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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks.finetuning import BackboneFinetuning, BaseFinetuning
from pytorch_lightning.callbacks.gpu_stats_monitor import GPUStatsMonitor
from pytorch_lightning.callbacks.gradient_accumulation_scheduler import GradientAccumulationScheduler
from pytorch_lightning.callbacks.lambda_function import LambdaCallback
from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase
from pytorch_lightning.callbacks.pruning import ModelPruning
from pytorch_lightning.callbacks.quantization import QuantizationAwareTraining
from pytorch_lightning.callbacks.stochastic_weight_avg import StochasticWeightAveraging
from pytorch_lightning.callbacks.timer import Timer
__all__ = [
'BackboneFinetuning',
'BaseFinetuning',
'Callback',
'EarlyStopping',
'GPUStatsMonitor',
'GradientAccumulationScheduler',
'LambdaCallback',
'LearningRateMonitor',
'ModelCheckpoint',
'ModelPruning',
'ProgressBar',
'ProgressBarBase',
'QuantizationAwareTraining',
'StochasticWeightAveraging',
'Timer',
]
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] | 3.299484 | 581 |
# Copyright 2017,2021 Niall McCarroll
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from tests.test_utils import TestUtils
import unittest
if __name__ == '__main__':
unittest.main() | [
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] | 3.357143 | 210 |
from .util import imgResample
from cloudvolume import CloudVolume
from argparse import ArgumentParser
import numpy as np
import SimpleITK as sitk
import tifffile as tf
def get_mip_at_res(vol, resolution):
"""Find the mip that is at least a given resolution
Args:
vol (cloudvolume.CloudVoluem): CloudVolume object for desired precomputed volume
resolution (int): Desired resolution in nanometers
Returns:
tuple: mip and resolution at that mip
"""
tmp_mip = 0
tmp_res = 0
for i, scale in enumerate(vol.scales):
if (scale["resolution"] <= resolution).all():
tmp_mip = i
tmp_res = scale["resolution"]
elif i == 0:
tmp_res = scale["resolution"]
return tmp_mip, tmp_res
return tmp_mip, tmp_res
def download_data(s3_path, outfile, desired_resolution, resample_isotropic=False, return_size=False):
"""Download whole precomputed volume from S3 at desired resolution and optionally resample data to be isotropic
Args:
s3_path (str): S3 path to precomputed volume
outfile (str): Path to output file
desired_resolution (int): Lowest resolution (in nanometers) at which to download data if desired_resolution isnt available.
resample_isotropic (bool, optional): If true, resample data to be isotropic at desired_resolution.
Returns:
resolution: Resoluton of downloaded data in microns
"""
vol = CloudVolume(s3_path)
mip_needed, resolution = get_mip_at_res(vol, np.array([desired_resolution] * 3))
vol = CloudVolume(s3_path, mip=mip_needed, parallel=True)
# download img and convert to C order
img = np.squeeze(vol[:, :, :]).T
# save out as correct file type
img_s = sitk.GetImageFromArray(img)
# set spacing in microns
resolution = np.divide(resolution, 1000.0).tolist()
img_s.SetSpacing(resolution)
if resample_isotropic:
img_s = imgResample(img_s, np.divide([desired_resolution]*3,1000.))
resolution = np.divide([desired_resolution]*3,1000.)
# if output is tiff, use tiffile
if 'tif' in outfile.split('.')[-1]:
tf.imwrite(outfile, sitk.GetArrayFromImage(img_s))
# else use SimpleITK
else:
sitk.WriteImage(img_s, outfile)
# tf.imsave(outfile, img.T, compress=3)
# return resolution in um
if return_size:
return (resolution, vol.scales[mip_needed]['size'])
return resolution
if __name__ == "__main__":
parser = ArgumentParser(
description="Download volume from S3 for subsequent registration."
)
parser.add_argument(
"s3_path",
help="S3 path to precomputed volume layer in the form s3://<bucket-name>/<path-to-precomputed-volume>",
)
parser.add_argument("outfile", help="name of output file with associated file extension. eg. /path/to/image.tif")
parser.add_argument(
"desired_resolution",
help="Desired minimum resolution for downloaded image in nanometers. Resolution assumed to be same in all 3 dimensions.",
type=int
)
parser.add_argument("--isotropic", help="Resample data to isotropic at desired_resolution.", action='store_true')
args = parser.parse_args()
download_data(
args.s3_path,
args.outfile,
args.desired_resolution,
resample_isotropic=args.isotropic
)
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] | 2.622291 | 1,292 |
from random import sample
import networkx as nx
from ns.flow.flow import Flow
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from __future__ import annotations
import voluptuous as vol
from datetime import datetime, timezone, timedelta
from homeassistant.core import HomeAssistant, callback
from homeassistant.helpers.entity import Entity, ToggleEntity
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, CoordinatorEntity
from homeassistant.config_entries import ConfigEntry, SOURCE_IMPORT
from homeassistant.components.binary_sensor import BinarySensorDeviceClass
from homeassistant.components.switch import SwitchDeviceClass
from homeassistant.helpers import entity_platform
import homeassistant.helpers.config_validation as cv
from .client import PowerPetDoorClient
from homeassistant.const import (
STATE_OPEN,
STATE_OPENING,
STATE_CLOSED,
STATE_CLOSING
)
from .const import (
DOMAIN,
CONF_HOST,
CONF_PORT,
CONF_NAME,
CONF_UPDATE,
CONF_HOLD,
COMMAND,
CONFIG,
DOOR_STATE_IDLE,
DOOR_STATE_CLOSED,
DOOR_STATE_HOLDING,
DOOR_STATE_KEEPUP,
DOOR_STATE_SLOWING,
DOOR_STATE_RISING,
CMD_GET_DOOR_STATUS,
CMD_GET_SENSORS,
CMD_GET_POWER,
CMD_GET_AUTO,
CMD_ENABLE_INSIDE,
CMD_DISABLE_INSIDE,
CMD_ENABLE_OUTSIDE,
CMD_DISABLE_OUTSIDE,
CMD_POWER_ON,
CMD_POWER_OFF,
CMD_ENABLE_AUTO,
CMD_DISABLE_AUTO,
CMD_OPEN,
CMD_OPEN_AND_HOLD,
CMD_CLOSE,
STATE_LAST_CHANGE,
FIELD_DOOR_STATUS,
FIELD_INSIDE,
FIELD_OUTSIDE,
FIELD_POWER,
FIELD_AUTO,
SERVICE_OPEN,
SERVICE_CLOSE,
SERVICE_TOGGLE,
)
from .schema import PP_SCHEMA, PP_OPT_SCHEMA, PP_SCHEMA_ADV, PP_OPT_SCHEMA_ADV, get_validating_schema
import logging
_LOGGER = logging.getLogger(__name__)
SWITCHES = {
"inside": {
"field": FIELD_INSIDE,
"update": CMD_GET_SENSORS,
"enable": CMD_ENABLE_INSIDE,
"disable": CMD_DISABLE_INSIDE,
"icon_on": "mdi:motion-sensor",
"icon_off": "mdi:motion-sensor-off"
},
"outside": {
"field": FIELD_OUTSIDE,
"update": CMD_GET_SENSORS,
"enable": CMD_ENABLE_OUTSIDE,
"disable": CMD_DISABLE_OUTSIDE,
"icon_on": "mdi:motion-sensor",
"icon_off": "mdi:motion-sensor-off"
},
"power": {
"field": FIELD_POWER,
"update": CMD_GET_POWER,
"enable": CMD_POWER_ON,
"disable": CMD_POWER_OFF,
"icon_on": "mdi:power",
"icon_off": "mdi:power-off"
},
"auto": {
"field": FIELD_AUTO,
"update": CMD_GET_AUTO,
"enable": CMD_ENABLE_AUTO,
"disable": CMD_DISABLE_AUTO,
"icon_on": "mdi:calendar-week",
"icon_off": "mdi:calendar-remove"
},
}
PLATFORM_SCHEMA = cv.PLATFORM_SCHEMA.extend(get_validating_schema(PP_SCHEMA)).extend(get_validating_schema(PP_OPT_SCHEMA)).extend(get_validating_schema(PP_SCHEMA_ADV)).extend(get_validating_schema(PP_OPT_SCHEMA_ADV))
DOOR_SCHEMA = {
vol.Optional(CONF_HOLD): cv.boolean
}
async def async_setup_platform(
hass: HomeAssistant,
config: ConfigType,
async_add_devices: AddEntitiesCallback,
discovery_info: DiscoveryInfoType | None = None,
) -> None:
"""Set up the Power Pet Door sensor."""
_LOGGER.warning(
"Configuration of the Power Pet Door platform in YAML is deprecated and "
"will be removed in Home Assistant 2022.6; Your existing configuration "
"has been imported into the UI automatically and can be safely removed "
"from your configuration.yaml file"
)
hass.async_create_task(
hass.config_entries.flow.async_init(
DOMAIN,
context={"source": SOURCE_IMPORT},
data=config,
)
)
# Right now this can be an alias for the above
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] | 2.213994 | 1,715 |
# uncompyle6 version 3.7.4
# Python bytecode 3.7 (3394)
# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]
# Embedded file name: T:\InGame\Gameplay\Scripts\Server\interactions\base\basic.py
# Compiled at: 2020-10-17 21:48:10
# Size of source mod 2**32: 42277 bytes
from animation.animation_xevt_element import AnimationXevtElement
from animation.animation_utils import get_tested_animation_override
from animation.arb_accumulator import with_skippable_animation_time
from autonomy.content_sets import ContentSetWithOverrides, ContentSet
from balloon.tunable_balloon import TunableBalloon
from broadcasters.broadcaster_request import BroadcasterRequest
from buffs.buff_element import BuffFireAndForgetElement
from buffs.tunable import TunableBuffElement
from business.business_elements import BusinessBuyLot, BusinessEmployeeAction
from call_to_action.call_to_action_elements import TurnOffCallToAction
from carry.carry_elements import TunableExitCarryWhileHolding, EnterCarryWhileHolding, TransferCarryWhileHolding
from crafting.create_photo_memory import CreatePhotoMemory
from crafting.set_photo_filter import SetPhotoFilter
from crafting.take_photo import TakePhoto
from drama_scheduler.festival_contest_ops import FestivalContestSubmitElement
from ensemble.ensemble_ops import AddToEnsemble, DestroyEnsemble, RemoveFromEnsemble
from event_testing.resolver import SingleSimResolver
from event_testing.tests import TunableTestSet
from familiars.familiar_elments import BindFamiliarElement, DismissFamiliarElement
from interactions import ParticipantType
from interactions.payment.payment_element import PaymentElement
from interactions.push_affordance_on_parent import PushAffordanceOnRandomParent
from interactions.push_npc_leave_lot_now import PushNpcLeaveLotNowInteraction
from interactions.utils.adventure import Adventure
from interactions.utils.animation_reference import TunableAnimationReference
from interactions.utils.audio import TunableAudioModificationElement, TunableAudioSting, TunablePlayStoredAudioFromSource
from interactions.utils.camera import CameraFocusElement, SetWallsUpOverrideElement
from interactions.utils.creation import ObjectCreationElement, SimCreationElement
from interactions.utils.destruction import ObjectDestructionElement
from interactions.utils.filter_elements import InviteSimElement
from interactions.utils.interaction_elements import FadeChildrenElement, SetVisibilityStateElement, UpdatePhysique, PutNearElement, AddToHouseholdElement, ReplaceObject, SaveParticipantElement, UpdateDisplayNumber, SetRoutingInfoAndState, ProceduralAnimationRotationElement
from interactions.utils.life_event import TunableLifeEventElement
from interactions.utils.loot_element import LootElement
from interactions.utils.notification import NotificationElement
from interactions.utils.parent_object import ParentObjectElement, ParentObjectWithRoutingFormationSlave
from interactions.utils.plumbbob import TunableReslotPlumbbob
from interactions.utils.reactions import ReactionTriggerElement
from interactions.utils.routing_elements import RouteToLocationElement
from interactions.utils.sim_focus import TunableFocusElement
from interactions.utils.statistic_element import PeriodicStatisticChangeElement, TunableProgressiveStatisticChangeElement, TunableStatisticIncrementDecrement, TunableStatisticDecayByCategory, TunableStatisticTransferRemove, TunableExitConditionSnippet, ConditionalActionRestriction, ConditionalInteractionAction
from interactions.utils.tunable import TunableSetClockSpeed, ServiceNpcRequest, TunableSetSimSleeping, DoCommand, SetGoodbyeNotificationElement
from interactions.utils.visual_effect import PlayVisualEffectElement
from lot_decoration.lot_decoration_elements import LotDecorationElement
from notebook.notebook_entry_elements import NotebookDisplayElement
from objects.components.autonomy import TunableParameterizedAutonomy
from objects.components.canvas_component import UpdateFamilyPortrait, PaintingStateTransfer, UpdateObjectValue
from objects.components.footprint_component import TunableFootprintToggleElement
from objects.components.game.game_element_join import GameElementJoin
from objects.components.game_component import TunableSetGameTarget
from objects.components.inventory_elements import DeliverBill, DestroySpecifiedObjectsFromTargetInventory, InventoryTransfer, PutObjectInMail
from objects.components.name_component import NameTransfer
from objects.components.object_relationship_social import ObjectRelationshipSocialTrigger
from objects.components.state import TunableStateChange, TunableTransienceChange
from objects.components.stolen_component import ReturnStolenObject, MarkObjectAsStolen
from objects.components.stored_audio_component import TransferStoredAudioComponent
from objects.components.stored_sim_info_component import StoreSimElement
from objects.household_inventory_management import SendToInventory
from objects.parenting_utils import SetAsHeadElement
from objects.set_location_element import SetLocationElement
from objects.slot_elements import SlotObjectsFromInventory, SlotItemTransfer
from open_street_director.open_street_director_element import ManipulateConditionalLayer
from postures.set_posture_element import SetPosture
from rabbit_hole.rabbit_hole_element import RabbitHoleElement
from relationships.relationship_bit_change import TunableRelationshipBitElement
from reservation.unreserve_object_element import UnreserveObjectElement
from retail.retail_elements import RetailCustomerAction
from routing.formation.formation_element import RoutingFormationElement, ReleaseRoutingFormationElement
from routing.route_events.route_event_provider import RouteEventProviderRequest
from sickness.sickness_elements import TrackDiagnosticAction
from sims.aging.aging_element import ChangeAgeElement
from sims.occult.switch_occult_element import SwitchOccultElement
from sims.outfits.outfit_change_element import ChangeOutfitElement
from sims.pregnancy.pregnancy_element import PregnancyElement
from sims.royalty_tracker import TunableRoyaltyPayment
from sims.university.university_elements import UniversityEnrollmentElement
from sims4.tuning.tunable import OptionalTunable, TunableVariant, TunableList, AutoFactoryInit, HasTunableSingletonFactory, TunableFactory, TunableReference, TunableTuple, Tunable, TunableEnumEntry
from sims4.tuning.tunable_base import FilterTag
from singletons import DEFAULT
from situations.tunable import CreateSituationElement, TunableUserAskNPCToLeave, TunableMakeNPCLeaveMustRun, TunableSummonNpc, JoinSituationElement, LeaveSituationElement, DestroySituationsByTagsElement
from travel_group.travel_group_elements import TravelGroupAdd, TravelGroupRemove, TravelGroupExtend, TravelGroupEnd
from trends.trend_recording import RecordTrendsElement
from world.dynamic_spawn_point import DynamicSpawnPointElement
import careers.career_tuning, interactions, services, sims4.log
logger = sims4.log.Logger('Basic')
AFFORDANCE_LOADED_CALLBACK_STR = 'on_affordance_loaded_callback'
BASIC_EXTRA_DESCRIPTION = "\n Basic extras add additional non-periodic behavior to an interaction.\n Elements in this list come in two kinds: ones that act once and ones\n that do something at the beginning and end of an interaction.\n \n The first kind generally causes a discrete change in the world at a\n specified moment. Most of these tunables give you the option of\n specifying the moment in time when the behavior should trigger,\n usually at the beginning of the interaction, the end of the\n interaction, or on an xevent.\n \n The other kind of element is one that starts some modifying behavior\n which ends at the end of the interaction. These do things like\n modify the Sim's focus or modify audio properties.\n \n The order of the elements you add to this list does matter: the\n elements that come earlier in the list surround the behavior of\n elements that come later. In most cases this order isn't\n significant, but it is possible that one element could depend on the\n behavior of another having already occurred. Consult a GPE if you\n aren't sure.\n \n e.g. You want a sound modifier to be in effect while running this\n interaction, and while the sound is playing, you want the Sim's\n focus to be affected:\n * add an 'audio_modification' element\n * add a 'focus' element\n \n In this case, the audio_modification element will start before the\n focus one, and it will end after the focus one. (This example is\n somewhat contrived since both the beginning and ending of both\n elements will happen on the same frame so the order doesn't actually\n matter.)\n \n e.g. You want an object state to change at a particular xevent, such\n as a toilet becoming flushed when the Sim touches the handle:\n * add a 'state' element, using the xevent id agreed on in the DR or\n IR to fill in the timing.\n "
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] | 3.80878 | 2,369 |
import folium
import geopandas as gpd
import matplotlib.cm as cm
import matplotlib.colors as colors
from branca.colormap import StepColormap
import contextily
import numpy as np
import pandas as pd
import pytest
from geopandas_view import view
nybb = gpd.read_file(gpd.datasets.get_path("nybb"))
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
cities = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
world["range"] = range(len(world))
missing = world.copy()
np.random.seed(42)
missing.loc[np.random.choice(missing.index, 40), "continent"] = np.nan
missing.loc[np.random.choice(missing.index, 40), "pop_est"] = np.nan
def test_simple_pass():
"""Make sure default pass"""
m = view(nybb)
m = view(world)
m = view(cities)
m = view(world.geometry)
def test_choropleth_pass():
"""Make sure default choropleth pass"""
m = view(world, column="pop_est")
def test_map_settings_default():
"""Check default map settings"""
m = view(world)
assert m.location == [
pytest.approx(-3.1774349999999956, rel=1e-6),
pytest.approx(2.842170943040401e-14, rel=1e-6),
]
assert m.options["zoom"] == 10
assert m.options["zoomControl"] == True
assert m.position == "relative"
assert m.height == (100.0, "%")
assert m.width == (100.0, "%")
assert m.left == (0, "%")
assert m.top == (0, "%")
assert m.global_switches.no_touch is False
assert m.global_switches.disable_3d is False
assert "openstreetmap" in m.to_dict()["children"].keys()
def test_map_settings_custom():
"""Check custom map settins"""
m = view(nybb, zoom_control=False, width=200, height=200, tiles="CartoDB positron")
assert m.location == [
pytest.approx(40.70582377450201, rel=1e-6),
pytest.approx(-73.9778006856748, rel=1e-6),
]
assert m.options["zoom"] == 10
assert m.options["zoomControl"] == False
assert m.height == (200.0, "px")
assert m.width == (200.0, "px")
assert "cartodbpositron" in m.to_dict()["children"].keys()
# custom XYZ tiles
m = view(
nybb,
zoom_control=False,
width=200,
height=200,
tiles="https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}",
attr="Google",
)
out_str = _fetch_map_string(m)
assert (
'tileLayer("https://mt1.google.com/vt/lyrs=m\\u0026x={x}\\u0026y={y}\\u0026z={z}",{"attribution":"Google"'
in out_str
)
m = view(nybb, location=(40, 5))
assert m.location == [40, 5]
assert m.options["zoom"] == 10
m = view(nybb, zoom_start=8)
assert m.location == [
pytest.approx(40.70582377450201, rel=1e-6),
pytest.approx(-73.9778006856748, rel=1e-6),
]
assert m.options["zoom"] == 8
m = view(nybb, location=(40, 5), zoom_start=8)
assert m.location == [40, 5]
assert m.options["zoom"] == 8
def test_simple_color():
"""Check color settings"""
# single named color
m = view(nybb, color="red")
out_str = _fetch_map_string(m)
assert '"fillColor":"red"' in out_str
# list of colors
colors = ["#333333", "#367324", "#95824f", "#fcaa00", "#ffcc33"]
m2 = view(nybb, color=colors)
out_str = _fetch_map_string(m2)
for c in colors:
assert f'"fillColor":"{c}"' in out_str
# column of colors
df = nybb.copy()
df["colors"] = colors
m3 = view(df, color="colors")
out_str = _fetch_map_string(m3)
for c in colors:
assert f'"fillColor":"{c}"' in out_str
# line GeoSeries
m4 = view(nybb.boundary, color="red")
out_str = _fetch_map_string(m4)
assert '"fillColor":"red"' in out_str
def test_choropleth_linear():
"""Check choropleth colors"""
# default cmap
m = view(nybb, column="Shape_Leng")
out_str = _fetch_map_string(m)
assert 'color":"#440154"' in out_str
assert 'color":"#fde725"' in out_str
assert 'color":"#50c46a"' in out_str
assert 'color":"#481467"' in out_str
assert 'color":"#3d4e8a"' in out_str
# named cmap
m = view(nybb, column="Shape_Leng", cmap="PuRd")
out_str = _fetch_map_string(m)
assert 'color":"#f7f4f9"' in out_str
assert 'color":"#67001f"' in out_str
assert 'color":"#d31760"' in out_str
assert 'color":"#f0ecf5"' in out_str
assert 'color":"#d6bedc"' in out_str
def test_choropleth_mapclassify():
"""Mapclassify bins"""
# quantiles
m = view(nybb, column="Shape_Leng", scheme="quantiles")
out_str = _fetch_map_string(m)
assert 'color":"#21918c"' in out_str
assert 'color":"#3b528b"' in out_str
assert 'color":"#5ec962"' in out_str
assert 'color":"#fde725"' in out_str
assert 'color":"#440154"' in out_str
# headtail
m = view(world, column="pop_est", scheme="headtailbreaks")
out_str = _fetch_map_string(m)
assert '"fillColor":"#3b528b"' in out_str
assert '"fillColor":"#21918c"' in out_str
assert '"fillColor":"#5ec962"' in out_str
assert '"fillColor":"#fde725"' in out_str
assert '"fillColor":"#440154"' in out_str
# custom k
m = view(world, column="pop_est", scheme="naturalbreaks", k=3)
out_str = _fetch_map_string(m)
assert '"fillColor":"#21918c"' in out_str
assert '"fillColor":"#fde725"' in out_str
assert '"fillColor":"#440154"' in out_str
def test_categorical():
"""Categorical maps"""
# auto detection
m = view(world, column="continent")
out_str = _fetch_map_string(m)
assert 'color":"#9467bd","continent":"Europe"' in out_str
assert 'color":"#c49c94","continent":"NorthAmerica"' in out_str
assert 'color":"#1f77b4","continent":"Africa"' in out_str
assert 'color":"#98df8a","continent":"Asia"' in out_str
assert 'color":"#ff7f0e","continent":"Antarctica"' in out_str
assert 'color":"#9edae5","continent":"SouthAmerica"' in out_str
assert 'color":"#7f7f7f","continent":"Oceania"' in out_str
assert 'color":"#dbdb8d","continent":"Sevenseas(openocean)"' in out_str
# forced categorical
m = view(nybb, column="BoroCode", categorical=True)
out_str = _fetch_map_string(m)
assert 'color":"#9edae5"' in out_str
assert 'color":"#c7c7c7"' in out_str
assert 'color":"#8c564b"' in out_str
assert 'color":"#1f77b4"' in out_str
assert 'color":"#98df8a"' in out_str
# pandas.Categorical
df = world.copy()
df["categorical"] = pd.Categorical(df["name"])
m = view(df, column="categorical")
out_str = _fetch_map_string(m)
for c in np.apply_along_axis(colors.to_hex, 1, cm.tab20(range(20))):
assert f'"fillColor":"{c}"' in out_str
# custom cmap
m = view(nybb, column="BoroName", cmap="Set1")
out_str = _fetch_map_string(m)
assert 'color":"#999999"' in out_str
assert 'color":"#a65628"' in out_str
assert 'color":"#4daf4a"' in out_str
assert 'color":"#e41a1c"' in out_str
assert 'color":"#ff7f00"' in out_str
# custom list of colors
cmap = ["#333432", "#3b6e8c", "#bc5b4f", "#8fa37e", "#efc758"]
m = view(nybb, column="BoroName", cmap=cmap)
out_str = _fetch_map_string(m)
for c in cmap:
assert f'"fillColor":"{c}"' in out_str
# shorter list (to make it repeat)
cmap = ["#333432", "#3b6e8c"]
m = view(nybb, column="BoroName", cmap=cmap)
out_str = _fetch_map_string(m)
for c in cmap:
assert f'"fillColor":"{c}"' in out_str
with pytest.raises(ValueError, match="'cmap' is invalid."):
view(nybb, column="BoroName", cmap="nonsense")
def test_column_values():
"""
Check that the dataframe plot method returns same values with an
input string (column in df), pd.Series, or np.array
"""
column_array = np.array(world["pop_est"])
m1 = view(world, column="pop_est") # column name
m2 = view(world, column=column_array) # np.array
m3 = view(world, column=world["pop_est"]) # pd.Series
assert m1.location == m2.location == m3.location
m1_fields = view(world, column=column_array, tooltip=True, popup=True)
out1_fields_str = _fetch_map_string(m1_fields)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out1_fields_str
)
assert (
'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out1_fields_str
)
m2_fields = view(world, column=world["pop_est"], tooltip=True, popup=True)
out2_fields_str = _fetch_map_string(m2_fields)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out2_fields_str
)
assert (
'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out2_fields_str
)
# GeoDataframe and the given list have different number of rows
with pytest.raises(ValueError, match="different number of rows"):
view(world, column=np.array([1, 2, 3]))
def test_no_crs():
"""Naive geometry get no tiles"""
df = world.copy()
df.crs = None
m = view(df)
assert "openstreetmap" not in m.to_dict()["children"].keys()
def test_style_kwds():
"""Style keywords"""
m = view(world, style_kwds=dict(fillOpacity=0.1, weight=0.5, fillColor="orange"))
out_str = _fetch_map_string(m)
assert '"fillColor":"orange","fillOpacity":0.1,"weight":0.5' in out_str
m = view(world, column="pop_est", style_kwds=dict(color="black"))
assert '"color":"black"' in _fetch_map_string(m)
def test_tooltip():
"""Test tooltip"""
# default with no tooltip or popup
m = view(world)
assert "GeoJsonTooltip" in str(m.to_dict())
assert "GeoJsonPopup" not in str(m.to_dict())
# True
m = view(world, tooltip=True, popup=True)
assert "GeoJsonTooltip" in str(m.to_dict())
assert "GeoJsonPopup" in str(m.to_dict())
out_str = _fetch_map_string(m)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str
)
assert (
'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out_str
)
# True choropleth
m = view(world, column="pop_est", tooltip=True, popup=True)
assert "GeoJsonTooltip" in str(m.to_dict())
assert "GeoJsonPopup" in str(m.to_dict())
out_str = _fetch_map_string(m)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str
)
assert (
'aliases=["pop_est","continent","name","iso_a3","gdp_md_est","range"]'
in out_str
)
# single column
m = view(world, tooltip="pop_est", popup="iso_a3")
out_str = _fetch_map_string(m)
assert 'fields=["pop_est"]' in out_str
assert 'aliases=["pop_est"]' in out_str
assert 'fields=["iso_a3"]' in out_str
assert 'aliases=["iso_a3"]' in out_str
# list
m = view(world, tooltip=["pop_est", "continent"], popup=["iso_a3", "gdp_md_est"])
out_str = _fetch_map_string(m)
assert 'fields=["pop_est","continent"]' in out_str
assert 'aliases=["pop_est","continent"]' in out_str
assert 'fields=["iso_a3","gdp_md_est"' in out_str
assert 'aliases=["iso_a3","gdp_md_est"]' in out_str
# number
m = view(world, tooltip=2, popup=2)
out_str = _fetch_map_string(m)
assert 'fields=["pop_est","continent"]' in out_str
assert 'aliases=["pop_est","continent"]' in out_str
# keywords tooltip
m = view(
world,
tooltip=True,
popup=False,
tooltip_kwds=dict(aliases=[0, 1, 2, 3, 4, 5], sticky=False),
)
out_str = _fetch_map_string(m)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str
)
assert "aliases=[0,1,2,3,4,5]" in out_str
assert '"sticky":false' in out_str
# keywords popup
m = view(
world,
tooltip=False,
popup=True,
popup_kwds=dict(aliases=[0, 1, 2, 3, 4, 5]),
)
out_str = _fetch_map_string(m)
assert (
'fields=["pop_est","continent","name","iso_a3","gdp_md_est","range"]' in out_str
)
assert "aliases=[0,1,2,3,4,5]" in out_str
assert "<th>${aliases[i]" in out_str
# no labels
m = view(
world,
tooltip=True,
popup=True,
tooltip_kwds=dict(labels=False),
popup_kwds=dict(labels=False),
)
out_str = _fetch_map_string(m)
assert "<th>${aliases[i]" not in out_str
def test_given_m():
"Check that geometry is mapped onto a given folium.Map"
m = folium.Map()
view(nybb, m=m, tooltip=False, highlight=False)
out_str = _fetch_map_string(m)
assert out_str.count("BoroCode") == 5
# should not change map settings
assert m.options["zoom"] == 1
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] | 2.277788 | 5,569 |
#####################################################################
# #
# /plugins/theme/__init__.py #
# #
# Copyright 2013, Monash University #
# #
# This file is part of the program BLACS, in the labscript suite #
# (see http://labscriptsuite.org), and is licensed under the #
# Simplified BSD License. See the license.txt file in the root of #
# the project for the full license. #
# #
#####################################################################
from __future__ import division, unicode_literals, print_function, absolute_import
from labscript_utils import PY2
if PY2:
str = unicode
import logging
import os
from qtutils import *
from blacs.plugins import PLUGINS_DIR
name = "GUI Theme"
module = "theme" # should be folder name
logger = logging.getLogger('BLACS.plugin.%s'%module)
DEFAULT_STYLESHEET = """DigitalOutput {
font-size: 12px;
background-color: rgb(50,100,50,255);
border: 1px solid rgb(50,100,50,128);
border-radius: 3px;
padding: 2px;
color: #202020;
}
DigitalOutput:hover {
background-color: rgb(50,130,50);
border: None;
}
DigitalOutput:disabled{
background-color: rgb(50,100,50,128);
color: #505050;
}
DigitalOutput:checked {
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgb(32,200,32), stop: 1 rgb(32,255,32));
border: 1px solid #8f8f91;
color: #000000;
}
DigitalOutput:hover:checked {
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgb(32,200,32), stop: 1 rgb(120,255,120));
border: 1px solid #8f8f91;
}
DigitalOutput:checked:disabled{
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgba(32,200,32,128), stop: 1 rgba(32,255,32,128));
color: #606060;
}
InvertedDigitalOutput {
font-size: 12px;
background-color: rgb(70,100,170,255);
border: 1px solid rgb(70,100,170,128);
border-radius: 3px;
padding: 2px;
color: #202020;
}
InvertedDigitalOutput:hover {
background-color: rgb(70, 130, 220);
border: None;
}
InvertedDigitalOutput:disabled{
background-color: rgba(70,100,170,128);
color: #505050;
}
InvertedDigitalOutput:checked {
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgb(50,150,221), stop: 1 rgb(32,192,255));
border: 1px solid #8f8f91;
color: #000000;
}
InvertedDigitalOutput:hover:checked {
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgb(50,150,221), stop: 1 rgb(120,192,255));
border: 1px solid #8f8f91;
}
InvertedDigitalOutput:checked:disabled{
background-color: qlineargradient(x1: 0, y1: 0, x2: 0, y2: 1,
stop: 0 rgba(50,150,221,128), stop: 1 rgba(32,192,255,128));
color: #606060;
}
"""
def is_default_stylesheet(stylesheet):
"""Return whether a stylesheet is the same as the default stylesheet, modulo whitespace"""
return no_whitespace(str(stylesheet)) == no_whitespace(DEFAULT_STYLESHEET)
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] | 1.9568 | 1,875 |
##############################################
### Author: Layane ([email protected])
##############################################
print "Hi world!"
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# Databricks notebook source
# MAGIC %md ### Drift Monitor
# MAGIC
# MAGIC This notebook is called by Orchestrate to determine if drift has occured over time.
# MAGIC
# MAGIC It takes in two time periods of data and determines if significant change has occurred in addition to a target directory to write the drift.
# MAGIC
# MAGIC Let's first load the data passed from Orchestrate.
# COMMAND ----------
dbutils.widgets.text("file_path_1", "Default")
dbutils.widgets.text("file_path_2", "Default")
dbutils.widgets.text("drift_path", "Default")
file_path_1 = dbutils.widgets.get("file_path_1")
file_path_2 = dbutils.widgets.get("file_path_2")
drift_path = dbutils.widgets.get("drift_path")
# COMMAND ----------
df1_featurized = spark.read.format("delta").load(file_path_1)
df2 = spark.read.format("delta").load(file_path_2)
# COMMAND ----------
# MAGIC %md Right now we have our featurized dataset we trained on for the first time period.
# MAGIC
# MAGIC The second time period still needs to be featurized, however.
# MAGIC
# MAGIC Let's identify the caterogical and numeric columns, since we will want those for drift monitoring, and featurize the second time window.
# COMMAND ----------
categorical_cols = [field for (field, dataType) in df2.dtypes if dataType == "string"]
numeric_cols = [field for (field, dataType) in df2.dtypes if ((dataType == "double"))]
cols = numeric_cols + categorical_cols
# COMMAND ----------
# MAGIC %md Create a path for the second time window featurized data.
# COMMAND ----------
dbutils.fs.rm(drift_path, True)
dbutils.fs.mkdirs(drift_path)
# COMMAND ----------
# MAGIC %md Run the Featurize notebook on the time window.
# COMMAND ----------
params = {
"file_path": file_path_2,
"save_path": drift_path
}
dbutils.notebook.run("./02-Featurize", 0, params)
# COMMAND ----------
# MAGIC %md Load the featurized second time window.
# COMMAND ----------
df2_featurized = spark.read.format("delta").load(drift_path)
# COMMAND ----------
# MAGIC %md Load the Monitor class from the lesson.
# COMMAND ----------
# Dependencies for Class Monitor
import pandas as pd
import seaborn as sns
from scipy import stats
import numpy as np
# COMMAND ----------
# MAGIC %md Finally, let's load in our data and run drift monitoring.
# COMMAND ----------
drift_monitor = Monitor(df1_featurized.select(cols).toPandas(), df2_featurized.select(cols).toPandas(), categorical_cols, numeric_cols)
drift_monitor.run()
# COMMAND ----------
drift_monitor.generate_percent_change()
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] | 2.984742 | 852 |
import os
import os.path as osp
import logging
import argparse
from pathlib import Path
from utils.log import get_logger
from yolov3_deepsort import VideoTracker
from utils.parser import get_config
import motmetrics as mm
mm.lap.default_solver = 'lap'
from utils.evaluation import Evaluator
if __name__ == '__main__':
#INFO:
#Bu .py dosyası videoları MOT16 kriterlerine göre değerlendiriyor.
#Önce yolov3_deepsort.py çağırılıp ilgili video için outputlar üretiliyor. (results.avi ve results.txt)
#daha sonra results.txt ve gt.txt kıyaslanarak mot skorları hesaplanıyor ve summary_global.xlsx ismiyle kaydediliyor.
args = parse_args() #save_path argumanı her video frame'inde değişeceği için daha sonra tekrar alınacak
#orj kod:
seqs_str = '''MOT16-02
MOT16-04
MOT16-05
MOT16-09
MOT16-10
MOT16-11
MOT16-13
'''
"""
seqs_str = '''MOT16-04
MOT16-05
MOT16-10
'''
"""
#olması gereken klasör düzeni:
#MOT16/train/MOT16-XX/
# /gt/gt.txt
# /v.mp4
# /results.txt (üretilecek)
# /results.avi (üretilecek)
# /summary_global.xlsx (üretilecek)
data_root = 'MOT16/train'
seqs = [seq.strip() for seq in seqs_str.split()]
main(data_root=data_root,
seqs=seqs,
args=args) | [
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] | 1.732906 | 936 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import matplotlib as plt
import numpy as np
sys.path.append('../')
from loglizer import dataloader
from loglizer.models import DeepLog
from loglizer.preprocessing import Vectorizer, Iterator
from loglizer import x_ai
batch_size = 32
hidden_size = 32
num_directions = 2
topk = 5
train_ratio = 0.2
window_size = 2#10
epoches = 2
num_workers = 2
device = 0
struct_log = '../data/HDFS/HDFS_100k.log_structured.csv' # The structured log file
label_file = '../data/HDFS/anomaly_label.csv' # The anomaly label file
if __name__ == '__main__':
Eventname=['E5', 'E22', 'E11', 'E9', 'E26', 'E3', 'E4', 'E2', 'E23', 'E21', 'E20',
'E25', 'E18', 'E6']
playerlist=[0,1,2,3,4,5,6,7,8,9,10,11,12,13]
#playerlist=[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51]
coalition=[]
coalition=x_ai.getcoaltionlist()
cvalue=[]
print("coalition number:" ,len(coalition))
(x_train, window_y_train, y_train), (x_test, window_y_test, y_test) = dataloader.load_HDFS(struct_log, label_file=label_file, window='session', window_size=window_size, train_ratio=train_ratio, split_type='uniform')
feature_extractor = Vectorizer()
train_dataset = feature_extractor.fit_transform(x_train, window_y_train, y_train)
test_dataset = feature_extractor.transform(x_test, window_y_test, y_test)
train_loader = Iterator(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers).iter
test_loader = Iterator(test_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers).iter
model = DeepLog(num_labels=feature_extractor.num_labels, hidden_size=hidden_size, num_directions=num_directions, topk=topk, device=device)
model.fit(train_loader, epoches)
print('Train validation:')
metrics = model.evaluate(train_loader)
print(metrics)
print('Test validation:')
metrics = model.evaluate(test_loader)
print(metrics)
print('calculate characteristic function for coalition')
x_ai.characteristicFunctionDL1(coalition, x_train, window_y_train, y_train, x_test, window_y_test, y_test, playerlist, model, cvalue)
print("V1-14:", cvalue[0:14])
print("V-1:", cvalue[-1])
print('prepared characteristic value')
shapleys = x_ai.calculatShapley(cvalue, coalition, playerlist)
print(shapleys)
plt.bar(range(14), shapleys, color='lightsteelblue')
plt.plot(range(14), shapleys, marker='o', color='coral') # coral
plt.xticks(range(14), Eventname)
plt.xlabel('Event')
plt.ylabel("Shapley addictive index")
plt.legend()
plt.show() | [
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] | 2.395944 | 1,134 |
# Copyright (c) 2020. JetBrains s.r.o.
# Use of this source code is governed by the MIT license that can be found in the LICENSE file.
from typing import TypeVar, Generic, Optional, List, Union
from lets_plot.geo_data.gis.geometry import GeoRect, GeoPoint
from lets_plot.geo_data.geocodes import _ensure_is_list
from lets_plot.geo_data.gis.request import Request, GeocodingRequest, RegionQuery, MapRegion, AmbiguityResolver, \
PayloadKind, MapRegionKind, IgnoringStrategyKind, LevelKind
T = TypeVar('T')
class eq_map_region_with_id(ValueMatcher[MapRegion]):
"""
Checks only id
"""
class ScopeMatcher:
'''
Scope can't be mixed with names and ids.
Scope with name should have length exactly 1.
Scope with ids should have length exactly 1.
'''
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# -*- coding: utf-8 -*-
"""
Test formatters.
Unit tests for column formatters.
"""
import uuid
from unittest.mock import patch
import pytest
from pheweb.load.field_formatter import (
str_formatter,
chromosome_formatter,
position_formatter,
parameterized_sequence_formatter,
p_value_formatter,
m_log_from_p_value_formatter,
se_beta_formatter,
m_log_from_beta_formatter,
parameterized_float_formatter,
)
from pheweb.utils import M_LOG_P_SENTINEL
def test_str_formatter() -> None:
"""
Test string formatter.
@return: None
"""
salt = str(uuid.uuid4())
assert salt == str_formatter(salt)
def test_chromosome_formatter() -> None:
"""
Test chromosome formatter.
@return: None
"""
assert "1" == chromosome_formatter("1")
assert "25" == chromosome_formatter("MT")
with pytest.raises(ValueError) as value_error:
chromosome_formatter("Z")
assert "Z" in str(value_error)
salt = str(uuid.uuid4())
with pytest.raises(ValueError) as value_error:
chromosome_formatter(salt)
assert salt in str(value_error)
def test_position_formatter_1() -> None:
"""
Test position formatter.
@return: None
"""
assert "1" == position_formatter("1")
assert "10" == position_formatter("10")
assert "100300000" == position_formatter("1.003e+08")
assert "249200000" == position_formatter("2.492e+08")
for bad_value in [
"-BAD VALUE",
"-2",
"2.492e+01",
"bad",
"2.492e+01call_formatter",
]:
with pytest.raises(ValueError) as value_error:
position_formatter(bad_value)
assert bad_value in str(value_error)
def test_parameterized_sequence_formatter() -> None:
"""
Test parameter sequence formatter.
@return: None
"""
column_name = str(uuid.uuid4())
formatter = parameterized_sequence_formatter(column_name)
assert "" == formatter("")
assert "G" == formatter("G")
assert "CAT" == formatter("CAT")
bad_value = "BAT"
with pytest.raises(ValueError) as value_error:
formatter(bad_value)
assert bad_value in str(value_error)
def test_p_value_formatter_good_values() -> None:
"""
Test p-value with 'good values'.
@return: None
"""
assert p_value_formatter("0") == "0.0"
assert p_value_formatter("0.5") == "0.5"
assert p_value_formatter("1.0") == "1.0"
def test_p_value_formatter_bad_values() -> None:
"""
Test p-value with 'bad' values.
@return: None
"""
for bad_value in ["2.0", "a", "-1"]:
with pytest.raises(ValueError) as value_error:
p_value_formatter(bad_value)
assert bad_value in str(value_error)
def test_m_log_from_m_log_from_p_value_formatter_bad() -> None:
"""
Test m log p-value with 'bad' values.
@return: None
"""
for bad_value in ["-1.0", "a", "2"]:
with pytest.raises(ValueError) as value_error:
m_log_from_p_value_formatter(bad_value)
assert bad_value in str(value_error)
@patch("pheweb.load.field_formatter.p_value_formatter", return_value="-1.0")
def test_m_log_from_m_log_from_p_value_formatter_edge_case(
mock_p_value_formatter,
) -> None:
"""
Test m log p-value with an edge case.
@param mock_p_value_formatter: mocker p-value formatter
@return: None
"""
weird_value = "0.666"
assert not mock_p_value_formatter.called
with pytest.raises(ValueError) as value_error:
m_log_from_p_value_formatter(weird_value)
mock_p_value_formatter.assert_called_once()
assert weird_value in str(value_error)
def test_m_log_from_p_value_formatter_valid() -> None:
"""
Test m log p-value with valid values.
@return: None
"""
assert m_log_from_p_value_formatter("0") == str(M_LOG_P_SENTINEL)
assert m_log_from_p_value_formatter("0.1") == "1.0"
assert m_log_from_p_value_formatter("0.01") == "2.0"
def test_se_beta_formatter() -> None:
"""
Test se beta formatter.
@return: None
"""
assert se_beta_formatter("0") == "0.0"
assert se_beta_formatter("1") == "1.0"
assert se_beta_formatter("1.0") == "1.0"
assert se_beta_formatter(" 5.9719201914e-10") == "5.9719201914e-10"
for bad_value in ["-1.0", "a", "-2"]:
with pytest.raises(ValueError) as value_error:
se_beta_formatter(bad_value)
assert bad_value in str(value_error)
def test_m_log_from_beta_formatter() -> None:
"""
Test m log from beta formatter.
@return: None
"""
for bad_beta, bad_se_beta in [("", ""), ("1", ""), ("", "1"), ("1", "0")]:
with pytest.raises(ValueError):
m_log_from_beta_formatter(bad_beta, bad_se_beta)
assert m_log_from_beta_formatter("0.0", "1.0") == "0.0"
def test_parameterized_float_formatter_valid() -> None:
"""
Test parameterized float formatter is valid.
@return: None
"""
column_name = uuid.uuid4()
formatter = parameterized_float_formatter(column_name)
assert formatter("-1") == "-1.0"
assert formatter("0") == "0.0"
assert formatter("0.1") == "0.1"
assert formatter("10.0") == "10.0"
def test_parameterized_float_formatter_bad() -> None:
"""
Test parameterized float formatter bad.
@return: None
"""
column_name = str(uuid.uuid4())
formatter = parameterized_float_formatter(column_name)
for bad_value in ["a", "*"]:
with pytest.raises(ValueError) as value_error:
formatter(bad_value)
assert bad_value in str(value_error)
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] | 2.333472 | 2,396 |
# -*- coding: utf-8 -*-
import subprocess
import json
import os
import io
from multiprocessing import Pool
import multiprocessing
import multiprocessing.pool
from operator import itemgetter
import random
import string
import pickle
import copy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import pysam
import mip_classes as mod
import pandas as pd
from pandas.errors import MergeError
import gzip
from primer3 import calcHeterodimerTm
import primer3
import traceback
from msa_to_vcf import msa_to_vcf as msa_to_vcf
import itertools
import sys
import allel
from Bio import SeqIO
print("functions reloading")
# backbone dictionary
mip_backbones = {
"hybrid_bb": "AGATCGGAAGAGCACACGTGACTCGCCAAGCTGAAGNNNNNNNNNNNN",
"hybrid_split": "NNNNAGATCGGAAGAGCACACGTGACTCGCCAAGCTGAAGNNNNNNNNNN",
"hybrid_split_hp": "AGATCGGAAGAGCACACGTGACTCGCCAAGCTGAAGNNNNNNNNNN",
"gc_bb": "GCAGATCGGAAGAGCACACCTCGCCAAGCTTTCGGCNNNNNNNNNNNN",
"slx_bb": "CTTCAGCTTCCCGATCCGACGGTAGTGTNNNNNNNNNNNN"
}
"""
# Below class allows processors from a pool from multiprocessing module to
create processor pools of their own.
# http://mindcache.io/2015/08/09/python-multiprocessing-module-daemonic-processes-are-not-allowed-to-have-children.html
class NoDaemonProcess(multiprocessing.Process):
# make 'daemon' attribute always return False
def _get_daemon(self):
return False
def _set_daemon(self, value):
pass
daemon = property(_get_daemon, _set_daemon)
# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool
# because the latter is only a wrapper function, not a proper class.
class NoDaemonProcessPool(multiprocessing.pool.Pool):
Process = NoDaemonProcess
"""
# above code was broken when switching to python 3. Below is taken from:
# https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic/8963618#8963618
# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool
# because the latter is only a wrapper function, not a proper class.
# Exception wrapper for multiprocessing taken from
# https://stackoverflow.com/questions/6126007/python-getting-a-traceback-from-a-multiprocessing-process/26096355#26096355
###############################################################
# Region prep related functions
###############################################################
def coordinate_to_target(coordinates, snp_locations, capture_size):
""" Create MIP targets starting from a snp file that is produced offline,
usually from Annovar. This is a tab separated file with the following
chr1 2595307 2595307 A G rs3748816.
This can be generalized to any target with coordinates.
"""
# create target regions to cover all snps
# start by getting snps on same chromosome together
snp_chroms = {}
reference_snp_locations = rsl = coordinates
for r in rsl:
chrom = rsl[r]["chrom"]
try:
snp_chroms[chrom].append([rsl[r]["begin"],
rsl[r]["end"]])
except KeyError:
snp_chroms[chrom] = [[rsl[r]["begin"],
rsl[r]["end"]]]
# merge snps that are too close to get separate regions
# the length should be twice the capture size
merged_snp_chroms = {}
for c in snp_chroms:
merged_snp_chroms[c] = merge_overlap(snp_chroms[c], 2 * capture_size)
# create regions for alignment
for c in merged_snp_chroms:
regions = merged_snp_chroms[c]
for r in regions:
snps_in_region = []
for s in reference_snp_locations:
if ((reference_snp_locations[s]["chrom"] == c)
and (r[0] <= reference_snp_locations[s]["begin"]
<= reference_snp_locations[s]["end"] <= r[1])):
snps_in_region.append(s)
r.append(snps_in_region)
for reg in regions:
snps = reg[2]
reg_begin = reg[0]
reg_end = reg[1]
reg_locations = []
for s in snps:
s_locations = []
locations = snp_locations[s]
ref_location = reference_snp_locations[s]
ref_begin = ref_location["begin"]
ref_end = ref_location["end"]
left_flank_buffer = ref_begin - reg_begin + capture_size
right_flank_buffer = reg_end - ref_end + capture_size
for l in locations:
snp_chrom = l["chrom"]
snp_begin = l["begin"]
snp_end = l["end"]
tar_begin = snp_begin - left_flank_buffer
tar_end = snp_end + right_flank_buffer
s_locations.append([snp_chrom, tar_begin, tar_end])
reg_locations.append(s_locations)
reg.append(reg_locations)
# create target coordinate for each region
target_coordinates = {}
for c in merged_snp_chroms:
regions = merged_snp_chroms[c]
for reg in regions:
region_name = "-".join(reg[2])
region_targets = reg[3][0]
for i in range(len(region_targets)):
reg_name = region_name + "-" + str(i)
if reg_name in target_coordinates:
print((reg_name, " is already in targets!"))
else:
target_coordinates[reg_name] = region_targets[i]
return target_coordinates
def rsid_to_target(resource_dir, snp_file):
""" Create MIP targets starting from a snp file that is produced offline,
usually from Annovar. This is a tab separated file with the following
content: chr1 2595307 2595307 A G rs3748816.
This can be generalized to any target with coordinates.
"""
# one snp can have multiple locations on the reference genome,
# this can happen with snps in regions where there are multiple different
# assemblies (HLA locus, for example). So first step is to get each of
# these locations in the genome.
snp_locations = {}
capture_types = {}
with io.open(os.path.join(resource_dir, snp_file),
encoding="utf-8") as infile:
for line in infile:
newline = line.strip().split("\t")
rsid = newline[5]
try:
# update the location dictionary if the rsid is already present
temp_dic = {"chrom": newline[0],
"begin": int(newline[1]),
"end": int(newline[2]),
"ref_base": newline[3],
"alt_bases": [newline[4]]}
# check if this location is already in the dict
# append the new alternative base to the dict
for snp in snp_locations[rsid]:
if ((snp["begin"] == temp_dic["begin"])
and (snp["end"] == temp_dic["end"])
and (snp["chrom"] == temp_dic["chrom"])
and (snp["ref_base"] == temp_dic["ref_base"])):
snp["alt_bases"].append(temp_dic["alt_bases"][0])
break
else:
# add the snp dict if the location is different than what
# is present in the location dict.
snp_locations[rsid].append(temp_dic)
except KeyError:
# add the new rsid to location dict if it is not already there
snp_locations[rsid] = [temp_dic]
capture_types[rsid] = newline[6]
# one reference location for each snp is required
# alternative assambly chromosomes have an underscore in their names,
# so that will be utilized to get the location in the orignal assembly,
# i.e. the chromosome that does not have the underscore
# (chr7 and not chr7_alt08)
reference_snp_locations = {}
problem_snps = []
for s in snp_locations:
if len(snp_locations[s]) == 1:
reference_snp_locations[s] = snp_locations[s][0]
else:
for i in range(len(snp_locations[s])):
if len(snp_locations[s][i]["chrom"].split("_")) == 1:
reference_snp_locations[s] = snp_locations[s][i]
break
else:
print(("Short chromosome name not found! "
"Please check the output list."))
problem_snps.append(s)
reference_snp_locations[s]["capture_type"] = capture_types[s]
return reference_snp_locations, snp_locations
def parse_alignment(reg_file):
""" Create a rinfo dictionary from a rinfo file."""
reg_dic = {}
with open(reg_file, "r") as infile:
for line in infile:
if line.startswith("REGION"):
newline = line.strip().split("\t")
key1 = newline[1].split(":")[0]
key2 = newline[1].split(":")[1]
if key1 not in reg_dic:
reg_dic[key1] = {key2: {"copyname": newline[2],
"chr": int(newline[3][3:]),
"begin": int(newline[4]),
"end": int(newline[5]),
"ori": (newline[6] == "F")}}
else:
reg_dic[key1][key2] = {"copyname": newline[2],
"chr": int(newline[3][3:]),
"begin": int(newline[4]),
"end": int(newline[5]),
"ori": (newline[6] == "F")}
return reg_dic
def update_rinfo_file(rinfo_file, update_file, output_file):
"""Update a rinfo file with the lines provided in the update_file.
This function will read all lines from a rinfo file and an update file.
First two columns of rinfo files describe the parameters while the
rest assign values. All the lines in the update file which share the
first column with a line in the original file will replace that line
in the original file. All other lines in the original file will remain.
"""
# read the update file
update_dict = {}
with open(update_file) as infile:
for line in infile:
if not line.startswith("#"):
newline = line.strip().split("\t")
update_dict[(newline[0], newline[1])] = line
# read the rinfo file and update as appropriate
with open(rinfo_file) as infile, open(output_file, "w") as outfile:
for line in infile:
if not line.startswith("#"):
newline = line.strip().split("\t")
line_key = (newline[0], newline[1])
try:
outfile.write(update_dict[line_key])
except KeyError:
outfile.write(line)
else:
outfile.write(line)
def get_target_coordinates(res_dir, species, capture_size,
coordinates_file=None, snps_file=None,
genes_file=None):
"""Extract MIP target coordinates from provided files."""
capture_types = {}
# Get target coordinates specified as genomic coordinates
if coordinates_file is None:
region_coordinates = {}
coord_names = []
else:
coordinates_file = os.path.join(res_dir, coordinates_file)
try:
coord_df = pd.read_table(coordinates_file, index_col=False)
coord_names = coord_df["Name"].tolist()
coord_df.rename(columns={"Name": "name", "Chrom": "chrom",
"Start": "begin", "End": "end"}, inplace=True)
region_coordinates = coord_df.set_index("name").to_dict(
orient="index")
# update capture types of targets
for g in region_coordinates:
if g not in capture_types:
capture_types[g] = region_coordinates[g]["CaptureType"]
except IOError:
print(("Target coordinates file {} could not be found.").format(
(coordinates_file)))
region_coordinates = {}
coord_names = []
# Get Gene target coordinates
if genes_file is None:
gene_coordinates = {}
gene_names = []
else:
# get the alias file (gene name to gene id mapping) if available
try:
with open(get_file_locations()[species]["alias"]) as infile:
alias = json.load(infile)
except (KeyError, IOError):
pass
try:
genes_file = os.path.join(res_dir, genes_file)
genes_df = pd.read_table(genes_file, index_col=False)
gene_names = genes_df["Gene"].tolist()
genes = genes_df.set_index("Gene").to_dict(orient="index")
gene_id_to_gene = {}
gene_ids = []
gene_coordinates = {}
for g in genes:
try:
if np.isnan(genes[g]["GeneID"]):
try:
gene_id = alias[g]
genes[g]["GeneID"] = gene_id
except KeyError:
print("""Alias for gene %s is not found.
Either provide a gene ID or use an alias
which is present in refgene file.""" % g)
continue
except NameError:
print(""" Gene ID is not provided for %s.
If gene name will be used to extract gene
ID an alias dictionary must be specified.
""" % g)
continue
except TypeError:
pass
gene_ids.append(genes[g]["GeneID"])
gene_id_to_gene[genes[g]["GeneID"]] = g
capture_types[g] = genes[g]["CaptureType"]
gene_id_coordinates = gene_to_target(gene_ids, species)
for gid in gene_id_coordinates:
gene_coordinates[gene_id_to_gene[gid]] = gene_id_coordinates[
gid]
except IOError:
print(("Target genes file {} could not be found.").format(
(genes_file)))
gene_coordinates = {}
gene_names = []
if snps_file is None:
snp_coordinates = {}
else:
# Get SNP target coordinates
try:
snps_file = os.path.join(res_dir, snps_file)
snp_df = pd.read_table(snps_file, index_col=False,
dtype={"Start": int, "End": int})
snp_df.rename(columns={"Name": "name", "Chrom": "chrom",
"Start": "begin", "End": "end"},
inplace=True)
snp_coordinates = snp_df.set_index("name").to_dict(orient="index")
for g in snp_coordinates:
if g not in capture_types:
capture_types[g] = "targets"
except IOError:
print(("Target SNPs file {} could not be found.").format(
(snps_file)))
snp_coordinates = {}
# merge coordinates dictionaries
all_coordinates = {}
all_coordinates.update(snp_coordinates)
all_coordinates.update(gene_coordinates)
all_coordinates.update(region_coordinates)
# Fix names that has unwanted characters
for c in list(all_coordinates.keys()):
clist = []
for ch in c:
if ch.isalnum():
clist.append(ch)
else:
clist.append("-")
newc = "".join(clist)
if newc != c:
print("%s is replaced with %s" % (c, newc))
all_coordinates[newc] = all_coordinates.pop(c)
capture_types[newc] = capture_types.pop(c)
target_regions, target_names = merge_coordinates(all_coordinates,
capture_size)
# prioritize gene names ond coordinate names over snp or other names
for t in list(target_names.keys()):
for n in target_names[t]:
if n in gene_names:
target_names[n] = target_names.pop(t)
target_regions[n] = target_regions.pop(t)
break
elif n in coord_names:
target_names[n] = target_names.pop(t)
target_regions[n] = target_regions.pop(t)
break
out_dict = {"target_regions": target_regions,
"target_names": target_names,
"capture_types": capture_types,
"gene_names": gene_names,
"snp_coordinates": snp_coordinates,
"gene_coordinates": gene_coordinates,
"region_coordinates": region_coordinates}
return out_dict
def merge_coordinates(coordinates, capture_size):
"""Merge overlapping coordinates for MIP targets.
Parameters
----------
coordinates: python dictionary
Coordinates to be merged in the form {target-name: {chrom: chrx,
begin: start-coordinate, end: end-coordinate}, ..}
capture_size: int
Anticipated MIP capture size. If two regions are as close as 2 times
this value, they will be merged.
Returns
-------
target_coordinates: python dictionary
merged coordinates dictionary
target_names: python dictionary
names of included targets in each merged region.
"""
# create target regions to cover all snps
# start by getting snps on same chromosome together
chroms = {}
for c in coordinates:
chrom = coordinates[c]["chrom"]
try:
chroms[chrom].append([coordinates[c]["begin"],
coordinates[c]["end"]])
except KeyError:
chroms[chrom] = [[coordinates[c]["begin"],
coordinates[c]["end"]]]
# merge snps that are too close to get separate regions
# the length should be twice the capture size
merged_chroms = {}
for c in chroms:
merged_chroms[c] = merge_overlap(chroms[c], 2 * capture_size)
# create regions for alignment
# create target coordinate for each region
target_coordinates = {}
target_names = {}
for c in merged_chroms:
regions = merged_chroms[c]
for reg in regions:
targets_in_region = []
for co in coordinates:
if (coordinates[co]["chrom"] == c
and reg[0] <= coordinates[co]["begin"]
<= coordinates[co]["end"] <= reg[1]):
targets_in_region.append(co)
region_name = targets_in_region[0]
target_names[region_name] = targets_in_region
r_start = reg[0]
r_end = reg[1]
target_coordinates[region_name] = [c, r_start, r_end]
return target_coordinates, target_names
def create_target_fastas(res_dir, targets, species, flank):
""" Create fasta files for a list of region coordinates provided as a dict
in the form {target1: [chrx, start, end], target2: [chrx, start, end], ..},
flank on both sides with the specified length. If beginning coordinate is
less than zero, reset the beginning coordinate to zero..
"""
for t in list(targets.keys()):
chrom = targets[t][0]
begin = targets[t][1] - flank + 1
if begin < 0:
begin = 0
end = targets[t][2] + flank
rk = chrom + ":" + str(begin) + "-" + str(end)
try:
with open(os.path.join(res_dir, t + ".fa"), "w") as outfile:
outfile.write(get_fasta(rk, species, header=t))
except Exception as e:
print(("Fasta file for {} could not be created, "
"due to error {}. It will be removed"
" from the target list.").format(t, e))
targets.pop(t)
return
def align_region_multi(alignment_list, pro):
"""Parallelize a list of lastz alignments."""
p = Pool(pro)
p.map_async(align_region_worker, alignment_list)
p.close()
p.join()
return
def align_region_worker(l):
"""Worker function for align_region_multi.
Aligns a single fasta query file to a target fasta file. Both query
and target fasta files can be multi sequence files.
"""
# get parameters from the input list
# first item is the fasta file name, including file extension
region_key = l[0]
# second item holds the run directory for lastz
resource_dir = l[1]
# output file is the target name + ".al" where the alignment output
# will be saved.
output_file = l[2]
# target fasta file is usually the reference genome
target_fasta = l[3]
# each action item will be appended to the target or query argument
# within brackets. [unmask] and [multiple] are important target actions
# unmask: allows starting alignments in masked(lowercase) parts of the
# target multiple: indicates there are multiple sequences in the target
# file (e.g. chromosomes, contigs)
target_actions = l[4]
# query file is always treated as a multiple sequence file
# so there is no need for the multiple action
query_actions = l[5]
# percent cutoff value for identity/coverage of query to target. This only
# affects reporting and not the alignment process itself.
identity_cutoff = l[6]
coverage_cutoff = l[7]
# format of the output, follows --format: argument in lastz
# if format is general, it should be followed by a comma separated list of
# fields to output, e.g. general:name1,text1,name2,text2,diff,score would
# seq of target, output the name of the query, sequence of the query, name
# of the target, a string showing the alignment and the alignment score
output_format = l[8]
# additional options to pass to lastz
options = l[9]
query_fasta = os.path.join(resource_dir, region_key)
# create target actions text
if len(target_actions) > 0:
target_act = "[" + ",".join(target_actions) + "]"
else:
target_act = ""
# create query actions text
if len(query_actions) > 0:
query_act = "[" + ",".join(query_actions) + "]"
else:
query_act = ""
# create the command list to pass to the processor
comm = ["lastz_32",
target_fasta + target_act,
query_fasta + query_act,
"--output=" + os.path.join(resource_dir, output_file),
"--format=" + output_format,
"--filter=identity:" + str(identity_cutoff),
"--filter=coverage:" + str(coverage_cutoff)]
# add any extra options to the end of the command
comm.extend(options)
# run the command using subprocess module
subprocess.check_output(comm)
return
def align_genes_for_design(fasta_list, res_dir,
alignment_types=["differences", "general"],
species="hs", num_processor=30):
"""Perform specified alignments given in an alignment dict.
This functions is called from align_targets function for the initial
target alignment to the reference genome.
It align sequences given in an alignment dict which contains alignment
specifics. Each entry in this dict must have a corresponding fasta file in
the res_dir specified. The alignment is performed against the reference
genome. This function merely prepares a list of commands to pass to
align_genes_for_design_worker function to carry out alignments in
parallel where multiple processors are available. Two types of alignment
outputs will be generated; one "general" informative about the alignment
such as where the alignment starts and ends, what is the percent identity,
coverage etc. The second output is the differences between the aligned
sequences, showing at which positions there are nucleotide changes and
what the changes are.
Parameters
----------
fasta_list: list
A list of dictionaries each of which contains specifics
for a single alignment, such as the name of the fasta file, coverage
and identity cut offs and any additional alignment parameters that are
passed to LastZ.
res_dir: str
Path to working directory where input and output files are located.
alignment_types: list
List of alignment types to be performed. Only "general" and/or
"differences" options are allowed.
species: str
Species whose reference genome will be used for alignment.
num_processor: int
Number of processors available for parallel processing.
"""
region_list = []
for gene_dict in fasta_list:
gene_name = gene_dict["gene_name"]
# percent cutoff value for identity/coverage of query to target.
# This only affects reporting and not the alignment process itself.
identity = gene_dict["identity"]
coverage = gene_dict["coverage"]
options = gene_dict["options"]
# alignment target is the reference genome of the specified species.
target = get_file_locations()[species]["fasta_genome"]
# alignment output should have the following fields.
# These are the bare minimum to be able to parse the alignment later.
out_fields = ["name1", "strand1", "zstart1", "end1", "length1",
"name2", "strand2", "zstart2", "end2", "zstart2+",
"end2+", "length2", "identity", "coverage"]
out_fields = ",".join(out_fields)
gen_out = "general:" + out_fields
# output fields for "differences" is fixed; it outputs the differences
# between the aligned sequence and the target.
dif_out = "differences"
if not os.path.exists(res_dir):
os.makedirs(res_dir)
# prepare a list of commands to feed to lastz for both alignment types
# i.e. "general" and "differences". Some of the additional parameters
# we are supplying here are the target and query actions.
# each action item will be appended to the target or query argument
# within brackets. [unmask] and [multiple] are important target actions
# unmask: allows starting alignments in masked(lowercase) parts of the
# target multiple: indicates there are multiple sequences in the target
# file (e.g. chromosomes, contigs)
if "general" in alignment_types:
al = [gene_name + ".fa", res_dir, gene_name + ".al", target,
["multiple", "unmask", "nameparse=darkspace"],
["unmask", "nameparse=darkspace"],
identity, coverage, gen_out, options]
region_list.append(al)
if "differences" in alignment_types:
al = [gene_name + ".fa", res_dir, gene_name + ".differences",
target, ["multiple", "unmask", "nameparse=darkspace"],
["unmask", "nameparse=darkspace"],
identity, coverage, dif_out, options]
region_list.append(al)
align_region_multi(region_list, num_processor)
return
def merge_alignments(resource_dir, fasta_list, output_prefix="merged"):
""" Merge the results of "general" type lastZ alignments into a
single file. This is used to process the alignment results from the
align_genes_for_design function where target sequences are aligned
against the reference genome.
Parameters
----------
resource_dir: str
Path to working directory where the alignment outputs are.
fasta_list: list
A list of dictionaries each of which has the specifics for a single
sequence alignment. It is used only to get alignment file names here.
output_prefix: str
Name for the output file. This will be appended by ".al" extension.
"""
# create a list for each alignment type (general and differences)
als_out = []
with open(os.path.join(
resource_dir, output_prefix + ".al"), "w") as alignment_file:
for f in fasta_list:
fnum = 0
with open(os.path.join(resource_dir, f + ".al")) as alignment:
linenum = 0
for line in alignment:
if linenum > 0:
als_out.append(line.strip())
elif fnum == 0:
als_out.append(line.strip())
linenum += 1
else:
linenum += 1
fnum += 0
alignment_file.write("\n".join(als_out))
return
def merge_alignment_diffs(resource_dir, fasta_list, output_prefix="merged"):
""" Merge the results of "differences" type lastZ alignments into a
single file. This is used to process the alignment results from the
align_genes_for_design function where target sequences are aligned
against the reference genome.
Parameters
----------
resource_dir: str
Path to working directory where the alignment outputs are.
fasta_list: list
A list of dictionaries each of which has the specifics for a single
sequence alignment. It is used only to get alignment file names here.
output_prefix: str
Name for the output file. This will be appended by ".al" extension.
"""
# create a list for each alignment type (general and differences)
diffs_out = []
with open(os.path.join(
resource_dir, output_prefix + ".differences"), "w") as diff_file:
for f in fasta_list:
fnum = 0
with open(os.path.join(resource_dir, f + ".differences")) as diffs:
for d in diffs:
diffs_out.append(d.strip())
fnum += 0
diff_file.write("\n".join(diffs_out))
return
def alignment_parser(wdir, name, spacer=0, gene_names=[]):
""" Parse merged genome alignment results file which is generated by
align_genes_for_design function to align design targets to reference
genomes. One query (target region) may have multiple alignments to the
genome.
Parameters
----------
wdir: str
Path to working directory
name: str
File name for the merged alignment file
spacer: int
Spacer length to use when merging overlapping regions. If two regions
are not overlapping but the distance between them is smaller than the
spacer, they will be merged.
Returns
-------
A list of dictionaries:
target_regions: merged genomic coordinates for grouped targets.
This dictionary is used as the final target regions.
For example: {r1: [[chr1, 100, 200], [chr3, 30, 300]],
r3: [chr4, 0, 300]]}
region_names: names for each region.
For example: {r1: [r1, r2], r3: [r3]}
imperfect_aligners: names of the target regions for which a perfect
alignment to the reference genome has not been found.
"""
alignment_dict = {}
# open alignment files
with open(os.path.join(wdir, name + ".al")) as infile:
# each line in the file is a separate alignment for which we'll
# prepare a dictionary.
for line in infile:
newline = line.strip().split("\t")
# first line has column names
if line.startswith("#"):
colnames = [newline[0][1:]]
colnames.extend(newline[1:])
else:
temp_dict = {}
for i in range(len(colnames)):
col = colnames[i]
value = newline[i]
temp_dict[col] = value
query_name = temp_dict["name2"]
try:
alignment_dict[query_name].append(temp_dict)
except KeyError:
alignment_dict[query_name] = [temp_dict]
# go through each target sequence and each alignment for that
# target to where in the genome it was aligned to.
aligned_regions = {}
for query in alignment_dict:
aligned_regions[query] = []
for a in alignment_dict[query]:
chrom = a["name1"]
begin = int(a["zstart1"])
end = int(a["end1"])
aligned_regions[query].append([chrom, begin, end])
# check for overlapping alignments. These can be the same target aligning
# to overlapping regions in the genome (internal duplications) or
# different targets aligning to the same (or overlapping) regions in the
# genome (paralogus sequences).
# overlapping regions will be grouped together to form the final target
# regions for probe design.
overlaps = {}
for q1 in aligned_regions:
# each target will have itself as overlapping
overlaps[q1] = [q1]
# get the genomic regions q1 was aligned to
reg1 = aligned_regions[q1]
# go through each region
for r1 in reg1:
# check overlap with other target regions
for q2 in aligned_regions:
if q1 == q2:
continue
reg2 = aligned_regions[q2]
for r2 in reg2:
if check_overlap(r1, r2, spacer):
overlaps[q1].append(q2)
break
# go through the overlaps and remove the overlapping overlaps
# e.g. if a overlaps b, b overlaps a also. We'll have {a: [a,b], b: [b, a]}
# in the overlaps dict. We want only one of these, so reduce to {a:[a, b]}
overlap_found = True
# place a failsafe counter to avoid unforseen infinite loops
exit_counter = 0
while (overlap_found and (exit_counter < 10000)):
overlap_found = False
for o in list(overlaps.keys()):
# check if o is still in the overlaps and has not been removed
if o in overlaps:
val = overlaps[o]
# get the overlapping regions for "val" and add them
# to overlapping regions for "o", then remove "val"
for v in val:
if (v in overlaps) and (o in overlaps) and (o != v):
overlaps[o].extend(overlaps[v])
overlaps.pop(v)
overlap_found = True
if exit_counter > 9999:
print("Overlap removal while loop limit is reached.")
# clean up overlapping region lists by removing duplicates.
for o in list(overlaps.keys()):
overlaps[o] = sorted(list(set(overlaps[o])))
##########################################################################
# create a new dictionary for target regions.
# for each target group in overlaps, we'll have genomic coordinates
# that will be used as final targets.
##########################################################################
# group regions according to their chromosomes
separated_regions = {}
for o in overlaps:
sep = separated_regions[o] = {}
for g in overlaps[o]:
regs = aligned_regions[g]
for r in regs:
try:
sep[r[0]].append(r[1:])
except KeyError:
sep[r[0]] = [r[1:]]
# merge each overlapping region
separated_merged_regions = {}
for s in separated_regions:
merged_sep = separated_merged_regions[s] = {}
for chrom in separated_regions[s]:
merged_region = merge_overlap(separated_regions[s][chrom])
merged_sep[chrom] = merged_region
###########################################
# organize target regions, assign region names based on the original
# target names. Assign a reference target.
###########################################
# sort target regions based on the length of
# chromosome name and the length of region. Sort is based on the region
# size and chromosome name is used as a tie-breaker
# to distinguish alternate contigs and not use them as reference, but
# it is not absolutely necessary and it would not behave as expected
# when chromosome names do not follow that convention, i.e, chr6 and
# chr6_altXYZ.
for ar in list(aligned_regions.keys()):
regs = aligned_regions[ar]
for r in regs:
r.append(0 - len(r[0]))
r.append(r[2] - r[1] + 1)
aligned_regions[ar] = sorted(regs, key=itemgetter(4, 3),
reverse=True)
target_regions = {}
region_names = {}
regions = separated_merged_regions
for r in regions:
target_regions[r] = []
for chrom in regions[r]:
for l in regions[r][chrom]:
temp_region = [chrom]
temp_region.extend(l)
temp_region.append(-len(chrom))
temp_region.append(l[1] - l[0])
target_regions[r].append(temp_region)
# sort target regions per target group based on the length of
# chromosome name and the length of region. Chromosome name is used
# to distinguish alternate contigs and not use them as reference, but
# it is not absolutely necessary and it would not behave as expected
# when chromosome names do not follow that convention, i.e, chr6 and
# chr6_altXYZ
target_regions[r] = sorted(target_regions[r], key=itemgetter(4, 3),
reverse=True)
# assign names to grouped targets
reg_names = []
# for each region we go back to individual region alignments and see
# if the individual alignment overlaps with this region. If it does
# we use the individual regions name for this region within the group.
for i in range(len(target_regions[r])):
reg = target_regions[r][i]
reg_chrom = reg[0]
reg_begin = reg[1]
reg_end = reg[2]
for c in aligned_regions:
main_region = aligned_regions[c][0]
if (reg_chrom == main_region[0]
and reg_begin <= main_region[1]
and reg_end >= main_region[2]):
reg_names.append(c)
break
else:
reg_names.append("na")
# assign a reference region for each group based on gene names provided
# this is mainly to used to have better names for regions. For example,
# if a gene is a target as well as a snp, we would like the gene name
# to be the name of the group as opposed to the SNP's name.
ref_found = False
for g in gene_names:
if g in reg_names:
ref_found = True
ref_index = reg_names.index(g)
ref_name = g
break
if not ref_found:
ref_name = r
ref_index = 0
ref_region = target_regions[r].pop(ref_index)
reg_names.pop(ref_index)
target_regions[r] = [ref_region] + target_regions[r]
reg_names = [ref_name] + reg_names
region_names[ref_name] = reg_names
target_regions[reg_names[0]] = target_regions.pop(r)
overlaps[reg_names[0]] = overlaps.pop(r)
# after the alignments are done, some regions will not have proper names
# and some will have "na". We'll change those to avoid repeating
# names.
for r in list(region_names.keys()):
rnames = region_names[r]
nnames = []
rn_counts = {}
for rn in rnames:
rnc = rnames.count(rn)
rn_counts[rn] = {"total_count": rnc,
"used_count": 0}
for rn in rnames:
if rn_counts[rn]["total_count"] > 1:
nnames.append(rn + "-" + str(rn_counts[rn]["used_count"]))
rn_counts[rn]["used_count"] += 1
else:
nnames.append(rn)
region_names[r] = nnames
# find target regions that could not be perfectly aligned to the genome
# these are usually extragenomic sequences supplied in fasa files, such as
# certain TCR haplotypes.
imperfect_aligners = []
for r in alignment_dict:
best_score = 0
alignments = alignment_dict[r]
for a in alignments:
cov = int(a["covPct"].split(".")[0])
idt = int(a["idPct"].split(".")[0])
score = cov * idt
if score > best_score:
best_score = score
if best_score != 10000:
imperfect_aligners.append(r)
return [target_regions, region_names, imperfect_aligners, aligned_regions,
overlaps]
def set_intra_alignment_options(target_regions, identity, coverage,
max_allowed_indel_size):
"""Set lastZ alignment options for intraparalog_aligner function."""
alignment_options_dict = {}
for t in target_regions:
temp_dict = {"gene_name": t, "identity": identity}
reference_len = target_regions[t][0][-1]
small_target = 0
for r in target_regions[t]:
if r[-1] < coverage:
small_target += 1
try:
smallest_target = min([smallest_target, r[-1]])
except NameError:
smallest_target = int(r[-1])
if small_target > 0:
print(("{} targets within {} are smaller than intra_coverage"
" value. This means that those targets will not be aligned."
" Smallest target's length was {}. Set intra_coverage"
" to a value smaller than this value to align all regions."
).format(small_target, t, smallest_target))
cover = round(coverage * 100 / reference_len, 1)
gap_open_penalty = 400
gap_extend_penalty = 30
ydrop = max_allowed_indel_size * gap_extend_penalty + gap_open_penalty
alignment_opts = ["--ydrop=" + str(ydrop), "--notransition",
"--ambiguous=iupac", "--noytrim"]
temp_dict["options"] = alignment_opts
if cover > 100:
cover = 100
temp_dict["coverage"] = cover
alignment_options_dict[t] = temp_dict
return alignment_options_dict
def intraparalog_aligner(resource_dir,
target_regions,
region_names,
imperfect_aligners,
fasta_sequences,
species,
num_process,
alignment_options_dict={}):
"""Align all regions within a target group.
Align all regions within a target group to the region selected
as the reference region.
Returns
-------
Returns nothing. It creates query.fa target.fa and .aligned files for each
target region group. These alignment have no genomic coordinates, so
all coordinates are relative to the given sequence. Also, the region names
are indicated as the reference gene name + copy name as this is originally
intended for use in paralog genes.
"""
alignment_commands = []
out_fields = "name1,strand1,zstart1,end1,length1,name2,strand2,zstart2,"
out_fields = out_fields + "end2,zstart2+,end2+,length2,identity,coverage"
gen_out = "general:" + out_fields
diff_out = "differences"
for t in target_regions:
alignment_options = alignment_options_dict[t]["options"]
identity = alignment_options_dict[t]["identity"]
coverage = alignment_options_dict[t]["coverage"]
tar_regs = target_regions[t]
# create a fasta file for the reference copy (or reference region)
target_keys = [tr[0] + ":" + str(tr[1] + 1)
+ "-" + str(tr[2]) for tr in tar_regs]
query_key = target_keys[0]
with open(os.path.join(resource_dir, t + ".query.fa"), "w") as outfile:
outfile.write(">" + t + "_ref\n")
outfile.write(get_sequence(query_key, species))
# create a fasta file that includes all target regions within a group.
with open(os.path.join(
resource_dir, t + ".targets.fa"), "w") as outfile:
outfile_list = []
for i in range(len(target_keys)):
k = target_keys[i]
cname = "_C" + str(i)
outfile_list.append(">" + t + cname)
outfile_list.append(get_sequence(k, species))
# add extragenomic (i.e. imperfect_aligners)
ols = region_names[t]
o_count = 0
for o in ols:
if o in imperfect_aligners:
outfile_list.append(">" + t + "_X" + str(o_count))
outfile_list.append(fasta_sequences[o])
o_count += 1
outfile.write("\n".join(outfile_list))
comm = [t + ".query.fa", resource_dir, t + ".aligned",
os.path.join(resource_dir, t + ".targets.fa"),
["multiple", "unmask", "nameparse=darkspace"],
["unmask", "nameparse=darkspace"],
identity, coverage, gen_out,
alignment_options, species]
alignment_commands.append(comm)
comm = [t + ".query.fa", resource_dir,
t + ".differences",
os.path.join(resource_dir, t + ".targets.fa"),
["multiple", "unmask", "nameparse=darkspace"],
["unmask", "nameparse=darkspace"],
identity, coverage,
diff_out, alignment_options, species]
alignment_commands.append(comm)
return align_region_multi(alignment_commands, num_process)
def intra_alignment_checker(family_name, res_dir, target_regions,
region_names):
"""
Parse intraparalog_aligner results.
Following a within group alignment, check if any individual region
within the group has multiple aligned parts. If found, split that region
into multiple regions to be re-aligned by intraparalog_aligner.
"""
alignment_file = family_name + ".aligned"
new_regions = {}
with open(os.path.join(res_dir, alignment_file), "r") as alignment:
for line in alignment:
# extract the column names from the first line
if line.startswith("#"):
newline = line.strip().split("\t")
newline[0] = newline[0][1:]
colnames = list(newline)
# assign values of each column for each alignment
else:
newline = line.strip().split("\t")
temp_dict = {}
for i in range(len(colnames)):
temp_dict[colnames[i]] = newline[i]
alignment_id = temp_dict["name1"]
ci = alignment_id.split("_")[-1]
ct = ci[0]
if ct == "C":
cn = int(ci[1:])
tr = target_regions[cn]
start = tr[1] + int(temp_dict["zstart1"])
end = tr[1] + int(temp_dict["end1"])
size = end - start + 1
try:
new_regions[cn].append([tr[0], start, end,
0 - len(tr[0]), size])
except KeyError:
new_regions[cn] = [[tr[0], start, end,
0 - len(tr[0]), size]]
# check if any paralog is missing after aligning to the reference copy
targeted_copies = list(range(len(target_regions)))
missing_copies = set(targeted_copies).difference(new_regions.keys())
if len(missing_copies) > 0:
print(("Paralog copies {} were not successfully aligned to "
"the reference copy for the target {}. You may consider "
"relaxing the alignment filters '--local-coverage' "
"and '--local-identity'").format(
", ".join(map(str, sorted(missing_copies))), family_name))
ret_regions = []
rnames = []
for ci in sorted(new_regions):
ret_regions.extend(sorted(new_regions[ci]))
if len(new_regions[ci]) > 1:
print(("Paralog copy {} for target region {} was aligned "
"to the reference copy multiple times. This copy will "
"be treated as multiple independent paralog copies and "
"realigned to the reference copy as separate "
"targets.").format(ci, family_name))
for i in range(len(new_regions[ci])):
rnames.append(region_names[ci] + "-" + str(i))
else:
rnames.append(region_names[ci])
return [ret_regions, rnames]
def alignment_mapper(family_name, res_dir):
"""Create a coordinate map of within group alignments."""
alignment_file = family_name + ".aligned"
difference_file = family_name + ".differences"
with open(os.path.join(res_dir, alignment_file), "r") as alignment, open(
os.path.join(res_dir, difference_file), "r") as difference:
# create an alignment dictionary for each region that a query
# aligns to these correspond to each line in the alignment file
# and thus, are relative coordinates.
alignment_dic = {}
for line in alignment:
# extract the column names from the first line
if line.startswith("#"):
newline = line.strip().split("\t")
newline[0] = newline[0][1:]
colnames = list(newline)
# assign values of each column for each alignment
else:
newline = line.strip().split("\t")
temp_dict = {"differences": []}
for i in range(len(colnames)):
temp_dict[colnames[i]] = newline[i]
alignment_id = temp_dict["name1"]
if alignment_id in alignment_dic:
print(("{} aligned to the reference copy multiple times. "
"Only the first alignment will be used for "
"coordinate mapping.").format(alignment_id))
continue
alignment_dic[alignment_id] = temp_dict
cov = float(alignment_dic[alignment_id]["covPct"][:-1])
idt = float(alignment_dic[alignment_id]["idPct"][:-1])
alignment_dic[alignment_id]["score"] = np.mean([idt, cov])
# differences file is a continuous file for all alignments
# extract differences for each alignment
for line in difference:
newline = line.strip().split("\t")
dname = newline[0]
alignment_dic[dname]["differences"].append(newline[:-2])
# map each position in each alignment to the query
for a in alignment_dic:
snps = alignment_dic[a]["snps"] = {}
co = alignment_dic[a]["coordinates"] = {}
rev_co = alignment_dic[a]["reverse_coordinates"] = {}
# if alignment on reverse strand
if alignment_dic[a]["strand2"] == "-":
# genomic coordinate of target start
# this position is zstart2+ away from query end
# (when it is a - alignment)
al_start = int(alignment_dic[a]["zstart1"])
query_plus_end = int(alignment_dic[a]["end2+"])
# assign start to the first key of the coord dictionary
first_key = query_plus_end - 1
co[first_key] = al_start
rev_co[al_start] = first_key
last_key = first_key
inserted = 0
for d in alignment_dic[a]["differences"]:
# start/end coordinates of diff relative to the query
diff_start = int(d[6])
diff_end = int(d[7])
query_length = int(d[9])
# for each diff, fill in the coordinates
# between the last_key in the coord dic and
# start_key - diff start
for j in range(last_key - 1, query_length
- diff_start - 1, -1):
# j decreases by one, starting from the last
# available key the value will be 1 more than the
# previous key (j+1)
if j == last_key - 1:
co[j] = round(co[j + 1] - 0.1) + 1 + inserted
else:
co[j] = round(co[j + 1] - 0.1) + 1
rev_co[co[j]] = j
# current last key is now first_key - diff_start
last_key = query_length - diff_start - 1
query_diff_end = last_key + 1
# genomic coordinate of target at diff start
tar_start = int(d[1])
# genomic coordinate of target at diff end
tar_end = int(d[2])
# if end and start are the same, there is a deletion
# in target compared to query
# all nucleotides from diff start to diff end will have
# the same coordinate
if tar_start == tar_end:
inserted = 0
for i in range(diff_end - diff_start):
co[last_key - i] = tar_start - 0.5
last_key -= diff_end - diff_start - 1
# in cases of deletion in query, only rev_co will be
# updated
elif diff_start == diff_end:
inserted = 0
for i in range(tar_end - tar_start):
rev_co[co[last_key + 1] + i + 1] = (
last_key + 0.5)
inserted += 1
last_key += 1
# last_key will be mapped to target start
# if there is only a SNP and no indel
else:
inserted = 0
co[last_key] = tar_start
rev_co[tar_start] = last_key
query_diff_start = last_key
diff_key = str(query_diff_start) + "-" + str(
query_diff_end)
snps[diff_key] = {"chrom": d[0],
"target_begin": int(d[1]),
"target_end": int(d[2]),
"target_orientation": d[3],
"query_start": diff_start,
"query_end": diff_end,
"query_orientation": d[8],
"target_base": d[10],
"query_base": d[11]}
# fill in the coordinates between last diff
# and the alignment end
query_plus_start = int(alignment_dic[a]["zstart2+"])
for k in range(last_key - 1, query_plus_start - 1, -1):
co[k] = round(co[k+1] - 0.1) + 1
rev_co[co[k]] = k
# when the alignment is on the forward strand
else:
# where on target sequence the alignment starts
tar_start = int(alignment_dic[a]["zstart1"])
# where in the query sequence the alinment starts
q_start = int(alignment_dic[a]["zstart2"])
co[q_start] = tar_start
rev_co[tar_start] = q_start
# last key used is q_start, last key is updated each time
# something is added to the coordinate dict.
last_key = first_key = q_start
inserted = 0
for d in alignment_dic[a]["differences"]:
# where on query sequence the difference starts and
# ends
diff_start = int(d[6])
diff_end = int(d[7])
diff_key = d[6] + "-" + d[7]
query_length = d[9]
snps[diff_key] = {"chrom": d[0],
"target_begin": int(d[1]),
"target_end": int(d[2]),
"target_orientation": d[3],
"query_start": diff_start,
"query_end": diff_end,
"query_orientation": d[8],
"target_base": d[10],
"query_base": d[11]}
# from the last key to the diff start the query and
# target sequences are the same in length and co dict
# is filled so
for i in range(last_key + 1, diff_start):
if i == last_key + 1:
co[i] = round(co[i-1] - 0.1) + 1 + inserted
inserted = 0
else:
co[i] = round(co[i-1] - 0.1) + 1
rev_co[co[i]] = i
# update last used key in co dict
last_key = diff_start
# genomic coordinate of target at diff start
tar_start = int(d[1])
# genomic coordinate of target at diff end
tar_end = int(d[2])
# if end and start are the same, there is a deletion
# in target compared to query
# all nucleotides from diff start to diff end will have
# the same coordinate
if tar_start == tar_end:
inserted = 0
for i in range(diff_end - diff_start):
co[last_key + i] = tar_start - 0.5
last_key += diff_end - diff_start - 1
# in cases of deletion in query (insertion in target)
# position will be mapped to the target end coordinate
elif diff_start == diff_end:
inserted = 0
for i in range(tar_end - tar_start):
rev_co[co[last_key - 1] + 1 + i] = (
last_key - 0.5)
inserted += 1
last_key -= 1
# if there is no indel
# last_key will be mapped to target start
else:
inserted = 0
co[last_key] = tar_start
rev_co[tar_start] = last_key
# fill in the coordinates between last diff
# and the alignment end
q_end = int(alignment_dic[a]["end2"])
for k in range(last_key + 1, q_end):
co[k] = round(co[k-1] - 0.1) + 1
rev_co[co[k]] = k
return alignment_dic
###############################################################
# Design related functions
###############################################################
def create_dirs(dir_name):
""" create subdirectory names for a given dir,
to be used by os.makedirs, Return a list of
subdirectory names."""
primer3_input_DIR = dir_name + "/primer3_input_files/"
primer3_output_DIR = dir_name + "/primer3_output_files/"
bowtie2_input_DIR = dir_name + "/bowtie2_input/"
bowtie2_output_DIR = dir_name + "/bowtie2_output/"
mfold_input_DIR = dir_name + "/mfold_input/"
mfold_output_DIR = dir_name + "/mfold_output/"
return [primer3_input_DIR, primer3_output_DIR, bowtie2_input_DIR,
bowtie2_output_DIR, mfold_input_DIR, mfold_output_DIR]
def get_snps(region, snp_file):
""" Take a region string and a tabix'ed snp file,
return a list of snps which are lists of
tab delimited information from the snp file. """
# extract snps using tabix, in tab separated lines
snp_temp = subprocess.check_output(["tabix", snp_file, region]).decode(
"UTF-8"
)
# split the lines (each SNP)
snps_split = snp_temp.split("\n")
# add each snp in the region to a list
# as lists of
snps = []
for line in snps_split:
snp = line.split('\t')
snps.append(snp)
# remove last item which is coming from the new line at the end
del snps[-1]
return snps
def get_vcf_snps(region, snp_file):
""" Take a region string and a tabix'ed snp file,
return a list of snps which are lists of
tab delimited information from the snp file. """
# extract snps using tabix, in tab separated lines
snp_temp = subprocess.check_output(["bcftools", "view", "-H", "-G", "-r",
region, snp_file]).decode("UTF-8")
# split the lines (each SNP)
snps_split = snp_temp.split("\n")[:-1]
# add each snp in the region to a list
# as lists of
snps = []
for line in snps_split:
snp = line.split('\t')[:8]
snps.append(snp)
return snps
def get_exons(gene_list):
""" Take a list of transcript information in refgene format and return a
list of exons in the region as [[e1_start, e1_end], [e2_start], [e2_end],
..]. The transcripts must belong to the same gene (i.e. have the same gene
name).Merge overlapping exons.
"""
# get start and end coordinates of exons in gene list
starts = []
ends = []
gene_names = []
gene_ids = []
chrom_list = []
for gene in gene_list:
chrom_list.append(gene[2])
chrom_set = list(set(chrom_list))
if len(chrom_set) == 0:
return {}
chrom_set = [c for c in chrom_set if len(c) < 6]
if len(chrom_set) > 1:
print(("More than one chromosomes, ",
chrom_set,
", has specified gene ",
gene[12]))
return {}
chrom = chrom_set[0]
for gene in gene_list:
if gene[2] == chrom:
starts.extend(list(map(int, gene[9].split(",")[:-1])))
ends.extend(list(map(int, gene[10].split(",")[:-1])))
gene_names.append(gene[12])
gene_ids.append(gene[1])
ori = gene[3]
# pair exon starts and ends
exons = []
for i in range(len(starts)):
exons.append([starts[i], ends[i]])
# check for overlapping exons and merge if any
overlapping = 1
while overlapping:
overlapping = 0
for i in range(len(exons)):
e = exons[i]
for j in range(len(exons)):
x = exons[j]
if (i != j) and ((e[0] <= x[0] <= e[1])
or (e[0] <= x[1] <= e[1])
or (x[0] <= e[0] <= x[1])):
# merge exons and add to the exon list
exons.append([min(e[0], x[0]), max(e[1], x[1])])
# remove the exons e and x
exons.remove(e)
exons.remove(x)
# change overlapping to 1 so we can stop the outer for loop
overlapping = 1
# once an overlapping exon is found, break the for loop
break
if overlapping:
# if an overlapping exon is found, stop this for loop and
# continue with the while loop with the updated exon list
break
# get the gene start and end coordinates
if (len(starts) >= 1) and (len(ends) >= 1):
start = min(starts)
end = max(ends)
else:
print(("No exons found for ", gene_list[0][1]))
return {}
# create an output dict
out = {}
out["chrom"] = chrom
out["begin"] = start + 1
out["end"] = end
out["exons"] = [[e[0] + 1, e[1]] for e in sorted(exons, key=itemgetter(0))]
out["names"] = gene_names
out["ids"] = gene_ids
out["orientation"] = ori
return out
def get_gene_name(region, species):
""" Return the gene(s) in a region. """
gene_names = []
try:
genes = get_snps(region, get_file_locations()[species][
"refgene_tabix"])
for g in genes:
gene_names.append(g[12])
except KeyError:
pass
return gene_names
def get_gene(gene_name, refgene_file, chrom=None, alternative_chr=1):
""" Return genomic coordinates of a gene extracted from the refseq genes file.
Refgene fields are as follows:
0:bin, 1:name, 2:chrom, 3:strand, 4:txStart, 5:txEnd, 6:cdsStart, 7:cdsEnd,
8:exonCount, 9:exonStarts, 10:exonEnds, 11:score, 12:name2,
13:cdsStartStat, 14:cdsEndStat, 15:exonFrames.
Field 12 will be used for name search."""
# all chromosomes must be included if chromosome of the gene is not
# provided therefore, chrom cannot be None when alternative_chr is set to 0
if not (chrom or alternative_chr):
print(("Chromosome of the gene %s must be specified "
"or all chromosomes must be searched."))
print(("Specify a chromosome or set alternative chromosome to 1."
% gene_name))
return 1
with open(refgene_file, 'r') as infile:
coord = []
for line in infile:
if not line.startswith('#'):
newline = line.strip().split('\t')
if newline[12] == gene_name:
coord.append(newline)
if len(coord) < 1:
print(("No gene found with the name ", gene_name))
return []
alter = []
if chrom:
# add each gene to alter dict, in the corresponding chromosome key
for c in coord:
if c[2] == chrom:
alter.append(c)
# find genes on alternate chromosomes if requested
elif alternative_chr:
for c in coord:
alter.append(c)
return alter
def create_gene_fasta(gene_name_list, wdir, species="hs", flank=150,
multi_file=False):
""" Get a list of genes, extract exonic sequence + flanking sequence.
Create fasta files in corresponding directory for each gene if multi_file
is True, create a single fasta file if False.
"""
region_list = []
for gene_name in gene_name_list:
if gene_name.startswith("chr"):
coord = get_coordinates(gene_name)
query = make_region(coord[0], coord[1] - flank, coord[2] + flank)
else:
e = get_exons(
get_gene(gene_name, get_file_locations()[species]["refgene"],
alternative_chr=1)
)
query = e["chrom"] + ":" + str(e["begin"] - flank) + "-" + str(
e["end"] + flank)
region_list.append(query)
regions = get_fasta_list(region_list, species)
fasta_dict = {}
for i in range(len(region_list)):
r = region_list[i]
gene_name = gene_name_list[i]
fasta_dict[gene_name] = regions[r]
if multi_file:
for gene_name in fasta_dict:
save_dict = {gene_name: fasta_dict[gene_name]}
filename = os.path.join(wdir, gene_name + ".fa")
save_fasta_dict(save_dict, filename)
else:
save_fasta_dict(fasta_dict, os.path.join(wdir, "multi.fa"))
def make_boulder(fasta, primer3_input_DIR, exclude_list=[],
output_file_name="", sequence_targets=[]):
""" Create a boulder record file in primer3_input_DIR from a given fasta
STRING. SEQUENCE_ID is the fasta header, usually the genomic region
(chrX:m-n) exclude_list is [coordinate,length] of any regions primers
cannot overlap.
"""
# parse fasta string, get header and remove remaining nextlines.
fasta_list = fasta.split("\n")
fasta_head = fasta_list[0][1:]
seq_template = "".join(fasta_list[1:])
# convert exclude list to strings
exclude_string_list = []
exclude_region = ""
for i in exclude_list:
exclude_string_list.append(str(i[0])+","+str(i[1]))
exclude_region = " ".join(exclude_string_list)
# create the boulder record
if len(sequence_targets) == 0:
sequence_target_string = ""
else:
sequence_target_string = " ".join([",".join(map(str, s))
for s in sequence_targets])
boulder = ("SEQUENCE_ID=" + fasta_head + "\n" +
"SEQUENCE_TEMPLATE=" + seq_template + "\n" +
"SEQUENCE_TARGET=" + sequence_target_string + "\n" +
"SEQUENCE_EXCLUDED_REGION=" + exclude_region + "\n" + "=")
if output_file_name == "":
outname = fasta_head
else:
outname = output_file_name
with open(os.path.join(primer3_input_DIR, outname), 'w') as outfile:
outfile.write(boulder)
return boulder
def make_primers_worker(l):
"""
Worker function to make_primers_multi.
A worker function to make primers for multiple regions using separate
processors. Read boulder record in given input directory and creates primer
output files in output directory
"""
# function arguments should be given as a list due to single
# iterable limitation of map_async function of multiprocessor.Pool
# input boulder record name
input_file = l[0]
# primer settings used
settings = l[1]
# output file name
output_file = l[2]
# locations of input/output dirs
primer3_input_DIR = l[3]
primer3_output_DIR = l[4]
primer3_settings_DIR = l[5]
subregion_name = l[6]
paralog_name = l[7]
primer_type = l[8]
input_file = os.path.join(primer3_input_DIR, input_file)
output_file = os.path.join(primer3_output_DIR, output_file)
settings = os.path.join(primer3_settings_DIR, settings)
# call primer3 program using the input and settings file
res = subprocess.run(["primer3_core",
"-p3_settings_file=" + settings, input_file],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
if res.returncode != 0:
print(("Primer design for the gene {} subregion {} {} arm failed "
"with error {}").format(paralog_name, subregion_name,
primer_type, res.stderr))
return
else:
primer3_output = res.stdout
# write boulder record to file.
with open(output_file, 'w') as outfile:
outfile.write(primer3_output.decode("UTF-8"))
return
def make_primers_multi(ext_list, lig_list, pro):
"""Design primers in parallel using the make_primers_worker function."""
# create a pool of twice the number of targets (for extension and ligation)
# p = Pool(2*pro)
p = Pool(pro)
# make extension primers using extension arm primer settings
p.map_async(make_primers_worker, ext_list)
# make ligation primers using ligation arm primer settings
p.map_async(make_primers_worker, lig_list)
# close pool
p.close()
# wait for processes to finish
p.join()
return
def primer_parser3(input_file, primer3_output_DIR, bowtie2_input_DIR,
parse_out, fasta=1, outp=1):
"""
Parse a primer3 output file and generate a primer fasta file.
The fasta file for the primers that only contains primer names and
sequences will be placed in the bowtie input directory to be
used as bowtie2 input.
Return a dictionary {sequence_information:{}, primer_information{}}
first dict has tag:value pairs for input sequence while second dict
has as many dicts as the primer number returned with primer name keys
and dicts as values {"SEQUENCE": "AGC..", "TM":"58"...}. Also write
this dictionary to a json file in primer3_output_DIR.
"""
primer_dic = {}
# all target sequence related information will be placed in
# sequence_information dictionary.
primer_dic["sequence_information"] = {}
# primer information will be kept in primer_information dicts.
primer_dic["primer_information"] = {}
# load the whole input file into a list.
infile = open(primer3_output_DIR + input_file, 'r')
lines = []
for line in infile:
# if a line starts with "=" that line is a record separator
if not line.startswith("="):
# boulder record tag-value pairs separated by "="
inline = line.strip('\n').split('=')
lines.append(inline)
infile.close()
# find sequence related information and add it to appropriate dic.
for pair in lines:
tag = pair[0]
value = pair[1]
if tag.startswith("SEQUENCE"):
if tag == "SEQUENCE_ID":
new_value = value.split(",")[-1].replace("CHR", "chr")
primer_dic["sequence_information"][tag] = new_value
else:
primer_dic["sequence_information"][tag] = value
# find how many left primers returned and create empty dictionary
# for each primer in primer_information dict.
for pair in lines:
tag = pair[0]
value = pair[1]
if tag == "PRIMER_LEFT_NUM_RETURNED":
# Add this to sequence information dic because it is sequence
# specific information
primer_dic["sequence_information"][
"SEQUENCE_LEFT_NUM_RETURNED"] = value
# create empty dictionaries with primer name keys
for i in range(int(value)):
primer_key = "PRIMER_LEFT_" + str(i)
primer_dic["primer_information"][primer_key] = {}
# do the same for right primers found
for pair in lines:
tag = pair[0]
value = pair[1]
if tag == "PRIMER_RIGHT_NUM_RETURNED":
primer_dic["sequence_information"][
"SEQUENCE_RIGHT_NUM_RETURNED"] = value
for i in range(int(value)):
primer_key = "PRIMER_RIGHT_" + str(i)
primer_dic["primer_information"][primer_key] = {}
# get sequence coordinate information to determine genomic coordinates of
# primers because primer information is relative to template sequence
sequence_coordinates = get_coordinates(primer_dic[
"sequence_information"]["SEQUENCE_ID"])
seq_chr = sequence_coordinates[0]
seq_start = int(sequence_coordinates[1])
# get primer information from input file and add to primer dictionary
for pair in lines:
tag = pair[0]
value = pair[1]
if ((tag.startswith("PRIMER_LEFT_")
or tag.startswith("PRIMER_RIGHT_"))
and (tag != "PRIMER_LEFT_NUM_RETURNED")
and (tag != "PRIMER_RIGHT_NUM_RETURNED")):
attributes = tag.split('_')
# primer coordinates tag does not include an attribute value
# it is only primer name = coordinates, so:
if len(attributes) > 3:
# then this attribute is not coordinates and should have an
# attribute value such as TM or HAIRPIN etc.
primer_name = '_'.join(attributes[0:3])
attribute_value = '_'.join(attributes[3:])
primer_dic["primer_information"][primer_name][
attribute_value] = value
else:
# then this attribute is coordinates and has no attribute value
# give it an attribute valute "COORDINATES"
primer_name = '_'.join(attributes[0:3])
primer_dic["primer_information"][primer_name][
'COORDINATES'] = value
# the coordinates are relative to sequence template
# find the genomic coordinates
coordinate_values = value.split(",")
if tag.startswith("PRIMER_LEFT"):
# sequence start is added to primer start to get genomic
# primer start
genomic_start = seq_start + int(coordinate_values[0])
# primer len is added "to genomic start because it is a
# left primer
genomic_end = genomic_start + int(coordinate_values[1]) - 1
primer_dic["primer_information"][primer_name][
'GENOMIC_START'] = genomic_start
primer_dic["primer_information"][primer_name][
'GENOMIC_END'] = genomic_end
primer_dic["primer_information"][primer_name][
'CHR'] = seq_chr
primer_dic["primer_information"][primer_name][
'ORI'] = "forward"
else:
# sequence start is added to primer start to get genomic
# primer start
genomic_start = seq_start + int(coordinate_values[0])
# primer len is subtracted from genomic start because it is
# a right primer
genomic_end = genomic_start - int(coordinate_values[1]) + 1
primer_dic["primer_information"][primer_name][
'GENOMIC_START'] = genomic_start
primer_dic["primer_information"][primer_name][
'GENOMIC_END'] = genomic_end
primer_dic["primer_information"][primer_name][
'CHR'] = seq_chr
primer_dic["primer_information"][primer_name][
'ORI'] = "reverse"
# add NAME as a key to primer information dictionary
primer_dic["primer_information"][primer_name]['NAME'] = primer_name
# if some primers were eliminated from initial primer3 output, remove from
# dictionary
for primer in list(primer_dic["primer_information"].keys()):
if primer_dic["primer_information"][primer] == {}:
primer_dic["primer_information"].pop(primer)
# dump the dictionary to json file in primer3_output_DIR if outp parameter
# is true
if outp:
dict_file = open(os.path.join(primer3_output_DIR, parse_out), 'w')
json.dump(primer_dic, dict_file, indent=1)
dict_file.close()
# generate a simple fasta file with primer names
if fasta:
outfile = open(bowtie2_input_DIR+parse_out, 'w')
for primer in primer_dic["primer_information"]:
# primer name is fasta header and sequence is fasta sequence
fasta_head = primer
fasta_line = primer_dic["primer_information"][primer]["SEQUENCE"]
outfile.write(">" + fasta_head + "\n" + fasta_line + "\n")
outfile.close()
return primer_dic
def paralog_primers(primer_dict, copies, coordinate_converter, settings,
primer3_output_DIR, outname, species, outp=0):
"""
Process primers generated for paralogs.
Take a primer dictionary file and add genomic start and end coordinates
of all its paralogs.
"""
# uncomment for using json object instead of dic
# load the primers dictionary from file
# with open(primer_file, "r") as infile:
# primer_dic = json.load(infile)
# primer dict consists of 2 parts, sequence_information dict
# and primer information dict. We wont'change the sequence_info part
primers = primer_dict["primer_information"]
primer_keys = set()
for primer in list(primers.keys()):
p_name = primer
p_dic = primers[primer]
p_coord = coordinate_converter
p_copies = copies
chroms = p_coord["C0"]["chromosomes"]
start = p_dic["GENOMIC_START"]
end = p_dic["GENOMIC_END"]
ref_coord = p_dic["COORDINATES"]
primer_ori = p_dic["ORI"]
p_dic["PARALOG_COORDINATES"] = {}
primer_seq = p_dic["SEQUENCE"]
# add reference copy as paralog
p_dic["PARALOG_COORDINATES"]["C0"] = {"SEQUENCE": primer_seq,
"ORI": primer_ori,
"CHR": chroms["C0"],
"NAME": p_name,
"GENOMIC_START": start,
"GENOMIC_END": end,
"COORDINATES": ref_coord}
for c in p_copies:
if c != "C0":
# check if both ends of the primer has aligned with reference
try:
para_start = p_coord["C0"][c][start]
para_end = p_coord["C0"][c][end]
except KeyError:
# do not add that copy if it is not aligned
continue
para_primer_ori = para_start < para_end
if para_primer_ori:
para_primer_key = (chroms[c] + ":" + str(para_start) + "-"
+ str(para_end))
p_dic["PARALOG_COORDINATES"][c] = {
"ORI": "forward", "CHR": chroms[c], "NAME": p_name,
"GENOMIC_START": para_start, "GENOMIC_END": para_end,
"COORDINATES": ref_coord, "KEY": para_primer_key}
primer_keys.add(para_primer_key)
else:
para_primer_key = chroms[c] + ":" + str(
para_end) + "-" + str(para_start)
p_dic["PARALOG_COORDINATES"][c] = {
"ORI": "reverse", "CHR": chroms[c], "NAME": p_name,
"GENOMIC_START": para_start, "GENOMIC_END": para_end,
"COORDINATES": ref_coord, "KEY": para_primer_key}
primer_keys.add(para_primer_key)
if len(primer_keys) > 0:
primer_sequences = get_fasta_list(primer_keys, species)
for p in primers:
para = primers[p]["PARALOG_COORDINATES"]
for c in para:
if c != "C0":
copy_dict = para[c]
p_ori = copy_dict["ORI"]
p_key = copy_dict["KEY"]
p_seq = primer_sequences[p_key]
if p_ori == "reverse":
p_seq = reverse_complement(p_seq)
copy_dict["SEQUENCE"] = primer_sequences[p_key]
if outp:
with open(os.path.join(primer3_output_DIR, outname), "w") as outf:
json.dump(primer_dict, outf, indent=1)
return primer_dict
def bowtie2_run(fasta_file, output_file, bowtie2_input_DIR,
bowtie2_output_DIR, species, process_num=4,
seed_MM=1, mode="-a", seed_len=18, gbar=1, local=0):
"""Align primers from a fasta file to specified species genome."""
file_locations = get_file_locations()
# check if entered species is supported
genome = file_locations[species]["bowtie2_genome"]
# determine what type of alignment is wanted
# local or end-to-end
if local:
check_local = "--local"
else:
check_local = "--end-to-end"
res = subprocess.Popen(["bowtie2", "-p", str(process_num), "-D", "20",
"-R", "3", "-N", str(seed_MM), "-L",
str(seed_len), "-i", "S,1,0.5", "--gbar",
str(gbar), mode, check_local, "-x", genome, "-f",
os.path.join(bowtie2_input_DIR, fasta_file), "-S",
os.path.join(bowtie2_output_DIR, output_file)],
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
log_file = os.path.join(
bowtie2_output_DIR, "log_" + species + "_" + id_generator(6))
with open(log_file, "wb") as outfile:
outfile.write(res.communicate()[1])
return 0
def bowtie(fasta_file, output_file, bowtie2_input_DIR, bowtie2_output_DIR,
options, species, process_num=4, mode="-a", local=0, fastq=0):
"""Align a fasta or fastq file to a genome using bowtie2."""
file_locations = get_file_locations()
# check if entered species is supported
genome = file_locations[species]["bowtie2_genome"]
# determine what type of alignment is wanted
# local or end-to-end
if local:
check_local = "--local"
else:
check_local = "--end-to-end"
com = ["bowtie2", "-p " + str(process_num)]
com.extend(options)
com.append(mode)
com.append(check_local)
com.append("-x " + genome)
if fastq:
com.append("-q " + os.path.join(bowtie2_input_DIR, fasta_file))
else:
com.append("-f " + os.path.join(bowtie2_input_DIR, fasta_file))
com.append("-S " + os.path.join(bowtie2_output_DIR, output_file))
subprocess.check_output(com)
return 0
def bwa(fastq_file, output_file, output_type, input_dir,
output_dir, options, species, base_name="None"):
"""
Align a fastq file to species genome using bwa.
Options should be a list that starts with the command (e.g. mem, aln etc).
Additional options should be appended as strings of "option value",
for example, "-t 30" to use 30 threads. Output type can be sam or bam.
Recommended options ["-t30", "-L500", "-T100"]. Here L500 penalizes
clipping severely so the alignment becomes end-to-end and T100 stops
reporting secondary alignments, assuming their score is below 100.
"""
genome_file = get_file_locations()[species]["bwa_genome"]
read_group = ("@RG\\tID:" + base_name + "\\tSM:" + base_name + "\\tLB:"
+ base_name + "\\tPL:ILLUMINA")
options = copy.deepcopy(options)
options.append("-R" + read_group)
if output_type == "sam":
com = ["bwa"]
com.extend(options)
com.append(genome_file)
com.append(os.path.join(input_dir, fastq_file))
with open(os.path.join(output_dir, output_file), "w") as outfile:
subprocess.check_call(com, stdout=outfile)
else:
com = ["bwa"]
com.extend(options)
com.append(genome_file)
com.append(os.path.join(input_dir, fastq_file))
sam = subprocess.Popen(com, stdout=subprocess.PIPE)
bam_com = ["samtools", "view", "-b"]
bam = subprocess.Popen(bam_com, stdin=sam.stdout,
stdout=subprocess.PIPE)
bam_file = os.path.join(output_dir, output_file)
sort_com = ["samtools", "sort", "-T", "/tmp/", "-o", bam_file]
subprocess.run(sort_com, stdin=bam.stdout)
subprocess.run(["samtools", "index", bam_file], check=True,
stderr=subprocess.PIPE)
def bwa_multi(fastq_files, output_type, fastq_dir, bam_dir, options, species,
processor_number, parallel_processes):
"""Align fastq files to species genome using bwa in parallel."""
if len(fastq_files) == 0:
fastq_files = [f.name for f in os.scandir(fastq_dir)]
if output_type == "sam":
extension = ".sam"
elif output_type == "bam":
extension = ".srt.bam"
else:
print(("Output type must be bam or sam, {} was given").format(
output_type))
return
if not os.path.exists(bam_dir):
os.makedirs(bam_dir)
if parallel_processes == 1:
for f in fastq_files:
# get base file name
base_name = f.split(".")[0]
bam_name = base_name + extension
options.extend("-t" + str(processor_number))
bwa(f, bam_name, output_type, fastq_dir, bam_dir, options, species,
base_name)
else:
processor_per_process = processor_number // parallel_processes
p = NoDaemonProcessPool(parallel_processes)
options = options + ["-t " + str(processor_per_process)]
results = []
errors = []
for f in fastq_files:
base_name = f.split(".")[0]
bam_name = base_name + extension
p.apply_async(bwa, (f, bam_name, output_type, fastq_dir, bam_dir,
options, species, base_name),
callback=results.append,
error_callback=errors.append)
p.close()
p.join()
if len(errors) > 0:
for e in errors:
print("Error in bwa_multi function", e.stderr)
def parse_cigar(cigar):
"""
Parse a CIGAR string.
CIGAR string is made up of numbers followed
by key letters that represent a sequence alignment; return a dictionary
with alignment keys and number of bases with that alignment key as values.
Below is some more information about cigar strings.
2S20M1I2M5D,for, example would mean that the 2 bases are "S"oft clipped
from 5' end of the sequence(read) aligned and it is not part of the
alignment; following that 2 bases, 20 bases of the read aligns or "M"atches
to the reference sequence, match here does not mean the bases are
identical, just that there is 1 base of reference for each base of the read
and there are enough similarity between the two sequences that they
aligned. 1 base following the 20M is an insertion, that is, it exists in
the read but not in the reference; 5 bases at the end are "D"eletions,
they are in the reference but not in the read.
"""
cig = {}
values = []
for c in cigar:
try:
values.append(str(int(c)))
except ValueError:
if c in list(cig.keys()):
cig[c] += int("".join(values))
else:
cig[c] = int("".join(values))
values = []
return cig
def get_cigar_length(cigar):
"""Get the length of the reference sequence from CIGAR string."""
try:
# parse cigar string and find out how many insertions are in the
# alignment
insertions = parse_cigar(cigar)["I"]
except KeyError:
# the key "I" will not be present in the cigar string if there is no
# insertion
insertions = 0
# all the values in the cigar dictionary represent a base in the reference
# seq,
# except the insertions, so they should be subtracted
return sum(parse_cigar(cigar).values()) - insertions
def parse_bowtie(primer_dict, bt_file, primer_out, primer3_output_DIR,
bowtie2_output_DIR, species, settings, outp=1):
"""
Take a bowtie output (sam) file and filter top N hits per primer.
When a primer has more than "upper_hit_limit" bowtie hits,
remove that primer.
Add the bowtie hit information, including hit sequence to
the primers dictionary.
"""
# extract how many bowtie hits should be added
# to the primer information for further TM analysis
N = int(settings["hit_limit"])
# how many total bowtie hits gets a primer fired
M = int(settings["upper_hit_limit"])
# read in bowtie file
infile = open(os.path.join(bowtie2_output_DIR, bt_file), 'r')
primers = copy.deepcopy(primer_dict)
# create a temp dic to count hits/primer
counter_dic = {}
# create a bowtie key that will be used when adding
# bowtie information to primers
bowtie_key = "bowtie_information_" + species
# all bowtie hits that will be used further for TM analysis
# will need to have sequence information with them
# region keys for hits (in chrx:begin-end format) will be
# kept in a list for mass fasta extraction later.
keys = set()
#
# read bowtie hits
for line in infile:
try:
if not line.startswith("@"):
record = line.strip('\n').split('\t')
primer_name = record[0]
# increment hit counter for primer
try:
counter_dic[primer_name] += 1
except KeyError:
counter_dic[primer_name] = 1
# check how many hits have been analyzed for this primer
# if upper hit limit has been reached, mark primer for removal
if counter_dic[primer_name] >= M:
primers['primer_information'][primer_name]["remove"] = True
continue
# move on to the next hit if primer hit limit has been reached.
# no further hits will be added for those primers
if counter_dic[primer_name] >= N:
continue
flag = record[1]
# a flag value of 4 means there was no hit, so pass those lines
if flag == "4":
continue
# chromosome of the bowtie hit
chrom = record[2]
# genomic position of bowtie hit
pos = int(record[3])
# get cigar string of alignment
cigar = record[5]
# extract which strand is the bowtie hit on
# true if forward
strand = ((int(record[1]) % 256) == 0)
# get hit coordinates
hit_start = pos
# bowtie gives us the start position of the hit
# end position is calculated using the cigar string
# of the hit
hit_end = pos + get_cigar_length(cigar) - 1
# create region keys required for sequence retrieval
# we want 3 nt extra on the 5' of the primer
# because when alternative primers for paralogs
# are considered we check +/- 3 nt from 5' end
# to balance TM.
if strand:
# Primer's 5' is the hit start when the hit is on forward
# strand so the nucleotides are added at start position
bt_start = hit_start
bt_end = hit_end
hit_str = "forward"
hit_region_key = (chrom + ":" + str(hit_start)
+ "-" + str(hit_end))
else:
bt_start = hit_end
bt_end = hit_start
hit_str = "reverse"
hit_region_key = (chrom + ":" + str(hit_start)
+ "-" + str(hit_end))
# add region key to keys list for fasta retrieval later
keys.add(hit_region_key)
# add all hit information to primer dictionary
try:
primers["primer_information"][primer_name][bowtie_key][
str(counter_dic[primer_name])
] = {"chrom": chrom, "begin": bt_start, "end": bt_end,
"key": hit_region_key, "strand": hit_str}
except KeyError:
primers["primer_information"][primer_name][bowtie_key] = {
str(counter_dic[primer_name]): {"chrom": chrom,
"begin": bt_start,
"end": bt_end,
"key": hit_region_key,
"strand": hit_str}
}
except KeyError:
# in earlier versions of this function the primers with
# excessive hits were removed during iteration and that lead
# to keyerrors. Now there should be no key error.
continue
# get the fasta sequences of all hits
sequence_dic = get_fasta_list(keys, species)
# remove primers with too many hits and add bowtie information for others.
for p in list(primers["primer_information"].keys()):
try:
if primers["primer_information"][p]["remove"]:
primers["primer_information"].pop(p)
continue
except KeyError:
pass
# add hit sequences to primer dictionary
# forward strand hits are added directly
# reverse strand hits are reversed-complemented
# so the hit is always in the primer orientation and
# and similar in sequence"
try:
for h in primers["primer_information"][p][bowtie_key]:
if (primers["primer_information"][p]
[bowtie_key][h]["strand"] == "forward"):
primers["primer_information"][p][bowtie_key][h][
"sequence"
] = sequence_dic[primers["primer_information"][p][
bowtie_key][h]["key"]
]
else:
primers["primer_information"][p][bowtie_key][h][
"sequence"
] = reverse_complement(
sequence_dic[primers["primer_information"]
[p][bowtie_key][h]["key"]]
)
except KeyError:
# if there is no bowtie hit for this primer (happens for host
# species):
primers["primer_information"][p][bowtie_key] = {}
# save the updated primers file
if outp:
with open(os.path.join(
primer3_output_DIR, primer_out), 'w') as outfile:
json.dump(primers, outfile, indent=1)
return primers
def process_bowtie(primers, primer_out, primer3_output_DIR,
bowtie2_output_DIR, species, settings, host=False, outp=1):
"""
Process a primer dict with bowtie information added.
Look at bowtie hits for each primer, determine if they
are on intended targets or nonspecific. In cases of paralogus
regions, check all paralogs and determine if the primer
will bind to any paralog. Create alternative primers if necessary
and allowed. Get melting temperatures of all hits and add
all these information to the primer dictionary.
"""
# get Na, Mg and oligo concentrations these are specified in M but primer3
# uses mM for ions and nM for oligos, so those will be adjusted.
Na = float(settings["Na"]) * 1000
Mg = float(settings["Mg"]) * 1000
conc = float(settings["oligo_conc"]) * pow(10, 9)
# are alternative mip arms allowed/desired
alt_arm = int(settings["alternative_arms"])
bowtie_key = "bowtie_information_" + species
alt_keys = set([])
# get reference chromosome lengths
genome_file = get_file_locations()[species]["fasta_genome"]
reference_lengths = {}
genome_sam = pysam.FastaFile(genome_file)
for r in genome_sam.references:
reference_lengths[r] = genome_sam.get_reference_length(r)
# read bowtie hits
for primer_name in primers['primer_information']:
try:
primer_seq = primers['primer_information'][primer_name]["SEQUENCE"]
if not host:
para = (primers['primer_information'][primer_name]
["PARALOG_COORDINATES"])
if ("BOWTIE_BINDS" not in
primers['primer_information'][primer_name]):
primers[
'primer_information'][primer_name]["BOWTIE_BINDS"] = []
if ("ALT_BINDS" not in
primers['primer_information'][primer_name]):
primers[
'primer_information'][primer_name]["ALT_BINDS"] = []
for bt_hit_name in list(primers['primer_information']
[primer_name][bowtie_key].keys()):
bt_hit = (primers['primer_information'][primer_name]
[bowtie_key][bt_hit_name])
bt_chrom = bt_hit["chrom"]
bt_begin = bt_hit["begin"]
bt_end = bt_hit["end"]
bt_ori = bt_hit["strand"]
bt_seq = bt_hit["sequence"]
if host:
bt_hit["TM"] = calcHeterodimerTm(
primer_seq,
reverse_complement(bt_seq),
mv_conc=Na,
dv_conc=Mg,
dntp_conc=0,
dna_conc=conc
)
continue
intended = 0
# para is a dict like:
# {C0:{"CHR": "chr4", "GENOMIC_START" ..}, C1:{..
# for non-CNV regions, bowtie mapping should be exactly the
# same as genomic coordinates, so even if there is 1 bp
# difference, we'll count this as off target. For CNV regions,
# a more generous 20 bp padding will be allowed to account for
# differences in our mapping and bowtie mapping. Bowtie mapping
# will be accepted as the accurate mapping and paralog
# coordinates will be changed accordingly.
map_padding = 1
if len(para) > 1:
map_padding = 20
for k in para:
para_ori = para[k]["ORI"]
para_chr = para[k]["CHR"]
para_begin = para[k]["GENOMIC_START"]
para_end = para[k]["GENOMIC_END"]
if ((para_ori == bt_ori) and (para_chr == bt_chrom)
and (abs(para_begin - bt_begin) < map_padding)
and (abs(para_end - bt_end) < map_padding)):
intended = 1
# Get bowtie determined coordinates and sequences
# for the paralog copy. These will have priority
# over GENOMIC_ values calculated internally.
para[k]["BOWTIE_END"] = bt_end
para[k]["BOWTIE_START"] = bt_begin
para[k]["BOWTIE_SEQUENCE"] = bt_seq
if intended:
# if the paralog sequence is the same as the reference
# this primer should bind to the paralog copy as well.
if bt_seq.upper() == primer_seq.upper():
para[k]["BOWTIE_BOUND"] = True
primers['primer_information'][
primer_name]["BOWTIE_BINDS"].append(k)
else:
# if the sequences are not exactly the same
# we'll assume the primer does not bind to the
# paralog and attempt to generate an alternative
# primer for this paralog.
para[k]["BOWTIE_BOUND"] = False
# Do this only if alternative MIP arms are allowed
# specified by alt_arm setting.
if alt_arm:
# get chromosome length to avoid setting
# alt arms beyon chromosome ends
para_chr_length = reference_lengths[para_chr]
al = {}
al["ref"] = {"ALT_SEQUENCE": primer_seq}
al["ref"]["ALT_TM"] = calcHeterodimerTm(
primer_seq,
reverse_complement(primer_seq),
mv_conc=Na,
dv_conc=Mg,
dntp_conc=0,
dna_conc=conc
)
for j in range(-3, 4):
if j == 0:
continue
alt_start = bt_begin + j
alt_end = bt_end
if ((alt_start < 0) or (alt_end < 0)
or (alt_start > para_chr_length)
or (alt_end > para_chr_length)):
continue
if para_ori == "forward":
alt_primer_key = create_region(
bt_chrom,
alt_start,
alt_end
)
else:
alt_primer_key = create_region(
bt_chrom,
alt_end,
alt_start
)
al[j] = {}
al[j]["ALT_START"] = alt_start
al[j]["ALT_END"] = alt_end
al[j]["ALT_ORI"] = para_ori
al[j]["ALT_KEY"] = alt_primer_key
alt_keys.add(alt_primer_key)
para[k]["ALTERNATIVES"] = al
else:
para[k]["ALTERNATIVES"] = {}
para[k]["ALT_TM"] = 0
para[k]["ALT_TM_DIFF"] = 100
para[k]["ALT_BOUND"] = False
# remove bowtie hit for intended target
primers['primer_information'][
primer_name][bowtie_key].pop(bt_hit_name)
break
# add TM value for unindended target
if not intended:
bt_hit["TM"] = calcHeterodimerTm(
primer_seq,
reverse_complement(bt_seq),
mv_conc=Na,
dv_conc=Mg,
dntp_conc=0,
dna_conc=conc
)
# Design alternative primers (if allowed) for paralogs
# when there is no bowtie hit for that paralog.
if not host:
for k in para:
try:
para[k]["BOWTIE_END"]
except KeyError:
para_ori = para[k]["ORI"]
para_chr = para[k]["CHR"]
para_begin = para[k]["GENOMIC_START"]
para_end = para[k]["GENOMIC_END"]
para[k]["BOWTIE_BOUND"] = False
if alt_arm:
# get chromosome length to avoid setting
# alt arms beyon chromosome ends
para_chr_length = reference_lengths[para_chr]
al = {}
al["ref"] = {"ALT_SEQUENCE": primer_seq}
al["ref"]["ALT_TM"] = calcHeterodimerTm(
primer_seq,
reverse_complement(primer_seq),
mv_conc=Na,
dv_conc=Mg,
dntp_conc=0,
dna_conc=conc
)
for j in range(-3, 4):
if j == 0:
continue
alt_start = para_begin + j
alt_end = para_end
if ((alt_start < 0) or (alt_end < 0)
or (alt_start > para_chr_length)
or (alt_end > para_chr_length)):
continue
if para_ori == "forward":
alt_primer_key = create_region(
para_chr,
alt_start,
alt_end
)
else:
alt_primer_key = create_region(
para_chr,
alt_end,
alt_start
)
al[j] = {}
al[j]["ALT_START"] = alt_start
al[j]["ALT_END"] = alt_end
al[j]["ALT_ORI"] = para_ori
al[j]["ALT_KEY"] = alt_primer_key
alt_keys.add(alt_primer_key)
para[k]["ALTERNATIVES"] = al
else:
para[k]["ALTERNATIVES"] = {}
para[k]["ALT_TM"] = 0
para[k]["ALT_TM_DIFF"] = 100
para[k]["ALT_BOUND"] = False
except KeyError:
continue
if len(alt_keys) > 0:
alt_sequences = get_fasta_list(alt_keys, species)
for primer_name in primers['primer_information']:
para = (primers['primer_information'][primer_name]
["PARALOG_COORDINATES"])
for k in para:
try:
alt_candidates = para[k]["ALTERNATIVES"]
except KeyError:
continue
for c in list(alt_candidates.keys()):
try:
alt_candidates[c]["ALT_TM"]
except KeyError:
alt_ori = alt_candidates[c]["ALT_ORI"]
alt_key = alt_candidates[c]["ALT_KEY"]
alt_seq = alt_sequences[alt_key]
if alt_ori == "reverse":
alt_seq = reverse_complement(alt_seq)
if alt_seq != "":
alt_tm = calcHeterodimerTm(
alt_seq,
reverse_complement(alt_seq),
mv_conc=Na,
dv_conc=Mg,
dntp_conc=0,
dna_conc=conc
)
alt_candidates[c]["ALT_TM"] = alt_tm
alt_candidates[c]["ALT_SEQUENCE"] = alt_seq
else:
alt_candidates.pop(c)
if outp:
with open(os.path.join(
primer3_output_DIR, primer_out), 'w') as outfile:
json.dump(primers, outfile, indent=1)
return primers
def filter_bowtie(primers, output_file, primer3_output_DIR, species, TM=46,
hit_threshold=0, lower_tm=46, lower_hit_threshold=3, outp=1):
"""
Check TMs of bowtie hits of given primers, on a given genome.
Filter the primers with too many nonspecific hits.
"""
for primer in list(primers["primer_information"].keys()):
# create a hit count parameter for hits with significant tm
# there are two parameters specified in the rinfo file
# high temp limit and low temp limit. The idea is to allow
# a very small (if any) number of nonspecific targets with high TM
# values but allow some low TM off targets.
hc = 0
lhc = 0
# check if bowtie information exists in dic
try:
bt_key = "bowtie_information_" + species
bowtie = primers["primer_information"][primer][bt_key]
for h in bowtie:
hit = bowtie[h]
try:
# if TM information is included in bowtie, compare with
# high and low TM, increment hc, lc if necessary and
# discard primers passing specified off target tresholds.
if float(hit["TM"]) >= TM:
hc += 1
if hc > hit_threshold:
primers["primer_information"].pop(primer)
break
elif float(hit["TM"]) >= lower_tm:
lhc += 1
if lhc > lower_hit_threshold:
primers["primer_information"].pop(primer)
break
except KeyError:
continue
# remove bowtie information once we use it.
primers["primer_information"][primer].pop(bt_key)
except KeyError:
continue
if outp:
# write dictionary to file in primer3_output_DIR
outfile = open(os.path.join(primer3_output_DIR, output_file), 'w')
json.dump(primers, outfile, indent=1)
outfile.close()
return primers
def alternative(primer_dic, output_file,
primer3_output_DIR, tm_diff, outp=1):
"""
Pick the best alternative arm for primers that do not bind all paralogs.
This is done by picking the alternative primer with melting temperature
that is closest to the original primer.
"""
primers = primer_dic["primer_information"]
try:
for primer_name in primers:
primer = primers[primer_name]
para = primer["PARALOG_COORDINATES"]
for c in para:
try:
alts = para[c]["ALTERNATIVES"]
# get the original primer TM
ref_tm = alts["ref"].pop("ALT_TM")
alts.pop("ref")
# sort alt primers by their TM difference from the ref
sorted_alts = sorted(
alts, key=lambda a: abs(alts[a]["ALT_TM"] - ref_tm)
)
# use the primer only if the TM difference is within
# specified limit.
if abs(alts[sorted_alts[0]]["ALT_TM"] - ref_tm) <= tm_diff:
primer["ALT_BINDS"].append(c)
para[c].update(alts[sorted_alts[0]])
para[c].pop("ALTERNATIVES")
except KeyError:
try:
para[c].pop("ALTERNATIVES")
except KeyError:
pass
except IndexError:
try:
para[c].pop("ALTERNATIVES")
except KeyError:
pass
except KeyError:
pass
if outp:
with open(os.path.join(
primer3_output_DIR, output_file), "w") as outfile:
json.dump(primer_dic, outfile, indent=1)
return primer_dic
def score_paralog_primers(primer_dict, output_file, primer3_output_DIR,
ext, mask_penalty, species, backbone, outp=1):
"""
Score primers in a dictionary according to a scoring matrix.
Scoring matrices are somewhat crude at this time.
Arm GC content weighs the most, then arms GC clamp and arm length
Next_base values are last.
"""
primers = primer_dict["primer_information"]
extension = (ext == "extension")
# primer scoring coefficients were calculated based on
# linear models of various parameters and provided as a dict
with open("/opt/resources/mip_scores.dict", "rb") as infile:
linear_coefs = pickle.load(infile)
# the model was developed using specific reaction conditions as below.
# actual conditions may be different from these but we'll use these
# for the model.
na = 25 # Sodium concentration
mg = 10 # magnesium concentration
conc = 0.04 # oligo concentration
# get extension arm sequence
if extension:
for p in primers:
extension_arm = primers[p]["SEQUENCE"]
# calculate gc content of extension arm
extension_gc = calculate_gc(extension_arm)
# count lowercase masked nucleotides. These would likely be masked
# for variation underneath.
extension_lowercase = sum([c.islower() for c in extension_arm])
# calculate TM with the model parameters for TM
ext_TM = primer3.calcTm(extension_arm, mv_conc=na, dv_conc=mg,
dna_conc=conc, dntp_conc=0)
# create a mip parameter dict
score_features = {"extension_gc": extension_gc,
"extension_lowercase": extension_lowercase,
"ext_TM": ext_TM}
# calculate primer score using the linear model provided
tech_score = 0
for feature in score_features:
degree = linear_coefs[feature]["degree"]
primer_feature = score_features[feature]
poly_feat = [pow(primer_feature, i) for i in range(degree + 1)]
tech_score += sum(linear_coefs[feature]["coef"] * poly_feat)
tech_score += linear_coefs[feature]["intercept"]
primers[p]["SCORE"] = tech_score
# get ligation arm parameters
else:
for p in primers:
ligation_arm = primers[p]["SEQUENCE"]
# calculate gc content of extension arm
ligation_gc = calculate_gc(ligation_arm)
# only the 3' end of the ligation arm was important in terms of
# lowercase masking.
ligation_lowercase_end = sum([c.islower()
for c in ligation_arm[-5:]])
# calculate TM of ligation sequence (actual ligation probe arm)
# agains probe backbone.
ligation_bb_TM = primer3.calcHeterodimerTm(
reverse_complement(ligation_arm), backbone,
mv_conc=na, dv_conc=mg, dna_conc=conc, dntp_conc=0)
# create a mip parameter dict
score_features = {"ligation_gc": ligation_gc,
"ligation_lowercase_end": ligation_lowercase_end,
"ligation_bb_TM": ligation_bb_TM}
# calculate primer score using the linear model provided
tech_score = 0
for feature in score_features:
degree = linear_coefs[feature]["degree"]
primer_feature = score_features[feature]
poly_feat = [pow(primer_feature, i) for i in range(degree + 1)]
tech_score += sum(linear_coefs[feature]["coef"] * poly_feat)
tech_score += linear_coefs[feature]["intercept"]
primers[p]["SCORE"] = tech_score
if outp:
# write dictionary to json file
outfile = open(os.path.join(primer3_output_DIR, output_file), "w")
json.dump(primer_dict, outfile, indent=1)
outfile.close()
return primer_dict
def filter_primers(primer_dict, output_file,
primer3_output_DIR, n, bin_size, outp=1):
"""
Filter primers so that only top n scoring primers remain for each bin.
Primers are divided into bins of the given size based on the 3' end of
the primer. Only top performing n primers ending in the same bin will
remain after filtering.
For example, bin_size=3 and n=1 would chose the best scoring primer
among primers that end within 3 bps of each other.
"""
# load extension and ligation primers from file
template_seq = primer_dict["sequence_information"]["SEQUENCE_TEMPLATE"]
template_len = len(template_seq)
forward_bins = {}
reverse_bins = {}
for i in range(template_len//bin_size + 1):
forward_bins[i] = []
reverse_bins[i] = []
for primer in list(primer_dict["primer_information"].keys()):
# get primer orientation
ori = primer_dict["primer_information"][primer]["ORI"]
# get primer start coordinate
start = int(primer_dict["primer_information"][primer]
["COORDINATES"].split(",")[0])
primer_len = int(primer_dict["primer_information"][primer]
["COORDINATES"].split(",")[1])
if ori == "forward":
end = start + primer_len - 1
elif ori == "reverse":
end = start - primer_len + 1
# which bin the start coordinate falls into
end_bin = end//bin_size
# get primer score
score = primer_dict["primer_information"][primer]["SCORE"]
# append the primer name/score to appropriate bin dic
if ori == "forward":
forward_bins[end_bin].append([primer, score])
elif ori == "reverse":
reverse_bins[end_bin].append([primer, score])
best_primer_dict = {}
best_primer_dict["sequence_information"] = primer_dict[
"sequence_information"]
best_primer_dict["primer_information"] = {}
# find best scoring mips in each forward bin
for key in forward_bins:
# sort primers for score
primer_set = sorted(forward_bins[key], key=itemgetter(1))
# get best scoring primers (all primers if there are less than n)
if len(primer_set) < n:
best_primers = primer_set
else:
best_primers = primer_set[-n:]
# add best primers do dictionary
for primers in best_primers:
primer_name = primers[0]
best_primer_dict["primer_information"][primer_name] = primer_dict[
"primer_information"][primer_name]
# find best scoring mips in each reverse bin
for key in reverse_bins:
# sort primers for score
primer_set = sorted(reverse_bins[key], key=itemgetter(1))
# get best scoring primers (all primers if there are less than n)
if len(primer_set) < n:
best_primers = primer_set
else:
best_primers = primer_set[-n:]
# add best primers do dictionary
for primers in best_primers:
primer_name = primers[0]
best_primer_dict["primer_information"][primer_name] = primer_dict[
"primer_information"][primer_name]
# write new dic to file
if outp:
with open(os.path.join(
primer3_output_DIR, output_file), "w") as outfile:
json.dump(best_primer_dict, outfile, indent=1)
return best_primer_dict
def pick_paralog_primer_pairs(extension, ligation, output_file,
primer3_output_DIR, min_size, max_size,
alternative_arms, region_insertions,
subregion_name, outp=1):
"""Pick primer pairs satisfying a given size range."""
# assign primer information dictionaries to a shorter name
ext = extension["primer_information"]
lig = ligation["primer_information"]
# check if extension and ligation dictionaries have primers
if len(ext) == 0:
return 1
if len(lig) == 0:
return 1
# create a primer pairs dic. This dictionary is similar to primer dic
primer_pairs = {}
# has the same sequence_information key:value pairs
primer_pairs["sequence_information"] = {}
# has pair information key instead of primer_information
primer_pairs["pair_information"] = {}
# populate sequence information (same as extension or ligation)
primer_pairs["sequence_information"]['SEQUENCE_TEMPLATE'] = extension[
"sequence_information"]['SEQUENCE_TEMPLATE']
primer_pairs["sequence_information"]['SEQUENCE_EXCLUDED_REGION'] = (
extension["sequence_information"]['SEQUENCE_EXCLUDED_REGION']
)
primer_pairs["sequence_information"]['SEQUENCE_TARGET'] = extension[
"sequence_information"]['SEQUENCE_TARGET']
primer_pairs["sequence_information"]['SEQUENCE_ID'] = extension[
"sequence_information"]['SEQUENCE_ID']
# pick primer pairs
for e in ext.keys():
# extension primer information for this mip will be e_info
e_info = ext[e]
# get primer coordinates
ext_start = e_info["GENOMIC_START"]
ext_end = e_info["GENOMIC_END"]
# get primer orientation
ext_ori = ext_end > ext_start
# if end is greater than start then it is a left(fw) primer,
# and ext_ori is True.
# get coordinates of this primer in paralog copies.
ep_info = e_info["PARALOG_COORDINATES"]
# the paralogs bound by primer according to bowtie mapping
e_binds = e_info["BOWTIE_BINDS"]
# paralogs that were not bound by the primer and alt primers were
# designed.
e_alt_binds = e_info["ALT_BINDS"]
# find a ligation primer
for l in list(lig.keys()):
l_info = lig[l]
# get primer coordinates
lig_start = l_info["GENOMIC_START"]
lig_end = l_info["GENOMIC_END"]
# get orientation of primer
lig_ori = lig_end < lig_start
# if end is less than start, it is a right primer
# create a list for start and end coordinates
coord = []
# continue only if the two orientations have the same value
if lig_ori == ext_ori:
# check if relative positions of primers are correct
if ext_ori:
# ligation end should be greater than extension end
# for forward pairs
position = lig_end > ext_end
else:
# extension end should be greater than ligation end
# for reverse pairs
position = ext_end > lig_end
# get pair information if relative positions of primers are
# correct
if position:
coord = [ext_start, ext_end, lig_start, lig_end]
coord.sort()
prod_size = coord[-1] - coord[0] + 1
pairs = {}
# get paralogus coordinates
lp_info = l_info["PARALOG_COORDINATES"]
l_binds = l_info["BOWTIE_BINDS"]
l_alt_binds = l_info["ALT_BINDS"]
# find the paralogs that are hybridized by both primers
# start with paralog copies that are bound by the
# original primers (not alts).
paralogs = list(set(l_binds).intersection(e_binds))
for p in paralogs:
try:
p_coord = []
ep_start = ep_info[p]["BOWTIE_START"]
ep_end = ep_info[p]["BOWTIE_END"]
ep_ori = ep_end > ep_start
lp_start = lp_info[p]["BOWTIE_START"]
lp_end = lp_info[p]["BOWTIE_END"]
lp_ori = lp_end < lp_start
lp_chrom = lp_info[p]["CHR"]
if lp_ori == ep_ori:
if lp_ori:
p_position = lp_end > ep_end
pair_ori = "forward"
else:
p_position = lp_end < ep_end
pair_ori = "reverse"
if p_position:
p_coord = [ep_start, ep_end,
lp_start, lp_end]
p_coord.sort()
prod_size = p_coord[-1] - p_coord[0] + 1
pairs[p] = {
"capture_size": prod_size,
"extension_start": ep_start,
"extension_end": ep_end,
"ligation_start": lp_start,
"ligation_end": lp_end,
"mip_start": p_coord[0],
"mip_end": p_coord[3],
"capture_start": p_coord[1] + 1,
"capture_end": p_coord[2] - 1,
"chrom": lp_chrom,
"orientation": pair_ori
}
except KeyError:
continue
# check if any pairs' product is within size limits
# taking into account reported insertions within
# the target region. If there are insertions, we reduce
# the max size to accomodate those insertions.
# Deletions are handled differently because their impact
# on the captures will be different. Any deletion that
# is small enough to be captured will still be captured
# without any alterations. However the capture size will
# become smaller, which is not detrimental.
pair_found = 0
captured_copies = []
for p in list(pairs.keys()):
if not region_insertions.empty:
max_insertion_size = region_insertions.loc[
(region_insertions["copy_chrom"]
== pairs[p]["chrom"])
& (region_insertions["copy_begin"]
> pairs[p]["capture_start"])
& (region_insertions["copy_end"]
< pairs[p]["capture_end"]),
"max_size"].sum()
else:
max_insertion_size = 0
adjusted_max_size = max_size - max_insertion_size
if adjusted_max_size < (min_size/2):
continue
# we do not have to adsjust min_size unless the max
# size get too close to min_size, in which case
# we leave a 30 bp distance between min an max so
# that we're not very limited in primer pair choices.
adjusted_min_size = min(adjusted_max_size - 30,
min_size)
if (adjusted_max_size
>= pairs[p]["capture_size"]
>= adjusted_min_size):
captured_copies.append(p)
pair_found = 1
if pair_found:
# if a pair is found for any copy
# remove minimum size restriction for other copies
for p in list(pairs.keys()):
if p in captured_copies:
continue
if not region_insertions.empty:
max_insertion_size = region_insertions.loc[
(region_insertions["copy_chrom"]
== pairs[p]["chrom"])
& (region_insertions["copy_begin"]
> pairs[p]["capture_start"])
& (region_insertions["copy_end"]
< pairs[p]["capture_end"]),
"max_size"].sum()
else:
max_insertion_size = 0
adjusted_max_size = max_size - max_insertion_size
if adjusted_max_size < (min_size/2):
continue
if (adjusted_max_size
>= pairs[p]["capture_size"] >= 0):
captured_copies.append(p)
# C0 must be in the captured copies because the
# reference copy is used for picking mip sets
if "C0" not in captured_copies:
continue
# create a pair name as
# PAIR_extension primer number_ligation primer number
ext_name = e.split('_')[2]
lig_name = l.split('_')[2]
pair_name = ("PAIR_" + subregion_name + "_" + ext_name
+ "_" + lig_name)
if ext_ori:
orientation = "forward"
pair_name = pair_name + "_F"
else:
orientation = "reverse"
pair_name = pair_name + "_R"
primer_pairs["pair_information"][pair_name] = {
"pairs": pairs,
"extension_primer_information": ext[e],
"ligation_primer_information": lig[l],
"orientation": orientation,
"captured_copies": captured_copies
}
# Check if there are any paralog copies that require
# alt primers to be used. If so, create those pairs.
alt_paralogs = list((set(l_alt_binds).union(
e_alt_binds)).difference(paralogs))
alts = {}
for a in alt_paralogs:
try:
alt_arms = []
p_coord = []
# check if the extension primer is the
# original or alt.
if ep_info[a]["BOWTIE_BOUND"]:
ep_start = ep_info[a]["BOWTIE_START"]
ep_end = ep_info[a]["BOWTIE_END"]
else:
try:
ep_start = ep_info[a]["ALT_START"]
ep_end = ep_info[a]["ALT_END"]
alt_arms.append("extension")
except KeyError:
continue
ep_ori = ep_end > ep_start
# check if ligation primer is the original
# or alternative designed.
if lp_info[a]["BOWTIE_BOUND"]:
lp_start = lp_info[a]["BOWTIE_START"]
lp_end = lp_info[a]["BOWTIE_END"]
else:
try:
lp_start = lp_info[a]["ALT_START"]
lp_end = lp_info[a]["ALT_END"]
alt_arms.append("ligation")
except KeyError:
continue
lp_ori = lp_end < lp_start
lp_chrom = lp_info[a]["CHR"]
if lp_ori == ep_ori:
if lp_ori:
p_position = lp_end > ep_end
pair_ori = "forward"
else:
p_position = lp_end < ep_end
pair_ori = "reverse"
if p_position:
p_coord = [ep_start, ep_end,
lp_start, lp_end]
p_coord.sort()
prod_size = (p_coord[-1]
- p_coord[0] + 1)
alts[a] = {
"capture_size": prod_size,
"extension_start": ep_start,
"extension_end": ep_end,
"ligation_start": lp_start,
"ligation_end": lp_end,
"mip_start": p_coord[0],
"mip_end": p_coord[3],
"capture_start": p_coord[1] + 1,
"capture_end": p_coord[2] - 1,
"chrom": lp_chrom,
"orientation": pair_ori,
"alternative_arms": alt_arms
}
except KeyError:
# if extension or ligation primer coordinates
# are not available for the paralog copy
# for any reason, e.g. the copy does not align
# to the ref for this primer, there will be
# a key error and it should be caught in this
# block.
continue
# check if any pairs' product is within size limits
captured_copies = []
for a in list(alts.keys()):
# does it satisfy arm setting?
good_alt = 0
# "any" means both ligation and extension arms
# are allowed to have alt sequences.
if alternative_arms == "any":
good_alt = 1
# if only one arm is allowed to have alt sequence,
# it could be specified as "one" or the specific
# arm (extension or ligation).
elif ((len(alts[a]["alternative_arms"]) == 1)
and ((alternative_arms
== alts[a]["alternative_arms"][0])
or (alternative_arms == "one"))):
good_alt = 1
# if the alt capture is valid, check the capture
# size and determined if it is likely to be
# captured.
if good_alt:
if not region_insertions.empty:
max_insertion_size = region_insertions.loc[
(region_insertions["copy_chrom"]
== alts[a]["chrom"])
& (region_insertions["copy_begin"]
> alts[a]["capture_start"])
& (region_insertions["copy_end"]
< alts[a]["capture_end"]),
"max_size"].sum()
else:
max_insertion_size = 0
adjusted_max_size = (max_size
- max_insertion_size)
if adjusted_max_size < (min_size/2):
continue
if (adjusted_max_size
>= alts[a]["capture_size"] >= 0):
captured_copies.append(a)
primer_pairs["pair_information"][
pair_name]["pairs"][a] = alts[a]
primer_pairs["pair_information"][pair_name][
"alt_copies"] = captured_copies
# return if no pairs found
if len(primer_pairs["pair_information"]) == 0:
# No primer pairs found.
return 1
# write dict to file in primer_output_DIR
if outp:
with open(os.path.join(
primer3_output_DIR, output_file), 'w') as outfile:
json.dump(primer_pairs, outfile, indent=1)
return primer_pairs
def add_capture_sequence(primer_pairs, output_file, primer3_output_DIR,
species, outp=1):
"""
Extract the sequence between primers.
Get captured sequence using the primer coordinates.
"""
capture_keys = set()
for p_pair in primer_pairs["pair_information"]:
pairs = primer_pairs["pair_information"][p_pair]["pairs"]
for p in pairs:
paralog_key = pairs[p]["chrom"] + ":" + str(pairs[p][
"capture_start"]) + "-" + str(pairs[p]["capture_end"])
pairs[p]["capture_key"] = paralog_key
capture_keys.add(paralog_key)
capture_sequence_dic = get_fasta_list(capture_keys, species)
for p_pair in primer_pairs["pair_information"]:
pairs = primer_pairs["pair_information"][p_pair]["pairs"]
for p in pairs:
if pairs[p]["orientation"] == "forward":
pairs[p]["capture_sequence"] = capture_sequence_dic[pairs[p][
"capture_key"]]
else:
pairs[p]["capture_sequence"] = reverse_complement(
capture_sequence_dic[pairs[p]["capture_key"]]
)
if outp:
with open(os.path.join(
primer3_output_DIR, output_file), "w") as outfile:
json.dump(primer_pairs, outfile, indent=1)
return primer_pairs
def make_mips(pairs, output_file, primer3_output_DIR, mfold_input_DIR,
backbone, outp=1):
"""
Make mips from primer pairs.
Take the reverse complement of ligation primer sequence, add the backbone
sequence and the extension primer. Standard backbone is used if none
specified.
Add a new key to each primer pair:
"mip_information" with a dictionary that has SEQUENCE key
and mip sequence as value.
"""
# check if the primer dictionary is empty
if len(pairs["pair_information"]) == 0:
return 1
# get primer sequences for each primer pair
for primers in pairs["pair_information"]:
extension_sequence = pairs["pair_information"][primers][
"extension_primer_information"]["SEQUENCE"]
ligation_sequence = pairs["pair_information"][primers][
"ligation_primer_information"]["SEQUENCE"]
# reverse complement ligation primer
ligation_rc = reverse_complement(ligation_sequence)
# add sequences to make the mip
mip_sequence = ligation_rc + backbone + extension_sequence
# create a dictionary to hold mip information
mip_dic = {"ref": {"SEQUENCE": mip_sequence,
"captures": copy.deepcopy(
pairs["pair_information"][primers]
["captured_copies"]
)}}
# create alternative mips where necessary
if "alt_copies" in list(pairs["pair_information"][primers].keys()):
alt_sequences = {}
alt_counter = 0
alt = pairs["pair_information"][primers]["alt_copies"]
p_para = pairs["pair_information"][primers]["pairs"]
e_para = pairs["pair_information"][primers][
"extension_primer_information"]["PARALOG_COORDINATES"]
l_para = pairs["pair_information"][primers][
"ligation_primer_information"]["PARALOG_COORDINATES"]
# since alt primers are created for each copy, it is possible
# that some copies have the same primer pair. Pick just one
# such pair and remove the others.
for a in alt:
if "extension" in p_para[a]["alternative_arms"]:
extension_sequence = e_para[a]["ALT_SEQUENCE"].upper()
if "ligation" in p_para[a]["alternative_arms"]:
ligation_sequence = l_para[a]["ALT_SEQUENCE"].upper()
value_found = 0
# search through already created alt pairs to see if this one
# is already there.
for key, value in list(alt_sequences.items()):
if ([extension_sequence, ligation_sequence]
== value["sequences"]):
value_found = 1
# add the copy name to the dict and not create
# a new key for this copy.
value["copies"].append(a)
break
# create new entry if this alt pair is new
if not value_found:
alt_sequences[alt_counter] = {
"sequences": [extension_sequence, ligation_sequence],
"copies": [a]
}
alt_counter += 1
# create mip sequence and dict for the alt pairs
for alt_pair in alt_sequences:
seq_dic = alt_sequences[alt_pair]["sequences"]
alt_copies = alt_sequences[alt_pair]["copies"]
# reverse complement ligation primer
ligation_rc = reverse_complement(seq_dic[1])
# add sequences to make the mip
mip = ligation_rc + backbone + seq_dic[0]
mip_dic["alt" + str(alt_pair)] = {"SEQUENCE": mip,
"captures": alt_copies}
pairs["pair_information"][primers]["mip_information"] = mip_dic
# write mip sequences to a fasta file in mfold_input_DIR
# to check hairpin formation
with open(os.path.join(mfold_input_DIR, output_file), "w") as outfile:
for primers in pairs["pair_information"]:
outline = (">" + primers + "\n" + pairs["pair_information"]
[primers]["mip_information"]["ref"]['SEQUENCE'] + "\n")
outfile.write(outline)
# write mip dictionary to file in primer3_output_DIR
if outp:
outfile = open(os.path.join(primer3_output_DIR, output_file), 'w')
json.dump(pairs, outfile, indent=1)
outfile.close()
return pairs
def check_hairpin(pairs, output_file, settings, output_dir, outp=1):
"""Check possible hairpin formation in MIP probe.
Calculate possible hiybridization between the MIP arms or between the MIP
arms and the probe backbone. Remove MIPs with likely hairpins.
"""
pairs = copy.deepcopy(pairs)
# get Na, Mg and oligo concentrations these are specified in M but primer3
# uses mM for ions and nM for oligos, so those will be adjusted.
Na = float(settings["mip"]["Na"]) * 1000
Mg = float(settings["mip"]["Mg"]) * 1000
conc = float(settings["mip"]["oligo_conc"]) * pow(10, 9)
# number of mips will be used to determine the bacbone concentration
mip_count = int(settings["mip"]["mipset_size"])
# get TM thresholds for hairpins, arm tms should be the same
# otherwise we'll use the lower of the two
ext_arm_tm = float(settings["extension"]["hairpin_tm"])
lig_arm_tm = float(settings["ligation"]["hairpin_tm"])
arm_tm = min([ext_arm_tm, lig_arm_tm])
# backbone tm will be used for interactions between arms and
# all the backbones (from other mips as well). This will cause a higher
# tm since the backbones will be more in concentration, so it could
# make sense to keep this threshold high. On the other hand, eliminating
# even low likelyhood interactions could be useful.
backbone_tm = float(settings["mip"]["hairpin_tm"])
backbone_name = settings["mip"]["backbone"]
backbone = mip_backbones[backbone_name]
# go through mips and calculate hairpins
# we will calculate hairpins by looking at TMs between arm sequences
# and backbone sequences since the whole MIP sequence is too long
# for nearest neighbor calculations (at least for primer3 implementation).
for p in list(pairs["pair_information"].keys()):
pair_dict = pairs["pair_information"][p]
mip_dict = pair_dict["mip_information"]
# for each primer pair we can have a number of mips due to paralog
# copies having alternative mips. We'll go through each mip.
for m in list(mip_dict.keys()):
mip_seq = mip_dict[m]["SEQUENCE"]
# extract arm and backbone sequences from the mip sequence
lig = mip_seq[:mip_seq.index(backbone)]
ext = mip_seq[mip_seq.index(backbone) + len(backbone):]
bb = backbone.replace("N", "")
# calculate dimer TMs between sequence combinations
ext_lig = calcHeterodimerTm(ext, lig, mv_conc=Na, dv_conc=Mg,
dntp_conc=0, dna_conc=conc)
bb_ext_arm = calcHeterodimerTm(ext, bb, mv_conc=Na, dv_conc=Mg,
dntp_conc=0, dna_conc=conc)
bb_lig_arm = calcHeterodimerTm(lig, bb, mv_conc=Na, dv_conc=Mg,
dntp_conc=0, dna_conc=conc)
# take the maximum TM for hairpin threshold comparison
arms = max([ext_lig, bb_ext_arm, bb_lig_arm])
# calculate TM between arms and the whole reaction backbones
# backbone concentration will be more for this calculation.
bb_ext = calcHeterodimerTm(ext, bb, mv_conc=Na, dv_conc=Mg,
dntp_conc=0, dna_conc=conc * mip_count)
bb_lig = calcHeterodimerTm(lig, bb, mv_conc=Na, dv_conc=Mg,
dntp_conc=0, dna_conc=conc * mip_count)
bb_temp = max([bb_ext, bb_lig])
# if either hairpin tms is higher than the limit, remove the mip
# and remove the paralog copy that is supposed to be captured
# by this specific mip from the pair dictionary.
if (arms > arm_tm) or (bb_temp > backbone_tm):
lost_captures = mip_dict[m]["captures"]
mip_copies = pair_dict["captured_copies"]
mip_copies = list(set(mip_copies).difference(lost_captures))
pair_dict["captured_copies"] = mip_copies
alt_copies = pair_dict["alt_copies"]
alt_copies = list(set(alt_copies).difference(lost_captures))
pair_dict["alt_copies"] = alt_copies
mip_dict.pop(m)
else:
mip_dict[m]["Melting Temps"] = {"arms_hp": ext_lig,
"ext_hp": bb_ext_arm,
"lig_hp": bb_lig_arm,
"ext_backbone": bb_ext,
"lig_backbone": bb_lig}
if len(mip_dict) == 0:
pairs["pair_information"].pop(p)
for p in pairs["pair_information"].keys():
pair_dict = pairs["pair_information"][p]
hp_dict = pair_dict["hairpin"] = {}
mip_dict = pair_dict["mip_information"]
for m in mip_dict:
hp_dict[m] = mip_dict[m]["Melting Temps"]
if outp:
output_file = os.path.join(output_dir, output_file)
with open(output_file, "w") as outfile:
json.dump(pairs, outfile)
return pairs
def filter_mips(mip_dic, bin_size, mip_limit):
"""
Filter MIPs covering similar regions.
Filter MIPs so that only top scoring mip ending within the "bin_size"
nucleotides on the same strand remain.
"""
# load extension and ligation primers from file
shuffled = list(mip_dic.keys())
random.shuffle(shuffled)
for m in shuffled:
if len(mip_dic) <= mip_limit:
return
try:
m_start = mip_dic[m].mip["C0"]["capture_start"]
m_end = mip_dic[m].mip["C0"]["capture_end"]
m_func = mip_dic[m].func_score
m_tech = mip_dic[m].tech_score
m_ori = mip_dic[m].mip["C0"]["orientation"]
for n in shuffled:
if len(mip_dic) <= mip_limit:
return
try:
if mip_dic[m].name != mip_dic[n].name:
n_start = mip_dic[n].mip["C0"]["capture_start"]
n_end = mip_dic[n].mip["C0"]["capture_end"]
n_func = mip_dic[n].func_score
n_tech = mip_dic[n].tech_score
n_ori = mip_dic[n].mip["C0"]["orientation"]
if (((abs(n_start - m_start) <= bin_size)
and (abs(n_end - m_end) <= bin_size))
and (m_ori == n_ori)):
if (m_tech + m_func) >= (n_tech + n_func):
mip_dic.pop(n)
else:
mip_dic.pop(m)
break
except KeyError:
continue
except KeyError:
continue
return
###############################################################
# Data analysis related functions
###############################################################
def get_analysis_settings(settings_file):
"""Convert analysis settings file to dictionary."""
settings = {}
with open(settings_file) as infile:
for line in infile:
try:
if not line.startswith("#"):
newline = line.strip().split("\t")
value = newline[1].split(",")
if len(value) == 1:
settings[newline[0]] = value[0]
else:
settings[newline[0]] = [v for v in value if v != ""]
except Exception as e:
print(("Formatting error in settings file, line {}"
"causing error '{}''").format(line, e))
print(newline)
return
return settings
def write_analysis_settings(settings, settings_file):
"""Create a settings file from a settings dictionary."""
outfile_list = [["# Setting Name", "Setting Value"]]
for k, v in settings.items():
if isinstance(v, list):
val = ",".join(map(str, v))
else:
val = str(v)
outfile_list.append([k, val])
with open(settings_file, "w") as outfile:
outfile.write("\n".join(["\t".join(o) for o in outfile_list]) + "\n")
return
###############################################################################
# New contig based analysis for vcf generation
###############################################################################
def map_haplotypes(settings):
"""Bwa-map haplotypes from MIPWrangler output to the reference genome.
Extract each unique haplotype sequence from the MIPWrangler output and
map to reference genome. MIPWrangler maps the sequencing data to the MIPs
used for an experiment based on the probe arms. We compare here whether
the best genomic loci for a given haplotype matches to the MIPWrangler
assignment. If not, we consider those off target and remove.
"""
wdir = settings["workingDir"]
haplotypes_fq_file = os.path.join(wdir, settings["haplotypesFastqFile"])
haplotypes_sam_file = os.path.join(wdir, settings["haplotypesSamFile"])
bwa_options = settings["bwaOptions"]
call_info_file = settings["callInfoDictionary"]
species = settings["species"]
try:
tol = int(settings["alignmentTolerance"])
except KeyError:
tol = 200
# DATA EXTRACTION ###
raw_results = pd.read_table(os.path.join(wdir,
settings["mipsterFile"]))
##########################################################
# Add the statistics for each haplotype to the data
# such as how many samples had a given haplotype
# and how many barcodes supported a given haplotype
# Filter the haplotypes for those criteria to
# remove possible noise and infrequent haplotypes
##########################################################
# Haplotype Filters from the settings file
haplotype_min_barcode_filter = int(settings["minHaplotypeBarcodes"])
haplotype_min_sample_filter = int(settings["minHaplotypeSamples"])
haplotype_min_sample_fraction_filter = float(
settings["minHaplotypeSampleFraction"]
)
# Gather per haplotype data across samples
hap_counts = raw_results.groupby(
"haplotype_ID"
)["barcode_count"].sum().reset_index().rename(
columns={"barcode_count": "Haplotype Barcodes"})
hap_sample_counts = raw_results.groupby("haplotype_ID")[
"sample_name"].apply(lambda a: len(set(a))).reset_index().rename(
columns={"sample_name": "Haplotype Samples"})
num_samples = float(raw_results["sample_name"].unique().size)
hap_sample_counts["Haplotype Sample Fraction"] = (
hap_sample_counts["Haplotype Samples"] / num_samples
)
hap_counts = hap_counts.merge(hap_sample_counts)
initial_hap_count = len(hap_counts)
hap_counts = hap_counts.loc[(hap_counts["Haplotype Samples"]
>= haplotype_min_sample_filter)
& (hap_counts["Haplotype Sample Fraction"]
>= haplotype_min_sample_fraction_filter)
& (hap_counts["Haplotype Barcodes"]
>= haplotype_min_barcode_filter)]
print(("Out of {} initial haplotypes, {} were filtered using {}, {}, and "
"{} as minimum total UMI count; number and fraction of samples "
" the haplotype was observed in, respectively.").format(
initial_hap_count, initial_hap_count - len(hap_counts),
haplotype_min_barcode_filter, haplotype_min_sample_filter,
haplotype_min_sample_fraction_filter))
hap_df = raw_results.loc[raw_results["haplotype_ID"].isin(
hap_counts["haplotype_ID"])].groupby(
["gene_name", "mip_name", "haplotype_ID"])[
"haplotype_sequence"].first().reset_index()
# fill in fake sequence quality scores for each haplotype. These scores
# will be used for mapping only and the real scores for each haplotype
# for each sample will be added later.This step is probably unnecessary
# as the bwa mem algorithm does not seem to use the quality scores.
hap_df["quality"] = hap_df["haplotype_sequence"].apply(
lambda a: "H" * len(a))
haps = hap_df.set_index("haplotype_ID").to_dict(orient="index")
# BWA alignment
# create a fastq file for bwa input
with open(haplotypes_fq_file, "w") as outfile:
for h in haps:
outfile.write("@" + h + "\n")
outfile.write(haps[h]["haplotype_sequence"] + "\n" + "+" + "\n")
outfile.write(haps[h]["quality"] + "\n")
# run bwa
bwa(haplotypes_fq_file, haplotypes_sam_file, "sam", "", "", bwa_options,
species)
# process alignment output sam file
header = ["haplotype_ID", "FLAG", "CHROM", "POS", "MAPQ", "CIGAR", "RNEXT",
"PNEXT", "TLEN", "SEQ", "QUAL"]
sam_list = []
with open(haplotypes_sam_file) as infile:
for line in infile:
if not line.startswith("@"):
newline = line.strip().split()
samline = newline[:11]
for item in newline[11:]:
value = item.split(":")
if value[0] == "AS":
samline.append(int(value[-1]))
break
else:
samline.append(-5000)
sam_list.append(samline)
sam = pd.DataFrame(sam_list, columns=header + ["alignment_score"])
# find alignment with the highest alignment score. We will consider these
# the primary alignments and the source of the sequence.
sam["best_alignment"] = (sam["alignment_score"] == sam.groupby(
"haplotype_ID")["alignment_score"].transform("max"))
# add MIP column to alignment results
sam["MIP"] = sam["haplotype_ID"].apply(lambda a: a.split(".")[0])
# create call_info data frame for all used probes in the experiment
probe_sets_file = settings["mipSetsDictionary"]
probe_set_keys = settings["mipSetKey"]
used_probes = set()
for psk in probe_set_keys:
with open(probe_sets_file) as infile:
used_probes.update(json.load(infile)[psk])
with open(call_info_file) as infile:
call_info = json.load(infile)
call_df_list = []
for g in call_info:
for m in call_info[g]:
if m in used_probes:
mip_number = int(m.split("_")[-1][3:])
sub_number = int(m.split("_")[-2][3:])
for c in call_info[g][m]["copies"]:
call_dict = call_info[g][m]["copies"][c]
try:
call_dict.pop("genes")
except KeyError:
pass
try:
call_dict.pop("variants")
except KeyError:
pass
call_dict["gene"] = g
call_dict["MIP"] = m
call_dict["copy"] = c
call_dict["mip_number"] = mip_number
call_dict["sub_number"] = sub_number
call_df_list.append(pd.DataFrame(call_dict, index=[0]))
call_df = pd.concat(call_df_list, ignore_index=True, sort=True)
# combine alignment information with design information (call_info)
haplotype_maps = call_df.merge(
sam[["MIP", "haplotype_ID", "CHROM", "POS", "best_alignment",
"alignment_score"]])
haplotype_maps["POS"] = haplotype_maps["POS"].astype(int)
haplotype_maps = haplotype_maps.merge(
hap_df[["haplotype_ID", "haplotype_sequence"]])
# determine which haplotype/mapping combinations are for intended targets
# first, compare mapping coordinate to the MIP coordinate to see if
# a MIP copy matches with the alignment.
haplotype_maps["aligned_copy"] = (
(haplotype_maps["CHROM"] == haplotype_maps["chrom"])
& (abs(haplotype_maps["POS"] - haplotype_maps["capture_start"]) <= tol)
)
# aligned_copy means the alignment is on the intended MIP target
# this is not necessarily the best target, though. For a haplotype sequence
# to be matched to a MIP target, it also needs to be the best alignment.
haplotype_maps["mapped_copy"] = (haplotype_maps["aligned_copy"]
& haplotype_maps["best_alignment"])
# rename some fields to be compatible with previous code
haplotype_maps.rename(columns={"gene": "Gene", "copy": "Copy",
"chrom": "Chrom"}, inplace=True)
# any haplotype that does was not best mapped to at least one target
# will be considered an off target haplotype.
haplotype_maps["off_target"] = ~haplotype_maps.groupby(
"haplotype_ID")["mapped_copy"].transform("any")
off_target_haplotypes = haplotype_maps.loc[haplotype_maps["off_target"]]
# filter off targets and targets that do not align to haplotypes
haplotypes = haplotype_maps.loc[(~haplotype_maps["off_target"])
& haplotype_maps["aligned_copy"]]
# each MIP copy/haplotype_ID combination must have a single alignment
# if there are multiple, the best one will be chosen
haplotypes = haplotypes.groupby(["MIP", "Copy", "haplotype_ID"],
as_index=False).apply(get_best_alignment)
haplotypes.index = (range(len(haplotypes)))
# filter to best mapping copy/haplotype pairs
mapped_haplotypes = haplotypes.loc[haplotypes["mapped_copy"]]
mapped_haplotypes["mapped_copy_number"] = mapped_haplotypes.groupby(
["haplotype_ID"])["haplotype_ID"].transform(len)
mapped_haplotypes.to_csv(os.path.join(
wdir, "mapped_haplotypes.csv"), index=False)
off_target_haplotypes.to_csv(os.path.join(
wdir, "offtarget_haplotypes.csv"), index=False)
haplotypes.to_csv(os.path.join(
wdir, "aligned_haplotypes.csv"), index=False)
haplotype_maps.to_csv(os.path.join(
wdir, "all_haplotypes.csv"), index=False)
num_hap = len(set(haplotype_maps["haplotype_ID"]))
num_off = len(set(off_target_haplotypes["haplotype_ID"]))
print(("{} of {} haplotypes were off-target, either not mapping to "
"the reference genome, or best mapping to a region which was "
"not targeted.").format(num_off, num_hap))
return
def get_vcf_haplotypes(settings):
"""
Reverse compatibile map_haplotypes function.
This is the old name for map_haplotypes function. Some notebooks might
use the old name. So this will just run the map_haplotypes when called
by the old name.
"""
map_haplotypes(settings)
def get_haplotype_counts(settings):
"""Get UMI and read counts for each on target haplotype for each sample.
MIPWrangler output has the UMI and read counts per haplotype but some of
those are off target and some are mapping to multiple loci by design.
The decision on whether a haplotype sequence is on or off target and where
it maps best or if it maps to multiple loci are made by the map_haplotypes
function. This function distributes the UMI and read counts in the
MIPWrangler output using the mapped haplotypes data for each sample.
If a haplotype sequence is uniquely mapping to a targeted locus, we
allocate all reads for that sample and haplotype sequence to that locus.
If it is mapping to multiple places, we determine the ratios of those
'paralogous copies' for that sample based on the average mapping around
each locus and allocate the reads for that sample and that haplotype
sequence proportionally to the mapped loci. If a haplotype sequence is
mapping best to an unintended locus, we remove those.
"""
wdir = settings["workingDir"]
##########################################################
##########################################################
# Process 1: use sample sheet to determine which data points from the
# mipster file should be used, print relevant statistics.
##########################################################
##########################################################
# process sample sheets
run_meta = pd.read_table(os.path.join(wdir, "samples.tsv"))
# create a unique sample ID for each sample using sample name,
# sample set and replicate fields from the sample list file.
run_meta["sample_name"] = (
run_meta["sample_name"].astype(str)
)
run_meta["Sample Name"] = run_meta["sample_name"]
run_meta["Sample ID"] = run_meta[
["sample_name", "sample_set", "replicate"]
].apply(lambda a: "-".join(map(str, a)), axis=1)
# Sample Set key is reserved for meta data
# but sometimes erroneously included in the
# sample sheet. It should be removed.
try:
run_meta.drop("Sample Set", inplace=True, axis=1)
except (ValueError, KeyError):
pass
# a change to the formatting of sample sheets uses library_prep
# instead of Library Prep, so the below line is for backwards compatibility
run_meta.rename(columns={"library_prep": "Library Prep"}, inplace=True)
# drop duplicate values originating from
# multiple sequencing runs of the same libraries
run_meta = run_meta.drop_duplicates()
run_meta = run_meta.groupby(
["Sample ID", "Library Prep"]
).first().reset_index()
run_meta.to_csv(os.path.join(wdir, "run_meta.csv"))
# get used sample ids
sample_ids = run_meta["Sample ID"].unique().tolist()
##########################################################
##########################################################
# Process 2: extract all observed variants from observed
# haplotypes and create a variation data frame that will
# be able to map haplotype IDs to variation.
##########################################################
##########################################################
# get the haplotype dataframe for all mapped haplotypes
mapped_haplotype_df = pd.read_csv(
os.path.join(wdir, "mapped_haplotypes.csv"))
##########################################################
##########################################################
# Process 3: load the MIPWrangler output which has
# per sample per haplotype information, such as
# haplotype sequence quality, barcode counts etc.
# Create a suitable dataframe that can be merged
# with variant data to get the same information for each
# variant (variant barcode count, variant quality, etc.)
##########################################################
##########################################################
# get the MIPWrangler Output
raw_results = pd.read_table(os.path.join(wdir, settings["mipsterFile"]))
# limit the results to the samples intended for this analysis
raw_results = raw_results.loc[
raw_results["sample_name"].isin(sample_ids)
]
# rename some columns for better visualization in tables
raw_results.rename(
columns={"sample_name": "Sample ID",
"mip_name": "MIP",
"gene_name": "Gene",
"barcode_count": "Barcode Count",
"read_count": "Read Count"},
inplace=True
)
# use only the data corresponding to mapped haplotypes
# filtering the off target haplotypes.
mapped_results = raw_results.merge(mapped_haplotype_df, how="inner")
# Try to estimate the distribution of data that is mapping
# to multiple places in the genome.
# This is done in 4 steps.
# 1) Get uniquely mapping haplotypes and barcode counts
unique_df = mapped_results.loc[mapped_results["mapped_copy_number"] == 1]
unique_table = pd.pivot_table(unique_df,
index="Sample ID",
columns=["Gene", "MIP", "Copy", "Chrom"],
values=["Barcode Count"],
aggfunc=np.sum)
# 2) Estimate the copy number of each paralog gene
# for each sample from the uniquely mapping data
# Two values from the settings are used to determine the copy number
# in a given gene. Average copy count is the ploidy of the organism
# and the normalization percentile is what percentile is used for
# normalizing data. For example, for human genes ACC is 2 and
# if the percentiles are given as 0.4, 0.6: we would calculate the
# take the 40th and 60th percentile of them barcode counts for each probe
# across the samples and assume that the average of 40th and 60 pctl values
# to represent the average copy count of 2. Then caluculate this value
# for each probe and each sample.
try:
average_copy_count = float(settings["averageCopyCount"])
norm_percentiles = list(map(float,
settings["normalizationPercentiles"]))
except KeyError:
average_copy_count = 2
norm_percentiles = [0.4, 0.6]
unique_df.loc[:, "Copy Average"] = average_copy_count
# Adjusted barcode count will represent the estimated barcode count
# for multimapping haplotypes. For example, if hap1 is mapping to 2
# places in the genome and its barcode count for a sample containing this
# haplotype is 100. If we determined the copy numbers of the two mapping
# regions to be 1 and 1, the adjusted barcode count for each region
# would be 50. We'll set this value for uniquely mapping haplotypes
# to the Barcode Count, as they are not multi mapping.
unique_df.loc[:, "Adjusted Barcode Count"] = unique_df["Barcode Count"]
unique_df.loc[:, "Adjusted Read Count"] = unique_df["Read Count"]
unique_table.fillna(0, inplace=True)
# calculate the copy counts using the get_copy_counts function.
# this function normalizes data for each probe across samples
# and estimates copy counts using the percentile values as mentioned.
copy_counts = get_copy_counts(unique_table,
average_copy_count,
norm_percentiles)
# 3) Estimate the copy number of each "Gene"
# from the average copy count of uniquely mapping
# data for all MIPs within the gene.
cc = copy_counts.groupby(level=["Gene", "Copy"], axis=1).sum()
gc = copy_counts.groupby(level=["Gene"], axis=1).sum()
ac = cc.div(gc, level="Gene")
# 4) Distribute multi mapping data proportional to
# Paralog's copy number determined from the
# uniquely mapping data
multi_df = mapped_results.loc[mapped_results["mapped_copy_number"] > 1]
if not multi_df.empty:
# get the average copy count for the gene the haplotype belongs to
mca = multi_df.apply(lambda r: get_copy_average(r, ac), axis=1)
multi_df.loc[mca.index, "Copy Average"] = mca
multi_df["copy_sum"] = multi_df.groupby(
["Sample ID", "haplotype_ID"])["Copy Average"].transform("sum")
multi_df["copy_len"] = multi_df.groupby(
["Sample ID", "haplotype_ID"])["Copy Average"].transform("size")
null_index = multi_df["copy_sum"] == 0
multi_df.loc[null_index, "Copy Average"] = (
average_copy_count / multi_df.loc[null_index, "copy_len"])
multi_df.loc[null_index, "copy_sum"] = average_copy_count
multi_df["Copy Average"].fillna(0, inplace=True)
multi_df["Adjusted Barcode Count"] = (multi_df["Barcode Count"]
* multi_df["Copy Average"]
/ multi_df["copy_sum"])
multi_df["Adjusted Read Count"] = (multi_df["Read Count"]
* multi_df["Copy Average"]
/ multi_df["copy_sum"])
# Combine unique and multimapping data
combined_df = pd.concat([unique_df, multi_df], ignore_index=True,
sort=True)
combined_df.rename(
columns={
"Barcode Count": "Raw Barcode Count",
"Adjusted Barcode Count": "Barcode Count",
"Read Count": "Raw Read Count",
"Adjusted Read Count": "Read Count"
},
inplace=True
)
# print total read and barcode counts
print(
(
"Total number of reads and barcodes were {0[0]} and {0[1]}."
" On target number of reads and barcodes were {1[0]} and {1[1]}."
).format(
raw_results[["Read Count", "Barcode Count"]].sum(),
combined_df[["Read Count", "Barcode Count"]].sum().astype(int)
)
)
combined_df.to_csv(os.path.join(wdir, "haplotype_counts.csv"), index=False)
# So far the count data only includes MIPs that has at least one read
# in at least one sample. We would like to include MIPs with no reads
# as well. So we'll create a dataframe that has all the intended MIPs
# and merge with the count data.
# create call_info data frame for all used probes in the experiment
call_info_file = settings["callInfoDictionary"]
probe_sets_file = settings["mipSetsDictionary"]
probe_set_keys = settings["mipSetKey"]
used_probes = set()
for psk in probe_set_keys:
with open(probe_sets_file) as infile:
used_probes.update(json.load(infile)[psk])
with open(call_info_file) as infile:
call_info = json.load(infile)
call_df_list = []
for g in call_info:
for m in call_info[g]:
if m in used_probes:
for c in call_info[g][m]["copies"]:
call_dict = {"MIP": m, "Copy": c}
call_df_list.append(pd.DataFrame(call_dict, index=[0]))
call_df = pd.concat(call_df_list, ignore_index=True, sort=True)
# merge the count data with probe data. Fill missing values with 0.
combined_df = call_df.merge(combined_df, how="left").fillna(0)
# Create pivot table of combined barcode counts
# This is a per MIP per sample barcode count table
# of the samples with sequencing data
barcode_counts = pd.pivot_table(combined_df,
index="Sample ID",
columns=["MIP",
"Copy"],
values=["Barcode Count"],
aggfunc=np.sum)
# Sample name for probes without data would be NA and replaced to 0
# remove that if it exists
try:
barcode_counts.drop(0, inplace=True)
except KeyError:
pass
print("There are {} samples with sequence data".format(
barcode_counts.shape[0]
))
# After pivot table is created, the column names have an extra
# row with the name "Barcode Count". Remove that from column names.
bc_cols = barcode_counts.columns
bc_cols = [bc[1:] for bc in bc_cols]
# barcode count data is only available for samples with data
# so if a sample has not produced any data, it will be missing
# these samples should be added with 0 values for each probe
all_barcode_counts = pd.merge(
run_meta[["Sample ID", "replicate"]].set_index("Sample ID"),
barcode_counts, left_index=True, right_index=True, how="left")
all_barcode_counts.drop("replicate", axis=1, inplace=True)
# fix column names
all_barcode_counts.columns = pd.MultiIndex.from_tuples(
bc_cols, names=["MIP", "Copy"]
)
all_barcode_counts.fillna(0, inplace=True)
print("There are {} total samples.".format(all_barcode_counts.shape[0]))
all_barcode_counts.to_csv(os.path.join(wdir, "barcode_counts.csv"))
# Create an overview statistics file for samples including
# total read count, barcode count, and how well they cover each MIP.
sample_counts = combined_df.groupby("Sample ID")[["Read Count",
"Barcode Count"]].sum()
# Find samples without any data and print the number
no_data = run_meta.loc[
~run_meta["Sample ID"].isin(sample_counts.index)
]
print(("{} out of {} samples had no data and they will be excluded from "
"the variant calls.").format(no_data.shape[0], run_meta.shape[0]))
# add samples with no data
sample_counts = pd.merge(
run_meta[["Sample ID", "replicate"]].set_index("Sample ID"),
sample_counts, left_index=True, right_index=True, how="left")
sample_counts.drop("replicate", axis=1, inplace=True)
target_cov = pd.concat(
[(all_barcode_counts >= 1).sum(axis=1),
(all_barcode_counts >= 5).sum(axis=1),
(all_barcode_counts >= 10).sum(axis=1)],
axis=1,
).rename(
columns={
0: "targets_with_1_barcodes",
1: "targets_with_5_barcodes",
2: "targets_with_10_barcodes"
}
)
sample_counts = sample_counts.merge(target_cov,
how="outer",
left_index=True,
right_index=True).fillna(0)
target_cov_file = os.path.join(wdir, "sample_summary.csv")
sample_counts.to_csv(target_cov_file)
return
def freebayes_call(bam_dir="/opt/analysis/padded_bams",
fastq_dir="/opt/analysis/padded_fastqs",
options=[],
vcf_file="/opt/analysis/variants.vcf.gz",
targets_file=None, make_fastq=True,
align=True, settings=None, settings_file=None,
bam_files=None, bam_list=None, verbose=True,
fastq_padding=20, min_base_quality=1,
errors_file="/opt/analysis/freebayes_errors.txt",
warnings_file="/opt/analysis/freebayes_warnings.txt",
merge_distance=1000, contig_padding=500):
"""Call variants for MIP data using freebayes.
A mapped haplotype file must be present in the working directory. This
is generated during haplotype processing. Per sample fastqs and bams
will be created if align=True. Fastqs are generated with a default 20 bp
padding on each side of the haplotype. This assumes that there were no
errors where the MIP arms bind to the DNA. It may cause some false negative
calls where there was imperfect binding, but it is crucial for determining
variants close to the MIP arms.
Parameters
----------
bam_dir: str/path, /opt/analysis/padded_bams
path to the directory where per sample bam files are or where they
will be created if align=True.
fastq_dir: str/path, /opt/analysis/padded_fastqs
path to the directory where per sample fastq files are or where they
will be created if align=True.
vcf_file: str/path, /opt/analysis/variants.vcf.gz
Output vcf file path.
options: list, []
options to pass to freebayes directly, such as --min-coverage
the list must have each parameter and value as separate items.
For example, ["--min-alternate-count", "2"] and not
["--min-alternate-count 2"]
align: bool, True
Set to false if fastq and bam files have already been created.
settings: dict, None
Analysis settings dictionary. Either this or settings_file must
be provided.
settings_file: str/path, None
Path to the analysis settings file. Either this or the settings dict
must be provided.
targets_file: str/path, None
Path to targets file to force calls on certain locations even if
those variants do not satisfy filter criteria. It must be a tab
separated text file with minimum columns CHROM, POS, REF, ALT.
bam_files: list, None
list of bam files within the bam_dir to pass to freebayes. If None (
default), all bam files in the bam_dir will be used.
verbose: bool, True
if set to True, print errors and warnings in addition to saving to
errors and warnings files.
errors_file: str/path, /opt/analysis/freebayes_errors.txt
file to save freebayes errors.
warnings_file: str/path, /opt/analysis/freebayes_warnings
file to save freebayes warnings
merge_distance: int, 200
When creating contigs from MIP target regions, merge targets closer
to each other than this distance.
contig_padding: int, 50
Add this much padding to the contigs when calling freebayes.
"""
# get the analysis settings
# check if both settings and the settings file are None:
if (settings is None) and (settings_file is None):
print("settings or settings file must be provided for freebayes_call.")
return
else:
if settings is None:
settings = get_analysis_settings(settings_file)
else:
settings = copy.deepcopy(settings)
# get the working directory from settings
wdir = settings["workingDir"]
# load mapped haplotypes file. This file has the genomic locations
# of the haplotypes in mip data
mapped_haplotypes_file = os.path.join(wdir, "mapped_haplotypes.csv")
# get the mip data file location. This file has per sample haplotype
# information including counts.
mipster_file = os.path.join(wdir, settings["mipsterFile"])
if make_fastq:
# create fastq files from MIP data. One read per UMI will be created.
generate_mapped_fastqs(fastq_dir, mipster_file,
mapped_haplotypes_file, settings["species"],
pro=int(settings["processorNumber"]),
pad_size=fastq_padding)
if align:
# map per sample fastqs to the reference genome, creating bam files.
# bam files will have sample groups added, which is required for
# calling variants across the samples.
bwa_multi([], "bam", fastq_dir, bam_dir,
settings["bwaOptions"], settings["species"],
int(settings["processorNumber"]),
int(settings["processorNumber"]))
# divide data into contigs to make parallelization more efficient
# we'll create contigs from overlapping MIPs.
# load the call info dictionary which contains per MIP information
call_file = settings["callInfoDictionary"]
with open(call_file) as infile:
call_dict = json.load(infile)
# create a dataframe that has the genomic coordinates of each MIP
call_df = []
for g in call_dict:
for m in call_dict[g]:
for c in call_dict[g][m]["copies"]:
cdict = call_dict[g][m]["copies"][c]
call_df.append([cdict["chrom"], cdict["capture_start"],
cdict["capture_end"]])
call_df = pd.DataFrame(call_df, columns=["chrom", "capture_start",
"capture_end"])
# create a function that generates contigs of MIPs which overlap
# with 1 kb padding on both sides.
# create contigs per chromosome
contigs = call_df.groupby("chrom").apply(get_contig)
contigs = contigs.reset_index()
contigs.rename(columns={"level_1": "contig", 0: "contig_capture_start",
1: "contig_capture_end"}, inplace=True)
contigs["contig_name"] = contigs["chrom"] + "_" + contigs["contig"].astype(
str)
# we'll call freebayes on each contig by providing a region string in the
# form chrx:begin-end. Create those strings for each contig with some
# padding. It is important to check that we don't end up with a start
# position of <1 or end position longer than chom length.
# Begin by adding chromosome length to contig info.
# get reference chromosome lengths
genome_file = get_file_locations()[settings["species"]]["fasta_genome"]
reference_lengths = {}
genome_sam = pysam.FastaFile(genome_file)
for r in genome_sam.references:
reference_lengths[r] = genome_sam.get_reference_length(r)
contigs["chromosome_length"] = contigs["chrom"].map(reference_lengths)
contigs["region_start"] = contigs["contig_capture_start"] - contig_padding
contigs.loc[contigs["region_start"] < 1, "region_start"] = 1
contigs["region_end"] = contigs["contig_capture_end"] + contig_padding
contigs["region_end"] = contigs[
["region_end", "chromosome_length"]].min(axis=1).values
contigs["region"] = contigs["chrom"] + ":" + (
contigs["region_start"]).astype(str) + "-" + (
contigs["region_end"]).astype(str)
# we'll force calls on targeted variants if so specified
if targets_file is not None:
# each contig must include at least one of the targets, otherwise
# freebayes throws an error. So we'll load the targets and add the
# targets option to only those contigs that contain targets
targets = pd.read_table(targets_file)
# merge targets and contigs dataframes to determine which contigs
# contain targets. chrom will be used as the common column name
targets["chrom"] = targets["CHROM"]
targets = targets.merge(contigs)
# remove rows where chrom is shared but target position is outside
# of contig boundries.
targets = targets.loc[
(targets["contig_capture_start"] <= targets["POS"])
& (targets["POS"] <= targets["contig_capture_end"])]
targets["contains_targets"] = True
# merge only two columns of the targets df to contigs so that
# the only shared column is contig_name. More than one target can
# be in a single contig, so we need to drop duplicates from targets.
contigs = contigs.merge(targets[
["contig_name", "contains_targets"]].drop_duplicates(), how="left")
contigs["contains_targets"].fillna(False, inplace=True)
# create a targets.vcf file for freebayes
targets_vcf = os.path.join(wdir, "targets.vcf")
with open(targets_vcf, "w") as outfile:
outfile.write('##fileformat=VCFv4.2\n')
outfile.write(
'##FILTER=<ID=PASS,Description="All filters passed">\n')
outfile.write('##INFO=<ID=TR,Number=.,Type=String,Description'
'="Targeted variant.">\n')
vcf_fields = ["ID", "QUAL", "FILTER"]
for vf in vcf_fields:
targets[vf] = "."
targets["INFO"] = "TR"
vcf_fields = ["#CHROM", "POS", "ID", "REF", "ALT", "QUAL",
"FILTER", "INFO"]
targets = targets.rename(columns={"CHROM": "#CHROM"})[vcf_fields]
targets.sort_values(["#CHROM", "POS"]).to_csv(
outfile, sep="\t", index=False)
# bgzip and index
res = subprocess.run(["bgzip", "-f", targets_vcf],
stderr=subprocess.PIPE)
if res.returncode != 0:
print("Error in compressing targets.vcf file", res.stderr)
targets_vcf = targets_vcf + ".gz"
res = subprocess.run(["tabix", "-s", "1", "-b", "2", "-e", "2", "-f",
targets_vcf], stderr=subprocess.PIPE)
if res.returncode != 0:
print("Error in indexing targets.vcf.gz file ", res.stderr)
else:
contigs["contains_targets"] = False
# create a contig dictionary from the contigs dataframe
# this dict will be passed to the worker function for parallelization
chrom_dict = {}
gb = contigs.groupby("chrom")
for g in gb.groups.keys():
gr = gb.get_group(g)
chrom_dict[g] = gr[["contig_name", "region",
"contains_targets"]].set_index(
"contig_name").to_dict(orient="index")
# populate the contigs dictionary for freebayes parameters
# start with options to be added for each contig
# get fasta genome location
genome_fasta = get_file_locations()[settings["species"]]["fasta_genome"]
# specify fasta genome file
options.extend(["-f", genome_fasta])
# add if bam files are specified. Nothing should be added to options
# after the bam files.
if bam_files is not None:
options.extend(bam_files)
if bam_list is not None:
options.extend(["-L", bam_list])
# create a file list in the bam_dir that has full path to all bam files
# if all bam files are to be used
else:
bam_list = os.path.join(bam_dir, "bamlist.txt")
with open(bam_list, "w") as outfile:
for f in os.scandir(bam_dir):
if os.path.splitext(f.name)[1] == ".bam":
outfile.write(f.path + "\n")
options.extend(["-L", bam_list])
# add minimum base quality parameter to options if not already provided
if ("--min-base-quality" not in options) and ("-q" not in options):
options.extend(["-q", str(min_base_quality)])
# create a list for keeping all contig vcf file paths to concatanate
# them at the end.
contig_vcf_paths = []
# create a similar list for zipped vcf files
contig_vcf_gz_paths = []
# create a list of per contig dictionary to feed to multiprocessing
# function apply_async
contig_dict_list = []
# create the contigs vcf directory
cvcfs_dir = os.path.join(wdir, "contig_vcfs")
if not os.path.exists(cvcfs_dir):
os.makedirs(cvcfs_dir)
# update contig_dict with contig specific options
for chrom in chrom_dict:
for contig_name in chrom_dict[chrom]:
contig_dict = chrom_dict[chrom][contig_name]
################################################################
# create contig specific options and
# add contigs region string (chrx:begin-end)
region = contig_dict["region"]
contig_options = ["-r", region]
# add contigs vcf file name
contig_vcf = os.path.join(wdir, "contig_vcfs",
contig_name + ".vcf")
contig_dict["vcf_path"] = contig_vcf
# add output file to the freebayes options
contig_options.extend(["-v", contig_vcf])
# add contig vcf path to the list
contig_vcf_paths.append(contig_vcf)
# add contigs vcf.gz file name
contig_vcf_gz = os.path.join(wdir, "contig_vcfs",
contig_name + ".vcf.gz")
contig_vcf_gz_paths.append(contig_vcf_gz)
contig_dict["vcf_gz_path"] = contig_vcf_gz
# if contig includes targets, we'll force calls on those
if contig_dict["contains_targets"]:
contig_options.extend(["-@", targets_vcf])
# we'll add the contig specific options to the beginning of
# the options list in case bam files were added to the options
# and they must stay at the end because they are positional args.
contig_dict["options"] = contig_options + options
# add the contig dict to contig dict list
contig_dict_list.append(contig_dict)
# create a processor pool for parallel processing
pool = Pool(int(settings["processorNumber"]))
# create a results container for the return values from the worker function
results = []
errors = []
# run the freebayes worker program in parallel
pool.map_async(freebayes_worker, contig_dict_list, callback=results.extend,
error_callback=errors.extend)
# join and close the processor pool.
pool.close()
pool.join()
# compare the length of the results object and the number of contigs
# print an error message if they are not the same
if len(contig_dict_list) != (len(results) + len(errors)):
print(("Number of contigs, {}, is not the same as number of results "
"from the variant caller, {}, plus number of errors, {}. "
"This means some calls have failed silently. "
"Results and errors should be inspected.").format(
len(contig_dict_list), len(results), len(errors)))
# check each contig's variant call results for errors and warnings
# open files to save errors and warnings
with open(errors_file, "w") as ef, open(warnings_file, "wb") as wf:
# keep a count of warnings an errors
error_count = 0
warning_count = 0
for res in results:
for r in res:
try:
r.check_returncode()
except subprocess.CalledProcessError as e:
error_count += 1
ef.write(str(e) + "\n")
if verbose:
print("Error in freebayes calls: ", e)
# print if any warnings were issued
if len(r.stderr) > 0:
warning_count += 1
wf.write(r.stderr + b"\n")
if verbose:
print("Warning in freebayes calls: ", r.stderr)
# if errors are not printed but present, print an message to indicate
# the presence of errors/warnings
if not verbose:
if error_count > 0:
print(("Errors were encountered in freebayes calls."
" Please inspect {} for errors.").format(errors_file))
if warning_count > 0:
print(("There were warnings in freebayes calls."
" Please inspect {} for warnings.").format(
warnings_file))
if len(errors) > 0:
print(("There were {} calls that failed").format(len(errors)))
# concatanate contig vcfs. The number of contigs may be high, so we'll
# write the vcf paths to a file and bcftools will read from that file
cvcf_paths_file = os.path.join(wdir, "contig_vcfs", "vcf_file_list.txt")
with open(cvcf_paths_file, "w") as outfile:
outfile.write("\n".join(contig_vcf_gz_paths) + "\n")
subprocess.run(["bcftools", "concat", "-f", cvcf_paths_file, "-Oz",
"-o", vcf_file], check=True)
subprocess.run(["bcftools", "index", "-f", vcf_file], check=True)
# fix vcf header if --gvcf option has been used
if "--gvcf" in options:
temp_vcf_path = os.path.join(wdir, "temp.vcf.gz")
vcf_reheader(os.path.basename(vcf_file), temp_vcf_path, wdir=wdir)
old_vcf_path = os.path.join(wdir, "unfixed.vcf.gz")
subprocess.run(["mv", vcf_file, old_vcf_path])
subprocess.run(["mv", temp_vcf_path, vcf_file])
subprocess.run(["bcftools", "index", "-f", vcf_file], check=True)
return (contig_dict_list, results, errors)
def freebayes_worker(contig_dict):
"""Run freebayes program with the specified options.
Run freebayes program with the specified options and return a
subprocess.CompletedProcess object.
"""
options = contig_dict["options"]
command = ["freebayes"]
command.extend(options)
# run freebayes command piping the output
fres = subprocess.run(command, stderr=subprocess.PIPE)
# check the return code of the freebayes run. if succesfull continue
if fres.returncode == 0:
# bgzip the vcf output, using the freebayes output as bgzip input
vcf_path = contig_dict["vcf_path"]
gres = subprocess.run(["bgzip", "-f", vcf_path],
stderr=subprocess.PIPE)
# make sure bugzip process completed successfully
if gres.returncode == 0:
# index the vcf.gz file
vcf_gz_path = contig_dict["vcf_gz_path"]
ires = subprocess.run(["bcftools", "index", "-f", vcf_gz_path],
stderr=subprocess.PIPE)
# return the CompletedProcess objects
return (fres, gres, ires)
else:
return (fres, gres)
# if freebayes call failed, return the completed process object
# instead of attempting to zip the vcf file which does not exist if
# freebayes failed.
else:
return (fres, )
def vcf_reheader(vcf_file, fixed_vcf_file, wdir="/opt/analysis/"):
"""Fix vcf header QA/QR fields.
When --gvcf option is used in freebayes variant calling pipeline,
the header of the vcf file comes out incorrect for QA/QR fields number
type, Integer instead of Float. This function fixes those lines from
the header and creates a new vcf file with the correct header.
"""
# get the current header
vcf_path = os.path.join(wdir, vcf_file)
header = subprocess.Popen(["bcftools", "view", "-h", vcf_path],
stderr=subprocess.PIPE, stdout=subprocess.PIPE)
com = header.communicate()
if header.returncode != 0:
print("Failed to extract vcf header. Header will not be fixed.")
return
# convert the header byte string to text and creale a list of lines
head = com[0].decode("utf-8").split("\n")
# create a new header with fixed fields
new_head = []
for line in head:
if ("ID=QA" in line) or ("ID=QR" in line):
new_head.append(line.replace("Type=Integer", "Type=Float"))
else:
new_head.append(line)
new_header_path = os.path.join(wdir, "new_vcf_header.txt")
with open(new_header_path, "w") as outfile:
outfile.write("\n".join(new_head) + "\n")
fixed_vcf_path = os.path.join(wdir, fixed_vcf_file)
subprocess.run(["bcftools", "reheader", "-h", new_header_path,
vcf_path, "-o", fixed_vcf_path], check=True)
return
def gatk(options):
"""GATK wrapper function.
Run gatk program with the given options. Return the subprocess result.
"""
return subprocess.run(["gatk", *options], stderr=subprocess.PIPE)
def gatk_file_prep(bam_dir="/opt/analysis/padded_bams",
fastq_dir="/opt/analysis/padded_fastqs",
targets_file=None,
settings=None, settings_file=None,
errors_file="/opt/analysis/gatk_file_prep_output.txt"):
"""Prepare files for calling variants for MIP data using gatk.
A mapped haplotype file must be present in the working directory. This
is generated during haplotype processing. Per sample fastqs and bams
will be created. Fastqs are generated with a default 20 bp
padding on each side of the haplotype. This assumes that there were no
errors where the MIP arms bind to the DNA. It may cause some false negative
calls where there was imperfect binding, but it is crucial for determining
variants close to the MIP arms.
Parameters
----------
bam_dir: str/path, /opt/analysis/padded_bams
path to the directory where per sample bam files are or where they
will be created if align=True.
fastq_dir: str/path, /opt/analysis/padded_fastqs
path to the directory where per sample fastq files are or where they
will be created if align=True.
settings: dict, None
Analysis settings dictionary. Either this or settings_file must
be provided.
settings_file: str/path, None
Path to the analysis settings file. Either this or the settings dict
must be provided.
targets_file: str/path, None
Path to targets file to force calls on certain locations even if
those variants do not satisfy filter criteria. It must be a tab
separated text file with minimum columns CHROM, POS, REF, ALT.
errors_file: str/path, /opt/analysis/gatk_file_prep_errors.txt
file to save freebayes errors.
"""
# get the analysis settings
# check if both settings and the settings file are None:
if (settings is None) and (settings_file is None):
print("settings or settings file must be provided for freebayes_call.")
return
else:
if settings is None:
settings = get_analysis_settings(settings_file)
else:
settings = copy.deepcopy(settings)
# get the working directory from settings
wdir = settings["workingDir"]
# load mapped haplotypes file. This file has the genomic locations
# of the haplotypes in mip data
mapped_haplotypes_file = os.path.join(wdir, "mapped_haplotypes.csv")
# get the mip data file location. This file has per sample haplotype
# information including counts.
mipster_file = os.path.join(wdir, settings["mipsterFile"])
# get the mip data file location. This file has per sample haplotype
# information including counts.
mipster_file = os.path.join(wdir, settings["mipsterFile"])
# create fastq files from MIP data. One read per UMI will be created.
generate_mapped_fastqs(fastq_dir, mipster_file,
mapped_haplotypes_file, settings["species"],
pro=int(settings["processorNumber"]))
# if there is a targets file provided, we'll create a hypothetical
# sample that has all of the targeted variants. This way, a variant site
# for each target will be created in the final vcf file even if a
# variant was not observed in the data.
if targets_file is not None:
# load the targets as dataframe converting field names to
# field names in a haplotypes file.
targets = pd.read_table(targets_file).rename(
columns={"CHROM": "Chrom", "POS": "capture_start",
"ALT": "haplotype_sequence",
"mutation_name": "haplotype_ID"})
# fill in orientation and copy number information for all targets.
targets["orientation"] = "forward"
targets["mapped_copy_number"] = 1
targets["capture_end"] = (targets["capture_start"]
+ targets["REF"].apply(len) - 1)
# create a haplotype file for the targeted mutations
haplotype_fields = ['capture_end', 'capture_start', 'Chrom',
'orientation', 'haplotype_ID',
'haplotype_sequence', 'mapped_copy_number']
mutant_haplotypes = "/opt/analysis/mutant_haplotypes.csv"
targets[haplotype_fields].to_csv(mutant_haplotypes, index=False)
# create a hypothetical sample that has all mutations and a
# corresponding mip data file that shows a UMI count of 20
# for each observation
targets["sample_name"] = "control_mutant"
targets["sequence_quality"] = targets["haplotype_sequence"].apply(
lambda a: "".join(["H" for i in range(len(a))]))
targets["barcode_count"] = 20
data_fields = ["sample_name", 'haplotype_ID', "haplotype_sequence",
'sequence_quality', 'barcode_count']
mutant_data_file = "/opt/analysis/mutant_data.tsv"
targets[data_fields].to_csv(mutant_data_file, index=False, sep="\t")
# create a fastq file for the "control_mutant" sample
padding = 100
generate_mapped_fastqs(fastq_dir, mutant_data_file,
mutant_haplotypes, settings["species"],
pro=int(settings["processorNumber"]),
pad_size=padding)
# map per sample fastqs to the reference genome, creating bam files.
# bam files will have sample groups added, which is required for
# calling variants across the samples.
bwa_multi([], "bam", fastq_dir, bam_dir,
settings["bwaOptions"], settings["species"],
int(settings["processorNumber"]),
int(settings["processorNumber"]))
# create an intervals file to be used in gatk call
intervals_bed = "/opt/analysis/intervals.bed"
call_file = settings["callInfoDictionary"]
with open(call_file) as infile:
call_dict = json.load(infile)
# create a dataframe that has the genomic coordinates of each MIP
probe_info = []
for g in call_dict:
for m in call_dict[g]:
for c in call_dict[g][m]["copies"]:
cdict = call_dict[g][m]["copies"][c]
probe_info.append([cdict["chrom"], cdict["capture_start"],
cdict["capture_end"]])
probe_info = pd.DataFrame(probe_info, columns=["chrom", "capture_start",
"capture_end"])
probe_info["bed_start"] = probe_info["capture_start"] - 200
probe_info["bed_end"] = probe_info["capture_end"] + 200
probe_info[["chrom", "bed_start", "bed_end"]].to_csv(
intervals_bed, index=False, header=(None), sep="\t")
intervals_list = "/opt/analysis/intervals.list"
genome_dict = get_file_locations()[settings["species"]]["genome_dict"]
interval_call = gatk(["BedToIntervalList", "-I", intervals_bed,
"-O", intervals_list, "-SD", genome_dict])
# check the return code and if not 0 print warning
if interval_call.returncode != 0:
print(("An error ocurred when creating the intervals list. "
"Please see the {} for details.").format(errors_file))
# save command output
with open(errors_file, "ab") as outfile:
outfile.write(interval_call.stderr)
def vcf_to_tables_fb(vcf_file, settings=None, settings_file=None,
annotate=True, geneid_to_genename=None,
target_aa_annotation=None, aggregate_aminoacids=False,
target_nt_annotation=None, aggregate_nucleotides=False,
decompose_options=[], annotated_vcf=False,
aggregate_none=False, min_site_qual=-1,
min_target_site_qual=-1, min_genotype_qual=-1,
min_alt_qual=-1, min_ref_qual=-1, min_mean_alt_qual=-1,
min_mean_ref_qual=-1, output_prefix=""):
"""Create various tables from a vcf file.
Create various tables from a vcf file generated by the freebayes
program. There are 3 different types of count output for each variant:
variant count, reference count and coverage. The vcf file will be split
into biallelic variants. Table versions of the input vcf will be created
but the info fields will be limited to the mandatory vcf fields and some
annotation data if avaliable.
In addition to the original vcf table, aa change tables can be generated.
These will be generated by filtering the vcf to missense variants only,
decomposing block substitutions (haplotypes) and combining the counts for
the same aminoacid changes. This operation is specifically intended for
generating data for targeted missense mutations and only reports that. All
other variants, even those complex variants including targeted variants
will not be reported. Finally, one specific mutation (dhps-437) will have
reference counts instead of variant counts if present. This is because this
drug resistance variant is encoded by the 3d7 reference sequence.
Parameters
----------
settings: dict, None
Analysis settings dictionary. Either this or settings_file must
be provided.
settings_file: str/path, None
Path to the analysis settings file. Either this or the settings dict
must be provided.
annotate: bool, True
Annotate variant file. This is required for protein level analysis.
vcf_file: str/path
Starting vcf file.
geneid2genename: str/path, None.
Path to a tab separated tex file that maps gene ids to gene names.
Column names must be gene_id and gene_name. Gene IDs
will populate the Gene field if this file is not provided.
target_aa_annotation: str/path, None.
Path to a tab separated text file with targeted variant information to
annotate and label targeted amino acid changes.
It must have gene_name, aminoacid_change, and mutation_name columns.
Amino acid changes should be represented as refAAPosAltAA. refAA and
AltAA must be three letter amino acid codes.
This file is required for targeted protein variant labeling.
target_nt_annotation: str/path, None.
Path to a tab separated text file with targeted variant information to
annotate and label targeted nucleotide changes.
It must have CHROM, POS, REF, ALT, NAME columns.
This file is required for targeted nucleotide variant labeling.
aggregate_aminoacids: bool, False
whether counts for same amino acids should be aggregated. This involves
decomposing multi amino acid changes for missense variants. If amino
acid based targets will be annotated, based on a provided annotation
dictionary, aggregation step must be completed. Targeted mutations
that are part of complex events (indels, stop loss/gain etc.) will not
be labeled as targeted.
aggregate_nucleotides: bool, False
whether the counts for nucleotide changes should be aggregated. This
involves decomposing all variants to the smallest units possible,
breaking all haplotype data. The level of decomposition should be
specified with the decompose_options parameter.
aggregate_none: bool, False.
Do no aggregation on counts, save the original (annotated if requested)
vcf file as 3 count tables. Three aggregation options are compatible
with each other and can be used all at once.
decompose_options: list, []
if aggregate nucleotides option is selected, these options will be
passed to vt program. "-a" for decomposing variants containing indels,
for example. "-p" for keeping phase information. Any option to vt
decompose_blocksub would be valid. By default indels will not be
decomposed.
annotated_vcf: bool, False
is the provided vcf file annotated using snpEff. These annotations
will be used if no count aggregation is to be done and annotate option
is False.
min_site_qual: float, -1
Filter variants with QUAL values less than this value if the site is
not a targeted site. If targeted, the site will be kept regardless of
the qual value for the site. freebayes manual indicates that
simulations showed a value between 1-30 would be good. So a minimum
value of 1 here would clean up most junk sites.
min_target_site_qual: float, -1
If a variant site is targeted but the site qual is lower than this,
reset the alternate observation counts to 0. It may be best to leave
this at the default value since there is usually additional evidence
that a targeted variant exists in a samples compared to a de novo
variant.
"""
# get the analysis settings
# check if both settings and the settings file are None:
if (settings is None) and (settings_file is None):
print("settings or settings file must be provided for freebayes_call.")
return
else:
if settings is None:
settings = get_analysis_settings(settings_file)
else:
settings = copy.deepcopy(settings)
# get the working directory from settings
wdir = settings["workingDir"]
# All postprocessing steps require biallelic variant representation.
# so we'll use bcftools to split multiallelics to their own lines.
genome_fasta = get_file_locations()[settings["species"]]["fasta_genome"]
vcf_path = os.path.join(wdir, vcf_file)
split_vcf_path = os.path.join(wdir, output_prefix + "split." + vcf_file)
subprocess.run(["bcftools", "norm", "-f", genome_fasta, "-m-both",
vcf_path, "-Oz", "-o", split_vcf_path], check=True,
stderr=subprocess.PIPE)
subprocess.run(["bcftools", "index", "-f", split_vcf_path], check=True,
stderr=subprocess.PIPE)
# Will protein level aggregation be performed on the variants?
# This will only be done for simple missense variants but it is important
# to annotate the vcf file before breaking down the haplotypes.
if annotate:
annotated_vcf_path = os.path.join(wdir, output_prefix + "split.ann."
+ vcf_file)
res = annotate_vcf_file(settings, split_vcf_path, annotated_vcf_path)
if res != 0:
print("Annotating the vcf file failed.")
return
else:
annotated_vcf_path = split_vcf_path
if aggregate_aminoacids:
if not (annotate or annotated_vcf):
print("annotate option must be set to true or an annotadet vcf "
"file must be provided and annotated_vcf option must be "
"set to true for amino acid level aggregation. \n"
"Exiting!")
return
# check if a target annotation dict is provided.
target_annotation_dict = {}
if target_aa_annotation is not None:
taa = pd.read_table(target_aa_annotation).set_index(
["gene_name", "aminoacid_change"]).to_dict(orient="index")
for k in taa.keys():
target_annotation_dict[k] = taa[k]["mutation_name"]
# check if a gene id to gene name file is provided
gene_ids = {}
if geneid_to_genename is not None:
gids = pd.read_table(geneid_to_genename).set_index("gene_id")
gids = gids.to_dict(orient="index")
for g in gids:
gene_ids[g] = gids[g]["gene_name"]
# load annotated vcf file
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1,
transformers=allel.ANNTransformer())
# allel import provides a variants dictionary with keys such as
# variants/AD, variants/POS for variant level information
# the values are arrays with each element corresponding to one variant.
# similarly, calldata/GT type keys hold the genotype level data.
#############################################################
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
# find missense variant locations in the data. We are going to split
# multi amino acid changes for missense variants only for target
# annotation and count aggregation.
missense = ["missense_variant" == variant for variant
in variants["variants/ANN_Annotation"]]
# spcecify fields of interest from the INFO fields
variant_fields = ["ANN_Gene_ID", "ANN_HGVS_p", "ANN_Annotation",
"QUAL"]
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO', 'calldata/DP',
'calldata/GT', 'calldata/GQ', 'calldata/QA',
'calldata/QR']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# so now we have a list of length equal to variant number.
# each item is a tuple such as ('PF3D7_0104300', 'Gln107Leu') or
# ('PF3D7_0104300', 'AspGluAsp144HisGlnTyr'). We'll split these
# compound SNVs later.
# get count data for missense variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# split the compound mutations
split_variants = []
split_calls = []
for i in range(len(missense)):
mv = variant_data[i][:3]
# get the aa change such as AspGluAsp144HisGlnTyr
aa_change = mv[1]
# if no aa change, skip
if aa_change == "":
continue
try:
# if a mapping dict is present, add the gene name
# this would get Pfubp1 from PF3D7_0104300, for example
gene_name = gene_ids[mv[0]]
except KeyError:
gene_name = mv[0]
# get site quality, remove those not satisfying min_site_qual
# unless they are targeted mutations
site_qual = float(variant_data[i][3])
if missense[i]:
# get the position of the change (144 above)
aa_pos = int("".join([c for c in aa_change if c.isdigit()]))
# split the aa change to reference aminoacid sequence and
# alt amino acid sequence.
aa_split = aa_change.split(str(aa_pos))
reference = aa_split[0]
alternate = aa_split[1]
# aa changes are in 3 letter format. Loop through each aa and
# split to single aa changes.
for j in range(0, len(reference), 3):
new_pos = int(aa_pos + j/3)
# convert single amino acid names to 1 letter code.
new_reference = reference[j:j+3]
new_alternate = alternate[j:j+3]
new_change = new_reference + str(new_pos) + new_alternate
try:
# if this variant is in the targets, annotate it so.
mut_name = target_annotation_dict[
(gene_name, new_change)]
targeted_mutation = "Yes"
# reset alt observation counts to 0 if quality is low
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low quality non-target alleles as well as
# synonymous changes
if ((site_qual < min_site_qual)
or (new_reference == new_alternate)):
continue
mut_name = gene_name + "-" + new_change
targeted_mutation = "No"
# add the split variant information split variants list
split_variants.append(mv + (new_change, gene_name,
mut_name, targeted_mutation))
# add the individual level data to split calls list.
split_calls.append(call_data[i])
else:
try:
# if this variant is in the targets, annotate it as such.
mut_name = target_annotation_dict[
(gene_name, aa_change)]
targeted_mutation = "Yes"
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low qual or synonymous changes
if ((site_qual < min_site_qual)
or (mv[2] == "synonymous_variant")):
continue
mut_name = gene_name + "-" + aa_change
targeted_mutation = "No"
# add compound variant data to split variant data
split_variants.append(mv + (aa_change, gene_name,
mut_name, targeted_mutation))
# add the individual level data to split calls list.
split_calls.append(call_data[i])
# get individual level data
genotype_quals = call_data[i][4]
ao_count = call_data[i][0]
alt_quals = call_data[i][5]
average_alt_quals = alt_quals / ao_count
ro_count = call_data[i][1]
ref_quals = call_data[i][6]
average_ref_quals = ref_quals / ro_count
gq_mask = genotype_quals < min_genotype_qual
qa_mask = alt_quals < min_alt_qual
qr_mask = ref_quals < min_ref_qual
av_qa_mask = average_alt_quals < min_mean_alt_qual
av_qr_mask = average_ref_quals < min_mean_ref_qual
# replace count data for individuals failing quality thresholds
# alt allele count AO
call_data[i][0][qa_mask] = 0
call_data[i][0][av_qa_mask] = 0
# ref allele count RO
call_data[i][1][qr_mask] = 0
call_data[i][1][av_qr_mask] = 0
# reset coverage for gq failure
call_data[i][2][gq_mask] = 0
# reset genotypes for gq failure
call_data[i][3][gq_mask] = -2
# create a multiindex for the variant df that we'll create next
index = pd.MultiIndex.from_tuples(
split_variants, names=["Gene ID", "Compound Change", "ExonicFunc",
"AA Change", "Gene", "Mutation Name",
"Targeted"])
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# combine counts for same changes
grouping_keys = ["Gene ID", "Gene", "Mutation Name", "ExonicFunc",
"AA Change", "Targeted"]
# replace -1 (allel assigned NA values) values with 0
# sum alt counts
mutation_counts = variant_counts.groupby(grouping_keys).sum()
# take the max of ref counts
mutation_refs = reference_counts.groupby(grouping_keys).min()
# take the max of coverage counts
mutation_coverage = coverage.groupby(grouping_keys).max()
# due to aggregating aa changes, ref counts can be overcounted even
# if the minimum ref count is taken for the aggregate. The reason for
# this is that each nucleotide variant's reference observation count
# may include the alternate alleles for another nucleotide variant
# that codes for the same aa change. So we'll set the ref counts
# to coverage - alt count where ref count exceeds this value
diff_count = mutation_coverage - mutation_counts
ref_difference = (mutation_refs > diff_count).sum()
# get the variant indices where ref count exceeds coverage - alt count
exceed_index = ref_difference.loc[ref_difference > 0].index
mutation_refs.loc[:, exceed_index] = diff_count.loc[:, exceed_index]
# get genotypes as called by the variant caller
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index)
gt_calls = gt_calls.groupby(grouping_keys).agg(combine_gt)
# for one pf mutation alt count will be replaced with ref count
# because reference allele is drug resistant
dhps_key = ("PF3D7_0810800", "dhps", "dhps-Gly437Ala",
"missense_variant", "Gly437Ala", "Yes")
dhps_new_key = ("PF3D7_0810800", "dhps", "dhps-Ala437Gly",
"missense_variant", "Ala437Gly", "Yes")
try:
mutation_counts.loc[dhps_new_key, :] = mutation_refs.loc[
dhps_key, :]
mutation_refs.loc[dhps_new_key, :] = mutation_counts.loc[
dhps_key, :]
mutation_coverage.loc[dhps_new_key, :] = mutation_coverage.loc[
dhps_key, :]
gt_calls.loc[dhps_new_key, :] = gt_calls.loc[
dhps_key, :].replace({2: 0, 0: 2})
gt_calls.drop(dhps_key, inplace=True)
mutation_counts.drop(dhps_key, inplace=True)
mutation_refs.drop(dhps_key, inplace=True)
mutation_coverage.drop(dhps_key, inplace=True)
mutation_counts = mutation_counts.sort_index()
mutation_refs = mutation_refs.sort_index()
mutation_coverage = mutation_coverage.sort_index()
gt_calls = gt_calls.sort_index()
except KeyError:
pass
# save count tables
mutation_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_AA_table.csv"))
mutation_refs.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_AA_table.csv"))
mutation_coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_AA_table.csv"))
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_AA_table.csv"))
if aggregate_nucleotides:
# aggregating counts of nucleotides requires decomposing block
# substitutions, at a minimum. If desired, complex variants involving
# indels can be decomposed as well.
decomposed_vcf = os.path.join(wdir, output_prefix
+ "decomposed." + vcf_file)
# prepare vt decompose command
comm = ["vt", "decompose_blocksub"] + decompose_options
comm.append(split_vcf_path)
comm.extend(["-o", decomposed_vcf])
# run decompose
subprocess.run(comm, check=True)
subprocess.run(["bcftools", "index", "-f", decomposed_vcf], check=True)
# load decomposed vcf file
variants = allel.read_vcf(decomposed_vcf, fields=["*"], alt_number=1)
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
# spcecify fields of interest from the INFO fields
variant_fields = ["CHROM", "POS", "REF", "ALT", "QUAL"]
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO', 'calldata/DP',
'calldata/GT', 'calldata/GQ', 'calldata/QA',
'calldata/QR']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# get count data for the variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
# check if a target annotation dict is provided.
target_annotation_dict = {}
if target_nt_annotation is not None:
taa = pd.read_table(target_nt_annotation).set_index(
["CHROM", "POS", "REF", "ALT"]).to_dict(orient="index")
for k in taa.keys():
target_annotation_dict[k] = taa[k]["mutation_name"]
grouping_keys = ["CHROM", "POS", "REF", "ALT", "Mutation Name",
"Targeted"]
split_variants = []
split_calls = []
for i in range(len(variant_data)):
vd = variant_data[i][:4]
site_qual = float(variant_data[i][4])
try:
t_anno = target_annotation_dict[vd]
targeted_mutation = "Yes"
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low qual and nonvariant sites
if ((site_qual < min_site_qual) or (vd[2] == vd[3])):
continue
t_anno = ":".join(map(str, vd))
targeted_mutation = "No"
split_variants.append(vd + (t_anno, targeted_mutation))
split_calls.append(call_data[i])
# get individual level data
genotype_quals = call_data[i][4]
ao_count = call_data[i][0]
alt_quals = call_data[i][5]
average_alt_quals = alt_quals / ao_count
ro_count = call_data[i][1]
ref_quals = call_data[i][6]
average_ref_quals = ref_quals / ro_count
gq_mask = genotype_quals < min_genotype_qual
qa_mask = alt_quals < min_alt_qual
qr_mask = ref_quals < min_ref_qual
av_qa_mask = average_alt_quals < min_mean_alt_qual
av_qr_mask = average_ref_quals < min_mean_ref_qual
# replace count data for individuals failing quality thresholds
# alt allele count AO
call_data[i][0][qa_mask] = 0
call_data[i][0][av_qa_mask] = 0
# ref allele count RO
call_data[i][1][qr_mask] = 0
call_data[i][1][av_qr_mask] = 0
# reset coverage for gq failure
call_data[i][2][gq_mask] = 0
# reset genotypes for gq failure
call_data[i][3][gq_mask] = -2
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# create a multiindex for the variant df that we'll create next
index = pd.MultiIndex.from_tuples(
split_variants, names=grouping_keys)
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# combine counts for same changes
# sum alt counts
mutation_counts = variant_counts.groupby(grouping_keys).sum()
# take the max of ref counts
mutation_refs = reference_counts.groupby(grouping_keys).min()
# take the max of coverage counts
mutation_coverage = coverage.groupby(grouping_keys).max()
# save count tables
mutation_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_AN_table.csv"))
mutation_refs.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_AN_table.csv"))
mutation_coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_AN_table.csv"))
# get genotypes
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index)
gt_calls = gt_calls.groupby(grouping_keys).agg(combine_gt)
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_AN_table.csv"))
if aggregate_none:
# if no aggregation will be done, load the vcf file
if annotate or annotated_vcf:
# if annotation was requested use the annotated vcf path
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1,
transformers=allel.ANNTransformer())
else:
# if the file is not annotated, don't try to parse ANN field.
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1)
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
variant_fields = ["CHROM", "POS", "REF", "ALT", "QUAL"]
if annotate or annotated_vcf:
variant_fields.extend(["ANN_Gene_ID", "ANN_HGVS_p"])
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO', 'calldata/DP',
'calldata/GT', 'calldata/GQ', 'calldata/QA',
'calldata/QR']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# get count data for the variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
split_variants = []
split_calls = []
for i in range(len(variant_data)):
vd = variant_data[i][:4]
site_qual = float(variant_data[i][4])
if site_qual < min_site_qual:
continue
if annotate or annotated_vcf:
g_ann = variant_data[i][5]
p_ann = variant_data[i][6]
if p_ann == "":
p_ann = "."
if g_ann == "":
g_ann = "."
else:
p_ann = "."
g_ann = "."
vd = vd + (g_ann, p_ann)
split_variants.append(vd)
split_calls.append(call_data[i])
# get individual level data
genotype_quals = call_data[i][4]
ao_count = call_data[i][0]
alt_quals = call_data[i][5]
average_alt_quals = alt_quals / ao_count
ro_count = call_data[i][1]
ref_quals = call_data[i][6]
average_ref_quals = ref_quals / ro_count
gq_mask = genotype_quals < min_genotype_qual
qa_mask = alt_quals < min_alt_qual
qr_mask = ref_quals < min_ref_qual
av_qa_mask = average_alt_quals < min_mean_alt_qual
av_qr_mask = average_ref_quals < min_mean_ref_qual
# replace count data for individuals failing quality thresholds
# alt allele count AO
call_data[i][0][qa_mask] = 0
call_data[i][0][av_qa_mask] = 0
# ref allele count RO
call_data[i][1][qr_mask] = 0
call_data[i][1][av_qr_mask] = 0
# reset coverage for gq failure
call_data[i][2][gq_mask] = 0
# reset genotypes for gq failure
call_data[i][3][gq_mask] = -2
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# create a multiindex for the variant df that we'll create next
variant_fields = variant_fields[:4] + [
"variants/Gene ID", "variants/AA Change"]
index = pd.MultiIndex.from_tuples(split_variants,
names=[v.split("variants/")[1]
for v in variant_fields])
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# save count tables
variant_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_table.csv"))
reference_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_table.csv"))
coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_table.csv"))
# get genotypes
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index).replace(-2, -1)
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_table.csv"))
def vcf_to_tables(vcf_file, settings=None, settings_file=None, annotate=True,
geneid_to_genename=None, target_aa_annotation=None,
aggregate_aminoacids=False, target_nt_annotation=None,
aggregate_nucleotides=False, decompose_options=[],
annotated_vcf=False, aggregate_none=False, min_site_qual=-1,
min_target_site_qual=-1, min_genotype_qual=None,
output_prefix=""):
"""Create various tables from a vcf file.
Create various tables from a vcf file generated by the freebayes
program. There are 3 different types of count output for each variant:
variant count, reference count and coverage. The vcf file will be split
into biallelic variants. Table versions of the input vcf will be created
but the info fields will be limited to the mandatory vcf fields and some
annotation data if avaliable.
In addition to the original vcf table, aa change tables can be generated.
These will be generated by filtering the vcf to missense variants only,
decomposing block substitutions (haplotypes) and combining the counts for
the same aminoacid changes. This operation is specifically intended for
generating data for targeted missense mutations and only reports that. All
other variants, even those complex variants including targeted variants
will not be reported. Finally, one specific mutation (dhps-437) will have
reference counts instead of variant counts if present. This is because this
drug resistance variant is encoded by the 3d7 reference sequence.
Parameters
----------
settings: dict, None
Analysis settings dictionary. Either this or settings_file must
be provided.
settings_file: str/path, None
Path to the analysis settings file. Either this or the settings dict
must be provided.
annotate: bool, True
Annotate variant file. This is required for protein level analysis.
vcf_file: str/path
Starting vcf file.
geneid2genename: str/path, None.
Path to a tab separated tex file that maps gene ids to gene names.
Column names must be gene_id and gene_name. Gene IDs
will populate the Gene field if this file is not provided.
target_aa_annotation: str/path, None.
Path to a tab separated text file with targeted variant information to
annotate and label targeted amino acid changes.
It must have gene_name, aminoacid_change, and mutation_name columns.
Amino acid changes should be represented as refAAPosAltAA. refAA and
AltAA must be three letter amino acid codes.
This file is required for targeted protein variant labeling.
target_nt_annotation: str/path, None.
Path to a tab separated text file with targeted variant information to
annotate and label targeted nucleotide changes.
It must have CHROM, POS, REF, ALT, NAME columns.
This file is required for targeted nucleotide variant labeling.
aggregate_aminoacids: bool, False
whether counts for same amino acids should be aggregated. This involves
decomposing multi amino acid changes for missense variants. If amino
acid based targets will be annotated, based on a provided annotation
dictionary, aggregation step must be completed. Targeted mutations
that are part of complex events (indels, stop loss/gain etc.) will not
be labeled as targeted.
aggregate_nucleotides: bool, False
whether the counts for nucleotide changes should be aggregated. This
involves decomposing all variants to the smallest units possible,
breaking all haplotype data. The level of decomposition should be
specified with the decompose_options parameter.
aggregate_none: bool, False.
Do no aggregation on counts, save the original (annotated if requested)
vcf file as 3 count tables. Three aggregation options are compatible
with each other and can be used all at once.
decompose_options: list, []
if aggregate nucleotides option is selected, these options will be
passed to vt program. "-a" for decomposing variants containing indels,
for example. "-p" for keeping phase information. Any option to vt
decompose_blocksub would be valid. By default indels will not be
decomposed.
annotated_vcf: bool, False
is the provided vcf file annotated using snpEff. These annotations
will be used if no count aggregation is to be done and annotate option
is False.
min_site_qual: float, -1
Filter variants with QUAL values less than this value if the site is
not a targeted site. If targeted, the site will be kept regardless of
the qual value for the site. freebayes manual indicates that
simulations showed a value between 1-30 would be good. So a minimum
value of 1 here would clean up most junk sites.
min_target_site_qual: float, -1
If a variant site is targeted but the site qual is lower than this,
reset the alternate observation counts to 0. It may be best to leave
this at the default value since there is usually additional evidence
that a targeted variant exists in a samples compared to a de novo
variant.
"""
# get the analysis settings
# check if both settings and the settings file are None:
if (settings is None) and (settings_file is None):
print("settings or settings file must be provided for freebayes_call.")
return
else:
if settings is None:
settings = get_analysis_settings(settings_file)
else:
settings = copy.deepcopy(settings)
# get the working directory from settings
wdir = settings["workingDir"]
# All postprocessing steps require biallelic variant representation.
# so we'll use bcftools to split multiallelics to their own lines.
genome_fasta = get_file_locations()[settings["species"]]["fasta_genome"]
vcf_path = os.path.join(wdir, vcf_file)
# filter genotype for quality if specified
if min_genotype_qual is not None:
if vcf_file.endswith(".gz"):
vtype = "--gzvcf"
else:
vtype = "--vcf"
filt_res = subprocess.Popen(["vcftools", vtype, vcf_path,
"--minGQ", str(min_genotype_qual),
"--recode", "--recode-INFO-all",
"--stdout"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
filt_vcf_path = os.path.join(
wdir, output_prefix + "variants.GQ."
+ str(min_genotype_qual) + ".vcf.gz")
with open(filt_vcf_path, "wb") as outfile:
zip_res = subprocess.run(["bgzip", "-f"], stdin=filt_res.stdout,
stdout=outfile,
stderr=subprocess.PIPE)
index_res = subprocess.run(
["bcftools", "index", "-f", filt_vcf_path],
stderr=subprocess.PIPE)
if zip_res.returncode != 0:
print(("Compression of GQ filtered vcf failed due to "
"error: {}. \n Genotypes will not be "
"filtered.").format(zip_res.stderr))
elif index_res.returncode != 0:
print(("Indexing GQ filtered vcf file failed "
"due to error: {}. \n Genotypes will not "
"be filtered.").format(index_res.stderr))
else:
vcf_path = filt_vcf_path
split_vcf_path = os.path.join(wdir, output_prefix + "split." + vcf_file)
subprocess.run(["bcftools", "norm", "-f", genome_fasta, "-m-both",
vcf_path, "-Oz", "-o", split_vcf_path], check=True,
stderr=subprocess.PIPE)
subprocess.run(["bcftools", "index", "-f", split_vcf_path], check=True,
stderr=subprocess.PIPE)
# Will protein level aggregation be performed on the variants?
# This will only be done for simple missense variants but it is important
# to annotate the vcf file before breaking down the haplotypes.
if annotate:
annotated_vcf_path = os.path.join(wdir, output_prefix + "split.ann."
+ vcf_file)
res = annotate_vcf_file(settings, split_vcf_path, annotated_vcf_path)
if res != 0:
print("Annotating the vcf file failed.")
return
else:
annotated_vcf_path = split_vcf_path
if aggregate_aminoacids:
if not (annotate or annotated_vcf):
print("annotate option must be set to true or an annotadet vcf "
"file must be provided and annotated_vcf option must be "
"set to true for amino acid level aggregation. \n"
"Exiting!")
return
# check if a target annotation dict is provided.
target_annotation_dict = {}
if target_aa_annotation is not None:
taa = pd.read_table(target_aa_annotation).set_index(
["gene_name", "aminoacid_change"]).to_dict(orient="index")
for k in taa.keys():
target_annotation_dict[k] = taa[k]["mutation_name"]
# check if a gene id to gene name file is provided
gene_ids = {}
if geneid_to_genename is not None:
gids = pd.read_table(geneid_to_genename).set_index("gene_id")
gids = gids.to_dict(orient="index")
for g in gids:
gene_ids[g] = gids[g]["gene_name"]
# load annotated vcf file
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1,
transformers=allel.ANNTransformer())
# allel import provides a variants dictionary with keys such as
# variants/AD, variants/POS for variant level information
# the values are arrays with each element corresponding to one variant.
# similarly, calldata/GT type keys hold the genotype level data.
#############################################################
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
# find missense variant locations in the data. We are going to split
# multi amino acid changes for missense variants only for target
# annotation and count aggregation.
missense = ["missense_variant" == variant for variant
in variants["variants/ANN_Annotation"]]
# spcecify fields of interest from the INFO fields
variant_fields = ["ANN_Gene_ID", "ANN_HGVS_p", "ANN_Annotation",
"QUAL"]
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO',
'calldata/DP', 'calldata/GT']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# so now we have a list of length equal to variant number.
# each item is a tuple such as ('PF3D7_0104300', 'Gln107Leu') or
# ('PF3D7_0104300', 'AspGluAsp144HisGlnTyr'). We'll split these
# compound SNVs later.
# get count data for missense variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# split the compound mutations
split_variants = []
split_calls = []
for i in range(len(missense)):
mv = variant_data[i][:3]
# get the aa change such as AspGluAsp144HisGlnTyr
aa_change = mv[1]
# if no aa change, skip
if aa_change == "":
continue
try:
# if a mapping dict is present, add the gene name
# this would get Pfubp1 from PF3D7_0104300, for example
gene_name = gene_ids[mv[0]]
except KeyError:
gene_name = mv[0]
# get site quality, remove those not satisfying min_site_qual
# unless they are targeted mutations
site_qual = float(variant_data[i][3])
if missense[i]:
# get the position of the change (144 above)
aa_pos = int("".join([c for c in aa_change if c.isdigit()]))
# split the aa change to reference aminoacid sequence and
# alt amino acid sequence.
aa_split = aa_change.split(str(aa_pos))
reference = aa_split[0]
alternate = aa_split[1]
# aa changes are in 3 letter format. Loop through each aa and
# split to single aa changes.
for j in range(0, len(reference), 3):
new_pos = int(aa_pos + j/3)
# convert single amino acid names to 1 letter code.
new_reference = reference[j:j+3]
new_alternate = alternate[j:j+3]
new_change = new_reference + str(new_pos) + new_alternate
try:
# if this variant is in the targets, annotate it so.
mut_name = target_annotation_dict[
(gene_name, new_change)]
targeted_mutation = "Yes"
# reset alt observation counts to 0 if quality is low
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low quality non-target alleles as well as
# synonymous changes
if ((site_qual < min_site_qual)
or (new_reference == new_alternate)):
continue
mut_name = gene_name + "-" + new_change
targeted_mutation = "No"
# add the split variant information split variants list
split_variants.append(mv + (new_change, gene_name,
mut_name, targeted_mutation))
# add the individual level data to split calls list.
split_calls.append(call_data[i])
else:
try:
# if this variant is in the targets, annotate it as such.
mut_name = target_annotation_dict[
(gene_name, aa_change)]
targeted_mutation = "Yes"
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low qual or synonymous changes
if ((site_qual < min_site_qual)
or (mv[2] == "synonymous_variant")):
continue
mut_name = gene_name + "-" + aa_change
targeted_mutation = "No"
# add compound variant data to split variant data
split_variants.append(mv + (aa_change, gene_name,
mut_name, targeted_mutation))
# add the individual level data to split calls list.
split_calls.append(call_data[i])
# create a multiindex for the variant df that we'll create next
index = pd.MultiIndex.from_tuples(
split_variants, names=["Gene ID", "Compound Change", "ExonicFunc",
"AA Change", "Gene", "Mutation Name",
"Targeted"])
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# combine counts for same changes
grouping_keys = ["Gene ID", "Gene", "Mutation Name", "ExonicFunc",
"AA Change", "Targeted"]
# replace -1 (allel assigned NA values) values with 0
# sum alt counts
mutation_counts = variant_counts.groupby(grouping_keys).sum()
# take the max of ref counts
mutation_refs = reference_counts.groupby(grouping_keys).min()
# take the max of coverage counts
mutation_coverage = coverage.groupby(grouping_keys).max()
# due to aggregating aa changes, ref counts can be overcounted even
# if the minimum ref count is taken for the aggregate. The reason for
# this is that each nucleotide variant's reference observation count
# may include the alternate alleles for another nucleotide variant
# that codes for the same aa change. So we'll set the ref counts
# to coverage - alt count where ref count exceeds this value
diff_count = mutation_coverage - mutation_counts
ref_difference = (mutation_refs > diff_count).sum()
# get the variant indices where ref count exceeds coverage - alt count
exceed_index = ref_difference.loc[ref_difference > 0].index
mutation_refs.loc[:, exceed_index] = diff_count.loc[:, exceed_index]
# get genotypes as called by the variant caller
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index)
gt_calls = gt_calls.groupby(grouping_keys).agg(combine_gt)
# for one pf mutation alt count will be replaced with ref count
# because reference allele is drug resistant
dhps_key = ("PF3D7_0810800", "dhps", "dhps-Gly437Ala",
"missense_variant", "Gly437Ala", "Yes")
dhps_new_key = ("PF3D7_0810800", "dhps", "dhps-Ala437Gly",
"missense_variant", "Ala437Gly", "Yes")
try:
mutation_counts.loc[dhps_new_key, :] = mutation_refs.loc[
dhps_key, :]
mutation_refs.loc[dhps_new_key, :] = mutation_counts.loc[
dhps_key, :]
mutation_coverage.loc[dhps_new_key, :] = mutation_coverage.loc[
dhps_key, :]
gt_calls.loc[dhps_new_key, :] = gt_calls.loc[
dhps_key, :].replace({2: 0, 0: 2})
gt_calls.drop(dhps_key, inplace=True)
mutation_counts.drop(dhps_key, inplace=True)
mutation_refs.drop(dhps_key, inplace=True)
mutation_coverage.drop(dhps_key, inplace=True)
mutation_counts = mutation_counts.sort_index()
mutation_refs = mutation_refs.sort_index()
mutation_coverage = mutation_coverage.sort_index()
gt_calls = gt_calls.sort_index()
except KeyError:
pass
# save count tables
mutation_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_AA_table.csv"))
mutation_refs.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_AA_table.csv"))
mutation_coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_AA_table.csv"))
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_AA_table.csv"))
if aggregate_nucleotides:
# aggregating counts of nucleotides requires decomposing block
# substitutions, at a minimum. If desired, complex variants involving
# indels can be decomposed as well.
decomposed_vcf = os.path.join(wdir, output_prefix
+ "decomposed." + vcf_file)
# prepare vt decompose command
comm = ["vt", "decompose_blocksub"] + decompose_options
comm.append(split_vcf_path)
comm.extend(["-o", decomposed_vcf])
# run decompose
subprocess.run(comm, check=True)
subprocess.run(["bcftools", "index", "-f", decomposed_vcf], check=True)
# load decomposed vcf file
variants = allel.read_vcf(decomposed_vcf, fields=["*"], alt_number=1)
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
# spcecify fields of interest from the INFO fields
variant_fields = ["CHROM", "POS", "REF", "ALT", "QUAL"]
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO',
'calldata/DP', 'calldata/GT']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# get count data for the variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
# check if a target annotation dict is provided.
target_annotation_dict = {}
if target_nt_annotation is not None:
taa = pd.read_table(target_nt_annotation).set_index(
["CHROM", "POS", "REF", "ALT"]).to_dict(orient="index")
for k in taa.keys():
target_annotation_dict[k] = taa[k]["mutation_name"]
grouping_keys = ["CHROM", "POS", "REF", "ALT", "Mutation Name",
"Targeted"]
split_variants = []
split_calls = []
for i in range(len(variant_data)):
vd = variant_data[i][:4]
site_qual = float(variant_data[i][4])
try:
t_anno = target_annotation_dict[vd]
targeted_mutation = "Yes"
if site_qual < min_target_site_qual:
call_data[i][0][:] = 0
except KeyError:
# remove low qual and nonvariant sites
if ((site_qual < min_site_qual) or (vd[2] == vd[3])):
continue
t_anno = ":".join(map(str, vd))
targeted_mutation = "No"
split_variants.append(vd + (t_anno, targeted_mutation))
split_calls.append(call_data[i])
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# create a multiindex for the variant df that we'll create next
index = pd.MultiIndex.from_tuples(
split_variants, names=grouping_keys)
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# combine counts for same changes
# sum alt counts
mutation_counts = variant_counts.groupby(grouping_keys).sum()
# take the max of ref counts
mutation_refs = reference_counts.groupby(grouping_keys).min()
# take the max of coverage counts
mutation_coverage = coverage.groupby(grouping_keys).max()
# save count tables
mutation_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_AN_table.csv"))
mutation_refs.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_AN_table.csv"))
mutation_coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_AN_table.csv"))
# get genotypes
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index)
gt_calls = gt_calls.groupby(grouping_keys).agg(combine_gt)
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_AN_table.csv"))
if aggregate_none:
# if no aggregation will be done, load the vcf file
if annotate or annotated_vcf:
# if annotation was requested use the annotated vcf path
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1,
transformers=allel.ANNTransformer())
else:
# if the file is not annotated, don't try to parse ANN field.
variants = allel.read_vcf(annotated_vcf_path, fields=["*"],
alt_number=1)
# Freebayes vcfs have AO and RO counts for alt and ref allele depths
# but GATK has a combined AD depth. Create AO and RO from AD if
# needed
try:
variants["calldata/AO"]
except KeyError:
variants["calldata/RO"] = variants["calldata/AD"][:, :, 0]
variants["calldata/AO"] = variants["calldata/AD"][:, :, 1]
variant_fields = ["CHROM", "POS", "REF", "ALT", "QUAL"]
if annotate or annotated_vcf:
variant_fields.extend(["ANN_Gene_ID", "ANN_HGVS_p"])
variant_fields = ["variants/" + v for v in variant_fields]
# specify fields of interest from individual level data
# that is basically the count data for tables. AO: alt allele count,
# RO ref count, DP: coverage.
call_data_fields = ['calldata/AO', 'calldata/RO',
'calldata/DP', 'calldata/GT']
variants["calldata/GT"] = variants["calldata/GT"].sum(axis=2)
# zip variant level information together, so we have a single value
# for each variant
variant_data = list(zip(*[variants[v] for v in variant_fields]))
# get count data for the variants
call_data = list(zip(*[variants[c] for c in call_data_fields]))
split_variants = []
split_calls = []
for i in range(len(variant_data)):
vd = variant_data[i][:4]
site_qual = float(variant_data[i][4])
if site_qual < min_site_qual:
continue
if annotate or annotated_vcf:
g_ann = variant_data[i][5]
p_ann = variant_data[i][6]
if p_ann == "":
p_ann = "."
if g_ann == "":
g_ann = "."
else:
p_ann = "."
g_ann = "."
vd = vd + (g_ann, p_ann)
split_variants.append(vd)
split_calls.append(call_data[i])
# first item of the above list is alt counts, then ref counts and
# coverage.
#############################
# create a multiindex for the variant df that we'll create next
variant_fields = variant_fields[:4] + [
"variants/Gene ID", "variants/AA Change"]
index = pd.MultiIndex.from_tuples(split_variants,
names=[v.split("variants/")[1]
for v in variant_fields])
# get alt counts
variant_counts = pd.DataFrame(np.array(split_calls)[:, 0],
columns=variants["samples"],
index=index).replace(-1, 0)
# get reference counts
reference_counts = pd.DataFrame(np.array(split_calls)[:, 1],
columns=variants["samples"],
index=index).replace(-1, 0)
# get coverage depth
coverage = pd.DataFrame(np.array(split_calls)[:, 2],
columns=variants["samples"],
index=index).replace(-1, 0)
# save count tables
variant_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "alternate_table.csv"))
reference_counts.T.to_csv(os.path.join(wdir, output_prefix
+ "reference_table.csv"))
coverage.T.to_csv(os.path.join(wdir, output_prefix
+ "coverage_table.csv"))
# get genotypes
gt_calls = pd.DataFrame((np.array(split_calls)[:, 3]),
columns=variants["samples"],
index=index).replace(-2, -1)
gt_calls.T.to_csv(os.path.join(wdir, output_prefix
+ "genotypes_table.csv"))
def annotate_vcf_file(settings, vcf_file, annotated_vcf_file, options=[]):
"""Annotate a vcf file using snpEff, bgzip and index the output file."""
# get the species information from settings
species = settings["species"]
try:
# find where snpEff files are located and which database should be used
ann_db_dir = get_file_locations()[species]["snpeff_dir"]
ann_db = get_file_locations()[species]["snpeff_db"]
except KeyError:
print("snpeff_dir and snpeff_db must be specified in the settings "
"to carry out snpeff annotations.")
return
# run snpeff program on the vcf file. Snpeff outputs to stdout so we'll
# redirect it to the annotated vcf file. If output file name provided
# ends with .gz, we will remove it here because bgzip will add that in
# the next step
if annotated_vcf_file.endswith(".gz"):
annotated_vcf_file = annotated_vcf_file[:-3]
with open(annotated_vcf_file, "wb") as avf:
comm = ["java", "-Xmx10g", "-jar",
os.path.join(ann_db_dir, "snpEff.jar"), ann_db, vcf_file]
comm.extend(options)
res = subprocess.run(comm, stdout=avf, stderr=subprocess.PIPE)
if res.returncode != 0:
print("Error in snpEff call ", res.stderr)
return res.returncode
# most vcf operations require a bgzipped indexed file, so do those
res = subprocess.run(["bgzip", "-f", annotated_vcf_file],
stderr=subprocess.PIPE)
if res.returncode != 0:
print("Error in compressing the annotated vcf file, ", res.stderr)
return res.returncode
res = subprocess.run(["bcftools", "index", "-f",
annotated_vcf_file + ".gz"], stderr=subprocess.PIPE)
if res.returncode != 0:
print("Error in indexing the annotated vcf file, ", res.stderr)
return res.returncode
return 0
###############################################################################
# general use functions.
###############################################################################
def parse_alignment_positions(alignment_file, contig_start, ref_key="ref"):
""" Parse a multiple sequence alignment file given in fasta format.
Using the genomic start position of the reference contig, create a
genome to alignment and alignment to genome position maps.
"""
alignments = fasta_parser(alignment_file)
ref_seq = alignments[ref_key]
alignment_to_genomic = {0: contig_start - 1}
insertion_count = 0
for i in range(len(ref_seq)):
if ref_seq[i] != "-":
alignment_to_genomic[i+1] = i + contig_start - insertion_count
else:
insertion_count += 1
genomic_to_alignment = {}
for alignment_position in alignment_to_genomic:
genomic_to_alignment[alignment_to_genomic[
alignment_position]] = alignment_position
return {"a2g": alignment_to_genomic, "g2a": genomic_to_alignment}
def check_overlap(r1, r2, padding=0):
""" Check if two regions overlap. Regions are given as lists of chrom (str),
begin (int), end (int)."""
# check chromosome equivalency
o1 = r1[0] == r2[0]
# check interval overlap
merged = merge_overlap([r1[1:], r2[1:]], padding)
o2 = len(merged) == 1
return o1 & o2
def make_region(chromosome, begin, end):
""" Create region string from coordinates.
takes 2 (1 for human 1-9) digit chromosome,
begin and end positions (1 indexed)"""
region = "chr" + str(chromosome) + ":" + str(begin) + "-" + str(end)
return region
def create_region(chromosome, begin, end):
""" Create region string from coordinates.
chromosome string,
begin and end positions (1 indexed)"""
region = chromosome + ":" + str(begin) + "-" + str(end)
return region
def get_coordinates(region):
""" Define coordinates chr, start pos and end positions
from region string chrX:start-end. Return coordinate list.
"""
chromosome = region.split(":")[0]
coord = region.split(":")[1]
coord_list = coord.split("-")
begin = int(coord_list[0])
end = int(coord_list[1])
return [chromosome, begin, end]
def get_fasta(region, species="pf", offset=1, header="na"):
""" Take a region string (chrX:begin-end (1 indexed)),
and species (human=hs, plasmodium= pf),Return fasta record.
"""
if offset == 0:
region_coordinates = get_coordinates(region)
region = (region_coordinates[0] + ":" + str(region_coordinates[1] + 1)
+ "-" + str(region_coordinates[2]))
region = region.encode("utf-8")
file_locations = get_file_locations()
genome_fasta = file_locations[species]["fasta_genome"].encode("utf-8")
fasta = pysam.faidx(genome_fasta, region)
if header != "na":
fasta_seq = "\n".join(fasta.split("\n")[1:])
fasta = ">" + header + "\n" + fasta_seq
return fasta
def get_fasta_list(regions, species):
""" Take a list of regions and return fasta sequences."""
if len(regions) == 0:
return {}
file_locations = get_file_locations()
genome_fasta = file_locations[species]["fasta_genome"]
region_file = "/tmp/region s_" + id_generator(10) + ".txt"
with open(region_file, "w") as outfile:
for r in regions:
outfile.write(r + "\n")
fasta_dic = {}
command = ["samtools", "faidx", "-r", region_file, genome_fasta]
out = subprocess.check_output(command).decode("UTF-8")
fasta_list = out.split(">")[1:]
for f in fasta_list:
fl = f.strip().split("\n")
fhead = fl[0]
fseq = "".join(fl[1:])
fasta_dic[fhead] = fseq
return fasta_dic
def merge_overlap(intervals, spacer=0):
"""Merge overlapping intervals.
Take a list of lists of 2 elements, [start, stop],
check if any [start, stop] pairs overlap and merge if any.
Return the merged [start, stop] list.
"""
# reuse a piece of code from get_exons:
#######################################
exons = copy.deepcopy(intervals)
exons = [e for e in exons if len(e) == 2]
for e in exons:
e.sort()
exons.sort()
if len(exons) < 2:
return exons
overlapping = 1
while overlapping:
overlapping = 0
for i in range(len(exons)):
e = exons[i]
for j in range(len(exons)):
x = exons[j]
if i == j:
continue
else:
if e[1] >= x[1]:
if (e[0] - x[1]) <= spacer:
overlapping = 1
elif x[1] >= e[1]:
if (x[0] - e[1]) <= spacer:
overlapping = 1
if overlapping:
# merge exons and add to the exon list
exons.append([min(e[0], x[0]), max(e[1], x[1])])
# remove the exons e and x
exons.remove(e)
exons.remove(x)
# break once an overlapping exon is found
break
if overlapping:
# if an overlapping exon is found,
# stop this for loop and continue with the
# while loop with the updated exon list
break
exons.sort()
return exons
def overlap(reg1, reg2):
"""
Return overlap between two regions.
e.g. [10, 30], [20, 40] returns [20, 30]
"""
try:
intersect = set(range(reg1[0], reg1[1] + 1)).intersection(
set(range(reg2[0], reg2[1] + 1)))
intersect = sorted(intersect)
return [intersect[0]] + [intersect[-1]]
except IndexError:
return []
def remove_overlap(reg1, reg2, spacer=0):
"""
Remove overlap between two regions.
e.g. [10, 30], [20, 40] returns [10, 20], [30, 40]
"""
regions = sorted([sorted(reg1), sorted(reg2)])
try:
if regions[0][1] - regions[1][0] >= spacer:
coords = sorted(reg1 + reg2)
return[[coords[0], coords[1] - 1],
[coords[2] + 1, coords[3]]]
else:
return regions
except IndexError:
return []
def complete_overlap(reg1, reg2):
"""
Return whether one of the two given regions contain the other.
e.g. [10, 40], [20, 30] returns True.
"""
regions = sorted([sorted(reg1), sorted(reg2)])
try:
return (((regions[0][0] == regions[1][0])
and (regions[0][1] <= regions[1][1]))
or ((regions[0][0] < regions[1][0])
and (regions[0][1] >= regions[1][1])))
except IndexError:
return False
def check_redundant_region(reg1, reg2, spacer=0):
"""
Return whether one of the two given regions is redundant.
i.e. one contains the other or there is less than 'spacer'
non-overlap between them.
"""
regions = sorted([sorted(reg1), sorted(reg2)])
try:
if complete_overlap(*regions):
return True
else:
non_overlap = remove_overlap(*regions)
if len(non_overlap) == 0:
return False
extra = sum([r[1] - r[0] + 1 for r in non_overlap])
if extra <= spacer:
return True
else:
return False
except IndexError:
return False
def subtract_overlap(uncovered_regions, covered_regions, spacer=0):
"""
Given two sets of regions in the form [[start, end], [start, end]],
return a set of regions that is the second set subtracted from the first.
"""
uncovered_set = set()
for r in uncovered_regions:
try:
uncovered_set.update(list(range(r[0], r[1] + 1)))
except IndexError:
pass
covered_set = set()
for r in covered_regions:
try:
covered_set.update(list(range(r[0], r[1] + 1)))
except IndexError:
pass
uncovered_remaining = sorted(uncovered_set.difference(covered_set))
if len(uncovered_remaining) > 0:
uncovered = [[uncovered_remaining[i-1], uncovered_remaining[i]]
for i in range(1, len(uncovered_remaining))
if uncovered_remaining[i] - uncovered_remaining[i-1] > 1]
unc = [uncovered_remaining[0]]
for u in uncovered:
unc.extend(u)
unc.append(uncovered_remaining[-1])
return [[unc[i], unc[i+1]]for i in range(0, len(unc), 2)
if unc[i+1] - unc[i] > spacer]
else:
return []
def trim_overlap(region_list, low=0.1, high=0.9, spacer=0):
"""
Given a set of regions in the form [[start, end], [start, end]],
return a set of regions with any overlapping parts trimmed
when overlap size / smaller region size ratio is lower than "low";
or flanking region outside of overlap is trimmed when the ratio
is higher than "high".
"""
do_trim = True
while do_trim:
do_trim = False
break_for = False
region_list = [r for r in region_list if r != "remove"]
for i in range(len(region_list)):
if break_for:
break
else:
for j in range(len(region_list)):
if i != j:
reg_i = region_list[i]
reg_j = region_list[j]
if reg_i == reg_j:
region_list[i] = "remove"
break_for = True
do_trim = True
break
else:
overlapping_region = overlap(reg_i, reg_j)
if len(overlapping_region) > 0:
reg_sizes = sorted([reg_i[1] - reg_i[0] + 1,
reg_j[1] - reg_j[0] + 1])
overlap_size = float(overlapping_region[1]
- overlapping_region[0])
overlap_ratio = overlap_size/reg_sizes[0]
if overlap_ratio <= low:
region_list[i] = "remove"
region_list[j] = "remove"
region_list.extend(remove_overlap(
reg_i, reg_j, spacer))
break_for = True
do_trim = True
break
elif overlap_ratio >= high:
region_list[i] = "remove"
region_list[j] = "remove"
region_list.append(overlapping_region)
break_for = True
do_trim = True
break
else:
print(overlap_ratio,
"is outside trim range for ",
reg_i, reg_j)
return region_list
def fasta_parser(fasta_file, use_description=False):
"""Convert a fasta file to python dict.
Convert a fasta file with multiple sequences to a dictionary with fasta
id as keys and sequences as values. The fasta id is the text in the fasta
header before the first space character. If the entire header line is
to be used, use_description=True should be passed.
"""
fasta_dic = {}
records = SeqIO.parse(fasta_file, format="fasta")
for rec in records:
if use_description:
header = rec.description
else:
header = rec.id
if header in fasta_dic:
print(("%s occurs multiple times in fasta file" % header))
fasta_dic[header] = str(rec.seq)
return fasta_dic
def fasta_parser_verbatim(fasta):
"""Convert a fasta file with multiple sequences to a dictionary.
Convert a fasta file with multiple sequences to a dictionary with fasta
headers as keys and sequences as values. Spaces are allowed in keys.
"""
fasta_dic = {}
with open(fasta) as infile:
for line in infile:
# find the headers
if line.startswith(">"):
header = line[1:-1]
if header in fasta_dic:
print(("%s occurs multiple times in fasta file" % header))
fasta_dic[header] = ""
continue
try:
fasta_dic[header] = fasta_dic[header] + line.strip()
except KeyError:
fasta_dic[header] = line.strip()
return fasta_dic
def fasta_to_sequence(fasta):
""" Convert a multiline fasta sequence to one line sequence"""
f = fasta.strip().split("\n")
if len(f) > 0:
return "".join(f[1:])
else:
return ""
def unmask_fasta(masked_fasta, unmasked_fasta):
""" Unmask lowercased masked fasta file, save """
with open(masked_fasta) as infile, open(unmasked_fasta, "w") as outfile:
for line in infile:
if not line.startswith((">", "#")):
outfile.write(line.upper())
else:
outfile.write(line)
return
def fasta_to_fastq(fasta_file, fastq_file):
""" Create a fastq file from fasta file with dummy quality scores."""
fasta = fasta_parser(fasta_file)
fastq_list = []
for f in fasta:
fastq_list.append("@" + f)
fastq_list.append(fasta[f])
fastq_list.append("+")
fastq_list.append("H" * len(fasta[f]))
with open(fastq_file, "w") as outfile:
outfile.write("\n".join(fastq_list))
return
def combine_sample_data(gr):
"""Combine data from multiple sequencing runs for the same sample.
Take a pandas groupby object representing multiple data points
corresponding the same sequence and sample, from multiple sequence runs.
Sum the barcode and read counts for the combined result. Use the sequencing
quality values for the record with most supporting barcodes.
Return a single combined record in pd.Series object so that all results can
be combined into a new pd.DataFrame for all samples.
"""
result = {}
result["barcode_count"] = gr["barcode_count"].sum()
result["read_count"] = gr["read_count"].sum()
result["sequence_quality"] = gr.sort_values(
"barcode_count",
ascending=False
)["sequence_quality"].iloc[0]
result["mip_name"] = gr["mip_name"].iloc[0]
result["gene_name"] = gr["gene_name"].iloc[0]
return pd.Series(result)
def combine_info_files(wdir,
settings_file,
info_files,
sample_sheets,
combined_file,
sample_sets=None):
"""Combine MIPWrangler outputs from multiple runs."""
settings = get_analysis_settings(os.path.join(wdir, settings_file))
colnames = dict(list(zip(settings["colNames"],
settings["givenNames"])))
c_keys = list(colnames.keys())
c_vals = [colnames[k] for k in c_keys]
data = []
run_meta = []
for i in range(len(sample_sheets)):
current_run_meta = pd.read_table(sample_sheets[i])
for k in ["sample_name", "sample_set", "replicate"]:
current_run_meta[k] = current_run_meta[k].astype(str)
current_run_meta["sheet_order"] = i
current_run_meta["Original SID"] = current_run_meta[
["sample_name", "sample_set", "replicate"]
].apply(lambda a: "-".join(a), axis=1)
run_meta.append(current_run_meta)
run_meta = pd.concat(run_meta, ignore_index=True)
if sample_sets is not None:
sps = pd.DataFrame(sample_sets, columns=["sample_set",
"probe_set"])
else:
sps = run_meta.groupby(
["sample_set", "probe_set"]
).first().reset_index()[["sample_set", "probe_set"]]
run_meta = run_meta.merge(sps, how="inner")
run_meta.rename(columns={"library_prep": "Library Prep"}, inplace=True)
run_meta_collapsed = run_meta.groupby(
["sample_name", "sample_set", "replicate", "Library Prep"]
).first().reset_index()[["sample_name", "sample_set",
"replicate", "Library Prep"]]
# check if there are repeating sample_name, sample_set, replicate
# combinations; which make up the sample ID. If there are, replicate
# numbers will need to be re-assigned so that each library has a unique
# ID. If no overlap, they should be left as they are.
repeat_found = False
# check if replicates are to be ignored, i.e. merge all libraries from
# the same DNA source.
merge_replicates = False
try:
if int(settings["mergeReplicates"]):
merge_replicates = True
except KeyError:
pass
run_meta_collapsed["new_replicate"] = run_meta_collapsed.groupby(
["sample_name", "sample_set"])["replicate"].transform(
assign_replicate).astype(str)
run_meta = run_meta.merge(run_meta_collapsed)
run_meta["Sample ID"] = run_meta[["sample_name",
"sample_set",
"new_replicate"]].apply(
lambda a: "-".join(a), axis=1
)
# load the probe set dictionary to extract the
# probes that we're interested in
probe_sets_file = settings["mipSetsDictionary"]
probe_set_keys = settings["mipSetKey"]
used_probes = set()
for psk in probe_set_keys:
with open(probe_sets_file) as infile:
used_probes.update(json.load(infile)[psk])
for i in range(len(info_files)):
i_file = info_files[i]
current_run_meta = run_meta.loc[run_meta["sheet_order"] == i]
current_run_dict = current_run_meta.set_index(
"Original SID"
).to_dict(orient="index")
line_number = 0
try:
gzip.open(i_file, "rb").readline()
inf_file = gzip.open(i_file, "rb")
except IOError:
inf_file = open(i_file, "rb")
with inf_file as infile:
for line in infile:
newline = line.decode("utf-8").strip().split("\t")
line_number += 1
if line_number == 1:
col_indexes = [
newline.index(ck)
for ck in c_keys
]
for ci in col_indexes:
if colnames[newline[ci]] == "sample_name":
si_index = ci
elif colnames[newline[ci]] == "mip_name":
mip_name_index = ci
else:
ori_sample_id = newline[si_index]
mip_fam_name = newline[mip_name_index]
if mip_fam_name in used_probes:
try:
library = current_run_dict[
ori_sample_id
]["Library Prep"]
sample_id = current_run_dict[
ori_sample_id
]["Sample ID"]
d = ([newline[ci] if ci != si_index else sample_id
for ci in col_indexes] + [library])
data.append(d)
except KeyError:
continue
info = pd.DataFrame(data, columns=c_vals + ["Library Prep"])
info["barcode_count"] = info["barcode_count"].astype(int)
info["read_count"] = info["read_count"].astype(int)
# check if replicates are to be ignored, i.e. merge all libraries from
# the same DNA source.
if merge_replicates:
info["original_sample_name"] = info["sample_name"]
info["sample_name"] = info["sample_name"].apply(
lambda a: "-".join(a.split("-")[:-1]) + "-1")
info["Library Prep"] = "merged"
info = info.groupby(
["sample_name", "haplotype_sequence", "Library Prep"]
).apply(combine_sample_data).reset_index()
m_groups = info.groupby("mip_name")
h_list = []
for m, g in m_groups:
md = pd.DataFrame(g.groupby(["mip_name",
"haplotype_sequence"]).size().sort_values(
ascending=False
).reset_index()).reset_index()
md["index"] = md["index"].astype(str)
md["haplotype_ID"] = md["mip_name"] + "." + md["index"]
h_list.append(md[["haplotype_sequence", "haplotype_ID"]])
hap_ids = pd.concat(h_list, ignore_index=True)
info = info.merge(hap_ids)
info.to_csv(os.path.join(wdir, combined_file), index=False, sep="\t")
info.groupby(["gene_name", "mip_name", "haplotype_ID"])[
"haplotype_sequence"].first().reset_index().to_csv(
os.path.join(wdir, "unique_haplotypes.csv"), index=False)
run_meta = run_meta.groupby("Sample ID").first().reset_index()
run_meta = run_meta.drop(["Sample ID",
"sheet_order",
"replicate"],
axis=1).rename(
columns={"new_replicate": "replicate"}
)
if merge_replicates:
run_meta["replicate"] = 1
run_meta.to_csv(os.path.join(wdir, "samples.tsv"), sep="\t", index=False)
def process_info_file(wdir,
settings_file,
info_files,
sample_sheets,
combined_file,
sample_sets=None):
"""
Process MIPWrangler output file.
This function extracts the relevant fields from a given MIPWrangler
output file, renames the columns to be used in downstream analysis and
merges the provided meta data.
"""
settings = get_analysis_settings(os.path.join(wdir, settings_file))
colnames = dict(list(zip(settings["colNames"],
settings["givenNames"])))
c_keys = list(colnames.keys())
c_vals = [colnames[k] for k in c_keys]
data = []
current_run_meta = pd.read_table(sample_sheets[0])
for k in ["sample_name", "sample_set", "replicate"]:
current_run_meta[k] = current_run_meta[k].astype(str)
current_run_meta["sheet_order"] = 0
current_run_meta["Original SID"] = current_run_meta[
["sample_name", "sample_set", "replicate"]
].apply(lambda a: "-".join(a), axis=1)
run_meta = current_run_meta
run_meta.rename(columns={"library_prep": "Library Prep"}, inplace=True)
if sample_sets is not None:
sps = pd.DataFrame(sample_sets, columns=["sample_set",
"probe_set"])
else:
sps = run_meta.groupby(
["sample_set", "probe_set"]
).first().reset_index()[["sample_set", "probe_set"]]
run_meta = run_meta.merge(sps, how="inner")
run_meta["Sample ID"] = run_meta["Original SID"]
# load the probe set dictionary to extract the
# probes that we're interested in
probe_sets_file = settings["mipSetsDictionary"]
probe_set_keys = settings["mipSetKey"]
used_probes = set()
for psk in probe_set_keys:
with open(probe_sets_file) as infile:
used_probes.update(json.load(infile)[psk])
i_file = info_files[0]
current_run_meta = run_meta
current_run_dict = current_run_meta.set_index(
"Original SID"
).to_dict(orient="index")
line_number = 0
try:
gzip.open(i_file, "rb").readline()
inf_file = gzip.open(i_file, "rb")
except IOError:
inf_file = open(i_file, "rb")
with inf_file as infile:
for line in infile:
newline = line.decode("utf-8").strip().split("\t")
line_number += 1
if line_number == 1:
col_indexes = [
newline.index(ck)
for ck in c_keys
]
for ci in col_indexes:
if colnames[newline[ci]] == "sample_name":
si_index = ci
elif colnames[newline[ci]] == "mip_name":
mip_name_index = ci
else:
ori_sample_id = newline[si_index]
mip_fam_name = newline[mip_name_index]
if mip_fam_name in used_probes:
try:
library = current_run_dict[
ori_sample_id
]["Library Prep"]
sample_id = current_run_dict[
ori_sample_id
]["Sample ID"]
d = ([newline[ci] if ci != si_index else sample_id
for ci in col_indexes] + [library])
data.append(d)
except KeyError:
continue
info = pd.DataFrame(data, columns=c_vals + ["Library Prep"])
info["barcode_count"] = info["barcode_count"].astype(int)
info["read_count"] = info["read_count"].astype(int)
info.to_csv(os.path.join(wdir, combined_file), index=False, sep="\t")
info.groupby(["gene_name", "mip_name", "haplotype_ID"])[
"haplotype_sequence"].first().reset_index().to_csv(
os.path.join(wdir, "unique_haplotypes.csv"), index=False)
run_meta = run_meta.groupby("Sample ID").first().reset_index()
run_meta = run_meta.drop("Sample ID", axis=1)
run_meta.to_csv(os.path.join(wdir, "samples.tsv"), sep="\t", index=False)
def generate_fastqs(wdir, mipster_files, min_bc_count, min_bc_frac):
"""
Generate fastq files for each sample in raw MIPWrangler output file(s).
These files will have stitched and barcode corrected reads.
"""
fastq_dir = os.path.join(wdir, "fastq")
if not os.path.exists(fastq_dir):
os.makedirs(fastq_dir)
mipster_dfs = pd.concat([pd.read_table(os.path.join(wdir, mfile),
usecols=[
"s_Sample",
'h_popUID',
"h_seq",
'c_qual',
'c_barcodeCnt',
"c_barcodeFrac"
])
for mfile in mipster_files],
axis=0,
ignore_index=True)
mipster = mipster_dfs.loc[
(mipster_dfs["c_barcodeCnt"] >= min_bc_count)
& (mipster_dfs["c_barcorac"] >= min_bc_frac)
].groupby("s_Sample").apply(lambda x: pd.DataFrame.to_dict(
x, orient="index"
)).to_dict()
for sample in mipster:
fastq_file = os.path.join(fastq_dir, sample + ".fq.gz")
with gzip.open(fastq_file, "wb") as outfile:
outfile_list = []
for ind in mipster[sample]:
row = mipster[sample][ind]
bc = int(row["c_barcodeCnt"])
hid = row["h_popUID"]
qual = row["c_qual"]
seq = row["h_seq"]
sample = row["s_Sample"]
for i in range(bc):
read_name = "_".join(["@", sample, hid, str(ind), str(i)])
outfile_list.extend([read_name, seq, "+", qual])
outfile.write(("\n".join(outfile_list) + "\n").encode("UTF-8"))
return
def generate_processed_fastqs_worker(fastq_file, sample_mipster):
"""Worker function for generate_processed_fastqs."""
with gzip.open(fastq_file, "wb") as outfile:
outfile_list = []
for ind in sample_mipster:
row = sample_mipster[ind]
bc = int(row["barcode_count"])
hid = row["haplotype_ID"]
qual = row["sequence_quality"]
seq = row["haplotype_sequence"]
sample = row["sample_name"]
for i in range(bc):
read_name = "_".join(["@", sample, hid, str(ind), str(i)])
outfile_list.extend([read_name, seq, "+", qual])
outfile.write(("\n".join(outfile_list) + "\n").encode("UTF-8"))
def generate_processed_fastqs(fastq_dir, mipster_file,
min_bc_count=1,
pro=8):
"""
Generate fastq files for each sample in processed MIPWrangler output file.
The resulting fastq files will have stitched and barcode corrected reads.
"""
if not os.path.exists(fastq_dir):
os.makedirs(fastq_dir)
mipster = pd.read_table(mipster_file,
usecols=[
"sample_name",
'haplotype_ID',
"haplotype_sequence",
'sequence_quality',
'barcode_count'
])
mipster = mipster.loc[mipster["barcode_count"] >= min_bc_count].groupby(
"sample_name"
).apply(lambda x: pd.DataFrame.to_dict(x, orient="index")).to_dict()
p = Pool(pro)
for sample in mipster:
fastq_file = os.path.join(fastq_dir, sample + ".fq.gz")
sample_mipster = mipster[sample]
p.apply_async(generate_processed_fastqs_worker, (fastq_file,
sample_mipster))
p.close()
p.join()
return
def generate_mapped_fastqs(fastq_dir, mipster_file,
mapped_haplotypes_file, species, min_bc_count=1,
pro=8, pad_size=20, save=False):
"""
Generate fastq files for each sample in a mapped MIPWrangler output file.
The input is file is free from off target reads etc. The resulting fastq
files will have stitched and barcode corrected on target reads.
"""
if not os.path.exists(fastq_dir):
os.makedirs(fastq_dir)
mipster = pd.read_table(mipster_file,
usecols=[
"sample_name",
'haplotype_ID',
"haplotype_sequence",
'sequence_quality',
'barcode_count'
])
mapped_haplotypes = pd.read_csv(mapped_haplotypes_file)
def get_padded_haplotype_sequence(row, pad_size, species):
"""Pad haplotype sequence with flanking sequence from the reference."""
chrom = row["Chrom"]
capture_start = int(row["capture_start"])
capture_end = int(row["capture_end"])
pad_start = capture_start - pad_size
pad_end = capture_end + pad_size
left_key = create_region(chrom, pad_start, capture_start - 1)
right_key = create_region(chrom, capture_end + 1, pad_end)
left_pad = get_sequence(left_key, species)
right_pad = get_sequence(right_key, species)
h_seq = row["haplotype_sequence"]
ori = row["orientation"]
if ori == "reverse":
hs = left_pad + reverse_complement(h_seq) + right_pad
return reverse_complement(hs)
else:
return left_pad + h_seq + right_pad
if pad_size > 0:
mapped_haplotypes["padded_haplotype_sequence"] = (
mapped_haplotypes.apply(get_padded_haplotype_sequence,
args=(pad_size, species), axis=1))
else:
mapped_haplotypes["padded_haplotype_sequence"] = (
mapped_haplotypes["haplotype_sequence"])
mipster = mipster.merge(mapped_haplotypes[["haplotype_ID",
"padded_haplotype_sequence",
"mapped_copy_number"]])
mipster["raw_haplotype_sequence"] = mipster["haplotype_sequence"]
mipster["haplotype_sequence"] = mipster["padded_haplotype_sequence"]
mipster["raw_sequence_quality"] = mipster["sequence_quality"]
mipster["sequence_quality"] = (
pad_size * "!" + mipster["sequence_quality"] + pad_size * "!")
mipster["adjusted_barcode_count"] = (
mipster["barcode_count"] / mipster["mapped_copy_number"]).astype(int)
mipster["raw_barcode_count"] = mipster["barcode_count"]
mipster["barcode_count"] = mipster["adjusted_barcode_count"]
mipster_dict = mipster.loc[mipster["barcode_count"]
>= min_bc_count].groupby(
"sample_name").apply(lambda x: pd.DataFrame.to_dict(
x, orient="index")).to_dict()
if save:
mipster.to_csv(mipster_file + ".padded")
p = Pool(pro)
for sample in mipster_dict:
fastq_file = os.path.join(fastq_dir, sample + ".fq.gz")
sample_mipster = mipster_dict[sample]
p.apply_async(generate_processed_fastqs_worker, (fastq_file,
sample_mipster))
p.close()
p.join()
return
def generate_unprocessed_fastqs(fastq_dir, mipster_file, min_bc_count=1,
pro=8):
"""
Generate fastq files for each sample. These files will have stitched and
barcode corrected reads.
"""
if not os.path.exists(fastq_dir):
os.makedirs(fastq_dir)
mipster = pd.read_table(mipster_file, usecols=["s_Sample", 'h_popUID',
"h_seq", 'c_qual',
'c_barcodeCnt'])
mipster = mipster.rename(columns={"s_Sample": "sample_name",
"h_popUID": "haplotype_ID",
"h_seq": "haplotype_sequence",
"c_qual": "sequence_quality",
"c_barcodeCnt": "barcode_count"})
mipster = mipster.loc[mipster["barcode_count"] >= min_bc_count].groupby(
"sample_name"
).apply(lambda x: pd.DataFrame.to_dict(x, orient="index")).to_dict()
p = Pool(pro)
for sample in mipster:
fastq_file = os.path.join(fastq_dir, sample + ".fq.gz")
sample_mipster = mipster[sample]
p.apply_async(generate_processed_fastqs_worker, (fastq_file,
sample_mipster))
p.close()
p.join()
return
def convert_to_int(n):
"""Convert values to integers.
This is to be used when a pandas dataframe converts integers to floats due
to the presence of NA values and integer values are
preferred over floats, e.g. string conversion/comparison.
"""
try:
return int(n)
except ValueError:
return np.nan
def get_ternary_genotype(gen):
"""Convert a 0/0, 0/1, 1/1 type genotype string to 0, 1, 2."""
try:
g = sum(map(int, gen.split(":")[0].split("/")))
except ValueError:
g = np.nan
return g
def variation_to_geno(settings, var_file, output_prefix):
"""Create PLINK files from variation table file."""
wdir = settings["workingDir"]
case = {}
with open(os.path.join(wdir, "case_file")) as infile:
for line in infile:
newline = line.strip().split("\t")
case[newline[0]] = newline[1]
with open(os.path.join(wdir, var_file)) as infile:
linenum = 0
map_snp_ids = []
geno_snp_ids = []
genes = {}
ordered_genes = []
all_geno_bases = []
all_geno_numbers = []
for line in infile:
newline = line.strip().split("\t")
if linenum == 0:
linenum += 1
header = newline
sample_start_index = header.index("FORMAT") + 1
sample_ids = header[sample_start_index:]
sample_names = ["-".join(s.split("-")[:-2])
for s in sample_ids]
samples_used = []
ped_sample_info = []
geno_sample_info = []
for i in range(len(sample_ids)):
s = sample_ids[i]
sam_name = sample_names[i]
try:
affected = case[s]
if affected == "case":
affected = "2"
elif affected == "control":
affected = "1"
ped_sample_info.append(["0", sam_name, "0", "0", "0",
affected])
geno_sample_info.append(["0", sam_name, sam_name,
affected])
samples_used.append(s)
except KeyError:
continue
used_sample_mask = np.array([s in samples_used
for s in sample_ids])
else:
chrom = newline[0]
pos = newline[1]
rsid = newline[2]
ref = newline[3]
alt = newline[4]
aa_change = newline[5]
gene_name = newline[7]
if rsid == ".":
rsid = chrom[3:] + "-" + pos + "-" + ref + "-" + alt
map_snp_ids.append([chrom, rsid, "0", pos])
geno_snp_ids.append([chrom, rsid, pos, ref,
alt, gene_name, aa_change])
try:
genes[gene_name].append(rsid)
except KeyError:
genes[gene_name] = [rsid]
ordered_genes.append(gene_name)
genotypes = np.array(newline[sample_start_index:])[
used_sample_mask]
geno_bases_1 = []
geno_bases_2 = []
geno_numbers = []
for g in genotypes:
temp_g = g.split(":")[0]
if temp_g == "0/0":
geno_bases_1.append(ref)
geno_bases_2.append(ref)
geno_numbers.append("0")
elif temp_g == "0/1":
geno_bases_1.append(ref)
geno_bases_2.append(alt)
geno_numbers.append("1")
elif temp_g == "1/1":
geno_bases_1.append(alt)
geno_bases_2.append(alt)
geno_numbers.append("2")
else:
geno_bases_1.append("0")
geno_bases_2.append("0")
geno_numbers.append(".")
all_geno_bases.extend([geno_bases_1,
geno_bases_2])
all_geno_numbers.append(geno_numbers)
all_geno_bases = list(zip(*all_geno_bases))
all_geno_numbers = list(zip(*all_geno_numbers))
for i in range(len(ped_sample_info)):
ped_sample_info[i].extend(all_geno_bases[i])
for i in range(len(geno_sample_info)):
geno_sample_info[i].extend(all_geno_numbers[i])
write_list(ped_sample_info,
os.path.join(wdir, output_prefix + ".ped"))
write_list(map_snp_ids,
os.path.join(wdir, output_prefix + ".map"))
header = ["FAMILY_ID", "INDIVIDUAL_ID", "SAMPLE_ID", "AFFECTION"]
header.extend([s[1] for s in geno_snp_ids])
geno_sample_info = [header] + geno_sample_info
write_list(geno_sample_info, os.path.join(wdir, output_prefix + ".geno"))
write_list([["**", o] + genes[o] for o in ordered_genes],
os.path.join(wdir, output_prefix + ".hlist"))
return
def absence_presence(col, min_val=1):
"""
Given a numerical dataframe column, convert to binary values for a minimum
threshold. This should be used by pandas transform or apply.
"""
return pd.Series([0 if (c < min_val or np.isnan(c))
else 1 for c in col.tolist()])
def plot_performance(barcode_counts,
tick_label_size=8,
cbar_label_size=5,
dpi=300,
barcode_threshold=1,
absent_color="black",
present_color="green",
save=False,
wdir=None,
ytick_freq=None,
xtick_freq=None,
xtick_rotation=90,
tick_genes=False,
gene_name_index=None):
"""Plot presence/absence plot for a mip run."""
if xtick_freq is None:
xtick_freq = barcode_counts.shape[1]//30
if xtick_freq == 0:
xtick_freq = 1
if ytick_freq is None:
ytick_freq = barcode_counts.shape[0]//30
if ytick_freq == 0:
ytick_freq = 1
fig, ax = plt.subplots()
cmap = colors.ListedColormap([absent_color, present_color])
boundaries = [-0.5, 0.5, 1.5]
norm = colors.BoundaryNorm(boundaries, cmap.N)
heat = ax.pcolormesh(
barcode_counts.applymap(
lambda a: np.nan if np.isnan(a)
else 0 if a < barcode_threshold
else 1
), cmap=cmap, norm=norm)
sample_ids = list(barcode_counts.index)
sample_locs = np.arange(1, len(sample_ids) + 1, ytick_freq) - 0.5
ylabs = sample_ids[::ytick_freq]
plt.yticks(sample_locs, ylabs)
if tick_genes:
bc_cols = barcode_counts.columns.tolist()
bc_cols = [c[gene_name_index] for c in bc_cols]
xlabs = bc_cols[::xtick_freq]
gene_locs = np.arange(1, len(bc_cols) + 1, xtick_freq) - 0.5
plt.xticks(gene_locs, xlabs, rotation=xtick_rotation, ha="right")
for ticklabel in ax.get_xticklabels():
ticklabel.set_fontsize(tick_label_size)
for ticklabel in ax.get_yticklabels():
ticklabel.set_fontsize(tick_label_size)
ax.set_ylabel("Samples")
ax.set_xlabel("Probes")
fig.suptitle("Performance",
verticalalignment="bottom")
fig.tight_layout()
cbar = fig.colorbar(heat, ticks=[0, 1], shrink=0.2)
cbar.ax.tick_params(labelsize=cbar_label_size)
cbar.ax.set_yticklabels(["Absent", "Present"])
fig.set_dpi(dpi)
fig.tight_layout()
if save:
fig.savefig(os.path.join(wdir, "performance.png"), dpi=dpi,
bbox_inches='tight')
plt.close("all")
else:
return fig, ax
return
def plot_coverage(barcode_counts,
tick_label_size=8,
cbar_label_size=5,
dpi=300,
log=None,
log_constant=1,
linthresh=0.0001,
save=False,
wdir=None,
ytick_freq=None,
xtick_freq=None,
xtick_rotation=90,
tick_genes=False,
gene_name_index=None,
figure_title="Coverage",
title_fontdict=None,
ylabel="Samples",
xlabel="Probes",
fig_size=None,
cbar_title=None):
"""Plot UMI coverage per MIP per sample for a mip run."""
if xtick_freq is None:
xtick_freq = barcode_counts.shape[1]//30
if xtick_freq == 0:
xtick_freq = 1
if ytick_freq is None:
ytick_freq = barcode_counts.shape[0]//30
if ytick_freq == 0:
ytick_freq = 1
fig, ax = plt.subplots()
if log is None:
heat = ax.pcolormesh(barcode_counts)
if cbar_title is None:
cbar_title = ""
elif log == 2:
if cbar_title is None:
cbar_title = "log2"
heat = ax.pcolormesh(np.log2(barcode_counts + log_constant))
elif log == 10:
if cbar_title is None:
cbar_title = "log10"
heat = ax.pcolormesh(np.log10(barcode_counts + log_constant))
elif log == "ln":
if cbar_title is None:
cbar_title = "log"
heat = ax.pcolormesh(np.log(barcode_counts + log_constant))
elif log == "symlog":
if cbar_title is None:
cbar_title = ""
heat = ax.pcolormesh(barcode_counts,
norm=colors.SymLogNorm(linthresh=linthresh))
else:
print("log can only be None, 2, 10, 'log', {} provided.".format(log))
sample_ids = list(barcode_counts.index)
sample_locs = np.arange(1, len(sample_ids) + 1, ytick_freq) - 0.5
ylabs = sample_ids[::ytick_freq]
plt.yticks(sample_locs, ylabs)
if tick_genes:
bc_cols = barcode_counts.columns.tolist()
bc_cols = [c[gene_name_index] for c in bc_cols]
xlabs = bc_cols[::xtick_freq]
gene_locs = np.arange(1, len(bc_cols) + 1, xtick_freq) - 0.5
plt.xticks(gene_locs, xlabs,
rotation=xtick_rotation,
ha="right")
for ticklabel in ax.get_xticklabels():
ticklabel.set_fontsize(tick_label_size)
for ticklabel in ax.get_yticklabels():
ticklabel.set_fontsize(tick_label_size)
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
ax.set_title(figure_title, fontdict=title_fontdict)
cbar = fig.colorbar(heat, shrink=0.5)
cbar.ax.tick_params(labelsize=cbar_label_size)
cbar.ax.set_ylabel(cbar_title,
fontsize=cbar_label_size,
rotation=90)
fig.set_dpi(dpi)
if fig_size is not None:
fig.set_size_inches(fig_size)
fig.tight_layout()
if save:
fig.savefig(os.path.join(wdir, "coverage.png"),
dpi=dpi,
bbox_inches='tight')
plt.close("all")
else:
return fig, ax
return
def get_copy_counts(count_table, average_copy_count=2,
norm_percentiles=[0.4, 0.6]):
"""
Given a table of barcode counts with samples on rows
and probes on columns, transform the table to return
estimated copy count.
Parameters
----------
count_table : numpy array/pandas dataframe
Table of barcode counts with samples on rows
and probes on columns
average_copy_count : float, 2
Most common copy number in population. 2 for humans
This number is used to assign the copy number for
median/average normalized barcode count.
norm_percentiles : length 2 list of floats between 0 and 1, [0.4, 0.6]
Percentiles used for calculating average. [0.5, 0.5] would be median.
"""
# Normalize samples (across columns)
s_norm = count_table.transform(
lambda a: a/a.sum(), axis=1)
# Normalize across samples. This achieves estimating
# the copy number, assuming the average normalized
# barcode value (at specified percentile) is the value
# provided by averageCopyCount setting. This should
# default to something like median barcode count
# corresponds to copy number 2.
p_norm = s_norm.transform(
lambda a: average_copy_count * a/(a.quantile(norm_percentiles).mean()))
return p_norm
def repool(wdir,
data_summary,
high_barcode_threshold,
target_coverage_count=None,
target_coverage_fraction=0.95,
target_coverage_key="targets_with_10_barcodes",
barcode_coverage_threshold=10,
barcode_count_threshold=100,
low_coverage_action="Repool",
assesment_key="targets_with_1_barcodes",
good_coverage_quantile=0.25,
output_file="repool.csv"):
"""
Analyze run statistics and determine repooling/recapturing
strategy for following runs.
Parameters
----------
wdir : str
Path to working directory, used only for saving the results.
data_summary : Pandas DataFrame
Dataframe containing all count information per sample per target.
high_barcode_threshold: int/ other number
Targeted barcode number to determine how much more of a
sample should be repooled. Should be set to the number where
majority of samples show good target coverage.
barcode_coverage_threshold : int / other number, 10
Average reads per barcode per sample to consider the sample
saturated and remove from pooling. If sample is not deemed
complete it will be set to be recaptured.
barcode_count_threshold : int / other number
Minimum number of barcodes per sample to determine if a sample
has very low coverage. Those samples' status will be set
to the action (recapture or repool) defined by
low_coverage_action parameter.
target_coverage_count : int / other number / None, None
Minimum number of targets (MIPs) that are sequenced/covered
to the given criteria to consider a sample complete. Defaults
to None, in which case target_coverage_fraction * total number
of possible targets will be used.
target_coverage_fraction : float, 0.95
See target_coverage_count.
target_coverage_key : str, "targets_with_10_barcodes"
Dataframe column name to use for assessing target coverage.
By default a target that is covered with >10 barcodes will
be considered covered.
assesment_key : str, "targets_with_1_barcodes"
Dataframe key to use for determining uneven coverage across targets
which happens when barcode number per sample is high but number of
targets covered is low. By default any target with sequence is
considered covered.
good_coverage_quantile : float, 0.25
Quantile of barcodes for "completed samples". This is used to determine
if a sample has good enough barcode numbers, then test if it has enough
targets covered, or the majority of barcodes cover only a small number
of targets (uneven coverage).
output_file: str, repool.csv
"""
if target_coverage_count is None:
target_coverage_count = (data_summary[target_coverage_key].max()
* target_coverage_fraction)
# make a copy of data_summary so the original df stays the same
data_summary = copy.deepcopy(data_summary)
try:
data_summary["total_barcode_count"]
except KeyError:
data_summary["total_barcode_count"] = data_summary["Barcode Count"]
data_summary["total_read_count"] = data_summary["Read Count"]
# mark samples that reached the desired outcome
data_summary.loc[
data_summary[target_coverage_key] >= target_coverage_count,
"Status"
] = "Complete"
# mark samples with low coverage
data_summary.loc[
(data_summary["Status"].isnull())
& (data_summary["total_barcode_count"] < barcode_count_threshold),
"Status"] = low_coverage_action
# mark samples with too high barcode coverage
# these samples will have been sequenced to a high depth but
# low barcode numbers, so sequencing these more would not make sense.
# They will be re-captured if more data is needed.
try:
data_summary["Barcode Coverage"]
except KeyError:
data_summary["Barcode Coverage"] = (
data_summary["total_read_count"]
/ data_summary["total_barcode_count"]).fillna(0)
data_summary.loc[
(data_summary["Status"].isnull())
& (data_summary["Barcode Coverage"] >= barcode_coverage_threshold),
"Status"] = "Recapture"
# Zero barcode coverage is presumably due to poor sequencing
# So low coverage action should be taken.
data_summary.loc[
(data_summary["Status"].isnull())
& (data_summary["Barcode Coverage"] == 0),
"Status"
] = low_coverage_action
# All remaining samples will be repooled
data_summary.loc[
(data_summary["Status"].isnull()),
"Status"
] = "Repool"
data_summary["Library to Completion"] = (
(high_barcode_threshold - data_summary["total_barcode_count"])
/ data_summary["total_barcode_count"])
# replace inf values with max
lc_max = data_summary.loc[
data_summary["Library to Completion"] < np.inf,
"Library to Completion"].max()
data_summary.loc[
data_summary["Library to Completion"] == np.inf,
"Library to Completion"] = lc_max
# Determine samples with good barcode counts but poor target coverage
# These should be investigated to decide what is the reason behind it
# and how to proceed.
##########################################
# Determine the average barcode count per target covered
# for all samples where there is targets covered
data_summary.loc[data_summary[target_coverage_key] > 0,
"Barcodes Per Target Covered"] = (
data_summary.loc[data_summary[target_coverage_key] > 0,
"total_barcode_count"]
/ data_summary.loc[data_summary[target_coverage_key] > 0,
target_coverage_key]
)
# Get the lower quartile of barcodes per target for good data
# This number will be used to determine if poor coverage samples
# have high enough barcode coverage despite having poor target coverage.
good_coverage_threshold = data_summary.loc[
data_summary["Status"] == "Complete",
"Barcodes Per Target Covered"].quantile(good_coverage_quantile)
# Determine samples where barcode coverage is high but target coverage
# is low
data_summary.loc[
(data_summary["Barcodes Per Target Covered"] > good_coverage_threshold)
& (data_summary[assesment_key] < target_coverage_count),
"Uneven Coverage"] = True
data_summary.loc[data_summary["Uneven Coverage"].isnull(),
"Uneven Coverage"] = False
try:
data_summary.to_csv(os.path.join(wdir, output_file), index=False)
except TypeError:
# in an older version of this function, settings dict
# was passed instead of wdir, for backwards compatibility
# we'll catch that error and use wdir from the settings dict
data_summary.to_csv(wdir["workingDir"] + output_file, index=False)
print(("Out of %d samples %d are completed,"
" %d will be recaptured and %d repooled" % (
data_summary.shape[0],
data_summary.loc[data_summary["Status"] == "Complete"].shape[0],
data_summary.loc[data_summary["Status"]
== "Recapture"].shape[0],
data_summary.loc[data_summary["Status"] == "Repool"].shape[0])))
print(("%d samples showed uneven coverage, %d complete,"
" %d to be recaptured, %d repooled" % (
data_summary.loc[data_summary["Uneven Coverage"]].shape[0],
data_summary.loc[data_summary["Uneven Coverage"] &
(data_summary["Status"]
== "Complete")].shape[0],
data_summary.loc[data_summary["Uneven Coverage"]
& (data_summary["Status"]
== "Recapture")].shape[0],
data_summary.loc[data_summary["Uneven Coverage"]
& (data_summary["Status"]
== "Repool")].shape[0])))
return
def aa_to_coordinate(gene, species, aa_start, aa_end=None, alias=False):
"""
Given a gene name and its amino acid location,
return the genomic coordinates of the aa.
This will work with most Plasmodium genes but will
be problematic when genes have multiple isoforms,
such as most human genes.
"""
if alias:
with open(get_file_locations()[species]["alias"]) as infile:
alias_dic = json.load(infile)
try:
gene = alias_dic[gene]
except KeyError:
pass
cds = get_cds(gene, species)
if len(cds) == 0:
return [np.nan, np.nan, np.nan,
np.nan, np.nan, np.nan]
ori = cds["orientation"]
coord = cds["coordinates"]
chrom = cds["chrom"]
if aa_end is None:
aa_end = aa_start
if ori == "+":
c_end = aa_end * 3 - 1
c_start = aa_start * 3 - 3
else:
c_start = aa_end * 3 - 1
c_end = aa_start * 3 - 3
cds_start = coord[c_start]
cds_end = coord[c_end]
if ori == "+":
codon = get_sequence(
create_region(
chrom, cds_start, cds_end
), species
)
else:
codon = reverse_complement(
get_sequence(
create_region(
chrom, cds_start, cds_end
), species
)
)
aa = translate(codon)
return [chrom, cds_start, cds_end, ori, codon, aa]
def merge_snps(settings):
"""
When more than one SNP affects the same codon of a gene,
merge the two SNPs and create a merged protein change
annotation.
"""
wdir = settings["workingDir"]
species = settings["species"]
# load haplotype dictionary, save a backup.
unique_haplotype_file = os.path.join(wdir, settings["haplotypeDictionary"])
with open(unique_haplotype_file) as infile:
haplotypes = json.load(infile)
# create output information list to report all changes
outlist = [["HaplotypeID", "Copy", "New AA", "Reference AA",
"ReplacedCDNAchanges", "New Codon", "Reference Codon",
"ReplacedAaChanges"]]
# go through each annotated haplotype and merge SNPs
for m in haplotypes:
for h in haplotypes[m]:
if haplotypes[m][h]["mapped"]:
for cp in haplotypes[m][h]["mapped_copies"]:
# get sequence of the haplotype
hap_seq = haplotypes[m][h]["sequence"]
# get SNPs present in the haplotype
diffs = haplotypes[m][h]["mapped_copies"][cp][
"differences"]
aa_changes = {}
multi_indels = []
for i in range(len(diffs)):
d = diffs[i]
# get protein change information for the SNP
aa = d["annotation"]["AA Change"]
try:
aa_pos = int(d["annotation"]["AA Change Position"])
try:
# add the aa change position to the changes
# dict.
aa_changes[aa_pos].append(i)
except KeyError:
aa_changes[aa_pos] = [i]
except ValueError:
continue
# after going through all diffs, look for mutliple diffs
# affecting single aminoacid
all_merges = []
for c in aa_changes:
if (len(aa_changes[c]) > 1) and (
c not in multi_indels):
# break out of loop if indels found
mindel = False
indexes = aa_changes[c]
# keep positions relative to cDNA in a list
c_positions = []
# keep positions relative to haplotype in a list
h_indexes = []
# keep genomic positions of changes in a list
g_positions = []
# keep the difference between the cDNA and genomic
# positions in a list. This will be used to
# determine the gene's orientation on the genome.
c_offsets = []
changes_to_cdna = []
changes_to_aa = []
for i in indexes:
d = diffs[i]
# for each diff get the annotation
# e.g. 'HBB:HBB:exon1:c.G673A:p.V225I'
ano = d["annotation"]
if ano["SNV"] != "SNV":
mindel = True
multi_indels.append(c)
break
aa = ano["AA Change"]
changes_to_aa.append(aa)
# get the aa of reference genome (V)
aa_ref = aa[0]
# get cdna change, e.g. G673A
cdna = ano["CDS Change"]
changes_to_cdna.append(cdna)
# get the mutant base (A)
cdna_change = cdna[-1]
# compare the sequence on the cDNA with
# to the sequence of the haplotype,
# to determine the MIP/haplotype's orientation
# relative to the cDNA
ori = cdna_change == d["hap_base"]
cdna_pos = int(ano["CDS Position"])
# get genomic position of the change
diff_start = int(d["annotation"]["Start"])
# get the difference between the genomic and
# cDNA position of the change, to be used
# in determining the gene's orientation
c_positions.append(cdna_pos)
h_indexes.extend(d["hap_index"])
g_positions.append(diff_start)
c_offsets.append(diff_start - cdna_pos)
if mindel:
break
c_positions = sorted(c_positions)
h_indexes = sorted(set(h_indexes))
g_positions = sorted(g_positions)
# if the offset between the cDNA and genomic
# positions of the changes are always the same,
# the gene is on the plus strand, else it is
# reverse.
if len(set(c_offsets)) > 1:
gene_ori = False
else:
gene_ori = True
# get the position of the first base of the codon
codon_offset = (c_positions[0] % 3) - 1
codon_pos = c_positions[0] - codon_offset
# get the codon's sequence from the haplotype
if ori:
# if the haplotype is in the same orientation
# as the cDNA
h_start_index = h_indexes[0] - codon_offset
h_end_index = h_start_index + 3
hap_codon = hap_seq[h_start_index:h_end_index]
codon = hap_codon
else:
# if the haplotype is in the opposite
# orientation as the cDNA
h_end_index = h_indexes[-1] + codon_offset + 1
h_start_index = h_end_index - 3
hap_codon = hap_seq[h_start_index:h_end_index]
codon = reverse_complement(hap_codon)
# get genomic position and sequence of the codon
if gene_ori:
g_start = g_positions[0] - codon_offset
g_end = g_start + 2
else:
g_end = g_positions[-1] + codon_offset
g_start = g_end - 2
# extract the reference codon sequence
Ref = get_sequence(create_region(
d["chrom"], g_start, g_end), species)
if gene_ori:
g_codon = Ref
Alt = codon
else:
g_codon = reverse_complement(Ref)
Alt = reverse_complement(codon)
# calculate merged codon's amino acid
merged_aa = translate(codon)
# recreate the annotation string for the merge
protein_change = aa_ref + str(c) + merged_aa
coding_change = g_codon + str(codon_pos) + codon
# determine if the merged change is synonymous
if aa_ref == merged_aa:
ExonicFunc = "synonymous_variant"
else:
ExonicFunc = "nonsynonymous_variant"
merged_dict = {'annotation': {
'AA Change': protein_change,
"AA Change Position": str(c),
"CDS Change": coding_change,
"CDS Position": str(codon_pos),
'Alt': Alt,
'Chr': d["chrom"],
'End': g_end,
'ExonicFunc': ExonicFunc,
'GeneID': d["annotation"]['GeneID'],
'Ref': Ref,
'Start': g_start,
"SNV": "MNV"},
'begin': g_start,
'chrom': d["chrom"],
'end': g_end,
'hap_base': hap_codon,
'hap_index': [h_start_index,
h_end_index - 1],
'ref_base': Ref,
'type': 'snp',
'vcf_normalized': ":".join(
[d["chrom"], str(g_start), ".",
Ref, Alt]),
'vcf_raw': ":".join(
[d["chrom"], str(g_start), ".",
Ref, Alt]
),
"gene_ori": gene_ori,
"ori": ori}
all_merges.append(merged_dict)
outlist.append([h, c, merged_aa, aa_ref,
",".join(changes_to_cdna),
codon, g_codon,
",".join(changes_to_aa)])
# Remove SNPs that were merged, add the merged SNP
for c in aa_changes:
if ((len(aa_changes[c]) > 1)
and (c not in multi_indels)):
indexes = aa_changes[c]
for i in indexes:
diffs[i] = "remove"
diffs.extend(all_merges)
diffs = [d for d in diffs if d != "remove"]
haplotypes[m][h]["mapped_copies"][cp][
"differences"] = diffs
with open(unique_haplotype_file, "w") as outfile:
json.dump(haplotypes, outfile, indent=1)
# save the report
write_list(outlist, os.path.join(wdir, "merge_snps_output.txt"))
return outlist
def load_processed_data(settings):
"""
Load the data after initial processing.
Data included will be barcode counts, meta data
and data summary.
"""
wdir = settings["workingDir"]
# load the barcode count data and save a transposed
# version of it, only to load the transposed version
# back again and re-transpose. The reason for this is
# that read_csv method of pandas dataframe does not
# interpret multi index column names as integers but
# it does so for multi index index names. begin and
# end multi index columns should have names as int.
bc = pd.read_csv(os.path.join(wdir, "barcode_counts.csv"),
header=[0, 1, 2, 3, 4, 5, 6], index_col=0)
bc.T.to_csv(os.path.join(wdir, "barcode_counts.T.csv"))
bc = pd.read_csv(os.path.join(wdir, "barcode_counts.T.csv"),
index_col=[0, 1, 2, 3, 4, 5, 6]).T
data_summary = pd.read_csv(os.path.join(wdir, "data_summary.csv"),
index_col=None)
merged_meta = pd.read_csv(os.path.join(wdir, "meta_data.csv"),
index_col=None)
bc.index.name = "Sample ID"
return {"Barcode Counts": bc,
"Data Summary": data_summary,
"Meta Data": merged_meta}
def vcf_to_df(vcf_file):
"""
Convert a possibly compressed (.gz) vcf file to a Pandas DataFrame.
Parameters:
----------
vcf_file : Path to the vcf file. The file must have the 8 columns
specified in vcf specifications: CHROM,POS,ID,REF,ALT,QUAL,FILTER,INFO
It can be compressed. Individual genotypes are not used, so they
can be present or absent. Each indel must have their own line
in the file, i.e. bcftools norm -m -indels
Returns : Pandas dataframe with each row corresponding to a position
and not an individual variant.
"""
# Check if the file is compressed
try:
op = gzip.open(vcf_file).readline()
op = gzip.open(vcf_file)
except IOError:
op = open(vcf_file).readline()
op = open(vcf_file)
# create a list of variants that behave unexpectedly
# this will be used at the end of the function to make sure
# everything went as expected.
problem_alts = []
# keep each variant information in a list to be corverted to Df
outfile_list = []
# keep INFO field headers in a list
info_cols = []
with op as infile:
for line in infile:
try:
line = line.decode("utf-8")
except AttributeError:
pass
if line.startswith("#"):
# extract INFO field headers
if line.startswith("##INFO="):
info_cols.append(line.split(",")[0].split("=")[2])
else:
newline = line.strip().split("\t")
chrom = newline[0]
pos = int(newline[1])
var_id = newline[2]
ref = newline[3]
alt = newline[4]
qual = newline[5]
filt = newline[6]
info_raw = newline[7].split(";")
info_dict = {}
for ir in info_raw:
split_info = ir.split("=")
# the variant info is coded as "field=info"
# when field has a vale, and just "field"
# when field is a flag.
# e.g. "AC=4" shows allele count is 4
# e.g. "STR" shows the variant is short tandem repeat
try:
info_dict[split_info[0]] = split_info[1].split(",")
# if field is a flag
except IndexError:
info_dict[split_info[0]] = [True]
alt_bases = alt.split(",")
alt_len = len(alt_bases[0])
# since all the indels have their own line on this vcf file
# all alt bases must have the same length (1 for SNPs and
# indel size for the indels
for a in alt_bases:
if alt_len != len(a):
problem_alts.append(newline)
break
# check if the var is SNP
if len(ref) == alt_len:
# SNPs must be length 1
if alt_len != 1:
problem_alts.append(newline)
break
for i in range(len(alt_bases)):
outlist = [chrom, pos, var_id, ref, alt_bases[i],
qual, filt]
var_info = []
for col in info_cols:
try:
var_info.append(info_dict[col][i])
except KeyError:
var_info.append(np.nan)
except IndexError:
var_info.append(info_dict[col][0])
outlist = outlist + var_info
outfile_list.append(outlist)
# if not a SNP, must be indel
# indels must have their own line, hence only 1 indel in alt
# bases
elif len(alt_bases) > 1:
problem_alts.append(newline)
break
# if conforming indel:
else:
alt_base = alt_bases[0]
# vcf files have the indels together with the preceding
# base such as REF: TA, ALT: T
# the same information is encoded as REF: A, ALT:- in table
if ref[0] != alt_base[0]:
problem_alts.append(newline)
break
else:
# remove preceding base
ref_base = ref[1:]
alt_bases = alt_base[1:]
# check if insertion
if ref_base == "":
ref_base = "-"
for i in range(2):
outlist = [chrom, pos + i, var_id,
"-", alt_bases, qual, filt]
var_info = []
for col in info_cols:
try:
var_info.append(info_dict[col][0])
except KeyError:
var_info.append(np.nan)
outlist = outlist + var_info
outfile_list.append(outlist)
# if deletion
else:
# increment position because pos is not the
# effected position (ATT-> A, A is not affected)
pos += 1
for i in range(len(ref_base)):
outlist = [chrom, pos + i, var_id,
ref_base[i], "-", qual, filt]
var_info = []
for col in info_cols:
try:
var_info.append(info_dict[col][0])
except KeyError:
var_info.append(np.nan)
outlist = outlist + var_info
outfile_list.append(outlist)
var_df = pd.DataFrame(
outfile_list, columns=["CHROM", "POS", "ID", "REF", "ALT",
"QUAL", "FILTER"] + info_cols)
var_df = var_df.astype({"AN": int, "AC": int})
if len(problem_alts) > 0:
print(("There are %d problematic alleles, see the output list for "
"details" % len(problem_alts)))
return var_df, problem_alts
def collapse_vcf_df(filt_df):
"""
Take a vcf which has been converted to a Pandas data frame,
groupby genomic position and add up the allele counts.
"""
columns = filt_df.columns
agg = {}
for col in columns:
if col not in ["AC", "AN", "CHROM", "POS"]:
agg[col] = "first"
elif col == "AC":
agg[col] = np.sum
elif col == "AN":
agg[col] = np.max
collapsed = filt_df.groupby(["CHROM", "POS"]).agg(agg).reset_index()
return collapsed
def vcf_to_ucsc_table(collapsed, output_file):
"""
Take a "per position" vcf dataframe, convert to UCSC genome browser
style variant table.
"""
# Alleles and allele counts are represented as A,T, and 2,20, in
# the UCSC table. We'll create those strings with the following
# functions
collapsed["AS"] = collapsed.apply(get_allele_strings, axis=1)
collapsed["CS"] = collapsed.apply(get_count_strings, axis=1)
collapsed["0-offset"] = collapsed["POS"] - 1
collapsed["BIN"] = 0
table = collapsed[["BIN", "CHROM", "0-offset", "POS", "AS", "CS"]]
table = table.sort_values(["CHROM", "0-offset"])
tc = list(table.columns)
tc_1 = tc[:4]
tc_2 = tc[4:]
for i in range(18):
table[i] = ""
tc_1.append(i)
table = table[tc_1 + tc_2]
table.to_csv(output_file, sep="\t", index=False, header=False)
subprocess.call(["bgzip", "-c", output_file], stdout=open(
output_file + ".gz", "w"))
subprocess.call(["tabix", "-0", "-s 2", "-b 3", "-e 3",
output_file + ".gz"])
return table
def header_to_primer(bc_dict,
header_string,
platform):
"""
Convert a demultiplexed fastq header to forward and
reverse primer numbers.
"""
# Create sequence to primer dictionary from primer to sequence dict
# bc_dict maps primer number to the sample barcode sequence
# such as 1: AAATGCCC. We would like to get to a dict like AAATGCCC: 1
seq_to_bc_dict = {v["sequence"]: int(k) for k, v in bc_dict.items()}
split_string = header_string.split("+")
if platform == "miseq":
try:
fw = seq_to_bc_dict[split_string[1]]
except KeyError:
fw = 999
try:
rev = seq_to_bc_dict[reverse_complement(split_string[0])]
except KeyError:
rev = 999
elif platform == "nextseq":
try:
fw = seq_to_bc_dict[reverse_complement(split_string[1])]
except KeyError:
fw = 999
try:
rev = seq_to_bc_dict[reverse_complement(split_string[0])]
except KeyError:
rev = 999
return fw, rev
def primer_to_header(bc_dict, primers, platform):
"""
Convert fw, rev primer numbers to demultiplexed fastq header.
"""
fw_seq = bc_dict[primers[0]]["sequence"]
rev_seq = bc_dict[primers[1]]["sequence"]
if platform == "nextseq":
return reverse_complement(rev_seq) + "+" + reverse_complement(fw_seq)
elif platform == "miseq":
return reverse_complement(rev_seq) + "+" + fw_seq
def check_stitching(stitch_file):
"""
Take a stitch log file from MIPWrangler output, return summary datframe.
"""
with open(stitch_file) as infile:
stitch = []
for line in infile:
newline = line.strip()
stitch.append(newline)
sti_sum = []
for l in stitch:
if '\t"stdOut_" : "[FLASH] Starting FLASH v' in l:
nl = l.split("\\n")
for i in range(len(nl)):
il = nl[i]
if "Input files" in il:
nil = nl[i+1].split(" ")
nil = [t for t in nil if t != ""]
sid = nil[-1].split("/")[1]
sti_sum.append(sid)
elif (("Total pairs" in il) or
("Combined pairs" in il)):
nil = il.split(" ")
nil = [t for t in nil if t != ""]
sti_sum.append(int(nil[-1]))
sti = []
for i in range(0, len(sti_sum), 3):
sti.append(sti_sum[i:i+3])
sti = pd.DataFrame(sti, columns=["Sample ID",
"Total Reads",
"Combined Reads"])
return sti
def filter_vcf(in_vcf, out_vcf, filters_to_remove):
"""
Filter a vcf (possibly gzipped) for given filters
such that all variants containing any of the filters
will be removed.
"""
filt = set(filters_to_remove)
# Check if the file is compressed
try:
input_vcf = gzip.open(in_vcf).readline()
input_vcf = gzip.open(in_vcf)
output_vcf = gzip.open(out_vcf, "w")
except IOError:
input_vcf = open(in_vcf)
output_vcf = open(out_vcf, "w")
with input_vcf as infile, output_vcf as outfile:
for line in infile:
if line.startswith("##"):
outfile.write(line)
elif line.startswith("#"):
cols = line.split("\t")
for i in range(len(cols)):
if cols[i] == "FILTER":
filter_index = i
break
else:
newline = line.split("\t")
var_filters = newline[filter_index].split(";")
if len(filt.intersection(var_filters)) == 0:
outfile.write(line)
return
def iupac_converter(iupac_code):
"""
Return a list of all possible bases corresponding to a given iupac
nucleotide code.
"""
iupac_dict = {"A": "A", "C": "C", "G": "G", "T": "T", "R": "AG", "Y": "CT",
"S": "GC", "W": "AT", "K": "GT", "M": "AC", "B": "CGT",
"D": "AGT", "H": "ACT", "V": "ACG", "N": "ACGT"}
try:
return list(iupac_dict[iupac_code.upper()])
except KeyError:
print(("Non-IUPAC nucleotide code {}. Code must be one of {}").format(
iupac_code, "".join(list(iupac_dict.keys()))
))
return []
def make_degenerate(base_set):
"""
Return IUPAC code of degenerate nucleotide corresponding to given base set.
"""
iupac_dict = {"A": "A", "C": "C", "G": "G", "T": "T", "R": "AG", "Y": "CT",
"S": "GC", "W": "AT", "K": "GT", "M": "AC", "B": "CGT",
"D": "AGT", "H": "ACT", "V": "ACG", "N": "ACGT"}
reverse_iupac = {frozenset(list(v)): k for k, v in iupac_dict.items()}
try:
base_set = frozenset(map(str.upper, frozenset(base_set)))
return reverse_iupac[base_set]
except (ValueError, KeyError, TypeError):
return np.nan
def iupac_fasta_converter(header, sequence, max_ns=10):
"""Convert IUPAC degenerate nucleotides to ATGC.
Given a sequence (header and sequence itself) containing iupac characters,
return a dictionary with all possible sequences converted to ATCG.
"""
iupac_dict = {"R": "AG", "Y": "CT", "S": "GC", "W": "AT", "K": "GT",
"M": "AC", "B": "CGT", "D": "AGT", "H": "ACT", "V": "ACG",
"N": "ACGT"}
iupac_dict = {k: list(iupac_dict[k])
for k in list(iupac_dict.keys())}
if sequence.upper().count("N") >= max_ns:
return {header: sequence}
sequence = list(sequence.upper())
result_list = []
iupac_recurse(sequence)
if len(result_list) == 1:
return {header: result_list[0]}
else:
return {header + "-" + str(i): result_list[i]
for i in range(len(result_list))}
def save_fasta_dict(fasta_dict, fasta_file, linewidth=60):
"""Save a fasta dictionary to file."""
with open(fasta_file, "w") as outfile:
for header in fasta_dict:
outfile.write(">" + str(header) + "\n")
fasta_seq = fasta_dict[header]
for i in range(0, len(fasta_seq), linewidth):
outfile.write(fasta_seq[i: i + linewidth] + "\n")
def generate_sample_sheet(sample_list_file,
barcode_dict_file,
sample_sheet_template,
platform,
output_dir,
warnings=False):
"""Create a sample sheet to be used by bcl2fasq file from sample list."""
with open(barcode_dict_file, "rb") as in1:
barcode_dic = pickle.load(in1)
# read in sample information
sample_names = []
sample_info = {}
with open(sample_list_file) as infile:
linenum = 0
for line in infile:
newline = line.strip().split("\t")
# first line is the header with column names
if linenum == 0:
colnames = newline
linenum += 1
else:
sample_dict = {colname: colvalue for colname, colvalue
in zip(colnames, newline)}
sample_set = sample_dict["sample_set"]
sample_name = sample_dict["sample_name"]
replicate_number = sample_dict["replicate"]
forward_index = sample_dict["fw"]
reverse_index = sample_dict["rev"]
sample_id = "-".join([sample_name,
sample_set,
replicate_number])
if sample_id in sample_info:
print("Repeating sample name ", sample_id)
if not sample_id.replace("-", "").isalnum():
print(("Sample IDs can only contain "
"alphanumeric characters and '-'. "
"{} has invalid characters.").format(sample_id))
continue
# nextseq and miseq barcodes are handled differently
if platform == "nextseq":
sample_dict.update(
{"i7": barcode_dic[reverse_index]["index_sequence"],
"i5": barcode_dic[forward_index]["index_sequence"]})
elif platform == "miseq":
sample_dict.update(
{"i7": barcode_dic[reverse_index]["index_sequence"],
"i5": barcode_dic[forward_index]["sequence"]})
sample_dict["sample_index"] = linenum
linenum += 1
sample_info[sample_id] = sample_dict
sample_names.append(sample_id)
# Check for samples sharing one or both barcodes. One barcode sharing is
# allowed but a warning can be printed if desired by setting the warning
# to True. If both barcodes are shared among two samples, those samples
# will be ignored and a message will be broadcast.
samples_sharing = []
for s1 in sample_info:
for s2 in sample_info:
if s1 != s2:
if ((sample_info[s1]["fw"] == sample_info[s2]["fw"])
and (sample_info[s1]["rev"] == sample_info[s2]["rev"])):
samples_sharing.append([s1, s2])
elif warnings and (
(sample_info[s1]["fw"] == sample_info[s2]["fw"])
or (sample_info[s1]["rev"] == sample_info[s2]["rev"])
):
print("Samples %s and %s share a barcode" % (s1, s2))
samples_sharing_set = []
if len(samples_sharing) > 0:
for s in samples_sharing:
samples_sharing_set.extend(s)
samples_sharing_set = set(samples_sharing_set)
print("There are %d samples sharing the same barcode pair"
% len(samples_sharing_set))
pd.DataFrame(samples_sharing).to_csv(
os.path.join(output_dir, "samples_sharing_barcodes.tsv"),
sep="\t"
)
# create sample sheet
sample_sheet = os.path.join(output_dir, "SampleSheet.csv")
with open(sample_sheet_template) as infile, \
open(sample_sheet, "w") as outfile:
outfile_list = infile.readlines()
outfile_list = [o.strip() for o in outfile_list]
for sample_id in sample_names:
if sample_id in samples_sharing_set:
continue
reverse_index = sample_info[sample_id]["rev"]
forward_index = sample_info[sample_id]["fw"]
sample_index = str(sample_info[sample_id]["sample_index"])
outlist = [sample_index, sample_id, "", "",
"S" + reverse_index,
sample_info[sample_id]["i7"],
"N" + forward_index,
sample_info[sample_id]["i5"], "", ""]
outfile_list.append(",".join(outlist))
outfile.write("\n".join(outfile_list))
def chromosome_converter(chrom, from_malariagen):
""" Convert plasmodium chromosome names from standard (chr1, etc) to
malariagen names (Pf3d7...) and vice versa.
"""
standard_names = ["chr" + str(i) for i in range(1, 15)]
standard_names.extend(["chrM", "chrP"])
malariagen_names = ["Pf3D7_0" + str(i) + "_v3" for i in range(1, 10)]
malariagen_names = malariagen_names + [
"Pf3D7_" + str(i) + "_v3" for i in range(10, 15)]
malariagen_names.extend(["Pf_M76611", "Pf3D7_API_v3"])
if from_malariagen:
return dict(zip(malariagen_names, standard_names))[chrom]
else:
return dict(zip(standard_names, malariagen_names))[chrom]
def write_list(alist, outfile_name):
""" Convert values of a list to strings and save to file."""
with open(outfile_name, "w") as outfile:
outfile.write("\n".join(["\t".join(map(str, l))
for l in alist]) + "\n")
return
##########################################################
# Core/shared functions
##########################################################
def aa_converter(aa_name):
"""
Output 3 letter and 1 letter amino acid codes for a given
3 letter or 1 letter amino acid code.
"""
gencode3 = {'A': 'Ala', 'C': 'Cys', 'D': 'Asp', 'E': 'Glu', 'F': 'Phe',
'G': 'Gly', 'H': 'His', 'I': 'Ile', 'K': 'Lys', 'L': 'Leu',
'M': 'Met', 'N': 'Asn', 'P': 'Pro', 'Q': 'Gln', 'R': 'Arg',
'S': 'Ser', 'T': 'Thr', 'V': 'Val', 'W': 'Trp', 'Y': 'Tyr'}
for a in list(gencode3.keys()):
gencode3[gencode3[a]] = a
return gencode3[aa_name.capitalize()]
def ntthal(s1, s2, Na=25, Mg=10, conc=0.4, print_command=False,
td_path="/opt/resources/primer3_settings/primer3_config/"):
""" Return the melting temperature of two oligos at given conditions,
using ntthal from primer3 software.
Parameters
-----------
s1 : str, sequence of first oligo.
s2 : str, sequence of second oligo
Na : int, Sodium (or other monovalent cation) concentration in mM
Mg : int, Magnesium (or other divalent cation) concentration in mM
conc : float, concentration of the more concentrated oligo in nM
td_path : str, path to thermodynamic alignment parameters.
"""
cmnd = ["ntthal", "-path", td_path, "-mv", str(Na), "-dv", str(Mg),
"-d", str(conc), "-s1", s1, "-s2", s2, "-r"]
if print_command:
return(" ".join(cmnd))
else:
ntt_res = subprocess.check_output(cmnd)
return float(ntt_res.decode("UTF-8").strip())
def oligoTM(s, Na=25, Mg=10, conc=0.4,
thermodynamic_parameters=1, salt_correction=2):
""" Return the melting temperature an oligo at given conditions,
using oligotm from primer3 software.
Parameters
-----------
s : str, sequence of the oligo.
Na : int, Sodium (or other monovalent cation) concentration in mM
Mg : int, Magnesium (or other divalent cation) concentration in mM
conc : float, concentration of the more concentrated oligo in nM
tp : [0|1], Specifies the table of thermodynamic parameters and
the method of melting temperature calculation:
0 Breslauer et al., 1986 and Rychlik et al., 1990
(used by primer3 up to and including release 1.1.0).
This is the default, but _not_ the recommended value.
1 Use nearest neighbor parameters from SantaLucia 1998
*THIS IS THE RECOMMENDED VALUE*
sc : [0..2], Specifies salt correction formula for the melting
temperature calculation
0 Schildkraut and Lifson 1965, used by primer3 up to
and including release 1.1.0.
This is the default but _not_ the recommended value.
1 SantaLucia 1998
*THIS IS THE RECOMMENDED VAULE*
2 Owczarzy et al., 2004
"""
ntt_res = subprocess.check_output(
["oligotm", "-mv", str(Na), "-dv", str(Mg),
"-d", str(conc), "-tp", str(thermodynamic_parameters),
"-sc", str(salt_correction), s])
return float(ntt_res.decode("UTF-8").strip())
def reverse_complement(sequence):
""" Return reverse complement of a sequence. """
complement_bases = {
'g': 'c', 'c': 'g', 'a': 't', 't': 'a', 'n': 'n',
'G': 'C', 'C': 'G', 'A': 'T', 'T': 'A', 'N': 'N', "-": "-",
"R": "Y", "Y": "R", "S": "W", "W": "S", "K": "M", "M": "K",
"B": "V", "V": "B", "D": "H", "H": "D",
"r": "y", "y": "r", "s": "w", "w": "s", "k": "m", "m": "k",
"b": "v", "v": "b", "d": "h", "h": "d"
}
bases = list(sequence)
bases.reverse()
revcomp = []
for base in bases:
try:
revcomp.append(complement_bases[base])
except KeyError:
print("Unexpected base encountered: ", base, " returned as X!!!")
revcomp.append("X")
return "".join(revcomp)
def get_file_locations():
""" All static files such as fasta genomes, snp files, etc. must be listed
in a file in the working directory. File name is file_locations.
It is a tab separated text file. First tab has 2 letter species name, or
"all" for general files used for all species. Second tab is the file name
and third is the location of the file, either relative to script working
directory, or the absolute path."""
file_locations = {}
with open("/opt/species_resources/file_locations.tsv", "r") as infile:
for line in infile:
if not line.startswith("#"):
newline = line.strip().split("\t")
if newline[0] not in list(file_locations.keys()):
file_locations[newline[0]] = {newline[1]: newline[2]}
else:
file_locations[newline[0]][newline[1]] = newline[2]
return file_locations
def id_generator(N):
""" Generate a random string of length N consisting of uppercase letters
and digits. Used for generating names for temporary files, etc.
"""
return ''.join(random.SystemRandom().choice(
string.ascii_uppercase + string.digits) for _ in range(N))
def alphanumerize(text, allowed_chars=["-"], replacement_char="-",
verbose=False):
"""Replace special characters, spaces etc in a text.
Replace characters which are not alphanumeric or in the allowed characters
list with the provided replacement character.
"""
clist = []
for ch in text:
if ch.isalnum() or (ch in allowed_chars):
clist.append(ch)
else:
clist.append(replacement_char)
newtext = "".join(clist)
if verbose and (newtext != text):
print(("{} is replaced with {}.").format(text, newtext))
return newtext
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3013,
79,
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2501,
62,
65,
1386,
1,
4083,
33295,
7,
29510,
62,
67,
291,
14692,
2501,
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1,
7131,
15,
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8,
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220,
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2845,
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25,
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1303,
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8006,
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1303,
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5559,
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15,
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220,
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2073,
25,
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220,
220,
220,
220,
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329,
1312,
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2837,
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17946,
602,
58,
82,
7131,
72,
7131,
1,
28663,
1,
4083,
35312,
7203,
62,
48774,
6624,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
4941,
62,
16184,
79,
62,
17946,
602,
58,
82,
60,
796,
3013,
79,
62,
17946,
602,
58,
82,
7131,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
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220,
220,
220,
220,
2073,
25,
198,
220,
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220,
220,
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220,
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220,
220,
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220,
3601,
7,
7203,
16438,
34348,
1438,
407,
1043,
0,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
5492,
2198,
262,
5072,
1351,
526,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1917,
62,
16184,
862,
13,
33295,
7,
82,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
62,
16184,
79,
62,
17946,
602,
58,
82,
7131,
1,
27144,
495,
62,
4906,
8973,
796,
8006,
62,
19199,
58,
82,
60,
198,
220,
220,
220,
1441,
4941,
62,
16184,
79,
62,
17946,
602,
11,
3013,
79,
62,
17946,
602,
628,
628,
198,
4299,
21136,
62,
282,
16747,
7,
2301,
62,
7753,
2599,
198,
220,
220,
220,
37227,
13610,
257,
374,
10951,
22155,
422,
257,
374,
10951,
2393,
526,
15931,
198,
220,
220,
220,
842,
62,
67,
291,
796,
23884,
198,
220,
220,
220,
351,
1280,
7,
2301,
62,
7753,
11,
366,
81,
4943,
355,
1167,
576,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1627,
287,
1167,
576,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1627,
13,
9688,
2032,
342,
7203,
31553,
2849,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
1370,
796,
1627,
13,
36311,
22446,
35312,
7203,
59,
83,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
16,
796,
649,
1370,
58,
16,
4083,
35312,
7,
2404,
38381,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
17,
796,
649,
1370,
58,
16,
4083,
35312,
7,
2404,
38381,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
16,
407,
287,
842,
62,
67,
291,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
67,
291,
58,
2539,
16,
60,
796,
1391,
2539,
17,
25,
19779,
22163,
2047,
480,
1298,
649,
1370,
58,
17,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
354,
81,
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3605,
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18,
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18,
25,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
366,
27471,
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7,
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19,
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220,
220,
220,
220,
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220,
220,
220,
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366,
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58,
20,
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220,
220,
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366,
10145,
1298,
357,
3605,
1370,
58,
21,
60,
6624,
366,
37,
4943,
11709,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
67,
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2539,
16,
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2539,
17,
60,
796,
19779,
22163,
2047,
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17,
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18,
25,
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220,
220,
220,
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220,
220,
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6624,
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3274,
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3696,
6901,
262,
10007,
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220,
220,
1334,
8333,
3815,
13,
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3951,
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4296,
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543,
2648,
262,
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220,
220,
717,
5721,
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2656,
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6330,
326,
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584,
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3520,
13,
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1303,
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611,
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2032,
342,
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22446,
35312,
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59,
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4296,
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60,
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62,
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2,
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25,
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25,
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2073,
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220,
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20218,
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220,
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1988,
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72,
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220,
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220,
220,
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220,
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20218,
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1988,
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1949,
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2845,
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12331,
25,
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19874,
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19874,
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25,
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19874,
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17635,
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16,
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220,
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220,
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886,
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7,
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16,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19874,
62,
2301,
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58,
22766,
4083,
33295,
26933,
28663,
11,
2221,
11,
886,
12962,
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385,
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737,
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1303,
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7652,
481,
307,
32824,
1978,
284,
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262,
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220,
220,
1303,
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329,
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1486,
13,
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25,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
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220,
220,
220,
220,
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507,
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80,
17,
60,
198,
220,
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220,
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220,
220,
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220,
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220,
220,
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17,
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25,
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220,
220,
220,
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220,
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62,
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81,
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11,
374,
17,
11,
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198,
220,
220,
220,
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220,
220,
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220,
220,
12893,
1686,
58,
80,
16,
4083,
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80,
17,
8,
198,
220,
220,
220,
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220,
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220,
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1303,
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13,
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13,
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11,
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257,
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13,
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25,
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64,
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65,
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13,
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62,
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198,
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1303,
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8635,
3753,
284,
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15541,
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8420,
62,
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657,
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220,
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220,
981,
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62,
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8,
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220,
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21721,
62,
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796,
10352,
198,
220,
220,
220,
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329,
267,
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1351,
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25,
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58,
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60,
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1,
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1,
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78,
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198,
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58,
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75,
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58,
85,
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198,
220,
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13,
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62,
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198,
220,
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25,
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220,
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318,
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220,
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510,
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13,
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220,
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75,
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13,
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58,
78,
60,
796,
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75,
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58,
78,
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220,
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220,
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13,
198,
220,
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329,
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287,
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11,
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423,
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198,
220,
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220,
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326,
481,
307,
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355,
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13,
198,
220,
220,
220,
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2,
198,
220,
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220,
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1864,
284,
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198,
220,
220,
220,
11266,
62,
2301,
507,
796,
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198,
220,
220,
220,
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267,
287,
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25,
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220,
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507,
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78,
60,
796,
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198,
220,
220,
220,
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220,
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220,
329,
308,
287,
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78,
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198,
220,
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220,
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220,
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220,
842,
82,
796,
19874,
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2301,
507,
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70,
60,
198,
220,
220,
220,
220,
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374,
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25,
198,
220,
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1949,
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220,
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220,
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220,
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41767,
58,
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58,
15,
60,
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81,
58,
16,
25,
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198,
220,
220,
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2845,
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12331,
25,
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220,
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220,
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41767,
58,
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58,
15,
11907,
796,
685,
81,
58,
16,
25,
11907,
198,
220,
220,
220,
1303,
20121,
1123,
32997,
3814,
198,
220,
220,
220,
11266,
62,
647,
2004,
62,
2301,
507,
796,
23884,
198,
220,
220,
220,
329,
264,
287,
11266,
62,
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507,
25,
198,
220,
220,
220,
220,
220,
220,
220,
23791,
62,
325,
79,
796,
11266,
62,
647,
2004,
62,
2301,
507,
58,
82,
60,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
329,
15358,
287,
11266,
62,
2301,
507,
58,
82,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23791,
62,
36996,
796,
20121,
62,
2502,
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7,
25512,
515,
62,
2301,
507,
58,
82,
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28663,
12962,
198,
220,
220,
220,
220,
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220,
23791,
62,
325,
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58,
28663,
60,
796,
23791,
62,
36996,
198,
220,
220,
220,
1303,
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2235,
198,
220,
220,
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11,
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1912,
319,
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198,
220,
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13,
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570,
257,
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13,
198,
220,
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198,
220,
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319,
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286,
198,
220,
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13,
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220,
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284,
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220,
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57,
13,
198,
220,
220,
220,
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287,
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507,
13,
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198,
220,
220,
220,
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220,
220,
220,
842,
82,
796,
19874,
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507,
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283,
60,
198,
220,
220,
220,
220,
220,
220,
220,
329,
374,
287,
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25,
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220,
220,
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374,
13,
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7,
15,
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15,
60,
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374,
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16,
60,
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220,
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220,
220,
220,
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19874,
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507,
58,
283,
60,
796,
23243,
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2301,
82,
11,
1994,
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19,
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220,
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9575,
28,
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198,
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62,
2301,
507,
796,
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198,
220,
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220,
3814,
62,
14933,
796,
23884,
198,
220,
220,
220,
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796,
11266,
62,
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2004,
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507,
198,
220,
220,
220,
329,
374,
287,
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25,
198,
220,
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81,
60,
796,
17635,
198,
220,
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329,
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287,
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58,
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58,
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198,
220,
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220,
220,
220,
220,
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20218,
62,
36996,
796,
685,
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198,
220,
220,
220,
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20218,
62,
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13,
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198,
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20218,
62,
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13,
33295,
32590,
11925,
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28663,
4008,
198,
220,
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220,
220,
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13,
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75,
58,
16,
60,
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58,
15,
12962,
198,
220,
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507,
58,
81,
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198,
220,
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1303,
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319,
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286,
198,
220,
220,
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290,
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286,
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13,
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418,
462,
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318,
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198,
220,
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284,
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13527,
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290,
407,
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11,
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198,
220,
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318,
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3306,
290,
340,
561,
407,
17438,
355,
2938,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
618,
34348,
3891,
466,
407,
1061,
326,
9831,
11,
1312,
13,
68,
11,
442,
81,
21,
290,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
442,
81,
21,
62,
2501,
34278,
57,
198,
220,
220,
220,
220,
220,
220,
220,
2496,
62,
2301,
507,
58,
81,
60,
796,
23243,
7,
16793,
62,
2301,
507,
58,
81,
4357,
1994,
28,
9186,
1136,
353,
7,
19,
11,
513,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9575,
28,
17821,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
8333,
3891,
284,
32824,
6670,
198,
220,
220,
220,
220,
220,
220,
220,
842,
62,
14933,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
329,
1123,
3814,
356,
467,
736,
284,
1981,
3814,
10548,
902,
290,
766,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
262,
1981,
19114,
12893,
1686,
351,
428,
3814,
13,
1002,
340,
857,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
356,
779,
262,
1981,
7652,
1438,
329,
428,
3814,
1626,
262,
1448,
13,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
16793,
62,
2301,
507,
58,
81,
12962,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
796,
2496,
62,
2301,
507,
58,
81,
7131,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
28663,
796,
842,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
27471,
796,
842,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
437,
796,
842,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
269,
287,
19874,
62,
2301,
507,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1388,
62,
36996,
796,
19874,
62,
2301,
507,
58,
66,
7131,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
2301,
62,
28663,
6624,
1388,
62,
36996,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
842,
62,
27471,
19841,
1388,
62,
36996,
58,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
290,
842,
62,
437,
18189,
1388,
62,
36996,
58,
17,
60,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
14933,
13,
33295,
7,
66,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
842,
62,
14933,
13,
33295,
7203,
2616,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
8333,
257,
4941,
3814,
329,
1123,
1448,
1912,
319,
9779,
3891,
2810,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
318,
8384,
284,
973,
284,
423,
1365,
3891,
329,
7652,
13,
1114,
1672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
257,
9779,
318,
257,
2496,
355,
880,
355,
257,
3013,
79,
11,
356,
561,
588,
262,
9779,
1438,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
307,
262,
1438,
286,
262,
1448,
355,
6886,
284,
262,
25632,
338,
1438,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
9275,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
329,
308,
287,
9779,
62,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
308,
287,
842,
62,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
9275,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
9630,
796,
842,
62,
14933,
13,
9630,
7,
70,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
3672,
796,
308,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1006,
62,
9275,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
3672,
796,
374,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
9630,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
36996,
796,
2496,
62,
2301,
507,
58,
81,
4083,
12924,
7,
5420,
62,
9630,
8,
198,
220,
220,
220,
220,
220,
220,
220,
842,
62,
14933,
13,
12924,
7,
5420,
62,
9630,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2496,
62,
2301,
507,
58,
81,
60,
796,
685,
5420,
62,
36996,
60,
1343,
2496,
62,
2301,
507,
58,
81,
60,
198,
220,
220,
220,
220,
220,
220,
220,
842,
62,
14933,
796,
685,
5420,
62,
3672,
60,
1343,
842,
62,
14933,
198,
220,
220,
220,
220,
220,
220,
220,
3814,
62,
14933,
58,
5420,
62,
3672,
60,
796,
842,
62,
14933,
198,
220,
220,
220,
220,
220,
220,
220,
2496,
62,
2301,
507,
58,
2301,
62,
14933,
58,
15,
11907,
796,
2496,
62,
2301,
507,
13,
12924,
7,
81,
8,
198,
220,
220,
220,
220,
220,
220,
220,
12893,
1686,
58,
2301,
62,
14933,
58,
15,
11907,
796,
12893,
1686,
13,
12924,
7,
81,
8,
198,
220,
220,
220,
1303,
706,
262,
10548,
902,
389,
1760,
11,
617,
7652,
481,
407,
423,
1774,
3891,
198,
220,
220,
220,
1303,
290,
617,
481,
423,
366,
2616,
1911,
775,
1183,
1487,
883,
284,
3368,
20394,
198,
220,
220,
220,
1303,
3891,
13,
198,
220,
220,
220,
329,
374,
287,
1351,
7,
36996,
62,
14933,
13,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
374,
14933,
796,
3814,
62,
14933,
58,
81,
60,
198,
220,
220,
220,
220,
220,
220,
220,
299,
14933,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
374,
77,
62,
9127,
82,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
329,
374,
77,
287,
374,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
10782,
796,
374,
14933,
13,
9127,
7,
35906,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
77,
62,
9127,
82,
58,
35906,
60,
796,
19779,
23350,
62,
9127,
1298,
374,
10782,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
1484,
62,
9127,
1298,
657,
92,
198,
220,
220,
220,
220,
220,
220,
220,
329,
374,
77,
287,
374,
14933,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
374,
77,
62,
9127,
82,
58,
35906,
7131,
1,
23350,
62,
9127,
8973,
1875,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
14933,
13,
33295,
7,
35906,
1343,
366,
21215,
1343,
965,
7,
35906,
62,
9127,
82,
58,
35906,
7131,
1,
1484,
62,
9127,
8973,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
374,
77,
62,
9127,
82,
58,
35906,
7131,
1,
1484,
62,
9127,
8973,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
299,
14933,
13,
33295,
7,
35906,
8,
198,
220,
220,
220,
220,
220,
220,
220,
3814,
62,
14933,
58,
81,
60,
796,
299,
14933,
198,
220,
220,
220,
1303,
1064,
2496,
7652,
326,
714,
407,
307,
7138,
19874,
284,
262,
19270,
198,
220,
220,
220,
1303,
777,
389,
3221,
22820,
11286,
10179,
16311,
14275,
287,
277,
15462,
3696,
11,
884,
355,
198,
220,
220,
220,
1303,
1728,
309,
9419,
42519,
13567,
13,
198,
220,
220,
220,
23162,
62,
31494,
364,
796,
17635,
198,
220,
220,
220,
329,
374,
287,
19114,
62,
11600,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
26675,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
10548,
902,
796,
19114,
62,
11600,
58,
81,
60,
198,
220,
220,
220,
220,
220,
220,
220,
329,
257,
287,
10548,
902,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
39849,
796,
493,
7,
64,
14692,
66,
709,
47,
310,
1,
4083,
35312,
7203,
19570,
58,
15,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
83,
796,
493,
7,
64,
14692,
312,
47,
310,
1,
4083,
35312,
7203,
19570,
58,
15,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4776,
796,
39849,
1635,
4686,
83,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4776,
1875,
1266,
62,
26675,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
26675,
796,
4776,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1266,
62,
26675,
14512,
33028,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23162,
62,
31494,
364,
13,
33295,
7,
81,
8,
198,
220,
220,
220,
1441,
685,
16793,
62,
2301,
507,
11,
3814,
62,
14933,
11,
23162,
62,
31494,
364,
11,
19874,
62,
2301,
507,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12893,
1686,
60,
628,
198,
4299,
900,
62,
600,
430,
62,
282,
16747,
62,
25811,
7,
16793,
62,
2301,
507,
11,
5369,
11,
5197,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3509,
62,
40845,
62,
521,
417,
62,
7857,
2599,
198,
220,
220,
220,
37227,
7248,
938,
57,
19114,
3689,
329,
23422,
1845,
11794,
62,
31494,
263,
2163,
526,
15931,
198,
220,
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220,
19114,
62,
25811,
62,
11600,
796,
23884,
198,
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220,
220,
329,
256,
287,
2496,
62,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
11600,
796,
19779,
70,
1734,
62,
3672,
1298,
256,
11,
366,
738,
414,
1298,
5369,
92,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
62,
11925,
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2496,
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15,
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12,
16,
60,
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220,
220,
220,
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1402,
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657,
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220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
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12,
16,
60,
1279,
5197,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1402,
62,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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67,
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1303,
7925,
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5721,
3891,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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13,
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2032,
342,
7203,
2,
1,
2599,
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220,
220,
220,
220,
220,
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220,
220,
220,
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13,
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35312,
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15,
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15,
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16,
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220,
220,
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220,
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220,
220,
220,
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220,
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14933,
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1351,
7,
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8,
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220,
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220,
220,
220,
220,
220,
220,
220,
1303,
8333,
3815,
286,
1123,
5721,
329,
1123,
19114,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
1370,
796,
1627,
13,
36311,
22446,
35312,
7203,
59,
83,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
11600,
796,
19779,
26069,
4972,
1298,
17635,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
4033,
14933,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
11600,
58,
4033,
14933,
58,
72,
11907,
796,
649,
1370,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19114,
62,
312,
796,
20218,
62,
11600,
14692,
3672,
16,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
19114,
62,
312,
287,
19114,
62,
67,
291,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
7203,
90,
92,
19874,
284,
262,
4941,
4866,
3294,
1661,
13,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
10049,
262,
717,
19114,
481,
307,
973,
329,
366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
37652,
4559,
16855,
526,
737,
18982,
7,
282,
16747,
62,
312,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19114,
62,
67,
291,
58,
282,
16747,
62,
312,
60,
796,
20218,
62,
11600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
39849,
796,
12178,
7,
282,
16747,
62,
67,
291,
58,
282,
16747,
62,
312,
7131,
1,
66,
709,
47,
310,
1,
7131,
21912,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4686,
83,
796,
12178,
7,
282,
16747,
62,
67,
291,
58,
282,
16747,
62,
312,
7131,
1,
312,
47,
310,
1,
7131,
21912,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19114,
62,
67,
291,
58,
282,
16747,
62,
312,
7131,
1,
26675,
8973,
796,
45941,
13,
32604,
26933,
312,
83,
11,
39849,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5400,
2393,
318,
257,
12948,
2393,
329,
477,
10548,
902,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7925,
5400,
329,
1123,
19114,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1627,
287,
3580,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
1370,
796,
1627,
13,
36311,
22446,
35312,
7203,
59,
83,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
3672,
796,
649,
1370,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
19114,
62,
67,
291,
58,
67,
3672,
7131,
1,
26069,
4972,
1,
4083,
33295,
7,
3605,
1370,
58,
21912,
17,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3975,
1123,
2292,
287,
1123,
19114,
284,
262,
12405,
198,
220,
220,
220,
220,
220,
220,
220,
329,
257,
287,
19114,
62,
67,
291,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3013,
862,
796,
19114,
62,
67,
291,
58,
64,
7131,
1,
16184,
862,
8973,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
763,
796,
19114,
62,
67,
291,
58,
64,
7131,
1,
37652,
17540,
8973,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2710,
62,
1073,
796,
19114,
62,
67,
291,
58,
64,
7131,
1,
50188,
62,
37652,
17540,
8973,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
19114,
319,
9575,
37923,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
19114,
62,
67,
291,
58,
64,
7131,
1,
2536,
392,
17,
8973,
6624,
27444,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
45752,
20435,
286,
2496,
923,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
2292,
318,
1976,
9688,
17,
10,
1497,
422,
12405,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
357,
12518,
340,
318,
257,
532,
19114,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
435,
62,
9688,
796,
493,
7,
282,
16747,
62,
67,
291,
58,
64,
7131,
1,
89,
9688,
16,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
9541,
62,
437,
796,
493,
7,
282,
16747,
62,
67,
291,
58,
64,
7131,
1,
437,
17,
10,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8333,
923,
284,
262,
717,
1994,
286,
262,
6349,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
717,
62,
2539,
796,
12405,
62,
9541,
62,
437,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
763,
58,
11085,
62,
2539,
60,
796,
435,
62,
9688,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2710,
62,
1073,
58,
282,
62,
9688,
60,
796,
717,
62,
2539,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
938,
62,
2539,
796,
717,
62,
2539,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
18846,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
288,
287,
19114,
62,
67,
291,
58,
64,
7131,
1,
26069,
4972,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
923,
14,
437,
22715,
286,
814,
3585,
284,
262,
12405,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
814,
62,
9688,
796,
493,
7,
67,
58,
21,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
814,
62,
437,
796,
493,
7,
67,
58,
22,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
13664,
796,
493,
7,
67,
58,
24,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
329,
1123,
814,
11,
6070,
287,
262,
22715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1022,
262,
938,
62,
2539,
287,
262,
6349,
288,
291,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
923,
62,
2539,
532,
814,
923,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
2837,
7,
12957,
62,
2539,
532,
352,
11,
12405,
62,
13664,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
532,
814,
62,
9688,
532,
352,
11,
532,
16,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
474,
20638,
416,
530,
11,
3599,
422,
262,
938,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1695,
1994,
262,
1988,
481,
307,
352,
517,
621,
262,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2180,
1994,
357,
73,
10,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
474,
6624,
938,
62,
2539,
532,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
763,
58,
73,
60,
796,
2835,
7,
1073,
58,
73,
1343,
352,
60,
532,
657,
13,
16,
8,
1343,
352,
1343,
18846,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
763,
58,
73,
60,
796,
2835,
7,
1073,
58,
73,
1343,
352,
60,
532,
657,
13,
16,
8,
1343,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2710,
62,
1073,
58,
1073,
58,
73,
11907,
796,
474,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1459,
938,
1994,
318,
783,
717,
62,
2539,
532,
814,
62,
9688,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
938,
62,
2539,
796,
12405,
62,
13664,
532,
814,
62,
9688,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12405,
62,
26069,
62,
437,
796,
938,
62,
2539,
1343,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
45752,
20435,
286,
2496,
379,
814,
923,
198,
220,
220,
220,
220,
220,
220,
220,
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1441,
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62,
67,
291,
628,
198,
29113,
14468,
7804,
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62,
292,
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7,
65,
10247,
11,
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3689,
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23838,
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3601,
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318,
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510,
286,
3146,
3940,
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416,
1994,
7475,
326,
2380,
257,
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26,
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257,
22155,
198,
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220,
220,
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12536,
351,
326,
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1994,
355,
3815,
13,
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220,
220,
10383,
318,
617,
517,
1321,
546,
24518,
13042,
13,
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220,
362,
50,
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44,
16,
40,
17,
44,
20,
35,
11,
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11,
1672,
561,
1612,
326,
262,
362,
12536,
389,
366,
50,
1,
11205,
49305,
198,
220,
220,
220,
422,
642,
6,
886,
286,
262,
8379,
7,
961,
8,
19874,
290,
340,
318,
407,
636,
286,
262,
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220,
19114,
26,
1708,
326,
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10548,
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1,
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10411,
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329,
1123,
2779,
286,
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220,
220,
290,
612,
389,
1576,
26789,
1022,
262,
734,
16311,
326,
484,
198,
220,
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220,
19874,
13,
352,
2779,
1708,
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1160,
44,
318,
281,
36075,
11,
326,
318,
11,
340,
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1,
68,
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13,
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37227,
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8594,
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198,
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3815,
796,
17635,
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329,
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24518,
25,
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1949,
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220,
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220,
220,
220,
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220,
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3815,
13,
33295,
7,
2536,
7,
600,
7,
66,
22305,
198,
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220,
220,
220,
220,
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2845,
11052,
12331,
25,
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220,
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220,
220,
220,
220,
220,
220,
611,
269,
287,
1351,
7,
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13,
13083,
3419,
2599,
198,
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220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
8594,
58,
66,
60,
15853,
493,
7203,
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7,
27160,
4008,
198,
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220,
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220,
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1949,
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1303,
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24518,
4731,
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2845,
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12331,
25,
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1303,
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1,
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62,
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283,
7,
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283,
737,
27160,
28955,
532,
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628,
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4299,
21136,
62,
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36224,
7,
19795,
263,
62,
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11,
275,
83,
62,
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11,
33270,
62,
448,
11,
33270,
18,
62,
22915,
62,
34720,
11,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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9563,
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17,
62,
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62,
34720,
11,
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11,
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11,
38701,
28,
16,
2599,
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220,
7214,
257,
9563,
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5072,
357,
37687,
8,
2393,
290,
8106,
1353,
399,
7127,
583,
33270,
13,
628,
220,
220,
220,
1649,
257,
33270,
468,
517,
621,
366,
45828,
62,
17945,
62,
32374,
1,
9563,
36224,
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11,
198,
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220,
220,
4781,
326,
33270,
13,
198,
220,
220,
220,
3060,
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9563,
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2277,
1321,
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220,
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2684,
364,
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257,
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220,
220,
337,
796,
493,
7,
33692,
14692,
45828,
62,
17945,
62,
32374,
8973,
8,
198,
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220,
1303,
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2393,
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220,
220,
1167,
576,
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1280,
7,
418,
13,
6978,
13,
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7,
8176,
36224,
17,
62,
22915,
62,
34720,
11,
275,
83,
62,
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828,
705,
81,
11537,
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220,
2684,
364,
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4866,
13,
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30073,
7,
19795,
263,
62,
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8,
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220,
1303,
2251,
257,
20218,
288,
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284,
954,
7127,
14,
19795,
263,
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3753,
62,
67,
291,
796,
23884,
198,
220,
220,
220,
1303,
2251,
257,
9563,
36224,
1994,
326,
481,
307,
973,
618,
4375,
198,
220,
220,
220,
1303,
9563,
36224,
1321,
284,
2684,
364,
198,
220,
220,
220,
9563,
36224,
62,
2539,
796,
366,
8176,
36224,
62,
17018,
62,
1,
1343,
4693,
198,
220,
220,
220,
1303,
477,
9563,
36224,
7127,
326,
481,
307,
973,
2252,
329,
21232,
3781,
198,
220,
220,
220,
1303,
481,
761,
284,
423,
8379,
1321,
351,
606,
198,
220,
220,
220,
1303,
3814,
8251,
329,
7127,
357,
259,
442,
40914,
25,
27471,
12,
437,
5794,
8,
481,
307,
198,
220,
220,
220,
1303,
4030,
287,
257,
1351,
329,
2347,
3049,
64,
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1568,
13,
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3419,
198,
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1303,
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329,
1627,
287,
1167,
576,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1627,
13,
9688,
2032,
342,
7203,
31,
1,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1700,
796,
1627,
13,
36311,
10786,
59,
77,
27691,
35312,
10786,
59,
83,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
3672,
796,
1700,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
18703,
2277,
3753,
329,
33270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3753,
62,
67,
291,
58,
19795,
263,
62,
3672,
60,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3753,
62,
67,
291,
58,
19795,
263,
62,
3672,
60,
796,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1303,
2198,
703,
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423,
587,
15475,
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33270,
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220,
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220,
1303,
611,
6727,
2277,
4179,
468,
587,
4251,
11,
1317,
33270,
329,
9934,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
3753,
62,
67,
291,
58,
19795,
263,
62,
3672,
60,
18189,
337,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2684,
364,
17816,
19795,
263,
62,
17018,
6,
7131,
19795,
263,
62,
3672,
7131,
1,
28956,
8973,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1445,
319,
284,
262,
1306,
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611,
33270,
2277,
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468,
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4251,
13,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
645,
2252,
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481,
307,
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329,
883,
2684,
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60,
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399,
25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6056,
796,
1700,
58,
16,
60,
198,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
257,
6056,
1988,
286,
604,
1724,
612,
373,
645,
2277,
11,
523,
1208,
883,
3951,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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611,
6056,
6624,
366,
19,
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220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
34348,
286,
262,
9563,
36224,
2277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15358,
796,
1700,
58,
17,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
45752,
2292,
286,
9563,
36224,
2277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1426,
796,
493,
7,
22105,
58,
18,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
24518,
4731,
286,
19114,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24518,
796,
1700,
58,
20,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
7925,
543,
37923,
318,
262,
9563,
36224,
2277,
319,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2081,
611,
2651,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
37923,
796,
14808,
600,
7,
22105,
58,
16,
12962,
4064,
17759,
8,
6624,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
2277,
22715,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
9688,
796,
1426,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9563,
36224,
3607,
514,
262,
923,
2292,
286,
262,
2277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
886,
2292,
318,
10488,
1262,
262,
24518,
4731,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
286,
262,
2277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
437,
796,
1426,
1343,
651,
62,
22683,
283,
62,
13664,
7,
22683,
283,
8,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
3814,
8251,
2672,
329,
8379,
45069,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
356,
765,
513,
299,
83,
3131,
319,
262,
642,
6,
286,
262,
33270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
780,
618,
5559,
2684,
364,
329,
1582,
11794,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
389,
3177,
356,
2198,
29694,
513,
299,
83,
422,
642,
6,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
5236,
21232,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
37923,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
11460,
263,
338,
642,
6,
318,
262,
2277,
923,
618,
262,
2277,
318,
319,
2651,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
37923,
523,
262,
17751,
313,
1460,
389,
2087,
379,
923,
2292,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
83,
62,
9688,
796,
2277,
62,
9688,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
83,
62,
437,
796,
2277,
62,
437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
2536,
796,
366,
11813,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
36996,
62,
2539,
796,
357,
28663,
1343,
366,
11097,
1343,
965,
7,
17945,
62,
9688,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
366,
21215,
1343,
965,
7,
17945,
62,
437,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
83,
62,
9688,
796,
2277,
62,
437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
83,
62,
437,
796,
2277,
62,
9688,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
2536,
796,
366,
50188,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2277,
62,
36996,
62,
2539,
796,
357,
28663,
1343,
366,
11097,
1343,
965,
7,
17945,
62,
9688,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1343,
366,
21215,
1343,
965,
7,
17945,
62,
437,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
3814,
1994,
284,
8251,
1351,
329,
3049,
64,
45069,
1568,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8251,
13,
2860,
7,
17945,
62,
36996,
62,
2539,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
477,
2277,
1321,
284,
33270,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2684,
364,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
7131,
8176,
36224,
62,
2539,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
965,
7,
24588,
62,
67,
291,
58,
19795,
263,
62,
3672,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2361,
796,
19779,
28663,
1298,
15358,
11,
366,
27471,
1298,
275,
83,
62,
9688,
11,
366,
437,
1298,
275,
83,
62,
437,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1298,
2277,
62,
36996,
62,
2539,
11,
366,
2536,
392,
1298,
2277,
62,
2536,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2684,
364,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
7131,
8176,
36224,
62,
2539,
60,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
965,
7,
24588,
62,
67,
291,
58,
19795,
263,
62,
3672,
60,
2599,
19779,
28663,
1298,
15358,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
27471,
1298,
275,
83,
62,
9688,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
437,
1298,
275,
83,
62,
437,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2539,
1298,
2277,
62,
36996,
62,
2539,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2536,
392,
1298,
2277,
62,
2536,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
2961,
6300,
286,
428,
2163,
262,
2684,
364,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13181,
7127,
547,
4615,
1141,
24415,
290,
326,
1085,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
1994,
48277,
13,
2735,
612,
815,
307,
645,
1994,
4049,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
1303,
651,
262,
3049,
64,
16311,
286,
477,
7127,
198,
220,
220,
220,
8379,
62,
67,
291,
796,
651,
62,
7217,
64,
62,
4868,
7,
13083,
11,
4693,
8,
198,
220,
220,
220,
1303,
4781,
2684,
364,
351,
1165,
867,
7127,
290,
751,
9563,
36224,
1321,
329,
1854,
13,
198,
220,
220,
220,
329,
279,
287,
1351,
7,
19795,
364,
14692,
19795,
263,
62,
17018,
1,
4083,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2684,
364,
14692,
19795,
263,
62,
17018,
1,
7131,
79,
7131,
1,
28956,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2684,
364,
14692,
19795,
263,
62,
17018,
1,
4083,
12924,
7,
79,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
2277,
16311,
284,
33270,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2651,
37923,
7127,
389,
2087,
3264,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9575,
37923,
7127,
389,
17687,
12,
785,
1154,
12061,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
523,
262,
2277,
318,
1464,
287,
262,
33270,
12852,
290,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
290,
2092,
287,
8379,
1,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
289,
287,
2684,
364,
14692,
19795,
263,
62,
17018,
1,
7131,
79,
7131,
8176,
36224,
62,
2539,
5974,
198,
220,
220,
220,
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9575,
62,
65,
1040,
796,
23884,
198,
220,
220,
220,
329,
1312,
287,
2837,
7,
28243,
62,
11925,
1003,
8800,
62,
7857,
1343,
352,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
2651,
62,
65,
1040,
58,
72,
60,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
9575,
62,
65,
1040,
58,
72,
60,
796,
17635,
198,
220,
220,
220,
329,
33270,
287,
1351,
7,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
4083,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
33270,
12852,
198,
220,
220,
220,
220,
220,
220,
220,
22812,
796,
33270,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
7131,
1,
1581,
40,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
33270,
923,
20435,
198,
220,
220,
220,
220,
220,
220,
220,
923,
796,
493,
7,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14631,
8220,
12532,
1268,
29462,
1,
4083,
35312,
7,
2430,
38381,
15,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
11925,
796,
493,
7,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14631,
8220,
12532,
1268,
29462,
1,
4083,
35312,
7,
2430,
38381,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
611,
22812,
6624,
366,
11813,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
796,
923,
1343,
33270,
62,
11925,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
22812,
6624,
366,
50188,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
796,
923,
532,
33270,
62,
11925,
1343,
352,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
543,
9874,
262,
923,
20435,
8953,
656,
198,
220,
220,
220,
220,
220,
220,
220,
886,
62,
8800,
796,
886,
1003,
8800,
62,
7857,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
33270,
4776,
198,
220,
220,
220,
220,
220,
220,
220,
4776,
796,
33270,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
7131,
1,
6173,
6965,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
24443,
262,
33270,
1438,
14,
26675,
284,
5035,
9874,
288,
291,
198,
220,
220,
220,
220,
220,
220,
220,
611,
22812,
6624,
366,
11813,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2651,
62,
65,
1040,
58,
437,
62,
8800,
4083,
33295,
26933,
19795,
263,
11,
4776,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
22812,
6624,
366,
50188,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9575,
62,
65,
1040,
58,
437,
62,
8800,
4083,
33295,
26933,
19795,
263,
11,
4776,
12962,
198,
220,
220,
220,
1266,
62,
19795,
263,
62,
11600,
796,
23884,
198,
220,
220,
220,
1266,
62,
19795,
263,
62,
11600,
14692,
43167,
62,
17018,
8973,
796,
33270,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
366,
43167,
62,
17018,
8973,
198,
220,
220,
220,
1266,
62,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
8973,
796,
23884,
198,
220,
220,
220,
1303,
1064,
1266,
9689,
285,
2419,
287,
1123,
2651,
9874,
198,
220,
220,
220,
329,
1994,
287,
2651,
62,
65,
1040,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3297,
2684,
364,
329,
4776,
198,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
2617,
796,
23243,
7,
11813,
62,
65,
1040,
58,
2539,
4357,
1994,
28,
9186,
1136,
353,
7,
16,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1266,
9689,
2684,
364,
357,
439,
2684,
364,
611,
612,
389,
1342,
621,
299,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
19795,
263,
62,
2617,
8,
1279,
299,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
364,
796,
33270,
62,
2617,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
364,
796,
33270,
62,
2617,
58,
12,
77,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
1266,
2684,
364,
466,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2684,
364,
287,
1266,
62,
19795,
364,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
3672,
796,
2684,
364,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
60,
796,
33270,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
60,
198,
220,
220,
220,
1303,
1064,
1266,
9689,
285,
2419,
287,
1123,
9575,
9874,
198,
220,
220,
220,
329,
1994,
287,
9575,
62,
65,
1040,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
3297,
2684,
364,
329,
4776,
198,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
2617,
796,
23243,
7,
50188,
62,
65,
1040,
58,
2539,
4357,
1994,
28,
9186,
1136,
353,
7,
16,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1266,
9689,
2684,
364,
357,
439,
2684,
364,
611,
612,
389,
1342,
621,
299,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
19795,
263,
62,
2617,
8,
1279,
299,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
364,
796,
33270,
62,
2617,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
364,
796,
33270,
62,
2617,
58,
12,
77,
47715,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
1266,
2684,
364,
466,
22155,
198,
220,
220,
220,
220,
220,
220,
220,
329,
2684,
364,
287,
1266,
62,
19795,
364,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33270,
62,
3672,
796,
2684,
364,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1266,
62,
19795,
263,
62,
11600,
14692,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
60,
796,
33270,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
19795,
263,
62,
17018,
1,
7131,
19795,
263,
62,
3672,
60,
198,
220,
220,
220,
1303,
3551,
649,
288,
291,
284,
2393,
198,
220,
220,
220,
611,
38701,
25,
198,
220,
220,
220,
220,
220,
220,
220,
351,
1280,
7,
418,
13,
6978,
13,
22179,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33270,
18,
62,
22915,
62,
34720,
11,
5072,
62,
7753,
828,
366,
86,
4943,
355,
503,
7753,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33918,
13,
39455,
7,
13466,
62,
19795,
263,
62,
11600,
11,
503,
7753,
11,
33793,
28,
16,
8,
198,
220,
220,
220,
1441,
1266,
62,
19795,
263,
62,
11600,
628,
198,
4299,
2298,
62,
1845,
11794,
62,
19795,
263,
62,
79,
3468,
7,
2302,
3004,
11,
300,
7065,
11,
5072,
62,
7753,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33270,
18,
62,
22915,
62,
34720,
11,
949,
62,
7857,
11,
3509,
62,
7857,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5559,
62,
8357,
11,
3814,
62,
28463,
507,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
850,
36996,
62,
3672,
11,
38701,
28,
16,
2599,
198,
220,
220,
220,
37227,
31686,
33270,
14729,
19201,
257,
1813,
2546,
2837,
526,
15931,
198,
220,
220,
220,
1303,
8333,
33270,
1321,
48589,
3166,
284,
257,
12238,
1438,
198,
220,
220,
220,
1070,
796,
7552,
14692,
19795,
263,
62,
17018,
8973,
198,
220,
220,
220,
26106,
796,
300,
7065,
14692,
19795,
263,
62,
17018,
8973,
198,
220,
220,
220,
1303,
2198,
611,
7552,
290,
300,
7065,
48589,
3166,
423,
2684,
364,
198,
220,
220,
220,
611,
18896,
7,
2302,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
352,
198,
220,
220,
220,
611,
18896,
7,
4604,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
352,
198,
220,
220,
220,
1303,
2251,
257,
33270,
14729,
288,
291,
13,
770,
22155,
318,
2092,
284,
33270,
288,
291,
198,
220,
220,
220,
33270,
62,
79,
3468,
796,
23884,
198,
220,
220,
220,
1303,
468,
262,
976,
8379,
62,
17018,
1994,
25,
8367,
14729,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
43167,
62,
17018,
8973,
796,
23884,
198,
220,
220,
220,
1303,
468,
5166,
1321,
1994,
2427,
286,
33270,
62,
17018,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
24874,
62,
17018,
8973,
796,
23884,
198,
220,
220,
220,
1303,
48040,
8379,
1321,
357,
31642,
355,
7552,
393,
300,
7065,
8,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
51,
3620,
6489,
6158,
20520,
796,
7552,
58,
198,
220,
220,
220,
220,
220,
220,
220,
366,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
51,
3620,
6489,
6158,
20520,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
6369,
39149,
1961,
62,
31553,
2849,
20520,
796,
357,
198,
220,
220,
220,
220,
220,
220,
220,
7552,
14692,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
6369,
39149,
1961,
62,
31553,
2849,
20520,
198,
220,
220,
220,
1267,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
51,
46095,
20520,
796,
7552,
58,
198,
220,
220,
220,
220,
220,
220,
220,
366,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
51,
46095,
20520,
198,
220,
220,
220,
33270,
62,
79,
3468,
14692,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
2389,
20520,
796,
7552,
58,
198,
220,
220,
220,
220,
220,
220,
220,
366,
43167,
62,
17018,
1,
7131,
6,
5188,
10917,
18310,
62,
2389,
20520,
198,
220,
220,
220,
1303,
2298,
33270,
14729,
198,
220,
220,
220,
329,
304,
287,
1070,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
7552,
33270,
1321,
329,
428,
285,
541,
481,
307,
304,
62,
10951,
198,
220,
220,
220,
220,
220,
220,
220,
304,
62,
10951,
796,
1070,
58,
68,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
33270,
22715,
198,
220,
220,
220,
220,
220,
220,
220,
1070,
62,
9688,
796,
304,
62,
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220,
220,
220,
220,
32774,
11,
38701,
28,
16,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6889,
285,
2419,
422,
33270,
14729,
13,
628,
220,
220,
220,
7214,
262,
9575,
16829,
286,
300,
7065,
33270,
8379,
11,
751,
262,
32774,
198,
220,
220,
220,
8379,
290,
262,
7552,
33270,
13,
8997,
32774,
318,
973,
611,
4844,
198,
220,
220,
220,
7368,
13,
198,
220,
220,
220,
3060,
257,
649,
1994,
284,
1123,
33270,
5166,
25,
198,
220,
220,
220,
366,
76,
541,
62,
17018,
1,
351,
257,
22155,
326,
468,
7946,
10917,
18310,
1994,
198,
220,
220,
220,
290,
285,
541,
8379,
355,
1988,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
2198,
611,
262,
33270,
22155,
318,
6565,
198,
220,
220,
220,
611,
18896,
7,
79,
3468,
14692,
24874,
62,
17018,
8973,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
352,
198,
220,
220,
220,
1303,
651,
33270,
16311,
329,
1123,
33270,
5166,
198,
220,
220,
220,
329,
2684,
364,
287,
14729,
14692,
24874,
62,
17018,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
7552,
62,
43167,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2302,
3004,
62,
19795,
263,
62,
17018,
1,
7131,
1,
5188,
10917,
18310,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
300,
7065,
62,
43167,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
4604,
341,
62,
19795,
263,
62,
17018,
1,
7131,
1,
5188,
10917,
18310,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
9575,
16829,
300,
7065,
33270,
198,
220,
220,
220,
220,
220,
220,
220,
300,
7065,
62,
6015,
796,
9575,
62,
785,
26908,
7,
4604,
341,
62,
43167,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
16311,
284,
787,
262,
285,
541,
198,
220,
220,
220,
220,
220,
220,
220,
285,
541,
62,
43167,
796,
300,
7065,
62,
6015,
1343,
32774,
1343,
7552,
62,
43167,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
257,
22155,
284,
1745,
285,
541,
1321,
198,
220,
220,
220,
220,
220,
220,
220,
285,
541,
62,
67,
291,
796,
19779,
5420,
1298,
19779,
5188,
10917,
18310,
1298,
285,
541,
62,
43167,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
27144,
942,
1298,
4866,
13,
22089,
30073,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14631,
27144,
1522,
62,
22163,
444,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1267,
11709,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
5559,
285,
2419,
810,
3306,
198,
220,
220,
220,
220,
220,
220,
220,
611,
366,
2501,
62,
22163,
444,
1,
287,
1351,
7,
79,
3468,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
4083,
13083,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
3107,
3007,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
24588,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
1,
2501,
62,
22163,
444,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
279,
62,
1845,
64,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
1,
79,
3468,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
304,
62,
1845,
64,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
2302,
3004,
62,
19795,
263,
62,
17018,
1,
7131,
1,
27082,
1847,
7730,
62,
8220,
12532,
1268,
29462,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
62,
1845,
64,
796,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
4604,
341,
62,
19795,
263,
62,
17018,
1,
7131,
1,
27082,
1847,
7730,
62,
8220,
12532,
1268,
29462,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1201,
5988,
2684,
364,
389,
2727,
329,
1123,
4866,
11,
340,
318,
1744,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
326,
617,
9088,
423,
262,
976,
33270,
5166,
13,
12346,
655,
530,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
884,
5166,
290,
4781,
262,
1854,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
257,
287,
5988,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
366,
2302,
3004,
1,
287,
279,
62,
1845,
64,
58,
64,
7131,
1,
33645,
876,
62,
8357,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7552,
62,
43167,
796,
304,
62,
1845,
64,
58,
64,
7131,
1,
31429,
62,
5188,
10917,
18310,
1,
4083,
45828,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
366,
4604,
341,
1,
287,
279,
62,
1845,
64,
58,
64,
7131,
1,
33645,
876,
62,
8357,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
7065,
62,
43167,
796,
300,
62,
1845,
64,
58,
64,
7131,
1,
31429,
62,
5188,
10917,
18310,
1,
4083,
45828,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
9275,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2989,
832,
1541,
2727,
5988,
14729,
284,
766,
611,
428,
530,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
318,
1541,
612,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1994,
11,
1988,
287,
1351,
7,
2501,
62,
3107,
3007,
13,
23814,
3419,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
29565,
2302,
3004,
62,
43167,
11,
300,
7065,
62,
43167,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6624,
1988,
14692,
3107,
3007,
8973,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
62,
9275,
796,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
4866,
1438,
284,
262,
8633,
290,
407,
2251,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
257,
649,
1994,
329,
428,
4866,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
14692,
22163,
444,
1,
4083,
33295,
7,
64,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
649,
5726,
611,
428,
5988,
5166,
318,
649,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
1988,
62,
9275,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
3107,
3007,
58,
2501,
62,
24588,
60,
796,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3107,
3007,
1298,
685,
2302,
3004,
62,
43167,
11,
300,
7065,
62,
43167,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
22163,
444,
1298,
685,
64,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
24588,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
285,
541,
8379,
290,
8633,
329,
262,
5988,
14729,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
5988,
62,
24874,
287,
5988,
62,
3107,
3007,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
33756,
62,
67,
291,
796,
5988,
62,
3107,
3007,
58,
2501,
62,
24874,
7131,
1,
3107,
3007,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
22163,
444,
796,
5988,
62,
3107,
3007,
58,
2501,
62,
24874,
7131,
1,
22163,
444,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9575,
16829,
300,
7065,
33270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
7065,
62,
6015,
796,
9575,
62,
785,
26908,
7,
41068,
62,
67,
291,
58,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
16311,
284,
787,
262,
285,
541,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
285,
541,
796,
300,
7065,
62,
6015,
1343,
32774,
1343,
33756,
62,
67,
291,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
285,
541,
62,
67,
291,
14692,
2501,
1,
1343,
965,
7,
2501,
62,
24874,
15437,
796,
19779,
5188,
10917,
18310,
1298,
285,
541,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
27144,
942,
1298,
5988,
62,
22163,
444,
92,
198,
220,
220,
220,
220,
220,
220,
220,
14729,
14692,
24874,
62,
17018,
1,
7131,
19795,
364,
7131,
1,
76,
541,
62,
17018,
8973,
796,
285,
541,
62,
67,
291,
628,
220,
220,
220,
1303,
3551,
285,
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13,
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326,
389,
636,
286,
3716,
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2245,
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14,
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481,
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13,
383,
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286,
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815,
307,
198,
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7368,
351,
262,
26969,
3455,
62,
25811,
11507,
13,
198,
220,
220,
220,
19406,
62,
23108,
25,
20512,
11,
10352,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2141,
645,
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319,
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11,
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262,
2656,
357,
34574,
515,
611,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
410,
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2393,
355,
513,
220,
954,
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13,
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3689,
389,
11670,
198,
220,
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351,
1123,
584,
290,
460,
307,
973,
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13,
198,
220,
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26969,
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25,
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198,
220,
220,
220,
220,
220,
220,
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611,
19406,
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313,
1460,
3038,
318,
6163,
11,
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307,
198,
220,
220,
220,
220,
220,
220,
220,
3804,
284,
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83,
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13,
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64,
1,
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26969,
32927,
17670,
7268,
773,
1424,
11,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1672,
13,
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1,
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1321,
13,
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284,
410,
83,
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220,
220,
220,
220,
220,
220,
220,
26969,
3455,
62,
27372,
549,
561,
307,
4938,
13,
2750,
4277,
773,
1424,
481,
407,
307,
198,
220,
220,
220,
220,
220,
220,
220,
38237,
1335,
13,
198,
220,
220,
220,
24708,
515,
62,
85,
12993,
25,
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11,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
318,
262,
2810,
410,
12993,
2393,
24708,
515,
1262,
3013,
79,
36,
487,
13,
2312,
37647,
198,
220,
220,
220,
220,
220,
220,
220,
481,
307,
973,
611,
645,
954,
46500,
318,
284,
307,
1760,
290,
24708,
378,
3038,
198,
220,
220,
220,
220,
220,
220,
220,
318,
10352,
13,
198,
220,
220,
220,
949,
62,
15654,
62,
13255,
25,
12178,
11,
532,
16,
198,
220,
220,
220,
220,
220,
220,
220,
25853,
17670,
351,
19604,
1847,
3815,
1342,
621,
428,
1988,
611,
262,
2524,
318,
198,
220,
220,
220,
220,
220,
220,
220,
407,
257,
7977,
2524,
13,
1002,
7977,
11,
262,
2524,
481,
307,
4030,
7692,
286,
198,
220,
220,
220,
220,
220,
220,
220,
262,
4140,
1988,
329,
262,
2524,
13,
1479,
24406,
274,
10107,
9217,
326,
198,
220,
220,
220,
220,
220,
220,
220,
27785,
3751,
257,
1988,
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352,
12,
1270,
561,
307,
922,
13,
1406,
257,
5288,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
286,
352,
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561,
3424,
510,
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18556,
5043,
13,
198,
220,
220,
220,
949,
62,
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62,
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25,
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11,
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16,
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220,
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220,
220,
220,
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2524,
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318,
2793,
621,
428,
11,
198,
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220,
220,
220,
220,
220,
220,
13259,
262,
13527,
13432,
9853,
284,
657,
13,
632,
743,
307,
1266,
284,
2666,
198,
220,
220,
220,
220,
220,
220,
220,
428,
379,
262,
4277,
1988,
1201,
612,
318,
3221,
3224,
2370,
198,
220,
220,
220,
220,
220,
220,
220,
326,
257,
7977,
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7160,
287,
257,
8405,
3688,
284,
257,
390,
645,
13038,
198,
220,
220,
220,
220,
220,
220,
220,
15304,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
651,
262,
3781,
6460,
198,
220,
220,
220,
1303,
2198,
611,
1111,
6460,
290,
262,
6460,
2393,
389,
6045,
25,
198,
220,
220,
220,
611,
357,
33692,
318,
6045,
8,
290,
357,
33692,
62,
7753,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
33692,
393,
6460,
2393,
1276,
307,
2810,
329,
1479,
24406,
274,
62,
13345,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
6460,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6460,
796,
651,
62,
20930,
62,
33692,
7,
33692,
62,
7753,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6460,
796,
4866,
13,
22089,
30073,
7,
33692,
8,
198,
220,
220,
220,
1303,
651,
262,
1762,
8619,
422,
6460,
198,
220,
220,
220,
266,
15908,
796,
6460,
14692,
16090,
35277,
8973,
198,
220,
220,
220,
1303,
1439,
1281,
36948,
4831,
2421,
275,
498,
293,
677,
15304,
10552,
13,
198,
220,
220,
220,
1303,
523,
356,
1183,
779,
47125,
701,
10141,
284,
6626,
1963,
498,
293,
677,
82,
284,
511,
898,
3951,
13,
198,
220,
220,
220,
19270,
62,
7217,
64,
796,
651,
62,
7753,
62,
17946,
602,
3419,
58,
33692,
14692,
35448,
8973,
7131,
1,
7217,
64,
62,
5235,
462,
8973,
198,
220,
220,
220,
410,
12993,
62,
6978,
796,
28686,
13,
6978,
13,
22179,
7,
86,
15908,
11,
410,
12993,
62,
7753,
8,
198,
220,
220,
220,
6626,
62,
85,
12993,
62,
6978,
796,
28686,
13,
6978,
13,
22179,
7,
86,
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11,
5072,
62,
40290,
1343,
366,
35312,
526,
1343,
410,
12993,
62,
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8,
198,
220,
220,
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6653,
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77,
51,
2417,
27691,
775,
1183,
6626,
777,
198,
220,
220,
220,
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220,
220,
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1303,
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23266,
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13,
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2845,
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257,
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1911,
22179,
26933,
66,
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62,
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13,
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328,
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3419,
60,
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1303,
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64,
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4941,
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290,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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1303,
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13,
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220,
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257,
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62,
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64,
62,
3803,
13,
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7,
2536,
7,
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62,
1930,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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4941,
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62,
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58,
15,
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13527,
796,
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62,
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16,
60,
198,
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220,
220,
220,
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1303,
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2458,
389,
287,
513,
3850,
5794,
13,
26304,
832,
1123,
257,
64,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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1303,
6626,
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2060,
257,
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13,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
2837,
7,
15,
11,
18896,
7,
35790,
828,
513,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
649,
62,
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796,
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7,
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62,
1930,
1343,
474,
14,
18,
8,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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1303,
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2060,
23206,
7408,
3891,
284,
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13,
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62,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
1303,
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318,
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262,
6670,
11,
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378,
340,
523,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
2496,
62,
1236,
14221,
62,
11600,
58,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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62,
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3803,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
5297,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
5988,
13432,
9853,
284,
657,
611,
3081,
318,
1877,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2524,
62,
13255,
1279,
949,
62,
16793,
62,
15654,
62,
13255,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
47715,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
1877,
3081,
1729,
12,
16793,
28654,
829,
355,
880,
355,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
33271,
2458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14808,
15654,
62,
13255,
1279,
949,
62,
15654,
62,
13255,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
357,
3605,
62,
35790,
6624,
649,
62,
33645,
378,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
9779,
62,
3672,
1343,
366,
21215,
1343,
649,
62,
3803,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
2949,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
6626,
15304,
1321,
6626,
17670,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
13,
33295,
7,
76,
85,
1343,
357,
3605,
62,
3803,
11,
9779,
62,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
11,
7977,
62,
76,
7094,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
1981,
1241,
1366,
284,
6626,
3848,
1351,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
13,
33295,
7,
13345,
62,
7890,
58,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
428,
15304,
318,
287,
262,
6670,
11,
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378,
340,
355,
884,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
2496,
62,
1236,
14221,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
70,
1734,
62,
3672,
11,
257,
64,
62,
3803,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
5297,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2524,
62,
13255,
1279,
949,
62,
16793,
62,
15654,
62,
13255,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
47715,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
1877,
4140,
393,
33271,
2458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14808,
15654,
62,
13255,
1279,
949,
62,
15654,
62,
13255,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
357,
76,
85,
58,
17,
60,
6624,
366,
28869,
6704,
62,
25641,
415,
4943,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
9779,
62,
3672,
1343,
366,
21215,
1343,
257,
64,
62,
3803,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
2949,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
13061,
15304,
1366,
284,
6626,
15304,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
13,
33295,
7,
76,
85,
1343,
357,
7252,
62,
3803,
11,
9779,
62,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
11,
7977,
62,
76,
7094,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
1981,
1241,
1366,
284,
6626,
3848,
1351,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
13,
33295,
7,
13345,
62,
7890,
58,
72,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1981,
1241,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2429,
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62,
421,
874,
796,
869,
62,
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58,
72,
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19,
60,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
78,
62,
9127,
796,
869,
62,
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58,
72,
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15,
60,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
421,
874,
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869,
62,
7890,
58,
72,
7131,
20,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
2501,
62,
421,
874,
796,
5988,
62,
421,
874,
1220,
257,
78,
62,
9127,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
686,
62,
9127,
796,
869,
62,
7890,
58,
72,
7131,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
421,
874,
796,
869,
62,
7890,
58,
72,
7131,
21,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
5420,
62,
421,
874,
796,
1006,
62,
421,
874,
1220,
686,
62,
9127,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
80,
62,
27932,
796,
2429,
8690,
62,
421,
874,
1279,
949,
62,
5235,
8690,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
64,
62,
27932,
796,
5988,
62,
421,
874,
1279,
949,
62,
2501,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
81,
62,
27932,
796,
1006,
62,
421,
874,
1279,
949,
62,
5420,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1196,
62,
20402,
62,
27932,
796,
2811,
62,
2501,
62,
421,
874,
1279,
949,
62,
32604,
62,
2501,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1196,
62,
80,
81,
62,
27932,
796,
2811,
62,
5420,
62,
421,
874,
1279,
949,
62,
32604,
62,
5420,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6330,
954,
1366,
329,
3925,
9894,
3081,
40885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5988,
45907,
954,
317,
46,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
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20402,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
615,
62,
20402,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1006,
45907,
954,
15107,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
16,
7131,
80,
81,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
16,
7131,
615,
62,
80,
81,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
5197,
329,
308,
80,
5287,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
17,
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70,
80,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
2429,
13567,
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308,
80,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
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18,
7131,
70,
80,
62,
27932,
60,
796,
532,
17,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
257,
5021,
9630,
329,
262,
15304,
47764,
326,
356,
1183,
2251,
1306,
198,
220,
220,
220,
220,
220,
220,
220,
6376,
796,
279,
67,
13,
29800,
15732,
13,
6738,
62,
28047,
2374,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
11,
3891,
28,
14692,
39358,
4522,
1600,
366,
7293,
633,
9794,
1600,
366,
3109,
9229,
37,
19524,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
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220,
220,
220,
220,
220,
366,
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1600,
366,
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1600,
366,
44,
7094,
6530,
1600,
198,
220,
220,
220,
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220,
220,
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366,
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276,
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198,
220,
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220,
220,
220,
1303,
651,
5988,
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198,
220,
220,
220,
220,
220,
220,
220,
15304,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
657,
4357,
198,
220,
220,
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220,
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220,
220,
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220,
15180,
28,
25641,
1187,
14692,
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33116,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
6376,
28,
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737,
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32590,
16,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
4941,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
352,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
1187,
14692,
82,
12629,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
9630,
737,
33491,
32590,
16,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
5197,
6795,
198,
220,
220,
220,
220,
220,
220,
220,
5197,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
362,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
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12629,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
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220,
220,
220,
220,
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6376,
28,
9630,
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32590,
16,
11,
657,
8,
198,
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220,
220,
220,
220,
220,
1303,
12082,
9853,
329,
976,
2458,
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220,
220,
220,
220,
220,
220,
220,
36115,
62,
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796,
14631,
39358,
4522,
1600,
366,
39358,
1600,
366,
44,
7094,
6530,
1600,
366,
3109,
9229,
37,
19524,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3838,
9794,
1600,
366,
21745,
276,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6330,
532,
16,
357,
29363,
8686,
11746,
3815,
8,
3815,
351,
657,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2160,
5988,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
15148,
62,
9127,
82,
796,
15304,
62,
9127,
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13,
8094,
1525,
7,
8094,
278,
62,
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737,
16345,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1011,
262,
3509,
286,
1006,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
15148,
62,
5420,
82,
796,
4941,
62,
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82,
13,
8094,
1525,
7,
8094,
278,
62,
13083,
737,
1084,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1011,
262,
3509,
286,
5197,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
15148,
62,
1073,
1857,
796,
5197,
13,
8094,
1525,
7,
8094,
278,
62,
13083,
737,
9806,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2233,
284,
13262,
803,
257,
64,
2458,
11,
1006,
9853,
460,
307,
625,
9127,
276,
772,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
262,
5288,
1006,
954,
318,
2077,
329,
262,
19406,
13,
383,
1738,
329,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
428,
318,
326,
1123,
17751,
45608,
15304,
338,
4941,
13432,
954,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
743,
2291,
262,
13527,
28654,
829,
329,
1194,
17751,
45608,
15304,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
326,
12416,
329,
262,
976,
257,
64,
1487,
13,
1406,
356,
1183,
900,
262,
1006,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
5197,
532,
5988,
954,
810,
1006,
954,
21695,
428,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
814,
62,
9127,
796,
15148,
62,
1073,
1857,
532,
15148,
62,
9127,
82,
198,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
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1981,
1241,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
326,
318,
6209,
262,
954,
1366,
329,
8893,
13,
317,
46,
25,
5988,
45907,
954,
11,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
15107,
1006,
954,
11,
27704,
25,
5197,
13,
198,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
62,
25747,
796,
37250,
9948,
335,
1045,
14,
32,
46,
3256,
705,
9948,
335,
1045,
14,
13252,
3256,
705,
9948,
335,
1045,
14,
6322,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
9948,
335,
1045,
14,
19555,
3256,
705,
9948,
335,
1045,
14,
38,
48,
3256,
705,
9948,
335,
1045,
14,
48,
32,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
9948,
335,
1045,
14,
48,
49,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
17670,
14692,
9948,
335,
1045,
14,
19555,
8973,
796,
17670,
14692,
9948,
335,
1045,
14,
19555,
1,
4083,
16345,
7,
22704,
28,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
19974,
15304,
1241,
220,
1321,
1978,
11,
523,
356,
423,
257,
2060,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
329,
1123,
15304,
198,
220,
220,
220,
220,
220,
220,
220,
15304,
62,
7890,
796,
1351,
7,
13344,
46491,
58,
25641,
1187,
58,
85,
60,
329,
410,
287,
15304,
62,
25747,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
954,
1366,
329,
262,
17670,
198,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
796,
1351,
7,
13344,
46491,
58,
25641,
1187,
58,
66,
60,
329,
269,
287,
869,
62,
7890,
62,
25747,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
611,
257,
2496,
23025,
8633,
318,
2810,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2496,
62,
1236,
14221,
62,
11600,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2496,
62,
429,
62,
1236,
14221,
318,
407,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
7252,
796,
279,
67,
13,
961,
62,
11487,
7,
16793,
62,
429,
62,
1236,
14221,
737,
2617,
62,
9630,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14631,
3398,
33676,
1600,
366,
37997,
1600,
366,
31688,
1600,
366,
31429,
8973,
737,
1462,
62,
11600,
7,
13989,
2625,
9630,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
479,
287,
256,
7252,
13,
13083,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2496,
62,
1236,
14221,
62,
11600,
58,
74,
60,
796,
256,
7252,
58,
74,
7131,
1,
76,
7094,
62,
3672,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
36115,
62,
13083,
796,
14631,
3398,
33676,
1600,
366,
37997,
1600,
366,
31688,
1600,
366,
31429,
1600,
366,
44,
7094,
6530,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
21745,
276,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
25641,
415,
62,
7890,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
410,
67,
796,
15304,
62,
7890,
58,
72,
7131,
25,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2524,
62,
13255,
796,
12178,
7,
25641,
415,
62,
7890,
58,
72,
7131,
19,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
62,
1236,
78,
796,
2496,
62,
1236,
14221,
62,
11600,
58,
20306,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
5297,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2524,
62,
13255,
1279,
949,
62,
16793,
62,
15654,
62,
13255,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
47715,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
1877,
4140,
290,
1729,
25641,
415,
5043,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14808,
15654,
62,
13255,
1279,
949,
62,
15654,
62,
13255,
8,
393,
357,
20306,
58,
17,
60,
6624,
410,
67,
58,
18,
12962,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
62,
1236,
78,
796,
366,
25,
1911,
22179,
7,
8899,
7,
2536,
11,
410,
67,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
2949,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
13,
33295,
7,
20306,
1343,
357,
83,
62,
1236,
78,
11,
7977,
62,
76,
7094,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
13,
33295,
7,
13345,
62,
7890,
58,
72,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
1981,
1241,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2429,
8690,
62,
421,
874,
796,
869,
62,
7890,
58,
72,
7131,
19,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
78,
62,
9127,
796,
869,
62,
7890,
58,
72,
7131,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5988,
62,
421,
874,
796,
869,
62,
7890,
58,
72,
7131,
20,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
2501,
62,
421,
874,
796,
5988,
62,
421,
874,
1220,
257,
78,
62,
9127,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
686,
62,
9127,
796,
869,
62,
7890,
58,
72,
7131,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1006,
62,
421,
874,
796,
869,
62,
7890,
58,
72,
7131,
21,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2811,
62,
5420,
62,
421,
874,
796,
1006,
62,
421,
874,
1220,
686,
62,
9127,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
80,
62,
27932,
796,
2429,
8690,
62,
421,
874,
1279,
949,
62,
5235,
8690,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
64,
62,
27932,
796,
5988,
62,
421,
874,
1279,
949,
62,
2501,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
81,
62,
27932,
796,
1006,
62,
421,
874,
1279,
949,
62,
5420,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1196,
62,
20402,
62,
27932,
796,
2811,
62,
2501,
62,
421,
874,
1279,
949,
62,
32604,
62,
2501,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1196,
62,
80,
81,
62,
27932,
796,
2811,
62,
5420,
62,
421,
874,
1279,
949,
62,
32604,
62,
5420,
62,
13255,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6330,
954,
1366,
329,
3925,
9894,
3081,
40885,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5988,
45907,
954,
317,
46,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
20402,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
615,
62,
20402,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1006,
45907,
954,
15107,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
16,
7131,
80,
81,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
16,
7131,
615,
62,
80,
81,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
5197,
329,
308,
80,
5287,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
17,
7131,
70,
80,
62,
27932,
60,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
2429,
13567,
329,
308,
80,
5287,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
18,
7131,
70,
80,
62,
27932,
60,
796,
532,
17,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
717,
2378,
286,
262,
2029,
1351,
318,
5988,
9853,
11,
788,
1006,
9853,
290,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
5197,
13,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
14468,
7804,
4242,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
257,
5021,
9630,
329,
262,
15304,
47764,
326,
356,
1183,
2251,
1306,
198,
220,
220,
220,
220,
220,
220,
220,
6376,
796,
279,
67,
13,
29800,
15732,
13,
6738,
62,
28047,
2374,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
11,
3891,
28,
8094,
278,
62,
13083,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
5988,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
15304,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
657,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
1187,
14692,
82,
12629,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
9630,
737,
33491,
32590,
16,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
4941,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
352,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
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66,
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62,
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62,
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60,
4008,
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6626,
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415,
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58,
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19,
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62,
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25,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
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13,
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25,
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11,
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198,
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220,
220,
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13,
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329,
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13,
198,
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62,
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25,
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14,
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17962,
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220,
9779,
312,
17,
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25,
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14,
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11,
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13,
198,
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220,
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284,
257,
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11266,
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2393,
326,
8739,
9779,
220,
2340,
284,
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3891,
13,
198,
220,
220,
220,
220,
220,
220,
220,
29201,
3891,
1276,
307,
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62,
312,
290,
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62,
3672,
13,
13005,
32373,
198,
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220,
220,
220,
220,
220,
481,
48040,
262,
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2214,
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318,
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2810,
13,
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62,
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11,
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62,
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13,
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39869,
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32,
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13,
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25,
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11,
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11,
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2239,
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5668,
13,
12744,
276,
23005,
198,
220,
220,
220,
220,
220,
220,
220,
326,
389,
636,
286,
3716,
2995,
357,
521,
1424,
11,
2245,
2994,
14,
48544,
3503,
2014,
481,
407,
198,
220,
220,
220,
220,
220,
220,
220,
307,
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355,
7977,
13,
198,
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19406,
62,
77,
14913,
313,
1460,
25,
20512,
11,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
1771,
262,
9853,
329,
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45608,
2458,
815,
307,
13262,
515,
13,
770,
198,
220,
220,
220,
220,
220,
220,
220,
9018,
26969,
32927,
477,
17670,
284,
262,
18197,
4991,
1744,
11,
198,
220,
220,
220,
220,
220,
220,
220,
7163,
477,
42519,
8690,
1366,
13,
383,
1241,
286,
26969,
9150,
815,
307,
198,
220,
220,
220,
220,
220,
220,
220,
7368,
351,
262,
26969,
3455,
62,
25811,
11507,
13,
198,
220,
220,
220,
19406,
62,
23108,
25,
20512,
11,
10352,
13,
198,
220,
220,
220,
220,
220,
220,
220,
2141,
645,
46500,
319,
9853,
11,
3613,
262,
2656,
357,
34574,
515,
611,
9167,
8,
198,
220,
220,
220,
220,
220,
220,
220,
410,
12993,
2393,
355,
513,
220,
954,
8893,
13,
7683,
46500,
3689,
389,
11670,
198,
220,
220,
220,
220,
220,
220,
220,
351,
1123,
584,
290,
460,
307,
973,
477,
379,
1752,
13,
198,
220,
220,
220,
26969,
3455,
62,
25811,
25,
1351,
11,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
611,
19406,
17751,
313,
1460,
3038,
318,
6163,
11,
777,
3689,
481,
307,
198,
220,
220,
220,
220,
220,
220,
220,
3804,
284,
410,
83,
1430,
13,
27444,
64,
1,
329,
26969,
32927,
17670,
7268,
773,
1424,
11,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1672,
13,
27444,
79,
1,
329,
5291,
7108,
1321,
13,
4377,
3038,
284,
410,
83,
198,
220,
220,
220,
220,
220,
220,
220,
26969,
3455,
62,
27372,
549,
561,
307,
4938,
13,
2750,
4277,
773,
1424,
481,
407,
307,
198,
220,
220,
220,
220,
220,
220,
220,
38237,
1335,
13,
198,
220,
220,
220,
24708,
515,
62,
85,
12993,
25,
20512,
11,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
318,
262,
2810,
410,
12993,
2393,
24708,
515,
1262,
3013,
79,
36,
487,
13,
2312,
37647,
198,
220,
220,
220,
220,
220,
220,
220,
481,
307,
973,
611,
645,
954,
46500,
318,
284,
307,
1760,
290,
24708,
378,
3038,
198,
220,
220,
220,
220,
220,
220,
220,
318,
10352,
13,
198,
220,
220,
220,
949,
62,
15654,
62,
13255,
25,
12178,
11,
532,
16,
198,
220,
220,
220,
220,
220,
220,
220,
25853,
17670,
351,
19604,
1847,
3815,
1342,
621,
428,
1988,
611,
262,
2524,
318,
198,
220,
220,
220,
220,
220,
220,
220,
407,
257,
7977,
2524,
13,
1002,
7977,
11,
262,
2524,
481,
307,
4030,
7692,
286,
198,
220,
220,
220,
220,
220,
220,
220,
262,
4140,
1988,
329,
262,
2524,
13,
1479,
24406,
274,
10107,
9217,
326,
198,
220,
220,
220,
220,
220,
220,
220,
27785,
3751,
257,
1988,
1022,
352,
12,
1270,
561,
307,
922,
13,
1406,
257,
5288,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
286,
352,
994,
561,
3424,
510,
749,
18556,
5043,
13,
198,
220,
220,
220,
949,
62,
16793,
62,
15654,
62,
13255,
25,
12178,
11,
532,
16,
198,
220,
220,
220,
220,
220,
220,
220,
1002,
257,
15304,
2524,
318,
7977,
475,
262,
2524,
4140,
318,
2793,
621,
428,
11,
198,
220,
220,
220,
220,
220,
220,
220,
13259,
262,
13527,
13432,
9853,
284,
657,
13,
632,
743,
307,
1266,
284,
2666,
198,
220,
220,
220,
220,
220,
220,
220,
428,
379,
262,
4277,
1988,
1201,
612,
318,
3221,
3224,
2370,
198,
220,
220,
220,
220,
220,
220,
220,
326,
257,
7977,
15304,
7160,
287,
257,
8405,
3688,
284,
257,
390,
645,
13038,
198,
220,
220,
220,
220,
220,
220,
220,
15304,
13,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1303,
651,
262,
3781,
6460,
198,
220,
220,
220,
1303,
2198,
611,
1111,
6460,
290,
262,
6460,
2393,
389,
6045,
25,
198,
220,
220,
220,
611,
357,
33692,
318,
6045,
8,
290,
357,
33692,
62,
7753,
318,
6045,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
33692,
393,
6460,
2393,
1276,
307,
2810,
329,
1479,
24406,
274,
62,
13345,
19570,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
611,
6460,
318,
6045,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6460,
796,
651,
62,
20930,
62,
33692,
7,
33692,
62,
7753,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
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284,
352,
3850,
2438,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
35790,
796,
4941,
58,
73,
25,
73,
10,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
33645,
378,
796,
13527,
58,
73,
25,
73,
10,
18,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
3803,
796,
649,
62,
35790,
1343,
965,
7,
3605,
62,
1930,
8,
1343,
649,
62,
33645,
378,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
428,
15304,
318,
287,
262,
6670,
11,
24708,
378,
340,
523,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
2496,
62,
1236,
14221,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
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357,
70,
1734,
62,
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11,
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62,
3803,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
5297,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13259,
5988,
13432,
9853,
284,
657,
611,
3081,
318,
1877,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2524,
62,
13255,
1279,
949,
62,
16793,
62,
15654,
62,
13255,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
47715,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
1877,
3081,
1729,
12,
16793,
28654,
829,
355,
880,
355,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
33271,
2458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14808,
15654,
62,
13255,
1279,
949,
62,
15654,
62,
13255,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
357,
3605,
62,
35790,
6624,
649,
62,
33645,
378,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
9779,
62,
3672,
1343,
366,
21215,
1343,
649,
62,
3803,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
2949,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
6626,
15304,
1321,
6626,
17670,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
13,
33295,
7,
76,
85,
1343,
357,
3605,
62,
3803,
11,
9779,
62,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
11,
7977,
62,
76,
7094,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
1981,
1241,
1366,
284,
6626,
3848,
1351,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
13,
33295,
7,
13345,
62,
7890,
58,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
428,
15304,
318,
287,
262,
6670,
11,
24708,
378,
340,
355,
884,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
2496,
62,
1236,
14221,
62,
11600,
58,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
357,
70,
1734,
62,
3672,
11,
257,
64,
62,
3803,
15437,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
5297,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2524,
62,
13255,
1279,
949,
62,
16793,
62,
15654,
62,
13255,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
869,
62,
7890,
58,
72,
7131,
15,
7131,
47715,
796,
657,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
4781,
1877,
4140,
393,
33271,
2458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
14808,
15654,
62,
13255,
1279,
949,
62,
15654,
62,
13255,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
393,
357,
76,
85,
58,
17,
60,
6624,
366,
28869,
6704,
62,
25641,
415,
4943,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
796,
9779,
62,
3672,
1343,
366,
21215,
1343,
257,
64,
62,
3803,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7977,
62,
76,
7094,
796,
366,
2949,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
13061,
15304,
1366,
284,
6626,
15304,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
13,
33295,
7,
76,
85,
1343,
357,
7252,
62,
3803,
11,
9779,
62,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4517,
62,
3672,
11,
7977,
62,
76,
7094,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
1981,
1241,
1366,
284,
6626,
3848,
1351,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
66,
5691,
13,
33295,
7,
13345,
62,
7890,
58,
72,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
257,
5021,
9630,
329,
262,
15304,
47764,
326,
356,
1183,
2251,
1306,
198,
220,
220,
220,
220,
220,
220,
220,
6376,
796,
279,
67,
13,
29800,
15732,
13,
6738,
62,
28047,
2374,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6626,
62,
25641,
1187,
11,
3891,
28,
14692,
39358,
4522,
1600,
366,
7293,
633,
9794,
1600,
366,
3109,
9229,
37,
19524,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3838,
9794,
1600,
366,
39358,
1600,
366,
44,
7094,
6530,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
21745,
276,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
5988,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
15304,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
657,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
1187,
14692,
82,
12629,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
9630,
737,
33491,
32590,
16,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
4941,
9853,
198,
220,
220,
220,
220,
220,
220,
220,
4941,
62,
9127,
82,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
352,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
1187,
14692,
82,
12629,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6376,
28,
9630,
737,
33491,
32590,
16,
11,
657,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
5197,
6795,
198,
220,
220,
220,
220,
220,
220,
220,
5197,
796,
279,
67,
13,
6601,
19778,
7,
37659,
13,
18747,
7,
35312,
62,
66,
5691,
38381,
45299,
362,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15180,
28,
25641,
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220,
351,
1280,
7,
34642,
62,
3099,
489,
8690,
62,
7753,
8,
355,
1167,
576,
25,
198,
220,
220,
220,
220,
220,
220,
220,
42519,
13567,
796,
33918,
13,
2220,
7,
259,
7753,
8,
198,
220,
220,
220,
1303,
2251,
5072,
1321,
1351,
284,
989,
477,
2458,
198,
220,
220,
220,
503,
4868,
796,
685,
14692,
23303,
489,
8690,
2389,
1600,
366,
29881,
1600,
366,
3791,
15923,
1600,
366,
26687,
15923,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3041,
21820,
8610,
4535,
36653,
1600,
366,
3791,
18720,
261,
1600,
366,
26687,
18720,
261,
1600,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
3041,
21820,
32,
64,
29238,
8973,
60,
198,
220,
220,
220,
1303,
467,
832,
1123,
24708,
515,
42519,
8690,
290,
20121,
11346,
12016,
198,
220,
220,
220,
329,
285,
287,
42519,
13567,
25,
198,
220,
220,
220,
220,
220,
220,
220,
329,
289,
287,
42519,
13567,
58,
76,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
42519,
13567,
58,
76,
7131,
71,
7131,
1,
76,
6320,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
31396,
287,
42519,
13567,
58,
76,
7131,
71,
7131,
1,
76,
6320,
62,
22163,
444,
1,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
8379,
286,
262,
42519,
8690,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
387,
79,
62,
41068,
796,
42519,
13567,
58,
76,
7131,
71,
7131,
1,
43167,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
11346,
12016,
1944,
287,
262,
42519,
8690,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
814,
82,
796,
42519,
13567,
58,
76,
7131,
71,
7131,
1,
76,
6320,
62,
22163,
444,
1,
7131,
13155,
7131,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
366,
26069,
4972,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
62,
36653,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5021,
62,
521,
1424,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
2837,
7,
11925,
7,
67,
10203,
8,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
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814,
82,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
7532,
1487,
1321,
329,
262,
25632,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
796,
288,
14692,
1236,
14221,
1,
7131,
1,
3838,
9794,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
62,
1930,
796,
493,
7,
67,
14692,
1236,
14221,
1,
7131,
1,
3838,
9794,
23158,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
262,
257,
64,
1487,
2292,
284,
262,
2458,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8633,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
62,
36653,
58,
7252,
62,
1930,
4083,
33295,
7,
72,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7383,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
62,
36653,
58,
7252,
62,
1930,
60,
796,
685,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
11052,
12331,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
706,
1016,
832,
477,
814,
82,
11,
804,
329,
4517,
75,
2480,
814,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
13891,
2060,
23206,
46309,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
477,
62,
647,
3212,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
269,
287,
257,
64,
62,
36653,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
357,
11925,
7,
7252,
62,
36653,
58,
66,
12962,
1875,
352,
8,
290,
357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
407,
287,
5021,
62,
521,
1424,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2270,
503,
286,
9052,
611,
773,
1424,
1043,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2000,
417,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
39199,
796,
257,
64,
62,
36653,
58,
66,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1394,
6116,
3585,
284,
269,
28886,
287,
257,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
62,
1930,
1756,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1394,
6116,
3585,
284,
42519,
8690,
287,
257,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
62,
9630,
274,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1394,
45752,
6116,
286,
2458,
287,
257,
1351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
62,
1930,
1756,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1394,
262,
3580,
1022,
262,
269,
28886,
290,
45752,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6116,
287,
257,
1351,
13,
770,
481,
307,
973,
284,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5004,
262,
9779,
338,
12852,
319,
262,
19270,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
62,
8210,
1039,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2458,
62,
1462,
62,
10210,
2616,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2458,
62,
1462,
62,
7252,
796,
17635,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
39199,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
796,
814,
82,
58,
72,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
329,
1123,
814,
651,
262,
23025,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
304,
13,
70,
13,
705,
39,
15199,
25,
39,
15199,
25,
1069,
261,
16,
25,
66,
13,
38,
45758,
32,
25,
79,
13,
53,
18182,
40,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
281,
78,
796,
288,
14692,
1236,
14221,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
281,
78,
14692,
15571,
53,
8973,
14512,
366,
15571,
53,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2000,
417,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5021,
62,
521,
1424,
13,
33295,
7,
66,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
796,
281,
78,
14692,
3838,
9794,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2458,
62,
1462,
62,
7252,
13,
33295,
7,
7252,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
257,
64,
286,
4941,
19270,
357,
53,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
257,
64,
62,
5420,
796,
257,
64,
58,
15,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
22927,
2616,
1487,
11,
304,
13,
70,
13,
402,
45758,
32,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22927,
2616,
796,
281,
78,
14692,
34,
5258,
9794,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2458,
62,
1462,
62,
10210,
2616,
13,
33295,
7,
10210,
2616,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
30118,
2779,
357,
32,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22927,
2616,
62,
3803,
796,
22927,
2616,
58,
12,
16,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8996,
262,
8379,
319,
262,
269,
28886,
351,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
262,
8379,
286,
262,
42519,
8690,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
284,
5004,
262,
337,
4061,
14,
3099,
489,
8690,
338,
12852,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3585,
284,
262,
269,
28886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22812,
796,
22927,
2616,
62,
3803,
6624,
288,
14692,
45897,
62,
8692,
8973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
22927,
2616,
62,
1930,
796,
493,
7,
5733,
14692,
34,
5258,
23158,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
45752,
2292,
286,
262,
1487,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
814,
62,
9688,
796,
493,
7,
67,
14692,
1236,
14221,
1,
7131,
1,
10434,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
3580,
1022,
262,
45752,
290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
269,
28886,
2292,
286,
262,
1487,
11,
284,
307,
973,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
287,
13213,
262,
9779,
338,
12852,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
62,
1930,
1756,
13,
33295,
7,
10210,
2616,
62,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
62,
9630,
274,
13,
2302,
437,
7,
67,
14692,
45897,
62,
9630,
8973,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
62,
1930,
1756,
13,
33295,
7,
26069,
62,
9688,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
62,
8210,
1039,
13,
33295,
7,
26069,
62,
9688,
532,
22927,
2616,
62,
1930,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2000,
417,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
62,
1930,
1756,
796,
23243,
7,
66,
62,
1930,
1756,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
62,
9630,
274,
796,
23243,
7,
2617,
7,
71,
62,
9630,
274,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
308,
62,
1930,
1756,
796,
23243,
7,
70,
62,
1930,
1756,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
262,
11677,
1022,
262,
269,
28886,
290,
45752,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
6116,
286,
262,
2458,
389,
1464,
262,
976,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
262,
9779,
318,
319,
262,
5556,
37923,
11,
2073,
340,
318,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
9575,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
2617,
7,
66,
62,
8210,
1039,
4008,
1875,
352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9779,
62,
10145,
796,
10352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9779,
62,
10145,
796,
6407,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
2292,
286,
262,
717,
2779,
286,
262,
14873,
261,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14873,
261,
62,
28968,
796,
357,
66,
62,
1930,
1756,
58,
15,
60,
4064,
513,
8,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14873,
261,
62,
1930,
796,
269,
62,
1930,
1756,
58,
15,
60,
532,
14873,
261,
62,
28968,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
14873,
261,
338,
8379,
422,
262,
42519,
8690,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
22812,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
611,
262,
42519,
8690,
318,
287,
262,
976,
12852,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
355,
262,
269,
28886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
62,
9688,
62,
9630,
796,
289,
62,
9630,
274,
58,
15,
60,
532,
14873,
261,
62,
28968,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
289,
62,
437,
62,
9630,
796,
289,
62,
9688,
62,
9630,
1343,
513,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
387,
79,
62,
19815,
261,
796,
387,
79,
62,
41068,
58,
71,
62,
9688,
62,
9630,
25,
71,
62,
437,
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] | 2.024292 | 183,188 |
"""WebSocket API for HACS."""
# pylint: disable=unused-argument
import sys
import os
import voluptuous as vol
from aiogithubapi import AIOGitHubException
from homeassistant.components import websocket_api
import homeassistant.helpers.config_validation as cv
from .hacsbase.exceptions import HacsException
from .store import async_load_from_store, async_save_to_store
from custom_components.hacs.globals import get_hacs
from custom_components.hacs.helpers.register_repository import register_repository
async def setup_ws_api(hass):
"""Set up WS API handlers."""
websocket_api.async_register_command(hass, hacs_settings)
websocket_api.async_register_command(hass, hacs_config)
websocket_api.async_register_command(hass, hacs_repositories)
websocket_api.async_register_command(hass, hacs_repository)
websocket_api.async_register_command(hass, hacs_repository_data)
websocket_api.async_register_command(hass, check_local_path)
websocket_api.async_register_command(hass, hacs_status)
websocket_api.async_register_command(hass, acknowledge_critical_repository)
websocket_api.async_register_command(hass, get_critical_repositories)
@websocket_api.async_response
@websocket_api.websocket_command(
{
vol.Required("type"): "hacs/settings",
vol.Optional("action"): cv.string,
vol.Optional("category"): cv.string,
}
)
async def hacs_settings(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
action = msg["action"]
hacs.logger.debug(f"WS action '{action}'")
if action == "set_fe_grid":
hacs.configuration.frontend_mode = "Grid"
elif action == "onboarding_done":
hacs.configuration.onboarding_done = True
elif action == "set_fe_table":
hacs.configuration.frontend_mode = "Table"
elif action == "set_fe_compact_true":
hacs.configuration.frontend_compact = False
elif action == "set_fe_compact_false":
hacs.configuration.frontend_compact = True
elif action == "reload_data":
hacs.system.status.reloading_data = True
hass.bus.async_fire("hacs/status", {})
await hacs.recuring_tasks_all()
hacs.system.status.reloading_data = False
hass.bus.async_fire("hacs/status", {})
elif action == "upgrade_all":
hacs.system.status.upgrading_all = True
hacs.system.status.background_task = True
hass.bus.async_fire("hacs/status", {})
for repository in hacs.repositories:
if repository.pending_upgrade:
repository.status.selected_tag = None
await repository.install()
hacs.system.status.upgrading_all = False
hacs.system.status.background_task = False
hass.bus.async_fire("hacs/status", {})
hass.bus.async_fire("hacs/repository", {})
elif action == "clear_new":
for repo in hacs.repositories:
if msg.get("category") == repo.data.category:
if repo.status.new:
hacs.logger.debug(
f"Clearing new flag from '{repo.data.full_name}'"
)
repo.status.new = False
else:
hacs.logger.error(f"WS action '{action}' is not valid")
hass.bus.async_fire("hacs/config", {})
await hacs.data.async_write()
@websocket_api.async_response
@websocket_api.websocket_command({vol.Required("type"): "hacs/config"})
async def hacs_config(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
config = hacs.configuration
content = {}
content["frontend_mode"] = config.frontend_mode
content["frontend_compact"] = config.frontend_compact
content["onboarding_done"] = config.onboarding_done
content["version"] = hacs.version
content["dev"] = config.dev
content["debug"] = config.debug
content["country"] = config.country
content["experimental"] = config.experimental
content["categories"] = hacs.common.categories
connection.send_message(websocket_api.result_message(msg["id"], content))
@websocket_api.async_response
@websocket_api.websocket_command({vol.Required("type"): "hacs/status"})
async def hacs_status(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
content = {
"startup": hacs.system.status.startup,
"background_task": hacs.system.status.background_task,
"lovelace_mode": hacs.system.lovelace_mode,
"reloading_data": hacs.system.status.reloading_data,
"upgrading_all": hacs.system.status.upgrading_all,
"disabled": hacs.system.disabled,
}
connection.send_message(websocket_api.result_message(msg["id"], content))
@websocket_api.async_response
@websocket_api.websocket_command({vol.Required("type"): "hacs/repositories"})
async def hacs_repositories(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
repositories = hacs.repositories
content = []
for repo in repositories:
if repo.data.category in hacs.common.categories:
data = {
"additional_info": repo.information.additional_info,
"authors": repo.data.authors,
"available_version": repo.display_available_version,
"beta": repo.status.show_beta,
"can_install": repo.can_install,
"category": repo.data.category,
"country": repo.data.country,
"config_flow": repo.config_flow,
"custom": repo.custom,
"default_branch": repo.data.default_branch,
"description": repo.data.description,
"domain": repo.integration_manifest.get("domain"),
"downloads": repo.releases.downloads,
"file_name": repo.data.file_name,
"first_install": repo.status.first_install,
"full_name": repo.data.full_name,
"hide": repo.status.hide,
"hide_default_branch": repo.data.hide_default_branch,
"homeassistant": repo.data.homeassistant,
"id": repo.information.uid,
"info": repo.information.info,
"installed_version": repo.display_installed_version,
"installed": repo.status.installed,
"javascript_type": repo.information.javascript_type,
"last_updated": repo.information.last_updated,
"local_path": repo.content.path.local,
"main_action": repo.main_action,
"name": repo.display_name,
"new": repo.status.new,
"pending_upgrade": repo.pending_upgrade,
"releases": repo.releases.published_tags,
"selected_tag": repo.status.selected_tag,
"stars": repo.data.stargazers_count,
"state": repo.state,
"status_description": repo.display_status_description,
"status": repo.display_status,
"topics": repo.data.topics,
"updated_info": repo.status.updated_info,
"version_or_commit": repo.display_version_or_commit,
}
content.append(data)
connection.send_message(websocket_api.result_message(msg["id"], content))
@websocket_api.async_response
@websocket_api.websocket_command(
{
vol.Required("type"): "hacs/repository",
vol.Optional("action"): cv.string,
vol.Optional("repository"): cv.string,
}
)
async def hacs_repository(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
try:
repo_id = msg.get("repository")
action = msg.get("action")
if repo_id is None or action is None:
return
repository = hacs.get_by_id(repo_id)
hacs.logger.debug(f"Running {action} for {repository.data.full_name}")
if action == "update":
await repository.update_repository()
repository.status.updated_info = True
repository.status.new = False
elif action == "install":
was_installed = repository.status.installed
await repository.install()
if not was_installed:
hass.bus.async_fire("hacs/reload", {"force": False})
elif action == "uninstall":
await repository.uninstall()
hass.bus.async_fire("hacs/reload", {"force": False})
elif action == "hide":
repository.status.hide = True
elif action == "unhide":
repository.status.hide = False
elif action == "show_beta":
repository.status.show_beta = True
await repository.update_repository()
elif action == "hide_beta":
repository.status.show_beta = False
await repository.update_repository()
elif action == "delete":
repository.status.show_beta = False
repository.remove()
elif action == "set_version":
if msg["version"] == repository.data.default_branch:
repository.status.selected_tag = None
else:
repository.status.selected_tag = msg["version"]
await repository.update_repository()
else:
hacs.logger.error(f"WS action '{action}' is not valid")
repository.state = None
await hacs.data.async_write()
except AIOGitHubException as exception:
hass.bus.async_fire("hacs/error", {"message": str(exception)})
except AttributeError as exception:
hass.bus.async_fire(
"hacs/error", {"message": f"Could not use repository with ID {repo_id}"}
)
except Exception as exception: # pylint: disable=broad-except
hass.bus.async_fire("hacs/error", {"message": str(exception)})
@websocket_api.async_response
@websocket_api.websocket_command(
{
vol.Required("type"): "hacs/repository/data",
vol.Optional("action"): cv.string,
vol.Optional("repository"): cv.string,
vol.Optional("data"): cv.string,
}
)
async def hacs_repository_data(hass, connection, msg):
"""Handle get media player cover command."""
hacs = get_hacs()
repo_id = msg.get("repository")
action = msg.get("action")
data = msg.get("data")
if repo_id is None:
return
if action == "add":
if "github." in repo_id:
repo_id = repo_id.split("github.com/")[1]
if repo_id in hacs.common.skip:
hacs.common.skip.remove(repo_id)
if not hacs.get_by_name(repo_id):
try:
registration = await register_repository(repo_id, data.lower())
if registration is not None:
raise HacsException(registration)
except Exception as exception: # pylint: disable=broad-except
hass.bus.async_fire(
"hacs/error",
{
"action": "add_repository",
"exception": str(sys.exc_info()[0].__name__),
"message": str(exception),
},
)
else:
hass.bus.async_fire(
"hacs/error",
{
"action": "add_repository",
"message": f"Repository '{repo_id}' exists in the store.",
},
)
repository = hacs.get_by_name(repo_id)
else:
repository = hacs.get_by_id(repo_id)
if repository is None:
hass.bus.async_fire("hacs/repository", {})
return
hacs.logger.debug(f"Running {action} for {repository.data.full_name}")
if action == "set_state":
repository.state = data
elif action == "set_version":
repository.status.selected_tag = data
await repository.update_repository()
repository.state = None
elif action == "add":
repository.state = None
else:
repository.state = None
hacs.logger.error(f"WS action '{action}' is not valid")
await hacs.data.async_write()
@websocket_api.async_response
@websocket_api.websocket_command(
{vol.Required("type"): "hacs/check_path", vol.Optional("path"): cv.string}
)
async def check_local_path(hass, connection, msg):
"""Handle get media player cover command."""
path = msg.get("path")
exist = {"exist": False}
if path is None:
return
if os.path.exists(path):
exist["exist"] = True
connection.send_message(websocket_api.result_message(msg["id"], exist))
@websocket_api.async_response
@websocket_api.websocket_command({vol.Required("type"): "hacs/get_critical"})
async def get_critical_repositories(hass, connection, msg):
"""Handle get media player cover command."""
critical = await async_load_from_store(hass, "critical")
if not critical:
critical = []
connection.send_message(websocket_api.result_message(msg["id"], critical))
@websocket_api.async_response
@websocket_api.websocket_command(
{vol.Required("type"): "hacs/critical", vol.Optional("repository"): cv.string}
)
async def acknowledge_critical_repository(hass, connection, msg):
"""Handle get media player cover command."""
repository = msg["repository"]
critical = await async_load_from_store(hass, "critical")
for repo in critical:
if repository == repo["repository"]:
repo["acknowledged"] = True
await async_save_to_store(hass, "critical", critical)
connection.send_message(websocket_api.result_message(msg["id"], critical))
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] | 2.244688 | 6,118 |
"""
Classes derived from Portfolio Optimisation module
"""
from mlfinlab.portfolio_optimization.cla import CLA
from mlfinlab.portfolio_optimization.hrp import HierarchicalRiskParity
from mlfinlab.portfolio_optimization.mean_variance import MeanVarianceOptimisation
from mlfinlab.portfolio_optimization.hcaa import HierarchicalClusteringAssetAllocation
from mlfinlab.portfolio_optimization.risk_metrics import RiskMetrics
from mlfinlab.portfolio_optimization.returns_estimators import ReturnsEstimation
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] | 3.375839 | 149 |
import re
import logging
import pandas as pd
import numpy as np
from .merging_functions import merge_with_msmsscanstxt, merge_with_msmstxt, merge_with_summarytxt
logger = logging.getLogger(__name__)
PHOSPHO_REGEX = re.compile(r'([STY])\(Phospho \(STY\)\)')
def transfer(summary_df, mask=False):
"""
Main function for transfers by clustering. Transfers identifications for merged dataframe and adds a column for
identification type. Transferred columns are Sequence, Modified sequence, Proteins, Gene names, Protein Names,
Charge, m/z, and Mass.
:param summary_df: Summary dataframe, merged from cleaned msms.txt and MaRaCluster clusters.tsv file
:param mask: if false, uses "identification" column from summary frame. If set to a value, transfer uses the
'identification_{mask}' column, needed for the masking analysis
:return: DataFrame with transferred identifications resembling MaxQuant msmsScans.txt
"""
if mask:
identification_column = f'identification_{mask}'
else:
identification_column = 'identification'
identified_scans = summary_df['Modified sequence'].notna()
agg_funcs = {'Sequence': get_unique_else_nan,
'Modified sequence': get_consensus_modified_sequence,
'Modifications': get_unique_else_nan,
'Proteins': get_unique_else_nan,
'Gene Names': get_unique_else_nan,
'Protein Names': get_unique_else_nan,
'Charge': get_unique_else_nan,
'm/z': 'mean',
'Mass': 'mean',
'Missed cleavages': get_unique_else_nan,
'Length': get_unique_else_nan,
'Reverse': get_unique_else_nan}
cluster_info_df = summary_df[identified_scans].groupby('clusterID', as_index=False).agg(agg_funcs)
# Mark all clusters with a unique identification as transferred ('t').
# Identifications by MQ will overwrite this column as direct identification ('d') a few lines below.
cluster_info_df[identification_column] = np.where(cluster_info_df['Modified sequence'].notna(), 't', None)
replacement_dict = {k: "d_" + k for k in agg_funcs.keys()}
cluster_info_df.rename(columns=replacement_dict, inplace=True)
summary_df = pd.merge(left=summary_df, right=cluster_info_df, on=['clusterID'], how='left')
summary_df.loc[identified_scans, identification_column] = 'd'
summary_df.loc[
summary_df[identification_column] == 't', replacement_dict.keys()] = summary_df.loc[
summary_df[identification_column] == 't', replacement_dict.values()].to_numpy()
summary_df.drop(columns=replacement_dict.values(), inplace=True)
return summary_df
def get_unique_else_nan(input_list):
"""
Returns nan if no unique sequence is found in inputlist, or the sequence if its unique
:param input_list: List of peptide sequences
:return: Returns peptide sequence if it is the only one in the input list, otherwise returns np.NaN
"""
x = remove_nan_values(set(input_list))
if len(x) == 1:
return x[0]
else:
return np.nan
def remove_nan_values(input_list):
"""
Eliminates NaNs from sets or lists, returns list of all other values
:param input_list: List (or set) of elements
:return: List with removed np.NaN values
"""
return [v for v in input_list if pd.notnull(v)]
def flag_ambiguous_clusters(sumdf, rawseq='Sequence', modseq='Modified sequence'):
"""
adds MIC flags to all scans in MICs
:param sumdf: Summary dataframe, merged from cleaned msms.txt and MaRaCluster clusters.tsv file
:param rawseq: Column name of the raw sequence column
:param modseq: Column name of the modified sequence column
:return:
"""
# Store number of unique modified sequences per cluster in group_dataframe
group_dataframe = sumdf.groupby('clusterID')[modseq].nunique(dropna=True).reset_index(name='seqcount')
# Filter for clusters with more than one modified sequence and flag with column mod_ambiguous
group_dataframe = group_dataframe[group_dataframe['seqcount'] >= 2]
group_dataframe.drop(columns=['seqcount'], axis=1, inplace=True)
group_dataframe['mod_ambiguous'] = 1
# merge with initial frame
merged_dataframe = pd.merge(left=sumdf, right=group_dataframe, on='clusterID', how='left')
# Store number of unique raw sequences per cluster in group_dataframe
group_dataframe = sumdf.groupby('clusterID')[rawseq].nunique(dropna=True).reset_index(name='seqcount')
# Filter for clusters with more than one raw sequence and flag with column raw_ambiguous
group_dataframe = group_dataframe[group_dataframe['seqcount'] >= 2]
group_dataframe.drop(columns=['seqcount'], axis=1, inplace=True)
group_dataframe['raw_ambiguous'] = 1
# merge with initial frame
merged_dataframe = pd.merge(left=merged_dataframe, right=group_dataframe, on='clusterID', how='left')
# remove mod_ambiguous flags if cluster is raw_ambiguous
merged_dataframe.loc[merged_dataframe['raw_ambiguous'] == 1, 'mod_ambiguous'] = np.nan
return merged_dataframe
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] | 2.738624 | 1,890 |
from typing import Callable, Optional
import numpy as np
from ._typing import NDArray
def phi1D(
x: NDArray,
B: NDArray,
fun: Callable[[NDArray], NDArray],
) -> NDArray:
"""
The function phi1D is called by phi2D.
It aims at applying a function ``fun`` after multiplying each row
of B by x.
Parameters
----------
x : NDArray of shape (n, )
1D vector.
B : NDArray of shape (k, n)
2D vector whose number of columns is the number of rows of x.
fun : function
Vectorized function applying to NDArray.
Returns
-------
NDArray
The function fun is applied to the product of ``x`` and ``B``.
Typically, ``fun`` is a numpy function, ignoring nan,
with argument ``axis=1``.
Examples
--------
>>> import numpy as np
>>> x = np.array([1, 2, 3, 4, 5])
>>> B = np.array([[1, 1, 1, np.nan, np.nan],
... [np.nan, np.nan, 1, 1, 1]])
>>> fun = lambda x: np.nanmean(x, axis=1)
>>> res = phi1D(x, B, fun)
>>> print(res)
[2. 4.]
"""
return fun(x * B)
def phi2D(
A: NDArray,
B: NDArray,
fun: Callable[[NDArray], NDArray],
) -> NDArray:
"""
The function phi2D is a loop applying phi1D on each row of A.
Parameters
----------
A : NDArray of shape (n_rowsA, n_columns)
B : NDArray of shape (n_rowsB, n_columns)
A and B must have the same number of columns.
fun : function
Vectorized function applying to NDArray, and that should ignore nan.
Returns
-------
NDArray of shape (n_rowsA, n_rowsB)
Applies phi1D(x, B, fun) to each row x of A.
Examples
--------
>>> import numpy as np
>>> A = np.array([[1, 2, 3, 4, 5],[6, 7, 8, 9, 10],[11, 12, 13, 14, 15]])
>>> B = np.array([[1, 1, 1, np.nan, np.nan],
... [np.nan, np.nan, 1, 1, 1]])
>>> fun = lambda x: np.nanmean(x, axis=1)
>>> res = phi2D(A, B, fun)
>>> print(res.ravel())
[ 2. 4. 7. 9. 12. 14.]
"""
return np.apply_along_axis(phi1D, axis=1, arr=A, B=B, fun=fun)
def aggregate_all(agg_function: Optional[str], X: NDArray) -> NDArray:
"""
Applies np.nanmean(, axis=1) or np.nanmedian(, axis=1) according
to the string ``agg_function``.
Parameters
-----------
X : NDArray of shape (n, p)
Array of floats and nans
Returns
--------
NDArray of shape (n, 1):
Array of the means or medians of each row of X
Raises
------
ValueError
If agg_function is ``None``
Examples
--------
>>> import numpy as np
>>> from mapie.aggregation_functions import aggregate_all
>>> agg_function = "mean"
>>> aggregate_all(agg_function,
... np.array([list(range(30)),
... list(range(30))]))
array([14.5, 14.5])
"""
if agg_function == "median":
return np.nanmedian(X, axis=1)
elif agg_function == "mean":
return np.nanmean(X, axis=1)
raise ValueError("Aggregation function called but not defined.")
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11052,
12331,
7203,
46384,
43068,
2163,
1444,
475,
407,
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19570,
198
] | 2.317496 | 1,326 |
from lambdas.nop import _nop, _cma_output
| [
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] | 2.315789 | 19 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Generated from FHIR 4.0.1-9346c8cc45 (http://hl7.org/fhir/StructureDefinition/ContactDetail) on 2020-02-03.
# 2020, SMART Health IT.
import sys
from dataclasses import dataclass, field
from typing import ClassVar, Optional, List
from .contactpoint import ContactPoint
from .element import Element
@dataclass
class ContactDetail(Element):
""" Contact information.
Specifies contact information for a person or organization.
"""
resource_type: ClassVar[str] = "ContactDetail"
name: Optional[str] = None
telecom: Optional[List[ContactPoint]] = None | [
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60,
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198,
220,
220,
220,
30452,
25,
32233,
58,
8053,
58,
17829,
12727,
11907,
796,
6045
] | 3.009615 | 208 |
from behavioral.interpreter.logic import Lexer, Parser
| [
6738,
17211,
13,
3849,
3866,
353,
13,
6404,
291,
1330,
17210,
263,
11,
23042,
263,
628
] | 3.5 | 16 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from .core import TensorRandomOperandMixin, TensorSimpleRandomData
def random_sample(random_state, size=None, chunk_size=None, gpu=None, dtype=None):
"""
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample :math:`Unif[a, b), b > a` multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
Parameters
----------
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
dtype : data-type, optional
Data-type of the returned tensor.
Returns
-------
out : float or Tensor of floats
Array of random floats of shape `size` (unless ``size=None``, in which
case a single float is returned).
Examples
--------
>>> import mars.tensor as mt
>>> mt.random.random_sample().execute()
0.47108547995356098
>>> type(mt.random.random_sample().execute())
<type 'float'>
>>> mt.random.random_sample((5,)).execute()
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])
Three-by-two array of random numbers from [-5, 0):
>>> (5 * mt.random.random_sample((3, 2)) - 5).execute()
array([[-3.99149989, -0.52338984],
[-2.99091858, -0.79479508],
[-1.23204345, -1.75224494]])
"""
if dtype is None:
dtype = np.dtype('f8')
size = random_state._handle_size(size)
op = TensorRandomSample(state=random_state.to_numpy(), size=size, gpu=gpu, dtype=dtype)
return op(chunk_size=chunk_size)
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7,
354,
2954,
62,
7857,
28,
354,
2954,
62,
7857,
8,
198
] | 2.718974 | 975 |
nome_vendendor = input()
sal_fixo = float(input())
vendas_total = float(input())
salario(nome_vendendor, sal_fixo, vendas_total)
| [
198,
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8,
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] | 2.444444 | 54 |
'''
'''
from ProjectFactory import ProjectFactory
import wexpect
wexpect.TIMEOUT
# Start cmd as child process
child = wexpect.spawn('cmd.exe')
project_factory = ProjectFactory( child )
project_factory.build_project()
| [
7061,
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] | 3.253731 | 67 |
# -*- coding: utf-8 -*-
# Автор: Гусев Илья
# Описание: Тесты считывателя разметок.
import unittest
from rupo.files.reader import Reader, FileType
from rupo.stress.predictor import CombinedStressPredictor
from rupo.main.markup import Markup, Line, Word
from rupo.settings import MARKUP_XML_EXAMPLE, TEXT_XML_EXAMPLE, MARKUP_JSON_EXAMPLE
| [
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16437,
11,
39641,
8577,
62,
40386,
62,
6369,
2390,
16437,
628
] | 2.02381 | 168 |
from tesserwrap import Tesseract
from PIL import Image
import re
| [
6738,
256,
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1330,
39412,
263,
529,
198,
6738,
350,
4146,
1330,
7412,
198,
11748,
302,
198
] | 3.421053 | 19 |
import numpy as np
import struct
import math
# split something into chunks
| [
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"""
Utilities related to project directory.
"""
import os
import subprocess
def git_restore(project_dir):
"""
This function executes "git restore ." on the given project directory.
:param project_dir: A string and Absolute path of the project's directory.
:return: None
"""
assert os.path.isdir(project_dir)
process = subprocess.Popen(["git", "restore", "."], cwd=project_dir)
process.wait()
def create_understand_database(project_dir, und_path='/home/ali/scitools/bin/linux64/'):
"""
This function creates understand database for the given project directory.
:param und_path: The path of und binary file for executing understand command-line
:param project_dir: The absolute path of project's directory.
:return: String path of created database.
"""
assert os.path.isdir(project_dir)
assert os.path.isdir(und_path)
db_name = os.path.basename(os.path.normpath(project_dir)) + ".udb"
db_path = os.path.join(project_dir, db_name)
assert os.path.exists(db_path) is False
# An example of command-line is:
# und create -languages c++ add @myFiles.txt analyze -all myDb.udb
process = subprocess.Popen(
['und', 'create', '-languages', 'Java', 'add', project_dir, 'analyze', '-all', db_path],
cwd=und_path
)
process.wait()
return db_path
def update_understand_database(udb_path, project_dir=None, und_path='/home/ali/scitools/bin/linux64/'):
"""
This function updates database due to file changes.
:param project_dir: If understand database file is not in project directory you can specify the project directory.
:param und_path: The path of und binary file for executing understand command-line
:param udb_path: The absolute path of understand database.
:return: None
"""
assert os.path.isfile(udb_path)
assert os.path.isdir(und_path)
if project_dir is None:
project_dir = os.path.dirname(os.path.normpath(udb_path))
process = subprocess.Popen(
['und', 'add', project_dir, 'analyze', '-all', udb_path],
cwd=und_path
)
process.wait()
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] | 2.775457 | 766 |
__pysd_version__ = "1.5.0"
_namespace = {}
_dependencies = {}
__data = {'scope': None, 'time': lambda: 0}
_control_vars = {
"initial_time": lambda: 0,
"final_time": lambda: 20,
"time_step": lambda: 1,
"saveper": lambda: time_step()
}
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] | 2.27193 | 114 |
"""
This file contains global options for rioxarray
Credits:
This file was adopted from: https://github.com/pydata/xarray # noqa
Source file: https://github.com/pydata/xarray/blob/2ab0666c1fcc493b1e0ebc7db14500c427f8804e/xarray/core/options.py # noqa
"""
from typing import Any
EXPORT_GRID_MAPPING = "export_grid_mapping"
OPTIONS = {
EXPORT_GRID_MAPPING: True,
}
OPTION_NAMES = set(OPTIONS)
VALIDATORS = {
EXPORT_GRID_MAPPING: lambda choice: isinstance(choice, bool),
}
def get_option(key: str) -> Any:
"""
Get the global rioxarray option.
Parameters
----------
key: str
The name of the option.
Returns
-------
Any: the value of the option.
"""
return OPTIONS[key]
class set_options: # pylint: disable=invalid-name
"""
Set the global rioxarray option.
Parameters
----------
export_grid_mapping: bool, optional
If True, this option will export the full Climate and Forecasts (CF)
grid mapping attributes for the CRS. This is useful if you are exporting
your file to netCDF using :meth:`xarray.Dataset.to_netcdf()`. When disabled,
only the ``crs_wkt`` and ``spatial_ref`` attributes will be written and the
program will be faster due to not needing to use
:meth:`pyproj.CRS.to_cf() <pyproj.crs.CRS.to_cf>`. Default is True.
Usage as a context manager::
with rioxarray.set_options(export_grid_mapping=False):
rds = rioxarray.open_rasterio(...)
Usage for global settings::
rioxarray.set_options(export_grid_mapping=False)
"""
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] | 2.529134 | 635 |
#!/usr/bin/env python
# coding: utf-8
import csv
import random
from pathlib import Path
Path("data").mkdir(parents=True, exist_ok=True)
def gen_sets(N_sets, N_elems, max_cost=10, sep=' ', filename='set_cover'):
"""Function that generates a file of integer tuples for using as input for
set cover problems.
For each tuple, the first element indicates the cost, which is in the
range [1, `max_cost`], and the rest of elements inficates the elements
of the set, which are between 1 and `N_elems`.
There is a total of `N_sets`+1 tuples, or rows written in the file,
where each row stands for a different set, and the first-one indicates
the problem conditions, namely, `N_sets`, `N_elems`.
The resulting file has the format:
N_sets N_elems
cost_1 e_11 e12 ...
cost_2 e_21 e22 ...
...
cost_n e_n1 en2 ...
Parameters
----------
N_sets : int
The number of sets/tuples.
N_elems : int
The problem restriction (e.g. the total number of elements to cover).
max_cost : int, optional
The maximum cost for each set. Default is 10.
"""
data = [[N_sets, N_elems]]
U = set(range(1, N_elems+1))
avail_elems = set()
while True:
for s in range(N_sets):
cost = random.randint(1, max_cost)
elems = gen_elems(N_elems)
avail_elems.update(elems)
elems.insert(0,cost)
data.append(elems)
# Assert all elements are contained in universe of sets
if U.issubset(avail_elems):
break
# Reset data
avail_elems.clear()
data = [[N_sets, N_elems]]
with open('data/'+filename+'_'+str(N_sets)+'.txt', 'w', newline='') as csv_file:
wr = csv.writer(csv_file, delimiter=sep)
wr.writerows(data)
if __name__ == "__main__":
gen_sets(200, 1000, max_cost=100)
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7,
2167,
11,
8576,
11,
3509,
62,
15805,
28,
3064,
8,
198
] | 2.343596 | 812 |
from datetime import datetime
import requests
def check_availability(url_str, datetime_fetched=None):
"""
Use the web api to see if a page is available.
https://archive.org/help/wayback_api.php
"""
wayback_url = "http://archive.org/wayback/available"
params = {
'url': url_str.split('?')[0],
}
if datetime_fetched is not None:
params['timestamp'] = datetime_fetched.strftime("%Y%m%d%H%M%S")
response = requests.get(wayback_url, params=params, timeout=30)
r_json = response.json()
# let's be nice and convert the returned timestamp to a datetime obj
# wayback timestamps are in the form YYYYMMDDhhmmss
if "archived_snapshots" in r_json and \
"closest" in r_json['archived_snapshots'] and \
"timestamp" in r_json['archived_snapshots']['closest']:
wb_timestamp = r_json['archived_snapshots']['closest']['timestamp']
r_json['archived_snapshots']['closest']['datetime'] = datetime.strptime(wb_timestamp, "%Y%m%d%H%M%S")
return r_json
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4,
50,
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628,
220,
220,
220,
1441,
374,
62,
17752,
198
] | 2.448357 | 426 |
# Copyright 2020 The MiNLP Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ahocorasick
from collections import Iterable
DEFAULT_INTERFERE_FACTOR = 2
| [
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1581,
796,
362,
628
] | 3.754098 | 183 |
"""
Given a non negative integer number num. For every numbers i in the range 0 ≤ i ≤ num calculate the number of 1's in their binary representation and return them as an array.
Example 1:
Input: 2
Output: [0,1,1]
Example 2:
Input: 5
Output: [0,1,1,2,1,2]
Follow up:
It is very easy to come up with a solution with run time O(n*sizeof(integer)). But can you do it in linear time O(n) /possibly in a single pass?
Space complexity should be O(n).
Can you do it like a boss? Do it without using any builtin function like __builtin_popcount in c++ or in any other language.
Solution:
x &= (x-1) bitwise AND 与，remove the least significant 1 of x
"""
# Time: O(n*sizeof(int))
# Space: O(n) | [
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] | 3.057522 | 226 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sqlite3
# Adapter und Konverter registrieren
sqlite3.register_adapter(Kreis, kreisadapter)
sqlite3.register_converter("KREIS", kreiskonverter)
# Hier wird eine Beispieldatenbank im Arbeitsspeicher mit
# einer einspaltigen Tabelle für Kreise definiert
connection = sqlite3.connect(":memory:",
detect_types=sqlite3.PARSE_DECLTYPES)
cursor = connection.cursor()
cursor.execute("CREATE TABLE kreis_tabelle(k KREIS)")
# Kreis in die Datenbank schreiben
kreis = Kreis(1, 2.5, 3)
cursor.execute("INSERT INTO kreis_tabelle VALUES (?)", (kreis,))
# Kreis wieder auslesen
cursor.execute("SELECT * FROM kreis_tabelle")
gelesener_kreis = cursor.fetchall()[0][0]
print(type(gelesener_kreis))
print(gelesener_kreis.Mx, gelesener_kreis.My, gelesener_kreis.R)
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] | 2.303621 | 359 |
import numpy as np
from scipy import interpolate, optimize
import torch
def interpolate_over_nans(input_mat, t=None):
""" Function to interpolate over NaN values along the first dimension of a matrix
Args:
input_mat: numpy array, [time, neurons]
t: optional time vector, only useful if input_mat is not sampled regularly in time
Returns: Interpolated input_mat, interpolated time
"""
# if t is not specified, assume it has been sampled at regular intervals
if t is None:
t = np.arange(input_mat.shape[0])
output_mat = np.zeros(input_mat.shape)
# calculate the average sample rate and uses this to create an interpolated t
sample_rate = 1 / np.mean(np.diff(t, axis=0))
t_interp = np.arange(input_mat.shape[0]) / sample_rate
# loop through each column of the data and interpolate them separately
for c in range(input_mat.shape[1]):
# check if all the data is nan and skip if it is
if np.all(np.isnan(input_mat[:, c])):
print('column ' + str(c) + ' is all NaN, skipping')
continue
# find the location of all nan values
no_nan_ind = ~np.isnan(input_mat[:, c])
# remove nans from t and the data
no_nan_t = t[no_nan_ind]
no_nan_data_mat = input_mat[no_nan_ind, c]
# interpolate values linearly
interp_obj = interpolate.interp1d(no_nan_t, no_nan_data_mat, kind='linear', fill_value='extrapolate')
output_mat[:, c] = interp_obj(t_interp)
return output_mat, t_interp
def photobleach_correction(time_by_neurons, t=None):
""" Function to fit an exponential with a shared tau to all the columns of time_by_neurons
This function fits the function A*exp(-t / tau) to the matrix time_by_neurons. Tau is a single time constant shared
between every column in time_by_neurons. A is an amplitude vector that is fit separately for each column. The
correction is time_by_neurons / exp(-t / tau), preserving the amplitude of the data.
Args:
time_by_neurons: numpy array [time, neurons]
t: optional, only important if time_by_neurons is not sampled evenly in time
Returns: time_by_neurons divided by the exponential
"""
if np.any(np.isnan(time_by_neurons)):
raise Exception('Photobleach correction cannot be performed with NaNs in data')
if t is None:
t = np.arange(time_by_neurons.shape[0])
device = 'cpu'
dtype = torch.float64
# convert inputs to tensors
t_torch = torch.tensor(t, dtype=dtype, device=device)
time_by_neurons_torch = torch.tensor(time_by_neurons, dtype=dtype, device=device)
tau_0 = t[-1, None]/2
a_0 = np.mean(time_by_neurons, axis=0)
p_0 = np.concatenate((tau_0, a_0), axis=0)
# optimization function with jacobian from pytorch
p_hat = optimize.minimize(loss_fn_np, p_0,
jac=loss_fn_jacobian_np,
method='BFGS')
time_by_neurons_corrected = time_by_neurons_torch / torch.exp(-t_torch[:, None] / p_hat.x[0])
return time_by_neurons_corrected.numpy()
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] | 2.472684 | 1,263 |
# Copyright 2019 HTCondor Team, Computer Sciences Department,
# University of Wisconsin-Madison, WI.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Callable, List, Iterator
import logging
import abc
import time
from pathlib import Path
import collections
import weakref
import htcondor
import classad
from . import jobs, exceptions, utils
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class Handle(abc.ABC):
"""
A connection to a set of jobs defined by a constraint.
The handle can be used to query, act on, or edit those jobs.
"""
@property
@property
def query(self, projection=None, options=htcondor.QueryOpts.Default, limit=-1):
"""
Query against this set of jobs.
Parameters
----------
projection
The :class:`classad.ClassAd` attributes to retrieve, as a list of case-insensitive strings.
If ``None`` (the default), all attributes will be returned.
options
limit
The total number of matches to return from the query.
If ``None`` (the default), return all matches.
Returns
-------
ads : Iterator[:class:`classad.ClassAd`]
An iterator over the :class:`classad.ClassAd` that match the constraint.
"""
return self.condor.query(
self.constraint_string, projection=projection, opts=options, limit=limit
)
def remove(self):
"""
Remove jobs from the queue.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Remove)
def hold(self):
"""
Hold jobs.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Hold)
def release(self):
"""
Release held jobs.
They will return to the queue in the idle state.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Release)
def pause(self):
"""
Pause jobs.
Jobs will stop running, but will hold on to their claimed resources.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Suspend)
def resume(self):
"""
Resume (un-pause) jobs.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Continue)
def vacate(self):
"""
Vacate running jobs.
This will force them off of their current execute resource, causing them to become idle again.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the action.
"""
return self._act(htcondor.JobAction.Vacate)
def edit(self, attr, value):
"""
Edit attributes of jobs.
.. warning::
Many attribute edits will not affect jobs that have already matched.
For example, changing ``RequestMemory`` will not affect the memory allocation
of a job that is already executing.
In that case, you would need to vacate (or release the job if it was held)
before the edit had the desired effect.
Parameters
----------
attr
The attribute to edit. Case-insensitive.
value
The new value for the attribute.
Returns
-------
ad : :class:`classad.ClassAd`
An ad describing the results of the edit.
"""
return self.condor.edit(self.constraint_string, attr, str(value))
class ConstraintHandle(Handle):
"""
A connection to a set of jobs defined by an :attr:`ConstraintHandle.constraint`.
The handle can be used to query, act on, or edit those jobs.
"""
@property
def constraint(self) -> classad.ExprTree:
"""
The constraint that defines this :class:`ConstraintHandle`,
as an :class:`classad.ExprTree`.
"""
return self._constraint
@property
def constraint_string(self) -> str:
"""
The constraint that defines this :class:`ConstraintHandle`,
as a string.
"""
return str(self.constraint)
class ClusterHandle(ConstraintHandle):
"""
A subclass of :class:`ConstraintHandle` that targets a single cluster of jobs,
as produced by :func:`Condor.submit`.
Because this handle targets a single cluster of jobs, it has superpowers.
If the cluster has an event log
(``log = <path>`` in the submit description,
see the `docs`_),
this handle's ``state`` attribute will be a :class:`ClusterState` that provides
information about the current state of the jobs in the cluster.
.. warning ::
You shouldn't have to construct a :class:`ClusterHandle` yourself.
Instead, use the ones returned by :func:`Condor.submit`.
.. _docs: https://htcondor.readthedocs.io/en/latest/man-pages/condor_submit.html
"""
@property
def clusterid(self):
"""The cluster's cluster ID."""
return self._clusterid
@property
def clusterad(self):
"""The cluster's cluster ad."""
return self._clusterad
@property
def first_proc(self):
"""The process ID of the first job in the cluster."""
return self._first_proc
@property
def num_procs(self):
"""The number of jobs in the cluster."""
return self._num_procs
@property
def job_ids(self) -> List[jobs.JobID]:
"""Return the list of :class:`JobID` in this :class:`ClusterHandle`."""
return [jobs.JobID(self.clusterid, proc) for proc in range(len(self))]
@property
def state(self):
"""A :class:`ClusterState` that provides information about job state for this cluster."""
if self._state is None:
self._state = ClusterState(self)
return self._state
@property
def event_log(self):
"""The :class:`EventLog` for this :class:`ClusterHandle`."""
if self._event_log is None:
self._event_log = EventLog(self)
return self._event_log
def wait(
self,
condition: Optional[Callable[["ClusterState"], bool]] = None,
fail_condition: Optional[Callable[["ClusterState"], bool]] = None,
timeout: int = 120,
verbose: bool = False,
) -> bool:
"""
Waits for the ``condition`` to become ``True``.
Parameters
----------
condition
The function to wait to become ``True``. It will be passed the
:class:`ClusterState` as its only argument.
Because of how Python calls unbound class methods, you may directly
pass :class:`ClusterState` methods as conditions
(e.g., ``handle.wait(condition = ClusterState.any_held)``).
The default condition is :meth:`ClusterState.all_complete`, which
means "wait until all the jobs in this cluster are completed".
fail_condition
If this function becomes ``True``, ``wait`` will immediately
return ``False``. Use this to avoid waiting for a long time when
a test is failing.
timeout
After this amount of time, ``wait`` will return ``False`` and emit
a warning in the log.
verbose
If ``True``, the handle's state counts will be logged during the wait.
Returns
-------
success : bool
``True`` if the wait finished because the condition became ``True``;
``False`` otherwise.
"""
if condition is None:
condition = ClusterState.all_complete
if fail_condition is None:
fail_condition = lambda _: False
start_time = time.time()
num_events_read = self.state.read_events()
while True:
if verbose:
logger.debug("Handle {} state: {}".format(self, self.state.counts()))
if condition(self.state):
break
if fail_condition(self.state):
logger.warning(
"Wait for handle {} triggered its failure condition".format(self)
)
return False
if timeout is not None and time.time() > start_time + timeout:
logger.warning("Wait for handle {} timed out".format(self))
return False
# Sleep a second here if no job log events were waiting for us to prevent
# busy waiting. However, if we did see an event, try to read another
# event as often they come in bunches - and we want to consume them
# as rapidly as possible.
if num_events_read == 0:
time.sleep(1)
num_events_read = self.state.read_events()
logger.debug("Wait for handle {} finished successfully".format(self))
return True
class _MockSubmitResult:
"""
This class is used purely to transform unpacked submit results back into
"submit results" to accommodate the :class:`ClusterHandle` constructor.
**Should not be used in user code.**
"""
JOB_EVENT_STATUS_TRANSITIONS = {
htcondor.JobEventType.SUBMIT: jobs.JobStatus.IDLE,
htcondor.JobEventType.JOB_EVICTED: jobs.JobStatus.IDLE,
htcondor.JobEventType.JOB_UNSUSPENDED: jobs.JobStatus.IDLE,
htcondor.JobEventType.JOB_RELEASED: jobs.JobStatus.IDLE,
htcondor.JobEventType.SHADOW_EXCEPTION: jobs.JobStatus.IDLE,
htcondor.JobEventType.JOB_RECONNECT_FAILED: jobs.JobStatus.IDLE,
htcondor.JobEventType.JOB_TERMINATED: jobs.JobStatus.COMPLETED,
htcondor.JobEventType.EXECUTE: jobs.JobStatus.RUNNING,
htcondor.JobEventType.JOB_HELD: jobs.JobStatus.HELD,
htcondor.JobEventType.JOB_SUSPENDED: jobs.JobStatus.SUSPENDED,
htcondor.JobEventType.JOB_ABORTED: jobs.JobStatus.REMOVED,
}
NO_EVENT_LOG = object()
class ClusterState:
"""
A class that manages the state of the cluster tracked by a :class:`ClusterHandle`.
It reads from the cluster's event log internally and provides a variety of views
of the individual job states.
.. warning::
:class:`ClusterState` objects should not be instantiated manually.
:class:`ClusterHandle` will create them automatically when needed.
"""
def counts(self):
"""
Return the number of jobs in each :class:`JobStatus`, as a :class:`collections.Counter`.
"""
return self._counts.copy()
@property
def all_complete(self) -> bool:
"""
Return ``True`` if **all** of the jobs in the cluster are complete.
Note that this definition does include jobs that have left the queue,
not just ones that are in the "Completed" state in the queue.
"""
return self.all_status(jobs.JobStatus.COMPLETED)
def any_complete(self) -> bool:
"""
Return ``True`` if **any** of the jobs in the cluster are complete.
Note that this definition does include jobs that have left the queue,
not just ones that are in the "Completed" state in the queue.
"""
return self.any_status(jobs.JobStatus.COMPLETED)
def any_idle(self) -> bool:
"""Return ``True`` if **any** of the jobs in the cluster are idle."""
return self.any_status(jobs.JobStatus.IDLE)
def none_idle(self) -> bool:
"""Return ``True`` if **none** of the jobs in the cluster are idle."""
return self.none_status(jobs.JobStatus.IDLE)
@staticmethod
def running_exactly(count) -> bool:
"""Returns ``True`` if **count** of the jobs in the cluster are running."""
return lambda self: self.status_exactly(count, jobs.JobStatus.RUNNING)
def any_running(self) -> bool:
"""Return ``True`` if **any** of the jobs in the cluster are running."""
return self.any_status(jobs.JobStatus.RUNNING)
def all_running(self) -> bool:
"""Return ``True`` if **all** of the jobs in the cluster are running."""
return self.all_status(jobs.JobStatus.RUNNING)
def all_held(self) -> bool:
"""Return ``True`` if **all** of the jobs in the cluster are held."""
return self.all_status(jobs.JobStatus.HELD)
def any_held(self) -> bool:
"""Return ``True`` if **any** of the jobs in the cluster are held."""
return self.any_status(jobs.JobStatus.HELD)
def none_held(self) -> bool:
"""Return ``True`` if **none** of the jobs in the cluster are held."""
return self.none_status(jobs.JobStatus.HELD)
def all_terminal(self) -> bool:
"""Return ``True`` if **all** of the jobs in the cluster are completed, held, or removed."""
return self.all_status(
jobs.JobStatus.COMPLETED, jobs.JobStatus.HELD, jobs.JobStatus.REMOVED
)
def any_terminal(self) -> bool:
"""Return ``True`` if **any** of the jobs in the cluster are completed, held, or removed."""
return self.any_status(
jobs.JobStatus.COMPLETED, jobs.JobStatus.HELD, jobs.JobStatus.REMOVED
)
def status_exactly(self, count, *statuses: jobs.JobStatus) -> bool:
"""
Return ``True`` if **exactly** ``count`` of the jobs in the cluster are
in one of the ``statuses``. Prefer one of the explicitly-named helper
methods when possible, and don't be afraid to make a new helper method!
"""
return self.count_status(*statuses) == count
def all_status(self, *statuses: jobs.JobStatus) -> bool:
"""
Return ``True`` if **all** of the jobs in the cluster are in one of the ``statuses``.
Prefer one of the explicitly-named helper methods when possible,
and don't be afraid to make a new helper method!
"""
return self.count_status(*statuses) == len(self)
def any_status(self, *statuses: jobs.JobStatus) -> bool:
"""
Return ``True`` if **any** of the jobs in the cluster are in one of the ``statuses``.
Prefer one of the explicitly-named helper methods when possible,
and don't be afraid to make a new helper method!
"""
return self.count_status(*statuses) > 0
def none_status(self, *statuses: jobs.JobStatus) -> bool:
"""
Return ``True`` if **none** of the jobs in the cluster are in one of the ``statuses``.
Prefer one of the explicitly-named helper methods when possible,
and don't be afraid to make a new helper method!
"""
return self.count_status(*statuses) == 0
def count_status(self, *statuses: jobs.JobStatus) -> int:
"""Return the total number of jobs in the cluster in any of the given statuses."""
counts = self.counts()
return sum(counts[status] for status in statuses)
class EventLog:
"""
This class represents the job event log for a :class:`ClusterHandle`.
.. warning ::
You shouldn't have to construct this yourself.
Instead, use :attr:`ClusterHandle.event_log`.
"""
def read_events(self) -> Iterator[htcondor.JobEvent]:
"""Yield all un-read events in the event log."""
if self._event_reader is None:
self._event_reader = htcondor.JobEventLog(
self._event_log_path.as_posix()
).events(0)
for event in self._event_reader:
if event.cluster != self._clusterid:
continue
self.events.append(event)
yield event
def filter(
self, condition: Callable[[htcondor.JobEvent], bool]
) -> List[htcondor.JobEvent]:
"""
Return a list containing the job events that the condition is ``True`` for.
"""
return [e for e in self.events if condition(e)]
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] | 2.519562 | 6,671 |
__author__ = 'mogui <[email protected]>'
#
# Driver Constants
#
NAME = "OrientDB Python binary client (pyorient)"
VERSION = "1.5.6"
SUPPORTED_PROTOCOL = 36
#
# Binary Types
#
# Types Constants
BOOLEAN = 1 # Single byte: 1 = true, 0 = false
BYTE = 2
SHORT = 3
INT = 4
LONG = 5
BYTES = 6 # Used for binary data.
STRING = 7
RECORD = 8
STRINGS = 9
CHAR = 10
LINK = 11
# Field types, needed for decoding
# we have the type definition and the number of first bytes to read
FIELD_BOOLEAN = {"type": BOOLEAN, "bytes": 1, "struct": None}
FIELD_BYTE = {"type": BYTE, "bytes": 1, "struct": None}
FIELD_CHAR = {"type": CHAR, "bytes": 1, "struct": None}
FIELD_SHORT = {"type": SHORT, "bytes": 2, "struct": None}
FIELD_INT = {"type": INT, "bytes": 4, "struct": None}
FIELD_LONG = {"type": LONG, "bytes": 8, "struct": None}
FIELD_BYTES = {"type": BYTES, "bytes": 4, "struct": None}
FIELD_STRING = {"type": STRING, "bytes": 4, "struct": None}
FIELD_STRINGS = {"type": STRINGS, "bytes": 4, "struct": None}
FIELD_RECORD = {"type": RECORD, "bytes": None, "struct": [
FIELD_CHAR, # record_type
FIELD_SHORT, # record_clusterID
FIELD_LONG, # record_position
FIELD_INT, # record_version
FIELD_BYTES # record_content
]}
FIELD_TYPE_LINK = {"type": LINK, "bytes": None, "struct": [
FIELD_SHORT, # record_clusterID
FIELD_LONG, # record_position
]}
SHUTDOWN = "ShutdownMessage"
CONNECT = "ConnectMessage"
DB_OPEN = "DbOpenMessage"
DB_CREATE = "DbCreateMessage"
DB_CLOSE = "DbCloseMessage"
DB_EXIST = "DbExistsMessage"
DB_DROP = "DbDropMessage"
DB_SIZE = "DbSizeMessage"
DB_COUNT_RECORDS = "DbCountRecordsMessage"
DATA_CLUSTER_ADD = "DataClusterAddMessage"
DATA_CLUSTER_DROP = "DataClusterDropMessage"
DATA_CLUSTER_COUNT = "DataClusterCountMessage"
DATA_CLUSTER_DATA_RANGE = "DataClusterDataRangeMessage"
RECORD_LOAD = "RecordLoadMessage"
RECORD_CREATE = "RecordCreateMessage"
RECORD_UPDATE = "RecordUpdateMessage"
RECORD_DELETE = "RecordDeleteMessage"
COMMAND = "CommandMessage"
DB_RELOAD = "DbReloadMessage"
TX_COMMIT = "TxCommitMessage"
#
# Orient Operations
#
SHUTDOWN_OP = chr(1)
CONNECT_OP = chr(2)
DB_OPEN_OP = chr(3)
DB_CREATE_OP = chr(4)
DB_CLOSE_OP = chr(5)
DB_EXIST_OP = chr(6)
DB_DROP_OP = chr(7)
DB_SIZE_OP = chr(8)
DB_COUNT_RECORDS_OP = chr(9)
DATA_CLUSTER_ADD_OP = chr(10)
DATA_CLUSTER_DROP_OP = chr(11)
DATA_CLUSTER_COUNT_OP = chr(12)
DATA_CLUSTER_DATA_RANGE_OP = chr(13)
RECORD_LOAD_OP = chr(30)
RECORD_CREATE_OP = chr(31)
RECORD_UPDATE_OP = chr(32)
RECORD_DELETE_OP = chr(33)
COMMAND_OP = chr(41)
TX_COMMIT_OP = chr(60)
DB_RELOAD_OP = chr(73)
DB_LIST_OP = chr(74)
#
# Orient Primitives
#
#: Document type
DB_TYPE_DOCUMENT = 'document'
#: Graph type
DB_TYPE_GRAPH = 'graph'
DB_TYPES = (
DB_TYPE_DOCUMENT,
DB_TYPE_GRAPH,
)
# LOCAL deprecated from version 1.5 and removed in protocol 24
STORAGE_TYPE_LOCAL = 'local'
STORAGE_TYPE_PLOCAL = 'plocal'
STORAGE_TYPE_MEMORY = 'memory'
STORAGE_TYPES = (
STORAGE_TYPE_LOCAL,
STORAGE_TYPE_PLOCAL,
STORAGE_TYPE_MEMORY,
)
QUERY_SYNC = "com.orientechnologies.orient.core.sql.query.OSQLSynchQuery"
QUERY_ASYNC = "com.orientechnologies.orient.core.sql.query.OSQLAsynchQuery"
QUERY_CMD = "com.orientechnologies.orient.core.sql.OCommandSQL"
QUERY_GREMLIN = "com.orientechnologies.orient.graph.gremlin.OCommandGremlin"
QUERY_SCRIPT = "com.orientechnologies.orient.core.command.script.OCommandScript"
QUERY_TYPES = (
QUERY_SYNC,
QUERY_ASYNC,
QUERY_CMD,
QUERY_GREMLIN,
QUERY_SCRIPT,
)
RECORD_TYPE_BYTES = 'b'
RECORD_TYPE_DOCUMENT = 'd'
RECORD_TYPE_FLAT = 'f'
RECORD_TYPES = (
RECORD_TYPE_BYTES,
RECORD_TYPE_DOCUMENT,
RECORD_TYPE_FLAT,
)
CLUSTER_TYPE_PHYSICAL = 'PHYSICAL'
CLUSTER_TYPE_MEMORY = 'MEMORY'
CLUSTER_TYPES = (
CLUSTER_TYPE_PHYSICAL,
CLUSTER_TYPE_MEMORY
)
# OTHER CONFIGURATIONS
SOCK_CONN_TIMEOUT = 30
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] | 2.322249 | 1,654 |
import tornado.ioloop
import tornado.web
import modules.Index
application = tornado.web.Application([
(r"/", modules.Index.Index),
(r"/hotdogs", modules.Index.Index),
])
if __name__ == "__main__":
application.listen(8888)
tornado.ioloop.IOLoop.instance().start() | [
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] | 2.782178 | 101 |
import pickle
from pathlib import Path
from collections import Counter
import pandas as pd
from .properties import DecayMode, calc_max_rel
class Injection:
"""Injection information and properties (for convenience and readability)"""
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] | 4.067797 | 59 |
import pandas as pd
import numpy as np
def select_restack(name, save=False):
'''
This functon retrieves the data sheets, then it reorients them such that the
features are the columns and the data frame is doube indexed by country_name
and year ('level_1')
Parameters
----------
name: string, name of csv in data_sheets to go find
save : bool, TRUE saves the intermediare step to disk
Returns
-------
df_reind : pandas dataframe, this is df after psudopivot
'''
path = '/home/jake/world-bank/data_sheets/{}.csv'.format(name)
df = pd.read_csv(path)
cols = ['Country Name', 'Indicator Name',
'1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',
'1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
'1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
'1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',
'2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
'2014', '2015', '2016']
#this line reorients the dataframe
df_reind = df[cols].set_index(['Country Name', 'Indicator Name']).stack().unstack(1)
# relitively arbitrary cutoff for how many values there must be in a column to keep it
minum=0.6*len(df_reind)
for c in df_reind.columns:
n=df_reind[c].count()
if n>minum:
print('{} has {} non null'.format(c,n))
else:
df_reind.drop(c, axis=1, inplace=True)
#option to save the constituant frames to disk
if save:
filename = '{}_restack.csv'.format(name)
df_reind.to_csv(filename,index=False)
df_reind.reset_index(inplace=True)
return df_reind
def joining(lst):
'''
calls previous function to create DataFrames for each name in the lst and
joins them on the year country name psudoindex and writes this to disk
Parameters
----------
lst: list of strings, names of csv in data_sheets to go find
Returns
-------
df : pandas dataframe, this is full joined DataFrame
'''
for i, name in enumerate(lst):
if i == 0:
df = select_restack(name)
else:
df_2 = select_restack(name)
duplicates = set(df_2.columns).intersection(df.columns)
duplicates.remove('level_1')
duplicates.remove('Country Name')
df_2.drop(duplicates,axis=1,inplace=True)
df = pd.merge(df,df_2, on=['Country Name', 'level_1'], how='inner')
filename = 'inner_restack.csv'
df.to_csv(filename,index=False)
return df
if __name__ == '__main__':
lst = ['HNP_StatsData','WDIData','EdStatsData']
# lst = ['HNP_StatsData','WDIData']
df = joining(lst)
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] | 2.362944 | 1,182 |
from collections import deque
n, Q = map(int, input().split())
graph = [[] for i in range(n)]
for i in range(n - 1):
a, b = map(int, input().split())
graph[a - 1].append(b - 1)
graph[b - 1].append(a - 1)
dist = [-1] * n
dist[0] = 0
q = deque()
q.append(0)
while q:
v = q.popleft()
for i in set(graph[v]):
if dist[i] != -1:
continue
dist[i] = dist[v] + 1
q.append(i)
for i in range(Q):
c, d = map(int, input().split())
c -= 1
d -= 1
if abs(dist[c] - dist[d]) % 2 == 1:
print("Road")
else:
print("Town")
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"""nornir_scrapli.functions.print_structured_result"""
import logging
import threading
from scrapli.response import Response
from nornir.core.task import AggregatedResult, MultiResult, Result
from nornir_scrapli.result import ScrapliResult
from rich.console import Console
from rich.table import Table
LOCK = threading.Lock()
def print_table_result(
result: ScrapliResult,
failed: bool = False,
severity_level: int = logging.INFO,
parser: str = "textfsm",
to_dict: bool = True,
fail_to_string: bool = False,
) -> None:
"""
Prints the :obj:`nornir.core.task.Result` from a previous task to screen
Arguments:
result: from a previous task
failed: if `True` assume the task failed
severity_level: Print only errors with this severity level or higher
parser: textfsm|genie -- parser to parse output with
to_dict: output structured data in dict form instead --
basically put k:v instead of just
lists of lists of values for textfsm output;
ignored if parser == "genie"
fail_to_string: fallback to printing unstructured output
or have tasks skipped (because
print_result won't print empty lists which scrapli
returns if parsing fails)
"""
updated_agg_result = AggregatedResult(result.name)
# breakpoint()
console = Console()
table = Table(
"Hostname",
"Version",
"Platform",
"Image ID",
"Image Type",
"Uptime",
"System Image",
"Compiled Date",
)
for hostname, multi_result in result.items():
updated_multi_result = MultiResult(result.name)
for individual_result in multi_result:
scrapli_responses = getattr(individual_result, "scrapli_response", None)
if isinstance(scrapli_responses, Response):
scrapli_responses = [scrapli_responses]
if not scrapli_responses:
updated_multi_result.append(individual_result)
continue
for scrapli_response in scrapli_responses:
parser_method = getattr(scrapli_response, f"{parser}_parse_output")
updated_result = Result(
host=individual_result.host,
changed=individual_result.changed,
diff=individual_result.diff,
exception=individual_result.exception,
failed=individual_result.failed,
name=individual_result.name,
severity_level=individual_result.severity_level,
stderr=individual_result.stderr,
stdout=individual_result.stdout,
)
if parser == "textfsm":
structured_result = parser_method(to_dict=to_dict)
else:
structured_result = parser_method()
if not structured_result and fail_to_string:
updated_result.result = scrapli_response.result
else:
updated_result.result = structured_result
updated_multi_result.append(updated_result)
try:
version = structured_result["version"]
table.add_row(
f'[green]{version["hostname"]}[/green]',
f'[blue]{version["version"]}[/blue]',
f'[magenta]{version["platform"]}[/magenta]',
f'[cyan]{version["image_id"]}[/cyan]',
f'[orange1]{version["image_type"]}[/orange1]',
f'[bright_green]{version["uptime"]}[/bright_green]',
f'[magenta]{version["system_image"]}[/magenta]',
f'[yellow]{version["compiled_date"]}[/yellow]',
)
except KeyError:
print("This command is not supported in Table format!")
if updated_multi_result:
updated_agg_result[hostname] = updated_multi_result # noqa
LOCK.acquire()
try:
console.print(table)
finally:
LOCK.release()
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] | 2.127114 | 1,951 |
# -*- coding: utf-8 -*-
import datetime
from django.utils import translation
from django.conf import settings
from cms.utils.compat.dj import MiddlewareMixin
| [
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from tamproxy import SyncedSketch, Timer
from tamproxy.devices import Motor
# Cycles a motor back and forth between -255 and 255 PWM every ~5 seconds
if __name__ == "__main__":
sketch = MotorWrite(1, -0.00001, 100)
sketch.run() | [
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# -*- coding: utf-8 -*-
"""
@Time : 2020/9/13
@Author : Shen Fang
"""
import os
import datetime
import os.path as osp
import numpy as np
import matplotlib.pyplot as plt
import argparse
if __name__ == '__main__':
# map_image = read_image("level_14.png")
# patch = map_image[900:2000, 900:]
# plt.imshow(patch)
# plt.show()
parser = argparse.ArgumentParser(description="Process data on each day")
parser.add_argument("year", type=str, default="2019")
parser.add_argument("month", type=str)
parser.add_argument("day", type=str)
args = parser.parse_args()
year = args.year
month = args.month
day = args.day
data_folder = "-".join([year, month, day])
result_file = "".join([year, month, day]) + ".npy"
obtain = ObtainInfo("image_coord.npy")
obtain.delete_image_file(data_folder)
obtain.rename_image_file(data_folder)
folder = os.listdir(data_folder)
if ".DS_Store" in folder:
folder.remove(".DS_Store")
num_files = len(folder)
if num_files == 288:
obtain.process_one_day(data_folder, result_file)
else:
print("[Warning]\nNum of Files is not correct: {:d}, left: {:d}".format(num_files, 288-num_files))
print("Lost Time:")
find_lost_time(folder, year, month, day)
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import json
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp2d, interp1d
from sklearn.preprocessing import scale
import pandas as pd
class Sample(object):
"""
Contains the data recorded from the sensors.
Provides methods to analyze, manage and persist Samples.
"""
def save_to_file(self, file_path):
"""
Save the sample to a file using the JSON format.
The absolute filename is specified by the "file_path" parameter.
"""
# Create a dictionary containing all the important data of the sample.
# NOTE: the numpy array must be converted to a list to serialize it using JSON.
output_data = {'gesture_id': self.gesture_id, 'data': self.data.tolist()}
# Save the sample to a file ( filename specified by the file_path param ).
with open(file_path, 'w') as output_file:
json.dump(output_data, output_file)
@staticmethod
def load_from_file(file_path):
"""
Return a Sample object by reading a sample file.
"""
# Open the file and read the content
with open(file_path) as input_file:
input_data = json.load(input_file)
# Create a Sample object with the read data
sample = Sample(data=input_data['data'], gesture_id=input_data['gesture_id'])
# Return the Sample object
return sample
def get_linearized(self, one_dimensional=False):
"""
Linearize the data, combining the axes data.
Useful to feed the data into a machine learning algorithm.
:param one_dimensional: if True, converts the 2-dim [[]] array to a 1-dim [] array
:return: the linearized array containing the sample data
"""
# Reshape the data
output = self.data.reshape(1, -1)
# If one_dimensional is True, makes the array truly one-dimensional
if one_dimensional:
# The array, after the reshape, is in this form [[1, 2, 3]]
# We take the first axis, so it becomes [1, 2, 3]
output = output[0]
return output
def scale_frames(self, n_frames=50):
"""
Scales the sample frames, interpolating the data.
:param n_frames: Final number of frames in the sample.
"""
# Correct the case with only one data frame
if self.data.shape[0] <= 1:
# Copy the only frame two times
# Basically, [[1, 2, 3]] becomes [[1, 2, 3], [1, 2, 3]]
self.data = sp.repeat(self.data, 2, axis=0)
# Check the number of axis
if self.data.shape[1] > 1: # More than 1 axis
# Get the Sample data axis dimensions
x_size = self.data.shape[0]
y_size = self.data.shape[1]
# Create the indexes in the axis
x = sp.arange(0, x_size)
y = sp.arange(0, y_size)
# Create a function that interpolates the data points
f = interp2d(y, x, self.data)
# Create a new index of the desired size ( n_frames ).
x_new = sp.linspace(0, x_size - 1, n_frames)
# Calculate the new interpolated data and change it.
self.data = f(y, x_new)
else: # The case with only one axis must be handled differently
# Reshape the data
reshaped = self.data.reshape(1, -1)[0]
# Get the Sample data axis dimensions
x_size = self.data.shape[0]
# Create the indexes in the axis
x = sp.arange(0, x_size)
# Create a function that interpolates the data points
f = interp1d(x, reshaped, kind="zero")
# Create a new index of the desired size ( n_frames ).
x_new = sp.linspace(0, x_size - 1, n_frames)
# Calculate the new interpolated data and reshape it.
self.data = f(x_new).reshape(-1, 1)
def framelen(self):
"""
:return: the number of frames of the sample
"""
return self.data.shape[0]
def subtract(self, amount=0):
"""
Subtract the amount from all the values
:param amount: the amount to subtract
"""
self.data = self.data - amount
def rolling_mean(self, window):
"""
Calculate the rolling mean for the sample data
:param window: rolling mean window
:return:
"""
self.data = pd.rolling_mean(self.data, window, min_periods=1)
def normalize_frames(self):
"""
Normalize each axis of the Sample data
"""
self.data = scale(self.data)
def abs(self):
"""
Make each axis of the sample data a positive number calculating the absolute value.
"""
# Calculate the absolute value of each axis
self.data = sp.absolute(self.data)
def trim(self, threshold=100):
"""
Trim the extremes of the sample data until they exceed the threshold.
Useful when using a stream and a bit of cleaning is needed.
"""
# Get the sample gradient
gradient = self.gradient()
# Calculate the average for each axis
average = sp.average(gradient, axis=1)
# Get the index of the first element grater than the threshold
initial = sp.argmax(average > threshold)
# Get a reversed view of the array
reverse = average[::-1]
# Get the index of the first element grater than the threshold, starting from the end
end = average.size - sp.argmax(reverse > threshold)
# Trim the data array by keeping only the sector between the two indexes
self.data = self.data[initial:end:]
def gradient(self):
"""
Return a numpy array containing the gradient of the sample data
"""
# Check the number of axis
if self.data.shape[1] > 1: # More than 1 axis
# Calculate the gradient and extract only the first element
return sp.gradient(self.data)[0]
else: # The case with only one axis must be handled differently
# Reshape the data
reshaped = self.data.reshape(1, -1)[0]
# Calculate the gradient and reshape the result
return sp.gradient(reshaped).reshape(-1, 1)
def fft(self, append=True):
"""
Calculates the FFT of the sample data and replace the original data with it.
"""
# Calculate the real FFT transform
fourier = np.fft.rfft(self.data, axis=0)
if fourier.shape[0] > 10:
# Delete the first term, it's usually too big and covers the other terms
fourier = fourier[10:]
# Calculate the absolute value ( complex number argument )
absolute = sp.absolute(fourier)
# If append=True, append the fourier transform to the data, if not replace the data
if append:
# Append the fft
self.data = np.append(self.data, absolute, axis=0)
else:
# Replace the data
self.data = absolute
def plot(self, block=True):
"""
Using matplotlib, open a dialog with the plotted Sample data.
:param block: if true, the plot will be displayed in a non-blocking way
"""
# Clear the plot
plt.clf()
# Add each axis to the plot
for axis in range(self.data.shape[1]):
plt.plot(self.data[:, axis], label="AXIS_{axis}".format(axis=axis))
# Add the axis labels
plt.xlabel('time', fontsize=18)
plt.ylabel('value', fontsize=16)
plt.legend(loc='best', frameon=False)
# Check if the plot display should be blocking
if block: # Blocking
plt.show()
else: # Non Blocking
# Draw the figure and pause to enable rendering
plt.draw()
plt.pause(.001)
# Show the plot
plt.show(block=False)
class CallbackManager(object):
"""
Receive a gesture_id and call the corresponding callback.
A callback can be associated to a gesture_id using the attach_callback method.
"""
def attach_callback(self, gesture_id, callback):
"""
Attach a callback to a gesture_id
"""
self.callbacks[gesture_id] = callback
def detach_callback(self, gesture_id):
"""
Detach the gesture from the CallbackManager
"""
self.callbacks.pop(gesture_id, None)
def notify_gesture(self, gesture_id):
"""
Notify a gesture to all the attached callbacks
"""
# If a callback is set, call it. If not, call the default callback
if gesture_id in self.callbacks:
# Call the attached callback
self.callbacks[gesture_id](gesture_id)
else:
# If not set, call the default callback
self.default_callback(gesture_id)
def receive_gesture(self, gesture_id):
"""
Called by a predictor when a new gesture is available
"""
# If verbose is set, print a notification when a gesture arrives
if self.verbose:
print("Received gesture: " + gesture_id)
# Notify the gesture to all the attached callbacks
self.notify_gesture(gesture_id)
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8,
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2116,
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1662,
1958,
62,
3495,
495,
7,
3495,
495,
62,
312,
8,
198
] | 2.380264 | 3,942 |
import configparser, time, os
| [
11748,
4566,
48610,
11,
640,
11,
28686,
628,
198
] | 3.555556 | 9 |
# to load signals receiver called in App.ready()
default_app_config = 'user.apps.UserConfig'
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2,
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] | 3.444444 | 27 |
from django.conf.urls import url, include
from .views import (
serv_wallet, accept_remote_registration, register_new_wallet_user, login,
update_settings
)
urlpatterns = [
url(r'registration/remote', accept_remote_registration),
url(r'registration', register_new_wallet_user, name='register_new_wallet_user'),
url(r'^login', login, name='login'),
url(r'^update_settings', update_settings, name='update_wallet_settings'),
url(r'^', serv_wallet, name="wallet"),
]
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61,
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1113,
62,
44623,
11,
1438,
2625,
44623,
12340,
198,
60,
198
] | 2.876471 | 170 |
my_list = [i**2 for i in range(1,11)]
my_file = open("output.txt", "r+")
# Add your code below!
for item in my_list:
my_file.write(str(item) + "\n")
my_file.close() | [
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] | 2.263158 | 76 |
#!/usr/bin/python
import sys
from workflow.workflow import Settings
from workflow import Workflow
from const import Const
LOG = None
if __name__=="__main__":
config = Config(' '.join(sys.argv[1:]))
config.execute()
#setter = ConfigSetter(' '.join(sys.argv[1:]))
#setter.execute()
| [
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] | 2.761468 | 109 |
from django.contrib import admin
# Register your models here.
from django.contrib import admin
from .models import Story_File_Status,Wonder_Archive_People,Wonder_Archive_GT,Chat_Log,Store_Card,Shopping_Cart,Shopping_Product,Daily_Story_File_Status,Basic_Setting,Current_Archive,Store_Card_Own
""" class Wonder_Archive_PeopleInline(admin.TabularInline):
model = Wonder_Archive_People
class Story_File_StatusAdmin(admin.ModelAdmin):
inlines = [
Wonder_Archive_PeopleInline,
] """
""" admin.site.register(Story_File_Status,Story_File_StatusAdmin) """
admin.site.register(Story_File_Status)
admin.site.register(Wonder_Archive_People)
admin.site.register(Wonder_Archive_GT)
admin.site.register(Chat_Log)
admin.site.register(Store_Card)
admin.site.register(Shopping_Cart)
admin.site.register(Shopping_Product)
admin.site.register(Daily_Story_File_Status)
admin.site.register(Basic_Setting)
admin.site.register(Current_Archive)
admin.site.register(Store_Card_Own) | [
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7,
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201,
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13,
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13,
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7,
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201,
198,
28482,
13,
15654,
13,
30238,
7,
22658,
62,
16962,
62,
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8
] | 2.849432 | 352 |
"""Handles conversion of `markdown`
Here, `toml` is used to communicate `attributes` as for `markdown`.
Main purpose is to describe `style` of the documentation.
* `toml`'s data is returned as Python primitive types.
Speficiation
-------------
If the firstline of the document starts with ```,
and it is a valid toml, then these are regarded as
`attributes` of the document.
Requirement
-------------
* `pandoc`.
Example
```toml
key1 = "value"
```
This is a sample documentation.
"""
import re
import toml
import itertools
def unite(markdown, config):
"""Integrate `markdown` and `toml`.
"""
raise RuntimeError()
_pattern = re.compile(r"^```([\w|\s]*)\n((?:^(?!```).*\n)*)```\s*", re.MULTILINE)
def separate(text):
"""Separate `markdown` and `toml`.
"""
m = _pattern.match(text)
if not m:
markdown, config = text, dict()
return markdown, config
filetype, filecontent = m.groups()
print(filetype)
print(filecontent)
config = toml.loads(filecontent)
# toml = text[m.start():m.end()]
markdown = text[m.end():].strip("\n").strip(" ")
# print("mark", markdown)
# print("toml", toml)
return markdown, config
TEXT = """```toml
key = "label"
value = "fafa"
[[table]]
name = "hoge"
[[table]]
name = "hoge2"
```
fafaefad
```
documentc
```
"""
if __name__ == "__main__":
separate(TEXT)
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366,
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1,
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366,
69,
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1,
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11907,
198,
3672,
796,
366,
8873,
469,
1,
198,
30109,
11487,
11907,
198,
3672,
796,
366,
8873,
469,
17,
1,
198,
15506,
63,
198,
69,
28485,
891,
324,
198,
198,
15506,
63,
198,
22897,
66,
198,
15506,
63,
198,
198,
37811,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
4553,
7,
32541,
8,
198
] | 2.529091 | 550 |
import torchvision
from pathlib import Path
import tqdm
if __name__ == "__main__":
main()
| [
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366,
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12417,
834,
1298,
198,
220,
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1388,
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198
] | 2.852941 | 34 |
from django.db import models
# Create your models here. | [
6738,
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14208,
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1330,
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628,
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2,
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] | 3.8 | 15 |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Helper functions for manipulating collections of variables during training.
"""
import logging
import re
import tensorflow as tf
slim = tf.contrib.slim
# TODO: Consider replacing with tf.contrib.filter_variables in
# tensorflow/contrib/framework/python/ops/variables.py
def filter_variables(variables, filter_regex_list, invert=False):
"""Filters out the variables matching the filter_regex.
Filter out the variables whose name matches the any of the regular
expressions in filter_regex_list and returns the remaining variables.
Optionally, if invert=True, the complement set is returned.
Args:
variables: a list of tensorflow variables.
filter_regex_list: a list of string regular expressions.
invert: (boolean). If True, returns the complement of the filter set; that
is, all variables matching filter_regex are kept and all others discarded.
Returns:
a list of filtered variables.
"""
kept_vars = []
variables_to_ignore_patterns = list(filter(None, filter_regex_list))
for var in variables:
add = True
for pattern in variables_to_ignore_patterns:
if re.match(pattern, var.op.name):
add = False
break
if add != invert:
kept_vars.append(var)
return kept_vars
def multiply_gradients_matching_regex(grads_and_vars, regex_list, multiplier):
"""Multiply gradients whose variable names match a regular expression.
Args:
grads_and_vars: A list of gradient to variable pairs (tuples).
regex_list: A list of string regular expressions.
multiplier: A (float) multiplier to apply to each gradient matching the
regular expression.
Returns:
grads_and_vars: A list of gradient to variable pairs (tuples).
"""
variables = [pair[1] for pair in grads_and_vars]
matching_vars = filter_variables(variables, regex_list, invert=True)
for var in matching_vars:
logging.info('Applying multiplier %f to variable [%s]',
multiplier, var.op.name)
grad_multipliers = {var: float(multiplier) for var in matching_vars}
return tf.contrib.training.multiply_gradients(grads_and_vars, grad_multipliers)
def freeze_gradients_matching_regex(grads_and_vars, regex_list):
"""Freeze gradients whose variable names match a regular expression.
Args:
grads_and_vars: A list of gradient to variable pairs (tuples).
regex_list: A list of string regular expressions.
Returns:
grads_and_vars: A list of gradient to variable pairs (tuples) that do not
contain the variables and gradients matching the regex.
"""
variables = [pair[1] for pair in grads_and_vars]
matching_vars = filter_variables(variables, regex_list, invert=True)
kept_grads_and_vars = [pair for pair in grads_and_vars
if pair[1] not in matching_vars]
for var in matching_vars:
logging.info('Freezing variable [%s]', var.op.name)
return kept_grads_and_vars
def get_variables_available_in_checkpoint(variables, checkpoint_path):
"""Returns the subset of variables available in the checkpoint.
Inspects given checkpoint and returns the subset of variables that are
available in it.
TODO: force input and output to be a dictionary.
Args:
variables: a list or dictionary of variables to find in checkpoint.
checkpoint_path: path to the checkpoint to restore variables from.
Returns:
A list or dictionary of variables.
Raises:
ValueError: if `variables` is not a list or dict.
"""
# 获取checkpoint变量
ckpt_reader = tf.train.NewCheckpointReader(checkpoint_path)
ckpt_vars = ckpt_reader.get_variable_to_shape_map().keys()
if isinstance(variables, list):
variable_names_map = {variable.op.name: variable for variable in variables}
elif isinstance(variables, dict):
variable_names_map = variables
# 如果未提供变量，则直接返回checkpoint中的变量
elif variables == None:
variable_names_map = {ckpt_var: ckpt_reader.get_tensor(ckpt_var) for ckpt_var in list(ckpt_vars)}
return variable_names_map.values()
else:
raise ValueError('`variables` is expected to be a list or dict.')
vars_in_ckpt = {}
for variable_name, variable in sorted(variable_names_map.items()):
if variable_name in ckpt_vars:
vars_in_ckpt[variable_name] = variable
else:
logging.warning('Variable [%s] not available in checkpoint',
variable_name)
if isinstance(variables, list):
return vars_in_ckpt.values()
return vars_in_ckpt
def get_init_fn(flags):
"""Returns a function run by the chief worker to warm-start the training.
Note that the init_fn is only run when initializing the model during the very
first global step.
Returns:
An init function run by the supervisor.
"""
if flags.fine_tune_checkpoint is None:
return None
exclusions = []
# if flags.checkpoint_exclude_scopes:
# exclusions = [scope.strip()
# for scope in flags.checkpoint_exclude_scopes.split(',')]
# TODO(sguada) variables.filter_variables()
# 加载哪些变量
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
# 修改加载的变量名
# for variable in variables_to_restore:
# print(variable.op.name)
# print(variables_to_restore)
# # Change model scope if necessary.
# if flags.checkpoint_model_scope is not None:
# variables_to_restore = \
# {var.op.name.replace(flags.model_name,
# flags.checkpoint_model_scope): var
# for var in variables_to_restore}
if tf.gfile.IsDirectory(flags.fine_tune_checkpoint):
checkpoint_path = tf.train.latest_checkpoint(flags.fine_tune_checkpoint)
else:
checkpoint_path = flags.fine_tune_checkpoint
return slim.assign_from_checkpoint_fn(
checkpoint_path,
variables_to_restore,
ignore_missing_vars=False) | [
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] | 2.836149 | 2,368 |
import hashlib
from tornado.web import RequestHandler
from tornado import gen
| [
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] | 4.388889 | 18 |
"""
Code by Tony Duan was forked from https://github.com/tonyduan/normalizing-flows
MIT License
Copyright (c) 2019 Tony Duan, 2019 Peter Zagubisalo
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from typing import Dict, Callable, Tuple, Type, List, Optional as Opt
import math
import numpy as np
import scipy as sp
import torch as tr
from torch import Tensor
from torch import nn
from torch.nn import init # type: ignore
from torch.nn import functional as func
from kiwi_bugfix_typechecker import nn as nn_, func as func_
from .utils import unconstrained_rqs
from .flow import Flow
from .types import ModuleXToX
ε = 1e-8
# supported non-linearities: note that the function must be invertible
XToX = Callable[[Tensor], Tensor]
functional_derivatives: Dict[XToX, XToX] = {tr.tanh: dtanhxdx, func.leaky_relu: dleakyreluxdx, func.elu: deluxdx}
class Planar(Flow):
"""
Planar flow.
z_out = f(z) = z + u h(wᵀz + b)
[Rezende and Mohamed, 2015]
"""
def forward_(self, z: Tensor) -> Tuple[Tensor, Tensor]:
"""
Given z, returns z_out and the log-determinant log|df/dx|.
Returns
-------
"""
w: Tensor = self.w
u: Tensor = self.u
if self.h in (func.elu, func.leaky_relu):
pass
elif self.h == tr.tanh:
scal = tr.log(1 + tr.exp(w @ u)) - w @ u - 1
u = u + scal * w / func_.norm(w)
else:
raise NotImplementedError("This non-linearity is not supported.")
lin = tr.unsqueeze(z @ w, 1) + self.b
z_out = z + u * self.h(lin)
ϕ = functional_derivatives[self.h](lin) * w
log_det = tr.log(tr.abs(1 + ϕ @ u) + ε)
return z_out, log_det
class Radial(Flow):
"""
Radial flow.
z_out = f(z) = z + β h(α, r)(z − z0)
[Rezende and Mohamed 2015]
"""
def forward_(self, z: Tensor) -> Tuple[Tensor, Tensor]:
"""
Given z, returns z_out and the log-determinant log|df/dx|.
"""
log_α, x0 = self.log_α, self.x0
_, n = z.shape
r = func_.norm(z - x0)
h = (tr.exp(log_α) + r)**-1
β = -tr.exp(log_α) + tr.log(1 + tr.exp(self.β))
z_out = z + β * h * (z - x0)
log_det = (n - 1) * tr.log(1 + β * h) + tr.log(1 + β * h - β * r / (tr.exp(log_α) + r)**2)
return z_out, log_det
class FCNN(ModuleXToX):
"""
Simple fully connected neural network.
"""
class RealNVP(Flow):
"""
Non-volume preserving flow.
[Dinh et. al. 2017]
"""
class MAF(Flow):
"""
Masked auto-regressive flow.
[Papamakarios et al. 2018]
"""
layers: List[FCNN]
class ActNorm(Flow):
"""
ActNorm layer.
[Kingma and Dhariwal, 2018.]
"""
class OneByOneConv(Flow):
"""
Invertible 1x1 convolution.
[Kingma and Dhariwal, 2018.]
"""
P: Tensor
W_inv: Opt[Tensor]
class NSFAR(Flow):
"""
Neural spline flow, auto-regressive.
[Durkan et al. 2019]
"""
layers: List[FCNN]
class NSFCL(Flow):
"""
Neural spline flow, coupling layer.
[Durkan et al. 2019]
"""
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] | 2.430006 | 1,693 |
from utlis import *
import cv2
w,h = 360,240
pid = [0.4,0.4,0]
pError = 0
startCounter = 0 # for no Flight 1 - for flight 0
myDrone = initializeTello()
while True:
## Flight
if startCounter == 0:
myDrone.takeoff()
startCounter = 1
## Step 1
img = telloGetFrame(myDrone,w,h)
## Step 2
img, info = findFace(img)
## Step 3
pError = trackFace(myDrone,info,w,pid,pError)
print('Info:*******', info[0][0]) #This gives cx i.e center of face at index i
cv2.imshow('Image',img)
if cv2.waitKey(1) & 0xFF == ord('q'):
myDrone.land()
break | [
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] | 2.031447 | 318 |
__version__ = '0.14.0a1'
| [
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] | 1.733333 | 15 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This script can generate automate scripts for open source python project.
Scroll to ``if __name__ == "__main__":`` for more info.
"""
from __future__ import print_function
import sys
import datetime
from os import walk, mkdir
from os.path import join, abspath, dirname, basename
def write(s, path, encoding="utf-8"):
"""Write string to text file.
"""
with open(path, "wb") as f:
f.write(s.encode(encoding))
def read(path, encoding="utf-8"):
"""Read string from text file.
"""
with open(path, "rb") as f:
return f.read().decode(encoding)
def initiate_project(
package_name,
repo_name,
python_version,
github_username,
author_name,
author_email,
maintainer_name,
maintainer_email,
year,
s3_bucket,
):
"""
Generate project start files.
"""
print("Initate '%s-project' from template ..." % package_name)
template_dir = join(dirname(abspath(__file__)), "template")
output_dir = join(dirname(abspath(__file__)), "%s-project" % package_name)
for src_dir, dir_list, file_list in walk(template_dir):
# destination directory
dst_dir = src_dir.replace(template_dir, output_dir, 1)
if basename(dst_dir) == "__package__":
dst_dir = join(dirname(dst_dir), package_name)
# make destination directory
try:
print(" Create '%s' ..." % dst_dir)
mkdir(dst_dir)
except:
pass
# files
for filename in file_list:
src = join(src_dir, filename)
dst = join(dst_dir, filename)
content = read(src).\
replace("{{ package_name }}", package_name).\
replace("{{ repo_name }}", repo_name).\
replace("{{ python_version }}", python_version).\
replace("{{ github_username }}", github_username).\
replace("{{ author_name }}", author_name).\
replace("{{ author_email }}", author_email).\
replace("{{ maintainer_name }}", maintainer_name).\
replace("{{ maintainer_email }}", maintainer_email).\
replace("{{ year }}", year).\
replace("{{ s3_bucket }}", s3_bucket)
print(" Create '%s' ..." % dst)
write(content, dst)
print(" Complete!")
if __name__ == "__main__":
# --- EDIT THESE VARIABLE based on your own situation ---
package_name = "picage" # IMPORTANT
repo_name = "{package_name}-project".format(package_name=package_name)
python_version = "python%s%s" % (
sys.version_info.major, sys.version_info.minor)
github_username = "MacHu-GWU" # IMPORTANT
author_name = "Sanhe Hu" # IMPORTANT
author_email = "[email protected]" # IMPORTANT
maintainer_name = author_name
maintainer_email = author_email
year = str(datetime.datetime.utcnow().year)
s3_bucket = "www.wbh-doc.com" # IMPORTANT
initiate_project(
package_name,
repo_name,
python_version,
github_username,
author_name,
author_email,
maintainer_name,
maintainer_email,
year,
s3_bucket,
) | [
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] | 2.226415 | 1,484 |
#!/usr/bin/env python
#
# Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""External script for generating Cloud Endpoints related files.
The gen_discovery_doc subcommand takes a list of fully qualified ProtoRPC
service names and calls a cloud service which generates a discovery document in
REST or RPC style.
Example:
endpointscfg.py gen_discovery_doc -o . -f rest postservice.GreetingsV1
The gen_client_lib subcommand takes a discovery document and calls a cloud
service to generate a client library for a target language (currently just Java)
Example:
endpointscfg.py gen_client_lib java -o . greetings-v0.1.discovery
The get_client_lib subcommand does both of the above commands at once.
Example:
endpointscfg.py get_client_lib java -o . -f rest postservice.GreetingsV1
The gen_api_config command outputs an .api configuration file for a service.
Example:
endpointscfg.py gen_api_config -o . -a /path/to/app \
--hostname myhost.appspot.com postservice.GreetingsV1
"""
from __future__ import with_statement
import collections
import contextlib
try:
import json
except ImportError:
import simplejson as json
import os
import re
import sys
import urllib
import urllib2
from protorpc import remote
from google.appengine.ext.endpoints import api_config
DISCOVERY_DOC_BASE = ('https://webapis-discovery.appspot.com/_ah/api/'
'discovery/v1/apis/generate/')
CLIENT_LIBRARY_BASE = 'https://google-api-client-libraries.appspot.com/generate'
class ServerRequestException(Exception):
"""Exception for problems with the request to a server."""
def __init__(self, http_error):
"""Create a ServerRequestException from a given urllib2.HTTPError.
Args:
http_error: The HTTPError that the ServerRequestException will be
based on.
"""
error_details = None
if http_error.fp:
try:
error_body = json.load(http_error.fp)
error_details = ['%s: %s' % (detail['message'], detail['debug_info'])
for detail in error_body['error']['errors']]
except (ValueError, TypeError, KeyError):
pass
if error_details:
error_message = ('HTTP %s (%s) error when communicating with URL: %s. '
'Details: %s' % (http_error.code, http_error.reason,
http_error.filename, error_details))
else:
error_message = ('HTTP %s (%s) error when communicating with URL: %s.' %
(http_error.code, http_error.reason,
http_error.filename))
super(ServerRequestException, self).__init__(error_message)
def _WriteFile(output_path, name, content):
"""Write given content to a file in a given directory.
Args:
output_path: The directory to store the file in.
name: The name of the file to store the content in.
content: The content to write to the file.close
Returns:
The full path to the written file.
"""
path = os.path.join(output_path, name)
with open(path, 'wb') as f:
f.write(content)
return path
def GenApiConfig(service_class_names, generator=None, hostname=None):
"""Write an API configuration for endpoints annotated ProtoRPC services.
Args:
service_class_names: A list of fully qualified ProtoRPC service classes.
generator: An generator object that produces API config strings using its
pretty_print_config_to_json method.
hostname: A string hostname which will be used as the default version
hostname. If no hostname is specificied in the @endpoints.api decorator,
this value is the fallback. Defaults to None.
Raises:
TypeError: If any service classes don't inherit from remote.Service.
messages.DefinitionNotFoundError: If a service can't be found.
Returns:
A map from service names to a string containing the API configuration of the
service in JSON format.
"""
api_service_map = collections.OrderedDict()
for service_class_name in service_class_names:
module_name, base_service_class_name = service_class_name.rsplit('.', 1)
module = __import__(module_name, fromlist=base_service_class_name)
service = getattr(module, base_service_class_name)
if not (isinstance(service, type) and issubclass(service, remote.Service)):
raise TypeError('%s is not a ProtoRPC service' % service_class_name)
services = api_service_map.setdefault((service.api_info.name,
service.api_info.version),
[])
services.append(service)
service_map = collections.OrderedDict()
generator = generator or api_config.ApiConfigGenerator()
for api_info, services in api_service_map.iteritems():
hostname = services[0].api_info.hostname or hostname
service_map['%s-%s' % api_info] = generator.pretty_print_config_to_json(
services, hostname=hostname)
return service_map
def GenDiscoveryDoc(service_class_names, doc_format,
output_path, hostname=None):
"""Write discovery documents generated from a cloud service to file.
Args:
service_class_names: A list of fully qualified ProtoRPC service names.
doc_format: The requested format for the discovery doc. (rest|rpc)
output_path: The directory to output the discovery docs to.
hostname: A string hostname which will be used as the default version
hostname. If no hostname is specificied in the @endpoints.api decorator,
this value is the fallback. Defaults to None.
Raises:
ServerRequestException: If fetching the generated discovery doc fails.
Returns:
A list of discovery doc filenames.
"""
output_files = []
service_configs = GenApiConfig(service_class_names, hostname=hostname)
for api_name_version, config in service_configs.iteritems():
body = json.dumps({'config': config}, indent=2, sort_keys=True)
request = urllib2.Request(DISCOVERY_DOC_BASE + doc_format, body)
request.add_header('content-type', 'application/json')
try:
with contextlib.closing(urllib2.urlopen(request)) as response:
content = response.read()
discovery_name = api_name_version + '.discovery'
output_files.append(_WriteFile(output_path, discovery_name, content))
except urllib2.HTTPError, error:
raise ServerRequestException(error)
return output_files
def GenClientLib(discovery_path, language, output_path):
"""Write a client library from a discovery doc, using a cloud service to file.
Args:
discovery_path: Path to the discovery doc used to generate the client
library.
language: The client library language to generate. (java)
output_path: The directory to output the client library zip to.
Raises:
IOError: If reading the discovery doc fails.
ServerRequestException: If fetching the generated client library fails.
Returns:
The path to the zipped client library.
"""
with open(discovery_path) as f:
discovery_doc = f.read()
client_name = re.sub(r'\.discovery$', '.zip',
os.path.basename(discovery_path))
_GenClientLibFromContents(discovery_doc, language, output_path, client_name)
def _GenClientLibFromContents(discovery_doc, language, output_path,
client_name):
"""Write a client library from a discovery doc, using a cloud service to file.
Args:
discovery_doc: A string, the contents of the discovery doc used to
generate the client library.
language: A string, the client library language to generate. (java)
output_path: A string, the directory to output the client library zip to.
client_name: A string, the filename used to save the client lib.
Raises:
IOError: If reading the discovery doc fails.
ServerRequestException: If fetching the generated client library fails.
Returns:
The path to the zipped client library.
"""
body = urllib.urlencode({'lang': language, 'content': discovery_doc})
request = urllib2.Request(CLIENT_LIBRARY_BASE, body)
try:
with contextlib.closing(urllib2.urlopen(request)) as response:
content = response.read()
return _WriteFile(output_path, client_name, content)
except urllib2.HTTPError, error:
raise ServerRequestException(error)
def GetClientLib(service_class_names, doc_format, language,
output_path, hostname=None):
"""Fetch discovery documents and client libraries from a cloud service.
Args:
service_class_names: A list of fully qualified ProtoRPC service names.
doc_format: The requested format for the discovery doc. (rest|rpc)
language: The client library language to generate. (java)
output_path: The directory to output the discovery docs to.
hostname: A string hostname which will be used as the default version
hostname. If no hostname is specificied in the @endpoints.api decorator,
this value is the fallback. Defaults to None.
Returns:
A tuple (discovery_files, client_libs):
discovery_files: A list of paths to discovery documents.
client_libs: A list of paths to client libraries.
"""
discovery_files = GenDiscoveryDoc(service_class_names, doc_format,
output_path, hostname=hostname)
client_libs = []
for discovery_path in discovery_files:
client_libs.append(
GenClientLib(discovery_path, language, output_path))
return discovery_files, client_libs
def _GenApiConfigCallback(args, api_func=GenApiConfig):
"""Generate an api file.
Args:
args: An argparse.Namespace object to extract parameters from.
api_func: A function that generates and returns an API configuration
for a list of services.
"""
service_class_names, output_path, hostname = (
args.service, args.output, args.hostname)
service_configs = api_func(service_class_names, hostname=hostname)
for api_name_version, config in service_configs.iteritems():
api_name = api_name_version + '.api'
_WriteFile(output_path, api_name, config)
def _GetClientLibCallback(args,
client_func=GetClientLib):
"""Generate discovery docs and client libraries to files.
Args:
args: An argparse.Namespace object to extract parameters from.
client_func: A function that generates client libraries and stores them to
files, accepting a list of service names, a discovery doc format, a client
library language, and an output directory.
"""
service_class_names, doc_format, language, output_path, hostname = (
args.service, args.format, args.language, args.output, args.hostname)
discovery_paths, client_paths = client_func(
service_class_names, doc_format, language, output_path, hostname=hostname)
for discovery_path in discovery_paths:
print 'API discovery document written to %s' % discovery_path
for client_path in client_paths:
print 'API client library written to %s' % client_path
def _GenDiscoveryDocCallback(args, discovery_func=GenDiscoveryDoc):
"""Generate discovery docs to files.
Args:
args: An argparse.Namespace object to extract parameters from
discovery_func: A function that generates discovery docs and stores them to
files, accepting a list of service names, a discovery doc format, and an
output directory.
"""
services, doc_format, output_path, hostname = (
args.service, args.format, args.output, args.hostname)
discovery_paths = discovery_func(services, doc_format,
output_path, hostname=hostname)
for discovery_path in discovery_paths:
print 'API discovery document written to %s' % discovery_path
def _GenClientLibCallback(args, client_func=GenClientLib):
"""Generate a client library to file.
Args:
args: An argparse.Namespace object to extract parameters from
client_func: A function that generates client libraries and stores them to
files, accepting a path to a discovery doc, a client library language, and
an output directory.
"""
discovery_path, language, output_path = (args.discovery_doc[0], args.language,
args.output)
client_path = client_func(discovery_path, language, output_path)
print 'API client library written to %s' % client_path
def MakeParser(prog):
"""Create an argument parser.
Args:
prog: The name of the program to use when outputting help text.
Returns:
An argparse.ArgumentParser built to specification.
"""
import argparse
def AddStandardOptions(parser, *args):
"""Add common endpoints options to a parser.
Args:
parser: The parser to add options to.
*args: A list of option names to add. Possible names are: application,
format, output, language, service, and discovery_doc.
"""
if 'application' in args:
parser.add_argument('-a', '--application', default='.',
help='The path to the Python App Engine App')
if 'format' in args:
parser.add_argument('-f', '--format', default='rest',
choices=['rest', 'rpc'],
help='The requested API protocol type')
if 'hostname' in args:
help_text = ('Default application hostname, if none is specified '
'for API service.')
parser.add_argument('--hostname', help=help_text)
if 'output' in args:
parser.add_argument('-o', '--output', default='.',
help='The directory to store output files')
if 'language' in args:
parser.add_argument('language', choices=['java'],
help='The target output programming language')
if 'service' in args:
parser.add_argument('service', nargs='+',
help='Fully qualified service class name')
if 'discovery_doc' in args:
parser.add_argument('discovery_doc', nargs=1,
help='Path to the discovery document')
parser = argparse.ArgumentParser(prog=prog)
subparsers = parser.add_subparsers(title='subcommands')
get_client_lib = subparsers.add_parser(
'get_client_lib', help=('Generates discovery documents and client '
'libraries from service classes'))
get_client_lib.set_defaults(callback=_GetClientLibCallback)
AddStandardOptions(get_client_lib, 'application', 'format', 'hostname',
'output', 'language', 'service')
gen_api_config = subparsers.add_parser(
'gen_api_config', help=('Generates an .api file for the given service '
'classes'))
gen_api_config.set_defaults(callback=_GenApiConfigCallback)
AddStandardOptions(gen_api_config, 'application', 'hostname', 'output',
'service')
gen_discovery_doc = subparsers.add_parser(
'gen_discovery_doc',
help='Generates discovery documents from service classes')
gen_discovery_doc.set_defaults(callback=_GenDiscoveryDocCallback)
AddStandardOptions(gen_discovery_doc, 'application', 'format', 'hostname',
'output', 'service')
gen_client_lib = subparsers.add_parser(
'gen_client_lib', help='Generates a client library from service classes')
gen_client_lib.set_defaults(callback=_GenClientLibCallback)
AddStandardOptions(gen_client_lib, 'output', 'language', 'discovery_doc')
return parser
if __name__ == '__main__':
sys.exit(main(sys.argv))
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7,
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7,
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13,
853,
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] | 2.85477 | 5,598 |
# -*- coding: utf-8 -*-
from typing import TYPE_CHECKING
from bkpaas_auth.core.constants import ProviderType
from bkpaas_auth.core.encoder import user_id_encoder
if TYPE_CHECKING:
from bkpaas_auth.models import User # noqa
class UserInfo:
"""Base class for Userinfo"""
provider_type: ProviderType
class RtxUserInfo(UserInfo):
"""User info for RTX user"""
provider_type = ProviderType.RTX
email_suffix = "@tencent.com"
class BkUserInfo(UserInfo):
"""User info for Bk user"""
provider_type = ProviderType.BK
| [
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] | 2.763819 | 199 |
from functools import reduce
import random
flip = lambda f : lambda x, y : f(y, x)
foldr = lambda f, z, xs : reduce(flip(f), reversed(xs), z)
foldl = lambda f, z, xs : reduce(f, xs, z)
removeDuplicates = lambda xs : list(set([tuple(x) for x in xs]))
# kier - heart
# karo - diamond
# trefl - club
# pik - spade
# as = Ace
# król - King
# królowa - Queen
# walet - Jack
# figury zapisuję od 1..9 aby zgadzało się że każdy jest jednoznakowy, a i
blotki = [str(i) for i in range(1,10)]
figury = ['J', 'Q', 'K', 'A']
kolory = ['h', 'd', 'c', 's']
order = {'1' : 1, '2' : 2, '3' : 3, '4' : 4, '5' : 5, '6' : 6, '7' : 7, '8' : 8, '9' : 9, 'J' : 11, 'Q' : 12, 'K' : 13, 'A' : 14}
orderReka = {'wysokakarta' : 0, 'para' : 1, 'dwiepary' : 2, 'trojka' : 3, 'street' : 4, 'kolor' : 5, 'full' : 6, 'kareta' : 7, 'poker' : 8}
# porownajUklady(uklady(["4h", '3s', '9d', '5c', '1h']), uklady(["4h", 'Qs', '9d', '1c', '5h']))
podstawowy()
usuwany()
prawdopodobienstwa(["9h", '8h', '7h', '6h', '5h'])
prawdopodobienstwaFiguranta()
prawdopodobienstwaBlotkarza()
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404,
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672,
2013,
301,
10247,
3629,
313,
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198
] | 1.935543 | 543 |
# -*- coding: utf-8 -*-
import sys; _stdout = sys.stdout; _stderr = sys.stderr;
import re
import time
import bottle
import os.path
import hashlib
import threading
import subprocess
bottle.debug(True)
srcroot = os.path.join(os.path.dirname(__file__), "..")
sphinxbuild = os.environ.get("SPHINXBUILD", "sphinx-build")
devnull = open(os.devnull, "w")
@bottle.route("<path:path>")
t = threading.Thread(target=monitor, args=(test_md5(), lambda: system("make", "SPHINXBUILD=" + sphinxbuild, "clean", "html")))
t.setDaemon(True)
t.start()
bottle.run(host="0.0.0.0", port=4080)
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13,
15,
13,
15,
1600,
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28,
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] | 2.389344 | 244 |
LUMPS = 3002001
SALLY = 3002002
SNIFFS = 3002003
TWITCH = 3002004
SILVER = 3002005
COMPASS = 3002006
TIMBER = 3002007
BROOK = 3002008
PATIENCE = 3002009
TUMBLEWEED = 3002105
sm.lockInGameUI(True)
sm.forcedInput(0)
sm.spawnNpc(LUMPS, 300, 89)
sm.showNpcSpecialActionByTemplateId(LUMPS, "summon")
sm.spawnNpc(SALLY, -175, 0)
sm.showNpcSpecialActionByTemplateId(SALLY, "summon")
sm.spawnNpc(SNIFFS, 360, 103)
sm.showNpcSpecialActionByTemplateId(SALLY, "summon")
sm.spawnNpc(TWITCH, -238, -33)
sm.showNpcSpecialActionByTemplateId(TWITCH, "summon")
sm.spawnNpc(SILVER, 84, 37)
sm.showNpcSpecialActionByTemplateId(SILVER, "summon")
sm.spawnNpc(COMPASS, 174, 55)
sm.showNpcSpecialActionByTemplateId(COMPASS, "summon")
sm.spawnNpc(TIMBER, -128, 51)
sm.showNpcSpecialActionByTemplateId(TIMBER, "summon")
sm.spawnNpc(BROOK, -190, 77)
sm.showNpcSpecialActionByTemplateId(BROOK, "summon")
sm.spawnNpc(PATIENCE, 241, 72)
sm.showNpcSpecialActionByTemplateId(PATIENCE, "summon")
sm.spawnNpc(TUMBLEWEED, -61, 41)
sm.showNpcSpecialActionByTemplateId(TUMBLEWEED, "summon")
sm.sendDelay(2000)
sm.removeEscapeButton()
sm.setSpeakerID(TIMBER)
sm.sendNext("We were really getting used to having you around here...")
sm.setSpeakerID(BROOK)
sm.sendSay("Have all three meals of the day. Especially breakfast. Foxes live to eat.")
sm.setSpeakerID(SILVER)
sm.sendSay("You're leaving this town, but don't ever forget you are one of us Pointy-Ear Foxes. Don't forget the pride of foxes. Come back anytime you like. We'll be waiting for you.")
sm.flipDialoguePlayerAsSpeaker()
sm.sendSay("Thank you for all you've done. And I will never...never forget.")
sm.setSpeakerID(TWITCH)
sm.sendSay("Shade! You sound like you're leaving forever! Come back soon, and bring yummy treats next time!")
sm.setSpeakerID(SILVER)
sm.sendSay("Ah, old age makes me tear up about anything. But enough about me, Moonbeam won't come out of her room. She doesn't want to see you leave. And that heavy rainfall... I don't think she'll stop crying anytime soon.")
sm.flipDialoguePlayerAsSpeaker()
sm.sendSay("......")
sm.setSpeakerID(TUMBLEWEED)
sm.sendSay("I still think you should wait a couple days and go with me, but I guess if you're busy that's too bad. Here, take this scroll, it will take you right to Pantheon. When you get there, talk to #rCartalion#k. Tell him you came on behalf of me and he'll take good care of you.")
sm.removeNpc(LUMPS)
sm.removeNpc(SALLY)
sm.removeNpc(SNIFFS)
sm.removeNpc(TWITCH)
sm.removeNpc(SILVER)
sm.removeNpc(COMPASS)
sm.removeNpc(TIMBER)
sm.removeNpc(BROOK)
sm.removeNpc(PATIENCE)
sm.removeNpc(TUMBLEWEED)
sm.lockInGameUI(False)
sm.warp(410000000, 6)
| [
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7,
3901,
24598,
11,
718,
8,
628
] | 2.696755 | 986 |
import datetime
from django.test import TestCase
from babik_card_primitives.two_field_date import TwoFieldDate
| [
11748,
4818,
8079,
198,
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19795,
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13,
11545,
62,
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62,
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10430,
628
] | 3.5 | 32 |
"""python variable scharer
the class implementation of a variable scharer for python
"""
import socket, threading, time
from jpe_types.paralel import LockableThread, threadInharitanceFilter
from jpe_types.conversions.intager import baseN
from torent.client import client
import logging
NoneType = type(None)
"the type of None"
class comunicator:
"""the basic comunicator
will be used by more advanced comunicators to comunicate with one another basicly averything that habpens on the server and the client related to socket
"""
_initiated = False
def __init__(self, port: int, protocol=socket.AF_INET, sockType=socket.SOCK_STREAM, proto=0, name=None, log=None, bufferSize=2048, initFunc=None):
"""construct the comunicator
@param port: witch port the sockets are crated with
@type port: int
@param protocol: These constants represent the address (and protocol) families, used for the first argument to socket(). If the AF_UNIX constant is not defined then this protocol is unsupported. More constants may be available depending on the system.
@type protocol: socket.AF_INET, socket.AF_INET6, socket.AF_UNIX, AF_CAN, AF_PACKET, or AF_RDS.
@param sockType: These constants represent the socket types, used for the second argument to socket(). More constants may be available depending on the system. (Only SOCK_STREAM and SOCK_DGRAM appear to be generally useful.)
@type sockType: socket.SOCK_STREAM, socket.SOCK_DGRAM, socket.SOCK_RAW, socket.SOCK_RDM, socket.SOCK_SEQPACKET
@param proto: The protocol number is usually zero and may be omitted or in the case where the address family is AF_CAN the protocol should be one of CAN_RAW, CAN_BCM, CAN_ISOTP or CAN_J1939.
@type proto: socket.constant
@param name: the name of the communicator will be used by other istances to reference this one if None (default) the server will construct a unique one
@type name: str
@param log: the logger used to log proceses made by the comunicator
@type log: loggging.logger
@param bufferSize: the buffersize for recv data
@type bufferSize: int
@param func: the function we want to run with parameter client when a new client is connected, if the return of the
function is True the client will not be saved, this function is blocking so the connector wont lissen while its running
@type func: function with 1 parameter
"""
assert type(port) is int, "port must be an int"
self.port = port
"""witch port to operate on"""
assert isinstance(name, (str, NoneType)), "the name must be as string"
self.name = self._getUUID_for_client(port) if name is None else name
"""the uuid of the instance if None server Generated"""
self.sockData = protocol, sockType, proto
"a tuple containing the the sockets protocol and the sockType"
self.connections = {}
"""a dict containing the socket connections of the comunicator by uuid
uuid is the unike unit id aka the id used by the name of the connected communicator
"""
assert isinstance(log, (logging.Logger, type(None))), "logger must be None or a logger"
self.logger = log
"the logger used to log activity made this class"
self.listeningThread=None
"""the thread used for listening
initates to none\n
to start listening run stert_listen() method
to resume run setListenActivity(True) and to pause listening run setListenActivity(False)"""
assert type(bufferSize) is int, f"buffersize must be an int not {type(bufferSize)}"
self.buffer = bufferSize
"the buffersize for socket recv"
self.data = {"torent_getClientInit": (self._torent_getClientInit, {}),
"torent_connectToNewConectors": (self._connectToNewConectors, {}),
"torent_finischedInput": (self._finischedInput, {}),
"torent_unlockClient": (self.unlockClient, {}),
"torent_endClientInit": (self._clientComleat, {})}
"""who is data updated
a dict refrencing torent names and function to update python variables
"""
self.init_Lock = threading.Lock()
"""initialisation lock
the initalisation lock that is used by holdTilInit to lock threads until initiation is done"""
self.start_listen(initFunc)
self._initiated = True
def addDataPoint(self, key, val, **specification):
"""adds a new data point
adds datapoint with internal name key and value val
@param val: the function to be executed when value is changed this schould set val to whatever u want
@type val: function
@param key: the internal name of the val
@type key: string
@param specification: a dict containing execution specification\n
built in opions are:
None at present
"""
assert type(key) is str, f"the key must be a string not {type(key)}"
assert type(val) is type(lambda x:None), f"val must be a function not {type(val)}"
self.data[key] = (val, specification)
def assertInit(self):
"""make sure instance is initated
make sure we initated the instance
"""
assert self._initiated, "communicator must be initiated"
def _log_info(self, msg, *args, **kwargs):
"""make an info call to self.logger
Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) No % formatting operation is performed on msg when no args are supplied.
There are four keyword arguments in kwargs which are inspected: exc_info, stack_info, stacklevel and extra.
see https://docs.python.org/3/library/logging.html logger.debug for details
"""
if not self.logger is None:
self.logger.info(msg)
def _log_warn(self, msg, *args, **kwargs):
"""make an warn call to self.logger
Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) No % formatting operation is performed on msg when no args are supplied.
There are four keyword arguments in kwargs which are inspected: exc_info, stack_info, stacklevel and extra.
see https://docs.python.org/3/library/logging.html logger.debug for details
"""
if not self.logger is None:
self.logger.warn(msg)
def __str__(self):
"convert to string currently prints its name"
return str(self.name)
def start_listen(self, func=None):
"""listens for client connections called by the server
will run in a Thread listen for connections ad add them to the connection dict
@param func: the function we want to run with parameter client when a new client is connected, if the return of the
function is True the client will not be saved, this function is blocking so the connector wont lissen while its running
@type func: function with 1 parameter
"""
assert not self._initiated, "dont call this function"
if func is None:
func = lambda x: None
#crate a listening socket
ListeningSock = socket.socket(self.sockData[0], self.sockData[1], proto=self.sockData[2])
# the thing the thread is gona run
def listen_InThread(*vals: tuple):
"subscript fore the actual connection"
self, ListeningSock, port = vals
self._log_info(f"crated listening Socket {ListeningSock}")
ListeningSock.bind((socket.gethostname(), port))
ListeningSock.listen(10)
while True:
self._log_info(f"waiting for clients")
sock, addres = ListeningSock.accept()
self._log_info("got client")
this_client = client(sock, addres, None)
this_client.name, this_client.ip = this_client.recv(2048)
this_client.send(self.name)
self._log_info(f"got client, client name is {this_client.name}")
if not this_client.name in self.connections and not func(this_client):
self.connections[this_client.name] = this_client
this_client.startListening(self._updateData, self.buffer)
self._log_info("sucessfuly initated the client")
else: self._log_info("did not crate client")
self.listeningThread = LockableThread(target=listen_InThread,
args=[self, ListeningSock, self.port],
name=f"{self.name}_listeningThread",
daemon=True)
"the thread used to listen for connections"
self.listeningThread.start()
self._log_info(f"started listening Thread for communicator {self.name}")
def setListenActivity(self, activity=False):
"""sets the activity of the listening thread
activates or distactivates the listening Thread
@param activity: if True unlock the thread if false lock it
@type activity: bool
"""
if activity:
self._log_info("activated listening Thread")
self.listeningThread.releace()
else:
self._log_info("locked listening Thread")
self.listeningThread.aquire()
def connect(self, ip, func=None, port=None):
"""connect to a new master
crate a conection to a comunicator at ip ip and port self.port
@param ip: the ip addres of the master
@type ip: string ipadress
@param func: the function we want to run with parameter client when a new client is connected, if the return of the
function is True the client will not be saved, this function is blocking so the connector wont lissen while its running
@type func: function with 1 parameter
@param port: the port to conect to defaults to the listening port
@type port: int
"""
if not type(port) is int:
port=self.port
assert type(port) is int, f"the port must be an int plz dont set self.port manulay {type(port)}"
if func is None:
func = lambda x: None
sock = socket.socket(self.sockData[0], self.sockData[1], proto=self.sockData[2])
sock.connect((ip, port))
#crate client
this_client = client(sock, (ip, port), None)
self._log_info("crated client")
# send owne name
this_client.send((self.name, (socket.gethostbyname(socket.gethostname()), self.port)))
this_client.name = this_client.recv(2048)
self._log_info(f"comunicator {self.name} has crated client with name {this_client.name}")
if not this_client.name in self.connections:
self.connections[this_client.name] = this_client
this_client.startListening(self._updateData, self.buffer)
self._log_info(f"crated newClient")
def _getUUID_for_client(self, port):
"""generats a uuid for the client
genrate a uuid form id
"""
ip = socket.gethostbyname(socket.gethostname())
uuid = baseN(int(str(port) + "".join([x.zfill(3) for x in ip.split(".")])), 36)
return uuid
def send(self, data, clients=None, blackList=[]):
"""send data to all clients
go throw every client and send data
@param clients: names of the clients to update if None(defauld) that means all
@type clients: list
@param blackList: what clients not to update under any circumstances
@type blackList: list
"""
if clients is None:
clients = self.connections
assert isinstance(blackList, (list, tuple, dict)), f"make blackList a list not {type(blackList)}"
assert isinstance(blackList, (dict, list, tuple)), f"clients must be list, tuple of dict not {type(clients)}"
for client in clients:
assert client in self.connections, "invalid client {client} not in client dict"
if client in blackList:
continue
self.connections[client].send(data)
self._log_info(f"send data {data} to client {self.connections[client].name}")
def _updateData(self, key, val, client):
"""run update function
runs the update function data[key] with args val[0] and kwargs val[1]
@param key: the torent name of the function to be run
@type key: string
@param val: a list containing a tuple at position 0 and a dict at position 1 containing args and kwargs respectivly
@type val: list, tuple, dict
"""
assert type(key) is str, f"the key must be a string not {type(key)}"
assert type(val) in [list, tuple, dict], f"val must be a list, tuple or dict not {type(val)}"
if type(val) is dict:
args = val["args"] if "args" in val else ()
kwargs = val["kwargs"] if "kwargs" in val else {}
else:
args = val[0] if len(args) else ()
kwargs = val[1] if len(args) >=2 else {}
if key in self.data:
self._log_info(f"execute function {key} from communicator {self.name} in thread")
fun, funSpecific = self.data[key]
assert type(fun) is type(lambda x:None) or type(fun) is type(self._torent_getClientInit), "fun must be a function or method not {type(fun)}"
t= LockableThread(target=fun,
args=(client, *args),
kwargs=kwargs,
name=f"runFun {key} from torent comunicator {self.name}")
t.start()
else:
self._log_warn(f"function {key} not found in data for comunicator {self.name}")
def updateVar(self, key, *args, **kwargs):
"""update a value
run function with torent name key on all possible clients
note: you may not use clients and blackList as kwargs in your function as they are pased to send
see send for more details
@param key: the torent name of the function to be executed folowed by the parametrs to be passed
@type key: string
"""
if "clients" in kwargs:
clients = kwargs["clients"]
del kwargs["clients"]
else: clients = None
if "blackList" in kwargs:
blackList = kwargs["blackList"]
del kwargs["blackList"]
else: blackList = []
self.send(self._encriptData(key, args, kwargs=kwargs), clients=clients, blackList=blackList)
execute = updateVar
def _encriptData(self, key, args, kwargs):
"get data to be serialized"
data = (key, {"args": args, "kwargs": kwargs})
return data
# init functions
def postInit(self):
"""updates scharing graph to incoporate this comunicator
it is not recomended to use this function as unforcean problems may arise
"""
self.getOtherConections()
self.holdTilInit()
def getOtherConections(self):
"""tells server to run functon on this client with all the ips as arguments
this dose only work if ips are diferent otherwise go wire propergation
"""
args = []
for client in self.connections:
args.append(self.connections[client].name)
self.updateVar("torent_getClientInit", *tuple(args))
def _torent_getClientInit(self, clientInit, *args):
"""run function with all conected ips as addreses
run a function containing the values of the ips adresses conected to it except the client and all in args
"""
ignore = (clientInit.name, *args)
newVals = []
for client in self.connections.values():
if not client.name in ignore:
newVals.append(client.ip)
self.updateVar("torent_connectToNewConectors", *tuple(newVals), clients=[clientInit.name])
def _connectToNewConectors(self, client, *args):
"run by _torent_getClientInit to initate all the clients not contained in this client conection dict"
for ip in args:
self._log_info(f"connecting to client with ip {ip}")
self.connect(ip[0], port=ip[1])
self.updateVar("torent_endClientInit", clients=[client.name])
def holdTilInit(self):
"""hold thread unit server has finisched setup
hold current thread until we finich initation of torent
"""
self.init_Lock.acquire()
while self.init_Lock.locked():
time.sleep(.1)
def unlockClient(self, *args):
"unclocks the client part of init protocol"
self._log_info(f"communicator {self.name} is ready to go")
if self.init_Lock.locked(): self.init_Lock.release()
def _finischedInput(self, *args, **kwargs):
"unock init hold lock"
args = []
for client in self.connections:
args.append(self.connections[client].name)
self.updateVar("torent_getClientInit", *tuple(args),)
def finischSetup(self):
"""finalizes setup for graphes
"""
self._clientsRemainingToInit = len(self.connections)
self.updateVar("torent_finischedInput")
self.holdTilInit()
def _clientComleat(self, client):
"""remove 1 from client to be initateded counter
"""
logging.info(f"client {client.name} has finisched init")
if hasattr(self, "_clientsRemainingToInit"):
self._clientsRemainingToInit -= 1
else:
"only if run from postscripter"
self._clientsRemainingToInit = 0
if self._clientsRemainingToInit <= 0:
self.updateVar("torent_unlockClient")
self.unlockClient()
def decriptUUIDname(uuid):
"""converts a uuid gerated by the system to
converts a uuid gerated by the system to ip and port
@param uuid: the uuid to decript
@type uui: str
"""
assert isinstance(uuid, str), f"the uuid must be a string not {type(uuid)}"
key = str(int(uuid, 36))
ip = str(int(key[-12:-9])) + "." + str(int(key[-9:-6])) + "."+ str(int(key[-6:-3])) + "." + str(int(key[-3:]))
return ip, int(key[:-12]) | [
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5793,
326,
318,
973,
416,
1745,
51,
346,
31768,
284,
5793,
14390,
1566,
30122,
318,
1760,
37811,
201,
198,
201,
198,
220,
220,
220,
220,
220,
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13,
9688,
62,
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268,
7,
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19524,
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201,
198,
220,
220,
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2116,
13557,
259,
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515,
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201,
198,
220,
220,
220,
220,
201,
198,
220,
220,
220,
825,
751,
6601,
12727,
7,
944,
11,
1994,
11,
1188,
11,
12429,
16684,
2649,
2599,
201,
198,
220,
220,
220,
220,
220,
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37227,
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198,
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220,
220,
220,
220,
220,
220,
220,
6673,
4818,
499,
1563,
351,
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1438,
1994,
290,
1988,
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201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
220,
220,
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220,
220,
220,
220,
2488,
17143,
1188,
25,
262,
2163,
284,
307,
10945,
618,
1988,
318,
3421,
428,
5513,
426,
900,
1188,
284,
4232,
334,
765,
201,
198,
220,
220,
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25,
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201,
198,
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198,
220,
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2488,
17143,
1994,
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5387,
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286,
262,
1188,
201,
198,
220,
220,
220,
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220,
220,
2488,
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1994,
25,
4731,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
17143,
20855,
25,
257,
8633,
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9706,
20855,
59,
77,
201,
198,
220,
220,
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220,
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3170,
287,
1034,
507,
389,
25,
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198,
220,
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220,
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6045,
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1944,
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198,
220,
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220,
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220,
37227,
201,
198,
220,
220,
220,
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220,
220,
220,
6818,
2099,
7,
2539,
8,
318,
965,
11,
277,
1,
1169,
1994,
1276,
307,
257,
4731,
407,
1391,
4906,
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2539,
8,
36786,
201,
198,
220,
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6818,
2099,
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277,
1,
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257,
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407,
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8,
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201,
198,
220,
220,
220,
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220,
220,
220,
2116,
13,
7890,
58,
2539,
60,
796,
357,
2100,
11,
20855,
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201,
198,
220,
220,
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198,
220,
220,
220,
825,
6818,
31768,
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944,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
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37227,
15883,
1654,
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318,
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515,
201,
198,
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198,
220,
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220,
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198,
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6818,
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259,
8846,
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11,
366,
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307,
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1,
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198,
220,
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198,
220,
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825,
4808,
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62,
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11,
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11,
12429,
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198,
220,
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15883,
281,
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284,
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198,
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257,
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11,
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262,
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262,
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6425,
326,
428,
1724,
326,
345,
460,
779,
26286,
287,
262,
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11,
1978,
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257,
2060,
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4578,
2014,
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4064,
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318,
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13,
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198,
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220,
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13,
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198,
220,
220,
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198,
220,
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407,
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13,
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318,
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220,
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13,
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198,
220,
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198,
220,
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62,
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11,
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11,
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11,
12429,
46265,
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198,
220,
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37227,
15883,
281,
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220,
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284,
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13,
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201,
198,
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5972,
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13,
383,
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11,
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6425,
326,
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1724,
326,
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262,
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11,
1978,
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4578,
2014,
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318,
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319,
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645,
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389,
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318,
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40539,
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201,
198,
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198,
220,
220,
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825,
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198,
220,
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366,
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7,
944,
11,
1366,
11,
7534,
28,
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11,
2042,
8053,
28,
21737,
2599,
201,
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220,
220,
220,
220,
220,
37227,
21280,
1366,
284,
477,
7534,
201,
198,
201,
198,
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220,
220,
220,
220,
220,
220,
467,
3714,
790,
5456,
290,
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1366,
201,
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2488,
17143,
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25,
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6045,
7,
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8,
326,
1724,
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7534,
25,
1351,
201,
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201,
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220,
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2488,
17143,
2042,
8053,
25,
644,
7534,
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284,
4296,
739,
597,
5917,
201,
198,
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220,
220,
220,
220,
220,
220,
2488,
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2042,
8053,
25,
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220,
220,
220,
220,
37227,
201,
198,
220,
220,
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220,
220,
220,
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611,
7534,
318,
6045,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7534,
796,
2116,
13,
8443,
507,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
318,
39098,
7,
13424,
8053,
11,
357,
4868,
11,
46545,
11,
8633,
36911,
277,
1,
15883,
2042,
8053,
257,
1351,
407,
220,
1391,
4906,
7,
13424,
8053,
8,
36786,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
318,
39098,
7,
13424,
8053,
11,
357,
11600,
11,
1351,
11,
46545,
36911,
277,
1,
565,
2334,
1276,
307,
1351,
11,
46545,
286,
8633,
407,
1391,
4906,
7,
565,
2334,
8,
36786,
201,
198,
220,
220,
220,
220,
220,
220,
220,
329,
5456,
287,
7534,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
5456,
287,
2116,
13,
8443,
507,
11,
366,
259,
12102,
5456,
1391,
16366,
92,
407,
287,
5456,
8633,
1,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5456,
287,
2042,
8053,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
8443,
507,
58,
16366,
4083,
21280,
7,
7890,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
6404,
62,
10951,
7,
69,
1,
21280,
1366,
1391,
7890,
92,
284,
5456,
1391,
944,
13,
8443,
507,
58,
16366,
4083,
3672,
92,
4943,
201,
198,
201,
198,
220,
220,
220,
825,
4808,
19119,
6601,
7,
944,
11,
1994,
11,
1188,
11,
5456,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
5143,
4296,
2163,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
4539,
262,
4296,
2163,
1366,
58,
2539,
60,
351,
26498,
1188,
58,
15,
60,
290,
479,
86,
22046,
1188,
58,
16,
60,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
17143,
1994,
25,
262,
284,
1156,
1438,
286,
262,
2163,
284,
307,
1057,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
4906,
1994,
25,
4731,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
17143,
1188,
25,
257,
1351,
7268,
257,
46545,
379,
2292,
657,
290,
257,
8633,
379,
2292,
352,
7268,
26498,
290,
479,
86,
22046,
2461,
452,
306,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
4906,
1188,
25,
1351,
11,
46545,
11,
8633,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
2099,
7,
2539,
8,
318,
965,
11,
277,
1,
1169,
1994,
1276,
307,
257,
4731,
407,
1391,
4906,
7,
2539,
8,
36786,
201,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
2099,
7,
2100,
8,
287,
685,
4868,
11,
46545,
11,
8633,
4357,
277,
1,
2100,
1276,
307,
257,
1351,
11,
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393,
8633,
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4906,
7,
2100,
8,
36786,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2099,
7,
2100,
8,
318,
8633,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
26498,
796,
1188,
14692,
22046,
8973,
611,
366,
22046,
1,
287,
1188,
2073,
7499,
201,
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220,
220,
220,
220,
220,
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220,
220,
220,
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22046,
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1,
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23884,
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220,
220,
220,
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2073,
25,
201,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26498,
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7,
22046,
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16,
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7,
22046,
8,
18189,
17,
2073,
23884,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
287,
2116,
13,
7890,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
6404,
62,
10951,
7,
69,
1,
41049,
2163,
1391,
2539,
92,
422,
1316,
26407,
1391,
944,
13,
3672,
92,
287,
4704,
4943,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1257,
11,
1257,
32419,
796,
2116,
13,
7890,
58,
2539,
60,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
2099,
7,
12543,
8,
318,
2099,
7,
50033,
2124,
25,
14202,
8,
393,
2099,
7,
12543,
8,
318,
2099,
7,
944,
13557,
83,
382,
429,
62,
1136,
11792,
31768,
828,
366,
12543,
1276,
307,
257,
2163,
393,
2446,
407,
1391,
4906,
7,
12543,
8,
36786,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
28,
13656,
540,
16818,
7,
16793,
28,
12543,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
26498,
16193,
16366,
11,
1635,
22046,
828,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
86,
22046,
28,
46265,
22046,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
28,
69,
1,
5143,
24629,
1391,
2539,
92,
422,
284,
1156,
401,
403,
26407,
1391,
944,
13,
3672,
92,
4943,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
13,
9688,
3419,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13557,
6404,
62,
40539,
7,
69,
1,
8818,
1391,
2539,
92,
407,
1043,
287,
1366,
329,
401,
403,
26407,
1391,
944,
13,
3672,
92,
4943,
201,
198,
201,
198,
220,
220,
220,
825,
4296,
19852,
7,
944,
11,
1994,
11,
1635,
22046,
11,
12429,
46265,
22046,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
19119,
257,
1988,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1057,
2163,
351,
284,
1156,
1438,
1994,
319,
477,
1744,
7534,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
3465,
25,
345,
743,
407,
779,
7534,
290,
2042,
8053,
355,
479,
86,
22046,
287,
534,
2163,
355,
484,
389,
279,
839,
284,
3758,
201,
198,
220,
220,
220,
220,
220,
220,
220,
766,
3758,
329,
517,
3307,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
17143,
1994,
25,
262,
284,
1156,
1438,
286,
262,
2163,
284,
307,
10945,
5955,
6972,
416,
262,
5772,
316,
3808,
284,
307,
3804,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
4906,
1994,
25,
4731,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
366,
565,
2334,
1,
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479,
86,
22046,
25,
201,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7534,
796,
479,
86,
22046,
14692,
565,
2334,
8973,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1619,
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22046,
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565,
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8973,
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220,
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220,
220,
220,
220,
2073,
25,
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796,
6045,
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201,
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220,
220,
220,
220,
220,
220,
611,
366,
13424,
8053,
1,
287,
479,
86,
22046,
25,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2042,
8053,
796,
479,
86,
22046,
14692,
13424,
8053,
8973,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1619,
479,
86,
22046,
14692,
13424,
8053,
8973,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
2042,
8053,
796,
17635,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
21280,
7,
944,
13557,
268,
6519,
6601,
7,
2539,
11,
26498,
11,
479,
86,
22046,
28,
46265,
22046,
828,
7534,
28,
565,
2334,
11,
2042,
8053,
28,
13424,
8053,
8,
201,
198,
220,
220,
220,
12260,
796,
4296,
19852,
201,
198,
201,
198,
220,
220,
220,
825,
4808,
268,
6519,
6601,
7,
944,
11,
1994,
11,
26498,
11,
479,
86,
22046,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
366,
1136,
1366,
284,
307,
11389,
1143,
1,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1366,
796,
357,
2539,
11,
19779,
22046,
1298,
26498,
11,
366,
46265,
22046,
1298,
479,
86,
22046,
30072,
201,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
1366,
201,
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220,
220,
201,
198,
2,
2315,
5499,
201,
198,
220,
220,
220,
825,
1281,
31768,
7,
944,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
929,
19581,
5513,
1723,
4823,
284,
753,
404,
16262,
428,
401,
403,
26407,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
220,
340,
318,
407,
664,
296,
1631,
284,
779,
428,
2163,
355,
555,
3174,
272,
2761,
743,
15058,
201,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
1136,
6395,
3103,
478,
507,
3419,
201,
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220,
220,
220,
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346,
31768,
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220,
220,
220,
37227,
33331,
82,
4382,
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1257,
310,
261,
319,
428,
5456,
351,
477,
262,
220,
2419,
355,
7159,
201,
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201,
198,
220,
220,
220,
220,
220,
220,
220,
428,
10742,
691,
670,
611,
220,
2419,
389,
288,
361,
9100,
4306,
467,
6503,
1774,
70,
341,
201,
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220,
220,
220,
220,
220,
220,
37227,
201,
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220,
220,
220,
220,
220,
26498,
796,
17635,
201,
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220,
220,
220,
220,
220,
220,
220,
329,
5456,
287,
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13,
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25,
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220,
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220,
220,
220,
26498,
13,
33295,
7,
944,
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507,
58,
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4083,
3672,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
19119,
19852,
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83,
382,
429,
62,
1136,
11792,
31768,
1600,
1635,
83,
29291,
7,
22046,
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220,
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751,
411,
274,
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201,
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220,
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220,
220,
220,
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262,
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220,
8856,
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31768,
13,
3672,
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22046,
8,
201,
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220,
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649,
53,
874,
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17635,
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220,
220,
220,
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5456,
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13,
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27160,
33529,
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220,
220,
220,
220,
220,
220,
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53,
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8,
201,
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201,
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220,
220,
220,
220,
220,
220,
2116,
13,
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19852,
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83,
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429,
62,
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2514,
3791,
3103,
478,
669,
1600,
1635,
83,
29291,
7,
3605,
53,
874,
828,
7534,
41888,
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31768,
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12962,
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3213,
8633,
1,
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220,
220,
220,
220,
220,
220,
329,
20966,
287,
26498,
25,
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220,
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220,
220,
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6404,
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69,
1,
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278,
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1391,
541,
92,
4943,
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220,
220,
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2116,
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7,
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2493,
28,
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220,
220,
220,
201,
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220,
220,
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2116,
13,
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19852,
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83,
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62,
437,
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41888,
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220,
37227,
2946,
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286,
284,
1156,
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220,
220,
220,
220,
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37227,
201,
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220,
220,
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220,
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2116,
13,
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62,
25392,
13,
330,
29782,
3419,
201,
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220,
220,
220,
220,
220,
220,
220,
981,
2116,
13,
15003,
62,
25392,
13,
24162,
33529,
201,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
640,
13,
42832,
7,
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8,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
366,
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] | 2.375061 | 8,188 |
## -------------------------------------------------------------------------------------------------
## -- Project : MLPro - A Synoptic Framework for Standardized Machine Learning Tasks
## -- Package : mlpro
## -- Module : Howto 14 - Train UR5 with SB3 wrapper
## -------------------------------------------------------------------------------------------------
## -- History :
## -- yyyy-mm-dd Ver. Auth. Description
## -- 2021-11-18 0.0.0 MRD Creation
## -- 2021-11-18 1.0.0 MRD Initial Release
## -- 2021-12-07 1.0.1 DA Refactoring
## -- 2022-02-11 1.1.0 DA Special derivate for publication
## -------------------------------------------------------------------------------------------------
"""
Ver. 1.1.0 (2022-02-11)
This module shows how to use SB3 wrapper to train UR5 robot.
"""
from mlpro.rl.models import *
from mlpro.rl.pool.envs.ur5jointcontrol import UR5JointControl
from stable_baselines3 import PPO
from mlpro.wrappers.sb3 import WrPolicySB32MLPro
from pathlib import Path
# 1 Make Sure training_env branch of ur_control is sourced:
# request access to the ur_control project
# 2 Implement your own RL scenario
# 3 Train agent in scenario
now = datetime.now()
training = RLTraining(
p_scenario_cls=ScenarioUR5A2C,
p_cycle_limit=5500,
p_cycles_per_epi_limit=-1,
p_stagnation_limit=5,
p_stagnation_entry=15,
p_end_at_stagnation=False,
p_eval_frequency=10,
p_eval_grp_size=2,
p_score_ma_horizon=20,
p_collect_states=True,
p_collect_actions=True,
p_collect_rewards=True,
p_collect_training=True,
p_visualize=False,
p_path=str(Path.home()),
p_logging=Log.C_LOG_WE)
training.run()
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] | 2.876047 | 597 |
"""
This python file inserts 100K rows into table prefetching in database end2end
"""
import pymysql
import string
import random
db = pymysql.connect(host='localhost', port=3306,
user='root', passwd='greeN7^Tr33', db='end2end', charset='utf8')
cursor = db.cursor()
sql = "INSERT INTO prefetching(ID, Age, Name, c4, c5, c6, c7, c8, c9, c10) VALUES (%s,%s,%s, %s,%s,%s, %s,%s,%s, %s)"
T = []
for i in range(1, 100001):
age = random.randint(1,50)
name = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c4 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c5 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c6 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c7 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c8 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c9 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
c10 = ''.join(random.sample(string.ascii_letters + string.digits, random.randint(1, 30)))
T.append((i, age, name, c4, c5, c6, c7, c8, c9, c10))
try:
cursor.executemany(sql, T)
db.commit()
except:
db.rollback()
cursor.close()
db.close()
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198,
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3419,
198
] | 2.327731 | 595 |
import requests, urllib, base64, time, re, pdb
from Crypto.PublicKey import RSA
from Crypto.Cipher import PKCS1_v1_5
from SteamMobileAuth import getDeviceID
from bs4 import BeautifulSoup
USER_AGENT = "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.106 Safari/537.36"
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] | 2.730435 | 115 |
from django.db import models
from django.db.transaction import atomic
from simhash import hamming_distance
from .calculate_simhash import hash_length
from .calculate_simhash import highest_value
# Although this mixin is extremely interconnected with the SimHash model,
# it was just too much code to dump in the models file
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# -*- coding: utf-8 -*-
""".. moduleauthor:: Artur Lissin"""
import re
from typing import Pattern, Final, final
from dataclasses import dataclass
_NOT_SPACE: Final[Pattern[str]] = re.compile(r'[^\s]')
_TIME_FORMAT: Final[Pattern[str]] = re.compile(r'(.*\d+:\d+:\d+)\..*$')
_NEW_LINE: Final[Pattern[str]] = re.compile(r'\n')
@final
@dataclass
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] | 2.362416 | 149 |
from django.contrib.auth.models import AbstractUser
from django.db import models
from django.utils.html import escape, mark_safe
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import StandardScaler
import pymc3 as pm
# 12H1
data = pd.read_csv('Hurricanes.csv', delimiter=';')
deaths = np.array(data['deaths'])
fem = np.array(data['femininity'])
ss = StandardScaler()
fem = ss.fit_transform(fem.reshape(-1,1)).reshape(-1,)
with pm.Model() as m0:
a = pm.Normal('a', 0, 1.5)
l = pm.math.exp(a)
d = pm.Poisson('D', mu=l, observed=deaths)
trace0 = pm.sample(500, chains=4, tune=1000)
with pm.Model() as m1:
a = pm.Normal('a', 0, 1.5)
bF = pm.Normal('b', 0, 0.5)
mm = a + bF * fem
l = pm.math.exp(mm)
d = pm.Poisson('D', mu=l, observed=deaths)
trace1 = pm.sample(500, chains=4, tune=1000)
tracePost = pm.sample_posterior_predictive(trace1, model=m1)
posteriorDeaths = tracePost['D']
alphas = trace1['a']
betas = trace1['b']
lamdas = (alphas + np.tile(fem, (2000, 1)).T * betas).T
lamdas = np.exp(lamdas)
mu = np.mean(lamdas, axis=0)
muCI = np.percentile(lamdas, [4.5, 95.5], axis=0).T
ind = np.argsort(fem)
muCI = muCI[ind]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(fem, deaths, color='C0', edgecolor='k')
ax.plot(fem[ind], mu[ind], color='k')
ax.fill_between(fem[ind], muCI[:,1], muCI[:,0], color='k', alpha=0.5)
ppCI = np.percentile(posteriorDeaths, [4.5, 95.5], axis=0).T
ppCI = ppCI[ind]
ax.fill_between(fem[ind], ppCI[:,1], ppCI[:,0], color='k', alpha=0.1)
# coefficient of femininity is reliably above 0, but model is really bad at predicting very large death counts
# 12H2
with pm.Model() as m2:
a = pm.Normal('a', 0, 1.5)
bF = pm.Normal('b', 0, 0.5)
phi = pm.Exponential('phi', 1.)
mm = a + bF * fem
l = pm.math.exp(mm)
d = pm.NegativeBinomial('D', mu=l, alpha=phi, observed=deaths)
trace2 = pm.sample(500, chains=4, tune=1000)
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28,
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] | 2.270911 | 801 |
from lib.commonkafka import *
from lib.commonsplunk import check_events_from_splunk
from lib.helper import get_test_folder
import pytest
import re
logging.config.fileConfig(os.path.join(get_test_folder(), "logging.conf"))
logger = logging.getLogger("test_case")
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] | 3 | 88 |
import update_judicial_data
update_judicial_data.refresh()
update_judicial_data.copy_fincial_judgement() | [
11748,
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] | 3.28125 | 32 |
#!/usr/bin/env python
"""
Halite engine warpped in SWIG
"""
from distutils.core import setup, Extension
halite_module = Extension(
'_halite',
sources=[
'halite_wrap.cxx',
'wrapped/core/Halite.cpp',
'wrapped/halite-core.cpp',
'wrapped/networking/Networking.cpp',
],
extra_compile_args=['-std=c++11'])
setup (
name = 'halite',
version = '0.1',
author = "tmgardner + sojumu",
description = """halite bindings?""",
ext_modules = [halite_module],
py_modules = ["halite"])
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] | 2.429245 | 212 |
#! python 3
# ch.py - scrapes denvercourt/themis for criminal
# history for a given defendant w/DOB
# TODO: show open cases (nxt court date set in noCos tab); sentence; FTAs
import requests, sys, time, datetime, bs4, subprocess
from tqdm import tqdm
bool_Emergency = False
###### ENTRY POINT TO FUNCTIONING CODE ######
input_first_name = input("Def First Name: ").upper() # todo: string verify
if "EMERGENCY" in input_first_name:
bool_Emergency = True
print("OK, I'll hurry.\n")
input_first_name = input("Def First Name: ").upper() # todo: string verify
input_last_name = input("Def Last Name: ").upper() # todo: string verify
input_DOB = input("Def DOB: ") # todo: string format/date verify
##### OPEN FILE HANDLE ########################
f = open("C:\\Users\\XXXXXX\\Downloads\\defendant.txt", 'w')
# COCOURTS VARIABLES
# Get webpage
# The URL for the authentication page for main website
# url = 'https://www.jbits.courts.state.co.us/pas/pubaccess/index.cfm' # no longer needed
url_splash = 'https://www.jbits.courts.state.co.us/pas/pubaccess/action.cfm'
url_terms = 'https://www.jbits.courts.state.co.us/pas/pubaccess/user/terms.cfm'
url_personsearch_splash = 'https://www.jbits.courts.state.co.us/pas/pubaccess/user/person.cfm'
url_personsearch = 'https://www.jbits.courts.state.co.us/pas/pubaccess/user/search.cfm'
payload_login = {'actionType':'Login',
'username' :'[email protected]',
'password' :'XXXXXXXXXXXX' }
payload_terms = {'commit' :'1',
'acceptButton.x':'61',
'acceptButton.y':'16' }
payload_personsearch = {
'searchType' :'person',
'locationCodes':'CO_DCY_*',
'caseTypeCR' :'CR',
'casetypeJV' :'JV',
'casetypeTR' :'TR',
'lastname1' :input_last_name,
'firstname1' :input_first_name,
'middlename1' :'',
'dob1' :input_DOB,
'lastname2' :'',
'firstname2' :'',
'middlename2' :'',
'dob2' :'',
'lastname3' :'',
'firstname3' :'',
'middlename3' :'',
'dob3' :'',
'lastname4' :'',
'firstname4' :'',
'middlename4' :'',
'dob4' :'',
'lastname5' :'',
'firstname5' :'',
'middlename5' :'',
'dob5' :'',
'dateBegin' :'',
'dateEnd' :'',
'includeAlias' :'1',
'x' :'59',
'y' :'9' }
# THEMIS VARIABLES
# The URL for the authentication page for main website
url = 'http://denvercourt.dcc.dnvr/courtnet/login.aspx'
# The URL for the get request to search a name
get_url = 'http://denvercourt.dcc.dnvr/courtnet/name_search.aspx?_'
# The URL for the get request to search a case
casesearch_url = 'http://denvercourt.dcc.dnvr/courtnet/court_result.aspx?caseNo='
# payload info generated from dev tools, Network tab after submitting request
# was able to find form data in the head tab and used that directly
payload = {'__LASTFOCUS':'',
'__EVENTTARGET':'',
'__EVENTARGUMENT':'',
'__VIEWSTATE':'/wEPDwUJNjQyODUzNDYyD2QWAmYPZBYCAgMPZBYCAgUPZBYCAgUPD2QWAh4Fc3R5bGUFGXRleHQtdHJhbnNmb3JtOnVwcGVyY2FzZTtkGAEFHl9fQ29udHJvbHNSZXF1aXJlUG9zdEJhY2tLZXlfXxYBBSJjdGwwMCRDb250ZW50UGxhY2VIb2xkZXIxJGNiX0FncmVlWbd1tFkrgCyhC18H9VpiFgelE+k=',
'__VIEWSTATEGENERATOR':'355B8235',
'__EVENTVALIDATION':'/wEWBQK90dzODALJ4fq4BwL90KKTCALC4Iy6CgK8haTMDE41xwE2XedmDLm1WF64yqlP/gCC',
'ctl00$ContentPlaceHolder1$txtUserName':'XXXX',
'ctl00$ContentPlaceHolder1$txtPassword':'XXXXXX',
'ctl00$ContentPlaceHolder1$cb_Agree':'on',
'ctl00$ContentPlaceHolder1$cmdEnter':'Enter'}
abbre_dict = { 'disturbing the peace':'disturb peace',
'court supervised probation':'Crt Sup Probation',
'jail time imposed':'jail',
'driving under restraint':'DUR',
'jail time susp cond.':'suspend',
'concurrent jail sent':'concurrent',
'consecutive jail sent':'consecutive',
'threats to person/property':'threats',
'interference w/ police officer':'interference',
'destruction of private property':'destruction (priv)',
'destruction private property':'destruction (priv)',
'supervised probation':'probation',
'fine/costs credit for jail':'jail credit for fine',
'pub consumption of alcohol':'public alcohol',
'urinating in public':'urin in public',
'poss drug paraphernalia':'drug paraph'}
count_FTA = 0
list_toWrite = list()
str_toWrite = ""
#### COCOURTS SEARCH ############################################################
with requests.Session() as ses:
f.write("COLORADO COURTS\n")
p2 = ses.post(url_splash, data=payload_login)
p3 = ses.post(url_terms, data=payload_terms)
p4 = ses.get(url_personsearch_splash)
p5 = ses.post(url_personsearch, data=payload_personsearch)
parser_HTML = bs4.BeautifulSoup(p5.text, "html.parser")
list_coCrts_caseNos = parser_HTML.select('#resultsSet a')
list_coCrts_openCases = parser_HTML.select('#resultsSet td')
for openCase in list_coCrts_openCases:
if "Open" in openCase.getText().strip():
f.write("Defendant has open cases. Check CoCourts!\n")
break
del list_coCrts_caseNos[:9]
print("Searching Colorado Courts for " + str(len(list_coCrts_caseNos)) + " listed cases")
for caseNo in tqdm(list_coCrts_caseNos):
p6 = ses.get(caseNo["href"])
parser_conviction_HTML = bs4.BeautifulSoup(p6.text, "html.parser")
list_coCrts_convictionData = parser_conviction_HTML.select('td')
for data_index in range(len(list_coCrts_convictionData)):
if "Disposition: Guilty" in list_coCrts_convictionData[data_index].getText():
f.write(caseNo.getText() + " -- ")
for data_check_string in range(15):
if "Class: " in list_coCrts_convictionData[data_index - data_check_string - 1].getText():
f.write(list_coCrts_convictionData[data_index - data_check_string - 1].getText().replace("Class: ", '')[:2] + " ")
if "Charge: " in list_coCrts_convictionData[data_index - data_check_string - 1].getText():
f.write(list_coCrts_convictionData[data_index - data_check_string - 1].getText().replace("Charge: ", '').strip())
for data_check_string in range(8):
if "Jail" in list_coCrts_convictionData[data_index + data_check_string].getText() or "Probation" in list_coCrts_convictionData[data_index + data_check_string].getText() or "Department of Corrections" in list_coCrts_convictionData[data_index + data_check_string].getText():
list_sentencing_data = bs4.BeautifulSoup(str(list_coCrts_convictionData[data_index + data_check_string]), "html.parser").select('td') # Get all tds in the sentencing mini table
for data_check_jail_string in range(1, len(list_sentencing_data)):
if "Jail" in list_sentencing_data[data_check_jail_string].getText() and "Cost" not in list_sentencing_data[data_check_jail_string].getText() or "Department of Corrections" in list_sentencing_data[data_check_jail_string].getText():
f.write(" -- " + list_sentencing_data[data_check_jail_string].getText() + " " + list_sentencing_data[data_check_jail_string + 1].getText() + " " + list_sentencing_data[data_check_jail_string + 2].getText() + " " + list_sentencing_data[data_check_jail_string + 3].getText())
if "Probation" in list_sentencing_data[data_check_jail_string].getText() and "Fee" not in list_sentencing_data[data_check_jail_string].getText():
f.write(" -- " + list_sentencing_data[data_check_jail_string].getText() + " " + list_sentencing_data[data_check_jail_string + 1].getText() + " " + list_sentencing_data[data_check_jail_string + 2].getText() + " " + list_sentencing_data[data_check_jail_string + 3].getText())
f.write(" -- " + list_coCrts_convictionData[2].getText().replace("Location: ",'') + '\n')
if bool_Emergency == False:
time.sleep(2.0) # BE POLITE TO SCRAPED SERVER
f.write("\nNumber of cases in Colorado Courts: " + str(len(list_coCrts_caseNos)) + '\n')
#### THEMIS SEARCH #############################################################
with requests.Session() as s:
p = s.post(url, data=payload)
p2 = s.get(get_url + str(now_milliseconds()) + '&last_name=' + input_last_name + '&first_name=' + input_first_name + '&dob=' + input_DOB)
searchHTML = bs4.BeautifulSoup(p2.text, "html.parser")
list_caseNos = searchHTML.select('a')
list_nxtCourtDates = searchHTML.select('#ContentWrapper td')
f.write("\nTHEMIS\nOpen Cases\n")
print("Searching Themis for " + str(int(len(list_nxtCourtDates) / 10)) + " listed cases")
for k in range(len(list_nxtCourtDates)):
if (k + 4) % 10 == 0: # checking every 7th td in the row
if list_nxtCourtDates[k].getText().strip():
f.write(list_nxtCourtDates[k - 6].getText().strip() + " - " + list_nxtCourtDates[k].getText().strip() + '\n')
f.write("\nConvictions\n")
for caseNo in tqdm(list_caseNos): # tqdm is progress bar
p3 = s.get(casesearch_url + caseNo.getText() + '&_' + str(now_milliseconds()))
# print(p3.text)
searchHTML = bs4.BeautifulSoup(p3.text, "html.parser")
list_convictions = searchHTML.select('#p_gen_offense td')
counted_FTAs_Before = False # a variable to check if we've counted FTAs for this case number before
for index in range(len(list_convictions)):
if "GUILTY" in list_convictions[index].getText():
if counted_FTAs_Before == False:
str_toWrite += caseNo.getText() + " -- "
else:
str_toWrite += caseNo.getText() + "*-- "
str_toWrite += abbre_dict.get(list_convictions[index - 2].getText().strip().lower(), list_convictions[index - 2].getText().strip().lower())
if counted_FTAs_Before == False: # check for additional info only if this is the first offense for the casenumber
list_sentence = searchHTML.select('#p_gen_sentence td')
for i in range(len(list_sentence)):
if "PROBATION" in list_sentence[i].getText() and "TERMS" not in list_sentence[i].getText() and "JOURNAL" not in list_sentence[i].getText():
str_toWrite += " -- " + abbre_dict.get(list_sentence[i].getText().strip().lower(), list_sentence[i].getText().strip().lower()) + " " + list_sentence[i + 1].getText().strip() + " " + list_sentence[i + 2].getText().strip().lower() + " " + list_sentence[i + 4].getText().strip().lower() + list_sentence[i - 1].getText().strip()
if "JAIL" in list_sentence[i].getText() and "CONCURRENT" not in list_sentence[i].getText():
str_toWrite += " -- " + abbre_dict.get(list_sentence[i].getText().strip().lower(), list_sentence[i].getText().strip().lower()) + " " + list_sentence[i + 1].getText().strip() + " " + list_sentence[i + 2].getText().strip().lower() + " " + list_sentence[i + 4].getText().strip().lower() + list_sentence[i - 1].getText().strip()
list_actions = searchHTML.select('#p_gen_actions td')
FTAd_before = False # bool to make sure we format FTAs to the far right only on the first FTA
for j in range(len(list_actions)):
if "FTA" in list_actions[j].getText() and "JUDGE" not in list_actions[j].getText():
if FTAd_before == False and counted_FTAs_Before == False: # tab a bunch of times only on the first FTA for this case no
str_toWrite += '\t\t\t\t'
count_FTA += 1
str_toWrite += " -- FTAd on " + list_actions[j - 4].getText().strip() # D's CAN have multiple FTAs for one case
FTAd_before = True
str_toWrite += '\n' # make sure newline by end
list_toWrite.append(str_toWrite)
counted_FTAs_Before = True # counted all case numbers
str_toWrite = "" # clean out the string once done
if bool_Emergency == False:
time.sleep(2.0) # BE POLITE TO SCRAPED SERVER
list_toWrite = sorted(list_toWrite) # , then Sort the list
for stg_Element in list_toWrite:
f.write(stg_Element)
f.write("\nNumber of Cases in Themis: " + str(int(len(list_nxtCourtDates) / 10)))
f.write("\nFailures to Appear: " + str(count_FTA))
f.close()
subprocess.call(['C:\\windows\\system32\\notepad.exe', 'C:\\Users\\XXXXXX\\Downloads\\defendant.txt'])
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] | 2.209691 | 5,799 |
# This file is part of GridCal.
#
# GridCal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GridCal is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GridCal. If not, see <http://www.gnu.org/licenses/>.
import pandas as pd
import numpy as np
import json
from matplotlib import pyplot as plt
from PySide2.QtCore import QThread, Signal
from GridCal.Engine.Simulations.PowerFlow.power_flow_results import PowerFlowResults
from GridCal.Engine.Simulations.PowerFlow.power_flow_worker import power_flow_post_process, PowerFlowOptions
from GridCal.Engine.Simulations.result_types import ResultTypes
from GridCal.Engine.Simulations.ContinuationPowerFlow.continuation_power_flow import continuation_nr, VCStopAt, VCParametrization
from GridCal.Engine.Core.multi_circuit import MultiCircuit
from GridCal.Engine.Core.snapshot_pf_data import compile_snapshot_circuit, split_into_islands
from GridCal.Engine.plot_config import LINEWIDTH
from GridCal.Gui.GuiFunctions import ResultsModel
########################################################################################################################
# Voltage collapse classes
########################################################################################################################
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13,
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13,
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62,
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13,
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62,
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69,
62,
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11,
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62,
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62,
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13,
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13,
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62,
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6217,
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13,
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72,
13,
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72,
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628,
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29113,
29113,
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2,
45444,
9807,
6097,
198,
29113,
29113,
29113,
14468,
7804,
628,
628,
628
] | 4.064439 | 419 |
from __future__ import absolute_import, division, print_function
import os.path
import torch
import torch.utils.data as data
import glob
import numpy as np
from torchvision import transforms as vision_transforms
from .common import read_image_as_byte, read_calib_into_dict, get_date_from_width
| [
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] | 3.547619 | 84 |
import os
import tensorflow as tf
import sonnet as snt
from root_gnn.utils import load_yaml
from root_gnn import model as GNN
| [
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] | 3.047619 | 42 |
# TODO: Add an appropriate license to your skill before publishing. See
# the LICENSE file for more information.
# Below is the list of outside modules you'll be using in your skill.
# They might be built-in to Python, from mycroft-core or from external
# libraries. If you use an external library, be sure to include it
# in the requirements.txt file so the library is installed properly
# when the skill gets installed later by a user.
from adapt.intent import IntentBuilder
#from mycroft.skills.core import MycroftSkill, intent_handler
from mycroft import MycroftSkill, intent_file_handler, intent_handler, AdaptIntent
from mycroft.util.log import LOG
import json
import os
from mycroft.filesystem import FileSystemAccess
#LOGGER = getLogger(__name__)
# The "create_skill()" method is used to create an instance of the skill.
# Note that it's outside the class itself.
| [
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2,
5740,
326,
340,
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1398,
2346,
13,
198
] | 3.830435 | 230 |
import unittest
from masonite.exceptions import RouteNotFoundException
from masonite.app import App
from masonite.exceptions import InvalidRouteCompileException, RouteException
from masonite.helpers.routes import create_matchurl, flatten_routes, group
from masonite.request import Request
from masonite.routes import (Connect, Delete, Get, Head, Match, Options, Patch,
Post, Put, Redirect, Route, RouteGroup, Trace)
from masonite.testing import TestCase
from masonite.testsuite.TestSuite import generate_wsgi
from app.http.controllers.subdirectory.SubController import SubController
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] | 3.158974 | 195 |
# Copyright (c) 2011 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'dest_dir': '<(PRODUCT_DIR)/syzygy',
},
'conditions': [
['syzyasan==1', {
'variables': {
'syzygy_exe_dir': '<(DEPTH)/third_party/syzygy/binaries/exe',
},
# Copy the SyzyASan runtime and logger to the syzygy directory.
'targets': [
{
'target_name': 'copy_syzyasan_binaries',
'type': 'none',
'outputs': [
'<(dest_dir)/agent_logger.exe',
'<(dest_dir)/syzyasan_rtl.dll',
'<(dest_dir)/syzyasan_rtl.dll.pdb',
],
'copies': [
{
'destination': '<(dest_dir)',
'files': [
'<(syzygy_exe_dir)/agent_logger.exe',
'<(syzygy_exe_dir)/minidump_symbolizer.py',
'<(syzygy_exe_dir)/syzyasan_rtl.dll',
'<(syzygy_exe_dir)/syzyasan_rtl.dll.pdb',
],
},
],
},
],
}],
['OS=="win" and fastbuild==0', {
'conditions': [
['syzygy_optimize==1 or syzyasan==1', {
'variables': {
'dll_name': 'chrome',
},
'targets': [
# GN version: //chrome/tools/build/win/syzygy:chrome_dll_syzygy
{
'target_name': 'chrome_dll_syzygy',
'type': 'none',
'sources' : [],
'includes': [
'chrome_syzygy.gypi',
],
},
],
}],
['chrome_multiple_dll==1', {
'conditions': [
['syzyasan==1 or syzygy_optimize==1', {
'variables': {
'dll_name': 'chrome_child',
},
'targets': [
# GN version: //chrome/tools/build/win/syzygy:chrome_child_dll_syzygy
{
'target_name': 'chrome_child_dll_syzygy',
'type': 'none',
# For the official SyzyASan builds just copy chrome_child.dll
# to the Syzygy directory.
'conditions': [
['syzyasan==1 and buildtype=="Official"', {
'dependencies': [
'chrome_child_dll_syzygy_copy'
],
}],
],
# For the official SyzyASan builds also put an instrumented
# version of chrome_child.dll into syzygy/instrumented.
'variables': {
'conditions': [
['syzyasan==1 and buildtype=="Official"', {
'dest_dir': '<(PRODUCT_DIR)/syzygy/instrumented',
}],
],
},
'sources' : [],
'includes': [
'chrome_syzygy.gypi',
],
},
],
}],
# For the official SyzyASan builds just copy chrome_child.dll to the
# Syzygy directory.
['syzyasan==1 and buildtype=="Official"', {
'targets': [
{
# GN version: //chrome/tools/build/win/syzygy:chrome_child_dll_syzygy_copy
'target_name': 'chrome_child_dll_syzygy_copy',
'type': 'none',
'inputs': [
'<(PRODUCT_DIR)/chrome_child.dll',
'<(PRODUCT_DIR)/chrome_child.dll.pdb',
],
'outputs': [
'<(dest_dir)/chrome_child.dll',
'<(dest_dir)/chrome_child.dll.pdb',
],
'copies': [
{
'destination': '<(dest_dir)',
'files': [
'<(PRODUCT_DIR)/chrome_child.dll',
'<(PRODUCT_DIR)/chrome_child.dll.pdb',
],
},
],
}],
}],
],
}],
],
}],
],
}
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# coding: utf-8
"""
Registry of Open Community Challenge API
The OpenAPI specification implemented by the Challenge Registries. # Introduction TBA # noqa: E501
The version of the OpenAPI document: 0.1.4
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import six
from roccclient.configuration import Configuration
class PageOfOrganizationsAllOf(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
openapi_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
openapi_types = {
'organizations': 'list[Organization]'
}
attribute_map = {
'organizations': 'organizations'
}
def __init__(self, organizations=None, local_vars_configuration=None): # noqa: E501
"""PageOfOrganizationsAllOf - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._organizations = None
self.discriminator = None
if organizations is not None:
self.organizations = organizations
@property
def organizations(self):
"""Gets the organizations of this PageOfOrganizationsAllOf. # noqa: E501
An array of Organizations # noqa: E501
:return: The organizations of this PageOfOrganizationsAllOf. # noqa: E501
:rtype: list[Organization]
"""
return self._organizations
@organizations.setter
def organizations(self, organizations):
"""Sets the organizations of this PageOfOrganizationsAllOf.
An array of Organizations # noqa: E501
:param organizations: The organizations of this PageOfOrganizationsAllOf. # noqa: E501
:type organizations: list[Organization]
"""
self._organizations = organizations
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, PageOfOrganizationsAllOf):
return False
return self.to_dict() == other.to_dict()
def __ne__(self, other):
"""Returns true if both objects are not equal"""
if not isinstance(other, PageOfOrganizationsAllOf):
return True
return self.to_dict() != other.to_dict()
| [
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] | 2.379288 | 1,574 |
import re
import constants
if __name__ == '__main__':
CHAR_CLUSTERS = '''
เc็c
เcctาะ
เccีtยะ
เccีtย(?=[เ-ไก-ฮ]|$)
เccอะ
เcc็c
เcิc์c
เcิtc
เcีtยะ?
เcืtอะ?
เc[ิีุู]tย(?=[เ-ไก-ฮ]|$)
เctา?ะ?
cัtวะ
c[ัื]tc[ุิะ]?
c[ิุู]์
c[ะ-ู]t
c็
ct[ะาำ]?
แc็c
แcc์
แctะ
แcc็c
แccc์
โctะ
[เ-ไ]ct
ๆ
ฯลฯ
ฯ
'''
sent = 'ฉันกินข้าวที่บ้าน'
max_len = 4
extractor = CCExtractor(CHAR_CLUSTERS)
ccs = extractor.create_all_char_clusters(sent)
print('sentence: {}'.format(ccs))
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] | 1.103125 | 640 |
from scipy.spatial import voronoi_plot_2d, Voronoi
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull, convex_hull_plot_2d
from numpy import genfromtxt
import pandas as pd
import numpy as np
from osgeo import gdal
import argparse
import os
import xarray as xr
#cLHS
import clhs as cl
import csv
from scipy.spatial import distance
from scipy.stats import entropy
from scipy.special import kl_div
from scipy.stats import ks_2samp
from scipy.stats import wasserstein_distance
import argparse
import numpy as np
import osgeo.gdal as gdal
import os
#by Anna
#from data_preparation import data_preparation
from maxvol_cut import rect_maxvol_cut, f_no_cut, f_penal_2D
from tools import norm_data, add_coords, gen_input, extend_score, points_selection, f_no_cut, f_cut_eps, calc_score, good_points_brute_force, idx_to_idx
import csv
class Divergence():
"""
class to proccess data with MaxVol, cLHS and Random
I hope it will return dist
"""
def data_preparation(self, wd, data_m, dem_dir):
"""
Function to orginize tif files in flatten vectos, remove NaN and stack vectors into matrix
"""
fl_names = list(filter(lambda fl: fl.endswith('.tif'), os.listdir(wd+'/features/')))
files = list(map(lambda x: gdal.Open(os.path.join(wd+'/features/', x)), fl_names))
# files = list(map(lambda x: gdal.Open(os.path.join(wd, x)), fl_names))
arrays = list(map(lambda x: x.ReadAsArray().flatten(), files))
shapes = [x.ReadAsArray().shape for x in files]
nodatas = list(map(lambda x: x.GetRasterBand(1).GetNoDataValue(), files))
names = list(map(lambda x: x.replace('.tif','').split('.')[0], fl_names))
if dem_dir is None:
dem_raw = gdal.Open(wd+'/dem.tif')
dem = dem_raw.ReadAsArray()
else:
dem_raw = gdal.Open(dem_dir)
dem = dem_raw.ReadAsArray()
dem_flat = dem.flatten()
dem_nodata = dem_raw.GetRasterBand(1).GetNoDataValue()
init_dem_shape = dem.shape
idx_nodata_0 = np.where(dem_flat == dem_nodata)[0]
arrays_no_nodatas = np.zeros((len(arrays[0])-len(idx_nodata_0), len(arrays)))
idx_dem_nodata = np.where(dem_flat == dem_nodata)[0]
idx_dem = np.where(dem_flat != dem_nodata)[0]
# print(idx_dem.shape)
dem_no_nodata = np.delete(dem_flat, idx_dem_nodata)
for i in range(len(arrays)):
idx_nodata = np.where(arrays[i] == nodatas[i])[0]
array = arrays[i].copy()
array[idx_nodata]=0
arrays_no_nodatas[:,i] = np.delete(array, idx_nodata_0)
data_arr = arrays_no_nodatas.copy()
# Prepare data
# U can normilize data, and/or add coords to it
mode = data_m # Change to 0, 1, 2 or 3
X, fn_X_embedded = gen_input(mode, data_arr, shapes, idx_dem)
self.X = X
# X = np.vstack((X, X[:1,:]))
return X, dem_flat, dem_nodata, init_dem_shape, idx_dem
def create_polygon(self, shape, vertices, value=1):
"""
Creates np.array with dimensions defined by shape
Fills polygon defined by vertices with ones, all other values zero"""
base_array = np.zeros(shape, dtype=float) # Initialize your array of zeros
fill = np.ones(base_array.shape) * True # Initialize boolean array defining shape fill
# Create check array for each edge segment, combine into fill array
for k in range(vertices.shape[0]):
fill = np.all([fill, self.check(vertices[k-1], vertices[k], base_array)], axis=0)
# Set all values inside polygon to one
base_array[fill] = value
return base_array,fill
def check(self, p1, p2, base_array):
"""
Uses the line defined by p1 and p2 to check array of
input indices against interpolated value
Returns boolean array, with True inside and False outside of shape
"""
idxs = np.indices(base_array.shape) # Create 3D array of indices
p1 = p1.astype(float)
p2 = p2.astype(float)
# Calculate max column idx for each row idx based on interpolated line between two points
if p1[0] == p2[0]:
max_col_idx = (idxs[0] - p1[0]) * idxs.shape[1]
sign = np.sign(p2[1] - p1[1])
else:
max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
sign = np.sign(p2[0] - p1[0])
return idxs[1] * sign <= max_col_idx * sign
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--soil_parameter', type=str, default='Moisture_perc_10', help='soil feature targer', required=True)
args = parser.parse_args()
csv_file_to_process = 'kshen_new_data_final.csv'
df_name = list(pd.read_csv(csv_file_to_process, sep=',').columns)
np.random.seed(42)
for parameter in [str(args.soil_parameter)]:
print(parameter)
field_parameter = parameter
df_for_plots = pd.DataFrame(columns=['Sampling', 'Points', 'Value'])
dict_for_parameter = {'MAXVOL':{},
'cLHS':{},
'Random':{}}
dict_for_wasserstein = {'MAXVOL':{},
'cLHS':{},
'Random':{}}
dict_for_plots = {'MAXVOL':{},
'cLHS':{},
'Random':{}}
dict_for_statistic_ks = {'MAXVOL':{},
'cLHS':{},
'Random':{}}
dict_for_pvalue = {'MAXVOL':{},
'cLHS':{},
'Random':{}}
number_of_points = range(3,51)
#Read indices of points for cLHS
# res = !ls one_mi*
for num_points in number_of_points:
SAR = Divergence()
SAR.soil_feature = field_parameter
SAR.num_of_points = num_points
SAR.soil_data = pd.read_csv('kshen_new_data_final.csv', sep=',')
SAR.path_to_file_with_indices = None
SAR.wd = '.'
_ =SAR.data_preparation(SAR.wd, data_m=3, dem_dir = None)
SAR.distr_from_voronoi()
list_for_ks = []
list_for_random_plots = []
cLHS_statistic = []
cLHS_pValue = []
cLHS_wasserstein = []
for iteration in range(100):
cLHS = SAR.i_am_clhs(10000)
cLHS_dist = SAR.voronoi_map[cLHS]
list_for_random_plots.append(cLHS_dist)
print('cLHS iteration:',iteration)
cLHS_statistic.append(ks_2samp(SAR.original_data, cLHS_dist)[0])
cLHS_pValue.append(ks_2samp(SAR.original_data, cLHS_dist)[1])
cLHS_wasserstein.append(wasserstein_distance(SAR.original_data, cLHS_dist))
dict_for_statistic_ks['cLHS'][num_points] = cLHS_statistic
dict_for_pvalue['cLHS'][num_points] = cLHS_pValue
dict_for_wasserstein['cLHS'][num_points] = cLHS_wasserstein
#MAXVOL
MAXVOL = SAR.voronoi_map[SAR.i_am_maxvol_function()]
dict_for_wasserstein['MAXVOL'][num_points] = wasserstein_distance(SAR.original_data, MAXVOL)
dict_for_statistic_ks['MAXVOL'][num_points] = ks_2samp(SAR.original_data, MAXVOL)[0]
dict_for_pvalue['MAXVOL'][num_points] = ks_2samp(SAR.original_data, MAXVOL)[1]
# print('stat',ks_2samp(SAR.original_data, MAXVOL)[0])
random_statistic=[]
random_pvalue = []
random_wasserstein = []
for iteration in range(100):
RANDOM = SAR.voronoi_map[SAR.i_am_random()]
random_statistic.append(ks_2samp(SAR.original_data, RANDOM)[0])
random_pvalue.append(ks_2samp(SAR.original_data, RANDOM)[1])
random_wasserstein.append(wasserstein_distance(SAR.original_data, RANDOM))
dict_for_statistic_ks['Random'][num_points] = random_statistic
dict_for_pvalue['Random'][num_points] = random_pvalue
dict_for_wasserstein['Random'][num_points] = random_wasserstein
np.save('./npy_files/'+parameter+'_wasserstein.npy', dict_for_wasserstein)
np.save('./npy_files/'+parameter+'_ks_distance.npy', dict_for_statistic_ks)
np.save('./npy_files/'+parameter+'_pvalue.npy', dict_for_pvalue)
print('Finished!')
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] | 1.973339 | 4,426 |
# Tool to prepare foggy lidar test set from Oxford RobotCar dataset.
# Adapted from https://github.com/princeton-computational-imaging/SeeingThroughFog/blob/master/tools/DatasetFoggification/lidar_foggification.py
# Licensed under the Apache License
import argparse, tqdm, os, errno
import numpy as np
from transform import build_se3_transform
LIDAR_FOG_PATH = None
LIDAR_HISTORY_FOG_PATH = None
LIDAR_PATH = None
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Prepare foggy lidar test set from Oxford RobotCar radar dataset')
parser.add_argument('--data_path', type=str, required=True, help='path to the data record folder')
parser.add_argument('--beta', type=float, required=True, help='fog density (0.005 - 0.08)')
args = parser.parse_args()
processed_path = os.path.join(args.data_path, 'processed')
if not os.path.isdir(processed_path):
os.mkdir(processed_path)
LIDAR_FOG_PATH = os.path.join(processed_path, 'lidar_fog_' + str(args.beta))
if not os.path.isdir(LIDAR_FOG_PATH):
os.mkdir(LIDAR_FOG_PATH)
LIDAR_HISTORY_FOG_PATH = os.path.join(processed_path, 'lidar_history_fog_' + str(args.beta))
if not os.path.isdir(LIDAR_HISTORY_FOG_PATH):
os.mkdir(LIDAR_HISTORY_FOG_PATH)
LIDAR_PATH = os.path.join(args.data_path, 'processed', 'lidar')
assert os.path.isdir(LIDAR_PATH), "Lidar data is missing, please generate lidar data first!"
main(args) | [
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] | 2.55614 | 570 |
__author__ = 'sibirrer'
from lenstronomy.LensModel.single_plane import SinglePlane
from lenstronomy.LensModel.MultiPlane.multi_plane import MultiPlane
from lenstronomy.Cosmo.lens_cosmo import LensCosmo
from lenstronomy.Util import constants as const
__all__ = ['LensModel']
class LensModel(object):
"""
class to handle an arbitrary list of lens models. This is the main lenstronomy LensModel API for all other modules.
"""
def __init__(self, lens_model_list, z_lens=None, z_source=None, lens_redshift_list=None, cosmo=None,
multi_plane=False, numerical_alpha_class=None, observed_convention_index=None,
z_source_convention=None, cosmo_interp=False, z_interp_stop=None, num_z_interp=100,
kwargs_interp=None):
"""
:param lens_model_list: list of strings with lens model names
:param z_lens: redshift of the deflector (only considered when operating in single plane mode).
Is only needed for specific functions that require a cosmology.
:param z_source: redshift of the source: Needed in multi_plane option only,
not required for the core functionalities in the single plane mode.
:param lens_redshift_list: list of deflector redshift (corresponding to the lens model list),
only applicable in multi_plane mode.
:param cosmo: instance of the astropy cosmology class. If not specified, uses the default cosmology.
:param multi_plane: bool, if True, uses multi-plane mode. Default is False.
:param numerical_alpha_class: an instance of a custom class for use in NumericalAlpha() lens model
(see documentation in Profiles/numerical_alpha)
:param kwargs_interp: interpolation keyword arguments specifying the numerics.
See description in the Interpolate() class. Only applicable for 'INTERPOL' and 'INTERPOL_SCALED' models.
:param observed_convention_index: a list of indices, corresponding to the lens_model_list element with same
index, where the 'center_x' and 'center_y' kwargs correspond to observed (lensed) positions, not physical
positions. The code will compute the physical locations when performing computations
:param z_source_convention: float, redshift of a source to define the reduced deflection angles of the lens
models. If None, 'z_source' is used.
:param cosmo_interp: boolean (only employed in multi-plane mode), interpolates astropy.cosmology distances for
faster calls when accessing several lensing planes
:param z_interp_stop: (only in multi-plane with cosmo_interp=True); maximum redshift for distance interpolation
This number should be higher or equal the maximum of the source redshift and/or the z_source_convention
:param num_z_interp: (only in multi-plane with cosmo_interp=True); number of redshift bins for interpolating
distances
"""
self.lens_model_list = lens_model_list
self.z_lens = z_lens
self.z_source = z_source
self._z_source_convention = z_source_convention
self.redshift_list = lens_redshift_list
if cosmo is None:
from astropy.cosmology import default_cosmology
cosmo = default_cosmology.get()
self.cosmo = cosmo
self.multi_plane = multi_plane
if multi_plane is True:
if z_source is None:
raise ValueError('z_source needs to be set for multi-plane lens modelling.')
self.lens_model = MultiPlane(z_source, lens_model_list, lens_redshift_list, cosmo=cosmo,
numerical_alpha_class=numerical_alpha_class,
observed_convention_index=observed_convention_index,
z_source_convention=z_source_convention, cosmo_interp=cosmo_interp,
z_interp_stop=z_interp_stop, num_z_interp=num_z_interp,
kwargs_interp=kwargs_interp)
else:
self.lens_model = SinglePlane(lens_model_list, numerical_alpha_class=numerical_alpha_class,
lens_redshift_list=lens_redshift_list,
z_source_convention=z_source_convention, kwargs_interp=kwargs_interp)
if z_lens is not None and z_source is not None:
self._lensCosmo = LensCosmo(z_lens, z_source, cosmo=cosmo)
def ray_shooting(self, x, y, kwargs, k=None):
"""
maps image to source position (inverse deflection)
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:return: source plane positions corresponding to (x, y) in the image plane
"""
return self.lens_model.ray_shooting(x, y, kwargs, k=k)
def fermat_potential(self, x_image, y_image, kwargs_lens, x_source=None, y_source=None):
"""
Fermat potential (negative sign means earlier arrival time)
for Multi-plane lensing, it computes the effective Fermat potential (derived from the arrival time and
subtracted off the time-delay distance for the given cosmology). The units are given in arcsecond square.
:param x_image: image position
:param y_image: image position
:param x_source: source position
:param y_source: source position
:param kwargs_lens: list of keyword arguments of lens model parameters matching the lens model classes
:return: fermat potential in arcsec**2 without geometry term (second part of Eqn 1 in Suyu et al. 2013) as a list
"""
if hasattr(self.lens_model, 'fermat_potential'):
return self.lens_model.fermat_potential(x_image, y_image, kwargs_lens, x_source, y_source)
elif hasattr(self.lens_model, 'arrival_time') and hasattr(self, '_lensCosmo'):
dt = self.lens_model.arrival_time(x_image, y_image, kwargs_lens)
fermat_pot_eff = dt * const.c / self._lensCosmo.ddt / const.Mpc * const.day_s / const.arcsec ** 2
return fermat_pot_eff
else:
raise ValueError('In multi-plane lensing you need to provide a specific z_lens and z_source for which the '
'effective Fermat potential is evaluated')
def arrival_time(self, x_image, y_image, kwargs_lens, kappa_ext=0, x_source=None, y_source=None):
"""
Arrival time of images relative to a straight line without lensing.
Negative values correspond to images arriving earlier, and positive signs correspond to images arriving later.
:param x_image: image position
:param y_image: image position
:param kwargs_lens: lens model parameter keyword argument list
:param kappa_ext: external convergence contribution not accounted in the lens model that leads to the same
observables in position and relative fluxes but rescales the time delays
:param x_source: source position (optional), otherwise computed with ray-tracing
:param y_source: source position (optional), otherwise computed with ray-tracing
:return: arrival time of image positions in units of days
"""
if hasattr(self.lens_model, 'arrival_time'):
arrival_time = self.lens_model.arrival_time(x_image, y_image, kwargs_lens)
else:
fermat_pot = self.lens_model.fermat_potential(x_image, y_image, kwargs_lens, x_source=x_source,
y_source=y_source)
if not hasattr(self, '_lensCosmo'):
raise ValueError("LensModel class was not initialized with lens and source redshifts!")
arrival_time = self._lensCosmo.time_delay_units(fermat_pot)
arrival_time *= (1 - kappa_ext)
return arrival_time
def potential(self, x, y, kwargs, k=None):
"""
lensing potential
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:return: lensing potential in units of arcsec^2
"""
return self.lens_model.potential(x, y, kwargs, k=k)
def alpha(self, x, y, kwargs, k=None, diff=None):
"""
deflection angles
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: None or float. If set, computes the deflection as a finite numerical differential of the lensing
potential. This differential is only applicable in the single lensing plane where the form of the lensing
potential is analytically known
:return: deflection angles in units of arcsec
"""
if diff is None:
return self.lens_model.alpha(x, y, kwargs, k=k)
elif self.multi_plane is False:
return self._deflection_differential(x, y, kwargs, k=k, diff=diff)
else:
raise ValueError('numerical differentiation of lensing potential is not available in the multi-plane '
'setting as analytical form of lensing potential is not available.')
def hessian(self, x, y, kwargs, k=None, diff=None, diff_method='square'):
"""
hessian matrix
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: float, scale over which the finite numerical differential is computed. If None, then using the
exact (if available) differentials.
:param diff_method: string, 'square' or 'cross', indicating whether finite differentials are computed from a
cross or a square of points around (x, y)
:return: f_xx, f_xy, f_yx, f_yy components
"""
if diff is None:
return self.lens_model.hessian(x, y, kwargs, k=k)
elif diff_method == 'square':
return self._hessian_differential_square(x, y, kwargs, k=k, diff=diff)
elif diff_method == 'cross':
return self._hessian_differential_cross(x, y, kwargs, k=k, diff=diff)
else:
raise ValueError('diff_method %s not supported. Chose among "square" or "cross".' % diff_method)
def kappa(self, x, y, kwargs, k=None, diff=None, diff_method='square'):
"""
lensing convergence k = 1/2 laplacian(phi)
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: float, scale over which the finite numerical differential is computed. If None, then using the
exact (if available) differentials.
:param diff_method: string, 'square' or 'cross', indicating whether finite differentials are computed from a
cross or a square of points around (x, y)
:return: lensing convergence
"""
f_xx, f_xy, f_yx, f_yy = self.hessian(x, y, kwargs, k=k, diff=diff, diff_method=diff_method)
kappa = 1./2 * (f_xx + f_yy)
return kappa
def curl(self, x, y, kwargs, k=None, diff=None, diff_method='square'):
"""
curl computation F_xy - F_yx
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: float, scale over which the finite numerical differential is computed. If None, then using the
exact (if available) differentials.
:param diff_method: string, 'square' or 'cross', indicating whether finite differentials are computed from a
cross or a square of points around (x, y)
:return: curl at position (x, y)
"""
f_xx, f_xy, f_yx, f_yy = self.hessian(x, y, kwargs, k=k, diff=diff, diff_method=diff_method)
return f_xy - f_yx
def gamma(self, x, y, kwargs, k=None, diff=None, diff_method='square'):
"""
shear computation
g1 = 1/2(d^2phi/dx^2 - d^2phi/dy^2)
g2 = d^2phi/dxdy
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: float, scale over which the finite numerical differential is computed. If None, then using the
exact (if available) differentials.
:param diff_method: string, 'square' or 'cross', indicating whether finite differentials are computed from a
cross or a square of points around (x, y)
:return: gamma1, gamma2
"""
f_xx, f_xy, f_yx, f_yy = self.hessian(x, y, kwargs, k=k, diff=diff, diff_method=diff_method)
gamma1 = 1./2 * (f_xx - f_yy)
gamma2 = f_xy
return gamma1, gamma2
def magnification(self, x, y, kwargs, k=None, diff=None, diff_method='square'):
"""
magnification
mag = 1/det(A)
A = 1 - d^2phi/d_ij
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: only evaluate the k-th lens model
:param diff: float, scale over which the finite numerical differential is computed. If None, then using the
exact (if available) differentials.
:param diff_method: string, 'square' or 'cross', indicating whether finite differentials are computed from a
cross or a square of points around (x, y)
:return: magnification
"""
f_xx, f_xy, f_yx, f_yy = self.hessian(x, y, kwargs, k=k, diff=diff, diff_method=diff_method)
det_A = (1 - f_xx) * (1 - f_yy) - f_xy*f_yx
return 1./det_A # attention, if dividing by zero
def flexion(self, x, y, kwargs, k=None, diff=0.000001, hessian_diff=True):
"""
third derivatives (flexion)
:param x: x-position (preferentially arcsec)
:type x: numpy array
:param y: y-position (preferentially arcsec)
:type y: numpy array
:param kwargs: list of keyword arguments of lens model parameters matching the lens model classes
:param k: int or None, if set, only evaluates the differential from one model component
:param diff: numerical differential length of Flexion
:param hessian_diff: boolean, if true also computes the numerical differential length of Hessian (optional)
:return: f_xxx, f_xxy, f_xyy, f_yyy
"""
if hessian_diff is not True:
hessian_diff = None
f_xx_dx, f_xy_dx, f_yx_dx, f_yy_dx = self.hessian(x + diff/2, y, kwargs, k=k, diff=hessian_diff)
f_xx_dy, f_xy_dy, f_yx_dy, f_yy_dy = self.hessian(x, y + diff/2, kwargs, k=k, diff=hessian_diff)
f_xx_dx_, f_xy_dx_, f_yx_dx_, f_yy_dx_ = self.hessian(x - diff/2, y, kwargs, k=k, diff=hessian_diff)
f_xx_dy_, f_xy_dy_, f_yx_dy_, f_yy_dy_ = self.hessian(x, y - diff/2, kwargs, k=k, diff=hessian_diff)
f_xxx = (f_xx_dx - f_xx_dx_) / diff
f_xxy = (f_xx_dy - f_xx_dy_) / diff
f_xyy = (f_xy_dy - f_xy_dy_) / diff
f_yyy = (f_yy_dy - f_yy_dy_) / diff
return f_xxx, f_xxy, f_xyy, f_yyy
def set_static(self, kwargs):
"""
set this instance to a static lens model. This can improve the speed in evaluating lensing quantities at
different positions but must not be used with different lens model parameters!
:param kwargs: lens model keyword argument list
:return: kwargs_updated (in case of image position convention in multiplane lensing this is changed)
"""
return self.lens_model.set_static(kwargs)
def set_dynamic(self):
"""
deletes cache for static setting and makes sure the observed convention in the position of lensing profiles in
the multi-plane setting is enabled. Dynamic is the default setting of this class enabling an accurate computation
of lensing quantities with different parameters in the lensing profiles.
:return: None
"""
self.lens_model.set_dynamic()
def _deflection_differential(self, x, y, kwargs, k=None, diff=0.00001):
"""
:param x: x-coordinate
:param y: y-coordinate
:param kwargs: keyword argument list
:param k: int or None, if set, only evaluates the differential from one model component
:param diff: finite differential length
:return: f_x, f_y
"""
phi_dx = self.lens_model.potential(x + diff/2, y, kwargs=kwargs, k=k)
phi_dy = self.lens_model.potential(x, y + diff/2, kwargs=kwargs, k=k)
phi_dx_ = self.lens_model.potential(x - diff/2, y, kwargs=kwargs, k=k)
phi_dy_ = self.lens_model.potential(x, y - diff/2, kwargs=kwargs, k=k)
f_x = (phi_dx - phi_dx_) / diff
f_y = (phi_dy - phi_dy_) / diff
return f_x, f_y
def _hessian_differential_cross(self, x, y, kwargs, k=None, diff=0.00001):
"""
computes the numerical differentials over a finite range for f_xx, f_yy, f_xy from f_x and f_y
The differentials are computed along the cross centered at (x, y).
:param x: x-coordinate
:param y: y-coordinate
:param kwargs: lens model keyword argument list
:param k: int, list of bools or None, indicating a subset of lens models to be evaluated
:param diff: float, scale of the finite differential (diff/2 in each direction used to compute the differential
:return: f_xx, f_xy, f_yx, f_yy
"""
alpha_ra_dx, alpha_dec_dx = self.alpha(x + diff/2, y, kwargs, k=k)
alpha_ra_dy, alpha_dec_dy = self.alpha(x, y + diff/2, kwargs, k=k)
alpha_ra_dx_, alpha_dec_dx_ = self.alpha(x - diff/2, y, kwargs, k=k)
alpha_ra_dy_, alpha_dec_dy_ = self.alpha(x, y - diff/2, kwargs, k=k)
dalpha_rara = (alpha_ra_dx - alpha_ra_dx_) / diff
dalpha_radec = (alpha_ra_dy - alpha_ra_dy_) / diff
dalpha_decra = (alpha_dec_dx - alpha_dec_dx_) / diff
dalpha_decdec = (alpha_dec_dy - alpha_dec_dy_) / diff
f_xx = dalpha_rara
f_yy = dalpha_decdec
f_xy = dalpha_radec
f_yx = dalpha_decra
return f_xx, f_xy, f_yx, f_yy
def _hessian_differential_square(self, x, y, kwargs, k=None, diff=0.00001):
"""
computes the numerical differentials over a finite range for f_xx, f_yy, f_xy from f_x and f_y
The differentials are computed on the square around (x, y). This minimizes curl.
:param x: x-coordinate
:param y: y-coordinate
:param kwargs: lens model keyword argument list
:param k: int, list of booleans or None, indicating a subset of lens models to be evaluated
:param diff: float, scale of the finite differential (diff/2 in each direction used to compute the differential
:return: f_xx, f_xy, f_yx, f_yy
"""
alpha_ra_pp, alpha_dec_pp = self.alpha(x + diff/2, y + diff/2, kwargs, k=k)
alpha_ra_pn, alpha_dec_pn = self.alpha(x + diff/2, y - diff/2, kwargs, k=k)
alpha_ra_np, alpha_dec_np = self.alpha(x - diff / 2, y + diff / 2, kwargs, k=k)
alpha_ra_nn, alpha_dec_nn = self.alpha(x - diff / 2, y - diff / 2, kwargs, k=k)
f_xx = (alpha_ra_pp - alpha_ra_np + alpha_ra_pn - alpha_ra_nn) / diff / 2
f_xy = (alpha_ra_pp - alpha_ra_pn + alpha_ra_np - alpha_ra_nn) / diff / 2
f_yx = (alpha_dec_pp - alpha_dec_np + alpha_dec_pn - alpha_dec_nn) / diff / 2
f_yy = (alpha_dec_pp - alpha_dec_pn + alpha_dec_np - alpha_dec_nn) / diff / 2
return f_xx, f_xy, f_yx, f_yy
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] | 2.399478 | 8,814 |
# -*- coding: utf-8 -*-
import os
try:
import Queue
except ImportError:
import queue as Queue
import re
import socket
import struct
import subprocess
import threading
import time
import traceback
import logging
import cv2
import numpy as np
from .MinicapInstall import *
SCREEN_ORI_LANDSCAPE = 0
SCREEN_ORI_PORTRAIT = 1
PULL_LOOP_RATE = 100
PULL_LOOP_SLEEP_TIME = 1./PULL_LOOP_RATE
DECODE_LOOP_RATE = 100
DECODE_LOOP_SLEEP_TIME = 1./DECODE_LOOP_RATE
LOG = logging.getLogger('minicap')
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] | 2.507614 | 197 |
'''
module that retreives and pack adms input info
'''
import rdflib
import requests
import math
import sys
import os
import json
import rdflib.plugins.sparql.results.jsonresults as jsresult
from collections import namedtuple
from pyproj import Proj, transform
if __name__ == "__main__":
q = shipRegionQuery(sys.argv[1],sys.argv[2],sys.argv[3],sys.argv[4],sys.argv[5],sys.argv[6],sys.argv[7])
res = q.queryRange()
pythonObj = {}
pythonObj["shipIRIs"] = res
print(json.dumps(pythonObj))
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] | 2.382609 | 230 |
# The MIT License (MIT)
#
# Copyright (c) 2015 Lucas Koegel
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from piko import Piko
from datetime import datetime
import csv
import time
import sys
import os.path
import logging, logging.handlers
if __name__ == '__main__':
logger = logging.getLogger()
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
fh = logging.handlers.RotatingFileHandler('pikoToCSV.log', maxBytes=1024*1024*512, backupCount=2)
fh.setLevel(logging.INFO)
format = logging.Formatter("%(asctime)s %(levelname)s: %(message)s")
ch.setFormatter(format)
logger.addHandler(ch)
logger.addHandler(fh)
logging.info('Started')
p = Piko(host='http://192.168.178.123')
FIELDNAMES = ['zeit', 'ost', 'west']
INTERVAL = 30 # seconds
while(True):
try:
string1Current = p.get_string1_current()
string2Current = p.get_string2_current()
string1Voltage = p.get_string1_voltage()
string2Voltage = p.get_string2_voltage()
if (string1Current < 0 or string2Current < 0 or string1Voltage < 0 or string2Voltage < 0):
# Piko is off
logging.info('Piko is off, going to sleep 10 minutes.')
time.sleep(600)
continue
# Calculate power
string1 = round(string1Current * string1Voltage)
string2 = round(string2Current * string2Voltage)
today = datetime.now()
fileName = 'piko-' + today.strftime('%d-%m-%Y') + '.csv'
if (not os.path.isfile(fileName)):
# New File -> write Headers
logging.info('Creating new file... ' + fileName)
with open(fileName, 'w') as newFile:
newFileWriter = csv.DictWriter(newFile, fieldnames=FIELDNAMES)
newFileWriter.writeheader()
with open(fileName, 'a') as csvfile:
# Existing file -> write piko values
logging.info('Writing to file ... ' + fileName)
writer = csv.DictWriter(csvfile, fieldnames=FIELDNAMES)
writer.writerow({'zeit': today.strftime('%X'), 'ost': string1, 'west': string2})
except: # catch *all* exceptions
logging.info("Error:", sys.exc_info()[0])
# Sleep
time.sleep(INTERVAL) | [
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] | 2.497085 | 1,372 |
# Copyright (c) 2009 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'includes': [
'../build/common.gypi',
],
'conditions': [
[ 'OS=="mac"', {
'target_defaults': {
'include_dirs': [
'src',
],
'configurations': {
'Debug': {
'defines': [
# This is needed for GTMLogger to work correctly.
'DEBUG',
],
},
},
},
'targets': [
{
'target_name': 'breakpad_utilities',
'type': '<(library)',
'sources': [
'src/common/convert_UTF.c',
'src/client/mac/handler/dynamic_images.cc',
'src/common/mac/file_id.cc',
'src/common/mac/MachIPC.mm',
'src/common/mac/macho_id.cc',
'src/common/mac/macho_utilities.cc',
'src/common/mac/macho_walker.cc',
'src/client/minidump_file_writer.cc',
'src/client/mac/handler/minidump_generator.cc',
'src/common/mac/SimpleStringDictionary.mm',
'src/common/string_conversion.cc',
'src/common/mac/string_utilities.cc',
],
'link_settings': {
'libraries': ['$(SDKROOT)/usr/lib/libcrypto.dylib'],
}
},
{
'target_name': 'crash_inspector',
'type': 'executable',
'dependencies': [
'breakpad_utilities',
],
'sources': [
'src/client/mac/crash_generation/Inspector.mm',
'src/client/mac/crash_generation/InspectorMain.mm',
],
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/Foundation.framework',
],
}
},
{
'target_name': 'crash_report_sender',
'type': 'executable',
'mac_bundle': 1,
'sources': [
'src/common/mac/HTTPMultipartUpload.m',
'src/client/mac/sender/crash_report_sender.m',
'src/common/mac/GTMLogger.m',
],
'mac_bundle_resources': [
'src/client/mac/sender/English.lproj/Localizable.strings',
'src/client/mac/sender/crash_report_sender.icns',
'src/client/mac/sender/Breakpad.nib',
'src/client/mac/sender/crash_report_sender-Info.plist',
],
'mac_bundle_resources!': [
'src/client/mac/sender/crash_report_sender-Info.plist',
],
'xcode_settings': {
'INFOPLIST_FILE': 'src/client/mac/sender/crash_report_sender-Info.plist',
},
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/AppKit.framework',
'$(SDKROOT)/System/Library/Frameworks/Foundation.framework',
'$(SDKROOT)/System/Library/Frameworks/SystemConfiguration.framework',
],
}
},
{
'target_name': 'dump_syms',
'type': 'executable',
'include_dirs': [
'src/common/mac',
],
'dependencies': [
'breakpad_utilities',
],
'sources': [
'src/common/mac/dwarf/bytereader.cc',
'src/common/mac/dwarf/dwarf2reader.cc',
'src/common/mac/dwarf/functioninfo.cc',
'src/common/mac/dump_syms.mm',
'src/tools/mac/dump_syms/dump_syms_tool.mm',
],
'xcode_settings': {
# The DWARF utilities require -funsigned-char.
'GCC_CHAR_IS_UNSIGNED_CHAR': 'YES',
},
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/Foundation.framework',
],
}
},
{
'target_name': 'symupload',
'type': 'executable',
'include_dirs': [
'src/common/mac',
],
'sources': [
'src/common/mac/HTTPMultipartUpload.m',
'src/tools/mac/symupload/symupload.m',
],
'link_settings': {
'libraries': [
'$(SDKROOT)/System/Library/Frameworks/Foundation.framework',
],
}
},
{
'target_name': 'breakpad',
'type': '<(library)',
'dependencies': [
'breakpad_utilities',
'crash_inspector',
'crash_report_sender',
],
'sources': [
'src/client/mac/handler/protected_memory_allocator.cc',
'src/client/mac/handler/exception_handler.cc',
'src/client/mac/Framework/Breakpad.mm',
'src/client/mac/Framework/OnDemandServer.mm',
],
},
],
}],
[ 'OS=="win"', {
'targets': [
{
'target_name': 'breakpad_handler',
'type': '<(library)',
'msvs_guid': 'B55CA863-B374-4BAF-95AC-539E4FA4C90C',
'sources': [
'src/client/windows/crash_generation/client_info.cc',
'src/client/windows/crash_generation/client_info.h',
'src/client/windows/crash_generation/crash_generation_client.cc',
'src/client/windows/crash_generation/crash_generation_client.h',
'src/client/windows/crash_generation/crash_generation_server.cc',
'src/client/windows/crash_generation/crash_generation_server.h',
'src/client/windows/handler/exception_handler.cc',
'src/client/windows/handler/exception_handler.h',
'src/common/windows/guid_string.cc',
'src/common/windows/guid_string.h',
'src/google_breakpad/common/minidump_format.h',
'src/client/windows/crash_generation/minidump_generator.cc',
'src/client/windows/crash_generation/minidump_generator.h',
'src/common/windows/string_utils-inl.h',
],
'include_dirs': [
'src',
],
'link_settings': {
'libraries': [
'-lurlmon.lib',
],
},
'direct_dependent_settings': {
'include_dirs': [
'src',
],
},
},
{
'target_name': 'breakpad_sender',
'type': '<(library)',
'msvs_guid': '9946A048-043B-4F8F-9E07-9297B204714C',
'sources': [
'src/client/windows/sender/crash_report_sender.cc',
'src/common/windows/http_upload.cc',
'src/client/windows/sender/crash_report_sender.h',
'src/common/windows/http_upload.h',
],
'include_dirs': [
'src',
],
'link_settings': {
'libraries': [
'-lurlmon.lib',
],
},
'direct_dependent_settings': {
'include_dirs': [
'src',
],
},
},
],
}],
[ 'OS=="linux"', {
'conditions': [
# Tools needed for archiving official build symbols.
['branding=="Chrome"', {
'targets': [
{
'target_name': 'symupload',
'type': 'executable',
# This uses the system libcurl, so don't use the default 32-bit
# compile flags when building on a 64-bit machine.
'variables': {
'host_arch': '<!(uname -m)',
},
'conditions': [
['host_arch=="x86_64"', {
'cflags!': ['-m32', '-march=pentium4', '-msse2',
'-mfpmath=sse'],
'ldflags!': ['-m32'],
'cflags': ['-O2'],
}],
],
'sources': [
'src/tools/linux/symupload/sym_upload.cc',
'src/common/linux/http_upload.cc',
],
'include_dirs': [
'src',
],
'link_settings': {
'libraries': [
'-ldl',
],
},
},
{
'target_name': 'dump_syms',
'type': 'executable',
'sources': [
'linux/dump_syms.cc',
'linux/dump_symbols.cc',
'linux/dump_symbols.h',
'linux/file_id.cc',
'linux/file_id.h',
],
'include_dirs': [
'src',
'..',
],
},
],
}],
],
'targets': [
{
'target_name': 'breakpad_client',
'type': '<(library)',
'sources': [
'linux/exception_handler.cc',
'linux/linux_dumper.cc',
'linux/minidump_writer.cc',
'src/common/linux/guid_creator.cc',
'src/common/string_conversion.cc',
'src/common/convert_UTF.c',
# TODO(agl): unfork this file
'linux/minidump_file_writer.cc',
],
'include_dirs': [
'src',
'..',
'.',
],
},
{
'target_name': 'breakpad_unittests',
'type': 'executable',
'dependencies': [
'../testing/gtest.gyp:gtest',
'../testing/gtest.gyp:gtestmain',
'breakpad_client',
],
'sources': [
'linux/directory_reader_unittest.cc',
'linux/exception_handler_unittest.cc',
'linux/line_reader_unittest.cc',
'linux/linux_dumper_unittest.cc',
'linux/linux_libc_support_unittest.cc',
'linux/memory_unittest.cc',
'linux/minidump_writer_unittest.cc',
],
'include_dirs': [
'src',
'..',
'.',
],
},
{
'target_name': 'generate_test_dump',
'type': 'executable',
'sources': [
'linux/generate-test-dump.cc',
],
'dependencies': [
'breakpad_client',
],
'include_dirs': [
'..',
],
},
{
'target_name': 'minidump_2_core',
'type': 'executable',
'sources': [
'linux/minidump-2-core.cc',
],
'include_dirs': [
'src',
'..',
],
},
],
}],
],
}
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] | 1.706916 | 6,203 |
#!/usr/bin/env/python
import os
import sys
import psycopg2
from sqlalchemy import Column, ForeignKey, Integer, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, validates
from sqlalchemy_utils import database_exists, create_database
Base = declarative_base()
engine = create_engine('postgresql://catuser:dbpass@localhost:5432/instrumentgarage')
if not database_exists(engine.url):
create_database(engine.url)
Base.metadata.create_all(engine)
| [
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] | 2.889474 | 190 |
# coding: utf-8
# Nuevo proyecto
import webapp2
import time
from webapp2_extras import jinja2
from google.appengine.ext import ndb
from model.proyecto import Proyecto
app = webapp2.WSGIApplication([
('/proyectos/nuevo', NuevoProyectoHandler)
], debug=True)
| [
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] | 2.436364 | 110 |
# Django Imports
from django.urls import path, include
# Standard Package Imports
# Project Imports
from . import views
# Third Party Imports
app_name = 'stock'
urlpatterns = [
# path('', views.home, name='home'),
path('', views.risk_return, name='risk-return'),
path('portfolio/contact-form/', views.contact_form, name='contact_form'),
# path('get_quote/', views.get_quote, name='get_quote'),
# path('<int:year>/', views.diary),
# path('<int:year>/<str:name>/', views.diary),
] | [
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] | 2.721925 | 187 |
#! /usr/bin/env python
import sys
import socket
import time
import subprocess
import subprocess32
import os
includeos_src = os.environ.get('INCLUDEOS_SRC',
os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))).split('/test')[0])
sys.path.insert(0,includeos_src)
from vmrunner import vmrunner
from vmrunner.prettify import color
test_name="Stresstest"
name_tag = "<" + test_name + ">"
# We assume malloc will increase / decrease heap pagewise
PAGE_SIZE = 4096
BURST_SIZE = 1000
BURST_COUNT = 10
BURST_INTERVAL = 0.1
HOST = "10.0.0.42"
PORT_FLOOD = 4242
PORT_MEM = 4243
memuse_at_start = 0
sock_timeout = 20
# Boot the VM, taking a timeout as parameter
thread_timeout = BURST_COUNT * 30
# It's to be expected that the VM allocates more room during the running of tests
# e.g. for containers, packets etc. These should all be freed after a run.
acceptable_increase = 12 * PAGE_SIZE
# A persistent connection to the VM for getting memory info
# TODO: This should be expanded to check more vital signs, such as time of day,
# connection / packet statistics (>= what we have sent) etc.
sock_mem = socket.socket
sock_mem = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
heap_verified = False
# Fire a single burst of UDP packets
# Fire a single burst of ICMP packets
# Fire a single burst of HTTP requests
# Fire a single burst of ARP requests
# Fire several bursts, e.g. trigger a function that fires bursts, several times
# Trigger several UDP bursts
# Trigger several UDP bursts
# Trigger several ICMP bursts
# Trigger several HTTP-brusts
# Get an auto-created VM from the vmrunner
vm = vmrunner.vms[0]
# Check for vital signs after all the bombardment is done
# Wait for sockets to exit TIME_WAIT status
# Add custom event-handlers
vm.on_output("Heap functioning as expected", heap_ok)
vm.on_output("Ready to start", crash_test)
vm.on_output("Ready for ARP", ARP)
vm.on_output("Ready for UDP", UDP)
vm.on_output("Ready for ICMP", ICMP)
vm.on_output("Ready for TCP", TCP)
vm.on_output("Ready to end", check_vitals)
if len(sys.argv) > 1:
thread_timeout = int(sys.argv[1])
if len(sys.argv) > 3:
BURST_COUNT = int(sys.argv[2])
BURST_SIZE = int(sys.argv[3])
print color.HEADER(test_name + " initializing")
print color.INFO(name_tag),"configured for ", BURST_COUNT,"bursts of", BURST_SIZE, "packets each"
vm.cmake().boot(thread_timeout).clean()
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16663,
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48678,
737,
27773,
3419,
198
] | 2.890346 | 839 |
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