content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from idact.detail.jupyter_app.app_allocation_parameters import \
AppAllocationParameters
def format_allocation_parameters(parameters: AppAllocationParameters):
"""Formats allocation parameters for the user.
:param parameters: Parameters to print.
"""
result = "Allocation parameters:\n"
result += " Nodes: {}\n".format(parameters.nodes)
result += " Cores: {}\n".format(parameters.cores)
result += " Memory per node: {}\n".format(parameters.memory_per_node)
result += " Walltime: {}\n".format(parameters.walltime)
if parameters.native_args[0] and parameters.native_args[1]:
result += " Native arguments:\n"
for key, value in zip(parameters.native_args[0],
parameters.native_args[1]):
if value == 'None':
result += " {}\n".format(key)
else:
result += " {} -> {}\n".format(key, value)
else:
result += " No native arguments.\n"
return result
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] | 2.358447 | 438 |
#!/usr/bin/env python3
import os, sys
service = "[Unit]\n"\
"Description={description}\n"\
"After=network.target\n"\
"StartLimitIntervalSec=0\n"\
"\n"\
"[Service]\n"\
"Type=simple\n"\
"Restart=always\n"\
"RestartSec=1\n"\
"User=root\n"\
"ExecStart={exec}\n"\
"\n"\
"[Install]\n"\
"WantedBy=multi-user.target"
name = False
desc = False
path = False
command = False
for arg in sys.argv:
if "--name=" in arg:
name = arg.split('=')[1] + ".service"
if "--path=" in arg:
path = arg.split('=')[1]
if "--command=" in arg:
command = arg.split('=')[1]
if "--desc=" in arg:
desc = arg.split('=')[1]
if arg == "-h" or arg == "--help":
print("Usage: python3 createservice.py [--name=NAME] [--path=PATH] [--command=COMMAND] [--desc=DESC]")
exit(0)
if not name:
name = input("Service name: ") + ".service"
if not path:
path = input("Executable binary path: ")
if not command:
command = input("Command and args: ")
if not desc:
desc = input("Description: ")
service = service.replace("{description}", desc).replace("{exec}", path + " " + command)
f = open("/lib/systemd/system/" + name, "w")
f.write(service)
f.close()
print(service)
print()
print("Wrote to /lib/systemd/system/" + name)
os.system("systemctl enable " + name)
os.system("systemctl start " + name)
print("Started and enabled service.") | [
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366,
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1438,
8,
198,
4798,
7203,
10434,
276,
290,
9343,
2139,
19570
] | 2.398601 | 572 |
from django.db import models
| [
6738,
42625,
14208,
13,
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1330,
4981,
628
] | 3.75 | 8 |
#!/usr/bin/python
import string
import random
import array
from copy import copy, deepcopy
import time
import numpy as np
import flatbuffers
import eas.FloatContentType as FloatContentType
import eas.ContentType as ContentType
import eas.ShapeType as ShapeType
import eas.Int64ContentType as Int64ContentType
import eas.PredictRequest as PredictRequest
import eas.DoubleContentType as DoubleContentType
import eas.IntContentType as IntContentType
from pb.tf_request import TFRequest
from pb import tf_request_pb2 as tf_pb
if __name__ == '__main__':
main()
| [
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705,
834,
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10354,
198,
220,
1388,
3419,
628
] | 3.602564 | 156 |
import requests
from .chunked_utils import chunked_bytes_from_object, chunked_object_from_bytes
| [
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] | 3.344828 | 29 |
import torch
import torch.nn as nn
from utils import tools
| [
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] | 3.263158 | 19 |
from typing import Optional
from uuid import UUID
from pydantic import BaseModel
from laa_court_data_api_app.models.hearing_events.hearing_event.result.hearing_event import HearingEvent
| [
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] | 3.375 | 56 |
from hestia.signal_decorators import ignore_raw, ignore_updates, ignore_updates_pre
from django.db.models.signals import post_save, pre_save
from django.dispatch import receiver
from constants.backends import NATIVE_BACKEND
from db.models.experiment_groups import ExperimentGroup, GroupTypes
from libs.repos.utils import assign_code_reference
from lifecycles.experiment_groups import ExperimentGroupLifeCycle
from schemas import SearchAlgorithms
from signals.backend import set_backend
from signals.names import set_name
from signals.persistence import set_persistence
from signals.tags import set_tags
@receiver(pre_save, sender=ExperimentGroup, dispatch_uid="experiment_group_pre_save")
@ignore_updates_pre
@ignore_raw
@receiver(post_save, sender=ExperimentGroup, dispatch_uid="experiment_group_saved")
@ignore_updates
@ignore_raw
| [
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#! /usr/bin/env python2.7
from numpy import random
import time
# change the seed
random.seed(int(time.time()))
class MCMC(object):
'''
MCMC implements Markov-Chain-Monte-Carlo algorithms.
'''
def __init__(self, Density, Proposal, Drawing_func, initial):
'''
Initialize an MCMC algorithm.
Parameters
----------
Density : density function whose normalization constant is not known
Proposal: proposal function
Drawing_func: a function that draws sample from Proposal
initial: the initial sample of the MCMC algorithm
'''
self.density = Density
self.proposal = Proposal
self.sample_proposal = Drawing_func
self.initial = initial
self.density_samples = []
self.prob_density_samples = []
self.num_proposals = 0
# stats are used to check how many
# proposals are accepted and rejected.
self.stats = {
"accepted": 0,
"rejected": 0,
}
def run(self, max_iter = 2000, burning=200, jupyter=False):
'''run MCMC.
Parameters
----------
max_iter: int
maximum number of iterations on this call
burning: int
ignores the initial samples upto the given number in the
Markov Chain.
'''
if jupyter:
from tqdm import tqdm_notebook as tqdm
else:
from tqdm import tqdm
if len(self.density_samples)>0:
x_old = self.density_samples[-1]
burning = 0
else:
x_old = self.initial
for it in tqdm(range(max_iter)):
# draw from proposal
x_prop = self.sample_proposal(x_old)
# compute alpha !!! assumption: symmetric proposal
try:
alpha = min(1.0, self.density(x_prop)/self.density(x_old) )
except:
print ("error in MCMC.run:")
print (self.density(x_prop), x_prop)
print (self.density(x_old), x_old)
alpha = 0.0
if ( alpha > random.uniform(0.0,1.0) ):
x_old = x_prop
if (it > burning):
self.density_samples.append(x_prop)
self.prob_density_samples.append(self.density(x_prop))
self.stats["accepted"] += 1
else:
if (it > burning):
self.stats["rejected"] += 1
self.num_proposals += 1
def write(self, filename, write_prob=False):
'''
write the samples into the file.
Parameters
-----------
filename: str
write_prob: bool
determines whether to write the probabilities in the last column or not.
'''
with open(filename, "w") as output:
for idx, sample in enumerate(self.density_samples):
for coef in sample[:-1]:
output.write(str(coef)+",")
output.write(str(sample[-1]))
if write_prob==True:
output.write(","+str(self.prob_density_samples[idx])+"\n")
else:
output.write("\n")
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] | 1.951163 | 1,720 |
# -*- coding: utf-8 -*-
'''
WikiLinkPlus Extension for Python-Markdown
===========================================
Converts [[WikiLinks]] to relative links.
See <https://github.com/neurobin/mdx_wikilink_plus> for documentation.
Copyright Md. Jahidul Hamid <[email protected]>
License: [BSD](http://www.opensource.org/licenses/bsd-license.php)
'''
from __future__ import absolute_import
from __future__ import unicode_literals
try:
from urllib.parse import urlparse
from urllib.parse import urlunparse
except ImportError:
from urlparse import urlparse
from urlparse import urlunparse
import markdown
from markdown.util import etree
import re
import os
from . import version
__version__ = version.__version__
MARKDOWN_MAJOR = markdown.version_info[0]
WIKILINK_PLUS_RE = r'\[\[\s*(?P<target>[^][|]+?)(\s*\|\s*(?P<label>[^][]+))?\s*\]\]'
def build_url(urlo, base, end, url_whitespace, url_case):
""" Build and return a valid url.
Parameters
----------
urlo A ParseResult object returned by urlparse
base base_url from config
end end_url from config
url_whitespace url_whitespace from config
url_case url_case from config
Returns
-------
URL string
"""
if not urlo.netloc:
if not end:
clean_target = re.sub(r'\s+', url_whitespace, urlo.path)
else:
clean_target = re.sub(r'\s+', url_whitespace, urlo.path.rstrip('/'))
if clean_target.endswith(end):
end = ''
if base.endswith('/'):
path = "%s%s%s" % (base, clean_target.lstrip('/'), end)
elif base and not clean_target.startswith('/'):
path = "%s/%s%s" % (base, clean_target, end)
else:
path = "%s%s%s" % (base, clean_target, end)
if url_case == 'lowercase':
urlo = urlo._replace(path=path.lower() )
elif url_case == 'uppercase':
urlo = urlo._replace(path=path.upper() )
else:
urlo = urlo._replace(path=path)
return urlunparse(urlo)
def title(subject):
"""Return title cased version of the given subject string"""
exceptions = ['a', 'an', 'the', 'v', 'vs', 'am', 'at', 'and', 'as', 'but','by', 'en', 'for', 'if', 'be', 'in', 'of', 'on', 'or', 'to', 'via',]
slst = list(filter(None, re.split(r'[ \t]+', subject)))
res = []
c = 0
for s in slst:
if re.match(r'^[^a-z]+$', s) or (s in exceptions and c != 0):
res.append(s)
else:
res.append(s.title())
c = c + 1
return ' '.join(res)
class WikiLinkPlusExtension(markdown.Extension):
"""WikiLinkPlus Extension class for markdown"""
if __name__ == "__main__":
import doctest
doctest.testmod()
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"""
self.all_things = {
"room_1": {
"room": {
"look": (
"You can see a bed and a desk with a phone resting on top. "
"There's nothing else."
),
"pickup": "Don't be ridiculous.",
"approach": "You're already in the room, man. No need.",
"hit": "You kick and hit around the room. Nothing happens.",
"synonyms": {
"floor",
"wall",
"walls",
"ceiling",
"space",
"area",
"environment",
},
},
"phone": {
"look": "A small cheap phone. It appears to be ringing.",
"pickup": (
"You have taken the phone. It is still ringing. Enter "
"'i' or 'inventory' at any time to bring up your inventory."
),
"approach": "You have approached the phone.",
"answer": (
"You answer it, the voice on the other line says 'You find "
"yourself in a room.' As the voice speaks, the room around you"
" starts to shift. You are now in a completely different room."
),
"hit": "You hit the phone. Nothing happens.",
"synonyms": {"device", "cellphone"},
},
"desk": {
"look": "A flimsy wooden desk.",
"pickup": (
"Please. This desk is too heavy to pick up and take with you."
),
"approach": "You have approached the desk.",
"hit": "You hit the desk. That was pointless.",
"synonyms": {"table"},
},
"bed": {
"look": "The bed you woke up from. Not sure how you got here.",
"pickup": "The bed's too big for that.",
"approach": "You have approached the bed.",
"sleep": "But you've just woke up. Get your head in the game, man!",
"hit": "You attack and hit the bed mercilessly. Nothing happens.",
"jump": (
"You jump on the bed for a bit, smiling and having a grand 'ol "
"time. Wow that was fun."
),
"synonyms": {"mattress", "sheets", "pillow"},
},
},
"room_2": {
"room": {
"look": (
"Behind you is a whiteboard. To the left of you is a "
"door.\nWritten on the floor are markings that read 'THREE "
"BLANKS FOR THREE NUMBERS. THERE IS NO ROOM FOR FOUR.'"
),
"pickup": "Don't be ridiculous.",
"approach": "You're already in the room, man. No need.",
"synonyms": {
"floor",
"wall",
"walls",
"ceiling",
"space",
"area",
"environment",
},
},
"whiteboard": {
"look": (
"A large whiteboard. It has this written on it:\n\n__ / __ = "
"__\n\n(Type and enter 'write' at any time if you wish to fill "
"in the blanks.)"
),
"pickup": "Lol. You can't pick up the whiteboard, silly.",
"approach": "You approach the whiteboard.",
"synonyms": {"board", "canvas"},
},
"cone": {
"look": "Some paper cones.",
"pickup": "You pick up the cone and toss it aside.",
"approach": "You approach the cone.",
"synonyms": {"cones"},
},
"door": {
"look": "A normal door.",
"pickup": (
"You are physically unable to pick up and take a door with you "
"unfortunately."
),
"approach": "You approach the door.",
"open": "You have opened the door and walked in.",
"synonyms": {"gate"},
},
"key": {
"look": "A pretty key.",
"pickup": "You have picked up the key.",
"approach": "You approach the key.",
"synonyms": {"keys"},
},
},
"room_3": {
"room": {
"look": (
"Except for a piece of chalk you see rested on the center of "
"the floor, this room is completely bare."
),
"pickup": "Don't be ridiculous.",
"approach": "You're already in the room, man. No need.",
"synonyms": {
"floor",
"wall",
"walls",
"ceiling",
"space",
"area",
"environment",
},
},
"chalk": {
"look": (
"A normal piece of chalk. There is a sticky note attached to "
"it."
),
"pickup": "You have picked up the chalk.",
"approach": "You have approached the chalk.",
"synonyms": {"chalks", "chlak"},
},
"note": {
"look": (
"A sticky note with a message written on it:\nYOU HAVE FOUND "
"THE KEY.\nNOW FIND THE DOOR."
),
"approach": "You're already in the room, man. No need.",
"read": "YOU HAVE FOUND THE KEY.\nNOW FIND THE DOOR.",
"synonyms": {
"paper",
"message",
"writing",
"writings",
"markings",
"marks",
"sticky",
},
},
"door": {
"look": (
"Some chalk markings that have created the appearance of a "
"door."
),
"pickup": "Don't be ridiculous.",
"approach": "You have approached the door.",
"draw": "You draw the door.",
"open": "You open the door and walk in.",
"synonyms": {"gate"},
},
},
"room_4": {
"room": {
"look": (
"The north wall that's facing me has some strange "
"writings/marks on it. There is a billiards table in the center"
" of the room in front of you."
),
"pickup": "Don't be ridiculous.",
"approach": "You're already in the room, man. No need.",
"synonyms": {
"floor",
"wall",
"walls",
"ceiling",
"space",
"area",
"environment",
},
},
"wall": {
"look": " 3 -> 1 -> 4",
"pickup": "Don't be ridiculous.",
"approach": "You have approached the wall.",
"hit": "You hit the wall. Completely useless.",
"read": "3 -> 1 -> 4",
"synonyms": {
"walls",
"marks",
"markings",
"writing",
"writings",
"drawing",
"drawings",
"symbol",
"hint",
"numbers",
},
},
"table": {
"look": (
"A billiards table. No balls or any functionality to speak of. "
"It would probably be an ideal location to PLACE objects here."
),
"pickup": "Don't be silly.",
"approach": "You have approached the table.",
"read": "insert symbol here",
"synonyms": {"desk"},
},
"rack": {
"look": "A large triangle rack used to play pool.",
"pickup": "You picked up the large triangle rack.",
"approach": "You have approached the large triangle rack.",
"place": (
"You have placed the large triangle rack down on the table."
),
"synonyms": {"triangle"},
},
"clock": {
"look": "A medium-sized circular clock.",
"pickup": "You picked up the medium-sized clock.",
"approach": "You have approached the clock.",
"place": (
"You have placed the medium-sized clock down on the table."
),
"synonyms": {"circle", "circular"},
},
"cube": {
"look": (
"A small rubix cube. Unfortunately doesn't work anymore and "
"might as well be a paperweight."
),
"pickup": "You picked up the small rubix cube.",
"approach": "You have approached the small rubix cube.",
"place": "You have placed the small rubix cube down on the table.",
"synonyms": {"rubix", "square"},
},
"end sequence": {
"end text": (
"A small rumbling starts to occur and the floor falls.\nYou "
"land clumsily.\n\nYou are no longer in a room. You are "
"outside.\n\nTHE END."
)
},
},
}
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] | 1.868145 | 4,869 |
# -*- coding: utf-8 -*-
"""Defines the output module for the MySQL database used by 4n6time."""
from __future__ import unicode_literals
try:
import MySQLdb
except ImportError:
MySQLdb = None
from plaso.output import logger
from plaso.output import manager
from plaso.output import shared_4n6time
class MySQL4n6TimeOutputModule(shared_4n6time.Shared4n6TimeOutputModule):
"""Class defining the MySQL database output module for 4n6time."""
NAME = '4n6time_mysql'
DESCRIPTION = 'MySQL database output for the 4n6time tool.'
_META_FIELDS = frozenset([
'sourcetype', 'source', 'user', 'host', 'MACB', 'type',
'record_number'])
_CREATE_TABLE_QUERY = (
'CREATE TABLE IF NOT EXISTS log2timeline ('
'rowid INT NOT NULL AUTO_INCREMENT, timezone VARCHAR(256), '
'MACB VARCHAR(256), source VARCHAR(256), sourcetype VARCHAR(256), '
'type VARCHAR(256), user VARCHAR(256), host VARCHAR(256), '
'description TEXT, filename VARCHAR(256), inode VARCHAR(256), '
'notes VARCHAR(256), format VARCHAR(256), '
'extra TEXT, datetime datetime, reportnotes VARCHAR(256), '
'inreport VARCHAR(256), tag VARCHAR(256), offset INT, '
'vss_store_number INT, URL TEXT, record_number VARCHAR(256), '
'event_identifier VARCHAR(256), event_type VARCHAR(256), '
'source_name VARCHAR(256), user_sid VARCHAR(256), '
'computer_name VARCHAR(256), evidence VARCHAR(256), '
'PRIMARY KEY (rowid)) ENGINE=InnoDB ROW_FORMAT=COMPRESSED')
_INSERT_QUERY = (
'INSERT INTO log2timeline(timezone, MACB, source, '
'sourcetype, type, user, host, description, filename, '
'inode, notes, format, extra, datetime, reportnotes, inreport, '
'tag, offset, vss_store_number, URL, record_number, '
'event_identifier, event_type, source_name, user_sid, computer_name, '
'evidence) '
'VALUES (:timezone, :MACB, :source, :sourcetype, :type, :user, :host, '
':description, :filename, :inode, :notes, :format, :extra, :datetime, '
':reportnotes, :inreport, :tag, :offset, :vss_store_number, :URL, '
':record_number, :event_identifier, :event_type, :source_name, '
':user_sid, :computer_name, :evidence)')
def __init__(self, output_mediator):
"""Initializes the output module object.
Args:
output_mediator (OutputMediator): mediates interactions between output
modules and other components, such as storage and dfvfs.
"""
super(MySQL4n6TimeOutputModule, self).__init__(output_mediator)
self._connection = None
self._count = None
self._cursor = None
self._dbname = 'log2timeline'
self._host = 'localhost'
self._password = 'forensic'
self._port = None
self._user = 'root'
def _GetTags(self):
"""Retrieves tags from the database.
Returns:
list[str]: tags.
"""
all_tags = []
self._cursor.execute('SELECT DISTINCT tag FROM log2timeline')
# This cleans up the messy SQL return.
tag_row = self._cursor.fetchone()
while tag_row:
tag_string = tag_row[0]
if tag_string:
tags = tag_string.split(',')
for tag in tags:
if tag not in all_tags:
all_tags.append(tag)
tag_row = self._cursor.fetchone()
# TODO: make this method an iterator.
return all_tags
def _GetUniqueValues(self, field_name):
"""Retrieves the unique values of a specific field from the database.
Args:
field_name (str): name of the field.
Returns:
dict[str, int]: number of instances of the field value per field name.
"""
self._cursor.execute(
'SELECT {0:s}, COUNT({0:s}) FROM log2timeline GROUP BY {0:s}'.format(
field_name))
result = {}
row = self._cursor.fetchone()
while row:
if row[0]:
result[row[0]] = row[1]
row = self._cursor.fetchone()
return result
def Close(self):
"""Disconnects from the database.
This method will create the necessary indices and commit outstanding
transactions before disconnecting.
"""
# Build up indices for the fields specified in the args.
# It will commit the inserts automatically before creating index.
if not self._append:
for field_name in self._fields:
query = 'CREATE INDEX {0:s}_idx ON log2timeline ({0:s})'.format(
field_name)
self._cursor.execute(query)
if self._set_status:
self._set_status('Created index: {0:s}'.format(field_name))
# Get meta info and save into their tables.
if self._set_status:
self._set_status('Creating metadata...')
for field in self._META_FIELDS:
values = self._GetUniqueValues(field)
self._cursor.execute('DELETE FROM l2t_{0:s}s'.format(field))
for name, frequency in iter(values.items()):
self._cursor.execute((
'INSERT INTO l2t_{0:s}s ({0:s}s, frequency) '
'VALUES("{1:s}", {2:d}) ').format(field, name, frequency))
self._cursor.execute('DELETE FROM l2t_tags')
for tag in self._GetTags():
self._cursor.execute(
'INSERT INTO l2t_tags (tag) VALUES ("{0:s}")'.format(tag))
if self._set_status:
self._set_status('Database created.')
self._connection.commit()
self._cursor.close()
self._connection.close()
self._cursor = None
self._connection = None
def Open(self):
"""Connects to the database and creates the required tables.
Raises:
IOError: if the 4n6time tables cannot be created or data not inserted
in the database.
OSError: if the 4n6time tables cannot be created or data not inserted
in the database.
ValueError: if no database name given.
"""
if not self._dbname:
raise ValueError('Missing database name.')
try:
if self._append:
self._connection = MySQLdb.connect(
self._host, self._user, self._password, self._dbname)
self._cursor = self._connection.cursor()
self._connection.set_character_set('utf8')
self._cursor.execute('SET NAMES utf8')
self._cursor.execute('SET CHARACTER SET utf8')
self._cursor.execute('SET character_set_connection=utf8')
self._cursor.execute('SET GLOBAL innodb_large_prefix=ON')
self._cursor.execute('SET GLOBAL innodb_file_format=barracuda')
self._cursor.execute('SET GLOBAL innodb_file_per_table=ON')
self._cursor.execute(
'CREATE DATABASE IF NOT EXISTS {0:s}'.format(self._dbname))
self._cursor.execute('USE {0:s}'.format(self._dbname))
# Create tables.
self._cursor.execute(self._CREATE_TABLE_QUERY)
if self._set_status:
self._set_status('Created table: log2timeline')
for field in self._META_FIELDS:
self._cursor.execute(
'CREATE TABLE IF NOT EXISTS l2t_{0}s ({0}s TEXT, frequency INT) '
'ENGINE=InnoDB ROW_FORMAT=COMPRESSED'.format(field))
if self._set_status:
self._set_status('Created table: l2t_{0:s}'.format(field))
self._cursor.execute(
'CREATE TABLE IF NOT EXISTS l2t_tags (tag TEXT) '
'ENGINE=InnoDB ROW_FORMAT=COMPRESSED')
if self._set_status:
self._set_status('Created table: l2t_tags')
self._cursor.execute(
'CREATE TABLE IF NOT EXISTS l2t_saved_query ('
'name TEXT, query TEXT) '
'ENGINE=InnoDB ROW_FORMAT=COMPRESSED')
if self._set_status:
self._set_status('Created table: l2t_saved_query')
self._cursor.execute(
'CREATE TABLE IF NOT EXISTS l2t_disk ('
'disk_type INT, mount_path TEXT, '
'dd_path TEXT, dd_offset TEXT, '
'storage_file TEXT, export_path TEXT) '
'ENGINE=InnoDB ROW_FORMAT=COMPRESSED')
self._cursor.execute(
'INSERT INTO l2t_disk ('
'disk_type, mount_path, dd_path, '
'dd_offset, storage_file, '
'export_path) VALUES '
'(0, "", "", "", "", "")')
if self._set_status:
self._set_status('Created table: l2t_disk')
except MySQLdb.Error as exception:
raise IOError('Unable to insert into database with error: {0!s}'.format(
exception))
self._count = 0
def SetCredentials(self, password=None, username=None):
"""Sets the database credentials.
Args:
password (Optional[str]): password to access the database.
username (Optional[str]): username to access the database.
"""
if password:
self._password = password
if username:
self._user = username
def SetDatabaseName(self, name):
"""Sets the database name.
Args:
name (str): name of the database.
"""
self._dbname = name
def SetServerInformation(self, server, port):
"""Sets the server information.
Args:
server (str): hostname or IP address of the database server.
port (int): port number of the database server.
"""
self._host = server
self._port = port
def WriteEventBody(self, event):
"""Writes the body of an event object to the output.
Args:
event (EventObject): event.
"""
if not hasattr(event, 'timestamp'):
return
row = self._GetSanitizedEventValues(event)
try:
self._cursor.execute(self._INSERT_QUERY, row)
except MySQLdb.Error as exception:
logger.warning(
'Unable to insert into database with error: {0!s}.'.format(
exception))
self._count += 1
# TODO: Experiment if committing the current transaction
# every 10000 inserts is the optimal approach.
if self._count % 10000 == 0:
self._connection.commit()
if self._set_status:
self._set_status('Inserting event: {0:d}'.format(self._count))
manager.OutputManager.RegisterOutput(
MySQL4n6TimeOutputModule, disabled=MySQLdb is None)
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#!/usr/bin/env python
# Author: David Kovar [dkovar <at> gmail [dot] com]
# Name: analyzeMFT.py
#
# Copyright (c) 2010 David Kovar. All rights reserved.
# This software is distributed under the Common Public License 1.0
#
# Date: May 2013
#
import sys
import os
import struct
import mbr_parser
from analyzemft import mft
from analyzemft import mftsession
VERSION = "v0.0.1"
# BIOS parameter block - http://www.ntfs.com/ntfs-partition-boot-sector.htm
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# Databricks notebook source
# MAGIC %md
# MAGIC **PROBLEM STATEMENT**
# MAGIC <br/>Predict the Active Covid cases of 2021 depending on 2020 statewise data in India.
# MAGIC <br/>Get Sample data from Source- https://www.kaggle.com/aritranandi23/covid-19-analysis-and-prediction/data
# MAGIC <br/>
# MAGIC <br/>**COLUMN DEFINITION**
# MAGIC <br/>Date:string
# MAGIC <br/>Time:string
# MAGIC <br/>State/UnionTerritory:string
# MAGIC <br/>ConfirmedIndianNational:string
# MAGIC <br/>ConfirmedForeignNational:string
# MAGIC <br/>Cured:integer
# MAGIC <br/>Deaths:integer
# MAGIC <br/>Confirmed:integer
# MAGIC
# MAGIC <br/>**STEPS IN MODELLING**
# MAGIC <br/>1.Data Acquisation
# MAGIC <br/>2.Data understanding
# MAGIC <br/>3.Data visualisation/EDA
# MAGIC <br/>4.Data cleaning/missing imputation/typecasting
# MAGIC <br/>5.Sampling/ bias removal
# MAGIC <br/>6.Anomaly detection
# MAGIC <br/>7.Feature selection/importance
# MAGIC <br/>8.Azure ML Model trigger
# MAGIC <br/>9.Model Interpretation
# MAGIC <br/>10.Telemetry
# MAGIC <br/>
# MAGIC <br/>
# MAGIC <br/>**FEATURE ENGINEERING**
# MAGIC <br/>1.Get Death rates, Discharge rates, Active cases rates from Confirmed, Cured, Death cases
# MAGIC <br/>2.In TS Forecasting each group must provide atleast 3 datapoints to obtain frequency, remove the records where frequency<=3 for the train set i.e. data from the year 2020
# COMMAND ----------
# MAGIC %md
# MAGIC ## Import functions from Master Notebook:
# MAGIC Import the Functions and dependencies from the Master notebook to be used in the Trigger Notebook
# COMMAND ----------
# MAGIC %run /Users/.../AMLMasterNotebook
# COMMAND ----------
# MAGIC %md
# MAGIC ## 1.Data Acquisition
# MAGIC 1.Acquisition of data from datasource ADLS path in CSV/Parquet/JSON etc format.
# MAGIC <br/>2.Logical Transformations in data.
# MAGIC <br/>3.Transforming columns into required datatypes, converting to pandas df, persisiting actual dataset, intoducing a column 'Index' to assign a unique identifier to each dataset row so that this canm be used to retrieve back the original form after any data manupulations.
# COMMAND ----------
# DBTITLE 1,Read Data from Lake
# MAGIC %scala
# MAGIC //<USER INPUT FILEPATH PARQUET OR CSV>
# MAGIC
# MAGIC val filepath1= "adl://<Your ADLS name>.azuredatalakestore.net/Temp/ML-PJC/covid_19_india.csv"
# MAGIC var df1=spark.read.format("com.databricks.spark.csv").option("inferSchema", "true").option("header", "true").option("delimiter", ",").load(filepath1)
# MAGIC df1.createOrReplaceTempView("CovidIndia")
# MAGIC
# MAGIC
# MAGIC
# MAGIC val filepath2= "adl://<Your ADLS name>.azuredatalakestore.net/Temp/ML-PJC/covid_vaccine_statewise.csv"
# MAGIC var df2=spark.read.format("com.databricks.spark.csv").option("inferSchema", "true").option("header", "true").option("delimiter", ",").load(filepath2)
# MAGIC df2.createOrReplaceTempView("Vaccine")
# MAGIC
# MAGIC val filepath3= "adl://<Your ADLS name>.azuredatalakestore.net/Temp/ML-PJC/StatewiseTestingDetails.csv"
# MAGIC var df3=spark.read.format("com.databricks.spark.csv").option("inferSchema", "true").option("header", "true").option("delimiter", ",").load(filepath3)
# MAGIC df3.createOrReplaceTempView("Testing")
# COMMAND ----------
# MAGIC %sql
# MAGIC with CTE1 as
# MAGIC (select
# MAGIC MAX(date_format(Date, 'MMMM')) AS Month
# MAGIC ,MAX(Year(Date)) AS Year
# MAGIC ,CONCAT(Year(Date),'-',RIGHT(CONCAT('00',MONTH(Date)),2),'-','01') AS Date
# MAGIC ,`State/UnionTerritory` as State
# MAGIC ,SUM(Cured) as Cured
# MAGIC ,SUM(Deaths) as Deaths
# MAGIC ,SUM(Confirmed) as Confirmed
# MAGIC ,((SUM(Confirmed)-SUM(Deaths)-SUM(Cured))/SUM(Confirmed) * 100) as ActiveCasesRate
# MAGIC ,(SUM(Cured)/SUM(Confirmed) * 100) AS DischargeRate
# MAGIC ,(SUM(Deaths)/SUM(Confirmed) * 100) as DeathsRate
# MAGIC from CovidIndia C
# MAGIC group by
# MAGIC CONCAT(Year(Date),'-',RIGHT(CONCAT('00',MONTH(Date)),2),'-','01')
# MAGIC ,`State/UnionTerritory`
# MAGIC )
# MAGIC
# MAGIC --select distinct state from CTE1 group by State having count(*)<=3
# MAGIC select * from CTE1 where State not in (select distinct state from CTE1 group by State having count(*)<=3)
# MAGIC and Year=2021
# COMMAND ----------
# DBTITLE 1,Logical transformation of data
df= spark.sql("""
with CTE1 as
(select
MAX(date_format(Date, 'MMMM')) AS Month
,MAX(Year(Date)) AS Year
,CONCAT(Year(Date),'-',RIGHT(CONCAT('00',MONTH(Date)),2),'-','01') AS Date
,`State/UnionTerritory` as State
,SUM(Cured) as Cured
,SUM(Deaths) as Deaths
,SUM(Confirmed) as Confirmed
,((SUM(Confirmed)-SUM(Deaths)-SUM(Cured))/SUM(Confirmed) * 100) as ActiveCasesRate
,(SUM(Cured)/SUM(Confirmed) * 100) AS DischargeRate
,(SUM(Deaths)/SUM(Confirmed) * 100) as DeathsRate
from CovidIndia C
group by
CONCAT(Year(Date),'-',RIGHT(CONCAT('00',MONTH(Date)),2),'-','01')
,`State/UnionTerritory`
)
select * from CTE1
where State not like 'Lakshadweep' and --Low Frequency for train as only one month dec in 2020 train
State not in (select distinct state from CTE1 group by State having count(*)<=3) --low frequency for train as atleast three months required """)
# COMMAND ----------
# DBTITLE 1,Data columns and structural transformation
import pandas as pd
import numpy as np
from pyspark.sql.functions import col
##df.select(*(col(c).cast("float").alias(c) for c in df.columns))
#cols=df.columns
#cols.remove('Index')
#for col_name in cols:
# df = df.withColumn(col_name, col(col_name).cast('float'))
#for col_name in ['Index']:
# df = df.withColumn(col_name, col(col_name).cast('Int'))
# <USER INPUT COLUMN NAMES WITH DATAYPES IN RESPECTIVE BUCKET>
cols_all=[
'Month'
,'Year'
,'Date'
,'State'
,'Cured'
,'Deaths'
,'Confirmed'
,'ActiveCasesRate'
,'DischargeRate'
,'DeathsRate'
]
cols_string=[
'Month'
,'Year'
,'Date'
,'State'
]
cols_int=[
'Cured'
,'Deaths'
,'Confirmed'
]
cols_bool=[]
cols_Float=[
'ActiveCasesRate'
,'DischargeRate'
,'DeathsRate'
]
for col_name in cols_int:
df = df.withColumn(col_name, col(col_name).cast('Int'))
for col_name in cols_Float:
df = df.withColumn(col_name, col(col_name).cast('float'))
for col_name in cols_bool:
df = df.withColumn(col_name, col(col_name).cast('bool'))
input_dataframe = df.toPandas()
input_dataframe['Index'] = np.arange(len(input_dataframe))
outdir = '/dbfs/FileStore/Covid.csv'
input_dataframe.to_csv(outdir, index=False)
#input_dataframe = pd.read_csv("/dbfs/FileStore/Dataframe.csv", header='infer')
# COMMAND ----------
# MAGIC %md
# MAGIC ## 2.Data Exploration
# MAGIC 1.Exploratory Data Analysis (EDA)- To understand the overall data at hand, analysing each feature independently for its' statistics, the correlation and interraction between variables, data sample etc.
# MAGIC <br/>2.Data Profiling Plots- To analyse the Categorical and Numerical columns separately for any trend in data, biasness in data etc.
# COMMAND ----------
# DBTITLE 1,EDA
input_dataframe = pd.read_csv("/dbfs/FileStore/Covid.csv", header='infer')
p=Data_Profiling_viaPandasProfiling(input_dataframe,'RealEstate','EDA')
displayHTML(p)
# COMMAND ----------
# DBTITLE 1,Data Profiling Plots
input_dataframe = pd.read_csv("/dbfs/FileStore/Covid.csv", header='infer')
#User Inputs
cols_all=[
'Month'
,'Year'
,'Date'
,'State'
,'Cured'
,'Deaths'
,'Confirmed'
,'ActiveCasesRate'
,'DischargeRate'
,'DeathsRate'
]
Categorical_cols=['Month'
,'Year'
,'Date'
,'State'
]
Numeric_cols=['Cured'
,'Deaths'
,'Confirmed'
,'ActiveCasesRate'
,'DischargeRate'
,'DeathsRate'
]
Label_col='ActiveCasesRate'
#Data_Profiling_Plots(input_dataframe,Categorical_cols,Numeric_cols,Label_col)
Data_Profiling_Plots(input_dataframe,Categorical_cols,Numeric_cols,Label_col)
# COMMAND ----------
# MAGIC %md
# MAGIC ## 4.Cleansing
# MAGIC To clean the data from NULL values, fix structural errors in columns, drop empty columns, encode the categorical values, normalise the data to bring to the same scale. We also check the Data Distribution via Correlation heatmap of original input dataset v/s the Cleansed dataset to validate whether or not the transformations hampered the original data trend/density.
# COMMAND ----------
# DBTITLE 1,Auto Cleanser
subsample_final = pd.read_csv("/dbfs/FileStore/Covid.csv", header='infer')
#subsample_final=subsample_final.drop(['Index'], axis = 1) # Index is highest variability column hence always imp along PC but has no business value. You can append columns to be dropped by your choice here in the list
inputdf_new=autodatacleaner(subsample_final,"/dbfs/FileStore/Covid.csv","Covid","Data Cleanser")
print("Total rows in the new pandas dataframe:",len(inputdf_new.index))
#persist cleansed data sets
filepath1 = '/dbfs/FileStore/Cleansed_Covid.csv'
inputdf_new.to_csv(filepath1, index=False)
# COMMAND ----------
# DBTITLE 1,Data profiling(Heatmap correlation)- User input dataframe
subsample_final = pd.read_csv("/dbfs/FileStore/Covid.csv", header='infer')
display(Data_Profiling_Fin(subsample_final))
# COMMAND ----------
# DBTITLE 1,Data profiling(Heatmap correlation)- Cleansed dataframe
Cleansed=pd.read_csv("/dbfs/FileStore/Cleansed_Covid.csv", header='infer')
display(Data_Profiling_Fin(Cleansed))
# COMMAND ----------
# MAGIC %md
# MAGIC ## 5.Anomaly Detection
# MAGIC Iterate data over various Anomaly-detection techniques and estimate the number of Inliers and Outliers for each.
# COMMAND ----------
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.font_manager
from pyod.models.abod import ABOD
from pyod.models.knn import KNN
from pyod.utils.data import generate_data, get_outliers_inliers
from pyod.models.cblof import CBLOF
#from pyod.models.feature_bagging import FeatureBagging
from pyod.models.hbos import HBOS
from pyod.models.iforest import IForest
from pyod.models.lof import LOF
from sklearn.preprocessing import MinMaxScaler
import warnings
from io import BytesIO
from pyspark.sql.functions import base64
from pyspark.sql.functions import unbase64
warnings.filterwarnings("ignore")
outliers_fraction = 0.05
#df = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/train.csv")
#target_variable = 'SalePrice'
#variables_to_analyze = '1stFlrSF'
output_path = '/dbfs/FileStore/AnomalyDetection_HTML'
#df.plot.scatter('1stFlrSF','SalePrice')
# COMMAND ----------
# DBTITLE 0,Anomaly Detection
#Calling the Anamoly Detection Function for identifying outliers
outliers_fraction = 0.05
df =pd.read_csv("/dbfs/FileStore/Cleansed_Covid.csv", header='infer')
target_variable = 'ActiveCasesRate'
variables_to_analyze='Confirmed'
AnomalyDetection(df,target_variable,variables_to_analyze,outliers_fraction,'anomaly_test','test')
# COMMAND ----------
# MAGIC %md
# MAGIC ## 6.Feature Selection
# MAGIC Perform feature selection on the basis of Feature Importance ranking, correlation values, variance within the column.
# MAGIC Choose features with High Importance value score, drop one of the two highly correlated features, drop features which offer zero variability to data and thus do not increase the entropy of dataset.
# COMMAND ----------
df =pd.read_csv("/dbfs/FileStore/Cleansed_Covid.csv", header='infer')
FeatureSelection(df,'ActiveCasesRate','Continuous',"/dbfs/FileStore/Cleansed_Covid.csv",'Covid','FeatureSelection')
# COMMAND ----------
# MAGIC %pip install azureml-train-automl-runtime
# MAGIC %pip install azureml-automl-runtime
# MAGIC %pip install azureml-widgets
# MAGIC %pip install azureml-sdk[automl]
# MAGIC %pip install ipywidgets
# MAGIC %pip install pandas-profiling
# MAGIC %pip install pyod
# MAGIC %pip install azureml-sdk
# MAGIC %pip install azureml-explain-model
# MAGIC %pip install imbalanced-learn
# MAGIC %pip install pyod
# MAGIC %pip install skfeature-chappers
# MAGIC %pip install raiwidgets
# MAGIC
# MAGIC %pip install ruamel.yaml==0.16.10
# MAGIC %pip install azure-core==1.8.0
# MAGIC %pip install liac-arff==2.4.0
# MAGIC %pip install msal==1.4.3
# MAGIC %pip install msrest==0.6.18
# MAGIC %pip install ruamel.yaml.clib==0.2.0
# MAGIC %pip install tqdm==4.49.0
# MAGIC %pip install zipp==3.2.0
# MAGIC %pip install interpret-community==0.15.0
# MAGIC %pip install azure-identity==1.4.0
# MAGIC %pip install dotnetcore2==2.1.16
# MAGIC %pip install jinja2==2.11.2
# MAGIC %pip install azure-core==1.15.0
# MAGIC %pip install azure-mgmt-containerregistry==8.0.0
# MAGIC %pip install azure-mgmt-core==1.2.2
# MAGIC %pip install distro==1.5.0
# MAGIC %pip install google-api-core==1.30.0
# MAGIC %pip install google-auth==1.32.1
# MAGIC %pip install importlib-metadata==4.6.0
# MAGIC %pip install msal==1.12.0
# MAGIC %pip install packaging==20.9
# MAGIC %pip install pathspec==0.8.1
# MAGIC %pip install requests==2.25.1
# MAGIC %pip install ruamel.yaml.clib==0.2.4
# MAGIC %pip install tqdm==4.61.1
# MAGIC %pip install zipp==3.4.1
# MAGIC %pip install scipy==1.5.2
# MAGIC %pip install charset-normalizer==2.0.3
# MAGIC %pip install websocket-client==1.1.0
# MAGIC %pip install scikit-learn==0.22.1
# MAGIC %pip install interpret-community==0.19.0
# MAGIC %pip install cryptography==3.4.7
# MAGIC %pip install llvmlite==0.36.0
# MAGIC %pip install numba==0.53.1
# COMMAND ----------
# MAGIC %md
# MAGIC ## 7.Auto ML Trigger - after preprocessing
# MAGIC Trigger Azure auto ML, pick the best model so obtained and use it to predict the label column. Calculate the Weighted Absolute Accuracy amd push to telemetry. also obtain the data back in original format by using the unique identifier of each row 'Index' and report Actual v/s Predicted Columns. We also provide the direct link to the azure Portal Run for the current experiment for users to follow.
# COMMAND ----------
# DBTITLE 1,Train-Test Split
##df has just index,y actual, y predicted cols, as rest all cols are encoded after manipulation
import pandas as pd
df =pd.read_csv("/dbfs/FileStore/Cleansed_Covid.csv", header='infer')
for col in df.columns:
if col not in ["Index"]:
df.drop([col], axis=1, inplace=True)
#dataframe is the actual input dataset
dataframe = pd.read_csv("/dbfs/FileStore/Covid.csv", header='infer')
#Merging Actual Input dataframe with AML output df using Index column
dataframe_fin = pd.merge(left=dataframe, right=df, left_on='Index', right_on='Index')
#dataframe_fin
#train-test split
train_data=dataframe_fin[dataframe_fin['Year']==2020]
test_data=dataframe_fin[dataframe_fin['Year']==2021]
label='DeathsRate'#'ActiveCasesRate'
test_labels = test_data.pop(label).values
train_data
# COMMAND ----------
#train_date duplicate check
train_data[train_data.duplicated(['State','Date'])]
# COMMAND ----------
#frequency <=3 check
df_new= train_data.groupby(['State']).count()
freq = df_new[(df_new['Index'] <= 3)]
freq
# COMMAND ----------
##2 removed lakshdweep
#frequency <=3 check
df_new= train_data.groupby(['State']).count()
freq = df_new[(df_new['Index'] <= 3)]
freq
# COMMAND ----------
# DBTITLE 1,Auto ML Run
time_series_settings = {
"time_column_name": "Date",
"grain_column_names": ["State"],
"max_horizon": 2,
"target_lags": 2,
"target_rolling_window_size": 2,
"featurization": "auto",
"short_series_handling_configuration":'auto',
"freq": 'MS',
"short_series_handling_config": "auto"
}
from azureml.core.workspace import Workspace
from azureml.core.experiment import Experiment
from azureml.train.automl import AutoMLConfig
import logging
from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
from azureml.core.experiment import Experiment
from azureml.core import Workspace
from azureml.core.authentication import ServicePrincipalAuthentication
from azureml.core.dataset import Dataset
from azureml.widgets import RunDetails
from azureml.core import Dataset, Datastore
from azureml.data.datapath import DataPath
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
import os
import warnings
from sklearn.metrics import mean_squared_error
from math import sqrt
warnings.filterwarnings('ignore')
automl_config = AutoMLConfig(task='forecasting',
primary_metric='normalized_root_mean_squared_error',
iterations= 1,
experiment_timeout_minutes=15,
enable_early_stopping=True,
n_cross_validations=2,
training_data=train_data,
label_column_name=label,
enable_ensembling=False,
verbosity=logging.INFO,
**time_series_settings)
ws = Workspace(subscription_id = '<subscription_id>', resource_group = '<resource_group>', workspace_name = '<workspace_name>')
#ws = Workspace.from_config()
# Verify that cluster does not exist already
compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D12_V2',
max_nodes=100)
compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)
compute_target.wait_for_completion(show_output=True)
datastore = ws.get_default_datastore()
train_dataset = Dataset.Tabular.register_pandas_dataframe(train_data,datastore,'Covid')
test_dataset = Dataset.Tabular.register_pandas_dataframe(test_data,datastore,'Covid')
experiment = Experiment(ws, "TS_forecasting")
remote_run = experiment.submit(automl_config, show_output=True)
remote_run.wait_for_completion()
best_run, fitted_model = remote_run.get_output()
# COMMAND ----------
# DBTITLE 1,Forecasting for Test set
y_predictions, X_trans = fitted_model.forecast(test_data)
y_predictions
# COMMAND ----------
# DBTITLE 1,Data Actuals v/s Predicted
result = test_data
y_predictions, X_trans = fitted_model.forecast(test_data)
result['Values_pred']=y_predictions
result['Values_actual']=test_labels
result['Error']=result['Values_actual']-result['Values_pred']
result['Percentage_change'] = ((result['Values_actual']-result['Values_pred']) / result['Values_actual'] )* 100
result=result.reset_index(drop=True)
result
# COMMAND ----------
# DBTITLE 1,Accuracy Calculation
y_actual = test_labels
sum_actuals = sum_errors = 0
for actual_val, predict_val in zip(y_actual, y_predictions):
abs_error = actual_val - predict_val
if abs_error < 0:
abs_error = abs_error * -1
sum_errors = sum_errors + abs_error
sum_actuals = sum_actuals + actual_val
mean_abs_percent_error = sum_errors / sum_actuals
Accuracy_score = 1 - mean_abs_percent_error
print(Accuracy_score)
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] | 2.714369 | 6,883 |
from django.contrib.auth.base_user import BaseUserManager
from django.utils.translation import ugettext_lazy as _
from django.db import models
from website.utils import log
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] | 3.55102 | 49 |
# @Author: riener
# @Date: 2018-12-19T17:30:53+01:00
# @Filename: gp_plus.py
# @Last modified by: riener
# @Last modified time: 2019-04-08T10:20:37+02:00
import itertools
import sys
import numpy as np
import lmfit
from lmfit import minimize as lmfit_minimize
from lmfit import Parameters
from gausspyplus.utils.determine_intervals import check_if_intervals_contain_signal
from gausspyplus.utils.fit_quality_checks import determine_significance, goodness_of_fit, check_residual_for_normality
from gausspyplus.utils.gaussian_functions import combined_gaussian, area_of_gaussian
from gausspyplus.utils.noise_estimation import determine_peaks, mask_channels
def say(message, verbose=False):
"""Diagnostic messages."""
if verbose is True:
print(message)
def split_params(params, ncomps):
"""Split params into amps, fwhms, offsets."""
amps = params[0:ncomps]
fwhms = params[ncomps:2*ncomps]
offsets = params[2*ncomps:3*ncomps]
return amps, fwhms, offsets
def number_of_components(params):
"""Compute number of Gaussian components."""
return int(len(params) / 3)
def gaussian_function(peak, FWHM, mean):
"""Return a Gaussian function."""
sigma = FWHM / 2.354820045 # (2 * sqrt( 2 * ln(2)))
return lambda x: peak * np.exp(-(x - mean)**2 / 2. / sigma**2)
def func(x, *args):
"""Return multi-component Gaussian model F(x).
Parameter vector kargs = [amp1, ..., ampN, width1, ..., widthN, mean1, ..., meanN],
and therefore has len(args) = 3 x N_components.
"""
ncomps = number_of_components(args)
yout = x * 0.
for i in range(ncomps):
yout = yout + gaussian_function(
args[i], args[i+ncomps], args[i+2*ncomps])(x)
return yout
def vals_vec_from_lmfit(lmfit_params):
"""Return Python list of parameter values from LMFIT Parameters object."""
if (sys.version_info >= (3, 0)):
vals = [value.value for value in list(lmfit_params.values())]
else:
vals = [value.value for value in lmfit_params.values()]
return vals
def errs_vec_from_lmfit(lmfit_params):
"""Return Python list of parameter uncertainties from LMFIT Parameters object."""
if (sys.version_info >= (3, 0)):
errs = [value.stderr for value in list(lmfit_params.values())]
else:
errs = [value.stderr for value in lmfit_params.values()]
# TODO: estimate errors via bootstrapping instead of setting them to zero
errs = [0 if err is None else err for err in errs]
return errs
def paramvec_to_lmfit(paramvec, max_amp=None, max_fwhm=None,
params_min=None, params_max=None):
"""Transform a Python iterable of parameters into a LMFIT Parameters object.
Parameters
----------
paramvec : list
Parameter vector = [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
max_amp : float
Enforced maximum value for amplitude parameter.
max_fwhm : float
Enforced maximum value for FWHM parameter. Use with caution! Can lead to artifacts in the fitting.
params_min : list
List of minimum limits for parameters: [min_amp1, ..., min_ampN, min_fwhm1, ..., min_fwhmN, min_mean1, ..., min_meanN]
params_max : list
List of maximum limits for parameters: [max_amp1, ..., max_ampN, max_fwhm1, ..., max_fwhmN, max_mean1, ..., max_meanN]
Returns
-------
params: lmfit.parameter.Parameters
"""
ncomps = number_of_components(paramvec)
params = Parameters()
if params_min is None:
params_min = len(paramvec)*[0.]
if params_max is None:
params_max = len(paramvec)*[None]
if max_amp is not None:
params_max[0:ncomps] = ncomps*[max_amp]
if max_fwhm is not None:
params_max[ncomps:2*ncomps] = ncomps*[max_fwhm]
for i in range(len(paramvec)):
params.add('p{}'.format(str(i + 1)), value=paramvec[i],
min=params_min[i], max=params_max[i])
return params
def remove_components_from_sublists(lst, remove_indices):
"""Remove items with indices idx1, ..., idxN from all sublists of a nested list.
Parameters
----------
lst : list
Nested list [sublist1, ..., sublistN].
remove_indices : list
List of indices [idx1, ..., idxN] indicating the items that should be removed from the sublists.
Returns
-------
lst : list
"""
for idx, sublst in enumerate(lst):
lst[idx] = [val for i, val in enumerate(sublst)
if i not in remove_indices]
return lst
def remove_components_above_max_ncomps(amps_fit, fwhms_fit, ncomps_max,
remove_indices, quality_control):
"""Remove all fit components above specified limit.
Parameters
----------
amps_fit : list
List containing amplitude values of all N fitted Gaussian components in the form [amp1, ..., ampN].
fwhms_fit : list
List containing FWHM values of all N fitted Gaussian components in the form [fwhm1, ..., fwhmN].
ncomps_max : int
Specified maximum number of fit components.
remove_indices : list
Indices of Gaussian components that should be removed from the fit solution.
quality_control : list
Log containing information about which in-built quality control parameters were not fulfilled (0: 'max_fwhm', 1: 'min_fwhm', 2: 'snr', 3: 'significance', 4: 'channel_range', 5: 'signal_range')
Returns
-------
remove_indices : list
Updated list with indices of Gaussian components that should be removed from the fit solution.
quality_control : list
Updated log containing information about which in-built quality control parameters were not fulfilled.
"""
ncomps_fit = len(amps_fit)
if ncomps_fit <= ncomps_max:
return remove_indices, quality_control
integrated_intensities = area_of_gaussian(
np.array(amps_fit), np.array(fwhms_fit))
indices = np.array(range(len(integrated_intensities)))
sort_indices = np.argsort(integrated_intensities)
for index in indices[sort_indices]:
if index in remove_indices:
continue
remove_indices.append(index)
quality_control.append(6)
remaining_ncomps = ncomps_fit - len(remove_indices)
if remaining_ncomps <= ncomps_max:
break
return remove_indices, quality_control
def check_params_fit(vel, data, errors, params_fit, params_errs, dct,
quality_control, signal_ranges=None,
params_min=None, params_max=None):
"""Perform quality checks for the fitted Gaussians components.
All Gaussian components that are not satisfying the criteria are discarded from the fit.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
params_errs : list
Parameter error vector in the form of [e_amp1, ..., e_ampN, e_fwhm1, ..., e_fwhmN, e_mean1, ..., e_meanN].
dct : dict
Dictionary containing parameter settings for the improved fitting.
rms : float
Root-mean-square noise of the spectrum.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
params_min : list
List of minimum limits for parameters: [min_amp1, ..., min_ampN, min_fwhm1, ..., min_fwhmN, min_mean1, ..., min_meanN]
params_max : list
List of maximum limits for parameters: [max_amp1, ..., max_ampN, max_fwhm1, ..., max_fwhmN, max_mean1, ..., max_meanN]
quality_control : list
Log containing information about which in-built quality control parameters were not fulfilled (0: 'max_fwhm', 1: 'min_fwhm', 2: 'snr', 3: 'significance', 4: 'channel_range', 5: 'signal_range')
Returns
-------
params_fit : list
Corrected version from which all Gaussian components that did not satisfy the quality criteria are removed.
params_err : list
Corrected version from which all Gaussian components that did not satisfy the quality criteria are removed.
ncomps_fit : int
Number of remaining fitted Gaussian components.
params_min : list
Corrected version from which all Gaussian components that did not satisfy the quality criteria are removed.
params_max : list
Corrected version from which all Gaussian components that did not satisfy the quality criteria are removed.
quality_control : list
Updated log containing information about which in-built quality control parameters were not fulfilled.
refit : bool
If 'True', the spectrum was refit because one or more of the Gaussian fit parameters did not satisfy the quality control parameters.
"""
ncomps_fit = number_of_components(params_fit)
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
amps_errs, fwhms_errs, offsets_errs = split_params(params_errs, ncomps_fit)
if params_min is not None:
amps_min, fwhms_min, offsets_min = split_params(params_min, ncomps_fit)
if params_max is not None:
amps_max, fwhms_max, offsets_max = split_params(params_max, ncomps_fit)
rms = errors[0]
# check if Gaussian components satisfy quality criteria
remove_indices = []
for i, (amp, fwhm, offset) in enumerate(
zip(amps_fit, fwhms_fit, offsets_fit)):
if dct['max_fwhm'] is not None:
if fwhm > dct['max_fwhm']:
remove_indices.append(i)
quality_control.append(0)
continue
if dct['min_fwhm'] is not None:
if fwhm < dct['min_fwhm']:
remove_indices.append(i)
quality_control.append(1)
continue
# discard the Gaussian component if its amplitude value does not satisfy the required minimum S/N value or is larger than the limit
if amp < dct['snr_fit']*rms:
remove_indices.append(i)
quality_control.append(2)
continue
# if amp > dct['max_amp']:
# remove_indices.append(i)
# quality_control.append(3)
# continue
# discard the Gaussian component if it does not satisfy the significance criterion
if determine_significance(amp, fwhm, rms) < dct['significance']:
remove_indices.append(i)
quality_control.append(3)
continue
# If the Gaussian component was fit outside the determined signal ranges, we check the significance of signal feature fitted by the Gaussian component. We remove the Gaussian component if the signal feature does not satisfy the significance criterion.
if (offset < np.min(vel)) or (offset > np.max(vel)):
remove_indices.append(i)
quality_control.append(4)
continue
if signal_ranges:
if not any(low <= offset <= upp for low, upp in signal_ranges):
low = max(0, int(offset - fwhm))
upp = int(offset + fwhm) + 2
if not check_if_intervals_contain_signal(
data, rms, [(low, upp)], snr=dct['snr'],
significance=dct['significance']):
remove_indices.append(i)
quality_control.append(5)
continue
if dct['max_ncomps'] is not None:
remove_indices, quality_control = remove_components_above_max_ncomps(
amps_fit, fwhms_fit, dct['max_ncomps'], remove_indices, quality_control)
remove_indices = list(set(remove_indices))
refit = False
if len(remove_indices) > 0:
amps_fit, fwhms_fit, offsets_fit = remove_components_from_sublists(
[amps_fit, fwhms_fit, offsets_fit], remove_indices)
params_fit = amps_fit + fwhms_fit + offsets_fit
amps_errs, fwhms_errs, offsets_errs = remove_components_from_sublists(
[amps_errs, fwhms_errs, offsets_errs], remove_indices)
params_errs = amps_errs + fwhms_errs + offsets_errs
if params_min is not None:
amps_min, fwhms_min, offsets_min = remove_components_from_sublists(
[amps_min, fwhms_min, offsets_min], remove_indices)
params_min = amps_min + fwhms_min + offsets_min
if params_max is not None:
amps_max, fwhms_max, offsets_max = remove_components_from_sublists(
[amps_max, fwhms_max, offsets_max], remove_indices)
params_max = amps_max + fwhms_max + offsets_max
params_fit, params_errs, ncomps_fit = perform_least_squares_fit(
vel, data, errors, params_fit, dct, params_min=params_min, params_max=params_max)
refit = True
return params_fit, params_errs, ncomps_fit, params_min, params_max, quality_control, refit
def check_which_gaussian_contains_feature(idx_low, idx_upp, fwhms_fit,
offsets_fit):
"""Return index of Gaussian component contained within a range in the spectrum.
The FWHM interval (mean - FWHM, mean + FWHM ) of the Gaussian component has to be fully contained in the range of the spectrum. If no Gaussians satisfy this criterion 'None' is returned. In case multiple Gaussians satisfy this criterion, the Gaussian with the highest FWHM parameter is selected.
Parameters
----------
idx_low : int
Index of the first channel of the range in the spectrum.
idx_upp : int
Index of the last channel of the range in the spectrum.
fwhms_fit : list
List containing FWHM values of all N fitted Gaussian components in the form [fwhm1, ..., fwhmN].
offsets_fit : list
List containing mean position values of all N fitted Gaussian components in the form [mean1, ..., meanN].
Returns
-------
index : int or None
Index of Gaussian component contained within the spectral range.
"""
lower = [int(offset - fwhm) for fwhm, offset in zip(fwhms_fit, offsets_fit)]
lower = np.array([0 if x < 0 else x for x in lower])
upper = np.array([int(offset + fwhm) + 2 for fwhm, offset in zip(fwhms_fit, offsets_fit)])
indices = np.arange(len(fwhms_fit))
conditions = np.logical_and(lower <= idx_low, upper >= idx_upp)
if np.count_nonzero(conditions) == 0:
return None
elif np.count_nonzero(conditions) == 1:
return int(indices[conditions])
else:
remaining_indices = indices[conditions]
select = np.argmax(np.array(fwhms_fit)[remaining_indices])
return int(remaining_indices[select])
def replace_gaussian_with_two_new_ones(data, vel, rms, snr, significance,
params_fit, exclude_idx, offset):
"""Replace broad Gaussian fit component with initial guesses for two narrower components.
Parameters
----------
data : numpy.ndarray
Original data of spectrum.
vel : numpy.ndarray
Velocity channels (unitless).
rms : float
Root-mean-square noise of the spectrum.
snr : float
Required minimum signal-to-noise ratio for data peak.
significance : float
Required minimum value for significance criterion.
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
exclude_idx : int
Index of the broad Gaussian fit component that will be removed from params_fit.
offset : type
Mean position of the broad Gaussian fit component that will be removed.
Returns
-------
params_fit : list
Updated list from which the parameters of the broad Gaussian fit components were removed and the parameters of the two narrower components were added.
"""
ncomps_fit = number_of_components(params_fit)
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
# TODO: check if this is still necessary?
if exclude_idx is None:
return params_fit
# remove the broad Gaussian component from the fit parameter list and determine new residual
idx_low_residual = max(0, int(
offsets_fit[exclude_idx] - fwhms_fit[exclude_idx]))
idx_upp_residual = int(
offsets_fit[exclude_idx] + fwhms_fit[exclude_idx]) + 2
mask = np.arange(len(amps_fit)) == exclude_idx
amps_fit = np.array(amps_fit)[~mask]
fwhms_fit = np.array(fwhms_fit)[~mask]
offsets_fit = np.array(offsets_fit)[~mask]
residual = data - combined_gaussian(amps_fit, fwhms_fit, offsets_fit, vel)
# search for residual peaks in new residual
for low, upp in zip([idx_low_residual, int(offset)],
[int(offset), idx_upp_residual]):
amp_guess, fwhm_guess, offset_guess = get_initial_guesses(
residual[low:upp], rms, snr, significance, peak='positive', maximum=True)
if amp_guess.size == 0:
continue
amps_fit, fwhms_fit, offsets_fit = list(amps_fit), list(fwhms_fit), list(offsets_fit)
amps_fit.append(amp_guess)
fwhms_fit.append(fwhm_guess)
offsets_fit.append(offset_guess + low)
params_fit = amps_fit + fwhms_fit + offsets_fit
return params_fit
def get_initial_guesses(residual, rms, snr, significance, peak='positive',
maximum=False, baseline_shift_snr=0):
"""Get initial guesses of Gaussian fit parameters for residual peaks.
Parameters
----------
residual : numpy.ndarray
Residual of the spectrum in which we search for unfit peaks.
rms : float
Root-mean-square noise of the spectrum.
snr : float
Required minimum signal-to-noise ratio for data peak.
significance : float
Required minimum value for significance criterion.
peak : str ('positive', 'negative')
Whether to search for positive (default) or negative peaks in the residual.
maximum : bool
Default is 'False'. If set to 'True', only the input parameter guesses for a single Gaussian fit component -- the one with the highest guessed amplitude value -- are returned.
baseline_shift_snr : float
Experimental feature that shifts the baseline of the residual before searching for peaks.
Returns
-------
amp_guesses : numpy.array or None
Initial guesses for amplitude values of Gaussian fit parameters for residual peaks.
fwhm_guesses : numpy.array or None
Initial guesses for FWHM values of Gaussian fit parameters for residual peaks.
offset_guesses : numpy.array or None
Initial guesses for mean positions of Gaussian fit parameters for residual peaks.
"""
# amp_guesses, ranges = determine_peaks(
# residual, peak=peak, amp_threshold=snr*rms)
amp_guesses, ranges = determine_peaks(
residual - baseline_shift_snr*rms, peak=peak,
amp_threshold=(snr - baseline_shift_snr)*rms)
if amp_guesses.size == 0:
return np.array([]), np.array([]), np.array([])
amp_guesses = amp_guesses + baseline_shift_snr*rms
sort = np.argsort(ranges[:, 0])
amp_guesses = amp_guesses[sort]
ranges = ranges[sort]
# determine whether identified residual peaks satisfy the significance criterion for signal peaks
keep_indices = np.array([])
significance_vals = np.array([])
for i, (lower, upper) in enumerate(ranges):
significance_val = np.sum(
np.abs(residual[lower:upper])) / (np.sqrt(upper - lower)*rms)
significance_vals = np.append(significance_vals, significance_val)
if significance_val > significance:
keep_indices = np.append(keep_indices, i)
keep_indices = keep_indices.astype('int')
amp_guesses = amp_guesses[keep_indices]
ranges = ranges[keep_indices]
significance_vals = significance_vals[keep_indices]
if amp_guesses.size == 0:
return np.array([]), np.array([]), np.array([])
amp_guesses_position_mask = np.in1d(residual, amp_guesses)
offset_guesses = np.where(amp_guesses_position_mask == True)[0]
# we use the determined significance values to get input guesses for the FWHM values
fwhm_guesses = (significance_vals*rms
/ (amp_guesses * 0.75269184778925247))**2
if maximum:
idx_max = np.argmax(amp_guesses)
amp_guesses = amp_guesses[idx_max]
fwhm_guesses = fwhm_guesses[idx_max]
offset_guesses = offset_guesses[idx_max]
return amp_guesses, fwhm_guesses, offset_guesses
def get_fully_blended_gaussians(params_fit, get_count=False,
separation_factor=0.8493218002991817):
"""Return information about blended Gaussian fit components.
A Gaussian fit component i is blended with another component if the separation of their mean positions is less than the FWHM value of the narrower component multiplied by the 'separation_factor'.
The default value for the separation_factor (0.8493218002991817) is based on the minimum required separation distance to distinguish two identical Gaussian peaks (= 2*std).
Parameters
----------
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
get_count : bool
Default is 'False'. If set to 'True' only the number of blended components is returned.
separation_factor : float
The required minimum separation between two Gaussian components (mean1, fwhm1) and (mean2, fwhm2) is determined as separation_factor * min(fwhm1, fwhm2).
Returns
-------
indices_blended : numpy.ndarray
Indices of fitted Gaussian components that satisfy the criterion for blendedness, sorted from lowest to highest amplitude values.
"""
ncomps_fit = number_of_components(params_fit)
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
# stddevs_fit = list(np.array(fwhms_fit) / 2.354820045)
indices_blended = np.array([])
blended_pairs = []
items = list(range(ncomps_fit))
N_blended = 0
for idx1, idx2 in itertools.combinations(items, 2):
min_separation = min(
fwhms_fit[idx1], fwhms_fit[idx2]) * separation_factor
separation = abs(offsets_fit[idx1] - offsets_fit[idx2])
if separation < min_separation:
indices_blended = np.append(indices_blended, np.array([idx1, idx2]))
blended_pairs.append([idx1, idx2])
N_blended += 1
if get_count:
return N_blended
indices_blended = np.unique(indices_blended).astype('int')
# sort the identified blended components from lowest to highest amplitude value
sort = np.argsort(np.array(amps_fit)[indices_blended])
return indices_blended[sort]
def remove_components(params_fit, remove_indices):
"""Remove parameters of Gaussian fit components.
Parameters
----------
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
remove_indices : int, list, np.ndarray
Indices of Gaussian fit components, whose parameters should be removed from params_fit.
Returns
-------
params_fit : list
Updated list from which the parameters of the selected Gaussian fit components were removed.
"""
ncomps_fit = number_of_components(params_fit)
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
if isinstance(remove_indices, np.ndarray):
remove_indices = list(remove_indices)
elif not isinstance(remove_indices, list):
remove_indices = [remove_indices]
amps_fit = list(np.delete(np.array(amps_fit), remove_indices))
fwhms_fit = list(np.delete(np.array(fwhms_fit), remove_indices))
offsets_fit = list(np.delete(np.array(offsets_fit), remove_indices))
params_fit = amps_fit + fwhms_fit + offsets_fit
return params_fit
def get_best_fit(vel, data, errors, params_fit, dct, first=False,
best_fit_list=None, signal_ranges=None, signal_mask=None,
force_accept=False, params_min=None, params_max=None,
noise_spike_mask=None):
"""Determine new best fit for spectrum.
If this is the first fit iteration for the spectrum a new best fit is assigned and its parameters are returned in best_fit_list.
If it is not the first fit iteration, the new fit is compared to the current best fit supplied in best_fit_list. If the new fit is preferred (decided via the AICc criterion), the parameters of the new fit are returned in best_fit_list. Otherwise, the old best_fit_list is returned.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
dct : dict
Dictionary containing parameter settings for the improved fitting.
first : bool
Default is 'False'. If set to 'True', the new fit will be assigned as best fit and returned in best_fit_list.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
params_min : list
List of minimum limits for parameters: [min_amp1, ..., min_ampN, min_fwhm1, ..., min_fwhmN, min_mean1, ..., min_meanN]
params_max : list
List of maximum limits for parameters: [max_amp1, ..., max_ampN, max_fwhm1, ..., max_fwhmN, max_mean1, ..., max_meanN]
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
if not first:
best_fit_list[7] = False
quality_control = best_fit_list[11]
else:
quality_control = []
ncomps_fit = number_of_components(params_fit)
params_fit, params_errs, ncomps_fit = perform_least_squares_fit(
vel, data, errors, params_fit, dct, params_min=params_min, params_max=params_max)
# check if fit components satisfy mandatory criteria
if ncomps_fit > 0:
refit = True
while refit:
params_fit, params_errs, ncomps_fit, params_min, params_max, quality_control, refit = check_params_fit(
vel, data, errors, params_fit, params_errs, dct,
quality_control, signal_ranges=signal_ranges)
best_fit = func(vel, *params_fit).ravel()
else:
best_fit = data * 0
rchi2, aicc = goodness_of_fit(
data, best_fit, errors, ncomps_fit, mask=signal_mask, get_aicc=True)
residual = data - best_fit
pvalue = check_residual_for_normality(residual, errors, mask=signal_mask,
noise_spike_mask=noise_spike_mask)
# return the list of best fit results if there was no old list of best fit results for comparison
if first:
new_fit = True
return [params_fit, params_errs, ncomps_fit, best_fit, residual, rchi2,
aicc, new_fit, params_min, params_max, pvalue, quality_control]
# return new best_fit_list if the AICc value is smaller
aicc_old = best_fit_list[6]
if ((aicc < aicc_old) and not np.isclose(aicc, aicc_old, atol=1e-1)) or force_accept:
new_fit = True
return [params_fit, params_errs, ncomps_fit, best_fit, residual, rchi2,
aicc, new_fit, params_min, params_max, pvalue, quality_control]
# return old best_fit_list if the aicc value is higher
best_fit_list[7] = False
return best_fit_list
def check_for_negative_residual(vel, data, errors, best_fit_list, dct,
signal_ranges=None, signal_mask=None,
force_accept=False, get_count=False,
get_idx=False, noise_spike_mask=None):
"""Check for negative residual features and try to refit them.
We define negative residual features as negative peaks in the residual that were introduced by the fit. These negative peaks have to have a minimum negative signal-to-noise ratio of dct['snr_negative'].
In case of a negative residual feature, we try to replace the Gaussian fit component that is causing the feature with two narrower components. We only accept this solution if it yields a better fit as determined by the AICc value.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
dct : dict
Dictionary containing parameter settings for the improved fitting.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
get_count : bool
Default is 'False'. If set to 'True', only the number of occurring negative residual features will be returned.
get_idx : bool
Default is 'False'. If set to 'True', the index of the Gaussian fit component causing the negative residual feature is returned. In case of multiple negative residual features, only the index of one of them is returned.
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
# in case a single rms value is given instead of an array
if not isinstance(errors, np.ndarray):
errors = np.ones(len(data)) * errors
if ncomps_fit == 0:
if get_count:
return 0
return best_fit_list
residual = best_fit_list[4]
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
amp_guesses, fwhm_guesses, offset_guesses = get_initial_guesses(
residual, errors[0], dct['snr_negative'], dct['significance'],
peak='negative')
# check if negative residual feature was already present in the data
remove_indices = []
for i, offset in enumerate(offset_guesses):
if residual[offset] > (data[offset] - dct['snr']*errors[0]):
remove_indices.append(i)
if len(remove_indices) > 0:
amp_guesses, fwhm_guesses, offset_guesses = remove_components_from_sublists(
[amp_guesses, fwhm_guesses, offset_guesses], remove_indices)
if get_count:
return (len(amp_guesses))
if len(amp_guesses) == 0:
return best_fit_list
# in case of multiple negative residual features, sort them in order of increasing amplitude values
sort = np.argsort(amp_guesses)
amp_guesses = np.array(amp_guesses)[sort]
fwhm_guesses = np.array(fwhm_guesses)[sort]
offset_guesses = np.array(offset_guesses)[sort]
for amp, fwhm, offset in zip(amp_guesses, fwhm_guesses, offset_guesses):
idx_low = max(0, int(offset - fwhm))
idx_upp = int(offset + fwhm) + 2
exclude_idx = check_which_gaussian_contains_feature(
idx_low, idx_upp, fwhms_fit, offsets_fit)
if get_idx:
return exclude_idx
if exclude_idx is None:
continue
params_fit = replace_gaussian_with_two_new_ones(
data, vel, errors[0], dct['snr'], dct['significance'],
params_fit, exclude_idx, offset)
best_fit_list = get_best_fit(
vel, data, errors, params_fit, dct, first=False,
best_fit_list=best_fit_list, signal_ranges=signal_ranges,
signal_mask=signal_mask, force_accept=force_accept,
noise_spike_mask=noise_spike_mask)
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
return best_fit_list
def try_fit_with_new_components(vel, data, errors, best_fit_list, dct,
exclude_idx, signal_ranges=None,
signal_mask=None, force_accept=False,
baseline_shift_snr=0, noise_spike_mask=None):
"""Exclude Gaussian fit component and try fit with new initial guesses.
First we try a new refit by just removing the component (i) and adding no new components. If this does not work we determine guesses for additional fit components from the residual that is produced if the component (i) is discarded and try a new fit. We only accept the new fit solution if it yields a better fit as determined by the AICc value.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
dct : dict
Dictionary containing parameter settings for the improved fitting.
exclude_idx : int
Index of Gaussian fit component that will be removed.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
baseline_shift_snr : float
Experimental feature that shifts the baseline of the residual before searching for peaks.
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
aicc_old = best_fit_list[6]
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
# idx_low_residual = max(
# 0, int(offsets_fit[exclude_idx] - fwhms_fit[exclude_idx]/2))
# idx_upp_residual = int(
# offsets_fit[exclude_idx] + fwhms_fit[exclude_idx]/2) + 2
# exclude component from parameter list of components
params_fit_new = remove_components(params_fit, exclude_idx)
# produce new best fit with excluded components
best_fit_list_new = get_best_fit(
vel, data, errors, params_fit_new, dct, first=True,
best_fit_list=None, signal_ranges=signal_ranges,
signal_mask=signal_mask, force_accept=force_accept,
noise_spike_mask=noise_spike_mask)
# return new best fit with excluded component if its AICc value is lower
aicc = best_fit_list_new[6]
if ((aicc < aicc_old) and not np.isclose(aicc, aicc_old, atol=1e-1)):
return best_fit_list_new
# search for new positive residual peaks
params_fit = best_fit_list_new[0]
ncomps_fit = best_fit_list_new[2]
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
residual = data - combined_gaussian(amps_fit, fwhms_fit, offsets_fit, vel)
# amp_guesses, fwhm_guesses, offset_guesses = get_initial_guesses(
# residual[idx_low_residual:idx_upp_residual], errors[0],
# dct['snr'], dct['significance'], peak='positive',
# baseline_shift_snr=baseline_shift_snr)
# offset_guesses = offset_guesses + idx_low_residual
amp_guesses, fwhm_guesses, offset_guesses = get_initial_guesses(
residual, errors[0], dct['snr'], dct['significance'], peak='positive',
baseline_shift_snr=baseline_shift_snr)
# return original best fit list if there are no guesses for new components to fit in the residual
if amp_guesses.size == 0:
return best_fit_list
# get new best fit with additional components guessed from the residual
amps_fit = list(amps_fit) + list(amp_guesses)
fwhms_fit = list(fwhms_fit) + list(fwhm_guesses)
offsets_fit = list(offsets_fit) + list(offset_guesses)
params_fit_new = amps_fit + fwhms_fit + offsets_fit
best_fit_list_new = get_best_fit(
vel, data, errors, params_fit_new, dct, first=False,
best_fit_list=best_fit_list_new, signal_ranges=signal_ranges,
signal_mask=signal_mask, force_accept=force_accept,
noise_spike_mask=noise_spike_mask)
# return new best fit if its AICc value is lower
aicc = best_fit_list_new[6]
if ((aicc < aicc_old) and not np.isclose(aicc, aicc_old, atol=1e-1)):
return best_fit_list_new
return best_fit_list
def check_for_broad_feature(vel, data, errors, best_fit_list, dct,
signal_ranges=None, signal_mask=None,
force_accept=False, noise_spike_mask=None):
"""Check for broad features and try to refit them.
We define broad fit components as having a FWHM value that is bigger by a factor of dct['fwhm_factor'] than the second broadest component in the spectrum.
In case of a broad fit component, we first try to replace it with two narrower components. If this does not work we determine guesses for additional fit components from the residual that is produced if the component (i) is discarded and try a new fit.
We only accept a new fit solution if it yields a better fit as determined by the AICc value.
If there is only one fit component in the spectrum, this check is not performed.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
dct : dict
Dictionary containing parameter settings for the improved fitting.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
best_fit_list[7] = False
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
if ncomps_fit < 2 and dct['fwhm_factor'] > 0:
return best_fit_list
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
fwhms_sorted = sorted(fwhms_fit)
if (fwhms_sorted[-1] < dct['fwhm_factor'] * fwhms_sorted[-2]):
return best_fit_list
exclude_idx = np.argmax(np.array(fwhms_fit))
params_fit = replace_gaussian_with_two_new_ones(
data, vel, errors[0], dct['snr'], dct['significance'],
params_fit, exclude_idx, offsets_fit[exclude_idx])
if len(params_fit) > 0:
best_fit_list = get_best_fit(
vel, data, errors, params_fit, dct, first=False,
best_fit_list=best_fit_list, signal_ranges=signal_ranges,
signal_mask=signal_mask, force_accept=force_accept,
noise_spike_mask=noise_spike_mask)
if best_fit_list[7]:
return best_fit_list
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
if ncomps_fit == 0:
return best_fit_list
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
exclude_idx = np.argmax(np.array(fwhms_fit))
# for baseline_shift_snr in range(int(dct['snr'])):
# best_fit_list = try_fit_with_new_components(
# vel, data, errors, best_fit_list, dct, exclude_idx,
# signal_ranges=signal_ranges, signal_mask=signal_mask,
# force_accept=force_accept, baseline_shift_snr=baseline_shift_snr)
# if best_fit_list[7]:
# break
best_fit_list = try_fit_with_new_components(
vel, data, errors, best_fit_list, dct, exclude_idx,
signal_ranges=signal_ranges, signal_mask=signal_mask,
force_accept=force_accept, noise_spike_mask=noise_spike_mask)
return best_fit_list
def check_for_blended_feature(vel, data, errors, best_fit_list, dct,
signal_ranges=None, signal_mask=None,
force_accept=False, noise_spike_mask=None):
"""Check for blended features and try to refit them.
We define two fit components as blended if the mean position of one fit component is contained within the standard deviation interval (mean - std, mean + std) of another fit component.
In case of blended fit components, we try to determine guesses for new fit components from the residual that is produced if one of the components is discarded and try a new fit. We start by excluding the fit component with the lowest amplitude value.
We only accept a new fit solution if it yields a better fit as determined by the AICc value.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
dct : dict
Dictionary containing parameter settings for the improved fitting.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
if ncomps_fit < 2:
return best_fit_list
exclude_indices = get_fully_blended_gaussians(
params_fit, separation_factor=dct['separation_factor'])
# skip if there are no blended features
if exclude_indices.size == 0:
return best_fit_list
for exclude_idx in exclude_indices:
best_fit_list = try_fit_with_new_components(
vel, data, errors, best_fit_list, dct, exclude_idx,
signal_ranges=signal_ranges, signal_mask=signal_mask,
force_accept=force_accept, noise_spike_mask=noise_spike_mask)
if best_fit_list[7]:
break
# for baseline_shift_snr in range(int(dct['snr'])):
# best_fit_list = try_fit_with_new_components(
# vel, data, errors, best_fit_list, dct, exclude_idx,
# signal_ranges=signal_ranges, signal_mask=signal_mask,
# force_accept=force_accept, baseline_shift_snr=baseline_shift_snr)
# if best_fit_list[7]:
# return best_fit_list
return best_fit_list
def quality_check(vel, data, errors, params_fit, ncomps_fit, dct,
signal_ranges=None, signal_mask=None,
params_min=None, params_max=None, noise_spike_mask=None):
"""Quality check for GaussPy best fit results.
All Gaussian fit components that are not satisfying the mandatory quality criteria get discarded from the fit.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN].
ncomps_fit : int
Number of fitted Gaussian components.
dct : dict
Dictionary containing parameter settings for the improved fitting.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
params_min : list
List of minimum limits for parameters: [min_amp1, ..., min_ampN, min_fwhm1, ..., min_fwhmN, min_mean1, ..., min_meanN]
params_max : list
List of maximum limits for parameters: [max_amp1, ..., max_ampN, max_fwhm1, ..., max_fwhmN, max_mean1, ..., max_meanN]
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
"""
if ncomps_fit == 0:
new_fit = False
best_fit_final = data*0
residual = data
params_fit, params_errs = [], []
rchi2, aicc = goodness_of_fit(
data, best_fit_final, errors, ncomps_fit, mask=signal_mask, get_aicc=True)
pvalue = check_residual_for_normality(
data, errors, mask=signal_mask, noise_spike_mask=noise_spike_mask)
quality_control = []
best_fit_list = [params_fit, params_errs, ncomps_fit, best_fit_final,
residual, rchi2, aicc, new_fit, params_min,
params_max, pvalue, quality_control]
return best_fit_list
best_fit_list = get_best_fit(
vel, data, errors, params_fit, dct, first=True,
best_fit_list=None, signal_ranges=signal_ranges,
signal_mask=signal_mask, noise_spike_mask=noise_spike_mask)
return best_fit_list
def check_for_peaks_in_residual(vel, data, errors, best_fit_list, dct,
fitted_residual_peaks, signal_ranges=None,
signal_mask=None, force_accept=False,
params_min=None, params_max=None, noise_spike_mask=None):
"""Try fit by adding new components, whose initial parameters were determined from residual peaks.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
best_fit_list : list
List containing parameters of the current best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
dct : dict
Dictionary containing parameter settings for the improved fitting.
fitted_residual_peaks : list
List of initial mean position guesses for new fit components determined from residual peaks that were already tried in previous iterations.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
signal_mask : numpy.ndarray
Boolean array containing the information of signal_ranges.
force_accept : bool
Experimental feature. Default is 'False'. If set to 'True', the new fit will be forced to become the best fit.
params_min : list
List of minimum limits for parameters: [min_amp1, ..., min_ampN, min_fwhm1, ..., min_fwhmN, min_mean1, ..., min_meanN]
params_max : list
List of maximum limits for parameters: [max_amp1, ..., max_ampN, max_fwhm1, ..., max_fwhmN, max_mean1, ..., max_meanN]
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
fitted_residual_peaks : list
Updated list of initial mean position guesses for new fit components determined from residual peaks.
"""
# TODO: remove params_min and params_max keywords
params_fit = best_fit_list[0]
ncomps_fit = best_fit_list[2]
residual = best_fit_list[4]
amps_fit, fwhms_fit, offsets_fit = split_params(params_fit, ncomps_fit)
amp_guesses, fwhm_guesses, offset_guesses = get_initial_guesses(
residual, errors[0], dct['snr'], dct['significance'],
peak='positive')
if amp_guesses.size == 0:
best_fit_list[7] = False
return best_fit_list, fitted_residual_peaks
if list(offset_guesses) in fitted_residual_peaks:
best_fit_list[7] = False
return best_fit_list, fitted_residual_peaks
fitted_residual_peaks.append(list(offset_guesses))
amps_fit = list(amps_fit) + list(amp_guesses)
fwhms_fit = list(fwhms_fit) + list(fwhm_guesses)
offsets_fit = list(offsets_fit) + list(offset_guesses)
params_fit = amps_fit + fwhms_fit + offsets_fit
best_fit_list = get_best_fit(
vel, data, errors, params_fit, dct, first=False,
best_fit_list=best_fit_list, signal_ranges=signal_ranges,
signal_mask=signal_mask, force_accept=force_accept,
params_min=params_min, params_max=params_max,
noise_spike_mask=noise_spike_mask)
return best_fit_list, fitted_residual_peaks
def log_new_fit(new_fit, log_gplus, mode='residual'):
"""Log the successful refits of a spectrum.
Parameters
----------
new_fit : bool
If 'True', the spectrum was successfully refit.
log_gplus : list
Log of all previous successful refits of the spectrum.
mode : str ('positive_residual_peak', 'negative_residual_peak', 'broad', 'blended')
Specifies the feature that was refit or used for a new successful refit.
Returns
-------
log_gplus : list
Updated log of successful refits of the spectrum.
"""
if not new_fit:
return log_gplus
modes = {'positive_residual_peak': 1, 'negative_residual_peak': 2, 'broad': 3, 'blended': 4}
log_gplus.append(modes[mode])
return log_gplus
def try_to_improve_fitting(vel, data, errors, params_fit, ncomps_fit, dct,
signal_ranges=None, noise_spike_ranges=None):
"""Short summary.
Parameters
----------
vel : numpy.ndarray
Velocity channels (unitless).
data : numpy.ndarray
Original data of spectrum.
errors : numpy.ndarray
Root-mean-square noise values.
params_fit : list
Parameter vector in the form of [amp1, ..., ampN, fwhm1, ..., fwhmN, mean1, ..., meanN]. Corresponds to the final best fit results of the GaussPy decomposition.
ncomps_fit : int
Number of fitted Gaussian components.
dct : dict
Dictionary containing parameter settings for the improved fitting.
signal_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain signal. The goodness-of-fit calculations are only performed for the spectral channels within these ranges.
noise_spike_ranges : list
Nested list containing info about ranges of the spectrum that were estimated to contain noise spike features. These will get masked out from goodness-of-fit calculations.
Returns
-------
best_fit_list : list
List containing parameters of the chosen best fit for the spectrum. It is of the form [{0} params_fit, {1} params_errs, {2} ncomps_fit, {3} best_fit, {4} residual, {5} rchi2, {6} aicc, {7} new_fit, {8} params_min, {9} params_max, {10} pvalue]
N_neg_res_peak : int
Number of negative residual features that occur in the best fit of the spectrum.
N_blended : int
Number of blended Gaussian components that occur in the best fit of the spectrum.
log_gplus : list
Log of all successful refits of the spectrum.
"""
n_channels = len(data)
if signal_ranges:
signal_mask = mask_channels(n_channels, signal_ranges,
remove_intervals=noise_spike_ranges)
else:
signal_mask = None
if noise_spike_ranges:
noise_spike_mask = mask_channels(n_channels, [[0, n_channels]],
remove_intervals=noise_spike_ranges)
else:
noise_spike_mask = None
# Check the quality of the final fit from GaussPy
best_fit_list = quality_check(
vel, data, errors, params_fit, ncomps_fit, dct,
signal_ranges=signal_ranges, signal_mask=signal_mask,
noise_spike_mask=noise_spike_mask)
params_fit, params_errs, ncomps_fit, best_fit_final, residual,\
rchi2, aicc, new_fit, params_min, params_max, pvalue, quality_control = best_fit_list
# Try to improve fit by searching for peaks in the residual
first_run = True
fitted_residual_peaks = []
log_gplus = []
# while (rchi2 > dct['rchi2_limit']) or first_run:
while (best_fit_list[10] < dct['min_pvalue']) or first_run:
new_fit = True
new_peaks = False
count_old = len(fitted_residual_peaks)
while new_fit:
best_fit_list[7] = False
best_fit_list, fitted_residual_peaks = check_for_peaks_in_residual(
vel, data, errors, best_fit_list, dct, fitted_residual_peaks,
signal_ranges=signal_ranges, signal_mask=signal_mask,
noise_spike_mask=noise_spike_mask)
new_fit = best_fit_list[7]
log_gplus = log_new_fit(new_fit, log_gplus,
mode='positive_residual_peak')
count_new = len(fitted_residual_peaks)
if count_old != count_new:
new_peaks = True
# stop refitting loop if no new peaks were fit from the residual
if (not first_run and not new_peaks) or (best_fit_list[2] == 0):
break
# try to refit negative residual feature
if dct['neg_res_peak']:
best_fit_list = check_for_negative_residual(
vel, data, errors, best_fit_list, dct,
signal_ranges=signal_ranges, signal_mask=signal_mask,
noise_spike_mask=noise_spike_mask)
new_fit = best_fit_list[7]
log_gplus = log_new_fit(new_fit, log_gplus,
mode='negative_residual_peak')
# try to refit broad Gaussian components
if dct['broad']:
new_fit = True
while new_fit:
best_fit_list[7] = False
best_fit_list = check_for_broad_feature(
vel, data, errors, best_fit_list, dct, signal_ranges=signal_ranges, signal_mask=signal_mask,
noise_spike_mask=noise_spike_mask)
new_fit = best_fit_list[7]
log_gplus = log_new_fit(new_fit, log_gplus, mode='broad')
# try to refit blended Gaussian components
if dct['blended']:
new_fit = True
while new_fit:
best_fit_list[7] = False
best_fit_list = check_for_blended_feature(
vel, data, errors, best_fit_list, dct,
signal_ranges=signal_ranges, signal_mask=signal_mask,
noise_spike_mask=noise_spike_mask)
new_fit = best_fit_list[7]
log_gplus = log_new_fit(new_fit, log_gplus, mode='blended')
if not first_run:
break
first_run = False
N_neg_res_peak = check_for_negative_residual(
vel, data, errors, best_fit_list, dct,
signal_ranges=signal_ranges, signal_mask=signal_mask,
get_count=True)
params_fit = best_fit_list[0]
N_blended = get_fully_blended_gaussians(
params_fit, get_count=True, separation_factor=dct['separation_factor'])
return best_fit_list, N_neg_res_peak, N_blended, log_gplus
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] | 2.4881 | 23,782 |
# coding=utf-8
# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (absolute_import, division, generators, nested_scopes, print_function,
unicode_literals, with_statement)
from abc import abstractmethod
from textwrap import dedent
from pants.base.deprecated import deprecated_conditional
from pants.base.exceptions import TestFailedTaskError
from pants.util.timeout import Timeout, TimeoutReached
class TestRunnerTaskMixin(object):
"""A mixin to combine with test runner tasks.
The intent is to migrate logic over time out of JUnitRun and PytestRun, so the functionality
expressed can support both languages, and any additional languages that are added to pants.
"""
@staticmethod
@classmethod
def execute(self):
"""Run the task."""
# Ensure that the timeout_maximum is higher than the timeout default.
if (self.get_options().timeout_maximum is not None
and self.get_options().timeout_default is not None
and self.get_options().timeout_maximum < self.get_options().timeout_default):
message = "Error: timeout-default: {} exceeds timeout-maximum: {}".format(
self.get_options().timeout_maximum,
self.get_options().timeout_default
)
self.context.log.error(message)
raise TestFailedTaskError(message)
if not self.get_options().skip:
test_targets = self._get_test_targets()
all_targets = self._get_targets()
for target in test_targets:
self._validate_target(target)
self._execute(all_targets)
def _spawn_and_wait(self, *args, **kwargs):
"""Spawn the actual test runner process, and wait for it to complete."""
test_targets = self._get_test_targets()
timeout = self._timeout_for_targets(test_targets)
process_handler = self._spawn(*args, **kwargs)
# TODO(sbrenn): We use process_handler.kill here because we want to aggressively terminate the
# process. It may make sense in the future to do a multi-stage approach
# where first we do process_handler.terminate to let it die gracefully, and
# then do process_handler.kill if that doesn't work. It will probably require
# adding poll() support to ProcessHandler.
try:
with Timeout(timeout, abort_handler=process_handler.kill):
return process_handler.wait()
except TimeoutReached as e:
raise TestFailedTaskError(str(e), failed_targets=test_targets)
@abstractmethod
def _spawn(self, *args, **kwargs):
"""Spawn the actual test runner process.
:rtype: ProcessHandler
"""
raise NotImplementedError
def _timeout_for_targets(self, targets):
"""Calculate the total timeout based on the timeout configuration for all the targets.
Because the timeout wraps all the test targets rather than individual tests, we have to somehow
aggregate all the target specific timeouts into one value that will cover all the tests. If some targets
have no timeout configured (or set to 0), their timeout will be set to the default timeout.
If there is no default timeout, or if it is set to zero, there will be no timeout, if any of the test targets
have a timeout set to 0 or no timeout configured.
TODO(sbrenn): This behavior where timeout=0 is the same as timeout=None has turned out to be very confusing,
and should change so that timeout=0 actually sets the timeout to 0, and only timeout=None
should set the timeout to the default timeout. This will require a deprecation cycle.
:param targets: list of test targets
:return: timeout to cover all the targets, in seconds
"""
if not self.get_options().timeouts:
return None
timeout_default = self.get_options().timeout_default
# Gather up all the timeouts.
timeouts = [self._timeout_for_target(target) for target in targets]
# If any target's timeout is None or 0, then set it to the default timeout.
# TODO(sbrenn): Change this so that only if the timeout is None, set it to default timeout.
timeouts_w_default = [timeout or timeout_default for timeout in timeouts]
# Even after we've done that, there may be a 0 or None in the timeout list if the
# default timeout is set to 0 or None. So if that's the case, then the timeout is
# disabled.
# TODO(sbrenn): Change this so that if the timeout is 0, it is actually 0.
if 0 in timeouts_w_default or None in timeouts_w_default:
return None
else:
# Sum the timeouts for all the targets, using the default timeout where one is not set.
return sum(timeouts_w_default)
def _get_targets(self):
"""This is separated out so it can be overridden for testing purposes.
:return: list of targets
"""
return self.context.targets()
def _get_test_targets(self):
"""Returns the targets that are relevant test targets."""
test_targets = list(filter(self._test_target_filter(), self._get_targets()))
return test_targets
@abstractmethod
def _test_target_filter(self):
"""A filter to run on targets to see if they are relevant to this test task.
:return: function from target->boolean
"""
raise NotImplementedError
@abstractmethod
def _validate_target(self, target):
"""Ensures that this target is valid. Raises TargetDefinitionException if the target is invalid.
We don't need the type check here because _get_targets() combines with _test_target_type to
filter the list of targets to only the targets relevant for this test task.
:param target: the target to validate
:raises: TargetDefinitionException
"""
raise NotImplementedError
@abstractmethod
def _execute(self, all_targets):
"""Actually goes ahead and runs the tests for the targets.
:param targets: list of the targets whose tests are to be run
"""
raise NotImplementedError
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] | 3.19086 | 1,860 |
import json
import requests
import webbrowser
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] | 4.7 | 10 |
import os
from pathlib import Path
import imageio
import numpy as np
import SimpleITK as sitk
import h5py
from pytorch3dunet.augment import transforms
from pytorch3dunet.datasets.utils import ConfigDataset, calculate_stats
from pytorch3dunet.unet3d.utils import get_logger, expand_as_one_hot
from pytorch3dunet.datasets.hdf5 import AbstractHDF5Dataset
from pytorch3dunet.datasets.visualize import visualizer
from scipy import ndimage
import re
import yaml
import ipdb
import torch
logger = get_logger('SXTHDataset')
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# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
from ._inputs import *
__all__ = ['InstanceArgs', 'Instance']
@pulumi.input_type
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] | 3.596774 | 124 |
# -*- encoding: utf-8 -*-
from __future__ import unicode_literals
from itertools import izip
from flask import render_template
from flask.ext.babel import gettext
from sqlalchemy import Text
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.sql.expression import and_, cast, desc
from pokr.models.candidacy import Candidacy
from pokr.models.election import Election
from pokr.models.person import Person
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] | 3.212121 | 132 |
from game import Game
import sys
if __name__ == '__main__':
game = Game(sys.argv[1])
game.start()
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12962,
198,
220,
220,
220,
983,
13,
9688,
3419,
198
] | 2.488372 | 43 |
#!/usr/bin/python
# Author: Jon Trulson <[email protected]>
# Copyright (c) 2015 Intel Corporation.
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import time, sys
import pyupm_i2clcd as lcdObj
# setup with default values
lcd = lcdObj.EBOLED();
lcd.clear();
lcd.setCursor(2, 0);
lcd.write("Hello");
lcd.setCursor(3, 5);
lcd.write("World!");
print "Sleeping for 5 seconds..."
time.sleep(5)
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] | 3.382212 | 416 |
import datetime, json
from lys import L
VIEW_SOURCE_URL = "/source/crxviewer.html?crx="
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] | 2.621622 | 37 |
"""
A module for processing experiment references in a human readable way
Three pieces of information are required to uniquely identify an experimental session: subject
nickname, the date, and the sequence number (whether the session was the first, second,
etc. on that date).
Alyx and ONE use uuids (a.k.a. eids) to uniquely identify sessions, however these are not
readable. This module converts between these uuids an readable references.
References may be strings in the form yyyy-mm-dd_n_subject, which may be easily sorted,
or as bunches (dicts) of the form {'subject': str, 'date': datetime.date, 'sequence', int}.
"""
import re
import functools
from pathlib import Path
from typing import Union, List, Iterable as Iter
from datetime import datetime
from collections.abc import Iterable, Mapping
from oneibl.one import ONE
from brainbox.core import Bunch
__all__ = [
'ref2eid', 'ref2dict', 'ref2path', 'eid2path', 'eid2ref', 'path2ref', 'ref2dj', 'is_exp_ref'
]
@recurse
def ref2eid(ref: Union[Mapping, str, Iter], one=None) -> Union[str, List]:
"""
Returns experiment uuid, given one or more experiment references
:param ref: One or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
:param one: An instance of ONE
:return: an experiment uuid string
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='test_user', password='TapetesBloc18', base_url=base)
Connected to...
>>> ref = {'date': datetime(2018, 7, 13).date(), 'sequence': 1, 'subject': 'flowers'}
>>> ref2eid(ref, one=one)
'4e0b3320-47b7-416e-b842-c34dc9004cf8'
>>> ref2eid(['2018-07-13_1_flowers', '2019-04-11_1_KS005'], one=one)
['4e0b3320-47b7-416e-b842-c34dc9004cf8',
'7dc3c44b-225f-4083-be3d-07b8562885f4']
"""
if not one:
one = ONE()
ref = ref2dict(ref, parse=False) # Ensure dict
session = one.search(
subjects=ref['subject'],
date_range=(str(ref['date']), str(ref['date'])),
number=ref['sequence'])
assert len(session) == 1, 'session not found'
return session[0]
@recurse
@parse_values
def ref2dict(ref: Union[str, Mapping, Iter]) -> Union[Bunch, List]:
"""
Returns a Bunch (dict-like) from a reference string (or list thereof)
:param ref: One or more objects with keys ('subject', 'date', 'sequence')
:return: A Bunch in with keys ('subject', 'sequence', 'date')
Examples:
>>> ref2dict('2018-07-13_1_flowers')
{'date': datetime.date(2018, 7, 13), 'sequence': 1, 'subject': 'flowers'}
>>> ref2dict('2018-07-13_001_flowers', parse=False)
{'date': '2018-07-13', 'sequence': '001', 'subject': 'flowers'}
>>> ref2dict(['2018-07-13_1_flowers', '2020-01-23_002_ibl_witten_01'])
[{'date': datetime.date(2018, 7, 13), 'sequence': 1, 'subject': 'flowers'},
{'date': datetime.date(2020, 1, 23), 'sequence': 2, 'subject': 'ibl_witten_01'}]
"""
if isinstance(ref, (Bunch, dict)):
return Bunch(ref) # Short circuit
ref = dict(zip(['date', 'sequence', 'subject'], ref.split('_', 2)))
return Bunch(ref)
@recurse
def ref2path(ref: Union[str, Mapping, Iter], one=None, offline: bool = False) -> Union[Path, List]:
"""
Convert one or more experiment references to session path(s)
:param ref: One or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
:param one: An instance of ONE
:param offline: Return path without connecting to database (unimplemented)
:return: a Path object for the experiment session
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='test_user', password='TapetesBloc18', base_url=base)
Connected to...
>>> ref = {'subject': 'flowers', 'date': datetime(2018, 7, 13).date(), 'sequence': 1}
>>> ref2path(ref, one=one)
WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001')
>>> ref2path(['2018-07-13_1_flowers', '2019-04-11_1_KS005'], one=one)
[WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001'),
WindowsPath('E:/FlatIron/cortexlab/Subjects/KS005/2019-04-11/001')]
"""
if not one:
one = ONE()
if offline:
raise NotImplementedError # Requires lab name :(
# root = Path(one._get_cache_dir(None))
# path = root / ref.subject / str(ref.date) / ('%03d' % ref.sequence)
else:
ref = ref2dict(ref, parse=False)
eid, (d,) = one.search(
subjects=ref['subject'],
date_range=(str(ref['date']), str(ref['date'])),
number=ref['sequence'],
details=True)
path = d.get('local_path')
if not path:
root = Path(one._get_cache_dir(None)) / 'Subjects' / d['lab']
return root / d['subject'] / d['start_time'][:10] / ('%03d' % d['number'])
else:
return Path(path)
@recurse
def eid2path(eid: Union[str, Iter], one=None, offline: bool = False) -> Union[Path, List]:
"""
Returns a local path from an eid, regardless of whether the path exists locally
:param eid: An experiment uuid
:param one: An instance of ONE
:param offline: If True, do not connect to database (not implemented)
:return: a Path instance
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='test_user', password='TapetesBloc18', base_url=base)
Connected to...
>>> eid = '4e0b3320-47b7-416e-b842-c34dc9004cf8'
>>> eid2path(eid, one=one)
WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001')
>>> eid2path([eid, '7dc3c44b-225f-4083-be3d-07b8562885f4'], one=one)
[WindowsPath('E:/FlatIron/zadorlab/Subjects/flowers/2018-07-13/001'),
WindowsPath('E:/FlatIron/cortexlab/Subjects/KS005/2019-04-11/001')]
"""
if not one:
one = ONE()
if offline:
raise NotImplementedError
# path = one.path_from_eid(eid, offline=True)
else:
d = one.get_details(eid)
path = d.get('local_path')
if not path:
root = Path(one._get_cache_dir(None)) / d['lab'] / 'Subjects'
path = root / d['subject'] / d['start_time'][:10] / ('%03d' % d['number'])
return path
@recurse
def eid2ref(eid: Union[str, Iter], one=None, as_dict=True, parse=True) \
-> Union[str, Mapping, List]:
"""
Get human-readable session ref from path
:param eid: The experiment uuid to find reference for
:param one: An ONE instance
:param as_dict: If false a string is returned in the form 'subject_sequence_yyyy-mm-dd'
:param parse: If true, the reference date and sequence are parsed from strings to their
respective data types
:return: one or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
Examples:
>>> base = 'https://test.alyx.internationalbrainlab.org'
>>> one = ONE(username='test_user', password='TapetesBloc18', base_url=base)
Connected to...
>>> eid = '4e0b3320-47b7-416e-b842-c34dc9004cf8'
>>> eid2ref(eid, one=one)
{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1}
>>> eid2ref(eid, parse=False, one=one)
{'subject': 'flowers', 'date': '2018-07-13', 'sequence': '001'}
>>> eid2ref(eid, as_dict=False, one=one)
'2018-07-13_1_flowers'
>>> eid2ref(eid, as_dict=False, parse=False, one=one)
'2018-07-13_001_flowers'
>>> eid2ref([eid, '7dc3c44b-225f-4083-be3d-07b8562885f4'], one=one)
[{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1},
{'subject': 'KS005', 'date': datetime.date(2019, 4, 11), 'sequence': 1}]
"""
if not one:
one = ONE()
d = one.get_details(eid)
if parse:
date = datetime.fromisoformat(d['start_time']).date()
ref = {'subject': d['subject'], 'date': date, 'sequence': d['number']}
format_str = '{date:%Y-%m-%d}_{sequence:d}_{subject:s}'
else:
date = d['start_time'][:10]
ref = {'subject': d['subject'], 'date': date, 'sequence': '%03d' % d['number']}
format_str = '{date:s}_{sequence:s}_{subject:s}'
return Bunch(ref) if as_dict else format_str.format(**ref)
@recurse
@parse_values
def path2ref(path_str: Union[str, Path, Iter]) -> Union[Bunch, List]:
"""
Returns a human readable experiment reference, given a session path. The path need not exist.
:param path_str: A path to a given session
:return: one or more objects with keys ('subject', 'date', 'sequence')
Examples:
>>> path_str = Path('E:/FlatIron/Subjects/zadorlab/flowers/2018-07-13/001')
>>> path2ref(path_str)
{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1}
>>> path2ref(path_str, parse=False)
{'subject': 'flowers', 'date': '2018-07-13', 'sequence': '001'}
>>> path_str2 = Path('E:/FlatIron/Subjects/churchlandlab/CSHL046/2020-06-20/002')
>>> path2ref([path_str, path_str2])
[{'subject': 'flowers', 'date': datetime.date(2018, 7, 13), 'sequence': 1},
{'subject': 'CSHL046', 'date': datetime.date(2020, 6, 20), 'sequence': 2}]
"""
pattern = r'(?P<subject>[\w-]+)([\\/])(?P<date>\d{4}-\d{2}-\d{2})(\2)(?P<sequence>\d{3})'
match = re.search(pattern, str(path_str)).groupdict()
return Bunch(match)
def ref2dj(ref: Union[str, Mapping, Iter]):
"""
Return an ibl-pipeline sessions table, restricted by experiment reference(s)
:param ref: one or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
:return: an acquisition.Session table
Examples:
>>> ref2dj('2020-06-20_2_CSHL046').fetch1()
Connecting...
{'subject_uuid': UUID('dffc24bc-bd97-4c2a-bef3-3e9320dc3dd7'),
'session_start_time': datetime.datetime(2020, 6, 20, 13, 31, 47),
'session_number': 2,
'session_date': datetime.date(2020, 6, 20),
'subject_nickname': 'CSHL046'}
>>> len(ref2dj({'date':'2020-06-20', 'sequence':'002', 'subject':'CSHL046'}))
1
>>> len(ref2dj(['2020-06-20_2_CSHL046', '2019-11-01_1_ibl_witten_13']))
2
"""
from ibl_pipeline import subject, acquisition
sessions = acquisition.Session.proj('session_number', session_date='date(session_start_time)')
sessions = sessions * subject.Subject.proj('subject_nickname')
ref = ref2dict(ref) # Ensure dict-like
@recurse
return sessions & restrict(ref)
@recurse
def is_exp_ref(ref: Union[str, Mapping, Iter]) -> Union[bool, List[bool]]:
"""
Returns True is ref is a valid experiment reference
:param ref: one or more objects with keys ('subject', 'date', 'sequence'), or strings with the
form yyyy-mm-dd_n_subject
:return: True if ref is valid
Examples:
>>> ref = {'date': datetime(2018, 7, 13).date(), 'sequence': 1, 'subject': 'flowers'}
>>> is_exp_ref(ref)
True
>>> is_exp_ref('2018-07-13_001_flowers')
True
>>> is_exp_ref('invalid_ref')
False
"""
if isinstance(ref, (Bunch, dict)):
if not {'subject', 'date', 'sequence'}.issubset(ref):
return False
ref = '{date}_{sequence}_{subject}'.format(**ref)
elif not isinstance(ref, str):
return False
return re.compile(r'\d{4}(-\d{2}){2}_(\d{1}|\d{3})_\w+').match(ref) is not None
if __name__ == "__main__":
import doctest
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS)
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220,
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220,
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220,
220,
220,
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13,
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28,
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62,
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2043,
1546,
47,
11598,
930,
10412,
395,
13,
23304,
47643,
1797,
8,
198
] | 2.436946 | 4,742 |
import pwndbg.auxv
import pwndbg.commands
import pwndbg.commands.telescope
import pwndbg.memory
import pwndbg.regs
import pwndbg.search
from pwndbg.color import message
@pwndbg.commands.ArgparsedCommand('Print out the current stack canary.')
@pwndbg.commands.OnlyWhenRunning
| [
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] | 2.622642 | 106 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
from candphy.logs import log
#creating the def for plotting the signals wave | [
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'''-------------------------------------------------------------------------------
Tool Name: CreateWeightTableFromECMWFRunoff
Source Name: CreateWeightTableFromECMWFRunoff.py
Version: ArcGIS 10.2
License: Apache 2.0
Author: Environmental Systems Research Institute Inc.
Updated by: Environmental Systems Research Institute Inc.
Description: Creates RAPID inflow file based on ECMWF runoff output
and the weight table previously created.
History: Initial coding - 10/21/2014, version 1.0
Updated: Version 1.0, 10/23/2014, modified names of tool and parameters
Version 1.0, 10/28/2014, added data validation
Version 1.1, 10/30/2014, added lon_index, lat_index in output weight table
Version 1.1, 11/07/2014, bug fixing - enables input catchment feature class
with spatial reference that is not PCS_WGS_1984.
Version 2.0, 06/04/2015, integrated Update Weight Table (according to Alan Snow, US Army ERDC)
-------------------------------------------------------------------------------'''
import os
import arcpy
import netCDF4 as NET
import numpy as NUM
import csv
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284,
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11,
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8,
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6,
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2010,
34,
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19,
355,
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198,
11748,
299,
32152,
355,
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198,
11748,
269,
21370,
198
] | 3.215259 | 367 |
from django.contrib import admin
from django.urls import path,include
urlpatterns = [
path('admin/', admin.site.urls),
path('api-auth/',include('rest_framework.urls')),
path('api/rest-auth/',include('rest_auth.urls')),
path('api/rest-auth/registration/',include('rest_auth.registration.urls')),
path('api/',include('rehber.api.urls')),
]
| [
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] | 2.538462 | 143 |
# Copyright 2016 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Runs a daemon to update git credentials from the GCE metadata server.
Only works inside a GCE with proper service accounts tagged.
Example invocation:
./run.py infra.services.git_cookie_daemon
"""
# This file is untested, keep as little code as possible in there.
import logging
import sys
from infra.services.git_cookie_daemon import git_cookie_daemon
from infra_libs import app
# https://chromium.googlesource.com/infra/infra/+/master/infra_libs/logs/README.md
LOGGER = logging.getLogger(__name__)
if __name__ == '__main__':
GitCookieDaemon().run()
| [
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198,
220,
15151,
34,
18055,
26531,
7966,
22446,
5143,
3419,
198
] | 3.26009 | 223 |
# Copyright 2016 Google Inc. 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import unittest
from capirca.lib import policy_simple
from absl import logging
if __name__ == '__main__':
unittest.main()
| [
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10354,
198,
220,
555,
715,
395,
13,
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3419,
198
] | 3.720339 | 236 |
include("common.py")
streckerDegradationDicarbonyl = [a for a in streckerDegradationDicarbonylGen()] | [
17256,
7203,
11321,
13,
9078,
4943,
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301,
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] | 2.564103 | 39 |
"""
SQLAlchemyIO (saio): Module hack for autoloading table definitions
## Usage
After
```python
import saio
saio.register_schema("model_draft", engine)
````
one can import table declarations easily using
```python
from saio.model_draft import lis_charging_poi as LisChargingPoi
```
Note that `ipython` and Jupyter Notebook, allow using `<TAB>` to auto-complete
table names.
## Implementation details
`saio.register_schema` instantiates a declarative base using
```python
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base(bind=engine)
# The Base can be imported using from saio.model_draft import Base
```
and then whenever one imports any table from `saio.model_draft`, ie. by calling
`from saio.model_draft import lis_charging_poi as LisChargingPoi`, saio does
approximately
```python
class LisChargingPoi(Base):
__tablename__ = 'lis_charging_poi'
__table_args__ = {'schema': 'model_draft', 'autoload': True}
```
"""
__copyright__ = "© Reiner Lemoine Institut"
__license__ = "MIT"
__url__ = "https://github.com/coroa/saio/blob/master/saio.py"
__author__ = "coroa"
import sqlalchemy as sa
import sqlalchemy.ext.declarative
import sqlalchemy.engine.reflection
import warnings
from types import ModuleType
import logging
logger = logging.getLogger(__name__)
del ModuleType
del memoize
try:
import pandas as pd
has_pandas = True
except ImportError:
has_pandas = False
try:
import geopandas as gpd
import shapely.wkb
import shapely.geos
has_geopandas = True
except ImportError:
has_geopandas = False
def as_pandas(query, index_col=None, coerce_float=True, params=None,
geometry='geom', crs=None, hex_encoded=True):
"""
Import a query into a pandas DataFrame or a geopandas GeoDataFrame
Arguments
---------
query : sqlalchemy.orm.query.Query
index_col : string or list of strings, optional, default: None
Column(s) to set as index(MultiIndex).
coerce_float : boolean, default: True
Attempts to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point. Useful for SQL result sets.
params : list, tuple or dict, optional, default: None
List of parameters to pass to execute method. The syntax used
to pass parameters is database driver dependent. Check your
database driver documentation for which of the five syntax styles,
described in PEP 249's paramstyle, is supported.
Eg. for psycopg2, uses %(name)s so use params={'name' : 'value'}
geometry : string, default: "geom"
Column name to convert to shapely geometries
crs : dict|string|None, default: None
CRS to use for the returned GeoDataFrame; if None, CRS is determined
from the SRID of the first geometry.
hex_encoded : bool, optional, default: True
Whether the geometry is in a hex-encoded string. Default is True,
standard for postGIS. Use hex_encoded=False for sqlite databases.
Note
----
Adapted from geopandas' read_postgis function.
Usage
-----
import saio
saio.register_schema("boundaries", engine)
from saio.boundaries import bkg_vg250_2_lan as BkgLan
df = saio.as_pandas(session.query(BkgLan.geom))
df.plot()
"""
assert has_pandas, "Pandas failed to import. Please check your installation!"
df = pd.read_sql_query(query.statement, query.session.bind,
index_col=index_col, coerce_float=coerce_float,
params=params)
if geometry not in df:
return df
if not has_geopandas:
warnings.warn("GeoPandas failed to import. Geometry column is left as WKB.")
return df
if len(df) > 0:
obj = df[geometry].iloc[0]
if isinstance(obj, bytes):
load_geom = lambda s: shapely.wkb.loads(s, hex=hex_encoded)
else:
load_geom = lambda s: shapely.wkb.loads(str(s), hex=hex_encoded)
srid = getattr(obj, 'srid', -1)
if srid == -1:
srid = shapely.geos.lgeos.GEOSGetSRID(load_geom(obj)._geom)
if crs is None and srid != 0:
crs = dict(init="epsg:{}".format(srid))
df[geometry] = df[geometry].map(load_geom)
return gpd.GeoDataFrame(df, crs=crs, geometry=geometry)
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] | 2.553105 | 1,723 |
# Librerias Django
from django.contrib.auth import authenticate
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.contrib.auth.models import User
from django.shortcuts import redirect, render
from django.urls import reverse, reverse_lazy
from django.views.generic import DetailView, ListView
from django.views.generic.edit import CreateView, DeleteView, UpdateView
# Librerias en carpetas locales
from ..models.stage import PyStage
""" BEGIN STAGE """
STAGE_FIELDS = [
{'string': 'Nombre', 'field': 'name'},
]
STAGE_FIELDS_SHORT = ['name']
@login_required(login_url="base:login")
""" END STAGE """
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] | 3 | 234 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import copy
from collections import defaultdict
from fractions import gcd
from .neurovis import NeuroVis
from .. import utils
from ..config import DEFAULT_POPULATION_COLORS
# Defines the default colors for a PSTH plot.
DEFAULT_PSTH_COLORS = ['Blues', 'Reds', 'Greens']
class PopVis(object):
'''Facilitates visualization of neuron population firing activity.
Args:
neuron_list (list of NeuroVis objects): The list of neurons to
visualize (see the NeuroVis class in
:class:`spykes.plot.neurovis`).
Attributes:
n_neurons (int): The number of neurons in the visualization.
name (str): The name of this visualization.
'''
@property
@property
def get_all_psth(self, event=None, df=None, conditions=None,
window=[-100, 500], binsize=10, conditions_names=None,
plot=True, colors=DEFAULT_PSTH_COLORS):
'''Iterates through all neurons and computes their PSTH's.
Args:
event (str): Column/key name of DataFrame/dictionary "data" which
contains event times in milliseconds (e.g.
stimulus/trial/ fixation onset, etc.).
df (DataFrame or dictionary): The data to use.
conditions (str): Column/key name of DataFrame/dictionary
:data:`df` which contains the conditions by which the trials
must be grouped
window (list of 2 elements): Time interval to consider, in
milliseconds.
binsize (int): Bin size, in milliseconds.
conditions_names (list of str): Legend names for conditions.
Default are the unique values in :data:`df['conditions']`.
plot (bool): If set, automatically plot; otherwise, don't.
colors (list): List of colors for heatmap (only if plot is True).
Returns:
dict: With keys :data:`event`, :data:`conditions`, :data:`binsize`,
:data:`window`, and :data:`data`. Each entry in
:data:`psth['data']` is itself a dictionary with keys of
each :data:`cond_id` that correspond to the means for that
condition.
'''
all_psth = {
'window': window,
'binsize': binsize,
'event': event,
'conditions': conditions,
'data': defaultdict(list),
}
for i, neuron in enumerate(self.neuron_list):
psth = neuron.get_psth(
event=event,
df=df,
conditions=conditions,
window=window,
binsize=binsize,
plot=False,
)
for cond_id in np.sort(list(psth['data'].keys())):
all_psth['data'][cond_id].append(psth['data'][cond_id]['mean'])
for cond_id in np.sort(list(all_psth['data'].keys())):
all_psth['data'][cond_id] = np.stack(all_psth['data'][cond_id])
if plot is True:
self.plot_heat_map(
all_psth,
conditions_names=conditions_names,
colors=colors,
)
return all_psth
def plot_heat_map(self, psth_dict, cond_id=None, conditions_names=None,
sortby=None, sortorder='descend', normalize=None,
neuron_names=True, colors=None, show=False):
'''Plots heat map for neuron population
Args:
psth_dict (dict): With keys :data:`event`, :data:`conditions`,
:data:`binsize`, :data:`window`, and :data:`data`. Each entry
in :data:`psth['data']` is itself a dictionary with keys of
each :data:`cond_id` that correspond to the means for that
condition.
cond_id (str): Which psth to plot indicated by the key in
:data:`all_psth['data']`. If None then all are plotted.
conditions_names (str or list of str): Name(s) to appear in the
title.
sortby (str or list): If :data:`rate`, sort by firing rate. If
:data:`latency`, sort by peak latency. If a list or array is
provided, it must correspond to integer indices to be used as
sorting indices. If no sort order is provided, the data
is returned as-is.
sortorder (str): The direction to sort in, either :data:`descend`
or :data:`ascend`.
normalize (str): If :data:`all`, divide all PSTHs by highest peak
firing rate in all neurons. If :data:`each`, divide each PSTH
by its own peak firing rate. If None, do not normalize.
neuron_names (bool): Whether or not to list the names of neurons on
the side.
colors (list of str): List of colors for the heatmap (as strings).
show (bool): If set, show the plot once finished.
'''
if colors is None:
colors = ['Blues', 'Reds', 'Greens']
window = psth_dict['window']
binsize = psth_dict['binsize']
conditions = psth_dict['conditions']
if cond_id is None:
keys = np.sort(list(psth_dict['data'].keys()))
else:
keys = cond_id
if conditions_names is None:
conditions_names = keys
for i, cond_id in enumerate(keys):
# Sorts and norms the data.
orig_data = psth_dict['data'][cond_id]
normed_data = self._get_normed_data(orig_data, normalize=normalize)
sort_idx = utils.get_sort_indices(
normed_data,
by=sortby,
order=sortorder,
)
data = normed_data[sort_idx, :]
plt.subplot(len(keys), 1, i + 1)
plt.pcolormesh(data, cmap=colors[i % len(colors)])
# Makes it visually appealing.
xtic_len = gcd(abs(window[0]), window[1])
xtic_labels = range(window[0], window[1] + xtic_len, xtic_len)
xtic_locs = [(j - window[0]) / binsize for j in xtic_labels]
if 0 not in xtic_labels:
xtic_labels.append(0)
xtic_locs.append(-window[0] / binsize)
plt.xticks(xtic_locs, xtic_labels)
plt.axvline((-window[0]) / binsize, color='r',
linestyle='--')
if neuron_names:
unsorted_ylabels = [neuron.name for neuron in self.neuron_list]
ylabels = [unsorted_ylabels[j] for j in sort_idx]
else:
ylabels = ["" for neuron in self.neuron_list]
plt.yticks(np.arange(data.shape[0]) + 0.5, ylabels)
ax = plt.gca()
ax.invert_yaxis()
ax.set_frame_on(False)
plt.tick_params(axis='x', which='both', top='off')
plt.tick_params(axis='y', which='both', left='off', right='off')
plt.xlabel('time [ms]')
plt.ylabel('Neuron')
plt.title("%s: %s" %
(conditions, conditions_names[i]))
plt.colorbar()
if show:
plt.show()
def plot_population_psth(self, all_psth=None, event=None, df=None,
conditions=None, cond_id=None, window=[-100, 500],
binsize=10, conditions_names=None,
event_name='event_onset', ylim=None,
colors=DEFAULT_POPULATION_COLORS, show=False):
'''Plots population PSTH's.
This involves two steps. First, it normalizes each neuron's PSTH across
the conditions. Second, it averages out and plots population PSTH.
Args:
all_psth (dict): With keys :data:`event`, :data:`conditions`,
:data:`binsize`, :data:`window`, and :data:`data`. Each entry
in :data:`psth['data']` is itself a dictionary with keys of
each :data:`cond_id` that correspond to the means for that
condition.
event (str): Column/key name of the :data:`df` DataFrame/dictionary
which contains event times in milliseconds
(stimulus/trial/fixation onset, etc.).
df (DataFrame or dictionary): DataFrame containing the data to
plot, or a dictionary corresponding to such a DataFrame.
conditions (str): Column/key name of DataFrame/dictionary
:data:`df` which contains the conditions by which the trials
must be grouped.
cond_id (list): Which psth to plot indicated by the key in
:data:`all_psth['data']``. If :data:`None` then all are
plotted.
window (list of 2 elements): Time interval to consider, in
milliseconds.
binsize (int): Bin size, in milliseconds.
conditions_names (list): Legend names for conditions. Default are
the unique values in :data:`df['conditions']`.
event_name (string): Legend name for event. Default is the actual
event name
ylim (list): The minimum and maximum values for Y.
colors (list): The colors to use for the plot (as strings).
show (bool): If set, show the plot once it has been created.
'''
# placeholder in order to use NeuroVis functionality
base_neuron = NeuroVis(spiketimes=range(10), name=self.name)
if all_psth is None:
psth = self.get_all_psth(
event=event,
df=df,
conditions=conditions,
window=window,
binsize=binsize,
plot=False,
)
else:
psth = copy.deepcopy(all_psth)
keys = np.sort(list(psth['data'].keys()))
# Normalizes each neuron across all conditions.
for i in range(self.n_neurons):
max_rates = list()
for key in keys:
max_rates.append(np.amax(psth['data'][key][i, :]))
norm_factor = max(max_rates)
for key in keys:
psth['data'][key][i, :] /= norm_factor
# Averages out all the neurons and plots.
for i, key in enumerate(keys):
normed_data = psth['data'][key]
psth['data'][key] = dict()
psth['data'][key]['mean'] = np.nanmean(normed_data, axis=0)
psth['data'][key]['sem'] = \
np.nanvar(normed_data, axis=0) / (len(self.neuron_list)**.5)
# Plots the PSTH.
base_neuron.plot_psth(
psth=psth,
event_name=event_name,
cond_id=cond_id,
conditions_names=conditions_names,
ylim=ylim,
colors=colors,
)
# Adds the appropriate title.
plt.title("%s Population PSTH: %s" % (self.name, psth['conditions']))
if show:
plt.show()
def _get_normed_data(self, data, normalize):
'''Normalizes all PSTH data
Args:
data (2-D numpy array): Array with shape
:data:`(n_neurons, n_bins)`
normalize (str): If :data:`all`, divide all PSTHs by highest peak
firing rate in all neurons. If :data:`each`, divide each PSTH
by its own peak firing rate. If None, do not normalize.
Returns:
array: The original data array, divided such
that all values fall between 0 and 1.
'''
max_rates = np.amax(data, axis=1)
# Computes the normalization factors.
if normalize == 'all':
norm_factors = np.ones([data.shape[0], 1]) * np.amax(max_rates)
elif normalize == 'each':
norm_factors = (
np.reshape(max_rates, (max_rates.shape[0], 1)) *
np.ones((1, data.shape[1]))
)
elif normalize is None:
norm_factors = np.ones([data.shape[0], 1])
else:
raise ValueError('Invalid norm factors: {}'.format(norm_factors))
return data / norm_factors
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] | 2.052474 | 6,022 |
import threading
import random
import time
import datetime
import pickle, os
| [
11748,
4704,
278,
198,
11748,
4738,
198,
11748,
640,
198,
11748,
4818,
8079,
198,
11748,
2298,
293,
11,
28686,
198
] | 3.85 | 20 |
from typing import List, Generator
from itertools import combinations
#################################
# String manipulation functions #
#################################
def make_ngrams(text: str, n: int) -> List[str]:
"""Turn a term string into a list of ngrams of size n
:param text: a text string
:type text: str
:param n: the ngram size
:type n: int
:return: a list of ngrams
:rtype: List[str]"""
if not isinstance(text, str):
raise TypeError('text must be a string')
if not isinstance(n, int):
raise TypeError('n must be a positive integer')
if n < 1:
raise ValueError('n must be a positive integer')
if n > len(text):
return []
text = "#{t}#".format(t=text)
max_start = len(text) - n + 1
return [text[start:start + n] for start in range(0, max_start)]
#####################################
# Term similarity scoring functions #
#####################################
def score_ngram_overlap(term1: str, term2: str, ngram_size: int):
"""Score the number of overlapping ngrams between two terms
:param term1: a first term string
:type term1: str
:param term2: a second term string
:type term2: str
:param ngram_size: the character ngram size
:type ngram_size: int
:return: the number of overlapping ngrams
:rtype: int
"""
term1_ngrams = make_ngrams(term1, ngram_size)
term2_ngrams = make_ngrams(term2, ngram_size)
overlap = 0
for ngram in term1_ngrams:
if ngram in term2_ngrams:
term2_ngrams.pop(term2_ngrams.index(ngram))
overlap += 1
return overlap
def score_ngram_overlap_ratio(term1, term2, ngram_size):
"""Score the number of overlapping ngrams between two terms as proportion of the length
of the first term
:param term1: a term string
:type term1: str
:param term2: a term string
:type term2: str
:param ngram_size: the character ngram size
:type ngram_size: int
:return: the number of overlapping ngrams
:rtype: int
"""
max_overlap = len(make_ngrams(term1, ngram_size))
overlap = score_ngram_overlap(term1, term2, ngram_size)
return overlap / max_overlap
def score_char_overlap_ratio(term1, term2):
"""Score the number of overlapping characters between two terms as proportion of the length
of the first term
:param term1: a term string
:type term1: str
:param term2: a term string
:type term2: str
:return: the number of overlapping ngrams
:rtype: int
"""
max_overlap = len(term1)
overlap = score_char_overlap(term1, term2)
return overlap / max_overlap
def score_char_overlap(term1: str, term2: str) -> int:
"""Count the number of overlapping character tokens in two strings.
:param term1: a term string
:type term1: str
:param term2: a term string
:type term2: str
:return: the number of overlapping ngrams
:rtype: int
"""
num_char_matches = 0
for char in term2:
if char in term1:
term1 = term1.replace(char, "", 1)
num_char_matches += 1
return num_char_matches
def score_levenshtein_similarity_ratio(term1, term2):
"""Score the levenshtein similarity between two terms
:param term1: a term string
:type term1: str
:param term2: a term string
:type term2: str
:return: the number of overlapping ngrams
:rtype: int
"""
max_distance = max(len(term1), len(term2))
distance = score_levenshtein_distance(term1, term2)
return 1 - distance / max_distance
def score_levenshtein_distance(term1: str, term2: str) -> int:
"""Calculate Levenshtein distance between two string.
:param term1: a term string
:type term1: str
:param term2: a term string
:type term2: str
:return: the number of overlapping ngrams
:rtype: int
"""
if len(term1) > len(term2):
term1, term2 = term2, term1
distances = range(len(term1) + 1)
for i2, c2 in enumerate(term2):
distances_ = [i2 + 1]
for i1, c1 in enumerate(term1):
if c1 == c2:
distances_.append(distances[i1])
else:
distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
distances = distances_
return distances[-1]
def insert_skips(window: str, skipgram_combinations: List[List[int]]):
"""For a given skip gram window, return all skip grams for a given configuration."""
for combination in skipgram_combinations:
# prev_index = 0
skip_gram = window[0]
try:
for index in combination:
# if index - prev_index > 1:
# skip_gram += "_"
skip_gram += window[index]
# prev_index = index
yield skip_gram, combination[-1] + 1
except IndexError:
pass
def text2skipgrams(text: str, ngram_size: int = 2, skip_size: int = 2) -> Generator[SkipGram, None, None]:
"""Turn a text string into a list of skipgrams.
:param text: an text string
:type text: str
:param ngram_size: an integer indicating the number of characters in the ngram
:type ngram_size: int
:param skip_size: an integer indicating how many skip characters in the ngrams
:type skip_size: int
:return: An iterator returning tuples of skip_gram and offset
:rtype: Generator[tuple]"""
if ngram_size <= 0 or skip_size < 0:
raise ValueError('ngram_size must be a positive integer, skip_size must be a positive integer or zero')
indexes = [i for i in range(0, ngram_size+skip_size)]
skipgram_combinations = [combination for combination in combinations(indexes[1:], ngram_size-1)]
for offset in range(0, len(text)-1):
window = text[offset:offset+ngram_size+skip_size]
for skipgram, skipgram_length in insert_skips(window, skipgram_combinations):
yield SkipGram(skipgram, offset, skipgram_length)
non_word_affixes_2 = {
". ", ", ", "! ", "? ",
" (", ") ", ").", ")!", "),", ")?",
" [", "] ", "].", "]!", "],", "]?",
}
non_word_affixes_1 = {
" ", ".", ",", "!", "?",
}
def get_non_word_prefix(string: str) -> str:
"""Check if a string has a non-word prefix and return it.
:param string: the string from which the prefix is to be return
:type string: str
:return: the non-word prefix
:rtype: str
"""
if string[:2] in non_word_affixes_2:
return string[:2]
elif string[:1] in non_word_affixes_1:
return string[:1]
else:
return ""
def get_non_word_suffix(string: str) -> str:
"""Check if a string has a non-word suffix and return it.
:param string: the string from which the suffix is to be return
:type string: str
:return: the non-word suffix
:rtype: str
"""
if string[-2:] in non_word_affixes_2:
return string[-2:]
elif string[-1:] in non_word_affixes_1:
return string[-1:]
else:
return ""
def strip_prefix(string: str) -> str:
"""Strip non-word prefix from string ending.
:param string: the string from which the prefix is to be stripped
:type string: str
:return: the stripped string
:rtype: str
"""
if len(string) <= 2:
pass
elif string[:2] in non_word_affixes_2:
string = string[2:]
elif string[1] in [" ", ","]:
string = string[2:]
elif string[0] in [" ", ","]:
string = string[1:]
return string
def strip_suffix(string: str) -> str:
"""Strip non-word suffix from string ending.
:param string: the string from which the suffix is to be stripped
:type string: str
:return: the stripped string
:rtype: str
"""
if len(string) <= 2:
pass
elif string[-2] in [" ", ","]:
string = string[:-2]
elif string[-2:] in [", ", ". ", "! ", "? "]:
string = string[:-2]
elif string[-1] in [" ", ","]:
string = string[:-1]
return string
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11,
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8,
628,
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62,
4775,
62,
2001,
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274,
62,
17,
796,
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220,
220,
27071,
33172,
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30,
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2001,
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4731,
628,
198,
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10283,
62,
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844,
7,
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8,
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965,
25,
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220,
220,
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1273,
5528,
1729,
12,
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35488,
422,
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13,
628,
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4731,
25,
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262,
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318,
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307,
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198,
220,
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1208,
198,
220,
220,
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1288,
361,
4731,
58,
12,
17,
60,
287,
14631,
33172,
366,
553,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
4731,
796,
4731,
58,
21912,
17,
60,
198,
220,
220,
220,
1288,
361,
4731,
58,
12,
17,
47715,
287,
685,
1600,
33172,
27071,
33172,
366,
0,
33172,
366,
30,
366,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
4731,
796,
4731,
58,
21912,
17,
60,
198,
220,
220,
220,
1288,
361,
4731,
58,
12,
16,
60,
287,
14631,
33172,
366,
553,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
4731,
796,
4731,
58,
21912,
16,
60,
198,
220,
220,
220,
1441,
4731,
198
] | 2.501558 | 3,210 |
from application import db
from datetime import datetime
from sqlalchemy import text
# Kilpailu-taulun luominen | [
6738,
3586,
1330,
20613,
198,
6738,
4818,
8079,
1330,
4818,
8079,
198,
6738,
44161,
282,
26599,
1330,
2420,
198,
198,
2,
19902,
79,
603,
84,
12,
83,
2518,
403,
300,
84,
6351,
268
] | 3.393939 | 33 |
import bpy
import arm.material.mat_utils
import arm.write_data
import arm.assets
import arm.make_renderpath
import arm.make_state
import arm.utils
| [
11748,
275,
9078,
198,
11748,
3211,
13,
33665,
13,
6759,
62,
26791,
198,
11748,
3211,
13,
13564,
62,
7890,
198,
11748,
3211,
13,
19668,
198,
11748,
3211,
13,
15883,
62,
13287,
6978,
198,
11748,
3211,
13,
15883,
62,
5219,
198,
11748,
3211,
13,
26791,
198
] | 3.266667 | 45 |
import cv2
import sys
from filter import *
from mix import *
TEST = []
TEST_out = []
SIZE_I = 16
TEST, TEST_out, PATH = create_test_tab_one_filter_rand_train()
# TEST, TEST_out = create_test_tab_filter()
TESTV, TESTV_out, PATHV = create_test_tab_one_filter_rand_valid()
#TESTV, TESTV_out = create_valid_tab_filter()
"""
print(len(TEST))
for im in TEST:
display_image(im)
l = ""
for t in TEST_out:
l += str(t) + " "
print(l)
"""
#cv2.imshow("Image", im)
#cv2.waitKey()
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2,
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17,
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198
] | 2.334951 | 206 |
import time
st = time.time()
fraction = "0."
places_of_digits = [1,10,100,1000,10000,100000,1000000]
pod = 0
d_index = places_of_digits[pod]
result = 1
present_num = 0
lenfp = 0
done = 0
while(lenfp <= places_of_digits[-1]):
present_num = present_num + 1
fraction = fraction + str(present_num)
lenpn = len(str(present_num))
if lenpn >= 1:
for i in xrange(1,lenpn+1):
lenfp = lenfp + 1
if lenfp == d_index:
result = result * int(str(present_num)[i-1])
pod = pod + 1
if pod < len(places_of_digits):
d_index = places_of_digits[pod]
#print "Fraction :: "+str(fraction)
print "Result :: "+str(result)
print "Time taken :: "+str(time.time()-st)+" seconds\n"
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2,
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37,
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7904,
43825,
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7,
69,
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8,
198,
4798,
366,
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43825,
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7,
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8,
198,
4798,
366,
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7,
2435,
13,
2435,
3419,
12,
301,
47762,
1,
4201,
59,
77,
1,
198
] | 2.326531 | 294 |
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CppExtension
from glob import glob
import os
sanitizers = [
#'-fsanitize=undefined', '-fsanitize-undefined-trap-on-error'
#'-fsanitize=address,undefined'
]
extra_compile_args = [
'-I' + os.path.realpath('.'),
'-march=native',
'-mtune=native',
'-fvisibility=hidden',
'-fvisibility-inlines-hidden',
# '-fno-omit-frame-pointer',
] + sanitizers
extra_link_args = [] + sanitizers
sources = glob('**/*.cpp', recursive=True)
sources = [file for file in sources if 'scripts' not in file]
setup(
name='torchy',
version='0.1',
description='Tracing JIT for PyTorch',
packages=['torchy'], #find_packages(exclude=['build']),
requires=['ninja', 'torch'],
ext_modules=[
CppExtension(
'_TORCHY',
sources,
extra_compile_args=extra_compile_args,
extra_link_args=extra_link_args
),
],
cmdclass={
'build_ext': BuildExtension
}
)
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] | 2.502551 | 392 |
"""Test decorator of torch.nn.Module subclasses."""
from torch import Tensor
from torch.nn import Linear
from bpexts.decorator import decorate
# decorated linear layer
DecoratedAffine = decorate(Linear)
def test_decorated_linear_properties():
"""Test name and docstring of decorated torch.nn.Linear."""
assert DecoratedAffine.__name__ == "DecoratedLinear"
assert DecoratedAffine.__doc__.startswith("[Decorated by bpexts]")
def dummy_backward_hook(module, grad_input, grad_output):
"""Dummy backward hook."""
def dummy_forward_hook(module, input, output):
"""Dummy forward hook."""
def dummy_pre_forward_hook(module, input):
"""Dummy pre forward hook."""
def test_exts_hooking():
"""Test tracking of hooks registrated with exts functions.
Return module with hooks (6 in total, 3 registered by torch.nn,
3 registered by exts.
"""
layer = DecoratedAffine(in_features=5, out_features=5)
# exts and nn hook registration functions
hooks = [dummy_backward_hook, dummy_forward_hook, dummy_pre_forward_hook]
exts_register = [
layer.register_exts_backward_hook,
layer.register_exts_forward_hook,
layer.register_exts_forward_pre_hook,
]
nn_register = [
layer.register_backward_hook,
layer.register_forward_hook,
layer.register_forward_pre_hook,
]
hooks_and_register = zip(hooks, exts_register, nn_register)
# iterate over hooks and registration methods
for i, (hook, exts_func, nn_func) in enumerate(hooks_and_register, 1):
# hooks registered by exts functions should be tracked
exts_func(hook)
assert len(layer.exts_hooks) == i
# usual hook registration should not affect extension hooks
nn_func(hook)
assert len(layer.exts_hooks) == i
# each hook type occurs twice (once from nn, once from exts)
for hook_type in [
layer._backward_hooks,
layer._forward_hooks,
layer._forward_pre_hooks,
]:
assert len(hook_type) == 2
return layer
def module_with_hooks():
"""Return module with 6 hooks.
Module contains a backward, a forward and a pre forward hook.
The hooks are registered using the torch.nn method and the
exts method, resultin in 2*3 = 6 hooks.
"""
return test_exts_hooking()
def test_hook_removing():
"""Test disabling of hooks tracked by exts."""
# 6 hooks, 3 tracked by exts, 3 tracked by torch.nn
layer = module_with_hooks()
# remove tracked hooks
layer.remove_exts_hooks()
assert len(layer.exts_hooks) == 0
# module has one forward, pre forward and backward hook each.
for hook_type in [
layer._backward_hooks,
layer._forward_hooks,
layer._forward_pre_hooks,
]:
assert len(hook_type) == 1
def module_with_buffers():
"""Return module that holds two buffers.
`'buffer1'` is registered by the torch.nn.Module method.
`'buffer2'` is registered by the exts method.
"""
layer = DecoratedAffine(in_features=5, out_features=5)
# buffer1 is registered normally, buffer2 with exts method
buffer_names = ["buffer1", "buffer2"]
tensors = [Tensor([1, 2, 3]), Tensor([4, 5, 6])]
buffer_register = [layer.register_buffer, layer.register_exts_buffer]
# register
for name, tensor, register in zip(buffer_names, tensors, buffer_register):
register(name, tensor)
return layer, buffer_names
def test_exts_buffering():
"""Test registration of buffers by exts methods."""
layer, buffer_names = module_with_buffers()
# check if all buffers are present
for b in buffer_names:
assert b in layer._buffers
# but only buffer2 is tracked by exts
assert buffer_names[0] not in layer.exts_buffers
assert buffer_names[1] in layer.exts_buffers
def test_buffer_removing():
"""Test removing of exts buffers."""
layer, buffer_names = module_with_buffers()
# remove buffer_names[1] which was registered with exts
layer.remove_exts_buffers()
# buffer1 is still existent
assert buffer_names[0] in layer._buffers
assert hasattr(layer, buffer_names[0])
# buffer2 not
assert not (buffer_names[1] in layer._buffers)
assert not hasattr(layer, buffer_names[1])
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] | 2.692839 | 1,592 |
from typing import Any, Callable, Dict
import pytest
from pydantic.fields import FieldInfo
from starlite import (
Body,
HTTPRouteHandler,
ImproperlyConfiguredException,
Parameter,
Provide,
RequestEncodingType,
Starlite,
get,
post,
websocket,
)
from starlite.constants import RESERVED_KWARGS
@get("/{my_key:str}")
@get("/{my_key:str}")
@get("/{my_key:str}")
@get("/{my_key:str}", dependencies={"my_key": Provide(my_dependency)})
@get("/", dependencies={"my_key": Provide(my_dependency)})
@get("/", dependencies={"my_key": Provide(my_dependency)})
@get("/", dependencies={"my_key": Provide(my_dependency)})
@pytest.mark.parametrize( # type: ignore[misc]
"handler",
[
handler_with_path_param_and_aliased_query_parameter_collision,
handler_with_path_param_and_aliased_header_parameter_collision,
handler_with_path_param_and_aliased_cookie_parameter_collision,
handler_with_path_param_dependency_collision,
handler_with_dependency_and_aliased_query_parameter_collision,
handler_with_dependency_and_aliased_header_parameter_collision,
handler_with_dependency_and_aliased_cookie_parameter_collision,
],
)
@pytest.mark.parametrize("reserved_kwarg", RESERVED_KWARGS) # type: ignore[misc]
@post("/", dependencies={"first": Provide(json_dependency)})
@post("/", dependencies={"first": Provide(url_encoded_dependency)})
@post("/", dependencies={"first": Provide(multi_part_dependency)})
@pytest.mark.parametrize("handler", [accepted_json_handler, accepted_url_encoded_handler, accepted_multi_part_handler]) # type: ignore[misc]
@pytest.mark.parametrize( # type: ignore[misc]
"body, dependency",
[
[Body(media_type=RequestEncodingType.JSON), url_encoded_dependency],
[Body(media_type=RequestEncodingType.JSON), multi_part_dependency],
[Body(media_type=RequestEncodingType.URL_ENCODED), json_dependency],
[Body(media_type=RequestEncodingType.URL_ENCODED), multi_part_dependency],
[Body(media_type=RequestEncodingType.MULTI_PART), json_dependency],
[Body(media_type=RequestEncodingType.MULTI_PART), url_encoded_dependency],
],
)
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"""
Tests Account API
"""
import json
import datetime
from django.test import TransactionTestCase
# Default user model may get swapped out of the system and hence.
from django.contrib.auth.models import User
from rest_framework.test import APIClient
from rest_framework import status
from seven23.models.accounts.models import Account, AccountGuests
from seven23.models.categories.models import Category
from seven23.models.transactions.models import DebitsCredits
from seven23.models.currency.models import Currency
class ApiCategoryTest(TransactionTestCase):
""" Account retrieve """
def test_categories_retrieve(self):
"""
Retrieve data with a user owning an account and being gest in an other one.
"""
self.client.force_authenticate(user=self.user)
response = self.client.get('/api/v1/categories')
data = response.json()
# Verify data structure
assert response.status_code == status.HTTP_200_OK
assert len(data) == 1
AccountGuests.objects.create(account=self.account2,
user=self.user,
permissions='W')
response = self.client.get('/api/v1/categories')
data = response.json()
# Verify data structure
assert response.status_code == status.HTTP_200_OK
assert len(data) == 2
def test_categories_post(self):
"""
We try to create a new category object
"""
blob = { 'name': 'Category post' }
self.client.force_authenticate(user=self.user)
response = self.client.post('/api/v1/categories',
{'account': self.account.id,
'blob': json.dumps(blob)
})
assert response.status_code == status.HTTP_201_CREATED
response = self.client.post('/api/v1/categories',
{'account':self.account2.id,
'blob': json.dumps(blob)
})
assert response.status_code == status.HTTP_201_CREATED
response = self.client.get('/api/v1/categories')
data = response.json()
# Verify data structure
assert response.status_code == status.HTTP_200_OK
assert len(data) == 2
minDate = datetime.datetime.now()
for category in data:
if datetime.datetime.strptime(category['last_edited'], '%Y-%m-%dT%H:%M:%S.%fZ') < minDate:
minDate = datetime.datetime.strptime(category['last_edited'], '%Y-%m-%dT%H:%M:%S.%fZ')
response = self.client.get('/api/v1/categories?last_edited=%s' % minDate)
data = response.json()
assert len(response.json()) == 1
def test_categories_bulk_delete(self):
"""
Retrieve data with a user owning an account and being gest in an other one.
"""
self.client.force_authenticate(user=self.user)
response = self.client.get('/api/v1/categories')
data = response.json()
# Verify data structure
assert response.status_code == status.HTTP_200_OK
assert len(data) == 1
# Make sure bulk delete with no param is disabled
response = self.client.delete('/api/v1/categories')
response = self.client.get('/api/v1/categories')
data = response.json()
# Verify data structure
assert response.status_code == status.HTTP_200_OK
assert len(data) == 1 | [
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220,
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220,
220,
220,
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6818,
18896,
7,
7890,
8,
6624,
352
] | 2.247793 | 1,586 |
# -*- coding: utf-8 -*-
import scoutnet
from dataimport import UserPrefs, ndb, Person, logging, TroopPerson, ScoutGroup
from flask import Blueprint, render_template, abort, redirect, request
persons = Blueprint('persons_page', __name__, template_folder='templates')
@persons.route('/')
@persons.route('/<sgroup_url>')
@persons.route('/<sgroup_url>/')
@persons.route('/<sgroup_url>/<person_url>')
@persons.route('/<sgroup_url>/<person_url>/')
@persons.route('/<sgroup_url>/<person_url>/<action>')
| [
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] | 2.654255 | 188 |
from instabot import Bot
COOKIE_FNAME = 'cookie.txt'
bot = Bot()
bot.login(username="[email protected]", password="reNcDjEgWp4X7Gt", use_cookie = False)
user_id = bot.get_user_id_from_username("lego")
user_info = bot.get_user_info(user_id)
print(user_info['biography']) | [
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] | 2.4375 | 112 |
from .sdk import MesoorRecommendation
from . import models
| [
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] | 3.933333 | 15 |
from java import dynamic_proxy
from java.lang import Runnable
import os
import youtube_dl as yt
from com.arthenica.mobileffmpeg import FFmpeg | [
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] | 3.525 | 40 |
from .edge import *
from .vertex import *
from .graph import *
from .generators import * | [
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num = (int(input('\033[0;34mDigite um numero: \033[m')))
print('\033[0;32mO numero digitado foi {},'.format(num))
print('seu sucessor é {}, e seu antecessor é {}.\033[m'.format(num + 1, num- 1))
| [
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] | 2.267442 | 86 |
class Predicate():
"""
This class should predicate most usefull floor to send elevator in idle state
The purpose to minimize wait time om next call. And just for fun
Predication may be performed by external tools, based on statistics table
For now it do nothing.
"""
predict = Predicate() | [
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] | 3.566667 | 90 |
# nsgaii.py
# Program to run NSGA-II on benchmark problems
# Author: Kristina Spencer
# Date: March 10, 2016
# i - bin index
# j - item index
# k - algorithm index
# m - solution index
# t - generation ind#x
from __future__ import print_function
import binpacking as bp
import datetime
import ga
import numpy as np
import os
import solutions as sols
from items import makeitems
from mop import dom
from operator import attrgetter
if __name__ == '__main__':
main()
| [
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
1388,
3419,
198
] | 2.936047 | 172 |
import numpy as np
from actor_base import Actor
class SimulatedPathFollowingHuman(Actor):
"""
A simulated human object that only follows a given path
"""
class SimulatedReactiveHuman(Actor):
"""
A reactive human object that follows a path but reacts
to robot's movement
"""
| [
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284,
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37227,
198
] | 3.336957 | 92 |
"""Module with functions for 'deploy' subpackage."""
from __future__ import annotations
import os
import shutil
import mylogging
from mypythontools.paths import validate_path, PathLike
from mypythontools.misc import delete_files
from mypythontools.system import check_script_is_available, terminal_do_command, PYTHON
from mypythontools_cicd.project_paths import PROJECT_PATHS
def deploy_to_pypi(
setup_path: None | PathLike = None, clean: bool = True, verbose: bool = True, pep517: bool = True
) -> None:
"""Publish python library to PyPi.
Username and password are set with env vars `TWINE_USERNAME` and `TWINE_PASSWORD`.
Note:
You need working `setup.py` file. If you want to see example, try the one from project-starter on
https://github.com/Malachov/mypythontools/blob/master/content/project-starter/setup.py
Args:
setup_path (None | PathLike, optional): Function suppose, that there is a setup.py somewhere in cwd.
If not, path will be inferred. Build and dist folders will be created in same directory.
Defaults to None.
clean (bool, optional): Whether delete created build and dist folders.
verbose (bool, optional): If True, result of terminal command will be printed to console.
Defaults to False.
pep517 (bool, optional): Whether using PEP 517, that use pyproject.toml to build distribution.
"""
usr = os.environ.get("TWINE_USERNAME")
password = os.environ.get("TWINE_PASSWORD")
if not usr or not password:
raise KeyError("Setup env vars TWINE_USERNAME and TWINE_PASSWORD to use deploy.")
check_script_is_available("twine", "twine")
setup_path = PROJECT_PATHS.root / "setup.py" if not setup_path else validate_path(setup_path)
setup_dir_path = setup_path.parent
dist_path = setup_dir_path / "dist"
build_path = setup_dir_path / "build"
delete_files(dist_path)
delete_files(build_path)
if pep517:
build_command = f"{PYTHON} -m build --wheel --sdist"
else:
build_command = f"{PYTHON} setup.py sdist bdist_wheel"
terminal_do_command(
build_command,
cwd=setup_dir_path.as_posix(),
verbose=verbose,
error_header="Build python distribution for PyPi deployment failed. Try to set 'pep517' to False.",
)
command = f"twine upload dist/*"
terminal_do_command(
command,
cwd=setup_dir_path.as_posix(),
verbose=verbose,
error_header="Deploying to PyPi failed.",
)
if clean:
delete_files(dist_path)
delete_files(build_path)
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13,
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220,
220,
220,
220,
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220,
220,
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62,
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54,
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220,
220,
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407,
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81,
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407,
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25,
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220,
220,
220,
220,
220,
220,
220,
5298,
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220,
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220,
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11,
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220,
220,
220,
220,
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28,
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220,
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62,
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2625,
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278,
284,
9485,
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4054,
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220,
220,
220,
1267,
628,
220,
220,
220,
611,
3424,
25,
198,
220,
220,
220,
220,
220,
220,
220,
12233,
62,
16624,
7,
17080,
62,
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8,
198,
220,
220,
220,
220,
220,
220,
220,
12233,
62,
16624,
7,
11249,
62,
6978,
8,
198
] | 2.633902 | 997 |
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="dj-sellsy",
version="0.1",
author="Mathieu Richardoz",
author_email="[email protected]",
description="A package to facilitate Django-Sellsy integration",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/bureauxlocaux/dj-sellsy",
packages=setuptools.find_packages(),
classifiers=[
"Development Status :: 4 - Beta",
"Environment :: Web Environment",
"Framework :: Django",
],
install_requires=[
'phpserialize',
'sellsy_api',
],
)
| [
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220,
220,
220,
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198,
220,
220,
220,
2721,
62,
47911,
41888,
198,
220,
220,
220,
220,
220,
220,
220,
705,
746,
862,
48499,
1096,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
705,
7255,
1837,
62,
15042,
3256,
198,
220,
220,
220,
16589,
198,
198,
8,
198
] | 2.519573 | 281 |
"""
Faça um programa que mostre na tela todos os números pares entre 1 e 50
"""
for i in range(1, 51):
if i % 2 == 0:
print(i, end=' ')
print('FIM')
| [
37811,
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] | 2.205479 | 73 |
from PIL import Image
import random
| [
6738,
350,
4146,
1330,
7412,
198,
11748,
4738,
628
] | 4.111111 | 9 |
#!/usr/bin/env python
'''
Oryol texture export functions
'''
import sys
import os
import platform
import subprocess
import tempfile
ProjectDirectory = os.path.dirname(os.path.abspath(__file__)) + '/..'
TexSrcDirectory = ProjectDirectory + '/data'
TexDstDirectory = ProjectDirectory + '/build/webpage'
# NOTE: PVRTexTools supports a lot more formats!
PVRFormats = ['PVRTC1_2', 'PVRTC1_4', 'PVRTC1_2_RGB', 'PVRTC1_4_RGB', 'PVRTC2_2', 'PVRTC2_4']
ETCFormats = ['ETC1', 'ETC2']
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
def configure(projDir, texSrcDir, texDstDir) :
'''
Configure the directories of the texture export module
'''
global ProjectDirectory
global TexSrcDirectory
global TexDstDirectory
ProjectDirectory = projDir
TexSrcDirectory = texSrcDir
TexDstDirectory = texDstDir
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
def toDDS(srcFilename, dstFilename, linearGamma, fmt, rgbFmt=None) :
'''
Convert a file to DDS format
'''
ensureDstDirectory()
ddsTool = getToolsBinPath() + 'nvcompress'
srcPath = TexSrcDirectory + '/' + srcFilename
dstPath = TexDstDirectory + '/' + dstFilename
print('=== toDDS: {} => {}:'.format(srcPath, dstPath))
if not needsExport(srcPath, dstPath) :
return
cmdLine = [ddsTool, '-'+fmt]
if rgbFmt != None :
cmdLine.append('-rgbfmt')
cmdLine.append(rgbFmt)
if linearGamma :
cmdLine.append('-tolineargamma')
cmdLine.append(srcPath)
cmdLine.append(dstPath)
subprocess.call(args=cmdLine)
#-------------------------------------------------------------------------------
def toCubeDDS(srcDir, srcExt, dstFilename, linearGamma, fmt, rgbFmt=None) :
'''
Generate a cube map and convert to dds.
'''
ensureDstDirectory()
nvassemble = getToolsBinPath() + 'nvassemble'
ddsTool = getToolsBinPath() + 'nvcompress'
srcFiles = ['posx', 'negx', 'posy', 'negy', 'posz', 'negz']
dstPath = TexDstDirectory + '/' + dstFilename
print('=== toCubeDDS: {}/{}/[posx,negx,posy,negy,posz,negz].{} => {}'.format(TexSrcDirectory, srcDir, srcExt, dstPath))
# call nvassemble to generate an uncompressed cube map...
cmdLine = [nvassemble, '-cube']
dirty = False
for src in srcFiles :
srcPath = TexSrcDirectory + '/' + srcDir + '/' + src + '.' + srcExt
dirty |= needsExport(srcPath, dstPath)
cmdLine.append(srcPath)
if not dirty :
return
cmdLine.append('-o')
cmdLine.append(dstPath)
subprocess.call(args=cmdLine)
# ...and compress/convert to the desired format
cmdLine = [ddsTool, '-'+fmt]
if rgbFmt != None :
cmdLine.append('-rgbfmt')
cmdLine.append(rgbFmt)
if linearGamma :
cmdLine.append('-tolineargamma')
cmdLine.append(dstPath)
cmdLine.append(dstPath)
subprocess.call(args=cmdLine)
#-------------------------------------------------------------------------------
def toPVR(srcFilename, dstFilename, format) :
'''
Convert a file to PVR format
'''
if format not in PVRFormats :
error('invalid PVR texture format {}!'.format(format))
ensureDstDirectory()
pvrTool = getToolsBinPath() + 'PVRTexToolCLI'
srcPath = TexSrcDirectory + '/' + srcFilename
dstPath = TexDstDirectory + '/' + dstFilename
print('=== toPVR: {} => {}:'.format(srcPath, dstPath))
if not needsExport(srcPath, dstPath) :
return
cmdLine = [pvrTool, '-i', srcPath, '-o', dstPath, '-square', '+', '-pot', '+', '-m', '-mfilter', 'cubic', '-f', format ]
subprocess.call(args=cmdLine)
#-------------------------------------------------------------------------------
def toCubePVR(srcDir, srcExt, dstFilename, format) :
'''
Generate a cube map and convert to PVR
'''
if format not in PVRFormats :
error('invalid PVR texture format {}!'.format(format))
ensureDstDirectory()
pvrTool = getToolsBinPath() + 'PVRTexToolCLI'
srcFiles = ['posx', 'negx', 'posy', 'negy', 'posz', 'negz']
dstPath = TexDstDirectory + '/' + dstFilename
print('=== toCubePVR: {}/{}/[posx,negx,posy,negy,posz,negz].{} => {}'.format(TexSrcDirectory, srcDir, srcExt, dstPath))
cmdLine = [pvrTool, '-i']
inputFiles = ''
dirty = False
for src in srcFiles :
srcPath = TexSrcDirectory + '/' + srcDir + '/' + src + '.' + srcExt
dirty |= needsExport(srcPath, dstPath)
inputFiles += srcPath + ','
if not dirty:
return
inputFiles = inputFiles[:-1]
cmdLine.append(inputFiles)
cmdLine.append('-o')
cmdLine.append(dstPath)
cmdLine.append('-cube')
cmdLine.append('-m')
cmdLine.append('-mfilter')
cmdLine.append('cubic')
cmdLine.append('-f')
cmdLine.append(format)
subprocess.call(args=cmdLine)
#-------------------------------------------------------------------------------
def toETC(srcFilename, dstFilename, format) :
'''
Convert a file to ETC2 in a KTX container file.
FIXME: alpha channel support
'''
if format not in ETCFormats :
error('invalid ETC texture format {}!'.format(format))
ensureDstDirectory()
tmpFilename, ext = os.path.splitext(dstFilename)
tmpFilename += '.ppm'
convTool = getToolsBinPath() + 'convert'
etcTool = getToolsBinPath() + 'etcpack'
srcPath = TexSrcDirectory + '/' + srcFilename
dstPath = TexDstDirectory + '/' + dstFilename
tmpPath = tempfile.gettempdir() + '/' + tmpFilename
print('=== toETC2: {} => {} => {}:'.format(srcPath, tmpPath, dstPath))
if not needsExport(srcPath, dstPath) :
return
# first convert file to PPM format
subprocess.call(args=[convTool, srcPath, tmpPath])
cmd = [etcTool, tmpPath, TexDstDirectory, '-mipmaps', '-ktx', '-c']
if format == 'etc1' :
cmd.append('etc1')
else :
cmd.append('etc2')
subprocess.call(args=cmd)
os.unlink(tmpPath)
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
if __name__ == '__main__' :
print("{}".format(__file__))
exportSampleTextures()
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10784,
36674,
39860,
3419,
198
] | 2.771008 | 2,380 |
from sanic.views import HTTPMethodView
from .errors import NotFound
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6738,
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] | 3.333333 | 21 |
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import os
import imageio
from timeit import timeit
from mpl_toolkits import mplot3d
from PIL import Image
#import png
import svd_tools_copy as svdt
import image_tools_copy as it
#import ../../../david/watermark as watermarktools
#sunset = it.load_image('../res/sunset.png')
#rainbow = it.load_image('../res/rainbow.png')
#view = it.load_image('../res/view.png')
view = it.load_image('../res/view.jpg')
tree = it.load_image('../res/tree.jpg')
plt.rcParams['font.size'] = '18'
#EXTRACTION ERROR = NORM(ORIGINAL WATERMARK - EXTRACTED WATERMARK)
#1. COMPUTE EMBEDDING AND EXTRACTION
#2. COMPUTE NORM(ORIGINAL WATERMARK - EXTRACTED WATERMARK)/NORM(ORIGINAL WATERMARK)
#lowrank extraction error
#cropping tests
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] | 2.15566 | 424 |
# -----------------------------------------------------------------------------
#
# Copyright 2013-2019 lispers.net - Dino Farinacci <[email protected]>
#
# 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.
#
# -----------------------------------------------------------------------------
#
# lisp-ms.py
#
# This file performs LISP Map-Server functionality.
#
# -----------------------------------------------------------------------------
import lisp
import lispconfig
import threading
import time
#------------------------------------------------------------------------------
#
# Global variables.
#
lisp_ipc_listen_socket = None
lisp_send_ipv4_socket = None
lisp_send_ipv6_socket = None
lisp_send_sockets = [None, None, None]
lisp_sites_by_name = {}
lisp_sites_by_name_sorted = []
lisp_site_timer = None
lisp_pubsub_timer = None
#------------------------------------------------------------------------------
#
# lisp_site_command
#
# Process the "lisp site" command.
#
#enddef
#
# lisp_ms_auth_prefix_command
#
# Process the "lisp ms-authoritative-prefix" command for the Map-Server.
#
#enddef
#
# lisp_ms_map_server_peer_command
#
# Process the "lisp map-server-peer" command.
#
#enddef
#
# lisp_ms_eid_crypto_hash_command
#
# Process the "lisp eid-crypto-hash" command.
#
#enddef
#
# lisp_ms_encryption_keys_command
#
# Process the "lisp encryption-keys" command.
#
#enddef
#
# lisp_ms_show_site_detail_command
#
# Show LISP site information for a particular EID-prefix..
#
#enddef
#
# lisp_ms_display_ddt_cache
#
# Display each entry in the lisp_ddt_cache.
#
#enddef
#
# lisp_ms_walk_ddt_cache
#
# Walk the entries in the lisp_ddt_cache(). And then subsequently walk the
# entries in lisp_ddt_entry.source_cache().
#
#enddef
#
# lisp_ms_show_ddt_entries
#
# Walk through the Map-Server's DDT entries and display them before the site
# cache.
#
#enddef
#
# lisp_ms_show_site_lookup
#
# Do a Map-Request lookup in the ddt-cache as well as the site cache. Return
# results. If not found, return negative prefix.
#
#enddef
#
# lisp_display_site_eid_entry
#
# Print one row for a site-eid entry.
#
#enddef
#
# lisp_ms_show_site_command
#
# Show LISP site information.
#
#enddef
#
# Map-Server commands processed by this process.
#
lisp_ms_commands = {
"lisp site" : [lisp_site_command, {
"site-name" : [False],
"description" : [False],
"shutdown" : [False],
"authentication-key" : [False],
"allowed-prefix" : [],
"instance-id" : [True, 0, 0xffffffff],
"eid-prefix" : [True],
"group-prefix" : [True],
"policy-name" : [True],
"accept-more-specifics" : [True, "yes", "no"],
"force-proxy-reply" : [True, "yes", "no"],
"force-nat-proxy-reply" : [True, "yes", "no"],
"echo-nonce-capable" : [True, "yes", "no"],
"force-ttl" : [True, 0, 0x7fffffff],
"pitr-proxy-reply-drop" : [True, "yes", "no"],
"proxy-reply-action" : [True, "native-forward", "drop"],
"require-signature" : [True, "yes", "no"],
"encrypt-json" : [True, "yes", "no"],
"allowed-rloc" : [],
"address" : [True],
"priority" : [True, 0, 255],
"weight" : [True, 0, 100] }],
"lisp ms-authoritative-prefix" : [lisp_ms_auth_prefix_command, {
"instance-id" : [False, 0, 0xffffffff, True],
"eid-prefix" : [False],
"group-prefix" : [False] }],
"lisp map-server-peer" : [lisp_ms_map_server_peer_command, {
"peer" : [],
"address" : [True],
"priority" : [True, 0, 255],
"weight" : [True, 0, 100],
"prefix" : [],
"instance-id" : [True, 0, 0xffffffff, True],
"eid-prefix" : [True],
"group-prefix" : [True] }],
"lisp eid-crypto-hash" : [lisp_ms_eid_crypto_hash_command, {
"instance-id" : [True, 0, 0xffffffff],
"eid-prefix" : [False] }],
"lisp encryption-keys" : [lisp_ms_encryption_keys_command, {
"json-key" : [False],
"map-register-key" : [False] }],
"lisp geo-coordinates" : [lispconfig.lisp_geo_command, {
"geo-name" : [False],
"geo-tag" : [False] }],
"lisp explicit-locator-path" : [lispconfig.lisp_elp_command, {
"elp-name" : [False],
"elp-node" : [],
"address" : [True],
"probe" : [True, "yes", "no"],
"strict" : [True, "yes", "no"],
"eid" : [True, "yes", "no"] }],
"lisp replication-list-entry" : [lispconfig.lisp_rle_command, {
"rle-name" : [False],
"rle-node" : [],
"address" : [True],
"level" : [True, 0, 255] }],
"lisp json" : [lispconfig.lisp_json_command, {
"json-name" : [False],
"json-string" : [False] }],
"show site" : [lisp_ms_show_site_command, { } ]
}
#
# lisp_timeout_site_eid
#
# Look at elapsed time for a parent site-eid or a child site-eid from an
# individual registrations due to merge-semantics.
#
#enddef
#
# lisp_timeout_individuals
#
# Time out the individual entries.
#
#enddef
#
# lisp_timeout_sites
#
# Time out entries that have not registered within their advertised register-
# ttl.
#
#enddef
#
# lisp_timeout_pubsub
#
# Time out entries in lisp_pubsub_cache.
#
#enddef
#
# lisp_ms_startup
#
# Intialize this LISP MS process. This function returns a LISP network
# listen socket.
#
#enddef
#
# lisp_ms_shutdown
#
# Shutdown process.
#
#enddef
#------------------------------------------------------------------------------
#
# Main entry point for process.
#
if (lisp_ms_startup() == False):
lisp.lprint("lisp_ms_startup() failed")
lisp.lisp_print_banner("Map-Server abnormal exit")
exit(1)
#endif
while (True):
opcode, source, port, packet = \
lisp.lisp_receive(lisp_ipc_listen_socket, True)
if (source == ""): break
if (opcode == "command"):
lispconfig.lisp_process_command(lisp_ipc_listen_socket, opcode,
packet, "lisp-ms", [lisp_ms_commands, lisp.lisp_policy_commands])
elif (opcode == "api"):
lisp.lisp_process_api("lisp-ms", lisp_ipc_listen_socket, packet)
else:
lisp.lisp_parse_packet(lisp_send_sockets, packet, source, port)
#endif
#endwhile
lisp_ms_shutdown()
lisp.lisp_print_banner("Map-Server normal exit")
exit(0)
#------------------------------------------------------------------------------
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1,
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685,
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198,
220,
220,
220,
220,
220,
220,
220,
366,
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1,
1058,
685,
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220,
198,
220,
220,
220,
220,
220,
220,
220,
366,
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1350,
1,
1058,
685,
17821,
11,
366,
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1600,
366,
3919,
33116,
198,
220,
220,
220,
220,
220,
220,
220,
366,
301,
2012,
1,
1058,
685,
17821,
11,
366,
8505,
1600,
366,
3919,
33116,
198,
220,
220,
220,
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366,
68,
312,
1,
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366,
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366,
3919,
8973,
1782,
4357,
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628,
220,
220,
220,
366,
75,
8802,
30330,
12,
4868,
12,
13000,
1,
1058,
685,
75,
8802,
11250,
13,
75,
8802,
62,
81,
293,
62,
21812,
11,
1391,
198,
220,
220,
220,
220,
220,
220,
220,
366,
81,
293,
12,
3672,
1,
1058,
685,
25101,
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198,
220,
220,
220,
220,
220,
220,
220,
366,
81,
293,
12,
17440,
1,
1058,
685,
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220,
198,
220,
220,
220,
220,
220,
220,
220,
366,
21975,
1,
1058,
685,
17821,
4357,
220,
198,
220,
220,
220,
220,
220,
220,
220,
366,
5715,
1,
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17821,
11,
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11,
14280,
60,
1782,
4357,
220,
628,
220,
220,
220,
366,
75,
8802,
33918,
1,
1058,
685,
75,
8802,
11250,
13,
75,
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62,
17752,
62,
21812,
11,
1391,
198,
220,
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220,
220,
220,
220,
220,
366,
17752,
12,
3672,
1,
1058,
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25101,
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198,
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220,
220,
220,
220,
220,
220,
366,
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12,
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1,
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25101,
60,
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366,
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2524,
1,
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62,
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62,
12860,
62,
15654,
62,
21812,
11,
1391,
1782,
2361,
198,
92,
198,
198,
2,
198,
2,
300,
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62,
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62,
15654,
62,
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198,
2,
198,
2,
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42118,
640,
329,
257,
2560,
2524,
12,
68,
312,
393,
257,
1200,
2524,
12,
68,
312,
422,
281,
198,
2,
1981,
47997,
2233,
284,
20121,
12,
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29320,
13,
198,
2,
198,
2,
437,
4299,
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300,
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48678,
62,
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82,
198,
2,
198,
2,
3862,
503,
262,
1981,
12784,
13,
198,
2,
198,
2,
437,
4299,
198,
198,
2,
198,
2,
300,
8802,
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49315,
198,
2,
198,
2,
3862,
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326,
423,
407,
6823,
1626,
511,
23944,
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12,
198,
2,
256,
28781,
13,
198,
2,
198,
2,
437,
4299,
198,
198,
2,
198,
2,
300,
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62,
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62,
12984,
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198,
2,
198,
2,
3862,
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287,
300,
8802,
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62,
23870,
13,
198,
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198,
2,
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198,
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198,
2,
300,
8802,
62,
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62,
9688,
929,
198,
2,
198,
2,
2558,
498,
1096,
428,
406,
1797,
47,
6579,
1429,
13,
770,
2163,
5860,
257,
406,
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47,
3127,
198,
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17802,
13,
198,
2,
198,
2,
437,
4299,
198,
198,
2,
198,
2,
300,
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62,
907,
62,
49625,
2902,
198,
2,
198,
2,
40411,
1429,
13,
198,
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198,
2,
437,
4299,
198,
198,
2,
10097,
26171,
198,
198,
2,
198,
2,
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5726,
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329,
1429,
13,
198,
2,
198,
361,
357,
75,
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62,
907,
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3419,
6624,
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198,
220,
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300,
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13,
75,
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75,
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907,
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3419,
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198,
220,
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13,
75,
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12,
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220,
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198,
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11,
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13,
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268,
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11,
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220,
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611,
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] | 2.456746 | 2,809 |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
import math
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from joblib import dump, load
def get_best_regressor(data):
"""
:param data: a pandas data frame with the last column being the dependent variable
:return: The best model for the data
"""
# TODO: Perform backwards elimination for the best model
# Identify data dimensions
n_inputs = len(data.iloc[0, :].values) - 1
sample_size = len(data.iloc[:, 0].values)
regressors = [LinearRegression(),
MyPolynomialRegressor(degree=5),
SVR(kernel="rbf"),
DecisionTreeRegressor(random_state=42),
RandomForestRegressor(n_estimators=20, random_state=42)]
best_model = None
best_mse = 10000
print(data)
X = data.iloc[:, 0:n_inputs].values
print(X)
y = data.iloc[:, n_inputs].values
# Feature scaling
sc_x = StandardScaler()
sc_y = StandardScaler()
X = sc_x.fit_transform(X)
y = sc_y.fit_transform(np.reshape(y, (-1, 1)))
y = y.ravel()
# Run each model
for regressor in regressors:
# initialize total mse
# TODO: maybe add vmse variance of mse or use adjusted r2
mmse_train = 0
mmse_test = 0
n_repeats = 50 # number of times the model is trained on different training and test sets
test_size = 0.2 # proportion of the data that will be part of test set
# Run each model with a new random state
for i in range(n_repeats):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
regressor.fit(X_train, y_train)
mse_train = mean_squared_error(y_train, regressor.predict(X_train))
mse_test = mean_squared_error(y_test, regressor.predict(X_test))
mmse_train += mse_train
mmse_test += mse_test
mmse_train = mmse_train / n_repeats
mmse_test = mmse_test / n_repeats
print(mmse_train)
print(mmse_test)
print("-----------")
mse = test_size * mmse_test + (1 - test_size) * mmse_train
print("Done training, now testing to see if its better")
# If the model is not over fitted and it has a better mse
#if (mmse_train < 1.1 * mmse_test) and mse < best_mse:
# TODO: Check this if statement
if mse < best_mse:
best_mse = mse
# To get the best possible version, we must train it on all the data we have
regressor.fit(X, y)
best_model = regressor
print(best_mse)
print(mean_squared_error(y, regressor.predict(X)))
plt.scatter(X[:, -1], y, color="red")
plt.plot(X[:, -1], regressor.predict(X), color="blue")
plt.xlabel("Level")
plt.ylabel("Salary")
plt.title("HI")
plt.show()
return best_model
def get_best_classifier(data):
"""
:param data: a pandas data frame with the last column being the dependent variable
:return: The best model for the data
"""
# TODO: Perform backwards elimination for the best model
# Identify data dimensions
n_inputs = len(data.iloc[0, :].values) - 1
sample_size = len(data.iloc[:, 0].values)
classifiers = [#KNeighborsClassifier(n_neighbors=5, metric="minkowski", p=2),
DecisionTreeClassifier(criterion="entropy", random_state=42),
LogisticRegression(random_state=0),
GaussianNB(),
RandomForestClassifier(n_estimators=20, max_leaf_nodes=10, criterion="entropy", random_state=42),
#SVC(kernel="rbf", random_state=0, C=100)
]
best_model = None
best_f1 = 0
print(data)
X = data.iloc[:, 0:n_inputs].values
print(X)
y = np.asarray(data.iloc[:, n_inputs].values, dtype=np.int)
# Feature scaling
sc_x = StandardScaler()
X = sc_x.fit_transform(X)
# Run each model
for classifier in classifiers:
# initialize total mse
# TODO: maybe add vmse variance of mse or use adjusted r2
mf1_train = 0
mf1_test = 0
n_repeats = 50 # number of times the model is trained on different training and test sets
test_size = 0.2 # proportion of the data that will be part of test set
# Run each model with a new random state
for i in range(n_repeats):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
classifier.fit(X_train, y_train)
f1_train = f1_score(y_train, classifier.predict(X_train))
f1_test = f1_score(y_test, classifier.predict(X_test))
mf1_train += f1_train
mf1_test += f1_test
mf1_train = mf1_train / n_repeats
mf1_test = mf1_test / n_repeats
print(mf1_train)
print(mf1_test)
f1 = test_size * mf1_test + (1 - test_size) * mf1_train
print("Done training, now testing to see if its better")
print("-----------")
# If the model is not over fitted and it has a better mse
# if (mmse_train < 1.1 * mmse_test) and mse < best_mse:
# TODO: Check this if statement
if f1 > best_f1:
best_f1 = f1
# To get the best possible version, we must train it on all the data we have
classifier.fit(X, y)
best_model = classifier
print(best_f1)
print(f1_score(y, classifier.predict(X)))
print(confusion_matrix(y, classifier.predict(X)))
dump(X, "X.joblib")
dump(y, "y.joblib")
return best_model
def impute(data):
"""
:param data: The pandas DataFrame to impute (only numbers allowed)
:return: the imputed dataframe with the average in place of empty cells
"""
# sklearn deletes the columns so we have to put them back later
colun_names = data.columns
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
imputer = imputer.fit(data.iloc[:, :])
data = imputer.transform(data)
data = pd.DataFrame(data, columns=colun_names)
return data
def encode_one_hot(data):
"""
:param data: The pandas DataFrame to perform the one hot encoding
:return: The original data but all text fields are replaced by one hot values
"""
data = pd.DataFrame(data)
columns = [] # a list of column names
new_columns = pd.DataFrame()
for i, item in enumerate(data.iloc[0, :].values):
if type(item) == str:
columns.append(data.columns[i])
new_column = pd.get_dummies(data.iloc[:, i], drop_first=True, dtype=np.float)
new_columns = pd.concat([new_columns.reset_index(drop=True), new_column.reset_index(drop=True)], axis=1)
data = data.drop(columns, axis=1)
data = pd.concat([new_columns.reset_index(drop=True), data.reset_index(drop=True)], axis=1)
return pd.DataFrame(data)
def encode_labels(data):
"""
:param data: The pandas DataFrame to perform the label encoding
:return: The original dataFrame but text fields are replaced by their integer values
It also returns an array of columns with 1 if the column is a category and 0 otherwise
"""
column_categories = []
data = pd.DataFrame(data)
for i, item in enumerate(data.iloc[0, :].values):
if type(item) == str:
column_categories.append(1)
data[data.columns[i]] = LabelEncoder().fit_transform(data[data.columns[i]])
else:
column_categories.append(0)
return data#, column_categories
def get_best_model_dependent(data, n_outputs, output_types):
"""
Calculates the best model for each output where the next output has all of the previous outputs as input too
:param output_types: An array of 1s or 0s. 0 if regression problem and 1 if classification problem
:param data: The pandas DataFrame to perform the modelling
:param n_outputs: The number of dependent variables to model
:return: An array of the best model for each output
"""
n_inputs = len(data.iloc[0, :]) - n_outputs
# For each output, find the best model from the previous
models = []
for i in range(n_outputs):
# Separate dependent and independent variables for preprocessing
# This stuff could go outside the for loop but as outputs become inputs, they get encoded differently
X = data.iloc[:, 0:n_inputs + i]
y = data.iloc[:, n_inputs + i]
X = encode_one_hot(X)
y = encode_labels(y)
category = output_types[i]
X = impute(X)
y = impute(y)
# Join them pack together
current_data = pd.concat([X.reset_index(drop=True), y.reset_index(drop=True)], axis=1)
if category == 0:
models.append(get_best_regressor(current_data))
else:
models.append(get_best_classifier(current_data))
return models
def get_best_model_independent(data, n_outputs, output_types):
"""
Calculates the best model for each output given the inputs where none of the outputs influence each other
:param output_types: An array of 1s or 0s. 0 if regression problem and 1 if classification problem
:param data: The pandas DataFrame to perform the modelling
:param n_outputs: The number of dependent variables to model
:return: An array of the best model for each output
"""
n_inputs = len(data.iloc[0, :]) - n_outputs
# Separate dependent and independent variables for preprocessing
X = data.iloc[:, 0:n_inputs]
Y = data.iloc[:, n_inputs:]
X = encode_one_hot(X)
Y = encode_labels(Y)
X = impute(X)
Y = impute(Y)
# For each output, find the best model from the previous
models = []
for i in range(n_outputs):
current_data = pd.concat([X.reset_index(drop=True), Y[Y.columns[i]].reset_index(drop=True)], axis=1)
if output_types[i] == 0:
models.append(get_best_regressor(current_data))
else:
models.append(get_best_classifier(current_data))
return models
if __name__ == "__main__":
start_time = time.time()
dataset = pd.read_csv("Churn_Modelling.csv")
dataset = dataset.iloc[:, 3:]
models = get_best_model_dependent(dataset, 1, [1])
dump(models, "Models.joblib")
print(models)
end_time = time.time()
print("Time taken:", end_time - start_time)
# models = load("Models.joblib")
# X = load("X.joblib")
# y = load("y.joblib")
# print(models[0])
# print(X)
# print(y)
#
# print(f1_score(y,models[0].predict(X)))
# cm = confusion_matrix(y,models[0].predict(X))
# print(cm)
# print((cm[0][0]+cm[1][1])/10000)
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] | 2.427075 | 4,772 |
#
# Collective Knowledge (environment)
#
# See CK LICENSE.txt for licensing details
# See CK COPYRIGHT.txt for copyright details
#
# Developer: Grigori Fursin, [email protected], http://fursin.net
#
cfg={} # Will be updated by CK (meta description of this module)
work={} # Will be updated by CK (temporal data)
ck=None # Will be updated by CK (initialized CK kernel)
# Local settings
##############################################################################
# Initialize module
def init(i):
"""
Input: {}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
return {'return':0}
##############################################################################
# set environment for tools and libraries
# (multiple versions of the same tools/libraries can co-exist)
def env_set(i):
"""
Input: {
(host_os) - host OS (detect, if ommitted)
(target_os) - target OS (detect, if ommitted)
(target_device_id) - target device ID (detect, if omitted)
or
(device_id)
(repo_uoa) - repo where to limit search
(uoa) - environment UOA entry
or
(tags) - search UOA by tags (separated by comma)
(or_tags) - add entries which has groups of tags separated by ;
(no_tags) - exclude entris with these tags separated by comma
(search_dict) - require verbatim matches of some attributes
(local) - if 'yes', add host_os, target_os, target_device_id to search
(key) - key from deps (to set env with path)
(name) - user-friendly name of the dependency (if needs to be resolved)
(deps) - already resolved deps
(reuse_deps) - if 'yes' reuse all deps if found in cache by tags
(deps_cache) - list with resolved deps
(skip_cache) - skip caching (sometimes needed to iterate over deps such as DNN libs
during autotuning, crowd-benchmarking and crowd-tuning)
(skip_auto_resolution) - if 'yes', do not check if deps are already resolved
(skip_default) - if 'yes', skip detection of default installed software version
(skip_installed) - dict to specify on which platforms not to search already installed version
(skip_pruning_by_other_deps) - if 'yes', do not prune available envs using other resolved deps
(bat_file) - if !='', use this filename to generate/append bat file ...
(bat_new) - if 'yes', start new bat file
(env) - existing environment
(print) - if 'yes', print found environment
(random) - if 'yes' and there is a choice, select random
(useful for quiet experiment crowdsourcing such as sw/hw crowdtuning)
(quiet) - if 'yes', automatically provide default answer to all questions when resolving dependencies ...
(force_env_init) - if 'yes', add '1' when calling env script (useful for LLVM plugins for example to force reinit)
(install_to_env) - install dependencies to env instead of CK-TOOLS (to keep it clean)!
(install_env) - customize installation (useful for replay to rebuild proper package with external env)
(version_from) - check version starting from ... (list of numbers)
(version_to) - check version up to ... (list of numbers)
(safe) - safe mode when searching packages first instead of detecting already installed soft
(to have more deterministic build)
(package_uoa) - force installation package
(also useful to rebuild deps during replay)
(rebuild) - if 'yes', attempt to set env to avoid downloading package again, just rebuild (if supported)
}
Output: {
return - return code = 0, if successful
= 32, if environment was deleted (env_uoa - env which was not found)
> 0, if error
(error) - error text if return > 0
env_uoa - found environment UOA
env - updated environment
bat - string for bat file
lst - all found entries
dict - meta of the selected env entry
detected_version - detected version of a software
}
"""
import os
import copy
import json
o=i.get('out','')
ooo = o if o=='con' else '' # shouldn't we consider other options?
ran=i.get('random','')
quiet=i.get('quiet','')
name=i.get('name','')
rebuild=i.get('rebuild','')
skip_cache=i.get('skip_cache','')
package_uoa=i.get('package_uoa','')
install_env=i.get('install_env',{})
# Clean output file
sar=i.get('skip_auto_resolution','')
cdeps=i.get('deps',{})
deps_cache=i.get('deps_cache',[])
reuse_deps=i.get('reuse_deps','')
skip_default=i.get('skip_default','')
skip_installed=i.get('skip_installed',{})
iev=i.get('install_to_env','')
safe=i.get('safe','')
bf=i.get('bat_file','')
if bf!='' and os.path.isfile(bf): os.remove(bf)
vfrom=i.get('version_from',[])
vto=i.get('version_to',[])
# Check host/target OS/CPU
hos=i.get('host_os','')
tos=i.get('target_os','')
tdid=i.get('target_device_id','')
if tdid=='': tdid=i.get('device_id','')
user_env = (hos!='' or tos!='' or tdid!='')
# Get some info about OS
ii={'action':'detect',
'module_uoa':cfg['module_deps']['platform.os'],
'host_os':hos,
'target_os':tos,
'device_id':tdid,
'skip_info_collection':'yes'}
r=ck.access(ii)
if r['return']>0: return r
hos=r['host_os_uid']
hosx=r['host_os_uoa']
hosd=r['host_os_dict']
ck_os_name=hosd['ck_name']
tos=r['os_uid']
tosx=r['os_uoa']
tosd=r['os_dict']
# Check if base is different
x1=hosd.get('base_uid','')
x2=hosd.get('base_uoa','')
if x1!='' and x2!='':
hos=x1
hosx=x2
x1=tosd.get('base_uid','')
x2=tosd.get('base_uoa','')
if x1!='' and x2!='':
tos=x1
tosx=x2
remote=tosd.get('remote','')
tbits=tosd.get('bits','')
hplat=hosd.get('ck_name','')
tplat2=i.get('original_target_os_name2','')
eset=hosd.get('env_set','')
svarb=hosd.get('env_var_start','')
svare=hosd.get('env_var_stop','')
sdirs=hosd.get('dir_sep','')
evs=hosd.get('env_var_separator','')
eifs=hosd.get('env_quotes_if_space','')
nout=hosd.get('no_output','')
# Check environment UOA
enruoa=i.get('repo_uoa','')
tags=i.get('tags','')
or_tags=i.get('or_tags','')
no_tags=i.get('no_tags','')
search_dict = i.get('search_dict')
duoa=i.get('uoa','')
lx=0
dd={}
setup={}
if user_env or i.get('local','')=='yes':
setup={'host_os_uoa':hos,
'target_os_uoa':tos,
'target_os_bits':tbits}
if reuse_deps=='yes' and skip_cache!='yes':
# Check in cache!
dep_query = {'setup':setup, 'tags':tags.split(','), 'or_tags':or_tags.split(','), 'no_tags':no_tags.split(',')}
for dc_q in deps_cache:
dep_meta = dc_q.get('meta',{})
r=ck.compare_dicts({'dict1':dep_meta, 'dict2':dep_query}) # if dep_meta contains dep_query...
if r['return']>0: return r
if r['equal']=='yes':
duoa = dc_q.get('uoa','')
reuse_deps='no' # to avoid updating cache
break
# Search
ii={'action':'search',
'module_uoa':work['self_module_uid'],
'tags':tags,
'repo_uoa':enruoa,
'data_uoa':duoa,
'add_info':'yes',
'add_meta':'yes'
} # Need to sort by version, if ambiguity
if search_dict!=None and len(search_dict)>0:
ii['search_dict']=search_dict
iii=copy.deepcopy(ii) # may need to repeat after registration
# Prepare possible warning
x = '"{}"'.format(name) if name else 'required software '
msg_env_not_found = 'no registered CK environment was found for {} dependency with tags="{}"'.format(x, tags)
if no_tags!='':
msg_env_not_found+=', no_tags="'+no_tags+'"'
if len(setup)>0:
ro=readable_os({'setup':setup})
if ro['return']>0: return ro
setup1=ro['setup1']
msg_env_not_found+=' and setup='+json.dumps(setup1)
if len(vfrom)>0 or len(vto)>0:
msg_env_not_found+=' and version constraints ('+json.dumps(vfrom)+' <= v <= '+json.dumps(vto)+')'
# Search for environment entries
r=ck.access(ii)
if r['return']>0: return r
# Prune if needed
r=prune_search_list({'lst':r['lst'],
'or_tags':or_tags,
'no_tags':no_tags,
'version_from':vfrom,
'version_to':vto,
'setup':setup,
})
if r['return']>0: return r
sbov=r.get('skipped_because_of_version','')
l=r['lst']
lx=len(l)
auoas=[]
dname=''
try_to_reinstall=False
if lx==0 and duoa!='':
# Check exact problem
rx=ck.access({'action':'load',
'module_uoa':work['self_module_uid'],
'data_uoa':duoa})
if rx['return']>0:
if rx['return']!=16: return rx
if package_uoa=='':
rx['error']='strange - missing environment ('+duoa+') and package not specified (may happen during replay from another machine)'
if o=='con' and quiet!='yes':
ck.out('')
ck.out(' WARNING: '+rx['error']+'!')
ck.out('')
# ry=ck.inp({'text':' Would you like to detect / reinstall again (Y/n)? '})
# x=ry['string'].strip()
#
# if x=='n' or x=='no':
# return rx
else:
# Otherwise can try to rebuild from provided package UOA
ck.out('')
ck.out('WARNING: environment doesn\'t exist but package provided - trying to redetect/reinstall ...')
duoa=''
iii['data_uoa']=duoa # since will be later new search
l=[]
lx=0
try_to_reinstall=True
# Repeat search if has without data uoa
r=ck.access(iii)
if r['return']>0: return r
# Prune if needed
r=prune_search_list({'lst':r['lst'],
'or_tags':or_tags,
'no_tags':no_tags,
'version_from':vfrom,
'version_to':vto,
'setup':setup,
'package_uoa':package_uoa})
if r['return']>0: return r
sbov=r.get('skipped_because_of_version','')
l=r['lst']
lx=len(l)
auoas=[]
dname=''
if not try_to_reinstall:
dds=rx['dict'].get('setup',{})
# Changed setup
if o=='con':
ck.out('')
ck.out('WARNING: requested host or target OS info is not matching info in env '+duoa+'!')
ck.out('')
rx=ck.access({'action':'convert_uid_to_alias', 'module_uoa':cfg['module_deps']['os'], 'uoa':dds.get('host_os_uoa','')})
if rx['return']>0: return rx
x=rx['string']
ck.out(' Host OS UOA in env '+duoa+' : '+x)
rx=ck.access({'action':'convert_uid_to_alias', 'module_uoa':cfg['module_deps']['os'], 'uoa':setup.get('host_os_uoa','')})
if rx['return']>0: return rx
x=rx['string']
ck.out(' Requested host OS UOA : '+x)
ck.out('')
rx=ck.access({'action':'convert_uid_to_alias', 'module_uoa':cfg['module_deps']['os'], 'uoa':dds.get('target_os_uoa','')})
if rx['return']>0: return rx
x=rx['string']
ck.out(' Target OS UOA in env '+duoa+' : '+x)
rx=ck.access({'action':'convert_uid_to_alias', 'module_uoa':cfg['module_deps']['os'], 'uoa':setup.get('target_os_uoa','')})
if rx['return']>0: return rx
x=rx['string']
ck.out(' Requested target OS UOA : '+x)
ck.out('')
ck.out(' Target OS bits in env '+duoa+' : '+dds.get('target_os_bits',''))
ck.out(' Requested target OS bits : '+setup.get('target_os_bits',''))
ck.out('')
ck.out(' Target or_tags : '+dds.get('tags',''))
ck.out(' Requested or_tags : '+or_tags)
ck.out('')
ck.out(' Target no_tags : '+dds.get('no_tags',''))
ck.out(' Requested no_tags : '+no_tags)
ck.out('')
ck.out(' This is a possible bug - please report here:')
ck.out(' * https://github.com/mlcommons/ck/issues')
ck.out(' * https://github.com/mlcommons/ck-mlops/issues')
ck.out('')
return {'return':33, 'error':'current host or target OS ('+str(setup)+' is not matching the one in software env '+duoa}
# If no entries and safe mode, search packages first
showed_warning=False
if lx==0 and duoa=='' and tags!='' and (safe=='yes' or package_uoa!=''):
ck.out('==========================================================================================')
ck.out('WARNING: '+msg_env_not_found)
showed_warning=True
if len(install_env)>0:
if o=='con':
ck.out('')
ck.out('Reusing original and slightly pruned environment ...')
ck.out('')
for k in list(install_env.keys()):
# TBD: quite ugly - maybe should record external env explicitly ...
if k in ['LFLAGS', 'CXXFLAGS', 'CK_HOST_CPU_NUMBER_OF_PROCESSORS', 'LCORE_FLAGS']:
del(install_env[k])
rx=internal_install_package({'out':ooo,
'package_uoa':package_uoa,
'tags':tags,
'or_tags':or_tags,
'no_tags':no_tags,
'quiet':quiet,
'install_to_env':iev,
'install_env':install_env,
'safe':safe,
'host_os':hos,
'target_os':tos,
'device_id':tdid,
'add_hint':'yes',
'reuse_deps':reuse_deps,
'deps_cache':deps_cache,
'version_from':vfrom,
'version_to':vto,
'deps':cdeps,
'rebuild':rebuild,
'sub_deps': i.get('current_deps',{}) if try_to_reinstall else {},
})
if rx['return']>0 and rx['return']!=16: return rx
if rx['return']==0:
duoa=rx['env_data_uoa']
duid=rx['env_data_uid']
# If no entries, try to detect default ones and repeat
if lx==0 and duoa=='':
history_deps=[]
if o=='con' and tags!='' and not showed_warning:
ck.out('')
ck.out(' ********')
ck.out(' WARNING: '+msg_env_not_found)
ck.out('')
showed_warning=True
# First, try to detect already installed software, but not registered (default)
# FGG changed on 2017-10-20 to be able to detect new versions of soft !
if not (skip_default=='yes' or skip_installed.get(tplat2,'')=='yes'): # or sbov=='yes'):
if o=='con':
ck.out(' Trying to automatically detect required software ...')
ii={'action':'search',
'module_uoa':cfg['module_deps']['soft'],
'tags':tags,
'add_meta':'yes'}
rx=ck.access(ii)
if rx['return']>0: return rx
slst=rx['lst']
# Sorting and checking which has detection module
detected=''
ssi=0
found=False
for q in sorted(slst, key=lambda v: v.get('meta',{}).get('sort',0)):
met=q.get('meta',{})
auoa=q['data_uoa']
auid=q['data_uid']
aname=met.get('soft_name','')
# Check no tags:
if no_tags!='':
split_no_tags=no_tags.split(',')
soft_tags=met.get('tags',[])
soft_skip=False
for st in soft_tags:
if st in split_no_tags:
soft_skip=True
break
if soft_skip:
continue
auoas.append(q['data_uoa'])
ds=met.get('auto_detect','')
if ds=='yes':
if auid not in history_deps:
# Check target
rx=ck.access({'action':'check_target',
'module_uoa':cfg['module_deps']['soft'],
'dict':met.get('customize',{}),
'host_os_uoa':hosx,
'host_os_dict':hosd,
'target_os_uoa':tosx,
'target_os_dict':tosd})
if rx['return']>0:
continue
history_deps.append(auid)
ssi+=1
if o=='con':
ck.out('')
ck.out(' '+str(ssi)+') Checking if "'+aname+'" ('+auoa+' / '+auid+') is installed ...')
# Detect software
ii={'action':'check',
'module_uoa':cfg['module_deps']['soft'],
'data_uoa':auid,
'skip_help':'yes',
'host_os':hos,
'target_os':tos,
'target_device_id':tdid,
'quiet':quiet,
'version_from':vfrom,
'version_to':vto,
# 'deps':cdeps,
'out':ooo}
if len(setup)>0:
ii.update(setup)
ry=ck.access(ii)
if ry['return']>0:
if o=='con':
ck.out(' (warning during intermediate step: '+ry['error']+')')
else:
found=True
hdeps=ry.get('deps',{})
for hd in hdeps:
xhd=hdeps[hd]
xxhd=xhd.get('dict',{}).get('soft_uoa','')
if xxhd not in history_deps:
history_deps.append(xxhd)
# repeat search if at least one above setup was performed
if not found:
if o=='con':
ck.out(' No software auto-detection scripts found for this software in CK :( ...')
if len(auoas)>0:
ck.out('')
ck.out(' Checked following related CK soft entries:')
for q in auoas:
ck.out(' * '+q)
else:
r=ck.access(iii)
if r['return']>0: return r
# Prune if needed
r=prune_search_list({'lst':r['lst'],
'or_tags':or_tags,
'no_tags':no_tags,
'version_from':vfrom,
'version_to':vto,
'setup':setup,
'package_uoa':package_uoa})
if r['return']>0: return r
l=r['lst']
lx=len(l)
# Re-check/prune existing environment using already resolved deps
if lx>0:
ilx=0
if i.get('skip_pruning_by_other_deps','')!='yes' and lx>1 and sar!='yes':
# Try auto-resolve or prune choices
nls=[]
for z in range(0, lx):
j=l[z]
zm=j.get('meta',{})
cus=zm.get('customize','')
zdeps=zm.get('deps',{})
skip=False
for q in zdeps:
jj=zdeps[q]
juoa=jj.get('uoa','')
for a in cdeps:
if a==q:
aa=cdeps[a]
if aa.get('skip_reuse','')!='yes':
auoa=aa.get('uoa','')
# Tricky part: basically if similar and already resolved current deps are not the same is underneath ones ...
if auoa!='' and auoa!=juoa:
skip=True
break
if skip: break
if not skip: nls.append(j)
l=nls
lx=len(l)
# Select sub-deps (sort by version)
if lx>1:
ls=sorted(l, key=lambda k: (k.get('meta',{}).get('customize',{}).get('sort', 0),
k.get('info',{}).get('data_name',k['data_uoa']),
internal_get_val(k.get('meta',{}).get('setup',{}).get('version_split',[]), 0, 0),
internal_get_val(k.get('meta',{}).get('setup',{}).get('version_split',[]), 1, 0),
internal_get_val(k.get('meta',{}).get('setup',{}).get('version_split',[]), 2, 0),
internal_get_val(k.get('meta',{}).get('setup',{}).get('version_split',[]), 3, 0),
internal_get_val(k.get('meta',{}).get('setup',{}).get('version_split',[]), 4, 0)),
reverse=True)
l=ls
if ran=='yes':
from random import randint
ilx=randint(0, lx-1)
elif quiet=='yes':
ilx=0
else:
if o=='con':
xq='required software'
if name!='': xq='"'+name+'"'
xq+=' with tags="'+tags+'"'
if len(setup)>0:
import json
ro=readable_os({'setup':setup})
if ro['return']>0: return ro
setup1=ro['setup1']
xq+=' and setup='+json.dumps(setup1)
ck.out('')
ck.out('More than one environment found for '+xq+':')
ck.out('')
dep_options = []
for opt_dict in l: # looping through all options for this environment
zi=opt_dict.get('info',{})
zm=opt_dict.get('meta',{})
zu=opt_dict.get('data_uid','')
zdn=zi.get('data_name','')
zdeps=zm.get('deps',{})
xtags=zm.get('tags','')
cus=zm.get('customize',{})
ver=cus.get('version','')
tags_csv = ','.join( [t for t in xtags if t] )
this_option = [ '{} - v{} ({} ({}))'.format(zdn, ver, tags_csv, zu) ]
if len(zdeps)>0:
for j in sorted(zdeps, key=lambda v: zdeps[v].get('sort',0)):
jj=zdeps[j]
juoa=jj.get('uoa','')
if juoa!='': # if empty, it most likely means that this unresolved dependency
# is for a different target
jtags=jj.get('tags','')
jver=jj.get('ver','')
this_option.append( '{}- Depends on "{}" (env UOA={}, tags="{}", version={})'.format(' '*35, j, juoa, jtags, jver))
dep_options.append(this_option)
select_adict = ck.access({'action': 'select_string',
'module_uoa': 'misc',
'options': dep_options,
'default': '0',
'question': 'Select one of the options for '+xq,
})
if select_adict['return']>0: return select_adict
ilx = select_adict['selected_index']
if ilx<len(l):
duid=l[ilx]['data_uid']
duoa=duid
dname=l[ilx].get('info',{}).get('data_name','')
dd=l[ilx].get('meta',{})
if o=='con' and i.get('print','')=='yes':
x=duoa
if duid!=duoa: x+=' ('+duid+')'
ck.out('CK environment found using tags "'+tags+'" : '+x)
# No registered environments found and environment UOA is not explicitly defined
if duoa=='':
if tags!='':
if not showed_warning:
ck.out('==========================================================================================')
ck.out('WARNING: '+msg_env_not_found)
rx=internal_install_package({'out':ooo,
'tags':tags,
'or_tags':or_tags,
'no_tags':no_tags,
'quiet':quiet,
'install_to_env':iev,
'install_env':install_env,
'safe':safe,
'host_os':hos,
'target_os':tos,
'device_id':tdid,
'reuse_deps':reuse_deps,
'deps_cache':deps_cache,
'version_from':vfrom,
'version_to':vto,
'rebuild':rebuild,
'deps':cdeps})
if rx['return']>0: return rx
duoa=rx['env_data_uoa']
duid=rx['env_data_uid']
if duoa=='':
if o=='con':
ck.out(' CK packages are not found for this software :( !')
ck.out('')
if len(auoas)>0:
if len(auoas)==1:
rx=ck.access({'action':'print_help',
'module_uoa':cfg['module_deps']['soft'],
'data_uoa':auoas[0],
'platform':hplat})
rx=ck.inp({'text':' Would you like to manually register software, i.e. if it is in an unusual path (y/N): '})
x=rx['string'].strip().lower()
if x=='yes' or x=='yes':
ck.out('')
rx=ck.access({'action':'setup',
'module_uoa':cfg['module_deps']['soft'],
'data_uoa':auoas[0],
'out':'con'})
if rx['return']>0: return rx
ck.out('')
else:
# Show possible Wiki page
rx=ck.inp({'text':' Would you like to open wiki pages about related software (with possible installation info) (y/N): '})
x=rx['string'].strip().lower()
if x=='yes' or x=='yes':
ck.out('')
for q in auoas:
rx=ck.access({'action':'wiki',
'module_uoa':cfg['module_deps']['soft'],
'data_uoa':q})
if rx['return']>0: return rx
ck.out('')
ck.out('')
return {'return':1, 'error':msg_env_not_found}
# Load selected environment entry
r=ck.access({'action':'load',
'module_uoa':work['self_module_uid'],
'data_uoa':duoa})
if r['return']>0:
if r['return']==16:
r['return']=32
r['env_uoa']=duoa
return r
d=r['dict']
p=r['path']
dname=r.get('data_name','')
if dname!='':
d['data_name']=dname
suoa=d.get('soft_uoa','')
cs=None
if suoa!='':
r=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['soft'],
'data_uoa':suoa})
if r['return']>0: return r
salias=r['data_alias']
d['soft_alias']=salias
# Check if has custom script
rx=ck.load_module_from_path({'path':r['path'], 'module_code_name':cfg['custom_script_name'], 'skip_init':'yes'})
if rx['return']==0:
cs=rx['code']
# Check that all sub dependencies still exists (if full path)
outdated=False
to_delete=False
err=''
subdeps=d.get('deps',{}) # sub-dependencies of the selected environment
# (normally already resolved, but check if software has changed in the meantime)
for subdep_name in subdeps:
subdep=subdeps[subdep_name]
subdep_cus=subdep.get('dict',{}).get('customize',{})
sfc=subdep_cus.get('skip_file_check','')
full_path=subdep_cus.get('full_path','')
if sfc!='yes' and full_path!='' and not os.path.isfile(full_path):
outdated=True
err='one of sub-dependencies ('+subdep_name+') have changed (file '+full_path+' not found)'
break
subdep_uoa=subdep.get('uoa','')
if subdep_uoa!='':
rx=ck.access({'action':'find',
'module_uoa':work['self_module_uid'],
'data_uoa':subdep_uoa})
if rx['return']>0:
if rx['return']!=16: return rx
outdated=True
err='one of sub-dependencies ('+subdep_name+') have changed (CK environment '+subdep_uoa+' not found)'
break
if reuse_deps=='yes' and skip_cache!='yes':
# Check in cache!
subdep_query = {'setup':setup, 'tags':subdep.get('tags','').split(','),
'or_tags':subdep.get('or_tags','').split(','),
'no_tags':subdep.get('no_tags','').split(',')}
dc_found = False
for dc_q in deps_cache:
dep_meta = dc_q.get('meta',{})
r=ck.compare_dicts({'dict1':dep_meta, 'dict2':subdep_query}) # if dep_meta contains subdep_query...
if r['return']>0: return r
if r['equal']=='yes':
dc_found = True
break
if not dc_found:
deps_cache.append({'meta':subdep_query, 'uoa':subdep_uoa, 'tags':tags.split(','),
'or_tags':or_tags.split(','),
'no_tags':no_tags.split(',')})
# Check if has changed but try to continue
if outdated and ' have changed ' in err and o=='con' and quiet!='yes':
ck.out('')
ck.out(' WARNING: '+err)
ck.out('')
rx=ck.inp({'text':' Would you like to continue at your own risk (y/N): '})
x=rx['string'].strip().lower()
if x=='y' or x=='yes':
outdated=False
# Check if file exists for current dependency
verx=''
cus=d.get('customize',{})
full_path=cus.get('full_path','')
tc='it appears that your environment has changed - '
if not outdated and full_path!='' and cus.get('skip_file_check','')!='yes' and not os.path.isfile(full_path):
err=tc+'software file not found in a specified path ('+full_path+')'
outdated=True
ver_in_env=cus.get('version','') # detected version during installation
if not outdated and ver_in_env!='':
scmd=cus.get('soft_version_cmd',{}).get(ck_os_name,'')
if cus.get('skip_version_recheck','')!='yes' and scmd!='' and 'parse_version' in dir(cs):
# Check version (via customized script) ...
ii={'action':'get_version',
'module_uoa':cfg['module_deps']['soft'],
'full_path':full_path,
'bat':'',
'host_os_dict':hosd,
'target_os_dict':tosd,
'cmd':scmd,
'custom_script_obj':cs,
'use_locale':cus.get('use_locale_for_version','')}
rx=ck.access(ii)
if rx['return']==0:
verx=rx['version']
if verx!='' and verx!=ver_in_env:
err=tc+'version during installation ('+ver_in_env+') is not the same as current version ('+verx+')'
outdated=True
if outdated:
if o=='con':
ck.out('')
ck.out('WARNING: '+err)
ck.out('')
rx=ck.inp({'text':'Would you like to remove outdated environment entry from CK (Y/n)? '})
x=rx['string'].strip().lower()
if x=='n' or x=='no':
x=''
if o=='con':
ck.out('')
rx=ck.inp({'text':'Would you like to continue at your own risk (y/N)? '})
x=rx['string'].strip().lower()
if x!='y':
return {'return':1, 'error':err}
else:
to_delete=True
# Deleting outdated environment
if to_delete:
if o=='con':
ck.out('')
ck.out('Removing outdated environment entry '+duoa+' ...')
rx=ck.access({'action':'delete',
'module_uoa':work['self_module_uid'],
'data_uoa':duoa})
if rx['return']>0: return rx
return {'return':1, 'error':'Outdated environment was removed - please, try again!'}
# Update cache
if reuse_deps=='yes' and skip_cache!='yes':
deps_cache.append({'meta':dep_query, 'uoa':duoa, 'tags':tags.split(','),
'or_tags':or_tags.split(','),
'no_tags':no_tags.split(',')})
# Prepare environment and bat
env=i.get('env',{})
xenv=d.get('env',{})
env.update(xenv)
env_call=hosd.get('env_call','')
bin_prefix=hosd.get('bin_prefix','')
# Process CMD first:
sb=''
es=d.get('env_script','') or ( cfg['default_bat_name'] + hosd.get('script_ext','') )
ppu=''
if i.get('force_env_init','')=='yes':
ppu=' 1'
if es!='':
pp=os.path.join(p,es)
if i.get('key','')!='':
sb+=eset+' CK_ENV_SCRIPT_'+i['key'].upper()+'='+pp+'\n'
sb+=env_call+' '+pp+ppu+'\n'
# Check bat file
if bf!='':
bn=i.get('bat_new','')
x='a'
if bn=='yes': x='w'
try:
fbf=open(bf, x)
fbf.write(sb)
except Exception as e:
fbf.close()
return {'return':1, 'error':'problem writing environment file ('+format(e)+')'}
fbf.close()
return {'return':0, 'env_uoa':duoa, 'env':env, 'bat':sb, 'lst':l, 'dict':d, 'detected_version':verx}
##############################################################################
# show all installed environment
def show(i):
"""
Input: {
(repo_uoa) - repository UOA (with wildcards)
(module_uoa) - module UOA (with wildcards)
(data_uoa) - data UOA (with wildcards)
(tags) - prune by tags
(target_os) - prune by target OS
(target_bits) - prune by target bits
(version) - prune by version
(name) - prune by name with wildcards
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
lst - list from search function
view - sorted view list
}
"""
o=i.get('out','')
tags=i.get('tags','')
extended_tags = [ tags ] if tags else []
tos_uoa=i.get('target_os','')
if tos_uoa!='':
# Load OS
ry=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['os'],
'data_uoa':tos_uoa})
if ry['return']>0: return ry
tos_uoa = ry['dict'].get('base_uoa','') or ry['data_uoa']
extended_tags.append( 'target-os-'+tos_uoa )
tb = i.get('target_bits','')
if tb!='': extended_tags.append( tb+'bits' )
ver = i.get('version','')
if ver!='': extended_tags.append( 'v'+ver )
tags = ','.join(extended_tags)
name=i.get('name','')
wname=False
if name.find('*')>=0 or name.find('?')>=0:
import fnmatch
wname=True
name=name.lower()
# creating a list of patterns from the "main xcid" and the list of auxiliary xcids:
full_xcids_list = [{
'repo_uoa': i.get('repo_uoa',''),
'module_uoa': i.get('module_uoa',''),
'data_uoa': i.get('data_uoa',''),
}] + i.get('xcids',[])
lst = []
# iterating through the list of patterns:
for xcid in full_xcids_list:
rx=ck.access({'action':'search',
'repo_uoa':xcid.get('repo_uoa',''),
'module_uoa':xcid.get('module_uoa',''),
'data_uoa':xcid.get('data_uoa',''),
'tags':tags,
'add_info':'yes',
'add_meta':'yes'})
if rx['return']>0: return rx
lst += rx['lst']
# prepare view
unsorted_dicts=[]
table_fields = [ # Rearranging the lines below will rearrange the fields in the output:
# (internal) (external) (align)
('data_uid', 'Env UID:', ':>'),
('target_os_uoa', 'Target OS:', ':>'),
('tbits', 'Bits:', ':>'),
('data_name', 'Name:', ':<'),
('version', 'Version:', ':<'),
('tags', 'Tags:', ':<'),
]
# width of each field (initialized with minimum required width)
max_width = { internal: len(external) for (internal, external, _) in table_fields }
target_os_name={} # Caching target OS names
for q in lst:
duoa=q['data_uoa']
duid=q['data_uid']
ruoa=q['repo_uoa']
ruid=q['repo_uid']
info=q['info']
meta=q['meta']
if len(meta)==0:
continue
cus=meta.get('customize',{})
setup=meta.get('setup',{})
tags=meta.get('tags',[])
host_os_uoa=setup.get('host_os_uoa','')
target_os_uoa=setup.get('target_os_uoa','')
tbits=setup.get('target_os_bits','')
version=cus.get('version','')
sversion=setup.get('version_split',[])
dname=info.get('data_name','')
if (not name) \
or ( ( fnmatch.fnmatch(dname.lower(), name)) if wname else (name == dname) ):
# Check target OS
if target_os_uoa in target_os_name:
tduoa=target_os_name[target_os_uoa]
elif target_os_uoa:
# Load
ry=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['os'],
'data_uoa':target_os_uoa})
if ry['return']>0: return ry
tduoa=ry['data_uoa']
target_os_name[target_os_uoa]=tduoa
else:
tduoa = 'Any'
tags_csv = ','.join( [t for t in tags if t] )
env_info = {
'data_uid': duid,
'repo_uid': ruid,
'tags': tags_csv,
'host_os_uoa': host_os_uoa,
'target_os_uoa': tduoa,
'tbits': tbits,
'version': version,
'version_split': sversion,
'data_name': dname,
}
# Find maximum width for each field:
for (internal, _, _) in table_fields:
field_width = len(str(env_info[internal]))
if field_width>max_width[internal]:
max_width[internal]=field_width
unsorted_dicts.append(env_info)
# Sort by target_os_uoa, name and split version
sorted_dicts=sorted(unsorted_dicts, key=lambda k: (k['target_os_uoa'],
k['tbits'],
k['data_name'],
internal_get_val(k.get('version_split',[]), 0, 0),
internal_get_val(k.get('version_split',[]), 1, 0),
internal_get_val(k.get('version_split',[]), 2, 0),
internal_get_val(k.get('version_split',[]), 3, 0),
internal_get_val(k.get('version_split',[]), 4, 0)),
reverse=True)
# Print
if o=='con' and len(sorted_dicts)>0:
last_field_name = table_fields[-1][0] # the last field is not padded, it is treated separately
# All fields of the header are aligned to the left
header_format = ' '.join( [ '{{p[{}]{}{}}}'.format(internal, ':<', max_width[internal]) for (internal, _, _) in table_fields[:-1]] \
+ [ '{{p[{}]}}'.format(last_field_name) ] )
# Fields of the body are aligned differently, depending on the third column of table_fields:
body_format = ' '.join( [ '{{p[{}]{}{}}}'.format(internal, body_align, max_width[internal]) for (internal, _, body_align) in table_fields[:-1]] \
+ [ '{{p[{}]}}'.format(last_field_name) ] )
header_line = header_format.format( p={ internal: external for (internal, external, _) in table_fields } )
ck.out(header_line)
ck.out('')
for field_values in sorted_dicts:
body_line = body_format.format( p=field_values )
ck.out( body_line )
return {'return':0, 'lst':lst, 'view':sorted_dicts}
###############################################################################
## A helper function to compute a given DNF on a given dictionary of attributes
##
## The assumed structure of the DNF is:
#
# [
# { # either one combination:
# 'left_hand': [ 'up', 'UP', 'raised', 'RAISED', '1' ],
# 'right_hand': [ 'down', 'DOWN', 'lowered', 'LOWERED', '0']
# },
# { # or another:
# 'right_hand': [ 'up', 'UP', 'raised', 'RAISED', '1' ],
# 'left_hand': [ 'down', 'DOWN', 'lowered', 'LOWERED', '0']
# }
# ]
##############################################################################
# resolve all dependencies
def resolve(i):
"""
Input: {
(host_os) - host OS (detect, if ommitted)
(target_os) - target OS (detect, if ommitted)
(target_device_id) - target device ID (detect, if omitted)
or
(device_id)
(repo_uoa) - repo where to limit search
deps - dependencies dict
(reuse_deps) - if 'yes' reuse all deps if found in cache by tags
(deps_cache) - list with resolved deps
(dep_add_tags) - a dictionary that maps extra tags to be added to specific subdictionaries of deps{}
for this particular resolution
(dep_add_tags.{KEY}) - extra tags added to specific subdictionary of deps{} for this particular resolution session
(remove_deps) [str] - a list of keys to remove from deps separated by comma.
Useful to run a given program workflow with an externally
installed dependency (compiler, library, model, tool).
(env) - env
(install_env) - env during installation
(add_customize) - if 'yes', add to deps customize field from the environment
(useful for program compilation)
(skip_dict) - if 'yes', do not add to deps dict field from the environment
(useful for program compilation)
(skip_auto_resolution) - if 'yes', do not check if deps are already resolved
(random) - if 'yes' and there is a choice, select random
(useful for quiet experiment crowdsourcing such as sw/hw crowdtuning)
(quiet) - if 'yes', automatically provide default answer to all questions when resolving dependencies ...
(install_to_env) - install dependencies to env instead of CK-TOOLS (to keep it clean)!
(safe) - safe mode when searching packages first instead of detecting already installed soft
(to have more deterministic build)
(rebuild) - if 'yes', attempt to set env to avoid downloading package again, just rebuild (if supported)
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
bat - string for bat file calling all bats ...
cut_bat - string for bat file calling all bats (does not include deps that are explicitly excluded) ...
deps - updated deps (with uoa)
env - updated env
}
Test:
cat deps_only.json
{
"deps": {
"weights": {
"force_target_as_host": "yes",
"local": "yes",
"name": "TensorFlow Lite model and weights",
"no_tags": "mobilenet-all",
"sort": 30,
"tags": "model,tflite,image-classification"
},
...
}
ck resolve env @deps_only.json "@@@{'dep_add_tags': {'weights':'resnet'}}" --out=json
ck resolve env @deps_only.json --dep_add_tags.weights=mobilenet --out=json
"""
import copy
o=i.get('out','')
if o=='con':
ck.out('')
ck.out(' -----------------------------------')
ck.out(' Resolving software dependencies ...')
combined_env_script_body=''
alternative_combined_env_script_body=''
install_env=i.get('install_env',{})
rebuild=i.get('rebuild','')
sar=i.get('skip_auto_resolution','')
deps=i.get('deps',{})
dep_add_tags = i.get('dep_add_tags', {})
for combi_opt in i:
if combi_opt.startswith('dep_add_tags.'):
_ , dep_name = combi_opt.split('.')
dep_add_tags[dep_name] = i[combi_opt]
for a_dep in dep_add_tags:
if a_dep in deps:
tags_to_be_added = dep_add_tags[a_dep]
if tags_to_be_added:
deps[a_dep]['tags'] += ',' + tags_to_be_added
else: # I'd rather raise() here, but we are in CK
ck.out("\n!!! Warning: dependency '{}' has not been found in the original entry - please check your input !!!\n".format(a_dep))
remove_deps=i.get('remove_deps','').strip()
remove_deps_list=[] if remove_deps=='' else remove_deps.split(',')
deps_cache=i.get('deps_cache',[])
reuse_deps=i.get('reuse_deps','')
ran=i.get('random','')
quiet=i.get('quiet','')
iev=i.get('install_to_env','')
safe=i.get('safe','')
# Check host/target OS/CPU
hos=i.get('host_os','')
tos=i.get('target_os','')
tdid=i.get('target_device_id','')
if tdid=='': tdid=i.get('device_id','')
# Get some info about OS
ii={'action':'detect',
'module_uoa':cfg['module_deps']['platform.os'],
'host_os':hos,
'target_os':tos,
'device_id':tdid,
'skip_info_collection':'yes'}
r=ck.access(ii)
if r['return']>0: return r
hos=r['host_os_uid']
hosx=r['host_os_uoa']
hosd=r['host_os_dict']
tos=r['os_uid']
tosx=r['os_uoa']
tosd=r['os_dict']
hplat=hosd.get('ck_name','')
hplat2=hosd.get('ck_name2','')
tplat=tosd.get('ck_name','')
tplat2=tosd.get('ck_name2','')
# Check if base is different
x1=hosd.get('base_uid','')
x2=hosd.get('base_uoa','')
if x1!='' and x2!='':
hos=x1
hosx=x2
x1=tosd.get('base_uid','')
x2=tosd.get('base_uoa','')
if x1!='' and x2!='':
tos=x1
tosx=x2
remote=tosd.get('remote','')
tbits=tosd.get('bits','')
# Checking deps
env=i.get('env',{})
enruoa=i.get('repo_uoa','')
ac=i.get('add_customize','')
sd=i.get('skip_dict','')
res=[]
iv=0
# Check if need to remove some deps
for k in remove_deps_list:
if k in deps:
del(deps[k])
sdeps=sorted(deps, key=lambda v: deps[v].get('sort',0))
xsb='' # Append to the end
xsb1='' # Append to the end
for k in sdeps:
q=deps[k]
if q.get('skipped','')=='yes':
continue
if q.get('enabled','')!='yes':
enable_DNF = q.get('enable_if_env')
if enable_DNF:
if match_attrib_DNF( install_env, enable_DNF ):
q['enabled']='yes'
else:
q['skipped']='yes'
continue
if q.get('skipped','')!='yes':
disable_DNF = q.get('disable_if_env')
if disable_DNF:
if match_attrib_DNF( install_env, disable_DNF ):
q['skipped']='yes'
continue
else:
q['enabled']='yes'
ytos=tos
ytdid=tdid
ytosx=tosx
ytosd=tosd
tags=q.get('tags','')
or_tags=q.get('or_tags','')
no_tags=q.get('no_tags','')
name=q.get('name','')
local=q.get('local','')
sd=q.get('skip_default','')
sinst=q.get('skip_installed',{})
vfrom=q.get('version_from',[])
vto=q.get('version_to',[])
ek=q.get('env_key','')
uoa=q.get('uoa','')
skip_cache=q.get('skip_cache','')
# Check if restricts dependency to a given host or target OS
rx=ck.access({'action':'check_target',
'module_uoa':cfg['module_deps']['soft'],
'dict':q,
'host_os_uoa':hosx,
'host_os_dict':hosd,
'target_os_uoa':ytosx,
'target_os_dict':ytosd})
if rx['return']>0:
continue
if q.get('force_target_as_host','')=='yes':
ytos=hos
ytdid=''
ytosx=hosx
ytosd=hosd
# Extra tags to be added conditionally:
#
xtags=[]
# Adding extra tags based on environment DNF:
#
update_tags_if_env = q.get('update_tags_if_env')
if update_tags_if_env:
for candidate_tag, add_tag_DNF in update_tags_if_env.items():
if match_attrib_DNF( install_env, add_tag_DNF ):
xtags.append( candidate_tag )
# Adding extra tags depending on host/target platform:
#
tx=q.get('update_tags_by_host_platform',{}).get(hplat,'')
if tx!='': xtags.append(tx)
tx=q.get('update_tags_by_host_platform2',{}).get(hplat2,'')
if tx!='': xtags.append(tx)
tx=q.get('update_tags_by_target_platform',{}).get(tplat,'')
if tx!='': xtags.append(tx)
tx=q.get('update_tags_by_target_platform2',{}).get(tplat2,'')
if tx!='': xtags.append(tx)
tx=q.get('update_tags_by_target_os_uoa',{}).get(ytosx,'') # Ugly, but quick
if tx!='': xtags.append(tx)
for tx in xtags:
if tags!='': tags+=','
tags+=tx.strip()
# Check if has associated package (in case of rebuilding deps for replay)
qdict=q.get('dict',{})
package_uoa=q.get('package_uoa','')
if package_uoa=='':
package_uoa=qdict.get('package_uoa','')
if package_uoa=='':
package_uoa=qdict.get('customize',{}).get('used_package_uid','')
xinstall_env=copy.deepcopy(qdict.get('customize',{}).get('install_env',{}))
# xinstall_env.update(install_env)
# install_env=copy.deepcopy(xinstall_env) # This is bug - install_env is a global env which is common for the first package!
# Try to set environment
iv+=1
if o=='con':
x='*** Dependency '+str(iv)+' = '+k
if name!='': x+=' ('+name+')'
x+=':'
ck.out('')
ck.out(x)
ii={'host_os':hos,
'original_target_os_name2':tplat2,
'target_os':ytos,
'target_device_id':ytdid,
'tags':tags,
'or_tags':or_tags,
'no_tags':no_tags,
'repo_uoa':enruoa,
'env':env,
'uoa':uoa,
'deps':deps,
'current_deps':qdict.get('deps',{}),
'deps_cache':deps_cache,
'reuse_deps':reuse_deps,
'skip_cache':skip_cache,
'skip_auto_resolution':sar,
'skip_default':sd,
'skip_installed':sinst,
'local':local,
'random':ran,
'name':name,
'key':ek,
'skip_pruning_by_other_deps':q.get('skip_pruning_by_other_deps',''),
'quiet':quiet,
'force_env_init':q.get('force_env_init',''),
'install_to_env':iev,
'install_env':xinstall_env,
'version_from':vfrom,
'version_to':vto,
'package_uoa':package_uoa,
'safe':safe
}
if rebuild=='yes': ii['rebuild']='yes'
if o=='con': ii['out']='con'
rx=env_set(ii)
if rx['return']>0: return rx
lst=rx['lst']
dd=rx['dict']
package_uoa=dd.get('package_uoa','')
if package_uoa=='':
package_uoa=dd.get('customize',{}).get('used_package_uid','')
if package_uoa!='':
q['package_uoa']=package_uoa # to be able to rebuild env for replay on another machine
dver=rx.get('detected_version','')
if dver!='': q['detected_ver']=dver
if 'choices' not in q or len('choices')==0:
q['choices'] = [zw['data_uid'] for zw in lst]
cus=dd.get('customize',{})
if ac=='yes': q['cus']=cus
if sd!='yes' or q.get('add_dict','')=='yes': q['dict']=dd
ver=cus.get('version','')
if ver!='': q['ver']=ver
uoa=rx['env_uoa']
q['uoa']=uoa
q['num_entries']=len(lst)
if o=='con':
ck.out('')
x=' Resolved. CK environment UID = '+uoa
if dver!='':
x+=' (detected version '+dver+')'
elif ver!='':
x+=' (version '+ver+')'
ck.out(x)
bdn=cus.get('build_dir_name','')
if bdn!='': q['build_dir_name']=bdn # Needed to suggest directory name for building libs
if uoa not in res: res.append(uoa)
env=rx['env']
individual_env_script_body = rx['bat']
q['bat'] = individual_env_script_body
combined_env_script_body += individual_env_script_body
if q.get('skip_from_bat','')!='yes':
alternative_combined_env_script_body += individual_env_script_body
pass_tags_to = q.get('pass_matching_tags_to', {})
if pass_tags_to:
resolved_tags = dd['tags']
for tag_prefix in pass_tags_to:
matching_tags = ','.join( [ matching_tag for matching_tag in resolved_tags if matching_tag.startswith(tag_prefix) ] )
if matching_tags:
matching_tags = ','+matching_tags
receiving_deps = pass_tags_to[tag_prefix]
if type(receiving_deps) != list:
receiving_deps = [ receiving_deps ]
for receiving_dep in receiving_deps:
deps[receiving_dep]['tags'] += matching_tags
if o=='con':
ck.out(' -----------------------------------')
return {'return':0, 'deps':deps, 'env': env, 'bat':combined_env_script_body, 'cut_bat':alternative_combined_env_script_body, 'res_deps':res}
##############################################################################
# refresh environment (re-setup soft)
def refresh(i):
"""
Input: {
(repo_uoa) - repository UOA (with wildcards), default = local (to avoid updating other repos)
(module_uoa) - module UOA (with wildcards)
(data_uoa) - data UOA (with wildcards)
(tags) - prune by tags
(target_os) - prune by target OS
(target_bits) - prune by target bits
(version) - prune by version
(name) - prune by name with wildcards
(reset_env) - if 'yes', do not use environment from existing entry, but use original one
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
lst - list from search function
view - sorted view list
}
"""
o=i.get('out','')
ruoa = i.get('repo_uoa','') or 'local'
muoa=i.get('module_uoa','')
duoa=i.get('data_uoa','')
tags=i.get('tags','')
extended_tags = [ tags ] if tags else []
tos_uoa=i.get('target_os','')
if tos_uoa!='':
# Load OS
ry=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['os'],
'data_uoa':tos_uoa})
if ry['return']>0: return ry
tos_uoa = ry['dict'].get('base_uoa','') or ry['data_uoa']
extended_tags.append( 'target-os-'+tos_uoa )
tb = i.get('target_bits','')
if tb!='': extended_tags.append( tb+'bits' )
ver = i.get('version','')
if ver!='': extended_tags.append( 'v'+ver )
tags = ','.join(extended_tags)
# FIXME: seems like the following bit was copied from show(), but the actual pattern matching bit was not, so there is no pattern matching actually.
name=i.get('name','')
wname=False
if name.find('*')>=0 or name.find('?')>=0:
import fnmatch
wname=True
name=name.lower()
ii={'action':'search',
'module_uoa':muoa,
'repo_uoa':ruoa,
'data_uoa':duoa,
'tags':tags,
'add_info':'yes',
'add_meta':'yes'}
rx=ck.access(ii)
if rx['return']>0: return rx
lst=rx['lst']
# prepare view
view=[]
target_os_name={} # Caching target OS names
for q in lst:
duoa=q['data_uoa']
duid=q['data_uid']
ruoa=q['repo_uoa']
ruid=q['repo_uid']
info=q['info']
meta=q['meta']
cus=meta.get('customize',{})
deps=meta.get('deps',{})
setup=meta.get('setup','')
tags=meta.get('tags',[])
if 'tmp' in tags:
continue
tags_csv = ','.join( [t for t in tags if t] )
host_os_uoa=setup.get('host_os_uoa','')
target_os_uoa=setup.get('target_os_uoa','')
tbits=setup.get('target_os_bits','')
version=setup.get('version','')
dname=info.get('data_name','')
ck.out('***********************************************************************')
ck.out(dname+' (Env: '+duid+')')
ck.out('')
ck.out(' Tags="'+tags_csv+'"')
soft_uoa=meta.get('soft_uoa','')
if soft_uoa=='':
# Trying to detect the soft entry by "abstracting out" some of the tags:
subtags=[]
for q in tags:
if not q.startswith('host-os-') and not q.startswith('target-os-') and \
not q.endswith('bits') and not q.startswith('v') and \
q!='retargeted':
subtags.append(q)
subtags_csv = ','.join( [t for t in subtags if t] )
ck.out(' All tags="'+tags_csv+'"')
ck.out(' Searching soft UOA by tags="'+subtags_csv+'" ...')
rx=ck.access({'action':'search',
'module_uoa':cfg['module_deps']['soft'],
'tags':subtags_csv})
if rx['return']>0: return rx
lst=rx['lst']
if len(lst)==0:
ck.out('')
ck.out(' No soft found')
rx=ck.inp({'text':' Please, enter soft UOA: '})
soft_uoa=rx['string'].strip()
elif len(lst)==1:
soft_uoa=lst[0]['data_uid']
ck.out(' Unique soft UOA found='+lst[0]['data_uoa'])
else:
ck.out('')
ck.out(' Available soft for these tags:')
num={}
ix=0
for q in lst:
num[str(ix)]=q['data_uid']
ck.out(' '+str(ix)+') '+q['data_uoa'])
ix+=1
rx=ck.inp({'text':' Select one of the options for soft UOA: '})
x=rx['string'].strip()
if x not in num:
return {'return':1, 'error':'option is not recognized'}
soft_uoa=num[x]
meta['soft_uoa']=soft_uoa
# Update environment entry
rx=ck.access({'action':'update',
'module_uoa':work['self_module_uid'],
'data_uoa':duoa,
'data_name':dname,
'dict':meta,
'sort_keys':'yes'})
if rx['return']>0: return rx
# Check if package available to take env
penv={}
package_uoa=meta.get('package_uoa','')
if package_uoa!='':
ck.out('')
ck.out(' Related package: '+package_uoa)
rx=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['package'],
'data_uoa':package_uoa})
if rx['return']>0: return rx
pdd=rx['dict']
penv=pdd.get('env',{})
# Trying new setup
ck.out('')
ck.out(' Refreshing setup ...')
ii={'action':'setup',
'module_uoa':cfg['module_deps']['soft'],
'host_os':host_os_uoa,
'target_os':target_os_uoa,
'data_uoa':soft_uoa,
'customize':cus,
'deps':deps,
'tags':tags_csv,
'package_uoa':package_uoa,
'skip_device_info_collection':'yes',
'soft_name':dname,
'env':penv,
'env_data_uoa':duid}
if i.get('reset_env','')!='': ii['reset_env']=i['reset_env']
rx=ck.access(ii)
if rx['return']>0:
rrx=rx['return']
if rrx!=32 and rrx!=33:
return rx
if o=='con':
if rrx==32:
ck.out('')
ck.out('One of the dependencies is missing for this CK environment!')
elif rrx==33:
ck.out('')
ck.out('This environment has either missing dependencies or strange mismatch between registered software environment and current setup!')
ck.out('')
ry=ck.inp({'text':'Would you like to delete it (Y/n)? '})
x=ry['string'].strip().lower()
if x!='n' and x!='no':
ry=ck.access({'action':'delete',
'module_uoa':work['self_module_uid'],
'data_uoa':duid})
if ry['return']>0: return ry
else:
return rx
return {'return':0}
##############################################################################
# internal function to convert host_os and target_os from UID to UOA to be readable
def readable_os(i):
"""
Input: {
setup
(host_os_uoa) - UID or UOA
(target_os_uoa) - UID or UOA
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
setup1 - processed setup with host_os_uoa and target_os_uoa as UOA
}
"""
original_setup = i.get('setup',{})
import copy
enriched_setup = copy.deepcopy( original_setup )
for setup_key in ('host_os_uoa', 'target_os_uoa'):
setup_value = original_setup.get(setup_key)
if setup_value:
r=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['os'],
'data_uoa':setup_value})
if r['return']>0: return r
enriched_setup[setup_key]=r['data_uoa']
return {'return':0, 'setup1':enriched_setup}
##############################################################################
# internal function: get value from list without error if out of bounds
##############################################################################
# parse 'A;B,C;D,~E,F' into [ [(True, A)], [(True, B), (True, C)], [(True, D), (False, E), (True, F)] ]
##############################################################################
# Prune search list by no_tags
def prune_search_list(i):
"""
Input: {
lst - list of entries after 'search'
(or_tags) - add entries which has groups of tags separated by ;
(no_tags) - string of tags to exclude
(version_from) - check version starting from ... (list of numbers)
(version_to) - check version up to ... (list of numbers)
(package_uoa) - prune by specific package
(setup) - prune by given setup dictionary
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
lst - pruned list
(skipped_because_of_version) - if 'yes', skip because of version check
}
"""
lst=i.get('lst',[])
no_tags_csv = i.get('no_tags','')
no_tags_set = set( no_tags_csv.split(',') if no_tags_csv!='' else [] )
otags = parse_disjunction( i.get('or_tags','') )
vfrom=i.get('version_from',[])
vto=i.get('version_to',[])
query_setup_dict=i.get('setup', {})
pruned_lst=[]
skipped_because_of_version=''
query_package_uoa=i.get('package_uoa','')
for entry in lst:
meta=entry.get('meta',{})
# Filter by package UOA
if query_package_uoa:
entry_package_uoa = meta.get('package_uoa','') or meta.get('customize',{}).get('used_package_uid','')
if entry_package_uoa and entry_package_uoa!=query_package_uoa:
continue
entry_tags_set = set( meta.get('tags',[]) )
# Filter out temporary entries (unfinished installation)
if 'tmp' in entry_tags_set:
continue
# Filter out matches agains 'no_tags'
if no_tags_set & entry_tags_set:
continue
# Filter by matching against 'or_tags' (full DNF support)
if len(otags)>0:
otags_ok = False
for conjunction in otags:
otags_ok = True
for (bsign, t) in conjunction:
if (t in entry_tags_set) != bsign: # Pythonic XOR
otags_ok = False # if at least one member of conjunction is False, the whole conjunction is False
break
if otags_ok: # if the current conjunction is True, the whole disjunction is True
break
if not otags_ok:
continue
v=meta.get('setup',{}).get('version_split',[])
# first check from env, but if not set, check from package
if len(v)==0:
v=meta.get('customize',{}).get('version_split',[])
if len(v)==0:
ver=meta.get('customize',{}).get('version','')
if ver!='' and ver[0].isdigit():
rx=ck.access({'action':'split_version',
'module_uoa':cfg['module_deps']['soft'],
'version':ver})
if rx['return']>0: return rx
v=rx['version_split']
# NB: a side-effect: the result of version splitting is stored here:
#
entry['meta']['customize']['version_split'] = v
if len(v)>0:
if len(vfrom)>0:
r=ck.access({'action':'compare_versions',
'module_uoa':cfg['module_deps']['soft'],
'version1':vfrom,
'version2':v})
if r['return']>0: return r
if r['result']=='>':
skipped_because_of_version='yes'
continue
if len(vto)>0:
r=ck.access({'action':'compare_versions',
'module_uoa':cfg['module_deps']['soft'],
'version1':v,
'version2':vto})
if r['return']>0: return r
if r['result']=='>':
skipped_because_of_version='yes'
continue
entry_setup_dict = meta.get('setup') # if entry_setup_dict is empty it matches anything
if entry_setup_dict and len(query_setup_dict)>0:
rx=ck.compare_dicts({'dict1':entry_setup_dict, 'dict2':query_setup_dict}) # check whether entry_setup_dict contains query_setup_dict
if rx['return']>0: return rx
if rx['equal']!='yes':
continue
# If we haven't hit "continue" anywhere above, the list element is ok to be included:
pruned_lst.append(entry)
return {'return':0, 'lst':pruned_lst, 'skipped_because_of_version':skipped_because_of_version}
##############################################################################
# remote env entry and installed package
def clean(i):
"""
Input: {
(data_uoa) - entries to be cleaned (wildcards can be used)
(repo_uoa)
(tags)
(force) - if 'yes', force delete
(f) - if 'yes', force delete
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
import copy
import os
import shutil
o=i.get('out','')
ooo = o if o=='con' else '' # shouldn't we consider other options?
force=i.get('force','')
if force=='': force=i.get('f','')
# If repo is not defined, use "local" to avoid removing env entries in other repos
ruoa=i.get('repo_uoa','')
if ruoa=='': ruoa='local'
i['repo_uoa']=ruoa
ii=copy.deepcopy(i)
ii['action']='show'
ii['out']=''
r=ck.access(ii)
if r['return']>0: return r
lst=r['lst']
# Check default path
r=ck.access({'action':'prepare_install_path',
'module_uoa':cfg['module_deps']['package']})
if r['return']>0: return r
install_path=r['path']
for q in lst:
ruid=q['repo_uid']
duid=q['data_uid']
duoa=q['data_uoa']
d=q['meta']
info_data_name = q.get('info',{}).get('data_name','UNKNOWN')
tags_csv = ','.join(d.get('tags',[]))
cus=d.get('customize',{})
full_path=cus.get('full_path','')
absolute_package_install_path=''
if full_path.startswith(install_path):
if install_path.endswith(os.path.sep) or install_path.endswith('/'): # is the first not enough?
j=0
else:
j=1
relative_path=full_path[len(install_path)+j:]
package_install_dir_name=relative_path.split(os.path.sep)[0]
if package_install_dir_name!='':
absolute_package_install_path = os.path.join(install_path, package_install_dir_name)
if absolute_package_install_path and os.path.isdir(absolute_package_install_path):
display_location = ' package in dir "'+absolute_package_install_path+'" and'
else:
display_location = ''
if force=='yes':
delete_bool = True
elif o=='con':
r=ck.inp({'text':'Are you sure to delete'+display_location+' CK entry env:"'+duoa+'" - a '+info_data_name+' with tags: '+tags_csv+' (y/N): '})
if r['return']>0: return r
delete_bool = r['string'].strip().lower() == 'y'
else:
delete_bool = False
if delete_bool:
# Delete entry
r=ck.access({'action':'rm',
'module_uoa':work['self_module_uid'],
'data_uoa':duid,
'repo_uoa':ruid,
'force': 'yes' if delete_bool else '', # convert it to 'yes' string (whether originally 'yes' or 'y')
'out':ooo})
if r['return']>0: return r
# Delete package
if absolute_package_install_path and os.path.isdir(absolute_package_install_path):
shutil.rmtree(absolute_package_install_path)
return {'return':0}
##############################################################################
# internal function to install package
def internal_install_package(i):
"""
Input: {
(host_os) - host OS (detect, if ommitted)
(target_os) - target OS (detect, if ommitted)
(target_device_id) - target device ID (detect, if omitted)
or
(device_id)
(package_uoa) - fix package (useful for replay ...)
(tags) - search UOA by tags (separated by comma)
(or_tags) - add entries which has groups of tags separated by ;
(no_tags) - exclude entris with these tags separated by comma
(deps) - already resolved deps
(sub_deps) - deps for the package to be installed (for replay mainly)
(reuse_deps) - if 'yes' reuse all deps if found in cache by tags
(deps_cache) - list with resolved deps
(quiet) - if 'yes', automatically provide default answer to all questions when resolving dependencies ...
(install_to_env) - install dependencies to env instead of CK-TOOLS (to keep it clean)!
(install_env) - customize installation (useful for replay to rebuild proper package with external env)
(safe) - safe mode when searching packages first instead of detecting already installed soft
(to have more deterministic build)
(version_from) - check version starting from ... (list of numbers)
(version_to) - check version up to ... (list of numbers)
(add_hint) - if 'yes', can skip package installation
(rebuild) - if 'yes', attempt to set env to avoid downloading package again, just rebuild (if supported)
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
env_data_uoa - installed package data UOA (can be "" if not found)
env_data_uid - installed package data UID (can be "" if not found)
}
"""
import os
o=i.get('out','')
ooo = o if o=='con' else '' # shouldn't we consider other options?
hos=i.get('host_os','')
tos=i.get('target_os','')
tdid=i.get('device_id','')
package_uoa=i.get('package_uoa','')
rebuild=i.get('rebuild','')
deps_cache=i.get('deps_cache',[])
reuse_deps=i.get('reuse_deps','')
tags=i.get('tags','')
or_tags=i.get('or_tags','')
no_tags=i.get('no_tags','')
quiet=i.get('quiet','')
iev=i.get('install_to_env','')
install_env=i.get('install_env',{})
safe=i.get('safe','')
ah=i.get('add_hint','')
vfrom=i.get('version_from',[])
vto=i.get('version_to',[])
cdeps=i.get('deps',{})
# Next, try to install via package for a given software
if o=='con':
ck.out('')
ck.out(' Searching and installing CK software packages ...')
if package_uoa!='':
# Try to detect alias
rx=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['package'],
'data_uoa':package_uoa})
if rx['return']>0: return rx
package_uid=rx['data_uid']
package_alias=rx['data_alias']
ck.out(' * package UOA: '+package_alias+' ('+package_uid+')')
ck.out(' * tags: '+tags)
ck.out(' * or tags: '+or_tags)
ck.out(' * no tags: '+no_tags)
ck.out('')
# if quiet=='yes':
# ck.out(' Searching and installing package with these tags automatically ...')
# a='y'
# else:
# rx=ck.inp({'text':' Would you like to search and install package with these tags automatically (Y/n)? '})
# a=rx['string'].strip().lower()
#
# if a!='n' and a!='no':
try:
save_cur_dir=os.getcwd()
except OSError:
os.chdir('..')
save_cur_dir=os.getcwd()
install_adict={'action':'install',
'module_uoa':cfg['module_deps']['package'],
'data_uoa':package_uoa,
'out':ooo,
'tags':tags,
'or_tags':or_tags,
'no_tags':no_tags,
'install_to_env':iev,
'env':install_env,
'safe':safe,
'host_os':hos,
'target_os':tos,
'device_id':tdid,
'reuse_deps':reuse_deps,
'deps_cache':deps_cache,
'version_from':vfrom,
'version_to':vto,
'quiet':quiet,
'add_hint':ah}
if rebuild=='yes': install_adict['rebuild']='yes'
# Check if there is a compiler in resolved deps to reuse it
sub_deps=i.get('sub_deps',{})
if cdeps.get('compiler',{}).get('uoa','')!='': sub_deps['compiler']=cdeps['compiler']
if cdeps.get('compiler-mcl',{}).get('uoa','')!='': sub_deps['compiler-mcl']=cdeps['compiler-mcl']
if len(sub_deps)>0: install_adict['deps']=sub_deps
duoa=''
duid=''
rx=ck.access(install_adict)
if rx['return']==0:
duoa=rx['env_data_uoa']
duid=rx['env_data_uid']
os.chdir(save_cur_dir)
elif rx['return']!=16: # 16 is a "magic number" meaning "user chose not to install" or "package not found"
return rx
return {'return':0, 'env_data_uoa':duoa, 'env_data_uid':duid}
##############################################################################
# DEPRECATED - use "ck virtual env" instead!
# set env for command line (pre-set various flags)
def xset(i):
"""
Input: {
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
# Generate tmp file
i['local']='yes'
i['bat_file']='tmp-ck-env.bat'
i['bat_new']='yes'
i['print']='yes'
return env_set(i)
##############################################################################
# pre-load environment for the shell
def virtual(i):
"""
Input: {
data_uoa or uoa - environment UOA to pre-load (see "ck show env")
(tags) - a combination of comma-separated tags to narrow down the search
(or_tags) - a combination of semicolon-separated and comma-separated tags to narrow down the search
(tag_groups) - independent groups of or_tags, separated by a space, refer to separate env entries
that can be combined together: '--tag_groups=alpha,beta gamma,~delta epsilon;lambda,mu'
(search_dict) - require verbatim matches of some attributes
(verbose) - set to 'yes' to see the script that will be used to build the environment
(shell_cmd) - command line to run in the "environment-enriched" shell (make sure it is suitably quoted)
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
Test:
ck virtual env --tags=model,onnx,resnet --shell_cmd='echo $ML_MODEL_DATA_LAYOUT'
"""
import copy
duoa = i.get('data_uoa', i.get('uoa','') )
tag_groups = i.get('tag_groups')
list_of_updates = []
if not duoa:
list_of_uoa = []
elif ',' in duoa: # TODO: becomes deprecated (but still works) in 1.10, becomes an error in 1.11
# ck.out('')
# ck.out('DEPRECATED: You seem to be using CSV format within a CID. Please list multiple CIDs on your command line instead.')
# ck.out('')
list_of_uoa = duoa.split(',')
else:
list_of_uoa = [ duoa ]
for xcid in i.get('xcids',[]):
if xcid['module_uoa'] == 'env':
list_of_uoa.append( xcid['data_uoa'] )
else:
return {'return':1, 'error':"all CID entries have to be of 'env' type"}
for uoa in list_of_uoa:
if uoa:
if '*' not in uoa: # be a wildcard or be present!
r = ck.access( {'action': 'load', 'module_uoa': 'env', 'data_uoa': uoa} )
if r['return']>0: return r
list_of_updates.append( {'uoa': uoa} )
if tag_groups and len(tag_groups)>0: # tag_groups are always added on top
list_of_updates += [ {'or_tags': or_tags} for or_tags in tag_groups.split()]
if not len(list_of_uoa) and ( i.get('tags') or i.get('or_tags') ): # but make sure tags were seen at least once
list_of_updates.append( {} )
if not len(list_of_updates): # if nothing else matched, let the user choose one env from the list
list_of_updates.append( { 'uoa' : '*'} )
prewrapper_lines = []
for dict_update in list_of_updates:
env_set_alist = copy.deepcopy( i )
env_set_alist.update( dict_update )
r=env_set( env_set_alist )
if r['return']>0: return r
prewrapper_lines.append( r['bat'].strip() )
return envsecute({ 'prewrapper_lines': prewrapper_lines, 'shell_cmd': i.get('shell_cmd'), 'verbose': i.get('verbose') })
################################################################################
# execute a shell command pre-wrapped with a given environment-modifying script
def envsecute(i):
"""
Input: {
(prewrapper_lines) - lines of the environment-setting pre-wrapper
(shell_cmd) - command line to run in the pre-wrapped shell (make sure it is suitably quoted)
(verbose) - set to 'yes' to see the pre_wrapper
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
Test:
ck envsecute env @@@"{'prewrapper_lines': ['export ALPHA=alpha','echo 12345']}" --shell_cmd='echo $ALPHA $BETA'
"""
# Run shell
import platform
import os
import subprocess
shell_cmd = i.get('shell_cmd', None)
prewrapper_lines = i.get('prewrapper_lines', [])
if i.get('verbose', '')=='yes':
ck.out("*** Preloading the following environment:\n")
ck.out("\n".join(prewrapper_lines))
ck.out('')
if not shell_cmd:
ck.out('')
ck.out('*** Warning: you are in a new shell with a pre-set CK environment. Enter "exit" to return to the original one!')
if platform.system().lower().startswith('win'): # pragma: no cover
if shell_cmd:
prewrapper_lines.append( shell_cmd )
termination_flag = '/C' # terminate the CMD shell when the environment script & shell_cmd are over
else:
termination_flag = '/K' # remain in the CMD shell
shell_script_contents = ' & '.join( prewrapper_lines )
p = subprocess.Popen(['cmd', termination_flag, shell_script_contents], shell = True, env=os.environ)
p.wait()
return_code = p.returncode
else:
rx=ck.gen_tmp_file({'prefix': 'ck_envsecute_', 'suffix': '.sh'})
if rx['return']>0: return rx
prewrapper_filename=rx['file_name']
shell_script_contents = '\n\n'.join( prewrapper_lines ) + '\n'
rx=ck.save_text_file({'text_file':prewrapper_filename, 'string':shell_script_contents })
if rx['return']>0: return rx
full_cmd_list = ['/bin/bash','--rcfile', prewrapper_filename, '-i'] + ( ['-c', shell_cmd] if shell_cmd else [] )
return_code = subprocess.call(full_cmd_list, shell = False)
return {'return':return_code, 'error':'Unknown error from the nested shell'}
##############################################################################
# return the install_location
def locate(i):
"""
Input: {
(data_uoa or uoa) - environment entry/entries
(tags) - prune by tags
(target_os) - prune by target OS
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
duoa = i.get('data_uoa', i.get('uoa', '*') )
tags_csv = i.get('tags')
tos_uoa = i.get('target_os','')
if tos_uoa!='':
# Load OS
ry=ck.access({'action':'load',
'module_uoa':cfg['module_deps']['os'],
'data_uoa':tos_uoa})
if ry['return']>0: return ry
tos_uoa = ry['dict'].get('base_uoa','') or ry['data_uoa']
target_os_tag = 'target-os-'+tos_uoa
i['tags'] = (tags_csv + ',' if tags_csv else '') + target_os_tag
if duoa.find('*')!=-1 or duoa.find('?')!=-1 or tags_csv:
searched_adict = ck.access( dict(i, action='search', out='') )
if searched_adict['return']>0: return rx
list_of_uoa = [ entry['data_uoa'] for entry in searched_adict['lst'] ]
else:
list_of_uoa = [ duoa ]
install_locations = {}
for uoa in list_of_uoa:
loaded_adict = ck.access({'action':'load',
'module_uoa': 'env',
'data_uoa': uoa,
})
if loaded_adict['return']>0: return loaded_adict
install_location = loaded_adict.get('dict', {}).get('install_location', '')
install_locations[uoa] = install_location
if i.get('out')=='con':
if len(list_of_uoa)>1:
ck.out("env:{} -> {}".format(uoa, install_location))
else:
ck.out(install_location)
return {'return': 0, 'install_locations': install_locations}
##############################################################################
# show the shell script for setting up this env
def cat(i):
"""
Input: {
data_uoa or uoa - environment UOA to pre-load (see "ck virtual env")
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
import os
duoa = i.get('data_uoa', i.get('uoa', '*') )
tags_csv = i.get('tags')
if duoa.find(',')!=-1: # TODO: becomes deprecated (but still works) in 1.10, becomes an error in 1.11
# ck.out('')
# ck.out('DEPRECATED: You seem to be using CSV format within a CID. Please list multiple CIDs on your command line instead.')
# ck.out('')
list_of_uoa = duoa.split(',')
elif duoa.find('*')!=-1 or duoa.find('?')!=-1 or tags_csv:
searched_adict = ck.access( dict(i, action='search') )
if searched_adict['return']>0: return rx
list_of_uoa = [ entry['data_uoa'] for entry in searched_adict['lst'] ]
else:
list_of_uoa = [ duoa ]
for xcid in i.get('xcids',[]):
if xcid['module_uoa'] == 'env':
list_of_uoa.append( xcid['data_uoa'] )
else:
return {'return':1, 'error':"all CID entries have to be of 'env' type"}
# Get some info about OS
r=ck.access({'action':'detect',
'module_uoa':cfg['module_deps']['platform.os'],
'skip_info_collection':'yes',
})
if r['return']>0: return r
hosd=r['host_os_dict']
rem_marker = hosd['rem']
for uoa in list_of_uoa:
loaded_adict = ck.access({'action':'load',
'module_uoa': 'env',
'data_uoa': uoa,
})
if loaded_adict['return']>0: return loaded_adict
entry_directory_path = loaded_adict['path']
env_script_name = loaded_adict['dict'].get('env_script') or ( cfg['default_bat_name'] + hosd.get('script_ext','') )
setup_script_path = os.path.join( entry_directory_path, env_script_name)
data_name = loaded_adict['data_name']
version = loaded_adict['dict'].get('customize',{}).get('version', 'UNKNOWN_VERSION')
tags_csv = ','.join( loaded_adict['dict'].get('tags',[]) )
header_lines = [
rem_marker,
'{} {}[ {} ver. {}, {} ]{}'.format(rem_marker, '-' * 20, data_name, version, setup_script_path, '-' * 20),
'{} Tags: {}'.format(rem_marker, tags_csv),
rem_marker,
]
with open(setup_script_path, 'r') as setup_script_file:
line_number = 0
for input_line in setup_script_file:
input_line = input_line.rstrip()
ck.out( input_line )
if not line_number: # insert the auto-generated header right after the first line
for header_line in header_lines:
ck.out( header_line )
line_number += 1
ck.out( "\n\n" )
return {'return':0}
##############################################################################
# remove tmp entries (when installation to env entry failed)
def rmtmp(i):
"""
Input: {
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
i['action']='rm'
i['data_uoa']='*'
tags=i.get('tags','')
if tags!='': tags+=','
tags+='tmp'
i['tags']=tags
return ck.access(i)
##############################################################################
# get all versions from deps (recursively)
def get_all_versions_in_deps(i):
"""
Input: {
deps - deps dict
(key) - current key
(only_root) - if 'yes', check only root keys
(versions) - current versions
(tag_versions) - current versions by tags
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
versions - all versions for all deps
tag_versions - all versions for all deps by tags
}
"""
deps=i['deps']
key=i.get('key','')
versions=i.get('versions',{})
tversions=i.get('tversions',[])
only_root=(i.get('only_root','')=='yes')
for k in deps:
xkey=key
if xkey!='': xkey+='#'
xkey+=k
d=deps[k]
dd=d.get('dict',{})
tags=dd.get('tags',[])
stags=','.join(tags)
ver=d.get('ver','')
versions[xkey]=ver
if stags not in tversions:
tversions.append(stags)
deps2=dd.get('deps',{})
if not only_root and len(deps2)>0:
r=get_all_versions_in_deps({'deps':deps2, 'key':xkey, 'versions':versions, 'tag_versions':tversions})
if r['return']>0: return r
return {'return':0, 'versions':versions, 'tag_versions':tversions}
##############################################################################
# extracting summary of all deps
def deps_summary(i):
"""
Input: {
deps - resolved deps
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
deps_summary - summary of deps
}
"""
deps=i['deps']
ds=i.get('deps_summary',{})
for x in deps:
d=deps[x]
dd=d.get('dict',{})
ds[x]={}
cx=dd.get('customize',{})
ds[x]['tags']=d.get('tags',[])
ds[x]['name']=d.get('name','')
ds[x]['package_tags']=','.join(dd.get('tags',[]))
ds[x]['data_name']=dd.get('data_name','')
puoa=dd.get('package_uoa','')
if puoa=='':
puoa=d.get('cus',{}).get('used_package_uid','')
ds[x]['package_uoa']=puoa
ds[x]['version']=cx.get('version','')
ds[x]['git_revision']=cx.get('git_info',{}).get('revision','')
ds[x]['git_iso_datetime_cut_revision']=cx.get('git_info',{}).get('iso_datetime_cut_revision','')
sdeps=dd.get('deps',{})
if len(sdeps)>0:
# Recursion
r=deps_summary({'deps':sdeps})
if r['return']>0: return r
ds[x]['deps']=r['deps_summary']
return {'return':0, 'deps_summary':ds}
##############################################################################
# delete env entries
def rm(i):
"""
Input: {
(repo_uoa) - local by default
(f) - force removing
}
Output: {
return - return code = 0, if successful
> 0, if error
(error) - error text if return > 0
}
"""
# Check that repo_uoa is local by default to avoid deleting permanent env in other entries
ruoa=i.get('repo_uoa','')
if ruoa=='': ruoa='local'
i['repo_uoa']=ruoa
i['common_func']='yes'
return ck.access(i)
##############################################################################
# delete env entry
def delete(i):
"""
Input: { See rm function }
Output: { See rm function }
"""
return rm(i)
##############################################################################
# delete env entry
def remove(i):
"""
Input: { See rm function }
Output: { See rm function }
"""
return rm(i)
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1635,
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10,
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1303,
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299,
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329,
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7544,
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35851,
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43979,
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220,
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220,
220,
220,
220,
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9575,
28,
17821,
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628,
220,
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220,
220,
220,
220,
220,
300,
28,
7278,
628,
220,
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220,
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220,
220,
611,
4966,
855,
6,
8505,
10354,
198,
220,
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422,
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198,
220,
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4229,
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4229,
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220,
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2073,
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611,
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198,
220,
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2124,
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220,
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220,
611,
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0,
28,
7061,
25,
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80,
11639,
30543,
10,
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10,
29653,
6,
628,
220,
220,
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10,
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1330,
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628,
220,
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686,
28,
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62,
418,
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30072,
198,
220,
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220,
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611,
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29,
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25,
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686,
198,
220,
220,
220,
220,
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220,
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220,
220,
9058,
16,
28,
305,
17816,
40406,
16,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
80,
10,
11639,
290,
9058,
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10,
17752,
13,
67,
8142,
7,
40406,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
10786,
5167,
621,
530,
2858,
1043,
329,
705,
10,
87,
80,
10,
10354,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
7061,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1207,
62,
25811,
796,
17635,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2172,
62,
11600,
287,
300,
25,
220,
1303,
9052,
278,
832,
477,
3689,
329,
428,
2858,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
72,
28,
8738,
62,
11600,
13,
1136,
10786,
10951,
3256,
90,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
76,
28,
8738,
62,
11600,
13,
1136,
10786,
28961,
3256,
90,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
84,
28,
8738,
62,
11600,
13,
1136,
10786,
7890,
62,
27112,
3256,
7061,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
32656,
28,
17027,
13,
1136,
10786,
7890,
62,
3672,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1976,
10378,
82,
28,
89,
76,
13,
1136,
10786,
10378,
82,
3256,
90,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
742,
3775,
28,
89,
76,
13,
1136,
10786,
31499,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
385,
28,
89,
76,
13,
1136,
10786,
23144,
1096,
3256,
90,
30072,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3326,
28,
9042,
13,
1136,
10786,
9641,
3256,
7061,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15940,
62,
40664,
796,
705,
4032,
13,
22179,
7,
685,
83,
329,
256,
287,
220,
742,
3775,
611,
256,
60,
1267,
628,
220,
220,
220,
220,
220,
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428,
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685,
705,
90,
92,
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90,
92,
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92,
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92,
4008,
4458,
18982,
7,
89,
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11,
3326,
11,
15940,
62,
40664,
11,
1976,
84,
8,
2361,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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611,
18896,
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89,
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8,
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198,
220,
220,
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220,
220,
220,
220,
220,
220,
329,
474,
287,
23243,
7,
89,
10378,
82,
11,
1994,
28,
50033,
410,
25,
1976,
10378,
82,
58,
85,
4083,
1136,
10786,
30619,
3256,
15,
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2599,
198,
220,
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474,
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60,
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220,
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7544,
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28,
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13,
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84,
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3256,
7061,
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198,
220,
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12162,
0,
28,
7061,
25,
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611,
6565,
11,
340,
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1884,
1724,
326,
428,
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198,
220,
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1303,
318,
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220,
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13,
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332,
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7061,
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628,
220,
220,
220,
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428,
62,
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13,
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7,
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92,
12,
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2412,
319,
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36786,
357,
24330,
471,
23621,
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5512,
15940,
2625,
90,
92,
1600,
2196,
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30072,
4458,
18982,
10786,
705,
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2327,
11,
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11,
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12162,
11,
474,
31499,
11,
474,
332,
4008,
628,
220,
220,
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1207,
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13,
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7,
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62,
18076,
8,
628,
220,
220,
220,
220,
220,
220,
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2922,
62,
324,
713,
796,
269,
74,
13,
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2673,
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705,
19738,
62,
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198,
220,
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21412,
62,
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198,
220,
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25811,
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198,
220,
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705,
12286,
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705,
15,
3256,
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220,
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705,
25652,
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705,
17563,
530,
286,
262,
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329,
705,
10,
87,
80,
11,
198,
220,
220,
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32092,
198,
220,
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220,
220,
220,
220,
611,
2922,
62,
324,
713,
17816,
7783,
20520,
29,
15,
25,
1441,
2922,
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324,
713,
628,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
4229,
87,
796,
2922,
62,
324,
713,
17816,
34213,
62,
9630,
20520,
628,
220,
220,
220,
220,
220,
220,
611,
4229,
87,
27,
11925,
7,
75,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
7043,
312,
28,
75,
58,
346,
87,
7131,
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7890,
62,
27112,
20520,
198,
220,
220,
220,
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220,
220,
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7043,
12162,
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646,
312,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
288,
3672,
28,
75,
58,
346,
87,
4083,
1136,
10786,
10951,
3256,
90,
92,
737,
1136,
10786,
7890,
62,
3672,
3256,
7061,
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628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
49427,
28,
75,
58,
346,
87,
4083,
1136,
10786,
28961,
3256,
90,
30072,
628,
220,
220,
220,
220,
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220,
220,
611,
267,
855,
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1102,
6,
290,
1312,
13,
1136,
10786,
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3256,
7061,
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6,
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10354,
198,
220,
220,
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2124,
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646,
12162,
198,
220,
220,
220,
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220,
611,
7043,
312,
0,
28,
646,
12162,
25,
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10,
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19203,
10,
646,
312,
10,
11537,
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220,
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269,
74,
13,
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34,
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10,
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1058,
705,
10,
87,
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628,
220,
220,
220,
1303,
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6823,
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15940,
0,
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198,
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407,
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269,
74,
13,
448,
10786,
23926,
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855,
11537,
198,
220,
220,
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21628,
47932,
16,
628,
220,
220,
220,
220,
220,
220,
220,
611,
267,
855,
6,
1102,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
11639,
8162,
37947,
1387,
705,
10,
2536,
7,
452,
47762,
6,
796,
705,
10,
74,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1438,
0,
28,
7061,
25,
2124,
10,
11639,
19203,
10,
3672,
10,
11537,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
47932,
10354,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
87,
8,
628,
220,
220,
220,
220,
220,
220,
220,
21065,
34758,
6,
4774,
62,
418,
10354,
71,
418,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14986,
62,
16793,
62,
418,
62,
3672,
17,
10354,
83,
489,
265,
17,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
16793,
62,
418,
10354,
20760,
418,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
16793,
62,
25202,
62,
312,
10354,
88,
8671,
312,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
31499,
10354,
31499,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
273,
62,
31499,
10354,
273,
62,
31499,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
3919,
62,
31499,
10354,
3919,
62,
31499,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
260,
7501,
62,
84,
12162,
10354,
268,
622,
12162,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
24330,
10354,
24330,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
84,
12162,
10354,
84,
12162,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10378,
82,
10354,
10378,
82,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14421,
62,
10378,
82,
10354,
80,
11600,
13,
1136,
10786,
10378,
82,
3256,
90,
92,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
10378,
82,
62,
23870,
10354,
10378,
82,
62,
23870,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
260,
1904,
62,
10378,
82,
10354,
260,
1904,
62,
10378,
82,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
48267,
62,
23870,
10354,
48267,
62,
23870,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
48267,
62,
23736,
62,
29268,
10354,
82,
283,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
48267,
62,
12286,
10354,
21282,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
48267,
62,
37050,
10354,
82,
8625,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
12001,
10354,
12001,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
25120,
10354,
2596,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
3672,
10354,
3672,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
2539,
10354,
988,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
48267,
62,
1050,
46493,
62,
1525,
62,
847,
62,
10378,
82,
10354,
80,
13,
1136,
10786,
48267,
62,
1050,
46493,
62,
1525,
62,
847,
62,
10378,
82,
3256,
7061,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
39624,
10354,
39624,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
3174,
62,
24330,
62,
15003,
10354,
80,
13,
1136,
10786,
3174,
62,
24330,
62,
15003,
3256,
7061,
828,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17350,
62,
1462,
62,
24330,
10354,
11203,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
17350,
62,
24330,
10354,
87,
17350,
62,
24330,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
9641,
62,
6738,
10354,
85,
6738,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
9641,
62,
1462,
10354,
85,
1462,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
26495,
62,
84,
12162,
10354,
26495,
62,
84,
12162,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
21230,
10354,
21230,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1782,
628,
220,
220,
220,
220,
220,
220,
220,
611,
17884,
855,
6,
8505,
10354,
21065,
17816,
260,
11249,
20520,
11639,
8505,
6,
198,
220,
220,
220,
220,
220,
220,
220,
611,
267,
855,
6,
1102,
10354,
21065,
17816,
448,
20520,
11639,
1102,
6,
628,
220,
220,
220,
220,
220,
220,
220,
374,
87,
28,
24330,
62,
2617,
7,
4178,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
374,
87,
17816,
7783,
20520,
29,
15,
25,
1441,
374,
87,
628,
220,
220,
220,
220,
220,
220,
220,
300,
301,
28,
40914,
17816,
75,
301,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
49427,
28,
40914,
17816,
11600,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
5301,
62,
84,
12162,
28,
1860,
13,
1136,
10786,
26495,
62,
84,
12162,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5301,
62,
84,
12162,
855,
7061,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5301,
62,
84,
12162,
28,
1860,
13,
1136,
10786,
23144,
1096,
3256,
90,
92,
737,
1136,
10786,
1484,
62,
26495,
62,
27112,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
5301,
62,
84,
12162,
0,
28,
7061,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
17816,
26495,
62,
84,
12162,
20520,
28,
26495,
62,
84,
12162,
1303,
284,
307,
1498,
284,
17884,
17365,
329,
24788,
319,
1194,
4572,
628,
220,
220,
220,
220,
220,
220,
220,
288,
332,
28,
40914,
13,
1136,
10786,
15255,
11197,
62,
9641,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
288,
332,
0,
28,
7061,
25,
10662,
17816,
15255,
11197,
62,
332,
20520,
28,
67,
332,
628,
220,
220,
220,
220,
220,
220,
220,
611,
705,
6679,
1063,
6,
407,
287,
10662,
393,
18896,
10786,
6679,
1063,
11537,
855,
15,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10662,
17816,
6679,
1063,
20520,
796,
685,
89,
86,
17816,
7890,
62,
27112,
20520,
329,
1976,
86,
287,
300,
301,
60,
628,
220,
220,
220,
220,
220,
220,
220,
269,
385,
28,
1860,
13,
1136,
10786,
23144,
1096,
3256,
90,
30072,
628,
220,
220,
220,
220,
220,
220,
220,
611,
936,
855,
6,
8505,
10354,
10662,
17816,
9042,
20520,
28,
9042,
198,
220,
220,
220,
220,
220,
220,
220,
611,
45647,
0,
11639,
8505,
6,
393,
10662,
13,
1136,
10786,
2860,
62,
11600,
3256,
7061,
8,
855,
6,
8505,
10354,
10662,
17816,
11600,
20520,
28,
1860,
628,
220,
220,
220,
220,
220,
220,
220,
3326,
28,
9042,
13,
1136,
10786,
9641,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3326,
0,
28,
7061,
25,
10662,
17816,
332,
20520,
28,
332,
628,
220,
220,
220,
220,
220,
220,
220,
334,
12162,
28,
40914,
17816,
24330,
62,
84,
12162,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17816,
84,
12162,
20520,
28,
84,
12162,
198,
220,
220,
220,
220,
220,
220,
220,
10662,
17816,
22510,
62,
298,
1678,
20520,
28,
11925,
7,
75,
301,
8,
628,
220,
220,
220,
220,
220,
220,
220,
611,
267,
855,
6,
1102,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
11639,
220,
220,
220,
1874,
5634,
13,
45233,
2858,
25105,
796,
705,
10,
84,
12162,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
288,
332,
0,
28,
7061,
25,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
10,
11639,
357,
15255,
11197,
2196,
705,
10,
67,
332,
10,
11537,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1288,
361,
3326,
0,
28,
7061,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2124,
10,
11639,
357,
9641,
705,
10,
332,
10,
11537,
6,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
269,
74,
13,
448,
7,
87,
8,
628,
220,
220,
220,
220,
220,
220,
220,
275,
32656,
28,
9042,
13,
1136,
10786,
11249,
62,
15908,
62,
3672,
3256,
7061,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
275,
32656,
0,
28,
7061,
25,
10662,
17816,
11249,
62,
15908,
62,
3672,
20520,
28,
17457,
77,
1303,
10664,
276,
284,
1950,
8619,
1438,
329,
2615,
9195,
82,
628,
220,
220,
220,
220,
220,
220,
220,
611,
334,
12162,
407,
287,
581,
25,
581,
13,
33295,
7,
84,
12162,
8,
628,
220,
220,
220,
220,
220,
220,
220,
17365,
28,
40914,
17816,
24330,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
1981,
62,
24330,
62,
12048,
62,
2618,
796,
374,
87,
17816,
8664,
20520,
628,
220,
220,
220,
220,
220,
220,
220,
10662,
17816,
8664,
20520,
796,
1981,
62,
24330,
62,
12048,
62,
2618,
198,
220,
220,
220,
220,
220,
220,
220,
5929,
62,
24330,
62,
12048,
62,
2618,
15853,
1981,
62,
24330,
62,
12048,
62,
2618,
628,
220,
220,
220,
220,
220,
220,
220,
611,
10662,
13,
1136,
10786,
48267,
62,
6738,
62,
8664,
3256,
7061,
31520,
11639,
8505,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5559,
62,
24011,
1389,
62,
24330,
62,
12048,
62,
2618,
15853,
1981,
62,
24330,
62,
12048,
62,
2618,
628,
220,
220,
220,
220,
220,
220,
220,
1208,
62,
31499,
62,
1462,
796,
10662,
13,
1136,
10786,
6603,
62,
15699,
278,
62,
31499,
62,
1462,
3256,
23884,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1208,
62,
31499,
62,
1462,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12939,
62,
31499,
796,
49427,
17816,
31499,
20520,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
7621,
62,
40290,
287,
1208,
62,
31499,
62,
1462,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12336,
62,
31499,
796,
705,
4032,
13,
22179,
7,
685,
12336,
62,
12985,
329,
12336,
62,
12985,
287,
12939,
62,
31499,
611,
12336,
62,
12985,
13,
9688,
2032,
342,
7,
12985,
62,
40290,
8,
2361,
1267,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
12336,
62,
31499,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12336,
62,
31499,
796,
705,
4032,
10,
15699,
278,
62,
31499,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6464,
62,
10378,
82,
796,
1208,
62,
31499,
62,
1462,
58,
12985,
62,
40290,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 1.863237 | 52,061 |
# Generated by Django 3.2 on 2021-05-10 16:23
import django.contrib.postgres.fields
from django.db import migrations, models
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] | 3.02381 | 42 |
import torch
import logging
import numpy as np
from data_management.vocabulary import Vocabulary
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
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] | 3.296296 | 54 |
from IPython.display import Image as Image_py
from src.data.google_vision_ocr_parsing import extract_full_text_annotation
from src.data.sroie import load_full_ocr, load_labels
from torch.utils.data import Dataset, ConcatDataset
from transformers import T5Tokenizer
from typing import List, Dict, Union
import gin
class T5BaselineDataset(Dataset):
"""Dataset for text-to-text training.
Args:
keynames (List[str]): filenames without extension.
t5_tokenizer_prefix (str): tokenizer prefix.
format_labels_fn (callable): function for labels preprocessing
format_inputs_fn (callable): function for inputs preprocessing
max_source_length (int): maximum length for toke sequences on encoder.
max_target_length (int): maximum length for labels token sequences.
"""
def inspect_example(self, idx):
"""Inspects examples for debugging purposes.
"""
image = Image_py(filename=f'{self.keynames[idx]}.jpg',
width=1200,
height=1200)
display(image)
example = self.__getitem__(idx)
return example
@gin.configurable(allowlist=['str_replace_newlines'])
def get_default_preprocessing_functions(
field_to_extract: str,
str_replace_newlines: str = None) -> Dict[str, callable]:
"""Convenience functions for generating preprocessing functions for labels
and inputs when using T5 text-to-text training.
Args:
field_to_extract (str): field_to_extract
str_replace_newlines (str): string to replace `\n`
characters. Usually spaces (` `) or pipes (`|`).
Returns:
Dict[str, callable]: dict with two keys:
- `labels`: function that extracts the ground truth labels for the
desired field.
- `inputs`: Replaces `\n` on the ocr output text and prepends the
question: `What is the <field_to_extract?`
"""
return {'inputs': format_inputs_fn, 'labels': format_labels_fn}
@gin.configurable(denylist=['keynames'])
def get_datasets_dict_from_task_functions_map(
keynames: List[str],
tasks_functions_maps: List[Dict[str, callable]],
t5_prefix: str,
max_source_length: int,
max_target_length: int) -> Dict[str, Dataset]:
"""Return dict with datasets for each individual task and one dataset with
all tasks concatenated.
Args:
keynames (List[str]): keynames (paths without extension) for receipts
on SROIE dataset.
t5_prefix (str, optional): prefix identifying the chosen t5 model.
Will be used to instantiate the T5Tokenizer.
max_source_length (int): Length of input token
sequences. Size is enforced with truncation or padding. Defaults to
max_target_length (int): length of labels token sequences.
Returns:
Dict[str, Dataset]: dataset for each task.
"""
# Dataset for each task
datasets = {
k: T5BaselineDataset(keynames=keynames,
format_labels_fn=v['labels'],
format_inputs_fn=v['inputs'],
t5_tokenizer_prefix=t5_prefix,
max_source_length=max_source_length,
max_target_length=max_target_length)
for k, v in tasks_functions_maps.items()
}
# dataset with all tasks
datasets['all_tasks_concat'] = ConcatDataset(datasets.values())
return datasets
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] | 2.36024 | 1,499 |
from django.shortcuts import render
from django.http import HttpResponse
from inventory.models import Product
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] | 3.8 | 30 |
import curio
from curio import socket
from ... import gvars
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] | 3.444444 | 18 |
import pytest
from kafka import KafkaConsumer
from kafka.errors import KafkaConfigurationError
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] | 4.041667 | 24 |
# -*- coding: utf-8 -*-
###
# © 2018 The Board of Trustees of the Leland Stanford Junior University
# Nathaniel Watson
# [email protected]
###
# For some useful documentation, see
# https://docs.python.org/2/distutils/setupscript.html.
# This page is useful for dependencies:
# http://python-packaging.readthedocs.io/en/latest/dependencies.html.
# PSF tutorial for packaging up projects:
# https://packaging.python.org/tutorials/packaging-projects
import glob
import os
from setuptools import setup, find_packages
SCRIPTS_DIR = "pulsarpy_to_dcc/scripts/"
scripts = glob.glob(os.path.join(SCRIPTS_DIR,"*.py"))
scripts.remove(os.path.join(SCRIPTS_DIR,"__init__.py"))
with open("README.md", "r") as fh:
long_description = fh.read()
setup(
author = "Nathaniel Watson",
author_email = "[email protected]",
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
description = "Client for submitting experiments in Pulsar LIMS to the ENCODE Portal",
install_requires = [
"dxpy3",
"encode-utils",
"pulsarpy",
"inflection",
"requests",
],
long_description = long_description,
long_description_content_type = "text/markdown",
name = "pulsarpy-to-encodedcc",
packages = find_packages(),
project_urls = {
"Read the Docs": "https://pulsarpy-to-encodedcc.readthedocs.io/en/latest",
},
scripts = scripts,
url = "https://github.com/nathankw/pulsarpy-to-encodedcc", # home page
version = "0.2.0",
)
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2196,
796,
366,
15,
13,
17,
13,
15,
1600,
198,
8,
198
] | 1.940114 | 1,052 |
#py_persistent_2.py
"""pickle your stuff for a long winter's nap"""
import pickle
from py_persistent_3 import THIS_YEAR
print("imported THIS_YEAR, which is {}".format(THIS_YEAR))
pickle_file='this_year.pkl'
with open(pickle_file, 'wb') as f:
pickle.dump(THIS_YEAR, f)
| [
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] | 2.555556 | 108 |
#!/usr/bin/env python3
# SPDX-License-Identifier: GPL-2.0+
#
# Copyright (c) 2012 The Chromium OS Authors.
#
"""See README for more information"""
import doctest
import multiprocessing
import os
import re
import sys
import unittest
# Bring in the patman libraries
our_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(1, os.path.join(our_path, '..'))
# Our modules
from buildman import board
from buildman import bsettings
from buildman import builder
from buildman import cmdline
from buildman import control
from buildman import toolchain
from patman import patchstream
from patman import gitutil
from patman import terminal
options, args = cmdline.ParseArgs()
# Run our meagre tests
if options.test:
RunTests(options.skip_net_tests)
# Build selected commits for selected boards
else:
bsettings.Setup(options.config_file)
ret_code = control.DoBuildman(options, args)
sys.exit(ret_code)
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] | 3.11745 | 298 |
from LAC import LAC
"""
本文件测试LAC的分词,词性标注及重要性功能
"""
os.environ['PYTHONIOENCODING'] = 'UTF-8'
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] | 1.305556 | 72 |
import time
import requests
import os
import json
from datetime import datetime
from dotenv import load_dotenv
from tinydb import TinyDB, where
from scrapers.domvast import Domvast
from scrapers.beumerutrecht import BeumerUtrecht
from scrapers.rvl import RVL
from scrapers.molenbeek import Molenbeek
from scrapers.makelaar1 import Makelaar1
from scrapers.lauteslager import Lauteslager
from scrapers.punt import Punt
from scrapers.debree import DeBree
from scrapers.realworks import createRealworksInstances
if __name__ == '__main__':
load_dotenv()
print('Waiting a few seconds for the VPN to start...')
time.sleep(5)
starttime = time.time()
print('Started at: ', starttime)
interval = 15.0 * 60.0
print('Checking every ' + str(interval) + ' seconds')
while True:
loop()
time.sleep(interval - ((time.time() - starttime) % interval))
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13,
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3419,
532,
923,
2435,
8,
4064,
16654,
4008,
198
] | 2.911765 | 306 |
#!/usr/bin/env python3
#
# --- Copyright 2021 Modern Electron
# --- Monte-Carlo Collision script to reproduce the benchmark tests from
# --- Turner et al. (2013) - https://doi.org/10.1063/1.4775084
import argparse
import sys
import numpy as np
from pywarpx import callbacks, fields, picmi
from scipy.sparse import csc_matrix
from scipy.sparse import linalg as sla
constants = picmi.constants
class PoissonSolver1D(picmi.ElectrostaticSolver):
"""This can be removed and the MLMG solver used once
https://github.com/ECP-WarpX/WarpX/issues/3123 is addressed."""
def __init__(self, grid, **kwargs):
"""Direct solver for the Poisson equation using superLU. This solver is
useful for 1D cases.
Arguments:
grid (picmi.Cartesian1DGrid): Instance of the grid on which the
solver will be installed.
"""
# Sanity check that this solver is appropriate to use
if not isinstance(grid, picmi.Cartesian1DGrid):
raise RuntimeError('Direct solver can only be used on a 1D grid.')
super(PoissonSolver1D, self).__init__(
grid=grid, method=kwargs.pop('method', 'Multigrid'),
required_precision=1, **kwargs
)
def initialize_inputs(self):
"""Grab geometrical quantities from the grid. The boundary potentials
are also obtained from the grid using 'warpx_potential_zmin' for the
left_voltage and 'warpx_potential_zmax' for the right_voltage.
These can be given as floats or strings that can be parsed by the
WarpX parser.
"""
# grab the boundary potentials from the grid object
self.right_voltage = (
self.grid.potential_zmax.replace('sin', 'np.sin').replace('pi', 'np.pi')
)
# set WarpX boundary potentials to None since we will handle it
# ourselves in this solver
self.grid.potential_xmin = None
self.grid.potential_xmax = None
self.grid.potential_ymin = None
self.grid.potential_ymax = None
self.grid.potential_zmin = None
self.grid.potential_zmax = None
super(PoissonSolver1D, self).initialize_inputs()
self.nz = self.grid.nx
self.dz = (self.grid.xmax - self.grid.xmin) / self.nz
self.nxguardphi = 1
self.nzguardphi = 1
self.phi = np.zeros(self.nz + 1 + 2*self.nzguardphi)
self.decompose_matrix()
callbacks.installpoissonsolver(self._run_solve)
def decompose_matrix(self):
"""Function to build the superLU object used to solve the linear
system."""
self.nsolve = self.nz + 1
# Set up the tridiagonal computation matrix in order to solve A*phi =
# rho for phi.
self.A_ldiag = np.ones(self.nsolve-1) / self.dz**2
self.A_mdiag = -2.*np.ones(self.nsolve) / self.dz**2
self.A_udiag = np.ones(self.nsolve-1) / self.dz**2
self.A_mdiag[0] = 1.
self.A_udiag[0] = 0.0
self.A_mdiag[-1] = 1.
self.A_ldiag[-1] = 0.0
# Set up the computation matrix in order to solve A*phi = rho
A = np.zeros((self.nsolve, self.nsolve))
idx = np.arange(self.nsolve)
A[idx, idx] = -2.0
A[idx[1:], idx[:-1]] = 1.0
A[idx[:-1], idx[1:]] = 1.0
A[0, 1] = 0.0
A[-1, -2] = 0.0
A[0, 0] = 1.0
A[-1, -1] = 1.0
A = csc_matrix(A, dtype=np.float32)
self.lu = sla.splu(A)
def _run_solve(self):
"""Function run on every step to perform the required steps to solve
Poisson's equation."""
# get rho from WarpX
self.rho_data = fields.RhoFPWrapper(0, False)[...][:,0]
# run superLU solver to get phi
self.solve()
# write phi to WarpX
fields.PhiFPWrapper(0, True)[...] = self.phi
def solve(self):
"""The solution step. Includes getting the boundary potentials and
calculating phi from rho."""
left_voltage = 0.0
t = self.sim_ext.gett_new()
right_voltage = eval(self.right_voltage)
# Construct b vector
rho = -self.rho_data / constants.ep0
b = np.zeros(rho.shape[0], dtype=np.float32)
b[:] = rho * self.dz**2
b[0] = left_voltage
b[-1] = right_voltage
phi = self.lu.solve(b)
self.phi[self.nzguardphi:-self.nzguardphi] = phi
self.phi[:self.nzguardphi] = left_voltage
self.phi[-self.nzguardphi:] = right_voltage
class CapacitiveDischargeExample(object):
'''The following runs a simulation of a parallel plate capacitor seeded
with a plasma in the spacing between the plates. A time varying voltage is
applied across the capacitor. The groups of 4 values below correspond to
the 4 cases simulated by Turner et al. (2013) in their benchmarks of
PIC-MCC codes.
'''
gap = 0.067 # m
freq = 13.56e6 # Hz
voltage = [450.0, 200.0, 150.0, 120.0] # V
gas_density = [9.64e20, 32.1e20, 96.4e20, 321e20] # m^-3
gas_temp = 300.0 # K
m_ion = 6.67e-27 # kg
plasma_density = [2.56e14, 5.12e14, 5.12e14, 3.84e14] # m^-3
elec_temp = 30000.0 # K
seed_nppc = 16 * np.array([32, 16, 8, 4])
nz = [128, 256, 512, 512]
dt = 1.0 / (np.array([400, 800, 1600, 3200]) * freq)
# Total simulation time in seconds
total_time = np.array([1280, 5120, 5120, 15360]) / freq
# Time (in seconds) between diagnostic evaluations
diag_interval = 32 / freq
def __init__(self, n=0, test=False):
"""Get input parameters for the specific case (n) desired."""
self.n = n
self.test = test
# Case specific input parameters
self.voltage = f"{self.voltage[n]}*sin(2*pi*{self.freq:.5e}*t)"
self.gas_density = self.gas_density[n]
self.plasma_density = self.plasma_density[n]
self.seed_nppc = self.seed_nppc[n]
self.nz = self.nz[n]
self.dt = self.dt[n]
self.max_steps = int(self.total_time[n] / self.dt)
self.diag_steps = int(self.diag_interval / self.dt)
if self.test:
self.max_steps = 50
self.diag_steps = 5
self.mcc_subcycling_steps = 2
else:
self.mcc_subcycling_steps = None
self.ion_density_array = np.zeros(self.nz + 1)
self.setup_run()
def setup_run(self):
"""Setup simulation components."""
#######################################################################
# Set geometry and boundary conditions #
#######################################################################
self.grid = picmi.Cartesian1DGrid(
number_of_cells=[self.nz],
warpx_max_grid_size=128,
lower_bound=[0],
upper_bound=[self.gap],
lower_boundary_conditions=['dirichlet'],
upper_boundary_conditions=['dirichlet'],
lower_boundary_conditions_particles=['absorbing'],
upper_boundary_conditions_particles=['absorbing'],
warpx_potential_hi_z=self.voltage,
)
#######################################################################
# Field solver #
#######################################################################
# self.solver = picmi.ElectrostaticSolver(
# grid=self.grid, method='Multigrid', required_precision=1e-6,
# warpx_self_fields_verbosity=2
# )
self.solver = PoissonSolver1D(grid=self.grid)
#######################################################################
# Particle types setup #
#######################################################################
self.electrons = picmi.Species(
particle_type='electron', name='electrons',
initial_distribution=picmi.UniformDistribution(
density=self.plasma_density,
rms_velocity=[np.sqrt(constants.kb * self.elec_temp / constants.m_e)]*3,
)
)
self.ions = picmi.Species(
particle_type='He', name='he_ions',
charge='q_e', mass=self.m_ion,
initial_distribution=picmi.UniformDistribution(
density=self.plasma_density,
rms_velocity=[np.sqrt(constants.kb * self.gas_temp / self.m_ion)]*3,
)
)
#######################################################################
# Collision initialization #
#######################################################################
cross_sec_direc = '../../../../warpx-data/MCC_cross_sections/He/'
mcc_electrons = picmi.MCCCollisions(
name='coll_elec',
species=self.electrons,
background_density=self.gas_density,
background_temperature=self.gas_temp,
background_mass=self.ions.mass,
ndt=self.mcc_subcycling_steps,
scattering_processes={
'elastic' : {
'cross_section' : cross_sec_direc+'electron_scattering.dat'
},
'excitation1' : {
'cross_section': cross_sec_direc+'excitation_1.dat',
'energy' : 19.82
},
'excitation2' : {
'cross_section': cross_sec_direc+'excitation_2.dat',
'energy' : 20.61
},
'ionization' : {
'cross_section' : cross_sec_direc+'ionization.dat',
'energy' : 24.55,
'species' : self.ions
},
}
)
mcc_ions = picmi.MCCCollisions(
name='coll_ion',
species=self.ions,
background_density=self.gas_density,
background_temperature=self.gas_temp,
ndt=self.mcc_subcycling_steps,
scattering_processes={
'elastic' : {
'cross_section' : cross_sec_direc+'ion_scattering.dat'
},
'back' : {
'cross_section' : cross_sec_direc+'ion_back_scatter.dat'
},
# 'charge_exchange' : {
# 'cross_section' : cross_sec_direc+'charge_exchange.dat'
# }
}
)
#######################################################################
# Initialize simulation #
#######################################################################
self.sim = picmi.Simulation(
solver=self.solver,
time_step_size=self.dt,
max_steps=self.max_steps,
warpx_collisions=[mcc_electrons, mcc_ions],
warpx_load_balance_intervals=self.max_steps//5000,
verbose=self.test
)
self.sim.add_species(
self.electrons,
layout = picmi.GriddedLayout(
n_macroparticle_per_cell=[self.seed_nppc], grid=self.grid
)
)
self.sim.add_species(
self.ions,
layout = picmi.GriddedLayout(
n_macroparticle_per_cell=[self.seed_nppc], grid=self.grid
)
)
self.solver.sim_ext = self.sim.extension
#######################################################################
# Add diagnostics for the CI test to be happy #
#######################################################################
field_diag = picmi.FieldDiagnostic(
name='diag1',
grid=self.grid,
period=0,
data_list=['rho_electrons', 'rho_he_ions'],
write_dir='.',
warpx_file_prefix='Python_background_mcc_1d_plt'
)
self.sim.add_diagnostic(field_diag)
##########################
# parse input parameters
##########################
parser = argparse.ArgumentParser()
parser.add_argument(
'-t', '--test', help='toggle whether this script is run as a short CI test',
action='store_true',
)
parser.add_argument(
'-n', help='Test number to run (1 to 4)', required=False, type=int,
default=1
)
args, left = parser.parse_known_args()
sys.argv = sys.argv[:1]+left
if args.n < 1 or args.n > 4:
raise AttributeError('Test number must be an integer from 1 to 4.')
run = CapacitiveDischargeExample(n=args.n-1, test=args.test)
run.run_sim()
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77,
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25,
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220,
220,
220,
5298,
3460,
4163,
12331,
10786,
14402,
1271,
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307,
281,
18253,
422,
352,
284,
604,
2637,
8,
198,
198,
5143,
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330,
1800,
7279,
10136,
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7,
77,
28,
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13,
77,
12,
16,
11,
1332,
28,
22046,
13,
9288,
8,
198,
5143,
13,
5143,
62,
14323,
3419,
198
] | 2.101176 | 6,039 |
import torch
import torchvision
from tqdm import notebook
import torch.nn.functional as F
from time import time
| [
11748,
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] | 3.230769 | 39 |
#!/usr/bin/env python3
# encoding: utf-8
import sys
from itertools import combinations
from random import randint
##############################################################################
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: {} words.txt".format(sys.argv[0]))
exit(1)
# else
process(sys.argv[1])
| [
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""" This module contains functions for reading CSV files containing pressure
and volume data.
"""
import numpy
def read_csv(input_file, delimiter=",", x="p", sigma_x="sigma_p",
y="v", sigma_y="sigma_v"):
"""
Reads a CSV file with x and y data and standard deviation columns.
Parameters
----------
input_file : str
delimiter : str
y : str
x : str
sigma_y : str
sigma_x : str
Returns
-------
x, y, sigma_x, sigma_y : tuple of array_like
"""
data = numpy.genfromtxt(input_file, delimiter=delimiter, names=True)
return (data[x].flatten(), data[sigma_x].flatten(),
data[y].flatten(), data[sigma_y].flatten())
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87,
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220,
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22784,
1366,
58,
82,
13495,
62,
88,
4083,
2704,
41769,
28955,
198
] | 2.412371 | 291 |
import fastai.torch_core as tc
import fastai.data.load as load
import torch
import PIL as pil
import pathlib as path
import dill
from numpy import prod
import utils
################################################################################
TARGET_FOLDER = 'data'
DATA_PATH = utils.download_dataset('cifar10', TARGET_FOLDER)
CLASSIFICATION_LABELS = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
EXPERIMENT_FOLDER = 'experiments'
################################################################################
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198,
29113,
29113,
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198
] | 3.598726 | 157 |
# =======================================================================================
# =======================================================================================
# =======================================================================================
if __name__ == '__main__':
solve() | [
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import logging
import tensorflow as tf
import d4.dsm.extensible_dsm as edsm
import d4.interpreter as si
from d4.dsm.loss import L2Loss
logging.basicConfig(level=logging.DEBUG)
if __name__ == "__main__":
test_learn_to_add_digits()
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] | 2.542553 | 94 |
from datetime import datetime
from datetime import timedelta
import jwt
from jwt import PyJWTError
from passlib.context import CryptContext
from fastapi import Depends
from fastapi import HTTPException
from fastapi.security import HTTPBearer
from starlette.status import HTTP_401_UNAUTHORIZED
from app.models.auth import UserModel
security = HTTPBearer()
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
# openssl rand -hex 32
SECRET_KEY = "c1669dfb649fdf59354d6cc7403cd4f16ea0f2f3d6ee6180bf452bf640472a6f"
ALGORITHM = "HS256"
ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24
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3843,
1546,
796,
3126,
1635,
1987,
628,
628,
628,
198
] | 2.88835 | 206 |
from __future__ import division, absolute_import, print_function
__copyright__ = "Copyright (C) 2016 Matt Wala"
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import six # noqa
import pytest
from six.moves import range
import sys
import logging
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
from py.test.cmdline import main
main([__file__])
# vim: foldmethod=marker
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12962,
198,
198,
2,
43907,
25,
5591,
24396,
28,
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263,
198
] | 3.394009 | 434 |
from collections import OrderedDict
from django.db import models
from django.utils.translation import ugettext_lazy as _
from modelutils import *
sheet_models = OrderedDict([
('Print', NewspaperSheet),
('Radio', RadioSheet),
('Television', TelevisionSheet),
('Internet', InternetNewsSheet),
('Twitter', TwitterSheet)
])
tm_sheet_models = OrderedDict([
('Print', NewspaperSheet),
('Radio', RadioSheet),
('Television', TelevisionSheet)
])
dm_sheet_models = OrderedDict([
('Internet', InternetNewsSheet),
('Twitter', TwitterSheet)
])
person_models = OrderedDict([
('Print', NewspaperPerson),
('Radio', RadioPerson),
('Television', TelevisionPerson),
('Internet', InternetNewsPerson),
('Twitter', TwitterPerson)]
)
tm_person_models = OrderedDict([
('Print', NewspaperPerson),
('Radio', RadioPerson),
('Television', TelevisionPerson),
])
dm_person_models = OrderedDict([
('Internet', InternetNewsPerson),
('Twitter', TwitterPerson)
])
journalist_models = OrderedDict([
('Print', NewspaperJournalist),
('Radio', RadioJournalist),
('Television', TelevisionJournalist),
('Internet', InternetNewsJournalist),
('Twitter', TwitterJournalist)
])
tm_journalist_models = OrderedDict([
('Print', NewspaperJournalist),
('Radio', RadioJournalist),
('Television', TelevisionJournalist),
])
broadcast_journalist_models = OrderedDict([
('Radio', RadioJournalist),
('Television', TelevisionJournalist),
])
dm_journalist_models = OrderedDict([
('Internet', InternetNewsJournalist),
('Twitter', TwitterJournalist)
])
all_models = OrderedDict([
('Sheets', sheet_models),
('Sources', person_models),
('Reporters', journalist_models)
])
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] | 2.97973 | 592 |
from graphene_django import DjangoObjectType
from graphql_jwt.decorators import login_required
import graphene
from .models import *
class ShopType(DjangoObjectType):
"""
Create ModelType for Shop model
"""
class Meta:
"""
Metadata class
"""
model = Shop
class LikeType(DjangoObjectType):
"""
Create ModelType for Like model
"""
class Meta:
"""
Metadata class
"""
model = Like
class DislikeType(DjangoObjectType):
"""
Create ModelType for Dislike model
"""
class Meta:
"""
Metadata class
"""
model = Dislike
class LikeShop(graphene.Mutation):
"""
LikeShop Mutation
"""
Output = LikeType
class Arguments:
"""
pk unique key of the shop to be liken by the
user in the session context.
"""
pk = graphene.ID()
@login_required
class DislikeShop(graphene.Mutation):
"""
DislikeShop Mutation
"""
Output = DislikeType
class Arguments:
"""
pk unique key of the shop to be liken by the
user in the session context.
"""
pk = graphene.ID()
@login_required
class RemoveShop(graphene.Mutation):
"""
DislikeShop Mutation
"""
ok = graphene.Boolean()
class Arguments:
"""
pk unique key of the shop to be liken by the user
in the session context.
"""
pk = graphene.ID()
@login_required
def mutate(self, info, pk):
"""
remove shop with primary key pk from favorite
shops of the user in the context.
"""
shop = Shop.objects.get(pk=pk)
like = Like.objects.get(shop=shop, user=info.context.user)
like.delete()
return RemoveShop(True)
class ShopQuery(graphene.ObjectType):
"""
Query class for model shop
"""
nearby_shops = graphene.List(ShopType)
preferred_shops = graphene.List(ShopType)
@login_required
def resolve_nearby_shops(self, info):
"""
Return all nearby shop for given user in the session
context.
"""
return Shop.get_sorted_by_distance(info.context.user)
@login_required
def resolve_preferred_shops(self, info):
"""
Return all preferred shops for given user in the session
context.
"""
return info.context.user.favorite_shops.all().distinct()
class ShopMutation(graphene.ObjectType):
"""
All Mutation related with shop model type.
"""
like = LikeShop.Field()
dislike = DislikeShop.Field()
remove = RemoveShop.Field()
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] | 2.292079 | 1,212 |
from django.contrib import admin
from users import models
# Register your models here.
admin.site.register(models.Profile, ProfileAdmin) | [
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] | 3.833333 | 36 |
import tensorflow as tf
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] | 3.25 | 8 |
"""
war
War card game written for fun while following the 'Complete Python Developer Certification Course' by Imtiaz Ahmad, on Udemy.
"""
# Add imports here
# from card_class import Card
# from .deck_class import Deck
# from .table_class import Table
# from .functions import get_players
# Handle versioneer
from ._version import get_versions
versions = get_versions()
__version__ = versions['version']
__git_revision__ = versions['full-revisionid']
del get_versions, versions
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] | 3.75 | 128 |
import json
import logging
import asyncio
from aiohttp import web
import voluptuous as vol
from concurrent import futures
from ledfxcontroller.api import RestEndpoint
_LOGGER = logging.getLogger(__name__)
MAX_PENDING_MESSAGES = 256
BASE_MESSAGE_SCHEMA = vol.Schema({
vol.Required('id'): vol.Coerce(int),
vol.Required('type'): str,
}, extra=vol.ALLOW_EXTRA)
# TODO: Have a more well defined registration and a more componetized solution.
# Could do something like have Device actually provide the handler for Device
# related functionality. This would allow easy access to internal workings and
# events.
websocket_handlers = {}
import numpy as np
@websocket_handler('get_pixels') | [
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] | 3.227907 | 215 |
# step 1 - capture first frame
# step 2 - capture other frame
# step 3 - calculate the difference in frisr and other frame
# step 4 - define threshold
# step 5 - define boarders
import cv2
from sendMail import send_mail
from datetime import datetime
first_frame = None
video = cv2.VideoCapture(0)
count = 0
photos = []
time1 = datetime.now()
while True:
check, frame = video.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
# first frame
if first_frame is None:
first_frame = gray
continue
# calculate difference
delta_frame = cv2.absdiff(first_frame, gray)
# define threshold
# if threshold is < 30 convert that pixels to black otherwise white
thresh_delta = cv2.threshold(delta_frame, 30, 225, cv2.THRESH_BINARY)[1]
thresh_delta = cv2.dilate(thresh_delta, None, iterations=0)
# define boarders or contours
(cnts, _) = cv2.findContours(thresh_delta.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in cnts:
if cv2.contourArea(contour) < 25000: # remove noises
continue
# define rectangle
count = count + 1
path = "Capture/ph_" + str(count) + ".jpg"
cv2.imwrite(path, frame)
# photos.append(path)
(x, y, w, h) = cv2.boundingRect(contour)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.imshow('frame', frame)
"""
cv2.imshow('gray', gray)
cv2.imshow('delta', delta_frame)
cv2.imshow('thresh ', thresh_delta)
"""
"""if len(photos) > 100 :
send_mail(photos[0])
photos.clear()
"""
"""time2 = datetime.now()
diff = time2 - time1
if diff.total_seconds() > 15:
send_mail(photos[:5])
photos.clear()
time1 = time2
"""
key = cv2.waitKey(1)
if key == ord('q'):
break
video.release()
cv2.destroyAllWindows()
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1994,
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269,
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17,
13,
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9218,
7,
16,
8,
201,
198,
220,
220,
220,
611,
1994,
6624,
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80,
6,
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201,
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220,
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220,
220,
220,
220,
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3419,
201,
198,
33967,
17,
13,
41659,
3237,
11209,
3419,
201,
198
] | 2.127368 | 950 |
from django.urls import path
from django.views.generic.base import TemplateView
from microsoft_student_partners.msp.views import MspView
app_name = "msp"
urlpatterns = [
path("", view=MspView.as_view(), name="home"),
path(
"services/",
TemplateView.as_view(template_name="services.html"),
name="services",
),
path("about", TemplateView.as_view(template_name="about.html"), name="about",),
path(
"contact", TemplateView.as_view(template_name="contact.html"), name="contact",
),
]
| [
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1438,
2625,
32057,
1600,
198,
220,
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10612,
198,
60,
198
] | 2.594203 | 207 |
from collections import namedtuple
from datetime import datetime
from subprocess import call
GitFileData = namedtuple("GitFileData", ["permissions", "type", "hash", "path"])
repos = dict()
def load_file(repo, file:GitFileData):
"""git checkout HEAD -- {path}"""
try:
get_repo(repo).checkout("master", "--", file.path)
# print("Successfully loaded: {}".format(file.path))
except Exception as e:
raise Exception("There was an error checking out file: {}\n{}".format(file.path, e))
call(["cp", "-vr", repo+"/" + file.path, "."])
def list_files(repo, filter=None, branch="HEAD"):
"""git ls-tree -r -t HEAD/{branch} --name-only"""
files = get_repo(repo)('ls-tree', "-r", "-t", branch).stdout.decode("utf8").strip().splitlines()
for data in files:
split, file = data.split("\t")
if filter is None or filter in file:
permissions, type, hash = split.split(" ")
yield GitFileData(permissions, type, hash, file)
# def select_file():
# pairs = {}
# for k, v in enumerate(list_files(str(resources_path))):
# print(f'{k}:\t{v.path}')
# pairs[k] = v
# id = int(input("File #:\n"))
# return pairs[id]
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48774,
198,
2,
220,
220,
220,
220,
1441,
14729,
58,
312,
60,
628
] | 2.432 | 500 |
import pandas as pd
import numpy as np
import sqlalchemy
from sqlalchemy import create_engine
from sqlalchemy.orm import Session
from password import pw
import json
from flask import Flask, jsonify, render_template
path = 'postgresql://postgres:' + pw + '@localhost:5432/rpi_comp'
engine = create_engine(path)
conn = engine.connect()
# Access our DB through pandas dataframe - convert to json - record oriented
data = pd.read_sql("SELECT * FROM rpi", conn).to_json(orient='records')
# Access database through sql alchemy
# db = engine.execute('SELECT * FROM "rpi"').fetchall()
# Create Session
session = Session(engine)
# Create Flask connection
app = Flask(__name__)
#################################################
# Flask Routes
#################################################
@app.route("/api/v1.0/rpi_comp")
def database():
"""Return the rpi_comp db"""
# Convert string to json
data_json = json.loads(data)
return jsonify(data_json)
@app.route("/")
@app.route("/index")
if __name__ == "__main__":
app.run(debug=True)
# Could this database be accessed in this method?
# DB through sql lite method
# reflect an existing database into a new model
# Base = automap_base()
# reflect the tables
# Base.prepare(engine, reflect=True)
# Accessing different tables
# Measurement = Base.classes.measurement
# Station = Base.classes.station
# Performing a query - Session.query
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2448,
15464,
257,
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532,
23575,
13,
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198
] | 3.292343 | 431 |
# -*- coding: utf-8 -*-
from hijack.tests.test_hijack import BaseHijackTests
from hijack_admin import settings as hijack_admin_settings
from hijack_admin.tests.test_app.models import RelatedModel
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] | 3 | 66 |
"""JTAGICE3 protocol mappings"""
from logging import getLogger
from .avrcmsisdap import AvrCommand
from ..util import binary
from ..util import print_helpers
from ..pyedbglib_errors import PyedbglibError
class Jtagice3Command(AvrCommand):
"""
Sends a "JTAGICE3" command frame, and received a response
JTAGICE3 protocol header is formatted:
JTAGICE3_TOKEN 0x0E
PROTOCOL_VERSION 0
SEQUENCE_NUMBER_L
SEQUENCE_NUMBER_H
HANDLER_ID
PAYLOAD
Response format is:
JTAGICE3_TOKEN 0x0E
SEQUENCE_NUMBER_L echo
SEQUENCE_NUMBER_H echo
HANDLER_ID
PAYLOAD
"""
# JTAGICE3 protocol token
JTAGICE3_TOKEN = 0x0E
JTAGICE3_PROTOCOL_VERSION = 0x00
# Handlers within JTAGICE3 protocol
HANDLER_DISCOVERY = 0x00
HANDLER_HOUSEKEEPING = 0x01
HANDLER_SPI = 0x11
HANDLER_AVR8_GENERIC = 0x12
HANDLER_AVR32_GENERIC = 0x13
HANDLER_TPI = 0x14
HANDLER_EDBG = 0x20
HANDLER_COPROCESSOR = 0x21
HANDLER_POWER = 0x22
HANDLER_SELFTEST = 0x81
def validate_response(self, response):
"""
Validates the response form the debugger
:param response: raw response bytes
"""
self.logger.debug("Checking response (%s)", print_helpers.bytelist_to_hex_string(response))
# Check length first
if len(response) < 5:
raise PyedbglibError("Invalid response length ({:d}).".format(len(response)))
# Check token
if response[0] != self.JTAGICE3_TOKEN:
raise PyedbglibError("Invalid token (0x{:02X}) in response.".format(response[0]))
# Check sequence
sequence = response[1] + (response[2] << 8)
if self.sequence_id != sequence:
raise PyedbglibError(
"Invalid sequence in response (0x{:04X} vs 0x{:04X}).".format(self.sequence_id, sequence))
# Check handler
if response[3] != self.handler:
raise PyedbglibError("Invalid handler (0x{:02X}) in response.".format(response[3]))
def jtagice3_command_response_raw(self, command):
"""
Sends a JTAGICE3 command and receives the corresponding response
:param command:
:return:
"""
# Header
header = bytearray([self.JTAGICE3_TOKEN, self.JTAGICE3_PROTOCOL_VERSION, self.sequence_id & 0xFF,
(self.sequence_id >> 8) & 0xFF, self.handler])
# Send command, receive response
packet = header + bytearray(command)
response = self.avr_command_response(packet)
return response
def jtagice3_command_response(self, command):
"""
Sends a JTAGICE3 command and receives the corresponding response, and validates it
:param command:
:return:
"""
response = self.jtagice3_command_response_raw(command)
# Increment sequence number
self.sequence_id += 1
if self.sequence_id > 0xFFFE:
self.sequence_id = 1
# Peel and return
return response[4:]
class Jtagice3ResponseError(Exception):
"""Exception type for JTAGICE3 responses"""
class Jtagice3Protocol(Jtagice3Command):
"""
Base class for all protocols in the JTAGICE3 family.
All sub-protocols support query, get and set commands.
"""
# Command versioning
CMD_VERSION0 = 0
CMD_VERSION1 = 1
# All handler share these functions:
CMD_QUERY = 0x00
CMD_SET = 0x01
CMD_GET = 0x02
# And these base responses
PROTOCOL_OK = 0x80
PROTOCOL_LIST = 0x81
PROTOCOL_DATA = 0x84
PROTOCOL_FAILED = 0xA0
# PROTOCOL_FAILED_WITH_DATA = 0xA1
# Failure codes
FAILURE_OK = 0
# CMD_SET and CMD_GET failure codes
SETGET_FAILURE_OK = 0x00
SETGET_FAILURE_NOT_IMPLEMENTED = 0x10
SETGET_FAILURE_NOT_SUPPORTED = 0x11
SETGET_FAILURE_INVALID_CLOCK_SPEED = 0x20
SETGET_FAILURE_ILLEGAL_STATE = 0x21
SETGET_FAILURE_JTAGM_INIT_ERROR = 0x22
SETGET_FAILURE_INVALID_VALUE = 0x23
SETGET_FAILURE_HANDLER_ERROR = 0x30
"""Mapping JTAGICE3 error codes to more human friendly strings"""
JTAGICE3_ERRORS = {0: 'SUCCESS'}
def check_response(self, response, expected=None):
"""
Checks the response for known errors
:param response: response bytes
:param expected: expected response
:return: data from response
"""
status, data = self.peel_response(response, expected)
if not status:
error_message = self.error_as_string(data[0])
msg = "JTAGICE3 error response code 0x{:02X}: '{:s}' ".format(data[0], error_message)
self.logger.error(msg)
raise Jtagice3ResponseError(error_message, data[0])
return data
def error_as_string(self, code):
"""
Get the response error as a string (error code translated to descriptive string)
:param code: error code
:return: error code as descriptive string
"""
try:
return self.JTAGICE3_ERRORS[code]
except KeyError:
return "Unknown error!"
def peel_response(self, response, expected=None):
"""
Process the response, extracting error codes and data
:param response: raw response bytes
:param expected: expected response
:return: status, data
"""
return_list = False, [0xFF]
# Special handling
if expected is not None and response[0] == expected:
return_list = True, response[2:]
else:
if response[0] == self.PROTOCOL_OK:
return_list = True, []
elif response[0] == self.PROTOCOL_LIST:
return_list = True, response[2:]
elif response[0] == self.PROTOCOL_DATA:
# Trailing status is not included on some handlers
if self.supports_trailing_status and response[-1] == self.FAILURE_OK:
return_list = True, response[2:-1]
else:
return_list = False, [response[-1]]
elif response[0] == self.PROTOCOL_FAILED:
return_list = False, [response[2]]
return return_list
def query(self, context):
"""
Queries functionality using the QUERY API
:param context: Query context
:return: List of supported entries
"""
self.logger.debug("Query to context 0x{:02X}".format(context))
resp = self.jtagice3_command_response([self.CMD_QUERY, self.CMD_VERSION0, context])
status, data = self.peel_response(resp)
if not status:
msg = "Unable to QUERY (failure code 0x{:02X})".format(data[0])
raise PyedbglibError(msg)
return data
def set_byte(self, context, offset, value):
"""
Sets a single byte parameter
:param context: context (address) to set
:param offset: offset address to set
:param value: value to set
:return:
"""
self._set_protocol(context, offset, bytearray([value]))
def set_le16(self, context, offset, value):
"""
Sets a little-endian 16-bit parameter
:param context: context (address) to set
:param offset: offset address to set
:param value: value to set
"""
self._set_protocol(context, offset, binary.pack_le16(value))
def set_le32(self, context, offset, value):
"""
Sets a little-endian 32-bit parameter
:param context: context (address) to set
:param offset: offset address to set
:param value: value to set
"""
self._set_protocol(context, offset, binary.pack_le32(value))
def _set_protocol(self, context, offset, data):
"""
Generic function for setting parameters
:param context: context (address) to set
:param offset: offset address to set
:param data: values to set
"""
self.logger.debug("JTAGICE3::set {:d} byte(s) to context {:d} offset {:d}".format(len(data),
context,
offset))
resp = self.jtagice3_command_response(
bytearray([self.CMD_SET, self.CMD_VERSION0, context, offset, len(data)]) + data)
resp_status, resp_data = self.peel_response(resp)
if not resp_status:
msg = "Unable to SET (failure code 0x{:02X})".format(resp_data[0])
raise PyedbglibError(msg)
def get_byte(self, context, offset):
"""
Get a single-byte parameter
:param context: context (address) to set
:param offset: offset address to set
:return: value read
"""
data = self._get_protocol(context, offset, 1)
return data[0]
def get_le16(self, context, offset):
"""
Get a little-endian 16-bit parameter
:param context: context (address) to set
:param offset: offset address to set
:return: value read
"""
data = self._get_protocol(context, offset, 2)
return binary.unpack_le16(data)
def get_le32(self, context, offset):
"""
Get a little-endian 32-bit parameter
:param context: context (address) to set
:param offset: offset address to set
:return: value read
"""
data = self._get_protocol(context, offset, 4)
return binary.unpack_le32(data)
def _get_protocol(self, context, offset, numbytes):
"""
Generic function to get a parameter
:param context: context (address) to set
:param offset: offset address to set
:param numbytes: number of bytes to get
:return: value read
"""
self.logger.debug("JTAGICE3::get {:d} byte(s) from context {:d} offset {:d}".format(numbytes, context, offset))
resp = self.jtagice3_command_response([self.CMD_GET, self.CMD_VERSION0, context, offset, numbytes])
status, data = self.peel_response(resp)
if not status:
msg = "Unable to GET (failure code 0x{:02X})".format(data[0])
raise Jtagice3ResponseError(msg, data)
return data
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] | 2.198695 | 4,751 |
from decimal import Decimal
from enerdata.profiles.profile import *
from expects import *
with description('A dragger object'):
with it('must return the integer of the number'):
d = Dragger()
aprox = d.drag(32.453)
expect(aprox).to(be(32))
with it('must keep the decimal part'):
d = Dragger()
aprox = d.drag(32.453)
expect(Decimal(d['default'])).to(equal(Decimal('0.453')))
with it('has to save the decimal part to default key if is not set'):
d = Dragger()
aprox = d.drag(1.01)
expect(d).to(have_key('default'))
with it('has to work with diferent keys independently'):
d = Dragger()
aprox = d.drag(1.6, key='key1')
aprox2 = d.drag(2.3, key='key2')
expect(d['key1']).to(equal(Decimal('-0.4')))
expect(d['key2']).to(equal(Decimal('0.3')))
aprox = d.drag(1.4, key='key1')
expect(aprox).to(be(1))
expect(d['key1']).to(equal(Decimal('0')))
aprox = d.drag(5.2, key='key2')
expect(aprox).to(be(6))
expect(d['key2']).to(equal(Decimal('-0.5')))
with context('Has to use the drag of antecesor drag'):
with context('If the current drag is >= 0.5'):
with it('has to return number + 1 and drag will be < 0'):
d = Dragger()
aprox = d.drag(32.453)
aprox2 = d.drag(1.1)
expect(aprox2).to(be(2))
expect(d['default']).to(be_below_or_equal(0))
with context('If the curren drag is < 0.5'):
with it('has to return number'):
d = Dragger()
aprox = d.drag(32.453)
aprox2 = d.drag(1.046)
expect(aprox2).to(be(1))
with context('if receives 0.5 and 0'):
with it('has to drag -0.5 and return 1 and 0'):
d = Dragger()
approx = d.drag(0.5)
dragging = d['default']
expect(approx).to(equal(1))
expect(dragging).to(equal(Decimal('-0.5')))
aprox = d.drag(0)
dragging = d['default']
expect(aprox).to(equal(0))
expect(dragging).to(equal(Decimal('-0.5')))
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6,
22305,
198
] | 1.881765 | 1,201 |
'''OpenGL extension NV.blend_square
This module customises the behaviour of the
OpenGL.raw.GL.NV.blend_square to provide a more
Python-friendly API
Overview (from the spec)
It is useful to be able to multiply a number by itself in the blending
stages -- for example, in certain types of specular lighting effects
where a result from a dot product needs to be taken to a high power.
This extension provides four additional blending factors to permit
this and other effects: SRC_COLOR and ONE_MINUS_SRC_COLOR for source
blending factors, and DST_COLOR and ONE_MINUS_DST_COLOR for destination
blending factors.
Direct3D provides capability bits for advertising these additional
blend modes.
The official definition of this extension is available here:
http://www.opengl.org/registry/specs/NV/blend_square.txt
'''
from OpenGL import platform, constant, arrays
from OpenGL import extensions, wrapper
import ctypes
from OpenGL.raw.GL import _types, _glgets
from OpenGL.raw.GL.NV.blend_square import *
from OpenGL.raw.GL.NV.blend_square import _EXTENSION_NAME
def glInitBlendSquareNV():
'''Return boolean indicating whether this extension is available'''
from OpenGL import extensions
return extensions.hasGLExtension( _EXTENSION_NAME )
### END AUTOGENERATED SECTION | [
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198,
201,
198,
21017,
23578,
47044,
7730,
1677,
1137,
11617,
44513
] | 3.292079 | 404 |
from chainer.training.triggers import interval
from chainer.training.triggers import minmax_value_trigger
IntervalTrigger = interval.IntervalTrigger
MaxValueTrigger = minmax_value_trigger.MaxValueTrigger
MinValueTrigger = minmax_value_trigger.MinValueTrigger
| [
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] | 3.728571 | 70 |
import urllib.request
wiki = "https://en.wikipedia.org/wiki/List_of_countries_by_the_number_of_billionaires"
page = urllib.request.urlopen(wiki)
from bs4 import BeautifulSoup
soup = BeautifulSoup(page)
print (soup.prettify())
soup.title.text
soup.a
soup.find_all("a")
right_table = soup.find('table',class_ = 'wikitable')
table_body = right_table.find('tbody')
rows = table_body.find_all('tr')
count = 0
data = []
for row in rows:
if count == 0:
cols = row.find_all('th')
cols = [elem.text.strip() for elem in cols]
data.append([elem for elem in cols if elem])
count = count + 1
else:
cols = row.find_all('td')
cols = [elem.text.strip() for elem in cols]
data.append([elem for elem in cols if elem])
import pandas as pd
dataframe = pd.DataFrame(columns = data[0])
for i in range(1,len(data)):
dataframe = dataframe.append(pd.Series(data[i],index = data[0]),ignore_index = True)
| [
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329,
9766,
76,
287,
951,
82,
611,
9766,
76,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
11748,
19798,
292,
355,
279,
67,
198,
7890,
14535,
796,
279,
67,
13,
6601,
19778,
7,
28665,
82,
796,
1366,
58,
15,
12962,
220,
220,
220,
220,
220,
220,
198,
198,
1640,
1312,
287,
2837,
7,
16,
11,
11925,
7,
7890,
8,
2599,
198,
220,
220,
220,
1366,
14535,
796,
1366,
14535,
13,
33295,
7,
30094,
13,
27996,
7,
7890,
58,
72,
4357,
9630,
796,
1366,
58,
15,
46570,
46430,
62,
9630,
796,
6407,
8,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
628,
198,
220,
220,
220,
220
] | 2.279176 | 437 |
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