content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
# iframe does not fire onload event if the response's content-type is not
# text/plain or text/html so this script exists if you want to test a 404 load
# in an iframe.
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# -*- coding: utf-8 -*-
from __future__ import absolute_import
import pytest
import salt.modules.chocolatey as choco
import salt.utils.platform
from tests.support.case import ModuleCase
from tests.support.helpers import destructiveTest
from tests.support.unit import skipIf
@skipIf(not salt.utils.platform.is_windows(), "Tests for only Windows")
@pytest.mark.windows_whitelisted
class ChocolateyModuleTest(ModuleCase):
"""
Validate Chocolatey module
"""
@destructiveTest
def setUp(self):
"""
Ensure that Chocolatey is installed
"""
self._chocolatey_bin = choco._find_chocolatey()
if "ERROR" in self._chocolatey_bin:
# self.fail("Chocolatey is not installed")
self.run_function("chocolatey.bootstrap")
super(ChocolateyModuleTest, self).setUp()
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88,
26796,
14402,
11,
2116,
737,
2617,
4933,
3419,
198
] | 2.684713 | 314 |
# Desenvolva um algoritmo em Python que exiba os números de 1 a 10
for i in range(1,11,1):
print(i) | [
2,
2935,
268,
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8
] | 2.395349 | 43 |
""" Authentication Managment API """
import json
from flask import jsonify, request
from flask_restful import Resource, reqparse
from aaxus import rest_api
from aaxus.models.user import User
from aaxus.models.token import RevokedToken
from aaxus.services.confirm_email import generate_confirmation_token, confirm_token
from itsdangerous import URLSafeTimedSerializer
from flask_jwt_extended import (create_access_token, create_refresh_token, jwt_required, jwt_refresh_token_required, get_jwt_identity, get_raw_jwt)
parser = reqparse.RequestParser()
parser.add_argument('username', help = 'This field cannot be blank', required = True)
parser.add_argument('password', help = 'This field cannot be blank', required = True) | [
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705,
1212,
2214,
2314,
307,
9178,
3256,
2672,
796,
6407,
8
] | 3.5 | 206 |
from widgets.button import DraggableButton
from prefabs.inputter import pressed
from base_node import get_surface
from widgets.progressbar import *
from prefabs.surface import blit
from shape import Circle
import pygame
__all__ = [
'HSeekbar'
]
| [
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] | 3.405405 | 74 |
from flask import current_app
from flask_login import UserMixin, AnonymousUserMixin
import hashlib
from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
from werkzeug.security import generate_password_hash, check_password_hash
from . import db, login_manager
login_manager.anonymous_user = AnonymousUser
@login_manager.user_loader
# 积分兑换商品 | [
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"""
Utility functions to support logging for the fitbenchmarking
project.
"""
import logging
import sys
def setup_logger(log_file='./fitbenchmarking.log', name='fitbenchmarking',
append=False, level='INFO'):
"""
Define the location and style of the log file.
:param log_file: path to the log file, defaults to './fitbenchmarking.log'
:type log_file: str, optional
:param name: The name of the logger to run the setup for,
defaults to fitbenchmarking
:type name: str, optional
:param append: Whether to append to the log or create a new one,
defaults to False
:type append: bool, optional
:param level: The level of error to print, defaults to 'INFO'
:type level: str, optional
"""
FORMAT = '[%(asctime)s] %(levelname)s %(filename)s: %(message)s'
formatter = logging.Formatter(FORMAT, "%H:%M:%S")
handler = logging.FileHandler(log_file, mode='a' if append else 'w')
handler.setFormatter(formatter)
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
for h in logger.handlers:
logger.removeHandler(h)
logger.addHandler(handler)
# Define a Handler which writes <level> or higher messages to console
levels = {'CRITICAL': logging.CRITICAL,
'ERROR': logging.ERROR,
'WARNING': logging.WARNING,
'INFO': logging.INFO,
'DEBUG': logging.DEBUG,
'NOTSET': logging.NOTSET}
log_level = levels.get(level.upper(), logging.INFO)
console = logging.StreamHandler(sys.stdout)
console.setLevel(log_level)
logger.addHandler(console)
logger.propagate = False
def get_logger(name='fitbenchmarking'):
"""
Get the unique logger for the given name.
This is a straight pass through but will be more intutive for people who
have not used python logging.
:param name: Name of the logger to use, defaults to 'fitbenchmarking'
:type name: str, optional
:return: The named logger
:rtype: logging.Logger
"""
return logging.getLogger(name)
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220,
1441,
18931,
13,
1136,
11187,
1362,
7,
3672,
8,
198
] | 2.644472 | 796 |
import numpy as np
from astropy.wcs import WCS
from glue.core import Data, DataCollection
from glue.plugins.wcs_autolinking.wcs_autolinking import wcs_autolink, WCSLink
from glue.core.link_helpers import MultiLink
from glue.core.tests.test_state import clone
from glue.dialogs.link_editor.state import EditableLinkFunctionState
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] | 3.37 | 100 |
import logging
import logging.config
import yaml
class StderrFilter(logging.Filter):
"""Simple filter which only outputs the following levels: WARNING, ERROR, CRITICAL.
"""
class FormatRecordFactory(logging.LogRecord):
"""A factory which formats messages with str.format."""
def getMessage(self):
"""
Return the message for this LogRecord, formatted with str.format.
Return the message for this LogRecord after merging any user-supplied
arguments with the message by using str.format.
"""
msg = str(self.msg)
if self.args:
msg = msg.format(self.args)
return msg
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] | 2.832618 | 233 |
from django.shortcuts import render, redirect
from django.views.generic import TemplateView
from .forms import BrukerForm
from .models import bruker
from vaskelister.models import Vaskeliste
from studentby.models import studentby
from kollektiv.models import kollektiv
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] | 3.69863 | 73 |
# -*- coding: utf-8 -*-
# @Time : 2020/12/27 10:58 PM
# @Author : Kevin
from src.utils.sentence_process import cut_sentence_by_character
from tqdm import tqdm
from src import config
if __name__ == '__main__':
# cut_chat_data_by_character()
clean_blank_pair()
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] | 2.513761 | 109 |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
import math
from scipy.optimize import fsolve
#Define Link Lengths
l1 = 2
l2 = 4.977
l3 = 2
l4 = 4
l5 = 1.5
l6 = 6
#Initial Motorized Joint Angles
theta_1 = -(math.pi/180)*110
theta_4 = -(math.pi/180)*80
#Fixed Joint A Position
A_x = 0
A_y = 0
#Fixed Joint E Position
E_x = 1.5
E_y = 0
#Initial Joint B Position
B_x = l1*math.cos(theta_1)
B_y = l1*math.sin(theta_1)
#Initial Joint D Position
D_x = l5+l4*math.cos(theta_4)
D_y = l4*math.sin(theta_4)
#Initial Joint C Position
C_x = 2.194879908034972
C_y = -5.93923099142825
theta_3 = -math.acos((C_x-D_x)/math.sqrt(((C_x-D_x)**2+(C_y-D_y)**2)))
print(theta_3)
#Initial Joint F Position
F_x = C_x+l6*math.cos(theta_3)
F_y = C_y+l6*math.sin(theta_3)
def calc_distance(p1, p2):
'''
p1: coordinates of the first point; it is a tuple
p2: coordinates of the second point; it is a tuple
returns the distance
'''
distance = math.sqrt((p1[0]-p2[0])**2+(p1[1]-p2[1])**2)
return distance
# Create the figure and the line that we will manipulate
fig, ax = plt.subplots()
link1, = plt.plot([A_x, B_x], [A_y, B_y], color="black")
link2, = plt.plot([C_x, B_x], [C_y, B_y], color="black")
link3, = plt.plot([C_x, D_x], [C_y, D_y], color="black")
link4, = plt.plot([E_x, D_x], [E_y, D_y], color="black")
link6, = plt.plot([C_x, F_x], [C_y, F_y], color="black")
jointA, = plt.plot(A_x, A_y, 'o', markersize=3, color="red")
jointB, = plt.plot(B_x, B_y, 'o', markersize=3, color="red")
jointC, = plt.plot(C_x, C_y, 'o', markersize=3, color="red")
jointD, = plt.plot(D_x, D_y, 'o', markersize=3, color="red")
jointE, = plt.plot(E_x, E_y, 'o', markersize=3, color="red")
jointF, = plt.plot(F_x, F_y, 'o', markersize=3, color="red")
ax.set_xlim(-3, 6)
ax.set_ylim(-15, 1)
# adjust the main plot to make room for the sliders
plt.subplots_adjust(left=0.25, bottom=0.25)
# Make a horizontal slider to control the frequency.
ax_motor1 = plt.axes([0.25, 0.1, 0.65, 0.03])
motor1 = Slider(
ax=ax_motor1,
label='Motor 1',
valmin=-math.pi,
valmax=math.pi/4,
valinit=theta_1,
)
# Make a vertically oriented slider to control the amplitude
ax_motor2 = plt.axes([0.1, 0.25, 0.0225, 0.63])
motor2 = Slider(
ax=ax_motor2,
label="Motor 2",
valmin=-7*math.pi/6,
valmax=0,
valinit=theta_4,
orientation="vertical"
)
# register the update function with each slider
motor1.on_changed(update)
motor2.on_changed(update)
# Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
resetax = plt.axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', hovercolor='0.975')
button.on_clicked(reset)
plt.show()
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] | 2.149525 | 1,264 |
import unittest
import numpy as np
from RyStats.common import entropy, hypersphere, procrustes_rotation
class TestCommonFunctions(unittest.TestCase):
"""Tests fixture for the common functions."""
def test_entropy(self):
"""Testing entropy calculations."""
# One value in each column should have zero entropy
dataset = np.eye((10))
result = entropy(dataset, axis=1)
self.assertAlmostEqual(result, 0.0, delta=1e-4)
result = entropy(dataset, axis=0)
self.assertAlmostEqual(result, 0.0, delta=1e-4)
# Constant data
dataset = np.ones((10, 5))
result = entropy(dataset, axis=1)
expected = np.log(5) * 10
self.assertAlmostEqual(result, expected, delta=1e-4)
dataset = np.ones((10, 5))
result = entropy(dataset, axis=0)
expected = np.log(10) * 5
self.assertAlmostEqual(result, expected, delta=1e-4)
def test_hypersphere(self):
"""Testing hypersphere calculations."""
# Testing random angles -> cartesian -> angles
rng = np.random.default_rng(34321)
random_angles = rng.uniform(0, np.pi, size=9)
cartesian = hypersphere.hyperspherical_vector(random_angles)
reconstructed_angles = hypersphere.hyperspherical_angles(cartesian)
np.testing.assert_allclose(random_angles, reconstructed_angles)
# Testing cartesian -> angles -> cartesian
cartesian = rng.uniform(-1, 1, 10)
cartesian /= np.linalg.norm(cartesian)
angles = hypersphere.hyperspherical_angles(cartesian)
reconstructed_cartesian = hypersphere.hyperspherical_vector(angles)
np.testing.assert_allclose(cartesian, reconstructed_cartesian)
def test_procustes(self):
"""Testing procustes rotation."""
rng = np.random.default_rng(84913)
dataset = rng.standard_normal(size=(30, 4))
rotation_matrix = rng.uniform(-2, 2, size=(40, 4))
rotation_matrix = rotation_matrix.T @ rotation_matrix
rotation_matrix, _, _ = np.linalg.svd(rotation_matrix)
rotated_dataset = dataset @ rotation_matrix.T
recovered_rotation = procrustes_rotation(dataset,
rotated_dataset)
np.testing.assert_allclose(rotation_matrix,
recovered_rotation)
if __name__ == "__main__":
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] | 2.279851 | 1,072 |
# Copyright 2020 The TensorFlow Authors
#
# 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
#
# https://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.
"""Util functions for rasterization tests."""
import os
import numpy as np
import tensorflow as tf
from tensorflow_graphics.geometry.transformation import look_at
from tensorflow_graphics.rendering.camera import perspective
from tensorflow_graphics.util import shape
def make_perspective_matrix(image_width=None, image_height=None):
"""Generates perspective matrix for a given image size.
Args:
image_width: int representing image width.
image_height: int representing image height.
Returns:
Perspective matrix, tensor of shape [4, 4].
Note: Golden tests require image size to be fixed and equal to the size of
golden image examples. The rest of the camera parameters are set such that
resulting image will be equal to the baseline image.
"""
field_of_view = (40 * np.math.pi / 180,)
near_plane = (0.01,)
far_plane = (10.0,)
return perspective.right_handed(field_of_view,
(float(image_width) / float(image_height),),
near_plane, far_plane)
def make_look_at_matrix(
camera_origin=(0.0, 0.0, 0.0), look_at_point=(0.0, 0.0, 0.0)):
"""Shortcut util function to creat model-to-eye matrix for tests."""
camera_up = (0.0, 1.0, 0.0)
return look_at.right_handed(camera_origin, look_at_point, camera_up)
def compare_images(test_case,
baseline_image,
image,
max_outlier_fraction=0.005,
pixel_error_threshold=0.04):
"""Compares two image arrays.
The comparison is soft: the images are considered identical if fewer than
max_outlier_fraction of the pixels differ by more than pixel_error_threshold
of the full color value.
Differences in JPEG encoding can produce pixels with pretty large variation,
so by default we use 0.04 (4%) for pixel_error_threshold and 0.005 (0.5%) for
max_outlier_fraction.
Args:
test_case: test_case.TestCase instance this util function is used in.
baseline_image: tensor of shape [batch, height, width, channels] containing
the baseline image.
image: tensor of shape [batch, height, width, channels] containing the
result image.
max_outlier_fraction: fraction of pixels that may vary by more than the
error threshold. 0.005 means 0.5% of pixels. Number of outliers are
computed and compared per image.
pixel_error_threshold: pixel values are considered to differ if their
difference exceeds this amount. Range is 0.0 - 1.0.
Returns:
Tuple of a boolean and string error message. Boolean indicates
whether images are close to each other or not. Error message contains
details of two images mismatch.
"""
tf.assert_equal(baseline_image.shape, image.shape)
if baseline_image.dtype != image.dtype:
return False, ("Image types %s and %s do not match" %
(baseline_image.dtype, image.dtype))
shape.check_static(
tensor=baseline_image, tensor_name="baseline_image", has_rank=4)
shape.check_static(tensor=image, tensor_name="image", has_rank=4)
# Flatten height, width and channels dimensions since we're interested in
# error per image.
image_height, image_width = image.shape[1:3]
baseline_image = tf.reshape(baseline_image, [baseline_image.shape[0]] + [-1])
image = tf.reshape(image, [image.shape[0]] + [-1])
abs_diff = tf.abs(baseline_image - image)
outliers = tf.math.greater(abs_diff, pixel_error_threshold)
num_outliers = tf.math.reduce_sum(tf.cast(outliers, tf.int32))
perc_outliers = num_outliers / (image_height * image_width)
error_msg = "{:.2%} pixels are not equal to baseline image pixels.".format(
test_case.evaluate(perc_outliers) * 100.0)
return test_case.evaluate(perc_outliers < max_outlier_fraction), error_msg
def load_baseline_image(filename, image_shape=None):
"""Loads baseline image and makes sure it is of the right shape.
Args:
filename: file name of the image to load.
image_shape: expected shape of the image.
Returns:
tf.Tensor with baseline image
"""
image_path = tf.compat.v1.resource_loader.get_path_to_datafile(
os.path.join("test_data", filename))
file = tf.io.read_file(image_path)
baseline_image = tf.cast(tf.image.decode_image(file), tf.float32) / 255.0
baseline_image = tf.expand_dims(baseline_image, axis=0)
if image_shape is not None:
# Graph-mode requires image shape to be known in advance.
baseline_image = tf.ensure_shape(baseline_image, image_shape)
return baseline_image
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] | 2.909505 | 1,757 |
from config import get_config
from utils import prepare_dirs_and_logger, save_config
if __name__ == "__main__":
config, unparsed = get_config()
main(config)
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651,
62,
11250,
3419,
198,
220,
220,
220,
1388,
7,
11250,
8,
198
] | 2.8 | 60 |
import os
PROJECT_ROOT_DIR = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir)) | [
11748,
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] | 2.275 | 40 |
import time
from collections import deque
from models.eligibility_trace_tf.world.memory.n_step_replay_memory import NStepReplayMemory, Transition, NStepTransition
| [
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] | 3.458333 | 48 |
import re
| [
11748,
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] | 3 | 4 |
from util.functions import *
from util import *
from tqdm import tqdm
import matplotlib.pyplot as plt
import itertools
def probabilistic_ordinal_embedding_prepare_data_for_training(dataset,
model):
"""
"""
triplets = []
for word_index, word in tqdm(enumerate(dataset)):
# Get words within windows
similar_window_words = dataset[ max(word_index-similar_window_size, 0):
min(word_index+similar_window_size+1, len(dataset))]
dissimilar_window_words = dataset[ max(word_index-similar_window_size-window_margin-dissimilar_window_size, 0):
min(word_index+similar_window_size+window_margin+dissimilar_window_size+1, len(dataset))]
buffer = dataset[ max(word_index-similar_window_size-window_margin, 0):
min(word_index+similar_window_size+window_margin+1, len(dataset))]
# Remove excess words
dissimilar_window_words = [word for word in dissimilar_window_words if word not in buffer]
similar_window_words.remove(word)
# Add to triplets
for pos_neg in itertools.product(similar_window_words, dissimilar_window_words):
triplets.append([word, pos_neg[0], pos_neg[1]])
model.X_train = triplets
model.initialize(synthetic_vocab_size) | [
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] | 1.993455 | 764 |
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 12 17:17:09 2017
@author: sh
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import tools_lib as tool
import math
load_predata_path = 'F:\\tcds\\shop\\predata\\'
file = open(load_predata_path + 'data_mall_shop.pkl', 'rb')
mall = pickle.load(file)
mall_shop = pickle.load(file)
file.close()
df = pd.read_hdf(load_predata_path + 'train_data.h5')
#提取商场为制定的样本
df_new = df[df.mall_id=='m_5085']
df_new.index =range(len(df_new))
#转换商场-商店对应关系为dataframe
shops = mall[df_new['mall_id'][0]]
df_shop = pd.DataFrame(index = range(len(shops)),columns = ['shop_id','category_id','longitude','latitude','price'])
i = 0
for shop in shops:
df_shop['shop_id'][i] = list(shop.keys())[0]
df_shop['category_id'][i] = int(list(shop.values())[0][0].strip('c_'))
df_shop['longitude'][i] = list(shop.values())[0][1]
df_shop['latitude'][i] = list(shop.values())[0][2]
df_shop['price'][i] = list(shop.values())[0][3]
i += 1
#创建样本
df_sample = pd.DataFrame(index = range(len(df_new)),
columns = ['index','delt_x','delt_y','delt_D','S','time','c','p','wifi_on'])
for i in range(len(df_new)):
for j in range(len(df_shop)):
if df_new['shop_id'][i] == df_shop['shop_id'][j]:
index = j
delt_x = df_new['longitude'][i] - df_shop['longitude'][j]
delt_y = df_new['latitude'][i] - df_shop['latitude'][j]
S = 2*math.asin(math.sqrt(math.sin(delt_x*math.pi/180/2)**2 + math.cos(df_new['latitude'][i]*math.pi/180) *
math.cos(df_shop['latitude'][j]*math.pi/180) * math.sin(delt_x*math.pi/180/2)**2))*math.pi/180 *63781370
delt_D = math.sqrt(delt_x**2 + delt_y**2)
time = int(df_new['time_stamp'][i][1].replace(':', ''))
time_c = df_shop['category_id'][j]
time_p = df_shop['price'][j]
wifi_on = 0
for wifi in df_new['wifi_info'][i]:
if wifi[2]:
wifi_on = 1
df_sample['index'][i] = index
df_sample['delt_x'][i] = abs(delt_x)
df_sample['delt_y'][i] = abs(delt_y)
df_sample['delt_D'][i] = delt_D
df_sample['S'][i] = S
df_sample['time'][i] = time
df_sample['c'][i] = time_c
df_sample['p'][i] = time_p
df_sample['wifi_on'][i] = wifi_on
continue
#贝叶斯分类器
limits_z =[ np.array([[0,1e-5],[1e-5,1e-3],[1e-3,1]]) ,
np.array([[0,1e-5],[1e-5,1e-3],[1e-3,1]]) ,
np.array([[0,5],[5,10],[10,20],[20,50],[50,1e4]]) ,
np.array([[0,1100],[1100,1300],[1300,1700],[1700,2000],[2000,2400]]) ,
np.array([[0,15],[15,30],[30,50]]) ,
np.array([[0,40],[40,60],[60,100]])]
lenght = len(df_shop)
str1 = 'index'
data = df_sample
P_z = np.zeros([1,len(df_shop)])
for i in range(len(df_shop)):
P_z[0,i] = len(df_sample[df_sample['index'] == i])/len(df_sample)
P_x_delt = tool.caculate_p(data=data ,str1=str1,str2 = 'delt_x',lenght=lenght ,limits = limits_z[0] )
P_y_delt = tool.caculate_p(data=data ,str1=str1,str2 = 'delt_y',lenght=lenght ,limits =limits_z[1])
P_s = tool.caculate_p(data=data ,str1=str1,str2 = 'S',lenght=lenght,limits =limits_z[2])
P_t = tool.caculate_p(data=data ,str1=str1 ,str2 = 'time',lenght=lenght ,limits =limits_z[3])
P_c = tool.caculate_p(data=data ,str1=str1 ,str2 = 'c',lenght=lenght ,limits =limits_z[4])
P_p = tool.caculate_p(data=data ,str1=str1 ,str2 = 'p',lenght=lenght ,limits =limits_z[5])
P = [P_z, P_x_delt,P_y_delt,P_s,P_t,P_c ]
#使用分类器预测
x_test = [0.01,0.0001,3,1100,33,60]
indexs = []
k=0
for limits in limits_z:
for i in range(len(limits)):
if limits[i][0] <= x_test[k] <= limits[i][1]:
index = i
break
k+=1
indexs.append(i)
P_caculate = P[0] * P[1][indexs[1],:] * P[2][indexs[2],:] * P[3][indexs[3],:] * P[4][indexs[4],:] * P[5][indexs[5],:] * P[6][indexs[6],:]
c = list(P_caculate[0])
re= c.index(max(c))
final_result = df_shop['shop_id'][re]
print(final_result)
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6,
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260,
60,
201,
198,
4798,
7,
20311,
62,
20274,
8,
201,
198
] | 1.769715 | 2,384 |
import logging
import threading
from time import sleep
import pytest
from parla import Parla, TaskEnvironment
from parla.cpu import cpu
from parla.environments import EnvironmentComponentInstance, EnvironmentComponentDescriptor
from parla.tasks import *
logger = logging.getLogger(__name__)
def repetitions():
"""Return an iterable of the repetitions to perform for probabilistic/racy tests."""
return range(5)
def sleep_until(predicate, timeout=2, period=0.05):
"""Sleep until either `predicate()` is true or 2 seconds have passed."""
for _ in range(int(timeout/period)):
if predicate():
break
sleep(period)
assert predicate(), "sleep_until timed out ({}s)".format(timeout)
thread_locals = threading.local()
@pytest.mark.skipif(len(cpu.devices) < 2, reason="Run with PARLA_CPU_ARCHITECTURE=cores")
@pytest.mark.skipif(len(cpu.devices) < 2, reason="Run with PARLA_CPU_ARCHITECTURE=cores")
@pytest.mark.skipif(len(cpu.devices) < 2, reason="Run with PARLA_CPU_ARCHITECTURE=cores")
@pytest.mark.skipif(len(cpu.devices) < 2, reason="Run with PARLA_CPU_ARCHITECTURE=cores")
@pytest.mark.skipif(len(cpu.devices) < 5, reason="Run with PARLA_CPU_ARCHITECTURE=cores")
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] | 2.846868 | 431 |
from unittest import mock
from datetime import date
from django.test import SimpleTestCase
from mtp_common.dates import WorkdayChecker
TEST_HOLIDAYS = {'england-and-wales': {
'division': 'england-and-wales',
'events': [
{'title': 'Boxing Day', 'date': '2016-12-26', 'notes': '', 'bunting': True},
{'title': 'Christmas Day', 'date': '2016-12-27', 'notes': 'Substitute day', 'bunting': True}]
}}
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] | 2.511905 | 168 |
"""
Test attention networks on ImageNet.
"""
gpu = input('GPU: ')
type_category_set = input('Category-set type in {diff, sem, sim, size}: ')
version_wnids = input('Version number (WNIDs): ')
version_weights = input('Version number (weights): ')
start = int(input('Start category set: '))
stop = int(input('Stop category set (inclusive): '))
import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
import csv
import numpy as np
import pandas as pd
from ..utils.paths import (path_category_sets, path_imagenet, path_init_model,
path_results, path_weights)
from ..utils.models import build_model
from ..utils.testing import predict_model, evaluate_predictions
ind_attention = 19
model = build_model(train=False, attention_position=ind_attention)
model.save_weights(path_init_model)
path_cat_sets = (
path_category_sets/f'{type_category_set}_v{version_wnids}_wnids.csv')
category_sets = [row for row in csv.reader(open(path_cat_sets), delimiter=',')]
scores_in, scores_out = [], []
for i in range(start, stop+1):
name_weights = f'{type_category_set}_v{version_weights}_{i:02}'
print(f'\nTesting on {name_weights}')
weights = np.load(path_weights/f'{name_weights}_weights.npy')
model.load_weights(path_init_model)
model.layers[ind_attention].set_weights([weights])
predictions, generator = predict_model(
model, 'dir', path_imagenet/'val_white/')
wnid2ind = generator.class_indices
labels = generator.classes
inds_in = []
for wnid in category_sets[i]:
inds_in.extend(np.flatnonzero(labels==wnid2ind[wnid]))
inds_out = np.setdiff1d(range(generator.n), inds_in)
print(f'''
In category_set: {len(inds_in)} examples
Out of category_set: {len(inds_out)} examples''')
scores_in.append(evaluate_predictions(predictions, labels, inds_in))
scores_out.append(evaluate_predictions(predictions, labels, inds_out))
cols_array = ['loss_in', 'acc_top1_in', 'acc_top5_in', 'loss_out',
'acc_top1_out', 'acc_top5_out']
cols_save = ['loss_in', 'loss_out', 'acc_top1_in', 'acc_top1_out',
'acc_top5_in', 'acc_top5_out']
scores_all = np.concatenate((np.array(scores_in), np.array(scores_out)), axis=1)
scores_df = pd.DataFrame(scores_all, columns=cols_array)
scores_df[cols_save].to_csv(
(path_results/
f'{type_category_set}_v{version_weights}_{start:02}-{stop:02}_results.csv'))
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statement = """"Institute of Medical Biology, Chinese Academy of Medical Sciences",Vaccine,Inactivated virus,Phase II,Phase II began June 2020,Inactivated,NCT04412538,Unknown,,,N/A,https://docs.google.com/document/d/1Y4nCJJ4njzD1wiHbufCY6gqfRmj49Qn_qNgOJD62Wik/edit,6/23/2020"""
parseRowToCell(statement) | [
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from PyQt5 import QtWidgets, QtGui, QtCore # import PyQt5 widgets
from cats import cat
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""" Helper class to train models using Ray backend
"""
import ray
from ray.tune import Trainable
from sklearn.base import clone
from sklearn.model_selection import cross_validate
from sklearn.utils.metaestimators import _safe_split
import numpy as np
import os
from pickle import PicklingError
import ray.cloudpickle as cpickle
import warnings
from tune_sklearn._detect_xgboost import is_xgboost_model
from tune_sklearn.utils import (check_warm_start, check_partial_fit,
_aggregate_score_dicts)
class _Trainable(Trainable):
"""Class to be passed in as the first argument of tune.run to train models.
Overrides Ray Tune's Trainable class to specify the setup, train, save,
and restore routines.
"""
estimator_list = None
@property
def _setup(self, config):
"""Sets up Trainable attributes during initialization.
Also sets up parameters for the sklearn estimator passed in.
Args:
config (dict): contains necessary parameters to complete the `fit`
routine for the estimator. Also includes parameters for early
stopping if it is set to true.
"""
self.estimator_list = clone(config.pop("estimator_list"))
self.early_stopping = config.pop("early_stopping")
X_id = config.pop("X_id")
self.X = ray.get(X_id)
y_id = config.pop("y_id")
self.y = ray.get(y_id)
self.groups = config.pop("groups")
self.fit_params = config.pop("fit_params")
self.scoring = config.pop("scoring")
self.max_iters = config.pop("max_iters")
self.cv = config.pop("cv")
self.return_train_score = config.pop("return_train_score")
self.n_jobs = config.pop("n_jobs")
self.estimator_config = config
self.train_accuracy = None
self.test_accuracy = None
self.saved_models = [] # XGBoost specific
if self.early_stopping:
assert self._can_early_start()
n_splits = self.cv.get_n_splits(self.X, self.y)
self.fold_scores = np.empty(n_splits, dtype=dict)
self.fold_train_scores = np.empty(n_splits, dtype=dict)
if not self._can_partial_fit() and self._can_warm_start():
# max_iter here is different than the max_iters the user sets.
# max_iter is to make sklearn only fit for one epoch,
# while max_iters (which the user can set) is the usual max
# number of calls to _trainable.
self.estimator_config["warm_start"] = True
self.estimator_config["max_iter"] = 1
for i in range(n_splits):
self.estimator_list[i].set_params(**self.estimator_config)
if self._is_xgb():
self.saved_models = [None for _ in range(n_splits)]
else:
self.main_estimator.set_params(**self.estimator_config)
def _train(self):
"""Trains one iteration of the model called when ``tune.run`` is called.
Different routines are run depending on if the ``early_stopping``
attribute is True or not.
If ``self.early_stopping`` is not None, each fold is fit with
`partial_fit`, which stops training the model if the validation
score is not improving for a particular fold.
Otherwise, run the full cross-validation procedure.
In both cases, the average test accuracy is returned over all folds,
as well as the individual folds' accuracies as a dictionary.
Returns:
ret (:obj:`dict): Dictionary of results as a basis for
``cv_results_`` for one of the cross-validation interfaces.
"""
if self.early_stopping:
for i, (train, test) in enumerate(self.cv.split(self.X, self.y)):
X_train, y_train = _safe_split(self.estimator_list[i], self.X,
self.y, train)
X_test, y_test = _safe_split(
self.estimator_list[i],
self.X,
self.y,
test,
train_indices=train)
if self._can_partial_fit():
self.estimator_list[i].partial_fit(X_train, y_train,
np.unique(self.y))
elif self._is_xgb():
self.estimator_list[i].fit(
X_train, y_train, xgb_model=self.saved_models[i])
self.saved_models[i] = self.estimator_list[i].get_booster()
elif self._can_warm_start():
self.estimator_list[i].fit(X_train, y_train)
else:
raise RuntimeError(
"Early stopping set but model is not: "
"xgb model, supports partial fit, or warm-startable.")
if self.return_train_score:
self.fold_train_scores[i] = {
name: score(self.estimator_list[i], X_train, y_train)
for name, score in self.scoring.items()
}
self.fold_scores[i] = {
name: score(self.estimator_list[i], X_test, y_test)
for name, score in self.scoring.items()
}
ret = {}
agg_fold_scores = _aggregate_score_dicts(self.fold_scores)
for name, scores in agg_fold_scores.items():
total = 0
for i, score in enumerate(scores):
total += score
key_str = f"split{i}_test_%s" % name
ret[key_str] = score
self.mean_score = total / len(scores)
ret["average_test_%s" % name] = self.mean_score
if self.return_train_score:
agg_fold_train_scores = _aggregate_score_dicts(
self.fold_train_scores)
for name, scores in agg_fold_train_scores.items():
total = 0
for i, score in enumerate(scores):
total += score
key_str = f"split{i}_train_%s" % name
ret[key_str] = score
self.mean_train_score = total / len(scores)
ret["average_train_%s" % name] = self.mean_train_score
return ret
else:
try:
scores = cross_validate(
self.main_estimator,
self.X,
self.y,
cv=self.cv,
n_jobs=self.n_jobs,
fit_params=self.fit_params,
groups=self.groups,
scoring=self.scoring,
return_train_score=self.return_train_score,
)
except PicklingError:
warnings.warn("An error occurred in parallelizing the cross "
"validation. Proceeding to cross validate with "
"one core.")
scores = cross_validate(
self.main_estimator,
self.X,
self.y,
cv=self.cv,
fit_params=self.fit_params,
groups=self.groups,
scoring=self.scoring,
return_train_score=self.return_train_score,
)
ret = {}
for name in self.scoring:
for i, score in enumerate(scores["test_%s" % name]):
key_str = f"split{i}_test_%s" % name
ret[key_str] = score
self.test_accuracy = sum(scores["test_%s" % name]) / len(
scores["test_%s" % name])
ret["average_test_%s" % name] = self.test_accuracy
if self.return_train_score:
for name in self.scoring:
for i, score in enumerate(scores["train_%s" % name]):
key_str = f"split{i}_train_%s" % name
ret[key_str] = score
self.train_accuracy = sum(scores["train_%s" % name]) / len(
scores["train_%s" % name])
ret["average_train_%s" % name] = self.train_accuracy
return ret
def _save(self, checkpoint_dir):
"""Creates a checkpoint in ``checkpoint_dir``, creating a pickle file.
Args:
checkpoint_dir (str): file path to store pickle checkpoint.
Returns:
path (str): file path to the pickled checkpoint file.
"""
path = os.path.join(checkpoint_dir, "checkpoint")
try:
with open(path, "wb") as f:
cpickle.dump(self.estimator_list, f)
except Exception:
warnings.warn("Unable to save estimator.", category=RuntimeWarning)
return path
def _restore(self, checkpoint):
"""Loads a checkpoint created from `save`.
Args:
checkpoint (str): file path to pickled checkpoint file.
"""
try:
with open(checkpoint, "rb") as f:
self.estimator_list = cpickle.load(f)
except Exception:
warnings.warn("No estimator restored", category=RuntimeWarning)
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5298,
43160,
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7,
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12225,
900,
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318,
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25,
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366,
87,
22296,
2746,
11,
6971,
13027,
4197,
11,
393,
5814,
12,
9688,
540,
19570,
628,
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611,
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13,
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25,
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62,
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1438,
25,
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13,
395,
320,
1352,
62,
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58,
72,
4357,
1395,
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27432,
11,
331,
62,
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8,
198,
220,
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220,
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329,
1438,
11,
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287,
2116,
13,
46536,
13,
23814,
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198,
220,
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220,
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1782,
198,
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2116,
13,
11379,
62,
1416,
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72,
60,
796,
1391,
198,
220,
220,
220,
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220,
220,
220,
1438,
25,
4776,
7,
944,
13,
395,
320,
1352,
62,
4868,
58,
72,
4357,
1395,
62,
9288,
11,
331,
62,
9288,
8,
198,
220,
220,
220,
220,
220,
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329,
1438,
11,
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287,
2116,
13,
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13,
23814,
3419,
198,
220,
220,
220,
220,
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220,
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220,
220,
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1782,
628,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
1005,
796,
23884,
198,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
4194,
62,
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62,
1416,
2850,
796,
4808,
9460,
49373,
62,
26675,
62,
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82,
7,
944,
13,
11379,
62,
1416,
2850,
8,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
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11,
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62,
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62,
1416,
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13,
23814,
33529,
198,
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220,
220,
220,
220,
220,
220,
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220,
220,
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2472,
796,
657,
198,
220,
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220,
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220,
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220,
220,
220,
220,
220,
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329,
1312,
11,
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1416,
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2599,
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2472,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
62,
2536,
796,
277,
1,
35312,
90,
72,
92,
62,
9288,
62,
4,
82,
1,
4064,
1438,
198,
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220,
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220,
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220,
220,
220,
1005,
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60,
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198,
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220,
220,
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220,
220,
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220,
220,
220,
220,
220,
220,
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2116,
13,
32604,
62,
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2472,
1220,
18896,
7,
1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1005,
14692,
23913,
62,
9288,
62,
4,
82,
1,
4064,
1438,
60,
796,
2116,
13,
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62,
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628,
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220,
220,
220,
220,
220,
220,
220,
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611,
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25,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
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4194,
62,
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62,
27432,
62,
1416,
2850,
796,
4808,
9460,
49373,
62,
26675,
62,
11600,
82,
7,
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220,
220,
220,
220,
220,
220,
220,
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220,
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2116,
13,
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62,
27432,
62,
1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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329,
1438,
11,
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287,
4194,
62,
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62,
27432,
62,
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2850,
13,
23814,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
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] | 1.935352 | 4,888 |
from typing import List, Tuple, Callable
from dsfs.linalg.vector import Vector
Matrix = List[List[float]]
| [
6738,
19720,
1330,
7343,
11,
309,
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11,
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198,
198,
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7343,
58,
8053,
58,
22468,
11907,
628,
628,
628
] | 3.054054 | 37 |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
dataset = pd.read_json('../data/renttherunway_final_data.json.gz', lines=True)
dataset = dataset.dropna()
train_data, validation_data, test_data = np.split(dataset.sample(frac=1, random_state=42),
[int(.7*len(dataset)), int(.85*len(dataset))])
| [
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13,
5332,
9,
11925,
7,
19608,
292,
316,
4008,
12962,
628,
198
] | 2.059783 | 184 |
from django.contrib.postgres.search import SearchVector
from ...product.models import Product
from ...order.models import Order
from ...userprofile.models import User
def search_products(phrase):
'''Dashboard full text product search'''
sv = SearchVector('name', 'description')
return Product.objects.annotate(search=sv).filter(search=phrase)
def search_orders(phrase):
'''Dashboard full text order search
When phrase is convertable to int, no full text search is performed,
just order with according id is looked up.
'''
try:
order_id = int(phrase.strip())
return Order.objects.filter(id=order_id)
except ValueError:
pass
sv = SearchVector('user__default_shipping_address__first_name',
'user__default_shipping_address__last_name',
'user__email')
return Order.objects.annotate(search=sv).filter(search=phrase)
def search_users(phrase):
'''Dashboard full text user search'''
sv = SearchVector('email',
'default_billing_address__first_name',
'default_billing_address__last_name')
return User.objects.annotate(search=sv).filter(search=phrase)
def search(phrase):
'''Dashboard full text postgres products, orders and users search
Composes independent search querysets into dictionary result.
Args:
phrase (str): searched phrase
'''
return {'products': search_products(phrase),
'orders': search_orders(phrase),
'users': search_users(phrase)}
| [
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220,
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893,
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656,
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13,
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25,
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220,
220,
220,
220,
220,
220,
220,
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2536,
2599,
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628,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
1441,
1391,
6,
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10354,
2989,
62,
29498,
7,
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828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
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7,
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828,
198,
220,
220,
220,
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220,
220,
220,
220,
705,
18417,
10354,
2989,
62,
18417,
7,
34675,
38165,
198
] | 2.729167 | 576 |
from math import log, floor
from two_thinning.strategies.strategy_base import StrategyBase
| [
6738,
10688,
1330,
2604,
11,
4314,
198,
198,
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] | 3.576923 | 26 |
# -*- enconding:utf-8 -*-
from django import template
from menubuilder.models import Menu
register = template.Library()
@register.inclusion_tag("tags/menu.html", takes_context=True)
| [
2,
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] | 3 | 62 |
from ArbitrageGraph import ArbitrageGraph
from ArbitrageGraphNeo import ArbitrageGraphNeo
from FeeStore import FeeStore
from OrderBook import OrderBook, OrderBookPair, Asset
from PriceStore import PriceStore
import datetime
import logging
from FWLiveParams import FWLiveParams
import asyncio
from utilities import timed
from TradingStrategy import TradingStrategy
from aiokafka import AIOKafkaProducer
import json
from multiprocessing import Process, Pipe, Queue
import numbers
from threading import Thread
from DealUUIDGenerator import DealUUIDGenerator
import time
logger = logging.getLogger('CryptoArbitrageApp')
| [
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13,
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1362,
10786,
23919,
78,
3163,
2545,
8394,
4677,
11537,
628
] | 3.8625 | 160 |
#!/usr/bin/env/python
# =============================================================================
# MODULE DOCSTRING
# =============================================================================
"""
Module which houses all the handling instructions for reading and writing to netCDF files for a given type.
This exists as its own module to keep the main storage module file smaller since any number of types may need to be
saved which special instructions for each.
"""
# =============================================================================
# GLOBAL IMPORTS
# =============================================================================
import os
import abc
import yaml
import warnings
import importlib
import collections
import numpy as np
import netCDF4 as nc
from sys import getsizeof
try:
from yaml import CLoader as Loader, CDumper as Dumper
except ImportError:
from yaml import Loader, Dumper
try:
from openmm import unit
except ImportError: # OpenMM < 7.6
from simtk import unit
from ..utils import typename, quantity_from_string
# TODO: Use the `with_metaclass` from .utils when we merge it in
ABC = abc.ABCMeta('ABC', (object,), {}) # compatible with Python 2 *and* 3
# =============================================================================
# MODULE VARIABLES
# =============================================================================
# =============================================================================
# MODULE FUNCTIONS
# =============================================================================
def decompose_path(path):
"""
Break a path down into individual parts
Parameters
----------
path : string
Path to variable on the
Returns
-------
structure : tuple of strings
Tuple of split apart path
"""
return tuple((path_entry for path_entry in path.split('/') if path_entry != ''))
def normalize_path(path):
"""
Remove trailing/leading slashes from each part of the path and combine them into a clean, normalized path
Similar to os.path.normpath, but just its own function
Parameters
----------
path : string
Path variable to normalize
Returns
-------
normalized_path : string
Normalized path as a single string
"""
split_path = decompose_path(path)
return '/'.join([path_part.strip('/ ') for path_part in split_path if path_part is not ''])
# =============================================================================
# CUSTOM EXCEPTIONS
# =============================================================================
# =============================================================================
# ABSTRACT DRIVER
# =============================================================================
class StorageIODriver(ABC):
"""
Abstract class to define the basic functions any storage driver needs to read/write to the disk.
The specific driver for a type of storage should be a subclass of this with its own
encoders and decoders for specific file types.
Each type of variable codec should subclass :class:`Codec` which has the minimum ``write``, ``read``, and ``append``
methods
Parameters
----------
file_name : string
Name of the file to read/write to of a given storage type
access_mode : string or None, Default None, accepts 'w', 'r', 'a'
Define how to access the file in either write, read, or append mode
None should behave like Python "a+" in which a file is created if not present, or opened in append if it is.
How this is implemented is up to the subclass
"""
def set_codec(self, type_key, codec):
"""
Add new codifier to the specific driver class. This coder must know how to read/write and append to disk.
This method also acts to overwrite any existing type <-> codec map, however, will not overwrite any codec
already in use by a variable. E.g. Variable X of type T has codec A as the codecs have {T:A}. The maps is
changed by set_codec(T,B) so now {T:B}, but X will still be on codec A. Unloading X and then reloading X will
bind it to codec B.
Parameters
----------
type_key : Unique immutable object
Unique key that will be added to identify this de_encoder as part of the class
codec : Specific codifier class
Class to handle all of the encoding of decoding of the variables
"""
self._codec_type_maps[type_key] = codec
@abc.abstractmethod
def create_storage_variable(self, path, type_key):
"""
Create a new variable on the disk and at the path location and store it as the given type.
Parameters
----------
path : string
The way to identify the variable on the storage system. This can be either a variable name or a full path
(such as in NetCDF files)
type_key : Immutable object
Type specifies the key identifier in the _codec_type_maps added by the set_codec function. If type is not in
_codec_type_maps variable, an error is raised.
Returns
-------
bound_codec : Codec which is linked to a specific reference on the disk.
"""
raise NotImplementedError("create_variable has not been implemented!")
@abc.abstractmethod
def get_storage_variable(self, path):
"""
Get a variable IO object from disk at path. Raises a KeyError or AttributeError if no storage object exists at
that level
Parameters
----------
path : string
Path to the variable/storage object on disk
Returns
-------
bound_codec : Codec which is linked to a specific reference on the disk.
"""
raise NotImplementedError("get_storage_variable has not been implemented!")
@abc.abstractmethod
def get_directory(self, path, create=True):
"""
Get a directory-like object located at path from disk.
Parameters
----------
path : string
Path to directory-like object on disk
create: boolean, default: True
Should create the stack of directories on the way down, similar function to `mkdir -p` in shell
Returns
-------
directory_handler : directory object as its stored on disk
"""
raise NotImplementedError("get_directory method has not been implemented!")
@abc.abstractmethod
def close(self):
"""
Instruct how to safely close down the file.
"""
raise NotImplementedError("close method has not been implemented!")
@abc.abstractmethod
def add_metadata(self, name, value, path=''):
"""
Function to add metadata to the file. This can be treated as optional and can simply be a `pass` if you do not
want your storage system to handle additional metadata
Parameters
----------
name : string
Name of the attribute you wish to assign
value : any, but preferred string
Extra meta data to add to the variable
path : string, Default: ''
Extra path pointer to add metadata to a specific location if platform allows it
"""
raise NotImplementedError("add_metadata has not been implemented!")
@property
def file_name(self):
"""File name of on hard drive"""
return self._file_name
@property
def access_mode(self):
"""Access mode of file on disk"""
return self._access_mode
# =============================================================================
# NetCDF IO Driver
# =============================================================================
class NetCDFIODriver(StorageIODriver):
"""
Driver to handle all NetCDF IO operations, variable creation, and other operations.
Can be extended to add new or modified type codecs
"""
def get_directory(self, path, create=True):
"""
Get the group (directory) on the NetCDF file, create the full path if not present
Parameters
----------
path : string
Path to group on the disk
create: boolean, default: True
Should create the directory/ies on the way down, similar function to `mkdir -p` in shell
If False, raise KeyError if not in the stack
Returns
-------
group : NetCDF Group
Group object requested from file. All subsequent groups are created on the way down and can be accessed
the same way.
"""
self._check_bind_to_file()
path = normalize_path(path)
try:
group = self._groups[path]
except KeyError:
if create:
group = self._bind_group(path)
else:
split_path = decompose_path(path)
target = self.ncfile
for index, fragment in enumerate(split_path):
target = target.groups[fragment]
# Do a proper bind group now since all other fragments now exist
group = self._bind_group(path)
finally:
return group
def get_storage_variable(self, path):
"""
Get a variable IO object from disk at path. Raises an error if no storage object exists at that level
Parameters
----------
path : string
Path to the variable/storage object on disk
Returns
-------
codec : Subclass of NCVariableCodec
The codec tied to a specific variable and bound to it on the disk
"""
self._check_bind_to_file()
path = normalize_path(path)
try:
# Check if the codec is already known to this instance
codec = self._variables[path]
except KeyError:
try:
# Attempt to read the disk and bind to that variable
# Navigate the path down from top NC file to last entry
head_group = self.ncfile
split_path = decompose_path(path)
for header in split_path[:-1]:
head_group = head_group.groups[header]
# Check if this is a group type
is_group = False
if split_path[-1] in head_group.groups:
# Check if storage object IS a group (e.g. dict)
try:
obj = head_group.groups[split_path[-1]]
store_type = obj.getncattr('IODriver_Storage_Type')
if store_type == 'groups':
variable = obj
is_group = True
except AttributeError: # Trap the case of no group name in head_group, non-fatal
pass
if not is_group:
# Bind to the specific variable instead since its not a group
variable = head_group.variables[split_path[-1]]
except KeyError:
raise KeyError("No variable found at {} on file!".format(path))
try:
# Bind to the storage type by mapping IODriver_Type -> Known Codec
data_type = variable.getncattr('IODriver_Type')
head_path = '/'.join(split_path[:-1])
target_name = split_path[-1]
# Remember the group for the future while also getting storage binder
if head_path == '':
storage_object = self.ncfile
else:
storage_object = self._bind_group(head_path)
uninstanced_codec = self._IOMetaDataReaders[data_type]
self._variables[path] = uninstanced_codec(self, target_name, storage_object=storage_object)
codec = self._variables[path]
except AttributeError:
raise AttributeError("Cannot auto-detect variable type, ensure that 'IODriver_Type' is a set ncattr")
except KeyError:
raise KeyError("No mapped type codecs known for 'IODriver_Type' = '{}'".format(data_type))
return codec
def check_scalar_dimension(self):
"""
Check that the `scalar` dimension exists on file and create it if not
"""
self._check_bind_to_file()
if 'scalar' not in self.ncfile.dimensions:
self.ncfile.createDimension('scalar', 1) # scalar dimension
def check_infinite_dimension(self, name='iteration'):
"""
Check that the arbitrary infinite dimension exists on file and create it if not.
Parameters
----------
name : string, optional, Default: 'iteration'
Name of the dimension
"""
self._check_bind_to_file()
if name not in self.ncfile.dimensions:
self.ncfile.createDimension(name, 0)
def check_iterable_dimension(self, length=0):
"""
Check that the dimension of appropriate size for a given iterable exists on file and create it if not
Parameters
----------
length : int, Default: 0
Length of the dimension, leave as 0 for infinite length
"""
if type(length) is not int:
raise TypeError("length must be an integer, not {}!".format(type(length)))
if length < 0:
raise ValueError("length must be >= 0")
name = 'iterable{}'.format(length)
if name not in self.ncfile.dimensions:
self.ncfile.createDimension(name, length)
def generate_infinite_dimension(self):
"""
Generate a new infinite dimension and return the name of that dimension
Returns
-------
infinite_dim_name : string
Name of the new infinite dimension on file
"""
self._check_bind_to_file()
created_dim = False
while not created_dim:
infinite_dim_name = 'unlimited{}'.format(self._auto_iterable_count)
if infinite_dim_name not in self.ncfile.dimensions:
self.ncfile.createDimension(infinite_dim_name, 0)
created_dim = True
else:
self._auto_iterable_count += 1
return infinite_dim_name
def add_metadata(self, name, value, path='/'):
"""
Add metadata to self on disk, extra bits of information that can be used for flags or other variables
Parameters
----------
name : string
Name of the attribute you wish to assign
value : any, but preferred string
Extra meta data to add to the variable
path : string, optional, Default: '/'
Path to the object to assign metadata. If the object does not exist, an error is raised
Not passing a path in attaches the data to the top level file
"""
self._check_bind_to_file()
path = normalize_path(path)
split_path = decompose_path(path)
if len(split_path) == 0:
self.ncfile.setncattr(name, value)
elif split_path[0].strip() == '': # Split this into its own elif since if the first is true this will fail
self.ncfile.setncattr(name, value)
elif path in self._groups:
self._groups[path].setncattr(name, value)
elif path in self._variables:
self._variables[path].add_metadata(name, value)
else:
raise KeyError("Cannot assign metadata at path {} since no known object exists there! "
"Try get_directory or get_storage_variable first.".format(path))
def _bind_group(self, path):
"""
Bind a group to a particular path on the nc file. Note that this method creates the cascade of groups all the
way to the final object if it can.
Parameters
----------
path : string
Absolute path to the group as it appears on the NetCDF file.
Returns
-------
group : NetCDF Group
The group that path points to. Can be accessed by path through the ._groups dictionary after binding
"""
# NetCDF4 creates the cascade of groups automatically or returns the group if already present
# To simplify code, the cascade of groups is not stored in this class until called
self._check_bind_to_file()
path = normalize_path(path)
self._groups[path] = self.ncfile.createGroup(path)
return self._groups[path]
def _check_bind_to_file(self):
"""
Bind to and create the file if it does not already exist (depending on access_mode)
"""
if self.ncfile is None:
if self.access_mode is None:
if os.path.isfile(self.file_name):
self.ncfile = nc.Dataset(self.file_name, 'a')
else:
self.ncfile = nc.Dataset(self.file_name, 'w')
else:
self.ncfile = nc.Dataset(self.file_name, self.access_mode)
# =============================================================================
# ABSTRACT TYPE Codecs
# =============================================================================
class Codec(ABC):
"""
Basic abstract codec class laying out all the methods which must be implemented in every Codec.
All codec need a ``write``, ``read``, and ``append`` method.
Parameters
----------
parent_driver : Parent StorageIODriver driver
Driver this instance of the codec is bound to which can manipulate the top level file and possible meta
data handling
target : string
String of the name of the object. Not explicitly a variable nor a group since the object could be either
"""
@abc.abstractmethod
def read(self):
"""
Return the property read from the file
Returns
-------
Given property read from the file and cast into the correct Python data type
"""
raise NotImplementedError()
@abc.abstractmethod
def write(self, data, at_index=None):
"""
Tell this writer how to write to the file given the final object that it is bound to
Alternately, tell a variable which is normally appended to to write a specific entry on the index at_index
Parameters
----------
data : any data you wish to write
at_index : None or Int, optional, default=None
Specify the index of a variable created by append to write specific data at the index entry.
When None, this option is ignored
The integer of at_index must be <= to the size of the appended data
"""
raise NotImplementedError()
@abc.abstractmethod
def append(self, data):
"""
Tell this codec how to append to the file given the final object that it is bound to. This should allways write
to the end of the currently existing data.
Some :class:`StorageIODriver``'s may not be able to append due to the type of storage medium. In this case, this
method should be implemented and raise a ``NotImplementedError`` or ``RuntimeError`` with an appropriate
message
To overwrite data at a specific index of the already appended data, use the :func:`write`` method with the
``at_index`` keyword.
Parameters
----------
data : any data you wish to append
"""
raise NotImplementedError
class NCVariableCodec(Codec):
"""
Pointer class which provides instructions on how to handle a given nc_variable
Bind to a given nc_storage_object on ncfile with given final_target_name,
If no nc_storage_object is None, it defaults to the top level ncfile
Parameters
----------
parent_driver : Parent NetCDF driver
Class which can manipulate the NetCDF file at the top level for dimension creation and meta handling
target : string
String of the name of the object. Not explicitly a variable nor a group since the object could be either
storage_object : NetCDF file or NetCDF group, optional, Default to ncfile on parent_driver
Object the variable/object will be written onto
"""
@abc.abstractproperty # TODO: Depreciate when we move to Python 3 fully with @abc.abstractmethod + @property
def dtype(self):
"""
Define the Python data type for this variable
Returns
-------
dtype : type
"""
raise NotImplementedError("dtype property has not been implemented in this subclass yet!")
# @abc.abstractproperty
@staticmethod
def dtype_string():
"""
Short name of variable for strings and errors
Returns
-------
string
"""
# TODO: Replace with @abstractstaticmethod when on Python 3
raise NotImplementedError("dtype_string has not been implemented in this subclass yet!")
@abc.abstractproperty
def _encoder(self):
"""
Define the encoder used to convert from Python Data -> netcdf
Returns
-------
encoder : function
Returns the encoder function
"""
raise NotImplementedError("Encoder has not yet been set!")
@abc.abstractproperty
def _decoder(self):
"""
Define the decoder used to convert from netCDF -> Python Data
Returns
-------
decoder : function
Returns the decoder function
"""
raise NotImplementedError("Decoder has not yet been set!")
def _bind_read(self):
"""
A one time event that binds this class to the object on disk. This method should set self._bound_target
This function is unique to the read() function in that no data is attempted to write to the disk.
Should raise error if the object is not found on disk (i.e. no data has been written to this location yet)
Should raise error if the object on disk is incompatible with this type of Codec.
This is normally a common action among codecs, but can be redefined as needed in subclasses
Returns
-------
None, but should set self._bound_target
"""
self._attempt_storage_read()
# Handle variable size objects
# This line will not happen unless target is real, so output_mode will return the correct value
if self._output_mode is 'a':
self._save_shape = self._bound_target.shape[1:]
else:
self._save_shape = self._bound_target.shape
@abc.abstractmethod
def _bind_write(self, data):
"""
A one time event that binds this class to the object on disk. This method should set self._bound_target
This function is unique to the write() function in that the data passed in should help create the storage object
if not already on disk and prepare it for a write operation.
Last action of this method should always be dump_metadata_buffer.
Parameters
----------
data : Any type this Codec can process
Data which will be stored to disk of type. The data should not be written at this stage, but inspected to
configure the storage as needed. In some cases, you may not even need the data.
Returns
-------
None, but should set self._bound_target
"""
raise NotImplementedError("_bind_write function has not been implemented in this subclass yet!")
@abc.abstractmethod
def _bind_append(self, data):
"""
A one time event that binds this class to the object on disk. This method should set self._bound_target
This function is unique to the append() function in that the data passed in should append what is at
the location, or should create the object, then write the data with the first dimension infinite in size.
Last action of this method should always be dump_metadata_buffer.
Parameters
----------
data : Any type this Codec can process
Data which will be stored to disk of type. The data should not be written at this stage, but inspected to
configure the storage as needed. In some cases, you may not even need the data.
Returns
-------
None, but should set self._bound_target
"""
raise NotImplementedError("_bind_append function has not been implemented in this subclass yet!")
def read(self):
"""
Return the property read from the ncfile
Returns
-------
Given property read from the nc file and cast into the correct Python data type
"""
if self._bound_target is None:
self._bind_read()
return self._decoder(self._bound_target)
def _common_bind_output_actions(self, type_string, append_mode, store_unit_string='NoneType'):
"""
Method to handle the common NetCDF variable/group Metadata actions when binding a new variable/group to the
disk in write/append mode. This code should be called in all the _bind_write and _bind_append blocks inside
the trapped error when _bind_read fails to find the object (i.e. new variable on disk creation)
Parameters
----------
type_string : String
Type of data being stored either as a single object, or the data being stored in the compound object.
For simple objects like ints and floats, this should just be the typename(self.dtype) and will align
with the codec's dtype_string
For compound objects such as lists, tuples, and np.ndarray's, this should be the string of the data stored
in the object and will be wholly different from the codec's dtype_string and dependent on what is being
stored in the codec
append_mode : Integer, 0 or 1
Integer boolean representation of if this is appended data or not.
_bind_write methods should pass a 0
_bind_append methods should pass 1
store_unit_string : String, optional, Default: 'NoneType'
String representation of the openmm.unit attached to this data. This string should be able to be fed into
quantity_from_string(store_unit_string) and return a valid openmm.Unit object. Typically generated from
str(unit).
If no unit is assigned to the data, then the default of 'NoneType' should be given.
"""
if append_mode not in [0, 1]:
raise ValueError('append_mode must be integer of 0 for _bind write, or 1 for _bind_append')
self.add_metadata('IODriver_Type', self.dtype_string())
self.add_metadata('type', type_string)
self._unit = store_unit_string
self.add_metadata('IODriver_Unit', self._unit)
# Specify the type of storage object this should tie to
self.add_metadata('IODriver_Storage_Type', self.storage_type)
self.add_metadata('IODriver_Appendable', append_mode)
def write(self, data, at_index=None):
"""
Tell this writer how to write to the NetCDF file given the final object that it is bound to
Alternately, tell a variable which is normally appended to to write a specific entry on the index at_index
Parameters
----------
data : any data you wish to write
at_index : None or Int, optional, default=None
Specify the index of a variable created by append to write specific data at the index entry.
When None, this option is ignored
The integer of at_index must be <= to the size of the appended data
"""
# Check type
if not isinstance(data, self.dtype):
raise TypeError("Invalid data type on variable {}.".format(self._target))
if at_index is not None:
self._write_to_append_at_index(data, at_index)
return
# Bind
if self._bound_target is None:
self._bind_write(data)
self._check_storage_mode('w')
self._check_data_shape_matching(data)
# Save data
packaged_data = self._encoder(data)
self._bound_target[:] = packaged_data
return
def append(self, data):
"""
Tell this writer how to write to the NetCDF file given the final object that it is bound to
To overwrite data at a specific index of the already appended data, use the .write(data, at_index=X) method
Parameters
----------
data :
"""
# Check type
if not isinstance(data, self.dtype):
raise TypeError("Invalid data type on variable {}.".format(self._target))
# Bind
if self._bound_target is None:
self._bind_append(data)
self._check_storage_mode('a')
self._check_data_shape_matching(data)
# Determine current current length and therefore the last index
length = self._bound_target.shape[0]
# Save data
self._bound_target[length, :] = self._encoder(data)
@abc.abstractmethod
def _check_data_shape_matching(self, data):
"""
Check to make sure that the appendable data is the same shape/size/compatible with the other data on the
appendable data.
e.g. Lists should be the same length, NumPy arrays should be the same shape and dtype, etc
For static shape objects such as Ints and Floats, the dtype alone is sufficient and this method can be
implemented with a simple `pass`
Parameters
----------
data
"""
raise NotImplementedError("I don't know how to compare data yet!")
@abc.abstractproperty
def storage_type(self):
"""
Tell the Codec what NetCDF storage type this Codec treats the data as.
This is explicitly either 'variables' or 'groups' so the driver knows which property to call on the NetCDF
storage object
Returns
-------
storage_type: string of either 'variables' or 'groups'
"""
raise NotImplementedError("I have not been set to 'variables' or 'groups'")
def add_metadata(self, name, value):
"""
Add metadata to self on disk, extra bits of information that can be used for flags or other variables
This is NOT a staticmethod of the top data set since you can buffer this before binding
Parameters
----------
name : string
Name of the attribute you wish to assign
value : any, but preferred string
Extra meta data to add to the variable
"""
if not self._bound_target:
self._metadata_buffer[name] = value
else:
self._bound_target.setncattr(name, value)
def _dump_metadata_buffer(self):
"""
Dump the metadata buffer to file
"""
if self._bound_target is None:
raise UnboundLocalError("Cannot dump the metadata buffer to target since no target exists!")
self._bound_target.setncatts(self._metadata_buffer)
self._metadata_buffer = {}
@staticmethod
def _convert_netcdf_store_type(stored_type):
"""
Convert the stored NetCDF data type from string to type without relying on unsafe eval() function
Parameters
----------
stored_type : string
Read from ncfile.Variable.type
Returns
-------
proper_type : type
Python or module type
"""
try:
# Check if it's a builtin type
try: # Python 2
module = importlib.import_module('__builtin__')
except ImportError: # Python 3
module = importlib.import_module('builtins')
proper_type = getattr(module, stored_type)
except AttributeError:
# if not, separate module and class
module, stored_type = stored_type.rsplit(".", 1)
module = importlib.import_module(module)
proper_type = getattr(module, stored_type)
return proper_type
@property
def _output_mode(self):
"""
Set the write and append flags. Code should only call this after being bound to a variable
Returns
-------
output_mode : string
Either 'a' for append or 'w' for write
"""
if self._bound_target.getncattr('IODriver_Appendable'):
output_mode = 'a'
else:
output_mode = 'w'
return output_mode
def _attempt_storage_read(self):
"""
This is a helper function to try and read the target from the disk then do some validation checks common to
every _bind_read call. Helps cut down on recoding.
Returns
-------
None, but should try to set _bound_target from disk
"""
self._bound_target = getattr(self._storage_object, self.storage_type)[self._target]
# Ensure that the target we bind to matches the type of driver
try:
if self._bound_target.getncattr('IODriver_Type') != self.dtype_string():
raise TypeError("Storage target on NetCDF file is of type {} but this driver is designed to handle "
"type {}!".format(self._bound_target.getncattr('IODriver_Type'), self.dtype_string()))
except AttributeError:
warnings.warn("This Codec cannot detect storage type from on-disk variable. .write() and .append() "
"operations will not work and .read() operations may work", RuntimeWarning)
def _check_storage_mode(self, expected_mode):
"""
Check to see if the data stored at this codec is actually compatible with the type of write operation that was
performed (write vs. append)
Parameters
----------
expected_mode : string, either "w' or "a"
Raises
------
TypeError if ._output_mode != expected mode
"""
# String fill in, uses the opposite of expected mode to raise warnings
saved_as = {'w': 'appendable', 'a': 'statically written'}
cannot = {'w': 'write', 'a': 'append'}
must_use = {'w': 'append() or the to_index keyword of write()', 'a': 'write()'}
if self._output_mode != expected_mode:
raise TypeError("{target} at {type} was saved as {saved_as} data! Cannot {cannot}, must use "
"{must_use}".format(target=self._target,
type=self.dtype_string(),
saved_as=saved_as[expected_mode],
cannot=cannot[expected_mode],
must_use=must_use[expected_mode])
)
def _write_to_append_at_index(self, data, index):
"""
Try to write data to a specific site on an append variable. This is a method which should be called in
every `write` call if the index is defined by something other than None.
Parameters
----------
data : Data to write to location on a previously appended variable
index : Int,
Index to write the data at, replacing what is already there
If index > size of written data, crash
"""
if self._bound_target is None:
try:
self._bind_read()
except KeyError:
# Trap the NetCDF Key Error to raise an issue that data must exist first
raise IOError("Cannot write to a specific index for data that does not exist!")
if type(index) is not int:
raise ValueError("to_index must be an integer!")
self._check_storage_mode('a') # We want this in append mode
self._check_data_shape_matching(data)
# Determine current current length and therefore if the index is too large
length = self._bound_target.shape[0]
# Must actually compare to full length so people don't fill an infinite variable with garbage that is just
# masked from empty entries
if index >= length or abs(index) > length:
raise ValueError("Cannot choose an index beyond the maximum length of the "
"appended data of {}".format(length))
self._bound_target[index, :] = self._encoder(data)
# =============================================================================
# NETCDF NON-COMPOUND TYPE CODECS
# =============================================================================
# Decoders: Convert from NC variable to python type
# Encoders: Decompose Python Type into something NC storable data
# There really isn't anything that needs to happen here, arrays are the ideal type
# Leaving these as explicit codecs in case we need to change them later
# List and tuple iterables, assumes contents are the same type.
# Use dictionaries for compound types
# Encoder for float, int, iterable, and numpy arrays
# Works for float and int
# =============================================================================
# HDF5 CHUNK SIZE ROUTINES
# =============================================================================
def determine_appendable_chunk_size(data, max_iteration=128, max_memory=104857600):
"""
Determine the chunk size of the appendable dimension, it will either be max_iterations in count or max_memory in
bytes where the function will try to reduce the number of iterations until it is under the max chunk size down to
a single iteration.
Parameters
----------
data : Data that will be saved to disk of shape that will be saved
This is a sample of what will be written at any one point in time.
max_iteration : int, Default: 128
Maximum number of iterations that will be chunked, either this limit or max_memory will be hit first, reducing
the max iterations by a factor of 2 until we are below the memory limit, to a minimum of 1
max_memory: int (bytes), Default: 104856700 (100MB)
Maximum number of bytes the chunk is allowed to have, if the 100 iterations exceeds this size, then we
reduce the number of iterations by half until we are below the memory limit
Returns
-------
iteration_chunk : int
Chunksize of the iteration dimension
"""
if max_iteration < 1 or not isinstance(max_iteration, int):
raise ValueError("max_iteration was {} but must be an integer greater than 1!".format(max_iteration))
iteration_chunk = int(max_iteration)
data_size = getsizeof(data)
while iteration_chunk * data_size > max_memory and iteration_chunk > 1:
iteration_chunk /= 2
# Ceiling and int since np.ceil returns a float
return int(np.ceil(iteration_chunk))
# =============================================================================
# REAL Codecs
# =============================================================================
# Generic codecs for non-compound data types: inf, float, string
class NCScalar(NCVariableCodec, ABC):
""""
This particular class is to minimize code duplication between some very basic data types such as int, str, float
It is itself an abstract class and requires the following functions to be complete:
dtype (@property)
dtype_string (@staticmethod)
"""
@property
@property
def _on_disk_dtype(self):
"""
Allow overwriting the dtype for storage for extending this method to cast data as a different type on disk
This is the property to overwrite the cast dtype if it is different than the input/output dtype
"""
return self.dtype
class NCInt(NCScalar):
"""
NetCDF codec for Integers
"""
@property
@property
@property
@staticmethod
class NCFloat(NCScalar):
"""
NetCDF codec for Floats
"""
@property
@property
@property
@staticmethod
class NCString(NCScalar):
"""
NetCDF codec for String
"""
@property
@property
@property
@staticmethod
# Array
class NCArray(NCVariableCodec):
"""
NetCDF Codec for numpy arrays
"""
@property
@property
@property
@staticmethod
@staticmethod
@property
class NCIterable(NCVariableCodec):
"""
NetCDF codec for lists and tuples
"""
@property
@staticmethod
@property
@property
@staticmethod
@property
class NCQuantity(NCVariableCodec):
"""
NetCDF codec for ALL openmm.unit.Quantity's
"""
@property
@staticmethod
@property
@property
@property
# =============================================================================
# NETCDF DICT YAML HANDLERS
# =============================================================================
class _DictYamlLoader(Loader):
"""PyYAML Loader that recognized !Quantity nodes, converts YAML output -> Python type"""
@staticmethod
class _DictYamlDumper(Dumper):
"""PyYAML Dumper that convert from Python -> YAML output"""
@staticmethod
def quantity_representer(dumper, data):
"""YAML Quantity representer."""
data_unit = data.unit
data_value = data / data_unit
data_dump = {'QuantityUnit': str(data_unit), 'QuantityValue': data_value}
# Uses "self (DictYamlDumper)" as the dumper to allow nested !Quantity types
return dumper.represent_mapping(u'!Quantity', data_dump)
class NCDict(NCScalar):
"""
NetCDF codec for Dict, which we store in YAML as a glorified String with some extra processing
"""
@staticmethod
@staticmethod
@property
@property
@property
@staticmethod
@property
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] | 2.684033 | 15,701 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from django.utils import timezone
from .models import RegVerifyMailModel, REG_EXPIRE_TIME
from package import mail_client
# 检查该用户当前是否可以推送验证码邮件
# 检查邮件和验证码是否正确
# 推送验证码邮件
# 推送订阅邮件（都调用这个api推送）
# 推送启动邮件邮件（都调用这个api推送）
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import numpy as np
import matplotlib.pyplot as plt
# Fixing random state for reproducibility
np.random.seed(19680801)
x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)
fig, axs = plt.subplots(2, 3, sharex=True, sharey=True)
# marker symbol
axs[0, 0].scatter(x, y, s=80, c=z, marker=">")
axs[0, 0].set_title("marker='>'")
plt.tight_layout()
plt.show() | [
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import copy
import csv
import json
import numpy as np
import os
import pickle
import random
import torch
from torch.utils.data.sampler import Sampler
import pdb
class ASMRSampler(Sampler):
"""
Total videos: 2794. The sampler ends when last $BATCH_SIZE videos are left.
"""
| [
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"""Leetcode 714. Best Time to Buy and Sell Stock with Transaction Fee
Medium
URL: https://leetcode.com/problems/best-time-to-buy-and-sell-stock-with-transaction-fee/
Your are given an array of integers prices, for which the i-th element is the price of
a given stock on day i; and a non-negative integer fee representing a transaction fee.
You may complete as many transactions as you like, but you need to pay the transaction
fee for each transaction. You may not buy more than 1 share of a stock at a time
(ie. you must sell the stock share before you buy again.)
Return the maximum profit you can make.
Example 1:
Input: prices = [1, 3, 2, 8, 4, 9], fee = 2
Output: 8
Explanation: The maximum profit can be achieved by:
- Buying at prices[0] = 1
- Selling at prices[3] = 8
- Buying at prices[4] = 4
- Selling at prices[5] = 9
The total profit is ((8 - 1) - 2) + ((9 - 4) - 2) = 8.
Note:
- 0 < prices.length <= 50000.
- 0 < prices[i] < 50000.
- 0 <= fee < 50000.
"""
if __name__ == '__main__':
main()
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628,
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198,
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3672,
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 3.091185 | 329 |
#!/usr/bin/env python
import ngs_utils.ensembl as ebl
import os
import shutil
from optparse import OptionParser, SUPPRESS_HELP
from os.path import isfile, join, basename, dirname, pardir
from ngs_utils import logger
from ngs_utils.file_utils import file_transaction, adjust_path, safe_mkdir, verify_file
''' Generates coding_regions BED file
Example usage:
python {__file__} -g GRCh37 --canonical | grep -v ^MT | grep -v ^GL | sort -k1,1V -k2,2n | bedtools merge -i - > coding_regions.canonical.clean.sort.merged.bed
'''
if __name__ == '__main__':
main()
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198,
220,
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220,
1388,
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198
] | 2.746411 | 209 |
import unittest
import asyncio
import moses.io as io
from . import dummy
| [
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] | 3.26087 | 23 |
import threading
thread_01 = threading.Thread(
# name='t1',
target=target_function,
kwargs={'person_01': 'Bruno', 'person_02': 'Hanna'})
thread_01.start()
thread_01.join()
print('executed after join thread 01')
| [
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] | 2.545455 | 88 |
"""Methods for building convolutional LSTM."""
import numpy
import keras
from gewittergefahr.gg_utils import error_checking
from gewittergefahr.deep_learning import architecture_utils
from ml4tc.machine_learning import cnn_architecture
INPUT_DIMENSIONS_KEY = 'input_dimensions'
NUM_LAYERS_BY_BLOCK_KEY = 'num_layers_by_block'
NUM_CHANNELS_KEY = 'num_channels_by_layer'
DROPOUT_RATES_KEY = 'dropout_rate_by_layer'
KEEP_TIME_DIMENSION_KEY = 'keep_time_dimension'
ACTIVATION_FUNCTION_KEY = 'activation_function_name'
ACTIVATION_FUNCTION_ALPHA_KEY = 'activation_function_alpha'
L2_WEIGHT_KEY = 'l2_weight'
USE_BATCH_NORM_KEY = 'use_batch_normalization'
NUM_NEURONS_KEY = cnn_architecture.NUM_NEURONS_KEY
INNER_ACTIV_FUNCTION_KEY = cnn_architecture.INNER_ACTIV_FUNCTION_KEY
INNER_ACTIV_FUNCTION_ALPHA_KEY = cnn_architecture.INNER_ACTIV_FUNCTION_ALPHA_KEY
OUTPUT_ACTIV_FUNCTION_KEY = cnn_architecture.OUTPUT_ACTIV_FUNCTION_KEY
OUTPUT_ACTIV_FUNCTION_ALPHA_KEY = (
cnn_architecture.OUTPUT_ACTIV_FUNCTION_ALPHA_KEY
)
DEFAULT_OPTION_DICT_GRIDDED_SAT = {
NUM_LAYERS_BY_BLOCK_KEY: numpy.array([2, 2, 2, 2, 2, 2, 2], dtype=int),
NUM_CHANNELS_KEY: numpy.array(
[8, 8, 16, 16, 24, 24, 32, 32, 48, 48, 64, 64, 128, 128], dtype=int
),
DROPOUT_RATES_KEY: numpy.full(14, 0.),
ACTIVATION_FUNCTION_KEY: architecture_utils.RELU_FUNCTION_STRING,
ACTIVATION_FUNCTION_ALPHA_KEY: 0.2,
USE_BATCH_NORM_KEY: True
}
DEFAULT_OPTION_DICT_UNGRIDDED_SAT = {
NUM_CHANNELS_KEY: numpy.array([100], dtype=int),
ACTIVATION_FUNCTION_KEY: architecture_utils.RELU_FUNCTION_STRING,
ACTIVATION_FUNCTION_ALPHA_KEY: 0.2,
USE_BATCH_NORM_KEY: True
}
DEFAULT_OPTION_DICT_SHIPS = {
NUM_CHANNELS_KEY: numpy.array([1000], dtype=int),
ACTIVATION_FUNCTION_KEY: architecture_utils.RELU_FUNCTION_STRING,
ACTIVATION_FUNCTION_ALPHA_KEY: 0.2,
USE_BATCH_NORM_KEY: True
}
DEFAULT_OPTION_DICT_DENSE = {
INNER_ACTIV_FUNCTION_KEY: architecture_utils.RELU_FUNCTION_STRING,
INNER_ACTIV_FUNCTION_ALPHA_KEY: 0.2,
USE_BATCH_NORM_KEY: True
}
# TODO(thunderhoser): Do I also need to pass normal (non-recurrent) activation
# and dropout rate to methods that create an LSTM layer?
def _get_lstm_layer(
num_output_units, recurrent_activation_func_or_name,
recurrent_dropout_rate, regularization_func, return_sequences):
"""Creates simple LSTM layer (with no convolution).
:param num_output_units: Number of output units.
:param recurrent_activation_func_or_name: Activation function for recurrent
step (may be passed as a function or string).
:param recurrent_dropout_rate: Dropout rate for recurrent step.
:param regularization_func: Regularization function (will be used for main
kernel weights, recurrent-kernel weights, and biases). If you do not
want regularization, make this None.
:param return_sequences: Boolean flag. If True (False), layer will return
full sequence (last output in output sequence).
:return: layer_object: Instance of `keras.layers.LSTM`.
"""
error_checking.assert_is_integer(num_output_units)
error_checking.assert_is_geq(num_output_units, 1)
error_checking.assert_is_boolean(return_sequences)
error_checking.assert_is_less_than(recurrent_dropout_rate, 1.)
if not recurrent_dropout_rate > 0.:
recurrent_dropout_rate = 0.
return keras.layers.LSTM(
units=num_output_units, activation=None, use_bias=True,
recurrent_activation=recurrent_activation_func_or_name,
kernel_initializer='glorot_uniform', bias_initializer='zeros',
recurrent_initializer='orthogonal',
unit_forget_bias=True,
kernel_regularizer=regularization_func,
recurrent_regularizer=regularization_func,
bias_regularizer=regularization_func,
activity_regularizer=None,
return_sequences=return_sequences,
dropout=0., recurrent_dropout=recurrent_dropout_rate
)
def _get_2d_conv_lstm_layer(
num_kernel_rows, num_kernel_columns, num_rows_per_stride,
num_columns_per_stride, num_filters, use_padding,
recurrent_activation_func_or_name,
recurrent_dropout_rate, regularization_func, return_sequences):
"""Creates LSTM layer with 2-D convolution.
:param num_kernel_rows: Number of spatial rows in convolutional filters.
:param num_kernel_columns: Number of spatial columns in convolutional
filters.
:param num_rows_per_stride: Number of spatial rows per filter stride.
:param num_columns_per_stride: Number of spatial columns per filter stride.
:param num_filters: Number of convolutional filters.
:param use_padding: Boolean flag. If True (False), will (not) pad edges
after convolution.
:param recurrent_activation_func_or_name: See doc for `_get_lstm_layer`.
:param recurrent_dropout_rate: Same.
:param regularization_func: Same.
:param return_sequences: Same.
:return: layer_object: Instance of `keras.layers.ConvLSTM2D`.
"""
error_checking.assert_is_integer(num_kernel_rows)
error_checking.assert_is_geq(num_kernel_rows, 1)
error_checking.assert_is_integer(num_kernel_columns)
error_checking.assert_is_geq(num_kernel_columns, 1)
error_checking.assert_is_integer(num_rows_per_stride)
error_checking.assert_is_geq(num_rows_per_stride, 1)
error_checking.assert_is_integer(num_columns_per_stride)
error_checking.assert_is_geq(num_columns_per_stride, 1)
error_checking.assert_is_integer(num_filters)
error_checking.assert_is_geq(num_filters, 1)
error_checking.assert_is_boolean(use_padding)
error_checking.assert_is_boolean(return_sequences)
error_checking.assert_is_less_than(recurrent_dropout_rate, 1.)
if not recurrent_dropout_rate > 0.:
recurrent_dropout_rate = 0.
return keras.layers.ConvLSTM2D(
filters=num_filters,
kernel_size=(num_kernel_rows, num_kernel_columns),
strides=(num_rows_per_stride, num_columns_per_stride),
padding='same' if use_padding else 'valid',
data_format='channels_last', dilation_rate=(1, 1),
activation=None, use_bias=True,
recurrent_activation=recurrent_activation_func_or_name,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=regularization_func,
recurrent_regularizer=regularization_func,
bias_regularizer=regularization_func,
return_sequences=return_sequences,
dropout=0.0, recurrent_dropout=recurrent_dropout_rate
)
def _create_layers_gridded_sat(option_dict):
"""Creates layers for gridded satellite data.
B = number of conv blocks
C = total number of conv layers
:param option_dict: Dictionary with the following keys.
option_dict['input_dimensions']:
length-4 numpy array with input dimensions:
(num_grid_rows, num_grid_columns, num_lag_times, num_channels).
option_dict['num_layers_by_block']: length-B numpy array with number of
conv layers for each block.
option_dict['num_channels_by_layer']: length-C numpy array with number of
channels for each conv layer.
option_dict['dropout_rate_by_layer']: length-C numpy array with dropout rate
for each conv layer. Use number <= 0 for no dropout.
option_dict['keep_time_dimension']: Boolean flag. If True, will keep time
dimension until the end and repeat conv-LSTM layers until the end. If
False, will remove time dimension after first conv-LSTM layer, then do
conv without LSTM.
option_dict['activation_function_name']: Name of activation function for all
conv layers. Must be accepted by
`architecture_utils.check_activation_function`.
option_dict['activation_function_alpha']: Alpha (slope parameter) for
activation function for all conv layers. Applies only to ReLU and eLU.
option_dict['l2_weight']: Weight for L_2 regularization in conv layers.
option_dict['use_batch_normalization']: Boolean flag. If True, will use
batch normalization after each conv layer.
:return: input_layer_object: Input layer for gridded satellite data
(instance of `keras.layers.Input`).
:return: last_layer_object: Last layer for processing only gridded satellite
data (instance of `keras.layers`).
"""
# Check input args.
input_dimensions = option_dict[INPUT_DIMENSIONS_KEY]
num_layers_by_block = option_dict[NUM_LAYERS_BY_BLOCK_KEY]
num_channels_by_layer = option_dict[NUM_CHANNELS_KEY]
dropout_rate_by_layer = option_dict[DROPOUT_RATES_KEY]
keep_time_dimension = option_dict[KEEP_TIME_DIMENSION_KEY]
activation_function_name = option_dict[ACTIVATION_FUNCTION_KEY]
activation_function_alpha = option_dict[ACTIVATION_FUNCTION_ALPHA_KEY]
l2_weight = option_dict[L2_WEIGHT_KEY]
use_batch_normalization = option_dict[USE_BATCH_NORM_KEY]
error_checking.assert_is_numpy_array(
input_dimensions, exact_dimensions=numpy.array([4], dtype=int)
)
error_checking.assert_is_integer_numpy_array(input_dimensions)
error_checking.assert_is_greater_numpy_array(input_dimensions, 0)
error_checking.assert_is_numpy_array(num_layers_by_block, num_dimensions=1)
error_checking.assert_is_integer_numpy_array(num_layers_by_block)
error_checking.assert_is_geq_numpy_array(num_layers_by_block, 1)
num_blocks = len(num_layers_by_block)
num_layers = numpy.sum(num_layers_by_block)
error_checking.assert_is_geq(num_blocks, 6)
error_checking.assert_is_leq(num_blocks, 7)
error_checking.assert_is_numpy_array(
num_channels_by_layer,
exact_dimensions=numpy.array([num_layers], dtype=int)
)
error_checking.assert_is_integer_numpy_array(num_channels_by_layer)
error_checking.assert_is_geq_numpy_array(num_channels_by_layer, 1)
error_checking.assert_is_numpy_array(
dropout_rate_by_layer,
exact_dimensions=numpy.array([num_layers], dtype=int)
)
error_checking.assert_is_leq_numpy_array(dropout_rate_by_layer, 1.)
error_checking.assert_is_boolean(keep_time_dimension)
error_checking.assert_is_geq(l2_weight, 0.)
error_checking.assert_is_boolean(use_batch_normalization)
# Do actual stuff.
input_layer_object = keras.layers.Input(
shape=tuple(input_dimensions.tolist())
)
layer_object = keras.layers.Permute(dims=(3, 1, 2, 4))(input_layer_object)
regularization_func = architecture_utils.get_weight_regularizer(
l2_weight=l2_weight
)
num_blocks = len(num_layers_by_block)
num_layers = numpy.sum(num_layers_by_block)
k = -1
return_sequences = True
for i in range(num_blocks):
for _ in range(num_layers_by_block[i]):
k += 1
return_sequences = keep_time_dimension and k != num_layers - 1
if keep_time_dimension or k == 0:
layer_object = _get_2d_conv_lstm_layer(
num_kernel_rows=3, num_kernel_columns=3,
num_rows_per_stride=1, num_columns_per_stride=1,
num_filters=num_channels_by_layer[k], use_padding=True,
recurrent_activation_func_or_name='hard_sigmoid',
recurrent_dropout_rate=0.,
regularization_func=regularization_func,
return_sequences=return_sequences
)(layer_object)
else:
layer_object = architecture_utils.get_2d_conv_layer(
num_kernel_rows=3, num_kernel_columns=3,
num_rows_per_stride=1, num_columns_per_stride=1,
num_filters=num_channels_by_layer[k],
padding_type_string=architecture_utils.YES_PADDING_STRING,
weight_regularizer=regularization_func
)(layer_object)
layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_name,
alpha_for_relu=activation_function_alpha,
alpha_for_elu=activation_function_alpha
)(layer_object)
if dropout_rate_by_layer[k] > 0:
layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=dropout_rate_by_layer[k]
)(layer_object)
if use_batch_normalization:
layer_object = architecture_utils.get_batch_norm_layer()(
layer_object
)
if return_sequences:
layer_object = architecture_utils.get_3d_pooling_layer(
num_rows_in_window=1, num_columns_in_window=2,
num_heights_in_window=2,
num_rows_per_stride=1, num_columns_per_stride=2,
num_heights_per_stride=2,
pooling_type_string=architecture_utils.MAX_POOLING_STRING
)(layer_object)
else:
layer_object = architecture_utils.get_2d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_rows_per_stride=2, num_columns_per_stride=2,
pooling_type_string=architecture_utils.MAX_POOLING_STRING
)(layer_object)
layer_object = architecture_utils.get_flattening_layer()(layer_object)
return input_layer_object, layer_object
def _create_layers_ungridded(option_dict):
"""Creates layers for ungridded data.
L = number of LSTM layers
:param option_dict: Dictionary with the following keys.
option_dict['input_dimensions']:
length-2 numpy array with input dimensions:
(num_lag_times, num_channels).
option_dict['num_channels_by_layer']: length-L numpy array with number of
channels for each LSTM layer.
option_dict['dropout_rate_by_layer']: length-L numpy array with dropout rate
for each LSTM layer. Use number <= 0 for no dropout.
option_dict['activation_function_name']: Name of activation function for all
LSTM layers. Must be accepted by
`architecture_utils.check_activation_function`.
option_dict['activation_function_alpha']: Alpha (slope parameter) for
activation function for all LSTM layers. Applies only to ReLU and eLU.
option_dict['l2_weight']: Weight for L_2 regularization in LSTM layers.
option_dict['use_batch_normalization']: Boolean flag. If True, will use
batch normalization after each LSTM layer.
:return: input_layer_object: Input layer for ungridded data (instance of
`keras.layers.Input`).
:return: last_layer_object: Last layer for processing only this set of
ungridded data (instance of `keras.layers`).
"""
# Check input args.
input_dimensions = option_dict[INPUT_DIMENSIONS_KEY]
num_channels_by_layer = option_dict[NUM_CHANNELS_KEY]
dropout_rate_by_layer = option_dict[DROPOUT_RATES_KEY]
activation_function_name = option_dict[ACTIVATION_FUNCTION_KEY]
activation_function_alpha = option_dict[ACTIVATION_FUNCTION_ALPHA_KEY]
l2_weight = option_dict[L2_WEIGHT_KEY]
use_batch_normalization = option_dict[USE_BATCH_NORM_KEY]
error_checking.assert_is_numpy_array(
input_dimensions, exact_dimensions=numpy.array([2], dtype=int)
)
error_checking.assert_is_integer_numpy_array(input_dimensions)
error_checking.assert_is_greater_numpy_array(input_dimensions, 0)
error_checking.assert_is_numpy_array(
num_channels_by_layer, num_dimensions=1
)
error_checking.assert_is_integer_numpy_array(num_channels_by_layer)
error_checking.assert_is_geq_numpy_array(num_channels_by_layer, 1)
num_layers = len(num_channels_by_layer)
error_checking.assert_is_numpy_array(
dropout_rate_by_layer,
exact_dimensions=numpy.array([num_layers], dtype=int)
)
error_checking.assert_is_leq_numpy_array(dropout_rate_by_layer, 1.)
error_checking.assert_is_geq(l2_weight, 0.)
error_checking.assert_is_boolean(use_batch_normalization)
# Do actual stuff.
input_layer_object = keras.layers.Input(
shape=tuple(input_dimensions.tolist())
)
regularization_func = architecture_utils.get_weight_regularizer(
l2_weight=l2_weight
)
num_layers = len(num_channels_by_layer)
layer_object = None
for i in range(num_layers):
if layer_object is None:
this_input_layer_object = input_layer_object
else:
this_input_layer_object = layer_object
layer_object = _get_lstm_layer(
num_output_units=num_channels_by_layer[i],
recurrent_activation_func_or_name='hard_sigmoid',
recurrent_dropout_rate=0.,
regularization_func=regularization_func,
return_sequences=i != num_layers - 1
)(this_input_layer_object)
layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_name,
alpha_for_relu=activation_function_alpha,
alpha_for_elu=activation_function_alpha
)(layer_object)
if dropout_rate_by_layer[i] > 0:
layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=dropout_rate_by_layer[i]
)(layer_object)
if use_batch_normalization:
layer_object = architecture_utils.get_batch_norm_layer()(
layer_object
)
layer_object = architecture_utils.get_flattening_layer()(layer_object)
return input_layer_object, layer_object
def create_model(
option_dict_gridded_sat, option_dict_ungridded_sat, option_dict_ships,
option_dict_dense, loss_function, metric_functions):
"""Creates conv-LSTM model.
:param option_dict_gridded_sat: See doc for `_create_layers_gridded_sat`.
If you do not want to use gridded satellite data, make this None.
:param option_dict_ungridded_sat: See doc for `_create_layers_ungridded`.
If you do not want to use ungridded satellite data, make this None.
:param option_dict_ships: See doc for `_create_layers_ungridded`. If you do
not want to use SHIPS data, make this None.
:param option_dict_dense: See doc for
`cnn_architecture.create_dense_layers`.
:param loss_function: Loss function.
:param metric_functions: 1-D list of metric functions.
:return: model_object: Untrained conv-LSTM model (instance of
`keras.models.Model`).
"""
input_layer_objects = []
flattening_layer_objects = []
if option_dict_gridded_sat is not None:
option_dict_gridded_sat_orig = option_dict_gridded_sat.copy()
option_dict_gridded_sat = DEFAULT_OPTION_DICT_GRIDDED_SAT.copy()
option_dict_gridded_sat.update(option_dict_gridded_sat_orig)
this_input_layer_object, this_flattening_layer_object = (
_create_layers_gridded_sat(option_dict_gridded_sat)
)
input_layer_objects.append(this_input_layer_object)
flattening_layer_objects.append(this_flattening_layer_object)
if option_dict_ungridded_sat is not None:
option_dict_ungridded_sat_orig = option_dict_ungridded_sat.copy()
option_dict_ungridded_sat = DEFAULT_OPTION_DICT_UNGRIDDED_SAT.copy()
option_dict_ungridded_sat.update(option_dict_ungridded_sat_orig)
this_input_layer_object, this_flattening_layer_object = (
_create_layers_ungridded(option_dict_ungridded_sat)
)
input_layer_objects.append(this_input_layer_object)
flattening_layer_objects.append(this_flattening_layer_object)
if option_dict_ships is not None:
option_dict_ships_orig = option_dict_ships.copy()
option_dict_ships = DEFAULT_OPTION_DICT_SHIPS.copy()
option_dict_ships.update(option_dict_ships_orig)
this_input_layer_object, this_flattening_layer_object = (
_create_layers_ungridded(option_dict_ships)
)
input_layer_objects.append(this_input_layer_object)
flattening_layer_objects.append(this_flattening_layer_object)
option_dict_dense_orig = option_dict_dense.copy()
option_dict_dense = DEFAULT_OPTION_DICT_DENSE.copy()
option_dict_dense.update(option_dict_dense_orig)
if len(flattening_layer_objects) > 1:
layer_object = keras.layers.concatenate(flattening_layer_objects)
else:
layer_object = flattening_layer_objects[0]
layer_object = cnn_architecture.create_dense_layers(
input_layer_object=layer_object, option_dict=option_dict_dense
)
model_object = keras.models.Model(
inputs=input_layer_objects, outputs=layer_object
)
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=metric_functions
)
model_object.summary()
return model_object
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"""Module defining Machinery, Species and ProcessingMap classes."""
# python 2/3 compatibility
from __future__ import division, print_function, absolute_import
# global imports
from collections import namedtuple
from scipy.sparse import (
csr_matrix, csc_matrix, lil_matrix, coo_matrix, hstack, eye
)
import numpy
# class storing machinery-related information
Machinery = namedtuple('Machinery', 'composition processing_cost weight')
class Species(object):
"""
Species-related information.
Parameters
----------
ids : list of str
Identifiers of species stored (metabolites/macromolecules).
production : sparse matrix
Production matrix (in terms of metabolites).
prod_proc_cost : sparse matrix
Production processing cost matrix.
degradation : sparse matrix
Degradation matrix (in terms of metabolites).
deg_proc_cost : sparsematrix
Degradation processing cost matrix.
weight : sparse matrix
Weight matrix (in terms of compartments).
"""
def __init__(self, data, metabolites):
"""Constructor."""
self._metabolites = metabolites
# extract composition of base species
self.ids = (metabolites
+ [m.id for m in data.proteins.macromolecules]
+ [m.id for m in data.rnas.macromolecules]
+ [m.id for m in data.dna.macromolecules])
# polymers and metabolites are allowed to have the same identifier
# by looping on reversed list, we ensure that the index of the
# metabolite is returned
self._index = {m: i for i, m in reversed(list(enumerate(self.ids)))}
# metabolites (weights and processing costs are zero)
nb_comp = len(data.metabolism.compartments)
nb_met = len(metabolites)
nb_processes = len(data.processes.processes)
met_comp = -eye(nb_met)
met_proc = csr_matrix((nb_processes, nb_met))
met_deg = eye(nb_met)
met_deg_proc = csr_matrix((nb_processes, nb_met))
met_weight = csr_matrix((nb_comp, nb_met))
# macromolecules
[macro_comp, macro_proc, macro_deg, macro_deg_proc, macro_weight] \
= compute_macromolecule_composition(data, metabolites)
self.production = hstack([met_comp, macro_comp]).tocsr()
self.prod_proc_cost = hstack([met_proc, macro_proc]).tocsr()
self.degradation = hstack([met_deg, macro_deg]).tocsr()
self.deg_proc_cost = hstack([met_deg_proc, macro_deg_proc]).tocsr()
self.weight = hstack([met_weight, macro_weight]).tocsr()
def create_machinery(self, machinery_set):
"""
Create machineries from a list of RBA machinery composition structures.
Parameters
----------
machinery_set : list of rba.xml.MachineryComposition
Machinery compositions.
Returns
-------
Machinery object
Contains the composition, processing cost and weight matrices of
machineries provided as input.
"""
species = lil_matrix((len(self.ids), len(machinery_set)))
for col, machinery in enumerate(machinery_set):
for reac in machinery.reactants:
species[self._index[reac.species], col] += reac.stoichiometry
for prod in machinery.products:
species[self._index[prod.species], col] -= prod.stoichiometry
return Machinery(self.production*species,
self.prod_proc_cost*species,
self.weight*species)
def metabolite_synthesis(self):
"""
Create reactions corresponding to synthesis of macrometabolites.
Macrometabolites are species that are both a metabolite and a
macromolecule (typically tRNAs).
Returns
-------
Tuple of 2 elements
First element is a list of stoichiometries vectors,
each vector representing a reaction. The second element are the
ids of the metabolites being synthesized by these reactions.
"""
names = []
reactions = []
nb_met = len(self._metabolites)
macrometabolites = self.ids[nb_met:]
for index, macro in enumerate(macrometabolites):
# if a macromolecule is also a metabolite,
# it appears twice in the species list.
met_index = self._index[macro]
macro_index = nb_met + index
if met_index < nb_met:
# create biosynthesis reaction
reaction = self.production[:, macro_index].tolil()
reaction[met_index, 0] = 1
reactions.append(reaction)
names.append(macro + '_synthesis')
return reactions, names
def compute_macromolecule_composition(data, metabolites):
"""
Compute base information of macromolecules.
Returns
-------
(production, production_processing_cost, degradation,
degradation_processing_cost, weight) tuple
"""
nb_processes = len(data.processes.processes)
compartments = [c.id for c in data.metabolism.compartments]
# get base macromolecule information
proteins = MacromoleculeSet(data.proteins, compartments,
metabolites, nb_processes)
rnas = MacromoleculeSet(data.rnas, compartments, metabolites, nb_processes)
dna = MacromoleculeSet(data.dna, compartments, metabolites, nb_processes)
# apply processing maps
macro_sets = {'protein': proteins, 'rna': rnas, 'dna': dna}
maps = {m.id: m for m in data.processes.processing_maps}
for p_index, process in enumerate(data.processes.processes):
for prod in process.processings.productions:
inputs = [i.species for i in prod.inputs]
macro_sets[prod.set].apply_production_map(
maps[prod.processing_map], p_index, inputs
)
for deg in process.processings.degradations:
inputs = [i.species for i in deg.inputs]
macro_sets[deg.set].apply_degradation_map(
maps[deg.processing_map], p_index, inputs
)
# aggregate matrices across sets
production_metabolites = [s.production for s in (proteins, rnas, dna)]
production_cost = [s.production_cost for s in (proteins, rnas, dna)]
degradation_metabolites = [s.degradation for s in (proteins, rnas, dna)]
degradation_cost = [s.degradation_cost for s in (proteins, rnas, dna)]
weight = [s.weight for s in (proteins, rnas, dna)]
return (hstack(production_metabolites), hstack(production_cost),
hstack(degradation_metabolites), hstack(degradation_cost),
hstack(weight))
class MacromoleculeSet(object):
"""Macromolecule information."""
def __init__(self, macro_set, compartments, metabolites, nb_processes):
"""Initialize set with zero production/degradation costs."""
self.components = [c.id for c in macro_set.components]
self._molecule_index = {
m.id: i for i, m in enumerate(macro_set.macromolecules)
}
self._component_matrix = self._extract_component_matrix(macro_set)
self.weight = self._extract_weight_matrix(
macro_set, self._component_matrix, compartments
)
self._metabolites = metabolites
nb_met = len(metabolites)
nb_mol = len(self._molecule_index)
self.production = coo_matrix((nb_met, nb_mol))
self.degradation = coo_matrix((nb_met, nb_mol))
self.production_cost = coo_matrix((nb_processes, nb_mol))
self.degradation_cost = coo_matrix((nb_processes, nb_mol))
def _extract_component_matrix(self, macro_set):
"""
Extract component matrix from macromolecule data.
A component matrix is the description of macromolecules in terms
of components (e.g. amino acids). Compare composition matrix, the
description in terms of metabolites consumed and
produced for synthesizing one macromolecule.
"""
nb_macros = len(macro_set.macromolecules)
C = lil_matrix((len(self.components), nb_macros))
for col, macro in enumerate(macro_set.macromolecules):
for c in macro.composition:
C[self.components.index(c.component), col] = c.stoichiometry
return C.tocsr()
def _extract_weight_matrix(self, macro_set, C, compartments):
"""Compute weight and associate weight with location."""
# we first compute weight per component, then weight per molecule
w = csr_matrix([c.weight for c in macro_set.components], dtype='float')
location = [compartments.index(m.compartment)
for m in macro_set.macromolecules]
nb_macros = len(macro_set.macromolecules)
W = csr_matrix(((w*C).toarray().ravel(),
(location, range(nb_macros))),
shape=(len(compartments), nb_macros))
return W
class ProcessingMap(object):
"""Class storing processing maps."""
def __init__(self, map_, components, metabolites):
"""
Constructor.
Parameters
----------
map_ : rba.xml.ProcessingMap
Structure containing processing map.
components : list of rba.xml.Components
Components handled by component map.
metabolites : list of str
Metabolites.
"""
nb_metabolites = len(metabolites)
nb_components = len(components)
met_index = {m: i for i, m in enumerate(metabolites)}
# store constant costs
self._metabolite_constant \
= self._cost_vector(map_.constant_processing, met_index)
self._processing_constant = numpy.zeros(1)
# store component based costs
self._metabolite_table = numpy.zeros([nb_metabolites, nb_components])
self._processing_table = numpy.zeros(nb_components)
for proc in map_.component_processings:
c_index = components.index(proc.component)
self._processing_table[c_index] += proc.machinery_cost
self._metabolite_table[:, c_index] += self._cost_vector(proc,
met_index)
def _cost_vector(self, proc, met_index):
"""Transform processing data into a metabolite vector."""
result = numpy.zeros(len(met_index))
for reac in proc.reactants:
result[met_index[reac.species]] -= reac.stoichiometry
for prod in proc.products:
result[met_index[prod.species]] += prod.stoichiometry
return result
def apply_map(self, component_matrix):
"""
Transform component matrix to metabolite matrix.
Parameters
----------
component_matrix: matrix
Description of macromolecules in terms of components
(columns are macromolecules, rows are components).
Returns
-------
(composition, processing_cost) tuple
composition is a metabolite matrix
describing metabolites consumed/produced during macromolecule
synthesis/degradation (depending on definition of the map).
Columns are macromolecules. Rows are metabolites. A negative
coefficient means the metabolite is *produced* (this is a
composition matrix, not a reaction matrix).
processing_cost is a matrix where columns are macromolecules and
lines are processes. It describes how many resources of a process
are used during macromolecule synthesis/degradation.
"""
# column selector used to duplicate vectors to match final matrix size
cols = numpy.zeros(component_matrix.shape[1], dtype = int)
metab_cost = (csr_matrix(self._metabolite_table) * component_matrix
+ csr_matrix(self._metabolite_constant).T[:, cols])
proc_cost = (csr_matrix(self._processing_table) * component_matrix
+ csr_matrix(self._processing_constant).T[:, cols])
return metab_cost, proc_cost
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] | 2.378068 | 5,134 |
# GNU MediaGoblin -- federated, autonomous media hosting
# Copyright (C) 2011, 2012 MediaGoblin contributors. See AUTHORS.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import logging
import os
from mediagoblin.tools.pluginapi import get_config
from mediagoblin.db.models import MediaEntry
from mediagoblin.tools import pluginapi
_log = logging.getLogger(__name__)
PLUGIN_DIR = os.path.dirname(__file__)
hooks = {
'setup': setup_plugin,
'template_context_prerender': make_stats
}
| [
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10354,
787,
62,
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198,
220,
220,
220,
1782,
198
] | 3.461059 | 321 |
#!/usr/bin/env python
import sys
import os
import glob
import numpy as np
from astropy.io import ascii
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import astropy.table as at
from numpy.lib.recfunctions import append_fields
from scipy.stats import linregress
import pymc3 as pm
import pandas as pd
from collections import OrderedDict
if __name__=='__main__':
sys.exit(main())
| [
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7,
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] | 2.979167 | 144 |
import logging
from django.test import Client, TestCase, override_settings
from everbug.middleware import Header
| [
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198,
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] | 4 | 29 |
from django.core.paginator import Paginator
from django.shortcuts import get_object_or_404, render
from django.urls import reverse
from django.views.generic.edit import CreateView, DeleteView, UpdateView
from .forms import RegForm
from .models import Cups, Participants, Races
| [
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] | 3.607595 | 79 |
self.description = "transferred file with glob characters that match a removed file"
lp = pmpkg("foo")
lp.files = ["foo/b*r", "foo/bar"]
self.addpkg2db("local", lp)
sp1 = pmpkg("foo", "1.0-2")
self.addpkg(sp1)
sp2 = pmpkg("bar", "1.0-2")
sp2.files = ["foo/b*r"]
self.addpkg(sp2)
self.args = "-U %s %s" % (sp1.filename(), sp2.filename())
self.addrule("PKG_VERSION=foo|1.0-2")
self.addrule("PKG_VERSION=bar|1.0-2")
self.addrule("FILE_EXIST=foo/b*r")
self.addrule("!FILE_EXIST=foo/bar")
| [
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] | 2.202703 | 222 |
# minent.utils
# Project level utilities
#
# Author: Benjamin Bengfort <[email protected]>
# Created: Thu Oct 23 14:09:04 2014 -0400
#
# Copyright (C) 2014 Bengfort.com
# For license information, see LICENSE.txt
#
# ID: utils.py [24fa113] [email protected] $
"""
Project level utilities
"""
##########################################################################
## Imports
##########################################################################
import re
import time
import base64
import bleach
import hashlib
from functools import wraps
from markdown import markdown
from dateutil.relativedelta import relativedelta
##########################################################################
## Utilities
##########################################################################
## Nullable kwargs for models
nullable = { 'blank': True, 'null': True, 'default':None }
## Not nullable kwargs for models
notnullable = { 'blank': False, 'null': False }
##########################################################################
## Helper functions
##########################################################################
def normalize(text):
"""
Normalizes the text by removing all punctuation and spaces as well as
making the string completely lowercase.
"""
return re.sub(r'[^a-z0-9]+', '', text.lower())
def signature(text):
"""
This helper method normalizes text and takes the SHA1 hash of it,
returning the base64 encoded result. The normalization method includes
the removal of punctuation and white space as well as making the case
completely lowercase. These signatures will help us discover textual
similarities between questions.
"""
text = normalize(text).encode('utf-8')
return base64.b64encode(hashlib.sha1(text).digest())
def htmlize(text):
"""
This helper method renders Markdown then uses Bleach to sanitize it as
well as convert all links to actual links.
"""
text = bleach.clean(text, strip=True) # Clean the text by stripping bad HTML tags
text = markdown(text) # Convert the markdown to HTML
text = bleach.linkify(text) # Add links from the text and add nofollow to existing links
return text
# Compile regular expression functions for query normalization
find_terms = re.compile(r'"([^"]+)"|(\S+)').findall
norm_space = re.compile(r'\s{2,}').sub
def normalize_query(terms):
"""
Splits the query string in individual keywords, getting rid of extra
spaces and grouping quoted words together.
Example:
>>> normalize_query(' some random words "with quotes " and spaces')
['some', 'random', 'words', 'with quotes', 'and', 'spaces']
"""
return [
norm_space(' ', (t[0] or t[1]).strip())
for t in find_terms(terms)
]
##########################################################################
## Memoization
##########################################################################
def memoized(fget):
"""
Return a property attribute for new-style classes that only calls its
getter on the first access. The result is stored and on subsequent
accesses is returned, preventing the need to call the getter any more.
https://github.com/estebistec/python-memoized-property
"""
attr_name = '_{0}'.format(fget.__name__)
@wraps(fget)
return property(fget_memoized)
##########################################################################
## Timer functions
##########################################################################
class Timer(object):
"""
A context object timer. Usage:
>>> with Timer() as timer:
... do_something()
>>> print timer.interval
"""
def __init__(self, wall_clock=True):
"""
If wall_clock is True then use time.time() to get the number of
actually elapsed seconds. If wall_clock is False, use time.clock to
get the process time instead.
"""
self.wall_clock = wall_clock
self.time = time.time if wall_clock else time.clock
def timeit(func, wall_clock=True):
"""
Returns the number of seconds that a function took along with the result
"""
@wraps(func)
def timer_wrapper(*args, **kwargs):
"""
Inner function that uses the Timer context object
"""
with Timer(wall_clock) as timer:
result = func(*args, **kwargs)
return result, timer
return timer_wrapper
def humanizedelta(*args, **kwargs):
"""
Wrapper around dateutil.relativedelta (same construtor args) and returns
a humanized string representing the detla in a meaningful way.
"""
delta = relativedelta(*args, **kwargs)
attrs = ('years', 'months', 'days', 'hours', 'minutes', 'seconds')
parts = [
'%d %s' % (getattr(delta, attr), getattr(delta, attr) > 1 and attr or attr[:-1])
for attr in attrs if getattr(delta, attr)
]
return " ".join(parts)
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] | 3.16206 | 1,592 |
#!/usr/bin/env python3
x = 0 # NOLINT
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import os
import sys
import bokeh.layouts as bkl
import bokeh.plotting as bkp
import numpy as np
from bokeh.models import Label
from bokeh.io import export_svgs
import cairosvg
# make it so we can import models/etc from parent folder
sys.path.insert(1, os.path.join(sys.path[0], '../common'))
from plotting import *
from skimage import measure
print('Loading data')
x = np.load('../data/prices2018.npy')
for trial_num in [1, 2, 3, 4]:
latbds = (x[:, 0].min(), x[:, 0].max())
lonbds = (x[:, 1].min(), x[:, 1].max())
lats = np.linspace(latbds[0], latbds[1], 100)
lons = np.linspace(lonbds[0], lonbds[1], 100)
longrid, latgrid = np.meshgrid(lons, lats)
contour_percentiles = np.linspace(0, 100, 10)
c = contour_percentiles / contour_percentiles.max()
contour_colors = ['#%02x%02x%02x' % (int(r), int(b), int(g)) for (r, b, g) in
zip(255 * c, 0 * np.ones(c.shape[0]), 255 * (1. - c))]
# algorithm / trial + Ms to plot
#nm = ('SVI', 'SparseVI')
nm = ('IHT-2', 'A-IHT II')
Ms = [220, 260, 300]
# plot the sequence of coreset pts and comparison of nonopt + opt
res = np.load('results/results_' + nm[0] + '_' + str(trial_num) + '.npz')
x = res['x']
wt = res['w']
mup = res['mup']
Sigp = res['Sigp']
muwt = res['muw']
Sigwt = res['Sigw']
basis_scales = res['basis_scales']
basis_locs = res['basis_locs']
datastd = res['datastd']
figs = []
# true posterior figure
fig = bkp.figure(x_range=lonbds, y_range=latbds, plot_width=1000, plot_height=1000)
# for f in [fig, fig_opt]:
for f in [fig]:
preprocess_plot(f, '32pt', False, False)
# plot data and coreset pts
f.scatter(x[:, 1], x[:, 0], fill_color='black', size=12, alpha=0.01, line_color=None)
# compute posterior mean regression on the grid
reg = np.zeros(longrid.shape)
for i in range(basis_scales.shape[0]):
reg += mup[i] * np.exp(
-(longrid - basis_locs[i, 1]) ** 2 / (2 * basis_scales[i] ** 2) - (latgrid - basis_locs[i, 0]) ** 2 / (
2 * basis_scales[i] ** 2))
# contour_levels
contour_levels = [np.percentile(reg, p) for p in contour_percentiles]
# plot contours
for color, level in zip(contour_colors, contour_levels):
contours = measure.find_contours(reg, level)
for contour in contours:
# interpolate values
latlons = np.hstack((np.interp(contour[:, 0], np.arange(lats.shape[0]), lats)[:, np.newaxis],
np.interp(contour[:, 1], np.arange(lons.shape[0]), lons)[:, np.newaxis]))
f.line(latlons[:, 1], latlons[:, 0], line_width=2, line_color=color)
for f in [fig]:
postprocess_plot(f, '32pt', orientation='horizontal', glyph_width=40)
f.legend.background_fill_alpha = 0.
f.legend.border_line_alpha = 0.
# f.legend.visible=False
f.xaxis.visible = False
f.yaxis.visible = False
countour_legend = Label(x=50, y=900, x_units='screen', y_units='screen',
text='True Posterior', text_font_size='32pt')
f.add_layout(countour_legend)
figs.append([fig])
# true contour
fig.output_backend = 'svg'
fig_name = 'exp2-contour_' + 'true_' + 'id_' + str(trial_num)
export_svgs(fig, filename=fig_name + '.svg')
# cairosvg.svg2pdf(url=fig_name+'.svg', write_to=fig_name+'.pdf')
cairosvg.svg2pdf(
file_obj=open(fig_name + '.svg', "rb"), write_to=fig_name + '.pdf')
# coreset figures
for m in Ms:
fig = bkp.figure(x_range=lonbds, y_range=latbds, plot_width=1000, plot_height=1000)
preprocess_plot(fig, '32pt', False, False)
# plot data and coreset pts
fig.scatter(x[:, 1], x[:, 0], fill_color='black', size=12, alpha=0.01, line_color=None)
# fig.scatter(x[:, 1], x[:, 0], fill_color='black', size=10*(wt[m, :]>0)+10*wt[m,:]/wt[m,:].max(), line_color=None)
fig.scatter(x[:, 1], x[:, 0], fill_color='black', size=30 * np.power(wt[m, :] / wt[m, :].max(), 0.4),
line_color=None)
# compute posterior mean regression on the grid
reg = np.zeros(longrid.shape)
for i in range(basis_scales.shape[0]):
reg += muwt[m, i] * np.exp(
-(longrid - basis_locs[i, 1]) ** 2 / (2 * basis_scales[i] ** 2) - (latgrid - basis_locs[i, 0]) ** 2 / (
2 * basis_scales[i] ** 2))
# plot contours
for color, level in zip(contour_colors, contour_levels):
contours = measure.find_contours(reg, level)
for contour in contours:
# interpolate values
latlons = np.hstack((np.interp(contour[:, 0], np.arange(lats.shape[0]), lats)[:, np.newaxis],
np.interp(contour[:, 1], np.arange(lons.shape[0]), lons)[:, np.newaxis]))
fig.line(latlons[:, 1], latlons[:, 0], line_width=2, line_color=color)
postprocess_plot(fig, '32pt', orientation='horizontal', glyph_width=40)
fig.legend.background_fill_alpha = 0.
fig.legend.border_line_alpha = 0.
fig.xaxis.visible = False
fig.yaxis.visible = False
countour_legend = Label(x=50, y=900, x_units='screen', y_units='screen',
text=nm[1] + ' Corset Posterior '+ 'Coreset size: ' + str(m), text_font_size='32pt')
fig.add_layout(countour_legend)
figs.append([fig])
fig.output_backend = 'svg'
fig_name = 'exp2-contour_' + nm[0] + '_m' + str(m)+ '_id_' + str(trial_num)
export_svgs(fig, filename=fig_name + '.svg')
# cairosvg.svg2pdf(url=fig_name+'.svg', write_to=fig_name+'.pdf')
cairosvg.svg2pdf(
file_obj=open(fig_name + '.svg', "rb"), write_to=fig_name + '.pdf')
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] | 2.067135 | 2,845 |
import os
import stat
import time
import re
import pwd
import logSupport
import condorPrivsep
from pidSupport import register_sighandler, unregister_sighandler
MY_USERNAME = pwd.getpwuid(os.getuid())[0]
cleaners = Cleanup()
class CredCleanup(Cleanup):
"""
Cleans up old credential files.
"""
cred_cleaners = CredCleanup()
# this class is used for cleanup
# INTERNAL
# return a dictionary of fpaths each having the os.lstat output
# this may reimplemented by the children
# this class is used for cleanup
class PrivsepDirCleanupCredentials(DirCleanup):
"""
Used to cleanup old credential files saved to disk by the factory for glidein submission (based on ctime).
"""
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] | 2.947154 | 246 |
from . import database
| [
6738,
764,
1330,
6831,
628
] | 4.8 | 5 |
import sys
from partsdb.partsdb import PartsDB
from tables import *
marpodb = PartsDB('postgresql:///'+sys.argv[1], Base = Base)
session = marpodb.Session()
n=0
i=0
for gene in session.query(Gene).all():
n = session.query(InterProHit).filter(InterProHit.targetID == gene.cds.id).count()
if n == 0:
session.query(BlastpHit).filter(BlastpHit.targetID == gene.cds.id).delete()
session.delete(gene)
n = n+1
if i % 10 == 0:
print "Processed {0} genes"
i = i+1
session.commit()
print "Deleted {0} genes".format(n) | [
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33342,
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15,
92,
10812,
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18982,
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8
] | 2.490566 | 212 |
from unittest import TestCase
import hummingbot.client.config.config_validators as config_validators
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] | 3.814815 | 27 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import yaml
| [
2,
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from functools import partial
class F(partial):
"""
Python Pipe. e.g.`range(10) | F(filter, lambda x: x % 2) | F(sum)`
WARNING: There will be a small performance loss when building a
pipeline. Please do not use it in performance-sensitive locations.
"""
class FF(partial):
"""
Python Pipe. e.g.`("f", 10) | FF(lambda letter, num: letter * num)`
WARNING: There will be a small performance loss when building a
pipeline. Please do not use it in performance-sensitive locations.
"""
| [
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12,
30176,
7064,
13,
198,
220,
220,
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37227,
628
] | 3.081871 | 171 |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Travis Yates
"""Tests for object_detection.utils.test_utils."""
import numpy as np
import tensorflow as tf
from object_detection.utils import test_utils
if __name__ == '__main__':
tf.test.main()
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] | 3.127907 | 86 |
import keyboard
keyboard.hook(abc)
keyboard.wait()
| [
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"""Consolidate repository data from several previous steps.
Use -h or --help for more information.
"""
import argparse
import csv
import logging
import sys
from util.parse import \
parse_repo_to_package_file, \
consolidate_data
__log__ = logging.getLogger(__name__)
FIELDNAMES = [
'id',
'name',
'full_name',
'description',
'size',
'private',
'fork',
'archived',
'created_at',
'updated_at',
'pushed_at',
'language',
'default_branch',
'homepage',
'forks_count',
'stargazers_count',
'subscribers_count',
'watchers_count',
'network_count',
'has_downloads',
'has_issues',
'has_pages',
'has_projects',
'has_wiki',
'owner_id',
'owner_login',
'owner_type',
'parent_id',
'source_id',
'commit_count',
'has_gradle_files',
'renamed_to',
'not_found',
'clone_status',
'clone_project_name',
'clone_project_id',
'clone_project_path',
'packages'
]
def define_cmdline_arguments(parser: argparse.ArgumentParser):
"""Add arguments to parser."""
parser.add_argument(
'ORIGINAL_REPO_LIST', type=argparse.FileType('r'),
help='''CSV file as created by subcommand 'get_repo_data' and augmented
by subcommand 'add_gradle_info'. This original file is necessary
because later versions have non ASCII characters wrongly encoded.
''')
parser.add_argument(
'NEW_REPO_LIST', type=argparse.FileType('r'),
help='''CSV file generated by external script to import GitHub
repositories to a local Gitlab instance. This file has the same
content as 'original_file' with some additional columns.
Unfortunately, there is an encoding issue.''')
parser.add_argument(
'MIRRORED_REPO_LIST', type=argparse.FileType('r'),
help='''CSV file generated by subcommand 'mirror_empty_repos'. This
file contains updated information on the snapshot repository in
Gitlab.''')
parser.add_argument(
'PACKAGE_LIST', type=argparse.FileType('r'),
help='''CSV file that lists package name and repository name in
a column each. The file should not have a header.''')
parser.add_argument(
'RENAMED_REPOS_LIST', type=argparse.FileType('r'),
help='''CSV file which lists GitHub IDs and new repo names of some
renamed repos.''')
parser.add_argument(
'-o', '--output', type=argparse.FileType('w'), default=sys.stdout,
help='File to write output CSV to. Default: stdout.')
parser.set_defaults(func=_main)
def _main(args: argparse.Namespace):
"""Pass arguments to respective function."""
__log__.info('------- Arguments: -------')
__log__.info('ORIGINAL_REPO_LIST: %s', args.ORIGINAL_REPO_LIST)
__log__.info('NEW_REPO_LIST: %s', args.NEW_REPO_LIST.name)
__log__.info('MIRRORED_REPO_LIST: %s', args.MIRRORED_REPO_LIST.name)
__log__.info('PACKAGE_LIST: %s', args.PACKAGE_LIST.name)
__log__.info('RENAMED_REPOS_LIST: %s', args.RENAMED_REPOS_LIST.name)
__log__.info('--output: %s', args.output.name)
__log__.info('------- Arguments end -------')
packages_by_repo = parse_repo_to_package_file(args.PACKAGE_LIST)
renamed_repos = {row['github_id']: row for row in csv.DictReader(
args.RENAMED_REPOS_LIST)}
data = consolidate_data(
args.ORIGINAL_REPO_LIST, args.NEW_REPO_LIST, args.MIRRORED_REPO_LIST,
renamed_repos, packages_by_repo)
writer = csv.DictWriter(args.output, FIELDNAMES)
writer.writeheader()
num_repos = 0
for row in data:
num_repos += 1
writer.writerow(row)
__log__.info('Number of successfully matched repos: %d', num_repos)
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1128,
418,
25,
4064,
67,
3256,
997,
62,
260,
1930,
8,
198
] | 2.385777 | 1,589 |
import unittest
from pubmed.package1.example_mod2 import ExampleMod2
if __name__ == '__main__':
# Run tests with the warning such as below ignored (not shown).
# InsecureRequestWarning: Unverified HTTPS request
# ResourceWarning: unclosed <ssl.SSLSocket fd=5, ...
unittest.main(warnings='ignore')
| [
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198
] | 3.048077 | 104 |
# The following code is of week one of python
print("Hello my name is George!")
print("This is my dog Max!")
print('o----')
print(' ||||')
# some maths!
print(50 - 10)
# let's make a variable for year I was born in by inputting!
# make sure to click on the black screen to type an press enter!
year = input("What year are you born in? ")
# Now lets see how old I am! Remeber to turn the variable into a number using int()
print(2019 - int(year))
| [
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198
] | 3.277372 | 137 |
import sys
from pyspark import SparkContext
sc = SparkContext(appName="WordCountExample")
lines = sc.textFile(sys.argv[1])
counts = lines.flatMap(lambda x: x.split(' ')) \
.map(lambda x: (x, 1)) \
.reduceByKey(lambda x, y: x+y)
output = counts.collect()
for (word, count) in output:
print("%s: %i" % (word, count))
sc.stop()
| [
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4008,
201,
198,
1416,
13,
11338,
3419,
201,
198
] | 2.186047 | 172 |
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class ModelSearchOptions(Model):
"""Model Search Options.
:param search_keyword: Gets or sets keyword for model search. Searches
within pre-defined properties.
:type search_keyword: str
:param model_type: Gets or sets the type of the model. Possible values
include: 'Interface', 'Undetermined'
:type model_type: str or ~pnp.models.enum
:param model_state: Gets or sets the state of the model. Possible values
include: 'Created', 'Listed'
:type model_state: str or ~pnp.models.enum
:param publisher_id: Gets or sets the publisher identifier.
:type publisher_id: str
:param created_by: Gets or sets the created by.
:type created_by: str
"""
_attribute_map = {
'search_keyword': {'key': 'searchKeyword', 'type': 'str'},
'model_type': {'key': 'modelType', 'type': 'str'},
'model_state': {'key': 'modelState', 'type': 'str'},
'publisher_id': {'key': 'publisherId', 'type': 'str'},
'created_by': {'key': 'createdBy', 'type': 'str'},
}
| [
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6,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
705,
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62,
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1391,
6,
2539,
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3886,
3256,
705,
4906,
10354,
705,
2536,
6,
5512,
198,
220,
220,
220,
1782,
198
] | 3.203704 | 486 |
#%%
import configparser
config = configparser.ConfigParser()
config.read('map_indicators.ini')
config.sections()
tables = config['Database']['tables'].split()
print(tables)
# %%
import configparser
def init(inifile : str) -> bool:
'''
check if everything is ok prior to entering the main loop
'''
config = configparser.ConfigParser()
config.read(inifile)
if 'Database' in config and 'Sessions' in config:
return True
else:
return False
assert init('map_indicators.ini') is True
assert init('m') is False
# %%
import os
import platform
print(os.name)
print(platform.system())
# %%
@decorate
l = ['t1', 't2']
select(l)
# %%
# Notre fonction décorée
@decorate
# Appel de la fonction
foobar("A", "B", "C", "D")
# %%
# It’s not black magic, you just have to let the wrapper
# pass the argument:
# Since when you are calling the function returned by the decorator, you are
# calling the wrapper, passing arguments to the wrapper will let it pass them to
# the decorated function
@a_decorator_passing_arguments
print_full_name("Peter", "Venkman")
# outputs:
#I got args! Look: Peter Venkman
#My name is Peter Venkman
# %%
@add_prefix('jules')
l = ['t1', 't2']
select(l)
# %%
myvar = 'content'
print('content of variable {v.__name__} {v}'.format(v=myvar))
# %%
import datetime
print(datetime.datetime.today())
# %%
context = 'datetime'
current_context = datetime.date.today() if context == 'date' else datetime.datetime.today()
print(current_context)
# %%
var = '/path/to/Session-2022-05-14-14'
session = var[var.rfind('-')+1:]
print(session)
# %%
returned_paths = ['/path/to/Session-2022-05-14-14', '/path/to/Session-2022-05-14-2']
current_context = str(datetime.date.today() if context == 'date' else datetime.datetime.today())
separator = '-'
sessions = [1]
for path in returned_paths:
sessions.append(int(path[path.rfind('-')+1:]))
x = max(sessions)
path_to_create = current_context+separator+str((max(sessions)+1))
print(path_to_create)
print(sessions)
print(x)
# %%
from prompt_toolkit import print_formatted_text, HTML, prompt
from prompt_toolkit.validation import Validator
validator_yn = Validator.from_callable(
is_ync,
error_message='enter (y)es; (n)o, (c)ancel',
move_cursor_to_end=True)
inputt = prompt('Your choice : (y/n/c) : ')
# %%
ask_ok('?')
# %%
@decorate("Arg 1", "Arg 2", "Arg 3")
foobar()
#%%
tables = ['adm_user', 'adm_profile_user']
tool_tables = ['user_acess', 'path']
query = 'select * from adm_user join adm_profile_user on a=a where access = user_access.access_enseigne'
brand = 'jules'
separator = '_'
print(query)
query = brand_query(query=query, tables=tables, brand=brand, separator=separator)
print(query)
#%%
s = "The quick brown fox jumps over the lazy dog"
for r in (("brown", "red"), ("lazy", "quick")):
s = s.replace(*r)
print(s)
#%%
l = []
f('5')
print(l)
print(id(l))
f('6')
print(l)
print(isinstance(l, list))
for x in l:
print(f'l:{x}')
f(None)
print(l) | [
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] | 2.602431 | 1,152 |
#!/usr/bin/env python
import pandas as pd
#Full outer join on the 3 tables produced based on the 3 XML Orphanet files.
filename1 = "orphanet_xml1_parsed"
filename4 = "orphanet_xml4_parsed"
filename6 = "orphanet_xml6_parsed"
f1_df = pd.read_csv(filename1, sep="\t", index_col=False, dtype=str, na_filter = False, encoding="latin")
f4_df = pd.read_csv(filename4, sep="\t", index_col=False, dtype=str, na_filter = False, encoding="latin")
f6_df = pd.read_csv(filename6, sep="\t", index_col=False, dtype=str, na_filter = False, encoding="latin")
df_merge1 = pd.merge(f1_df, f6_df, on='orphanet_id', how='outer')
df_merge2 = pd.merge(df_merge1, f4_df, on='orphanet_id', how='outer')
df_merge2.to_csv("orphanet_all_parsed.txt", sep="\t", na_rep="NA", index=False, encoding='utf-8')
| [
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628
] | 2.360606 | 330 |
import tixte
import discord
client = discord.Client(intents=discord.Intents.all(), command_prefix='!')
client.tixte = tixte.Client('your-master-token')
@client.event | [
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] | 2.929825 | 57 |
import logging
from PIL import Image
from pyscreenshot.plugins.backend import CBackend
from pyscreenshot.util import platform_is_osx, py2
if py2():
to_bytes = buffer
else:
to_bytes = bytes
log = logging.getLogger(__name__)
# based on:
# http://stackoverflow.com/questions/69645/take-a-screenshot-via-a-python-script-linux
app = None
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] | 2.784 | 125 |
from .api import call
# Retrieve arrival timings for all buses operating for specified bus stop. Each bus has 3 recurring timings.
# Retrieve data of all operating bus services.
# Retrieve data of all operating bus routes.
# Retrieve data of bus stops. Max number of records per page is 500.
| [
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# -*- coding: utf-8 -*-
"""
Malort Tests
Test Runner: PyTest
Notes:
* Expected values for string samples are any values that the sample
could contain, not the exact values.
"""
import os
import unittest
import pytest
import malort as mt
from malort.test_helpers import TestHelpers, TEST_FILES_1, TEST_FILES_2
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from .labelsmoothing import LabelSmoothingLoss | [
6738,
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] | 3.285714 | 14 |
import numpy as np
import itertools
from pyquil import Program
from pyquil.gates import MEASURE
# all references are to chapter 4 of Gottesman's thesis, chapter 4
# https://arxiv.org/pdf/quant-ph/9705052.pdf
tuple2pauli = {(0,0):'I', (0,1): 'Z', (1,0): 'X', (1,1): 'Y'}
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] | 2.4375 | 112 |
__author__ = "Prakash Manandhar"
__copyright__ = "Copyright 2021, Prakash Manandhar"
__credits__ = ["Prakash Manandhar"]
__license__ = "MIT"
__version__ = "1.1"
__maintainer__ = "Prakash Manandhar"
__email__ = "[email protected]"
__status__ = "Production"
import argparse
import re
import os.path
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
"""
Source: https://developers.google.com/docs/api/samples/extract-text#python
"""
def read_paragraph_element(element):
"""Returns the text in the given ParagraphElement.
Args:
element: a ParagraphElement from a Google Doc.
"""
text_run = element.get('textRun')
if not text_run:
return ''
return text_run.get('content')
"""
Source: https://developers.google.com/docs/api/samples/extract-text#python
"""
def read_strucutural_elements(elements):
"""Recurses through a list of Structural Elements to read a document's text where text may be
in nested elements.
Args:
elements: a list of Structural Elements.
"""
text = ''
for value in elements:
if 'paragraph' in value:
elements = value.get('paragraph').get('elements')
for elem in elements:
text += read_paragraph_element(elem)
elif 'table' in value:
# The text in table cells are in nested Structural Elements and tables may be
# nested.
table = value.get('table')
for row in table.get('tableRows'):
cells = row.get('tableCells')
for cell in cells:
text += read_strucutural_elements(cell.get('content'))
elif 'tableOfContents' in value:
# The text in the TOC is also in a Structural Element.
toc = value.get('tableOfContents')
text += read_strucutural_elements(toc.get('content'))
return text
if __name__ == "__main__":
parser = init_argparse()
args, unknwon_args = parser.parse_known_args()
SCOPES = ['https://www.googleapis.com/auth/documents.readonly']
creds = None
# The file token.json stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists(args.token):
creds = Credentials.from_authorized_user_file(args.token, SCOPES)
# If there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
args.secret, SCOPES)
creds = flow.run_local_server(port=0)
# Save the credentials for the next run
with open(args.token, 'w') as token:
token.write(creds.to_json())
service = build('docs', 'v1', credentials=creds)
# Retrieve the documents contents from the Docs service.
print("Retrieving document ....")
document = service.documents().get(documentId=args.id).execute()
doc_content = document.get('body').get('content')
doc_text = read_strucutural_elements(doc_content)
find_text(doc_text, args.regex, args.outfile)
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] | 2.505503 | 1,363 |
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 27 13:53:50 2016
@author: au194693
"""
import numpy as np
import scipy.io as sio
import pandas as pd
from my_settings import (tf_folder, subjects_ctl)
data = sio.loadmat("/Volumes/My_Passport/agency_connectivity/" +
"results/data_all_Ctrl.mat")["data_all"]
b_df = pd.DataFrame()
for j in range(len(data)):
baseline = data[j, 0].mean()
invol_trials = data[j, 3].squeeze()
if len(invol_trials) is 90:
invol_trials = invol_trials[1:]
error = (np.std(data[j, 3]) * 2 + invol_trials.mean(),
-np.std(data[j, 3]) * 2 + invol_trials.mean())
for i in range(len(invol_trials)):
row = pd.DataFrame([{
"subject": "p%s" % (j + 21),
"group": "ctl",
"condition": "invol",
"binding": invol_trials[i] - baseline,
"trial_number": i + 1,
"trial_status":
error[1] <= (invol_trials[i] - baseline) <= error[0],
"error": error,
"raw_trial": invol_trials[i],
"baseline": baseline
}])
b_df = b_df.append(row, ignore_index=True)
# b_df = pd.DataFrame()
for subject in subjects_ctl[:1]:
eeg = np.load(tf_folder + "%s_test_HT-comp.npy" % subject)
eeg_data = eeg[:, 52, 768:1024, 4]
if eeg_data.shape[0] is 90:
eeg_data = eeg_data[1:, :]
eeg_data = np.mean(np.abs(eeg_data)**2, axis=1)
test_eeg = eeg_data[b_df[b_df.subject == subject]
.trial_status.values].mean()
test_binding = b_df[b_df.subject == subject][b_df[b_df.subject ==
subject].trial_status ==
True].binding.values.mean()
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6407,
4083,
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13,
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3419,
198
] | 1.909774 | 931 |
#!/usr/bin/env python
"""
lambdata - a collection of Data Science helper functions
"""
| [
2,
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] | 3.133333 | 30 |
# -*- coding: utf-8 -*-
"""
@time : 2020/05/10 12:14
@author : 姚明伟
"""
import json
import pickle
from flask import request, jsonify, make_response
from info.modules.search_content.search_script_conf import get_tips_word
from info.utils.content import search_content_indistinct, search_content_exact
from info.utils.thread_return_value import MyThread
from loggers import *
from config import r_decode, r
from tools.response_code import RET
from . import search_content_blu
TREE = None
@search_content_blu.route('/', methods=['GET'])
@search_content_blu.route('/update/', methods=['GET'])
@search_content_blu.route('/su/')
def index():
"""
搜索提示功能
根据输入的值自动联想,支持中文,英文,英文首字母
:return: response
"""
start_time = time.time()
# 输入词转小写
wd = request.args.get('wd').lower()
user_id = request.args.get('user_id')
if user_id and user_id != 'None':
print(user_id)
print(type(user_id))
if not wd:
return make_response("""queryList({"q":"","p":false,"bs":"","csor":"0","status":770,"s":[]});""")
# 搜索词(支持中文，英文，英文首字母)
s = wd
# result = search_script_conf.get_tips_word(search_script_conf.sug, search_script_conf.data, s)
# # print('前缀：',result)
global TREE
if TREE is None:
# 第一次为空，需要在接口中加载一次已经生成好的字典树，pickle.loads这一步耗时接近1s
temp = r.get('tree')
TREE = pickle.loads(temp)
# 内容中有字典树，直接获取
suggest = get_tips_word(TREE[0], TREE[1], s)
print('前缀：', suggest)
data_top = {}
if len(suggest) > 0:
# 从redis获取热度值
heat_list = r_decode.hmget("hot_word_heat", suggest)
_map = dict(zip(suggest, heat_list))
# 按照热度值排序
data = dict(sorted(_map.items(), key=lambda x: int(x[1]), reverse=True))
print("热度值排序:", data)
# TODO 获取个性化搜索结果展示
suggest = list(data.keys())[:15]
data_top = {i: data[i] for i in suggest}
response = make_response(
"""queryList({'q':'""" + wd + """','p':false,'s':""" + str(suggest) + """});""")
response.headers['Content-Type'] = 'text/javascript; charset=utf-8'
end_time = time.time()
# 记录日志
ret = dict()
ret['code'] = 200
ret['msg'] = "ok"
ret['search_word'] = wd
ret['search_suggest'] = suggest
ret['heat_rank'] = data_top
ret['search_type'] = 'search_suggest'
ret['gmt_created'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
ret['user_id'] = ''
ret['platformCode'] = ''
ret['total_time'] = end_time - start_time
info(json.dumps(ret, ensure_ascii=False))
return response
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] | 1.875453 | 1,381 |
# Copyright 2015 OpenStack Foundation
# 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 datetime
import iso8601
import mock
from watcher.common import exception
from watcher.common import rpc
from watcher.common import utils as w_utils
from watcher.db.sqlalchemy import api as db_api
from watcher import notifications
from watcher import objects
from watcher.tests.db import base
from watcher.tests.db import utils
from watcher.tests.objects import utils as objutils
| [
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] | 3.517123 | 292 |
import os
import torch
import torch.nn.functional as F
from collections import OrderedDict
import argparse
from ptsemseg.models import get_model
from ptsemseg.utils import convert_state_dict
import numpy as np
#based on https://github.com/ducha-aiki/ucn-pytorch/blob/master/Utils.py ; commit de5bdec from Oct 4, 2017
#optimized uint8 HWC BGR -> float 1CHW directly in CUDA/pytorch code
if __name__ == "__main__":
sys.exit(main_export_onnx(sys.argv[1:]))
| [
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7,
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4008,
198
] | 2.747059 | 170 |
from django import forms
from .models.taste_note import TasteNoteModel
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] | 3.789474 | 19 |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
* Copyright (C) 2017 Hendrik van Essen
*
* This file is subject to the terms and conditions of the MIT License
* See the file LICENSE in the top level directory for more details.
"""
"""setup script"""
"""
BASIC SYSTEM PRIVATE COMPUTER
sudo apt-get install python3-pip
sudo apt-get install python-pip
sudo apt-get install python-zmq
ARDUINO
sudo apt-get install minicom python3-serial
Kookye FM-70
http://kookye.com/2016/07/24/use-arduino-drive-fingerprint-sensor/
unzip and copy to <arduinosketchfolder>/libraries/
cd <arduinosketchfolder>/libraries/
git clone https://github.com/adafruit/DHT-sensor-library
git clone https://github.com/adafruit/Adafruit_Sensor
install ArduinoJson within Arduino IDE from Library Manager (Sketch -> Include Library -> Manage Libraries)
"""
"""
BASIC SYSTEM RASPBERRY PI
enable SSH
change lines in /etc/ssh/sshd_config
####################################################
PermitRootLogin no
PasswordAuthentication no
####################################################
banning ips trying to login with wrong credentials
sudo apt-get install fail2ban
DISPLAY
https://learn.adafruit.com/adafruit-2-2-pitft-hat-320-240-primary-display-for-raspberry-pi/detailed-installation
SOUND
sudo apt-get install python3-pygame
sudo apt-get install python-pygame
Dynamic DNS client with NoIP.com
(https://www.noip.com/support/knowledgebase/installing-the-linux-dynamic-update-client-on-ubuntu/)
cd /usr/local/src/
wget http://www.no-ip.com/client/linux/noip-duc-linux.tar.gz
tar xf noip-duc-linux.tar.gz
cd noip-2.1.9-1/
make install
Autostart noip2 on boot
sudo nano /etc/systemd/system/noip2.service
script from https://gist.github.com/NathanGiesbrecht/da6560f21e55178bcea7fdd9ca2e39b5
####################################################
[Unit]
Description=No-ip.com dynamic IP address updater
After=network.target
After=syslog.target
[Install]
WantedBy=multi-user.target
Alias=noip.service
[Service]
# Start main service
ExecStart=/usr/local/bin/noip2
Restart=always
Type=forking
####################################################
sudo systemctl enable noip2
sudo systemctl start noip2
APACHE
sudo a2enmod cgi
/etc/apache2/sites-available/mypidrei.ddns.net.conf
####################################
<VirtualHost *:80>
<Directory /var/www/my_pi_drei/webserver/www/>
Options +ExecCGI
DirectoryIndex index.py
</Directory>
AddHandler cgi-script .py
ServerAdmin webmaster@localhost
DocumentRoot /var/www/my_pi_drei/webserver/www/
ServerName mypidrei.ddns.net
</VirtualHost>
####################################
sudo a2ensite mypidrei.ddns.net.conf
disable indexing in /etc/apache2/apache2.conf
####################################
<Directory /var/www/>
Options Indexes FollowSymLinks
AllowOverride None
Require all granted
</Directory>
####################################
to
####################################
<Directory /var/www/>
Options -Indexes +FollowSymLinks
AllowOverride None
Require all granted
</Directory>
####################################
sudo service apache2 restart
add in /etc/hosts
####################################
127.0.0.2 mypidrei.com
####################################
You will need to forward port 80 (Webserver) and 22 (SSH, optional) in your router settings
SSL For Apache using Certbot
You will need to forward port 443 (HTTPS) in your router settings
sudo apt-get install python-certbot-apache
sudo certbot --apache -d mypidrei.ddns.net (needs user input. Recommended option "Secure"(redirection of http to https))
CONFIG
copy from webserver/config config_example and rename the copy to config.py
-> replace all placeholder in the file
""" | [
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] | 3.067254 | 1,249 |
import re
import sys
import nltk
import json
import argparse
import pandas as pd
from nltk.corpus import stopwords
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.feature_extraction.text import TfidfVectorizer
# creates a genre predictor/classifier which does multi-class (many genres) and multi-label (each movie may have more than one genre) classification
# init function of the class where the classifier training occurs
# function for cleaning descriptive text
# the output of the program is the json version of this class
# create an argument parser to input the title and description from command line in the required format
try:
parser=argparse.ArgumentParser()
parser.add_argument('--title', help='the movie title', type= str)
parser.add_argument('--description', help='the movie description', type= str)
args=parser.parse_args()
if(len(args.title)==0 or len(args.description)==0):
print("Both title and description are mandatory non-empty strings. Please re-run command with non-empty inputs.")
raise Exception
except:
print("Error in input format. Run command using this format:\npython3 movie_genre_predictor.py --title <title> --description <description>")
sys.exit()
# create the predictor using the arguments passed
try:
mgp = MovieGenrePredictor()
description = args.description
description = mgp.clean_text(str(description))
description_tfidf = mgp.tfidf_vectorizer.transform(pd.Series(description))
output_genre_vector = mgp.classifier.predict(description_tfidf)
output_genre = mgp.multilabel_binarizer.inverse_transform(output_genre_vector)
except IOError:
print("Error occured on trying to read data file.")
sys.exit()
except:
print("Error occured while performing classification.")
sys.exit()
# create the object of Output class which is then dumped as json and printed as output.
try:
output = Output(args.title,args.description,output_genre[0])
j = json.dumps(output.__dict__, indent = 4)
print(j)
except:
print("Error occurred during conversion of predicted target output to json format.")
sys.exit()
| [
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] | 3.347639 | 699 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import precision_recall_curve, roc_curve, auc
import pandas as pd
import numpy as np
def pearsonr_cor(pred, label):
""" Return Pearson's correlation between prediction and label
"""
cor, _ = pearsonr(pred, label)
return cor
def spearmanr_cor(pred, label):
""" Return Spearman's correlation between prediction and label
"""
cor, _ = spearmanr(pred, label)
return cor
def compute_auroc(pred, label):
""" Calculate AUROC of predictions in terms of label
"""
#label = np.array(label)
#pred = np.array(pred)
fpr, tpr, thresholds = roc_curve(label, pred, pos_label =1)
auroc = auc(fpr, tpr)
return auroc
def compute_auprc(pred, label):
""" Calculate AUPRC of predictions in terms of label
"""
#label = np.array(label)
#pred = np.array(pred)
precision, recall, thresholds = precision_recall_curve(label, pred)
auprc = auc(recall, precision)
return auprc
def c_index(pred, label):
""" Compute C-idex of the predictions in terms of ground truth
Parameters:
-----------
pred: list
prediction
label: list
ground truth
pred and label are the same length
Yields:
-------
cidx: float
C-index (between 0 to 1)
"""
from itertools import permutations
pred = list(pred)
label = list(label)
perm = permutations(list(range(len(pred))), 2)
survive = 0
total = 0
for i, j in perm:
if label[i]<label[j]:
total +=1
if pred[i]<pred[j]:
survive += 1
cidx = survive/total
return cidx
def boostrapping_confidence_interval(pred_all, gs_all, eva_func, ci):
""" Boostrapping to get a 95 confidence interval for prediction performance
Parameters:
-----------
pred_all: list
all predictions from k-fold cross-validations
gs_all: list
all gold standards from k-fold cross-validations
eva_func: function
evaludation function
ci: confidence interval
Yields:
-------
mb: float
middle bound
lb: float
lower bound
ub: float
upper bound
"""
import numpy as np
import random
# set random seed
random.seed(0)
# prediction-groundtruth pairs from all five fold cross validation
tmp = np.array([pred_all, gs_all]).T
# calculate overall correlation
mb = eva_func(tmp[:,0], tmp[:,1])
# start boostrapping ...
eva_all = []
for i in range(100):
tmp_new = random.choices(tmp, k = len(tmp))
tmp_new = np.array(tmp_new)
eva = eva_func(tmp_new[:,0], tmp_new[:,1])
eva_all.append(eva)
eva_all = sorted(eva_all)
#print(eva_all)
lb = eva_all[round(100*(0.5-ci*0.5))]
ub = eva_all[round(100*(0.5+ci*0.5))]
return mb, lb, ub
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] | 2.375927 | 1,213 |
#!/usr/bin/env python3
#
# Copyright (c) 2018, Intel Corporation
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Show installed versions of doc building tools
import os.path
import sys
import pkg_resources
import subprocess
# Check all requirements listed in requirements.txt and print out version installed (if any)
print ("doc build tool versions found on your system...\n")
rf = open(os.path.join(sys.path[0], "requirements.txt"),"r")
for reqs in pkg_resources.parse_requirements(rf):
try:
ver = pkg_resources.get_distribution(reqs.project_name).version
print (" " + reqs.project_name.ljust(25," ") + " version: " + ver)
except:
print (color.RED + color.BOLD + reqs.project_name + " is missing." + color.END +
" (Hint: install all dependencies with " + color.YELLOW +
"\"pip3 install --user -r scripts/requirements.txt\"" + color.END + ")")
rf.close()
# Print out the version of Doxygen (not installed via pip3)
print (" " + "doxygen".ljust(25," ") + " version: " + subprocess.check_output(["doxygen", "-v"]).decode("utf-8"))
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366,
8,
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198,
198,
41871,
13,
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3419,
198,
198,
2,
12578,
503,
262,
2196,
286,
360,
23536,
5235,
357,
1662,
6589,
2884,
7347,
18,
8,
198,
4798,
5855,
220,
366,
1343,
366,
67,
23536,
5235,
1911,
75,
3137,
7,
1495,
553,
366,
8,
1343,
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2196,
25,
366,
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850,
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13,
9122,
62,
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7,
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67,
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27444,
85,
8973,
737,
12501,
1098,
7203,
40477,
12,
23,
48774,
198
] | 2.700246 | 407 |
import FWCore.ParameterSet.Config as cms
process = cms.Process("Demo")
process.load("FWCore.MessageService.MessageLogger_cfi")
process.MessageLogger = cms.Service("MessageLogger",
debugModules = cms.untracked.vstring("*"),
cout = cms.untracked.PSet(
threshold = cms.untracked.string('DEBUG')
),
destinations = cms.untracked.vstring('cout')
)
# How to use the EmptyIOVSource:
# the EmptyIOVSource will generate N events with a given interval.
# the N events must be specified in the maxEvents as usual but the
# first value, last value, timetype (runnumber, timestamp or lumiid) must be specified
# in the EmptyIOVSource configuration block. It will then generate events with the given
# interval.
# To generate one event per run in a given range of runs you should then use:
# - first - last value as the run range
# - interval == 1 (means move of one run unit at a time)
# - maxEvents = lastValue - firstValue (so that there is one event per run
# otherwise it will stop before completing the range or it will go beyond (to infinity).
process.maxEvents = cms.untracked.PSet(
input = cms.untracked.int32(5)
)
process.source = cms.Source("EmptyIOVSource",
timetype = cms.string('runnumber'),
firstValue = cms.uint64(97),
lastValue = cms.uint64(102),
interval = cms.uint64(1)
)
#process.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) )
#
#process.source = cms.Source("PoolSource",
# # replace 'myfile.root' with the source file you want to use
# fileNames = cms.untracked.vstring(
# 'file:myfile.root'
# )
#)
process.load("CalibTracker.SiStripESProducers.fake.Phase2TrackerConfigurableCablingESSource_cfi")
process.demo = cms.EDAnalyzer('CheckPhase2Cabling')
process.p = cms.Path(process.demo)
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2,
8,
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13,
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17,
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34,
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62,
66,
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13,
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78,
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1961,
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17,
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198,
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79,
796,
269,
907,
13,
15235,
7,
14681,
13,
9536,
78,
8,
198
] | 2.875606 | 619 |
import re
cases = int(input())
for case in range(cases):
try:
re.compile(input())
print(True)
except re.error:
print(False)
| [
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] | 2.135135 | 74 |
import copy
import io
from collections import defaultdict
import click
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import rcParams
from matplotlib.backends.backend_pdf import PdfPages
from openff.toolkit.topology import Molecule
from PIL import Image
from simtk import unit
from tabulate import tabulate
from visualization import show_oemol_struc
PARTICLE = unit.mole.create_unit(
6.02214076e23 ** -1,
"particle",
"particle",
)
HARTREE_PER_PARTICLE = unit.hartree / PARTICLE
HARTREE_TO_KCALMOL = HARTREE_PER_PARTICLE.conversion_factor_to(
unit.kilocalorie_per_mole
)
BOLTZMANN_CONSTANT = unit.constants.BOLTZMANN_CONSTANT_kB
REF_SPEC = "MP2/heavy-aug-cc-pVTZ"
def get_mae_score(spec_ener_dict):
"""
Gives the RMSE of each key/spec
:param spec_ener_dict:
:return:
"""
ref_ener = spec_ener_dict[REF_SPEC]
other_ener = copy.deepcopy(spec_ener_dict)
other_ener.pop(REF_SPEC)
score = defaultdict(float)
for key, values in other_ener.items():
n_val = len(values)
for i, value in enumerate(values):
n_grid = len(value[1])
ener_diff_abs = np.abs(np.subtract(value[1], ref_ener[i][1]))
score[key] += np.sum(ener_diff_abs / n_grid)
score[key] = score[key] / n_val
return score
def get_rmse_score(spec_ener_dict):
"""
Gives the RMSE of each key/spec
:param spec_ener_dict:
:return:
"""
ref_ener = spec_ener_dict[REF_SPEC]
other_ener = copy.deepcopy(spec_ener_dict)
other_ener.pop(REF_SPEC)
score = defaultdict(float)
for key, values in other_ener.items():
n_val = len(values)
for i, value in enumerate(values):
n_grid = len(value[1])
ener_diff_sqrd = np.multiply(
np.subtract(value[1], ref_ener[i][1]),
np.subtract(value[1], ref_ener[i][1]),
)
score[key] += np.sqrt(np.sum(ener_diff_sqrd / n_grid))
score[key] = score[key] / n_val
return score
@click.command()
@click.option(
"--data_pickle",
"data_pickle",
type=click.STRING,
required=False,
default="./torsiondrive_data.pkl",
help="pickle file in which the energies dict is stored",
)
if __name__ == "__main__":
main()
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220,
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] | 2.251931 | 1,036 |
class elements(object):
"""docstring for elements"""
class linkedList(object):
"""docstring for linkedList"""
# l = linkedList()
# x1 = elements(key=5)
# x2 = elements(key=6)
# x3 = elements(key=7)
# x4 = elements(key=8)
# x5 = elements(key=9)
# l.list_insert(x1)
# l.list_insert(x2)
# l.list_insert(x3)
# l.list_insert(x4)
# l.list_insert(x5)
# s1 = l.list_search(k=7)
# print s1.key
# l.list_delete(s1)
# s1 = l.list_search(k=7)
# print s1.key
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8,
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198
] | 2.213592 | 206 |
from argparse import ArgumentParser
from argparse import RawDescriptionHelpFormatter
import sys
import build
import os
from pathlib import Path
args = get_args(sys.argv)
if args.listadversaries == True:
adversary_list = os.listdir(os.path.join(Path(__file__).parent, "adversary"))
for item in adversary_list:
if os.path.isdir(os.path.join(Path(__file__).parent, "adversary", item)) and "__" not in item:
print(item)
if not args.listadversaries and args.adversary and args.outdir:
if os.path.isdir(args.outdir) == False:
print("\n[!] outdir is either not defined or is not a valid folder path\n")
raise ValueError
build.build_files(
adversary=args.adversary,
out_dir=args.outdir,
count=int(args.count),
filetype=args.filetype,
extension=args.extension,
debug=args.debug
)
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220,
14257,
28,
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13,
24442,
198,
220,
220,
220,
1267,
628
] | 2.505682 | 352 |
import sys
import snowflake.connector
from snowflake.sqlalchemy import URL
from sqlalchemy import create_engine
| [
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] | 3.862069 | 29 |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from model import Directory
from model import NormalFile
class VirusFile(NormalFile):
"""The VirusFile class is the structure that acts as a Virus File on the
filesystem that still has lines of data but also deletes files that are
not other virus files in the filesystem
:param number: The number to give to the VirusFile
:param name: The name to give to the VirusFile
:param parent: The parent directory of this VirusFile
"""
IDENTIFYING_BYTES = [124, 56, 198, 248, 119, 64, 87, 12]
# in hex: 7c, 38, c6, f8, 77, 40, 57, 0c
# # # # # # # # # # # # # # # # # # # #
def get_number(self) -> int:
"""Returns the number of this VirusFile"""
return self.__number
# # # # # # # # # # # # # # # # # # # #
@staticmethod
def from_json(json: dict):
"""Converts a JSON object into a VirusFile object
:param json: The JSON object to convert
:raises TypeError: When json.type is not 'VirusFile'
:raises KeyError: When the VirusFile's number or name is not specified in the JSON object
"""
if json["type"] != "VirusFile":
raise TypeError(f"Type of JSON object must match (\"{json['type']}\" != \"VirusFile\")")
if "number" not in json:
raise KeyError("\"number\" key must exist to create VirusFile object")
if "name" not in json:
raise KeyError("\"name\" key must exist to create VirusFile object")
return VirusFile(json["number"], json["name"])
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220,
220,
220,
220,
220,
220,
220,
220,
220,
5298,
7383,
12331,
7203,
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17618,
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1994,
1276,
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284,
2251,
40584,
8979,
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4943,
198,
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220,
220,
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220,
611,
366,
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287,
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25,
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220,
220,
220,
220,
220,
220,
220,
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220,
5298,
7383,
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7,
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14692,
17618,
33116,
33918,
14692,
3672,
8973,
8,
198
] | 2.690878 | 592 |
#coding:utf-8
# K-means
import numpy as np
import sys
import matplotlib.pyplot as plt
argvs = sys.argv
if __name__ == "__main__":
data = np.genfromtxt(argvs[1], delimiter=",")
print(data)
#plt.subplot(2, 1, 1)
plt.plot(data[:,3])
data2 = np.genfromtxt(argvs[2], delimiter=",")
print(data2)
#plt.subplot(2, 1, 2)
plt.plot(data2[:,4])
plt.show()
filename = argvs[1] + ".png"
plt.savefig(filename)
| [
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7,
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] | 1.982833 | 233 |
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from libs.net_utils import NLM, NLM_dot, NLM_woSoft, NLM_NC_woSoft, Batch_Contrastive
from torchvision.models import resnet18
from libs.autoencoder import encoder3, decoder3, encoder_res18, encoder_res50
from libs.utils import *
import pdb
def transform(aff, frame1):
"""
Given aff, copy from frame1 to construct frame2.
INPUTS:
- aff: (h*w)*(h*w) affinity matrix
- frame1: n*c*h*w feature map
"""
b,c,h,w = frame1.size()
frame1 = frame1.view(b,c,-1)
frame2 = torch.bmm(frame1, aff)
return frame2.view(b,c,h,w)
class normalize(nn.Module):
"""Given mean: (R, G, B) and std: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = (channel - mean) / std
"""
def create_flat_grid(F_size, GPU=True):
"""
INPUTS:
- F_size: feature size
OUTPUT:
- return a standard grid coordinate
"""
b, c, h, w = F_size
theta = torch.tensor([[1,0,0],[0,1,0]])
theta = theta.unsqueeze(0).repeat(b,1,1)
theta = theta.float()
# grid is a uniform grid with left top (-1,1) and right bottom (1,1)
# b * (h*w) * 2
grid = torch.nn.functional.affine_grid(theta, F_size)
grid[:,:,:,0] = (grid[:,:,:,0]+1)/2 * w
grid[:,:,:,1] = (grid[:,:,:,1]+1)/2 * h
grid_flat = grid.view(b,-1,2)
if(GPU):
grid_flat = grid_flat.cuda()
return grid_flat
def coords2bbox(coords, patch_size, h_tar, w_tar):
"""
INPUTS:
- coords: coordinates of pixels in the next frame
- patch_size: patch size
- h_tar: target image height
- w_tar: target image widthg
"""
b = coords.size(0)
center = torch.mean(coords, dim=1) # b * 2
center_repeat = center.unsqueeze(1).repeat(1,coords.size(1),1)
dis_x = torch.sqrt(torch.pow(coords[:,:,0] - center_repeat[:,:,0], 2))
dis_x = torch.mean(dis_x, dim=1).detach()
dis_y = torch.sqrt(torch.pow(coords[:,:,1] - center_repeat[:,:,1], 2))
dis_y = torch.mean(dis_y, dim=1).detach()
left = (center[:,0] - dis_x*2).view(b,1)
left[left < 0] = 0
right = (center[:,0] + dis_x*2).view(b,1)
right[right > w_tar] = w_tar
top = (center[:,1] - dis_y*2).view(b,1)
top[top < 0] = 0
bottom = (center[:,1] + dis_y*2).view(b,1)
bottom[bottom > h_tar] = h_tar
new_center = torch.cat((left,right,top,bottom),dim=1)
return new_center
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197,
7783,
649,
62,
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628,
628,
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] | 2.279397 | 995 |
import day11 as day
from helper import loadingUtils
INPUTFOLDER = day.get_path()
| [
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] | 3 | 29 |
from unittest import TestCase, main
import overdoses
import pandas as pd
from pandas.util.testing import assert_series_equal
if __name__ == '__main__':
main()
| [
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"""wargame.test.test_wargame
This module contains unit tests for various modules in wargame package.
This module is compatible with Python 2.7.9. It contains
supporting code for the book, Learning Python Application Development,
Packt Publishing.
:copyright: 2016, Ninad Sathaye
:license: The MIT License (MIT) . See LICENSE file for further details.
"""
from __future__ import print_function
import mock
import unittest
import sys
if sys.version_info >= (3, 0):
print("This code requires Python 2.7.9 ")
print("Looks like you are trying to run this using "
"Python version: %d.%d " % (sys.version_info[0],
sys.version_info[1]))
print("Exiting...")
sys.exit(1)
# Add the top level directory wargame to sys,path
sys.path.append("../")
from knight import Knight
from orcrider import OrcRider
from abstractgameunit import AbstractGameUnit
from gameutils import weighted_random_selection
from hut import Hut
from attackoftheorcs import AttackOfTheOrcs
class TestWarGame(unittest.TestCase):
"""This class contains unit tests for the game Attack of The Orcs."""
def setUp(self):
"""Overrides the setUp fixture of the superclass.
This method is called just before the calling each unit test.
Here, it creates instances of Knight and OrcRider for use by
various unit tests.
.. seealso:: TestCase.tearDown()
"""
self.knight = Knight()
self.enemy = OrcRider()
def test_injured_unit_selection(self):
"""Unit test to verify working of weighted_random_selection()
.. seealso:: gameutils.weighted_random_selection
"""
print("\nCalling test_wargame.test_injured_unit_selection..")
for i in range(100):
injured_unit = weighted_random_selection(self.knight,
self.enemy)
self.assertIsInstance(
injured_unit,
AbstractGameUnit,
"Injured unit must be an instance of AbstractGameUnit")
def test_acquire_hut(self):
"""Unit test to ensure that when hut is 'acquired'
The test asserts if the hut.occupant is updated to the Knight instance.
"""
print("\nCalling test_wargame.test_acquire_hut..")
hut = Hut(4, None)
hut.acquire(self.knight)
self.assertIs(hut.occupant, self.knight)
def test_occupy_huts(self):
"""Unittest to verify number of huts and the occupants.
This test verifies that the total number of `hut` instance created
and also checks the type of its `occupant` attribute.
.. seealso::
:py:meth:`attackoftheorcs.AttackOfTheOrcs._occupy_huts` ,
:py:meth:`attackoftheorcs.AttackOfTheOrcs.setup_game_scenario`
"""
print("\nCalling test_wargame.test_occupy_huts..")
game = AttackOfTheOrcs()
game.setup_game_scenario()
# Verify that only 5 huts are created.
self.assertEqual(len(game.huts), 5)
# Huts occupant must be an instance of a Knight or OrcRider
# or it could be set to None.
for hut in game.huts:
assert((hut.occupant is None) or
isinstance(hut.occupant, AbstractGameUnit))
def user_input_processor(self, prompt):
"""Simulate user input based on user prompt
:param prompt: The question asked to the user
:return: The simulated user input
"""
# The prompt can be either of the following:
# 1. "choose a hut number to enter (1-5):"
# 2. "...continue attack? (y/n):"
# Check if some keywords exist in the prompt
print("DEBUG: in user_input_processor")
if 'hut' in prompt.lower():
# 'The prompt contains 'hut'..should be asking for a hut number.
self.hut_selection_counter += 1
assert self.hut_selection_counter <= 5
return self.hut_selection_counter
elif 'attack' in prompt.lower():
# This prompt should be asking permission to continue attack
return 'y'
else:
raise Exception("Got an unexpected prompt!", prompt)
##@unittest.skip("skipping test test_play")
def test_play(self):
"""Unit test to verify AttackOfTheOrcs.play method."""
print("\nCalling test_wargame.test_play..")
game = AttackOfTheOrcs()
self.hut_selection_counter = 0
# Python 2.7 compatibility : replace builtins.input with raw_input
# TODO: Note that this patching technique does NOT work in Python 2.7
# It still seems to prompt the user input when the test is run.
with mock.patch('__builtin__.raw_input', new=self.user_input_processor):
game.play()
# Create a list that collects information on whether the
# huts are acquired. It is a boolean list
acquired_hut_list = [h.is_acquired for h in game.huts]
# Player wins if all huts are acquired AND the player health
# is grater than 0.
if all(acquired_hut_list):
# All the huts are acquired (winning criteria).
# check the player's health!
self.assertTrue(game.player.health_meter > 0)
else:
# This is the losing criteria., Player health can not be
# positive when he/she loses.
self.assertFalse(game.player.health_meter > 0)
if __name__ == '__main__':
unittest.main()
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] | 2.427269 | 2,303 |
from data.classes.players import *
| [
6738,
1366,
13,
37724,
13,
32399,
1330,
1635,
628,
628
] | 3.8 | 10 |
import os
from glob import glob
from ete3 import Tree
from plot_module import *
os.chdir("../DataEmpirical")
exp_dirs = sorted(
set(["_".join(i.split("_")[:-1]) for i in os.listdir("Experiments") if ("Replicates" in i) and ("Isopods" in i)]))
os.makedirs("Analysis/DataFrame", exist_ok=True)
file_format = "pdf"
dataset = pd.read_csv("Isopods/dataset.tsv", delimiter="\t")
for exp in exp_dirs:
nhx_dict = dict()
for exp_replicate in sorted(glob("Experiments/" + exp + "_*")):
for nhx_path in sorted(glob(exp_replicate + "/*run*.nhx")):
if nhx_path.count(" ") > 0:
continue
nhx_file = os.path.basename(nhx_path)
if nhx_file not in nhx_dict:
nhx_dict[nhx_file] = list()
nhx_dict[nhx_file].append(exp_replicate)
os.makedirs("Analysis/" + exp, exist_ok=True)
for tree_name, folder_list in nhx_dict.items():
print(tree_name, folder_list)
feature = remove_units(tree_name.split(".")[-2])
axis_dict, err_dict, taxa_dict = dict(), dict(), dict()
nb_values = -1
for index, folder in enumerate(sorted(folder_list)):
name = folder.split("_")[-1]
with open(folder + "/" + tree_name, 'r') as tree_file:
tree = Tree(remove_units(tree_file.readline()), format=1)
values = np.array([to_float(getattr(n, feature)) for n in tree.traverse() if feature in n.features])
taxa_dict["Rep. {0}".format(index + 1)] = np.array(
[to_float(getattr(n, feature)) for n in tree if feature in n.features])
if nb_values == -1:
nb_values = len(values)
if len(values) != nb_values:
print(name + " don't have the same number of values")
continue
min_values = np.array(
[to_float(getattr(n, feature + "_min")) for n in tree.traverse() if feature + "_min" in n.features])
max_values = np.array(
[to_float(getattr(n, feature + "_max")) for n in tree.traverse() if feature + "_max" in n.features])
axis_dict[name] = values
err_dict[name] = np.vstack((np.abs(values - min_values), np.abs(max_values - values)))
plot_correlation("Analysis/" + exp + "/" + tree_name.replace(".nhx", '.' + file_format), axis_dict, err_dict,
global_min_max=True)
if ("LogPopulationSize" not in tree_name) and ("LogMutationRate" not in tree_name): continue
df = pd.DataFrame.from_dict(taxa_dict)
df = df.apply(np.exp)
ratio = ["\\textbf{ " + "{0:.3g}".format(max(df[col]) / min(df[col])) + "}" for col in df]
if "Isopods" in exp:
label = label_transform(tree_name.split(".")[-2])
dict_trait = {"eco": "Habitat", "pig": "Pigmentation", "eye": "Ocular structure"}
dict_trait_def = {"eco": {"epi": "Surface", "hypo": "Underground"},
"pig": {"D": "Depigmented", "DP": "Part. dep.", "P": "Pigmented"},
"eye": {"A": "Anophthalmia", "M": "Microphthalmia", "O": "Ocular"}}
for trait, trait_name in dict_trait.items():
df[trait_name] = pd.Series([dict_trait_def[trait][dataset[dataset['code'] == i].iloc[0][trait]] for i in
tree.get_leaf_names()])
axes = df.boxplot(column=list(taxa_dict.keys()), by=trait_name, figsize=(len(taxa_dict), 2),
layout=(1, len(taxa_dict)), rot=90, fontsize=6)
axes[0].set_ylabel(label, fontsize=6)
for ax in axes:
ax.set_xlabel("")
ax.set_title(ax.get_title(), {'fontsize': 8})
plt.suptitle("")
plt.tight_layout()
plt.savefig("Analysis/{0}/{1}.{2}.{3}".format(exp, tree_name, trait, file_format), format=file_format)
plt.clf()
df["Code"] = pd.Series(tree.get_leaf_names())
taxa = [dataset[dataset['code'] == i].iloc[0]["morphosp"] for i in tree.get_leaf_names()]
taxa = ["\\textit{" + " ".join(str(i).replace('nan', '-').split(" ")[:2]) + "}" for i in taxa]
ratio += ['-'] * 4
frames = []
for k in taxa_dict.keys():
d = {label: df[k], 'Code': df["Code"], "Rep": [k] * len(df[k])}
for trait_name in dict_trait.values():
d[trait_name] = df[trait_name]
frames.append(pd.DataFrame(d))
merged = pd.concat(frames)
for trait, trait_name in dict_trait.items():
bp = merged.boxplot(column=[label], by=trait_name, fontsize=18, notch=True,
patch_artist=True, return_type="dict")
[[item.set_linewidth(3) for item in bp[key]['boxes']] for key in bp.keys()]
[[item.set_linewidth(3) for item in bp[key]['fliers']] for key in bp.keys()]
[[item.set_linewidth(3) for item in bp[key]['medians']] for key in bp.keys()]
[[item.set_linewidth(3) for item in bp[key]['whiskers']] for key in bp.keys()]
[[item.set_linewidth(3) for item in bp[key]['caps']] for key in bp.keys()]
[[item.set_color(BLUE) for item in bp[key]['boxes']] for key in bp.keys()]
[[item.set_color("black") for item in bp[key]['fliers']] for key in bp.keys()]
[[item.set_color(GREEN) for item in bp[key]['medians']] for key in bp.keys()]
[[item.set_color(BLUE) for item in bp[key]['whiskers']] for key in bp.keys()]
[[item.set_color("black") for item in bp[key]['caps']] for key in bp.keys()]
ax = plt.gca()
ax.set_ylabel(label, fontsize=22)
ax.set_xlabel("")
ax.set_title(trait_name, fontsize=26)
plt.suptitle("")
plt.tight_layout()
plt.savefig("Analysis/{0}/{1}.{2}.merged.{3}".format(exp, tree_name, trait, file_format),
format=file_format)
plt.clf()
merged.to_csv("Analysis/DataFrame/" + exp + "_merged_" + tree_name.replace(".nhx", '.tsv'), index=False,
sep="\t")
merged.to_latex("Analysis/DataFrame/" + exp + "_merged_" + tree_name.replace(".nhx", '.tex'), index=False,
float_format="%.3g")
else:
taxa = [(("\\textit{" + i.replace('_', ' ') + "}") if ("_" in i) else i) for i in tree.get_leaf_names()]
df["Taxon"] = pd.Series(taxa)
df.loc[len(df.index)] = ratio + ["\\textbf{Maximum range}"]
df.to_csv("Analysis/DataFrame/" + exp + "_" + tree_name.replace(".nhx", '.tsv'), index=False, sep="\t")
df.to_latex("Analysis/DataFrame/" + exp + "_" + tree_name.replace(".nhx", '.tex'), index=False, escape=False,
float_format=lambda x: ("{0:.3g}".format(x) if is_float(x) else x))
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17,
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64,
60,
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13431,
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329,
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64,
62,
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13,
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33529,
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220,
220,
220,
220,
220,
220,
220,
220,
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220,
288,
796,
1391,
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25,
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58,
74,
4357,
705,
10669,
10354,
47764,
14692,
10669,
33116,
366,
6207,
1298,
685,
74,
60,
1635,
18896,
7,
7568,
58,
74,
12962,
92,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
16708,
62,
3672,
287,
8633,
62,
9535,
270,
13,
27160,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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288,
58,
9535,
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62,
3672,
60,
796,
47764,
58,
9535,
270,
62,
3672,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13431,
13,
33295,
7,
30094,
13,
6601,
19778,
7,
67,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
23791,
796,
279,
67,
13,
1102,
9246,
7,
37805,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
16708,
11,
16708,
62,
3672,
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8633,
62,
9535,
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13,
23814,
33529,
198,
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220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
275,
79,
796,
23791,
13,
3524,
29487,
7,
28665,
41888,
18242,
4357,
416,
28,
9535,
270,
62,
3672,
11,
10369,
7857,
28,
1507,
11,
29838,
28,
17821,
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,
8529,
62,
49016,
28,
17821,
11,
1441,
62,
4906,
2625,
11600,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16410,
9186,
13,
2617,
62,
2815,
413,
5649,
7,
18,
8,
329,
2378,
287,
275,
79,
58,
2539,
7131,
6,
29305,
6,
11907,
329,
1994,
287,
275,
79,
13,
13083,
3419,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16410,
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13,
2617,
62,
2815,
413,
5649,
7,
18,
8,
329,
2378,
287,
275,
79,
58,
2539,
7131,
6,
2704,
3183,
6,
11907,
329,
1994,
287,
275,
79,
13,
13083,
3419,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16410,
9186,
13,
2617,
62,
2815,
413,
5649,
7,
18,
8,
329,
2378,
287,
275,
79,
58,
2539,
7131,
6,
1150,
1547,
6,
11907,
329,
1994,
287,
275,
79,
13,
13083,
3419,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16410,
9186,
13,
2617,
62,
2815,
413,
5649,
7,
18,
8,
329,
2378,
287,
275,
79,
58,
2539,
7131,
6,
1929,
1984,
364,
6,
11907,
329,
1994,
287,
275,
79,
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] | 1.93237 | 3,667 |
import random
import numpy as np
from autotune.param_space import Param
from typing import Callable
from .hyper_param_opt import AbstractHyperParameterOptimizer
| [
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] | 3.880952 | 42 |
# Test suite for DeriveAlive module
# These lines should be included for Travis/Coverall
import sys
sys.path.append('../')
import DeriveAlive.DeriveAlive as da
import numpy as np
import math
def test_DeriveAlive_Var():
'''Test constructor of Var class to ensure proper variable initializations.'''
# Run tests within test_DeriveAliveVar
test_scalar_without_bracket()
test_scalar_with_bracket()
test_vector_input()
test_with_preset_der()
test_repr()
def test_DeriveAlive_scalar_functions():
'''Test scalar functions split up by operation type.'''
# Run tests within test_DeriveAlive_scalar_functions()
test_neg()
test_abs()
test_constant()
test_add()
test_radd()
test_sub()
test_rsub()
test_mul()
test_rmul()
test_truediv()
test_rtruediv()
test_sin()
test_cos()
test_tan()
test_arcsin()
test_arccos()
test_arctan()
test_sinh()
test_cosh()
test_tanh()
test_pow()
test_rpow()
test_sqrt()
test_log()
test_exp()
test_logistic()
def test_DeriveAlive_vector_construction():
'''Test constructor of Var class to ensure proper variable initializations for vectors.'''
test_vector_input()
def test_DeriveAlive_vector_functions_m_to_1():
'''Test vector functions from m dimensions to 1 dimension, split up by operation type.'''
# Run tests within test_DeriveAlive_vector_functions_m_to_1()
test_neg()
test_abs()
test_constant()
test_add()
test_radd()
test_sub()
test_rsub()
test_mul()
test_rmul()
test_truediv()
test_rtruediv()
test_sin()
test_cos()
test_tan()
test_arcsin()
test_arccos()
test_arctan()
test_sinh()
test_cosh()
test_tanh()
test_pow()
test_rpow()
test_sqrt()
test_log()
test_exp()
test_logistic()
def test_DeriveAlive_vector_functions_1_to_n():
'''Test vector functions from 1 dimension to n dimensions, split up by operation type.'''
# Run tests within test_DeriveAlive_vector_functions_1_to_n()
test_neg()
test_abs()
test_constant()
test_add()
test_radd()
test_sub()
test_rsub()
test_mul()
test_rmul()
test_truediv()
test_rtruediv()
test_sin()
test_cos()
test_tan()
test_arcsin()
test_arccos()
test_arctan()
test_sinh()
test_cosh()
test_tanh()
test_pow()
test_rpow()
test_sqrt()
test_log()
test_exp()
test_logistic()
def test_DeriveAlive_vector_functions_m_to_n():
'''Test vector functions from m dimensions to n dimensions, split up by operation type.'''
# Run tests within test_DeriveAlive_vector_functions_m_to_n()
test_neg()
test_abs()
test_constant()
test_add()
test_radd()
test_sub()
test_rsub()
test_mul()
test_rmul()
test_truediv()
test_rtruediv()
test_sin()
test_cos()
test_tan()
test_arcsin()
test_arccos()
test_arctan()
test_sinh()
test_cosh()
test_tanh()
test_sqrt()
test_pow()
test_rpow()
test_log()
test_exp()
test_logistic()
# Without pytest, user can run these tests manually
test_DeriveAlive_Var()
test_DeriveAlive_vector_construction()
test_DeriveAlive_scalar_functions()
test_DeriveAlive_comparisons()
test_DeriveAlive_vector_functions_1_to_n()
test_DeriveAlive_vector_functions_m_to_1()
test_DeriveAlive_vector_functions_m_to_n()
print ("All tests passed!") | [
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] | 2.429348 | 1,288 |
"""Subpackage for handling import and export of sequencing data.
"""
import os
from .GenomFeatureReader import GenomFeatureReader
from .GenomReader import GenomReader
from .SamReader import SamReader, SamAlignedRead
from .BaseAlignedRead import BaseAlignedRead
from .BedtoolsIntersectionReader import BedtoolsIntersectionReader, BedtoolsIntersectionItem
_extension_to_reader = {
"sam": SamReader.open,
"fasta": GenomReader.open,
"fa": GenomReader.open,
"fna": GenomReader.open,
"gff": GenomFeatureReader.open
}
def read(filename):
"""Tries to read a given filename.
This function guesses the filetype based in the filename and returns
the appropriate object or raises an exception if the filetype is unknown.
Parameters
----------
filename : str
The filename of the file to open.
Returns
-------
Reader
A reader representing the file and offering methods to work with the file.
Supported file formats
----------------------
A list of files supported by this function.
.sam Sequence Alignment/Map ngsTools.io.SamReader
.fa
.fasta FASTA file format ngsTools.io.GenomReader
"""
basename = os.path.basename(filename)
extension = basename.split(".")[-1]
if extension not in _extension_to_reader:
raise TypeError("ngsTools.io does not support this file extension «" + extension + "»")
return _extension_to_reader[extension](filename)
| [
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] | 2.996 | 500 |
# -*- coding: utf-8 -*-
# Author: Simon Bussy <[email protected]>
import numpy as np
from collections import defaultdict
class History():
"""A class to manage the history along iterations of a solver.
"""
def clear(self):
"""Reset history values"""
self.values = defaultdict(list)
def update(self, **kwargs):
"""Update the history along the iterations.
For each keyword argument, we apply the history function corresponding
to this keyword, and use its results in the history
"""
n_iter = kwargs["n_iter"]
self.n_iter = n_iter
history_func = self.history_func
history = self.values
for key, func in history_func.items():
history[key].append(func(**kwargs))
def set_print_order(self, *args):
"""Allows to set the print order of the solver's history
"""
self.print_order = list(*args)
self.clear()
return self
def print_history(self):
"""Verbose the current line of history
"""
values = self.values
n_iter = self.n_iter
print_order = self.print_order
# If this is the first iteration, plot the history's column names
if n_iter == 0:
min_width = self.minimum_col_width
line = ' | '.join([name.center(min_width) for name in
print_order if name in values])
names = [name.center(min_width) for name in print_order]
self.col_widths = list(map(len, names))
print(line)
col_widths = self.col_widths
print_style = self.print_style
line = ' | '.join([(print_style[name] % values[name][-1])
.rjust(col_widths[i])
for i, name in enumerate(print_order)
if name in values])
print(line)
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220,
220,
220,
220,
220,
220,
37227,
13414,
65,
577,
262,
1459,
1627,
286,
2106,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
3815,
796,
2116,
13,
27160,
198,
220,
220,
220,
220,
220,
220,
220,
299,
62,
2676,
796,
2116,
13,
77,
62,
2676,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
2875,
796,
2116,
13,
4798,
62,
2875,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1002,
428,
318,
262,
717,
24415,
11,
7110,
262,
2106,
338,
5721,
3891,
198,
220,
220,
220,
220,
220,
220,
220,
611,
299,
62,
2676,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
949,
62,
10394,
796,
2116,
13,
39504,
62,
4033,
62,
10394,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1627,
796,
705,
930,
45302,
22179,
26933,
3672,
13,
16159,
7,
1084,
62,
10394,
8,
329,
1438,
287,
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,
3601,
62,
2875,
611,
1438,
287,
3815,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3891,
796,
685,
3672,
13,
16159,
7,
1084,
62,
10394,
8,
329,
1438,
287,
3601,
62,
2875,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2116,
13,
4033,
62,
10394,
82,
796,
1351,
7,
8899,
7,
11925,
11,
3891,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
1370,
8,
628,
220,
220,
220,
220,
220,
220,
220,
951,
62,
10394,
82,
796,
2116,
13,
4033,
62,
10394,
82,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
62,
7635,
796,
2116,
13,
4798,
62,
7635,
198,
220,
220,
220,
220,
220,
220,
220,
1627,
796,
705,
930,
45302,
22179,
26933,
7,
4798,
62,
7635,
58,
3672,
60,
4064,
3815,
58,
3672,
7131,
12,
16,
12962,
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,
764,
81,
3137,
7,
4033,
62,
10394,
82,
58,
72,
12962,
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,
329,
1312,
11,
1438,
287,
27056,
378,
7,
4798,
62,
2875,
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,
611,
1438,
287,
3815,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
1370,
8,
198
] | 2.22468 | 859 |
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