xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

097c50a96728dff3d3f2f66802f6917cbcd87b74

20,517

py

Python

scripts/all_to_all_analyzer.py

jweckstr/westmetro_scripts

a16385b00ac8d80f0068f348226ed89e2d0425a9

[ "MIT" ]

null

scripts/all_to_all_analyzer.py

jweckstr/westmetro_scripts

a16385b00ac8d80f0068f348226ed89e2d0425a9

[ "MIT" ]

null

scripts/all_to_all_analyzer.py

jweckstr/westmetro_scripts

a16385b00ac8d80f0068f348226ed89e2d0425a9

[ "MIT" ]

null

import sqlite3 import pandas import itertools import networkx as nx from gtfspy.gtfs import GTFS from gtfspy.util import timeit from scripts.all_to_all_settings import * def attach_database(conn, other_db_path, name="other"): cur = conn.cursor() cur.execute("ATTACH '%s' AS '%s'" % (str(other_db_path), name)) cur.execute("PRAGMA database_list") print("other database attached:", cur.fetchall()) return conn """ AllToAllDifferenceAnalyzer calculates the difference between various summary statistics of temporal distance and number of boardings, stores the values in a database and handles calls to this database. """ def stops_to_exclude(return_sqlite_list=False): gtfs_lm = GTFS(LM_DICT["gtfs_dir"]) areas_to_remove = gtfs_lm.execute_custom_query_pandas( "SELECT * FROM stops WHERE CASE WHEN substr(stop_id,1, 5) = '__b__' THEN CAST(substr(stop_id,6, 1) AS integer) ELSE CAST(substr(stop_id,1, 1) AS integer) END >4") if return_sqlite_list: return "(" + ",".join([str(x) for x in areas_to_remove["stop_I"].tolist()]) + ")" return areas_to_remove class AllToAllDifferenceAnalyzer: def __init__(self, gtfs_path, before_db_path, after_db_path, output_db): self.gtfs = GTFS(gtfs_path) print(output_db) self._create_indecies(before_db_path) self._create_indecies(after_db_path) self.conn = sqlite3.connect(output_db) self.conn = attach_database(self.conn, before_db_path, name="before") self.conn = attach_database(self.conn, after_db_path, name="after") def _create_indecies(self, db_path): conn = sqlite3.connect(db_path) cur = conn.cursor() for table in ["journey_duration", "n_boardings", "temporal_distance"]: query = """CREATE INDEX IF NOT EXISTS %s_from_stop_I_idx ON %s (from_stop_I); CREATE INDEX IF NOT EXISTS %s_to_stop_I_idx ON %s (to_stop_I);""" % (table, table, table, table) conn.commit() def diff_table(self, groupby="to_stop_I", measure="temporal_distance", ignore_stops=None): """ Creates a table with the before-after difference of mean, min and max temporal distance or number of boardings on a stop to stop basis :return: """ cur = self.conn.cursor() query = """DROP TABLE IF EXISTS diff_{groupby}_{measure}""".format(measure=measure, groupby=groupby) cur.execute(query) multiplier = 1 first = 0.5 second = 1 third = 1.5 threshold = 10800 # threshold for change in mean temporal distance if measure == "temporal_distance" or "journey_duration": multiplier = 60 first = 5 second = 10 third = 20 first_str = str(first).replace(".", "_") second_str = str(second).replace(".", "_") third_str = str(third).replace(".", "_") if ignore_stops: ignore_stops = " AND t1.to_stop_I NOT IN " + ignore_stops + " AND t1.from_stop_I NOT IN " + ignore_stops else: ignore_stops = "" query = """CREATE TABLE IF NOT EXISTS diff_{groupby}_{measure} ({groupby} INT, min_diff_mean REAL, mean_diff_mean REAL, max_diff_mean REAL, incr_count_over_{0} INT, incr_count_over_{1} INT, incr_count_over_{2} INT, decr_count_over_{0} INT, decr_count_over_{1} INT, decr_count_over_{2} INT ) """.format(first_str, second_str, third_str, measure=measure, groupby=groupby) cur.execute(query) query = """INSERT OR REPLACE INTO diff_{groupby}_{measure} ({groupby}, min_diff_mean, mean_diff_mean, max_diff_mean, incr_count_over_{first_str}, incr_count_over_{second_str}, incr_count_over_{third_str}, decr_count_over_{first_str}, decr_count_over_{second_str}, decr_count_over_{third_str}) SELECT {groupby}, min(diff_mean) AS min_diff_mean, avg(diff_mean) AS mean_diff_mean, max(diff_mean) AS max_diff_mean, sum(CASE WHEN diff_mean >= {0}*{multiplier} THEN 1 ELSE 0 END) AS incr_count_over_{first_str}, sum(CASE WHEN diff_mean >= {1}*{multiplier} THEN 1 ELSE 0 END) AS incr_count_over_{second_str}, sum(CASE WHEN diff_mean >= {2}*{multiplier} THEN 1 ELSE 0 END) AS incr_count_over_{third_str}, sum(CASE WHEN diff_mean <= -{0}*{multiplier} THEN 1 ELSE 0 END) AS decr_count_over_{first_str}, sum(CASE WHEN diff_mean <= -{1}*{multiplier} THEN 1 ELSE 0 END) AS decr_count_over_{second_str}, sum(CASE WHEN diff_mean <= -{2}*{multiplier} THEN 1 ELSE 0 END) AS decr_count_over_{third_str} FROM (SELECT t1.from_stop_I AS from_stop_I, t1.to_stop_I AS to_stop_I, t2.mean-t1.mean AS diff_mean FROM before.{measure} AS t1, after.{measure} AS t2 WHERE t1.from_stop_I = t2.from_stop_I AND t1.to_stop_I = t2.to_stop_I {ignore_stops} AND abs(t2.mean-t1.mean) < {threshold}) q1 GROUP BY {groupby}""".format(first, second, third, first_str=first_str, second_str=second_str, third_str=third_str, measure=measure, groupby=groupby, multiplier=multiplier, threshold=threshold, ignore_stops=ignore_stops) cur.execute(query) self.conn.commit() def get_mean_change_for_all_targets(self, groupby="to_stop_I", measure="temporal_distance", ignore_stops=None): """ Returns pre generated differences table as pandas DataFrame :param groupby: "to_stop_I" or "from_stop_I" designating if calculating the measure to the target or from the target :param measure: "temporal_distance", "n_boardings", :return: if ignore_stops: ignore_stops = " WHERE " + groupby + " IN " + ignore_stops else: ignore_stops = "" """ query = """SELECT * FROM diff_{groupby}_{measure}""".format(measure=measure, groupby=groupby) print("running query") df = pandas.read_sql_query(query, self.conn) df = self.gtfs.add_coordinates_to_df(df, stop_id_column=groupby, lat_name="lat", lon_name="lon") if measure == "temporal_distance": df["mean_diff_mean"] = df["mean_diff_mean"].apply(lambda x: x / 60) return df def extreme_change_od_pairs(self, threshold): """ Returns O-D pairs where the absolute change is larger than the threshold. Returns increase in travel time with positive thresholds and decrease in travel time with negative thresholds :param threshold: int :return: Pandas DataFrame """ if threshold < 0: string_to_add = " <= " + str(threshold) else: string_to_add = " >= " + str(threshold) query = """SELECT t1.from_stop_I AS from_stop_I, t1.to_stop_I AS to_stop_I, t2.mean-t1.mean AS diff_mean FROM before.temporal_distance AS t1, after.temporal_distance AS t2 WHERE t1.from_stop_I = t2.from_stop_I AND t1.to_stop_I = t2.to_stop_I AND t2.mean-t1.mean %s AND t2.mean-t1.mean < 10800""" % (string_to_add,) df = pandas.read_sql_query(query, self.conn) return df def get_global_mean_change(self, measure, threshold=10800, ignore_stops=False): ignore_list = "" if ignore_stops: ignore_list=stops_to_exclude(return_sqlite_list=True) query = """SELECT before_global_mean, after_global_mean, after_global_mean-before_global_mean AS global_mean_difference FROM (SELECT avg(mean) AS before_global_mean FROM before.{measure} WHERE mean <= {threshold} AND mean >0 AND from_stop_I NOT IN {ignore_stops} AND to_stop_I NOT IN {ignore_stops}) t1, (SELECT avg(mean) AS after_global_mean FROM after.{measure} WHERE mean <= {threshold} AND mean >0 AND from_stop_I NOT IN {ignore_stops} AND to_stop_I NOT IN {ignore_stops}) t2 """.format(measure=measure, threshold=threshold, ignore_stops=ignore_list) df = pandas.read_sql_query(query, self.conn) return df @timeit def get_rows_with_abs_change_greater_than_n(self, stops, measure, n, sign, unit="s"): stops = ",".join([str(x) for x in stops]) divisors = {"s": 1, "m": 60, "h": 3600} divisor = divisors[unit] query = """SELECT t1.{measure}/{divisor} AS before_{measure}, t2.{measure}/{divisor} AS after_{measure}, (t2.{measure}-t1.{measure})/{divisor} AS diff_{measure} FROM before.temporal_distance AS t1, after.temporal_distance AS t2 WHERE t1.from_stop_I != t1.to_stop_I AND t1.from_stop_I = t2.from_stop_I AND t1.to_stop_I = t2.to_stop_I AND t1.from_stop_I NOT IN ({stops}) AND t2.to_stop_I NOT IN ({stops}) AND t2.{measure}-t1.{measure} {sign} {n}""".format(measure=measure, divisor=divisor, stops=stops, n=n, sign=sign) df = pandas.read_sql_query(query, self.conn) return df @timeit def get_rows_based_on_stop_list(self, from_stops, to_stops, measure, measure_mode, unit="s"): """ :param from_stops: list :param to_stops: list :param measure: string (mean, min, max, median) :param unit: string :param measure_mode: string :return: """ assert measure_mode in ["n_boardings", "temporal_distance"] from_stops = ",".join([str(x) for x in from_stops]) to_stops = ",".join([str(x) for x in to_stops]) divisors = {"s": 1, "m": 60, "h": 3600} divisor = divisors[unit] query = """SELECT t1.{measure}/{divisor} AS before_{measure}, t2.{measure}/{divisor} AS after_{measure}, (t2.{measure}-t1.{measure})/{divisor} AS diff_{measure} FROM before.{mode} AS t1, after.{mode} AS t2 WHERE t1.from_stop_I != t1.to_stop_I AND t1.from_stop_I = t2.from_stop_I AND t1.to_stop_I = t2.to_stop_I AND t1.from_stop_I IN ({from_stops}) AND t2.to_stop_I IN ({to_stops})""".format(measure=measure, mode=measure_mode, divisor=divisor, from_stops=from_stops, to_stops=to_stops) df = pandas.read_sql_query(query, self.conn) return df def get_data_for_target(self, target, measure, direction="to", threshold=10800, unit="s", ignore_stops=False): divisors = {"s": 1, "m": 60, "h": 3600} divisor = divisors[unit] ignore_list = "" if ignore_stops: ignore_list = stops_to_exclude(return_sqlite_list=True) ignore_list = " AND t1.from_stop_I NOT IN {ignore_list} AND t1.to_stop_I NOT IN {ignore_list}".format(ignore_list=ignore_list) query = """SELECT t1.from_stop_I, t1.to_stop_I, t1.mean/{divisor} AS before_mean, t2.mean/{divisor} AS after_mean, (t2.mean-t1.mean)/{divisor} AS diff_mean, COALESCE((t2.mean/t1.mean)- 1, 0) AS diff_mean_relative FROM before.{measure} t1, after.{measure} t2 WHERE t1.from_stop_I=t2.from_stop_I AND t1.to_stop_I=t2.to_stop_I AND t1.mean <= {threshold} AND t2.mean <= {threshold} AND t1.{direction}_stop_I={target} {ignore_list}""".format(measure=measure, target=target, direction=direction, threshold=threshold, divisor=divisor, ignore_list=ignore_list) df = pandas.read_sql_query(query, self.conn) return df def get_mean_change(self, measure, threshold=10800, descening_order=False, include_list=None): if descening_order: order_by = "DESC" else: order_by = "ASC" include_list = "(" + ",".join([str(x) for x in include_list]) + ")" query = """SELECT t1.to_stop_I, t2.mean AS before, t2.mean-t1.mean AS diff_mean FROM (SELECT to_stop_I, avg(mean) AS mean FROM before.{measure} WHERE mean <= {threshold} AND to_stop_I IN {include_list} GROUP BY to_stop_I) t1, (SELECT to_stop_I, avg(mean) AS mean FROM after.{measure} WHERE mean <= {threshold} AND to_stop_I IN {include_list} GROUP BY to_stop_I) t2 WHERE t1.to_stop_I=t2.to_stop_I ORDER BY diff_mean {order_by} """.format(measure=measure, threshold=threshold, order_by=order_by, include_list=include_list) df = pandas.read_sql_query(query, self.conn) return df def get_n_winning_targets_using_change_in_mean(self, n, measure, distance=500, threshold=10800, losers=False, include_list=None): if losers: order_by = "DESC" else: order_by = "ASC" include_list = "(" + ",".join([str(x) for x in include_list]) + ")" query = """SELECT t1.to_stop_I, t2.mean-t1.mean AS diff_mean FROM (SELECT to_stop_I, avg(mean) AS mean FROM before.{measure} WHERE mean <= {threshold} AND to_stop_I IN {include_list} GROUP BY to_stop_I) t1, (SELECT to_stop_I, avg(mean) AS mean FROM after.{measure} WHERE mean <= {threshold} AND to_stop_I IN {include_list} GROUP BY to_stop_I) t2 WHERE t1.to_stop_I=t2.to_stop_I ORDER BY diff_mean {order_by} """.format(measure=measure, threshold=threshold, order_by=order_by, include_list=include_list) df = pandas.read_sql_query(query, self.conn) # exclude nearby stops nearby_excluded_stops = [] stops_remaining = [] gtfs = GTFS(GTFS_PATH) for value in df.itertuples(): if not value.to_stop_I in nearby_excluded_stops: exclude_df = gtfs.get_stops_within_distance(value.to_stop_I, distance) nearby_excluded_stops += list(exclude_df["stop_I"]) stops_remaining.append(value.to_stop_I) if len(stops_remaining) == n: break df = df.loc[df['to_stop_I'].isin(stops_remaining)] return df def n_inf_stops_per_stop(self, measure, indicator, threshold, group_by="to_stop_I", routing="before"): if group_by == "to_stop_I": stop_I = "from_stop_I" elif group_by == "from_stop_I": stop_I = "to_stop_I" else: raise AssertionError("Group_by should be to_stop_I or from_stop_I") query = """SELECT {group_by}, count(to_stop_I) AS N_stops FROM {routing}.{measure} WHERE {indicator} >{threshold} GROUP by {group_by} ORDER BY count(to_stop_I)""".format(measure=measure, threshold=threshold, indicator=indicator, routing=routing, group_by=group_by, stop_I=stop_I) df = pandas.read_sql_query(query, self.conn) return df def find_stops_where_all_indicators_are_finite(self, measure="temporal_distance", indicator="max", routing="after", threshold=10800): stops_to_ignore = [] ignore_statement = "" while True: query = """SELECT from_stop_I, count(to_stop_I) as invalid_connections FROM {routing}.{measure} WHERE {indicator} >= {threshold} {ignore_statement} group by from_stop_I order by invalid_connections""".format(measure=measure, indicator=indicator, threshold=threshold, routing=routing, ignore_statement=ignore_statement) df = pandas.read_sql_query(query, self.conn) print("query has run, with {n} stops remaining".format(n=len(df.index))) df['removal_column'] = df.index+df.invalid_connections n_stops_in_iteration = len(df.index) df_to_remove = df.loc[df['removal_column'] > n_stops_in_iteration] print("{n} stops removed".format(n=len(df_to_remove.index))) if len(df_to_remove.index) == 0: break stops_to_ignore += list(df_to_remove['from_stop_I']) stops_to_ignore_str = "" for stop in stops_to_ignore: if not stops_to_ignore_str == "": stops_to_ignore_str += "," stops_to_ignore_str += str(stop) # stops_to_ignore_str = ','.join(stops_to_ignore_str) ignore_statement = "AND from_stop_I NOT IN ({stops_comma}) " \ "AND to_stop_I NOT IN ({stops_comma})".format(stops_comma=stops_to_ignore_str) return list(df['from_stop_I']), stops_to_ignore def find_stops_where_all_indicators_are_finite_using_network(self, measure="temporal_distance", indicator="max", routing="after", threshold=10800): pass """ nodes = [x[0] for x in nodes] edges = itertools.combinations(nodes, 2) print("combinations") G = nx.Graph() G.add_edges_from(edges) print("initial edges in place") for row in df.iterrows(): G.remove_edge(row.from_stop_I, row.to_stop_I) print("removing stuff") """ if __name__ == "__main__": for time in TIMES: a2aa = AllToAllDifferenceAnalyzer(GTFS_PATH, get_a2aa_db_path(time, "old"), get_a2aa_db_path(time, "lm"), get_a2aa_db_path(time, "output")) ignore_list = stops_to_exclude(return_sqlite_list=True) a2aa.diff_table(groupby="to_stop_I", measure="n_boardings", ignore_stops=ignore_list) a2aa.diff_table(groupby="from_stop_I", measure="n_boardings", ignore_stops=ignore_list) a2aa.diff_table(groupby="to_stop_I", measure="temporal_distance", ignore_stops=ignore_list) a2aa.diff_table(groupby="from_stop_I", measure="temporal_distance", ignore_stops=ignore_list) #a2aa.diff_table(groupby="to_stop_I", measure="journey_duration", ignore_stops=ignore_list) #a2aa.diff_table(groupby="from_stop_I", measure="journey_duration", ignore_stops=ignore_list)

53.569191

173

0.561096

2,521

20,517

4.267751

0.107497

0.049726

0.040338

0.013384

0.513059

0.447346

0.401803

0.374012

0.331443

0.322614

0

0.016798

0.344251

20,517

383

174

53.569191

0.78289

0.057318

0

0.326316

0

0.077193

0.393647

0.054255

0.007018

0

0.007018

1

0.05614

false

0.003509

0.024561

0

0.129825

0.017544

0

null

0

null

0

1

0

097cf870cfdf8eb690e3cbf5e80ead9f28adc1b0

2,258

py

Python

tests/test_flow/test_snakemake_tutorial.py

flowsaber/flowsaber

7d68d085bbd9165d2bc0e0acd7826e70569c5fa3

[ "MIT" ]

31

2021-05-08T06:35:07.000Z

2022-03-05T05:58:24.000Z

tests/test_flow/test_snakemake_tutorial.py

flowsaber/flowsaber

7d68d085bbd9165d2bc0e0acd7826e70569c5fa3

[ "MIT" ]

3

2021-05-10T12:36:57.000Z

2021-05-15T14:01:15.000Z

tests/test_flow/test_snakemake_tutorial.py

zhqu1148980644/flowsaber

7d68d085bbd9165d2bc0e0acd7826e70569c5fa3

[ "MIT" ]

1

2021-03-09T06:18:17.000Z

from flowsaber.api import * def test_snakemake_workflow(): # EnvTask is the real dependent task when using conda/image option @shell def bwa(self, fa: File, fastq: File): # input will be automatically converted if has type annotation """bwa mem -t {self.config.cpu} {fa} {fastq} | samtools view -Sb - > {fastq.stem}.bam""" return "*.bam" # for ShellTask, str variable in the return will be treated as File and globed @shell def sort(bam: File): # self is optional in case you don't want to access the current task """samtools sort -o {sorted_bam} {bam}""" sorted_bam = f"{bam.stem}.sorted.bam" return sorted_bam @shell(publish_dirs=["results/vcf"]) def call(fa: File, bams: list): # In case you need to write some python codes """samtools mpileup -g -f {fa} {bam_files} | bcftools call -mv - > all.vcf""" bam_files = ' '.join(str(bam) for bam in bams) return "all.vcf" @task def stats(vcf: File): import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from pysam import VariantFile quals = [record.qual for record in VariantFile(str(vcf))] plt.hist(quals) plt.savefig("report.svg") @flow def call_vcf_flow(): """Call vcf from fastq file. Parameters ---------- fa: : str The path of genome file fastq: List[str] list of fastq files """ def _call(bams): # task is normal function, use python as wish return call(fa, bams) context = flowsaber.context fa = Channel.value(context.fa) fastq = Channel.values(*context.fastq) bam1 = bwa(fa, fastq) # automatically clone channel bam2 = bwa(fa, fastq) mix(bam1, bam2) | sort | collect | _call | stats prefix = 'tests/test_flow/snamke-demo.nosync/data' with flowsaber.context({ "fa": f'{prefix}/genome.fa', "fastq": [f'{prefix}/samples/{sample}' for sample in ['A.fastq', 'B.fastq', 'C.fastq']] }): # resolve dependency workflow = call_vcf_flow() run(workflow) if __name__ == "__main__": test_snakemake_workflow() pass

30.931507

105

0.59876

296

2,258

4.483108

0.452703

0.026375

0.03165

0

0.002468

0.282108

2,258

72

106

31.361111

0.816163

0.320638

0

0.047619

0

0.121463

0.058661

0

1

0.166667

false

0.02381

0.095238

0.02381

0.357143

0

null

0

null

0

1

0

098125b6bdbfea383598e527bbb70b034cf26260

1,324

py

Python

py_ad_1_4.py

aisolab/con-par-python

e74cb9c30acfdd78c12c9f7aba039d16ed1f7e78

[ "MIT" ]

1

2022-02-20T03:14:50.000Z

py_ad_1_4.py

aisolab/con-par-python

e74cb9c30acfdd78c12c9f7aba039d16ed1f7e78

[ "MIT" ]

null

py_ad_1_4.py

aisolab/con-par-python

e74cb9c30acfdd78c12c9f7aba039d16ed1f7e78

[ "MIT" ]

null

""" Section 1 Multithreading - Thread (2) - Daemon, Join Keyword - DaemonThread, Join """ """ DaemonThread(데몬스레드) (1). 백그라운드에서 실행 (2). 메인스레드 종료시 즉시 종료 (서브 스레드의 경우는 메인 스레드와 상관없이 자기 작업을 끝까지 수행함.) (3). 주로 백그라운드 무한 대기 이벤트 발생 실행하는 부분 담당 -> JVM(가비지 컬렉션), 자동 저장 (4). 일반 스레드는 작업 종료시까지 실행 """ import logging import threading # 스레드 실행 함수 def thread_func(name, d): logging.info("Sub-Thread %s: starting", name) for i in d: print(name, i) logging.info("Sub-Thread %s: finishing", name) # 메인 영역 if __name__ == "__main__": # Logging format 설정 format = "%(asctime)s: %(message)s" logging.basicConfig(format=format, level=logging.INFO, datefmt="%H:%M:%S") logging.info("Main-Thread: before creating thread") # 함수 인자 확인 # Daemon: Default False x = threading.Thread(target=thread_func, args=("First", range(200)), daemon=True) y = threading.Thread(target=thread_func, args=("Two", range(10)), daemon=False) logging.info("Main-Thread: before running thread") # 서브 스레드 시작 x.start() y.start() # DaemonThread 확인 print(x.isDaemon()) print(y.isDaemon()) # 주석 전후 결과 확인 # x.join() # 서브 스레드의 작업이 끝날 떄까지, 메인 스레드가 기다림. # y.join() logging.info("Main-Thread: wait for the thread to finish") logging.info("Main-Thread: all done")

24.981132

85

0.632175

199

1,324

4.150754

0.552764

0.09322

0.072639

0.101695

0.200969

0.084746

0

0.010669

0.221299

1,324

52

86

25.461538

0.790495

0.179003

0

0.259132

0

1

0.05

false

0

0.1

0

0.15

0

null

0

null

0

1

0

09821682a814779b24686f7214f05d5600259f1a

287

py

Python

listTest.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

listTest.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

listTest.py

diallog/GCPpy

dabd55ece1c12c1a390a228cd04cb7eb110e564b

[ "Unlicense" ]

null

#!/usr/bin/env python3 # PURPOSE: studying function side effects import os os.system('clear') orgList = [5, 3, 2, 1, 4] def sumList(myList): for i in range(1, len(myList)): myList[i] += myList[i-1] return myList[len(myList)-1] print(sumList(orgList)) print(orgList)

16.882353

41

0.655052

45

287

4.177778

0.644444

0.095745

0

0.038462

0.184669

287

16

42

17.9375

0.764957

0.212544

0

0.022321

0

1

0.111111

false

0

0.111111

0

0.333333

0.222222

0

null

0

null

0

1

0

09828a4b8ceea5e0df2ba0674a51b0b2f6523586

2,029

py

Python

class/pandas_class.py

Danigore25/python2

de6d582fcc35107aa21a1bd73fdf04a0d4209d31

[ "MIT" ]

null

class/pandas_class.py

Danigore25/python2

de6d582fcc35107aa21a1bd73fdf04a0d4209d31

[ "MIT" ]

null

class/pandas_class.py

Danigore25/python2

de6d582fcc35107aa21a1bd73fdf04a0d4209d31

[ "MIT" ]

2

2021-09-07T00:30:49.000Z

2021-10-19T15:14:54.000Z

import pandas as pd import numpy as np serie = pd.Series(['a', 'b', 'c', 'd', 'e'], index=['a', 'b', 'c', 'd', 'e'], name="Ejemplo Serie") print(serie) ecoli_matraz = pd.Series([0.1, 0.15, 0.19, 0.5, 0.9, 1.4, 1.8, 2.1, 2.3], index=['t1', 't2', 't3', 't4', 't5', 't6', 't7', 't8', 't9'], name='Matraz') print(ecoli_matraz) ODs = pd.Series([0.2, 0.2, 0.4, 0.1, 0.2, 0.1, 0.2, 0.4, 0.1], index=[8, 4, 1, 2, 3, 0, 5, 7, 6], name='Ajustes') # EJERCICIO 1 ---------------------------------------------------------------------- produccion = pd.Series([5, 11, 4, 7, 2], index=['gen1', 'gen2', 'gen3', 'gen4', 'gen5']) costos = pd.Series([5, 4.3, 7, 3.5], index=['gen1', 'gen2', 'gen3', 'gen5']) costo_unitario = costos/produccion.T print(costo_unitario) print(costo_unitario.min()) # ----------------------------------------------------- nan_test = pd.Series([0.1, None, 2.1, 2.3], name='Matraz') print(nan_test.count()) # loc y iloc series_test = pd.Series([5.1, 2.2, 1.1, 3.1, 4.2], index=[5, 2, 1, 3, 4]) print(series_test) print(series_test.loc[1]) print(series_test.iloc[1]) # EJERCICIO 2 ------------------------------------------------------------------ bool_min = costo_unitario == costo_unitario.min() bool_max = costo_unitario == costo_unitario.max() print(costo_unitario[bool_min | bool_max]) # Repetir índices regulon = pd.Series(['aidB', 'alaS', 'accB', 'accC', 'bhsA'], index=['AidB', 'AlaS', 'AccB', 'AccB', 'ComR'], name='Genes regulados') print(regulon.loc['AccB']) print(regulon.loc['AidB']) # Clases en series class Mamifero: vertebrado = True def haz_ruido(self): print('aaaaaaaaaaaaaaaaaaaaaaaaaaa') array_clase = pd.Series([np.sum, 'a', Mamifero], name='objetos') jerbo = array_clase.iloc[2] print(jerbo.haz_ruido())

30.283582

110

0.485461

274

2,029

3.507299

0.332117

0.074922

0.012487

0.008325

0.027055

0.012487

0

0.066537

0.237063

2,029

66

111

30.742424

0.554264

0.127649

0

0.114319

0.01591

0

1

0.025

false

0

0.05

0

0.125

0.325

0

null

0

null

0

1

0

0986c2b4d466c529bcf1de02d35647e1f00797b3

6,209

py

Python

scripts/datasets/mit67_install.py

cclauss/archai

a5fb8f937f7f1319e3204120803b2a045e9f768b

[ "MIT" ]

344

2020-06-12T22:12:56.000Z

2022-03-29T06:48:20.000Z

scripts/datasets/mit67_install.py

cclauss/archai

a5fb8f937f7f1319e3204120803b2a045e9f768b

[ "MIT" ]

29

2020-06-13T19:56:49.000Z

2022-03-30T20:26:48.000Z

scripts/datasets/mit67_install.py

cclauss/archai

a5fb8f937f7f1319e3204120803b2a045e9f768b

[ "MIT" ]

68

2020-06-12T19:32:43.000Z

2022-03-05T06:58:40.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT license. """ Script to prepare mit67 dataset for pytorch dataloader. """ from typing import List, Dict, Tuple, Union, Optional import os import pdb import time import argparse import os import tempfile import requests from torchvision.datasets.utils import download_and_extract_archive, download_url from torch.utils.model_zoo import tqdm from PIL import Image import shutil from collections import defaultdict import pathlib from archai.common import utils def check_mit67(dataroot: str) -> bool: mit67 = os.path.join(dataroot, 'mit67') train = os.path.join(mit67, 'train') test = os.path.join(mit67, 'test') meta = os.path.join(mit67, 'meta') if not os.path.isdir(mit67) or not os.path.isdir(train) or not os.path.isdir(test) or not os.path.isdir(meta): return False num_train_files = 0 for base, dirs, files in os.walk(train): for file in files: num_train_files += 1 if num_train_files != 12466: return False num_test_files = 0 for base, dirs, files in os.walk(test): for file in files: num_test_files += 1 if num_test_files != 3153: return False # all checks passed return True def download(dataroot: str): DOWNLOAD_URL = 'http://groups.csail.mit.edu/vision/LabelMe/NewImages/indoorCVPR_09.tar' with tempfile.TemporaryDirectory() as tempdir: download_and_extract_archive( DOWNLOAD_URL, tempdir, extract_root=dataroot, remove_finished=True) def load_test_csv_data(filename: str) -> Dict[str, List[str]]: ''' Loads the data in csv files into a dictionary with class names as keys and list of image names as values. Works only for test data csv''' data_dict = defaultdict(list) with open(filename, 'r') as f: lines = f.readlines() assert len(lines) > 0 for line in lines[1:]: words = line.rstrip().split(',') assert len(words) > 0 data_dict[words[0]] = words[1:] return data_dict def load_train_csv_data(filename: str) -> Dict[str, List[str]]: ''' Loads the data in csv files into a dictionary with class names as keys and list of image names as values. Works only for train data csv ''' data_dict = defaultdict(list) with open(filename, 'r') as f: lines = f.readlines() assert len(lines) > 0 for line in lines[1:]: words = line.rstrip().split(',') assert len(words) > 0 data_dict[words[1]] = words[2:] return data_dict def copy_data_helper(data: Dict[str, List[str]], imagesroot: str, foldername: str) -> None: for key in data.keys(): images = data[key] for im in images: if not im: continue source = os.path.join(imagesroot, key, im) target = os.path.join(foldername, key, im) if not os.path.isfile(target): utils.copy_file(source, target) def prepare_data(mit67_root: str): test_file = os.path.join(mit67_root, 'meta', 'MIT67_test.csv') test_data = load_test_csv_data(test_file) # train data is split into 4 files for some reason train1_file = os.path.join(mit67_root, 'meta', 'MIT67_train1.csv') train2_file = os.path.join(mit67_root, 'meta', 'MIT67_train2.csv') train3_file = os.path.join(mit67_root, 'meta', 'MIT67_train3.csv') train4_file = os.path.join(mit67_root, 'meta', 'MIT67_train4.csv') train_files = [train1_file, train2_file, train3_file, train4_file] train_data = defaultdict(list) for tf in train_files: this_data = load_train_csv_data(tf) train_data.update(this_data) # make classname directories for train and test for key in test_data.keys(): os.makedirs(os.path.join(mit67_root, 'test', key), exist_ok=True) os.makedirs(os.path.join(mit67_root, 'train', key), exist_ok=True) # copy images to the right locations imagesroot = os.path.join(mit67_root, 'Images') testfoldername = os.path.join(mit67_root, 'test') copy_data_helper(test_data, imagesroot, testfoldername) trainfoldername = os.path.join(mit67_root, 'train') copy_data_helper(train_data, imagesroot, trainfoldername) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--dataroot', type=str, default='C:\\Users\\dedey\\dataroot', help='root directory where mit67 folder is intended to exist. If mit67 already exists in the format required this script will skip downloading') args = parser.parse_args() # check that dataset is in format required # else download and prepare dataset if not check_mit67(args.dataroot): # make mit67 directory mit67 = os.path.join(args.dataroot, 'mit67') train = os.path.join(mit67, 'train') test = os.path.join(mit67, 'test') meta = os.path.join(mit67, 'meta') os.makedirs(mit67, exist_ok=True) os.makedirs(train, exist_ok=True) os.makedirs(test, exist_ok=True) os.makedirs(meta, exist_ok=True) # this step will create folder mit67/Images # which has all the images for each class in its own subfolder download(mit67) # download the csv files for the train and test split # from 'NAS Evaluation is Frustrating' repo # note that download_url doesn't work in vscode debug mode test_file_url = 'https://raw.githubusercontent.com/antoyang/NAS-Benchmark/master/data/MIT67_test.csv' train_file_urls = ['https://raw.githubusercontent.com/antoyang/NAS-Benchmark/master/data/MIT67_train1.csv', 'https://raw.githubusercontent.com/antoyang/NAS-Benchmark/master/data/MIT67_train2.csv', 'https://raw.githubusercontent.com/antoyang/NAS-Benchmark/master/data/MIT67_train3.csv', 'https://raw.githubusercontent.com/antoyang/NAS-Benchmark/master/data/MIT67_train4.csv'] download_url(test_file_url, meta, filename=None, md5=None) for tu in train_file_urls: download_url(tu, meta, filename=None, md5=None) prepare_data(mit67)

35.683908

204

0.671445

876

6,209

4.619863

0.244292

0.037064

0.049419

0.059303

0.410427

0.355819

0.324191

0.309859

0.270324

0.255498

0

0.026562

0.223869

6,209

173

205

35.890173

0.813239

0.144146

0

0.258929

0

0.053571

0.158354

0.004931

0

0.035714

1

0.053571

false

0

0.133929

0

0.241071

0

null

0

null

0

1

0

09887c8ffc4485168a4cf1dc2d552eb82e642cda

713

py

Python

src/python/T0/WMBS/Oracle/RunConfig/InsertRecoReleaseConfig.py

silviodonato/T0

a093729d08b31175ed35cd20e889bd7094ce152a

[ "Apache-2.0" ]

6

2016-03-09T14:36:19.000Z

2021-07-27T01:28:00.000Z

src/python/T0/WMBS/Oracle/RunConfig/InsertRecoReleaseConfig.py

silviodonato/T0

a093729d08b31175ed35cd20e889bd7094ce152a

[ "Apache-2.0" ]

193

2015-01-07T21:03:43.000Z

2022-03-31T12:22:18.000Z

src/python/T0/WMBS/Oracle/RunConfig/InsertRecoReleaseConfig.py

silviodonato/T0

a093729d08b31175ed35cd20e889bd7094ce152a

[ "Apache-2.0" ]

36

2015-01-28T19:01:54.000Z

2021-12-15T17:18:20.000Z

""" _InsertRecoReleaseConfig_ Oracle implementation of InsertRecoReleaseConfig """ from WMCore.Database.DBFormatter import DBFormatter class InsertRecoReleaseConfig(DBFormatter): def execute(self, binds, conn = None, transaction = False): sql = """INSERT INTO reco_release_config (RUN_ID, PRIMDS_ID, FILESET, DELAY, DELAY_OFFSET) VALUES (:RUN, (SELECT id FROM primary_dataset WHERE name = :PRIMDS), :FILESET, 0, 0) """ self.dbi.processData(sql, binds, conn = conn, transaction = transaction) return

28.52

79

0.548387

59

713

6.491525

0.677966

0.046997

0

0.004505

0.377279

713

24

80

29.708333

0.858108

0.105189

0

0.505564

0

1

0.071429

false

0

0.071429

0

0.285714

0

null

0

null

0

1

0

0988ffb2a91dd9ac6ea127ee5939338c9d7b530e

1,652

py

Python

split_wav.py

tanacchi/sound-dataset-generator

a74363c35652dbb7e7cb2dfd390cf89302f3827e

[ "MIT" ]

1

2020-12-02T02:31:33.000Z

split_wav.py

tanacchi/sound_dataset_generator

a74363c35652dbb7e7cb2dfd390cf89302f3827e

[ "MIT" ]

null

split_wav.py

tanacchi/sound_dataset_generator

a74363c35652dbb7e7cb2dfd390cf89302f3827e

[ "MIT" ]

null

import wave import os import sys from glob import glob import argparse parser = argparse.ArgumentParser() parser.add_argument("--length", type=int, default=30) parser.add_argument("--offset", type=int, default=15) args = parser.parse_args() unit_time_length = args.length start_time_offset = args.offset output_dir = os.path.join(".", "output") os.makedirs(output_dir, exist_ok=True) downloads_dir = os.path.join(".", "downloads") target_files = glob(os.path.join(downloads_dir, "*.wav")) for base_filepath in target_files: base_filename = os.path.basename(base_filepath) print(f"Processing for {base_filename}...") params = None data_raw = None with wave.open(base_filepath, "rb") as wave_read: params = wave_read.getparams() data_raw = wave_read.readframes(params.nframes) wave_read.close() unit_nframes = unit_time_length * params.framerate * params.nchannels * params.sampwidth start_frame_offset = start_time_offset * params.framerate * params.nchannels * params.sampwidth file_count = 0 for t in range(0, len(data_raw), start_frame_offset): file_count += 1 picked_data = data_raw[t:t+unit_nframes] output_filename = os.path.join(output_dir, f"{base_filename}_{file_count:09}.wav") with wave.open(output_filename, "wb") as wave_write: wave_write.setparams(( params.nchannels, params.sampwidth, params.framerate, len(picked_data), params.comptype, params.compname )) wave_write.writeframes(picked_data) wave_write.close() # os.remove(base_filepath) print("Done.")

33.714286

99

0.696126

221

1,652

4.963801

0.352941

0.027347

0.036463

0.082042

0

0.006711

0.188257

1,652

48

100

34.416667

0.811335

0.014528

0

0.070726

0.021525

0

1

0

false

0

0.128205

0

0.128205

0.051282

0

null

0

null

0

1

0

098a8775723a6e3a315440de72e96cd1befcdb31

2,454

py

Python

ex075A.py

gabrieleliasdev/python-cev

45390963b5112a982e673f6a6866da422bf9ae6d

[ "MIT" ]

null

ex075A.py

gabrieleliasdev/python-cev

45390963b5112a982e673f6a6866da422bf9ae6d

[ "MIT" ]

null

ex075A.py

gabrieleliasdev/python-cev

45390963b5112a982e673f6a6866da422bf9ae6d

[ "MIT" ]

null

from tkinter import * janela = Tk() lista = [] texto1 = StringVar() texto2 = StringVar() texto3 = StringVar() texto4 = StringVar() #--------------------- PROCESSAMENTO DO COMANDO ------ def click_bt1(): lista.append(int(et1.get())) lista.append(int(et2.get())) lista.append(int(et3.get())) lista.append(int(et4.get())) txt1.delete(0.0,'end') for i in lista: if i % 2 == 0: txt1.insert(0.0, f'{i} ') txt1.insert(0.0, '- Os valores pares digitados foram: ') if 3 in lista: txt1.insert(0.0, f'- O número 3 apareceu na {lista.index(3)+1} posição.\n') else: txt1.insert(0.0,'- O número 3 não apareceu na lista.\n') txt1.insert(0.0, f'- Você digitou o número "9" {lista.count(9)} vezes.\n') texto1.set(str('')) texto2.set(str('')) texto3.set(str('')) texto4.set(str('')) print(lista) #------------------------------------------------------ #---------------INSERÇÃO DOS WIDGETS --------------- lb1 = Label(janela, text='Digite o primeiro número: ') lb1.grid(row=0,column=0, stick=W) lb2 = Label(janela, text='Digite o segundo número: ') lb2.grid(row=1,column=0, stick=W) lb3 = Label(janela, text='Digite o terceiro número: ') lb3.grid(row=2,column=0, stick=W) lb4 = Label(janela, text='Digite o quarto número: ') lb4.grid(row=3,column=0, stick=W) et1 = Entry(janela, textvariable=texto1, width=5) et1.grid(row=0,column=1,sticky=E) et2 = Entry(janela, textvariable=texto2, width=5) et2.grid(row=1,column=1,sticky=E) et3 = Entry(janela, textvariable=texto3, width=5) et3.grid(row=2,column=1,sticky=E) et4 = Entry(janela, textvariable=texto4, width=5) et4.grid(row=3,column=1,sticky=E) bt1 = Button(janela,text='PROCESSAR', font=('arialblack',11,'bold'),command=click_bt1) bt1.grid(row=0,column=2,rowspan=4) txt1 = Text(janela,width=40,height=10,bd=5) txt1.grid(row=5,column=0,columnspan=3) #---------------------------------------------------------------------- #------------------- DIMENSIONAMENTO E CENTRALIZAÇÃO DA JANELA -------- janela.title('Exercicio - Ex075') janela_width = 330 janela_height = 260 scream_width = janela.winfo_screenwidth() scream_height = janela.winfo_screenheight() cord_x = int((scream_width/2) - (janela_width/2)) cord_y = int((scream_height/2) - (janela_height/2)) janela.geometry(f'{janela_width}x{janela_height}+{cord_x}+{cord_y}') #--------------------------------------------------------------------- janela.mainloop()

31.87013

86

0.601059

351

2,454

4.150997

0.327635

0.048044

0.037749

0.041181

0.087165

0

0.051893

0.128362

2,454

77

87

31.87013

0.629266

0.149552

0

0.180596

0.023055

0

1

0.017857

false

0

0.017857

0

0.035714

0.017857

0

null

0

null

0

1

0

098d7c6c55f7415535fddaa88a483e5bc3bc96a3

650

py

Python

Python/[4 kyu] Sum of Intervals.py

KonstantinosAng/CodeWars

9ec9da9ed95b47b9656a5ecf77f486230fd15e3a

[ "MIT" ]

null

Python/[4 kyu] Sum of Intervals.py

KonstantinosAng/CodeWars

9ec9da9ed95b47b9656a5ecf77f486230fd15e3a

[ "MIT" ]

null

Python/[4 kyu] Sum of Intervals.py

KonstantinosAng/CodeWars

9ec9da9ed95b47b9656a5ecf77f486230fd15e3a

[ "MIT" ]

null

# More details on this kata # https://www.codewars.com/kata/52b7ed099cdc285c300001cd def sum_of_intervals(intervals): s, ret = [list(x) for x in sorted(intervals)], 0 if len(s) == 1: return abs(s[0][0] - s[0][1]) for i in range(len(s)): if i + 1 > len(s) - 1: break if s[i][0] <= s[i + 1][0] <= s[i][1]: if i + 1 > len(s) - 1: break while s[i][0] <= s[i + 1][0] <= s[i][1]: if s[i][1] <= s[i + 1][1]: s[i][1] = s[i + 1][1] del s[i + 1] if i + 1 > len(s) - 1: break for i in s: ret += abs(i[0] - i[1]) return ret

32.5

56

0.432308

118

650

2.364407

0.271186

0.09319

0.096774

0.057348

0.290323

0.189964

0

0.106796

0.366154

650

19

57

34.210526

0.570388

0.123077

0

0.2

0

1

0.066667

false

0

0.133333

0

null

0

null

0

1

0

0990bfe14e23c72b11bf2defe5e3302294dbdd91

11,197

py

Python

unit_list.py

guliverza/AdditionalPylons

37336dcd1678c6cdfa22d881c2178ba65cb1fd61

[ "MIT" ]

null

unit_list.py

guliverza/AdditionalPylons

37336dcd1678c6cdfa22d881c2178ba65cb1fd61

[ "MIT" ]

null

unit_list.py

guliverza/AdditionalPylons

37336dcd1678c6cdfa22d881c2178ba65cb1fd61

[ "MIT" ]

null

import sc2 from sc2.constants import * #our own classes from unit_counters import UnitCounter from warpprism import WarpPrism as wpControl from immortal import Immortal as imControl from stalker import Stalker as skControl from zealot import Zealot as zlControl from sentry import Sentry as snControl from adept import Adept as adControl from colossus import Colossus as coControl from voidray import VoidRay as vrControl from tempest import Tempest as tpControl from phoenix import Phoenix as pxControl from probe import Probe as pbControl from shade import Shade as sdControl from hightemplar import HighTemplar as htControl from observer import Observer as obControl from disruptor import Disruptor as dsControl from disruptor_phased import DisruptorPhased as dpControl from carrier import Carrier as crControl from mothership import Mothership as msControl from archon import Archon as arControl from cannon import Cannon as cnControl class UnitList(): def __init__(self): self.unit_objects = {} self.unitCounter = UnitCounter() def make_decisions(self, game): self.game = game self.update_units() for unit in self.game.units(): obj = self.unit_objects.get(unit.tag) if obj: obj.make_decision(self.game, unit) def update_units(self): for unit in self.game.units(): obj = self.unit_objects.get(unit.tag) if obj: obj.unit = unit def getObjectByTag(self, unit_tag): if self.unit_objects.get(unit_tag): return self.unit_objects.get(unit_tag) return None def remove_object(self, unit_tag): if self.unit_objects.get(unit_tag): unit_obj = self.unit_objects.get(unit_tag) #check to see if it's a probe, if so remove it from gathering. if unit_obj.unit.name == 'Probe': unit_obj.removeGatherer() if unit_obj.unit.name == 'DisruptorPhased': unit_obj.clearMines() unit_obj.clearLurkers() #check to see if it's our probe scout, if so create another. # if unit_obj.unit.name == 'Probe' and unit_obj.scout: # #was a scout, create a new one. # self.assignScout() del self.unit_objects[unit_tag] def load_object(self, unit): #print ('Unit Created:', unit.name, unit.tag) #check to see if an object already exists for this tag if self.getObjectByTag(unit.tag): return if unit.name == 'WarpPrism': obj = wpControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Immortal': obj = imControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Stalker': obj = skControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Zealot': obj = zlControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Sentry': obj = snControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Adept': obj = adControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Colossus': obj = coControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'VoidRay': obj = vrControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Phoenix': obj = pxControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Probe': obj = pbControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Tempest': obj = tpControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'AdeptPhaseShift': obj = sdControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'HighTemplar': obj = htControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Observer': obj = obControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Disruptor': obj = dsControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'DisruptorPhased': obj = dpControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Carrier': obj = crControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Mothership': obj = msControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'Archon': obj = arControl(unit) self.unit_objects.update({unit.tag:obj}) elif unit.name == 'PhotonCannon': obj = cnControl(unit) self.unit_objects.update({unit.tag:obj}) # else: # print ('Unit Created:', unit.name, unit.tag) def unitPosition(self, ownerUnit): if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.saved_position return None def phaseTargets(self): phaseList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'DisruptorPhased' } targets = [] for key, phase in phaseList.items(): targets.append(phase.currentTarget) return targets def adeptChaseTarget(self, ownerUnit): #get the object by the unit_tag. if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.chasePosition return None def unitDamaged(self, ownerUnit): if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.wasDamaged return False def unitHomeTarget(self, ownerUnit): #get the object by the unit_tag. if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.homeTarget return None def unitTarget(self, ownerUnit): #get the object by the unit_tag. if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.last_target return None def disruptorBallCancel(self, owner_tag) -> bool: ballList = {k : v for k,v in self.unit_objects.items() if v.unit.type_id == DISRUPTORPHASED and v.requestCancel and v.ownerTag == owner_tag} if len(ballList) > 0: return True return False def adeptOrder(self, ownerUnit): #get the object by the unit_tag. if self.unit_objects.get(ownerUnit.tag): unit_obj = self.unit_objects.get(ownerUnit.tag) return unit_obj.shadeOrder return None def assignScout(self): #if it's late in the game and we aren't attacking, then don't make a replacement. if self.game.defend_only and self.game.time > 360: return #find a probe to assign as a scout. probeList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Probe' and not v.collect_only and not v.scout } for key, probe in probeList.items(): probe.becomeScout() probe.removeGatherer() return def unitCount(self, unit_name): unitList = {k : v for k,v in self.unit_objects.items() if v.unit.name == unit_name } return len(unitList) def shieldSafe(self, inc_unit): #check for other sentries near by with shields that are active. shieldingList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Sentry' and v.shieldActive and v.unit.distance_to(inc_unit.unit) < 2.5 } if len(shieldingList) > 0: return False return True def freeNexusBuilders(self): probeList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Probe' and v.nexus_builder } if len(probeList) > 0: for key, probe in probeList.items(): probe.nexus_builder = False probe.nexus_position = None @property def nexusBuilderAssigned(self) -> bool: probeList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Probe' and v.nexus_builder } if len(probeList) > 0: return True return False @property def hallucinationScore(self) -> int: hallList = {k : v for k,v in self.unit_objects.items() if v.isHallucination } hall_score = 0 for key, unit_obj in hallList.items(): hall_score += self.unitCounter.getUnitPower(unit_obj.unit.name) return hall_score def phoenixScouting(self): phoenixList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Phoenix' and v.isHallucination } if len(phoenixList) > 0: return True return False def getGravitonTarget(self, inc_unit): phoenixList = {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Phoenix' and v.isBeaming } #print (len(phoenixList), inc_unit.unit.name, len(self.unit_objects)) target = None #get the closest. mindist = 1000 for key, phoenix in phoenixList.items(): #get the distance to th if inc_unit.unit.position.to2.distance_to(phoenix.position.to2) < mindist: target = phoenix.beam_unit mindist = inc_unit.unit.position.to2.distance_to(phoenix.unit.position.to2) if mindist < 10: return target return None def getWorkers(self): return {k : v for k,v in self.unit_objects.items() if v.unit.name == 'Probe' }.items() def friendlyEngagedFighters(self, closestEnemy, friendRange=10): #find all the units near the closest Enemy that aren't retreating. baselist = {k : v for k,v in self.unit_objects.items() if v.unit.position.to2.distance_to(closestEnemy.position.to2) < friendRange } #find out how much DPS we have going on. friendDPStoGround = 0 friendDPStoAir = 0 friendAirHealth = 0 friendGroundHealth = 0 friendTotalDPS = 0 for k, friendObj in baselist.items(): if friendObj.unit.is_flying: friendAirHealth += friendObj.unit.health + friendObj.unit.shield else: friendGroundHealth += friendObj.unit.health + friendObj.unit.shield friendDPStoGround += friendObj.unit.ground_dps friendDPStoAir += friendObj.unit.air_dps if friendObj.unit.ground_dps > friendObj.unit.air_dps: friendTotalDPS += friendObj.unit.ground_dps else: friendTotalDPS += friendObj.unit.air_dps return [friendDPStoGround, friendDPStoAir, friendAirHealth, friendGroundHealth, friendTotalDPS] def friendlyFighters(self, inc_unit, friendRange=10): #find all the units near the passed units position that aren't retreating. #baselist = {k : v for k,v in self.unit_objects.items() if not v.isRetreating and v.unit.position.to2.distance_to(inc_unit.position.to2) < friendRange } baselist = {k : v for k,v in self.unit_objects.items() if v.unit.position.to2.distance_to(inc_unit.position.to2) < friendRange } #find out how much DPS we have going on. friendDPStoGround = 0 friendDPStoAir = 0 friendAirHealth = 0 friendGroundHealth = 0 friendTotalDPS = 0 for k, friendObj in baselist.items(): if friendObj.unit.is_flying: friendAirHealth += friendObj.unit.health + friendObj.unit.shield else: friendGroundHealth += friendObj.unit.health + friendObj.unit.shield friendDPStoGround += friendObj.unit.ground_dps friendDPStoAir += friendObj.unit.air_dps if friendObj.unit.ground_dps > friendObj.unit.air_dps: friendTotalDPS += friendObj.unit.ground_dps else: friendTotalDPS += friendObj.unit.air_dps return [friendDPStoGround, friendDPStoAir, friendAirHealth, friendGroundHealth, friendTotalDPS] #properties. @property def amount(self) -> int: return len(self.unit_objects)

34.24159

155

0.703224

1,573

11,197

4.91227

0.149396

0.06212

0.10871

0.049178

0.556231

0.551314

0.533713

0.508477

0.484794

0

0.004752

0.191926

11,197

326

156

34.346626

0.849248

0.10476

0

0.408

0

0.025129

0

1

0.104

false

0

0.092

0.008

0.336

0

null

0

null

0

1

0

09912f75595653975287507558557321b7720adb

619

py

Python

src/lib/bver/Versioned/Addon.py

backboneHQ/bver

c3c929442fadb28a3f39d0ddec19fb2dfc7a4732

[ "MIT" ]

1

2021-09-09T01:22:37.000Z

src/lib/bver/Versioned/Addon.py

backboneHQ/bver

c3c929442fadb28a3f39d0ddec19fb2dfc7a4732

[ "MIT" ]

null

src/lib/bver/Versioned/Addon.py

backboneHQ/bver

c3c929442fadb28a3f39d0ddec19fb2dfc7a4732

[ "MIT" ]

1

2021-09-03T18:45:15.000Z

from .Versioned import Versioned class Addon(Versioned): """ Implements the addon support to the versioned. """ def __init__(self, *args, **kwargs): """ Create an addon object. """ super(Addon, self).__init__(*args, **kwargs) # setting default options self.setOption('enabled', True) def bverEnabledName(self, software): """ Return the enabled environment variable name for the addon versioned. """ return 'BVER_{}_{}_ENABLED'.format( software.name().upper(), self.name().upper() )

23.807692

77

0.568659

60

619

5.683333

0.566667

0.082111

0

0.308562

619

25

78

24.76

0.796729

0.266559

0

0.063939

0

1

0.2

false

0

0.1

0

0.5

0

null

0

null

0

1

0

09928e74c332f7b48d51ab003cf566958a601031

5,988

py

Python

backend/filing/admin.py

bhardwajRahul/sec-filings-app

8cf7f5956717db8fee1f9a20445986ad9cb831ca

[ "MIT" ]

36

2020-12-04T08:16:38.000Z

2022-03-22T02:30:49.000Z

backend/filing/admin.py

bhardwajRahul/sec-filings-app

8cf7f5956717db8fee1f9a20445986ad9cb831ca

[ "MIT" ]

1

2021-10-14T22:20:40.000Z

2021-10-17T17:29:50.000Z

backend/filing/admin.py

briancaffey/sec-filings-app

8cf7f5956717db8fee1f9a20445986ad9cb831ca

[ "MIT" ]

16

2020-11-30T18:46:51.000Z

2022-01-20T23:01:58.000Z

from datetime import date from django.contrib import admin, messages from django.core.management import call_command from django.utils.html import format_html from django.http import HttpResponseRedirect from django.urls import path # Register your models here. from .models import ( FilingList, Filing, Holding, Cik, Cusip, CikObservation, CusipObservation, ) from .tasks import process_filing, process_filing_list class CikAdmin(admin.ModelAdmin): class Meta: model = Cik search_fields = ("cik_number",) list_display = ("cik_number", "filer_name") class CusipAdmin(admin.ModelAdmin): class Meta: model = Cusip search_fields = ("cusip_number", "company_name", "symbol") list_display = ("cusip_number", "company_name", "symbol") class CikObservationAdmin(admin.ModelAdmin): class Meta: model = CikObservation search_fields = ("cik__cik_number", "name") readonly_fields = ("cik", "filing_list", "name") list_display = ("id", "cik", "name", "filing_list") class CusipObservationAdmin(admin.ModelAdmin): class Meta: model = CusipObservation search_fields = ("cusip__cusip_number", "name") # raw_id_fields = ["cusip", "name"] readonly_fields = ("cusip", "name", "filing") list_display = ("id", "cusip", "name", "filing") class FilingListAdmin(admin.ModelAdmin): class Meta: model = FilingList list_display = ("id", "datafile", "quarter", "filing_count") readonly_fields = ("quarter",) change_form_template = "admin/filing/filinglist/change_form.html" def save_model(self, request, obj, form, change): if not obj.quarter: obj.quarter = date(int(obj.filing_year), ((int(obj.filing_quarter) - 1) * 3) + 1, 1) super(FilingListAdmin, self).save_model(request, obj, form, change) def process_filings(self, request, queryset): for filing_list in queryset: filing_list.process_filing_list() messages.add_message(request, messages.INFO, 'Processing filing') actions = [process_filings] process_filings.short_description = "Process Filings" def get_urls(self): urls = super().get_urls() filing_list_urls = [path("generate/", self.generate_filing_lists)] return urls + filing_list_urls def generate_filing_lists(self, request): call_command("generate_filing_lists") self.message_user( request, "Filing lists have been generated (1993 - 2020)." ) return HttpResponseRedirect("../") def response_change(self, request, obj): if "_process_filing_list" in request.POST: process_filing_list.apply_async(args=(obj.id,)) self.message_user(request, "Filing list is being processed.") return HttpResponseRedirect(".") return super().response_change(request, obj) class FilingAdmin(admin.ModelAdmin): class Meta: model = Filing # https://stackoverflow.com/questions/46756086/django-admin-edit-model-select-prefetch-related list_select_related = ("filing_list", "cik") readonly_fields = ("filing_list",) list_display = ( "id", "cik", "form_type", "date_filed", "filing_list_link", "datafile", "holding_count", ) search_fields = ("form_type",) # def holding_count(self, obj=None): # return obj.holding_count() def holding_count(self, obj=None): return format_html( f"<a href='/admin/filing/holding/?filing__id={obj.id}'>{obj.holding_count()}</a>" # noqa ) holding_count.admin_order_field = "holdingcount" def filing_list_link(self, obj=None): return format_html( f'<a target="_blank" href="/admin/filing/filinglist/{obj.filing_list.id}/change/">{str(obj.filing_list)}</a>' # noqa ) change_form_template = "admin/filing/filing/change_form.html" def response_change(self, request, obj): if "_process_filing" in request.POST: process_filing.apply_async(args=(obj.id,)) self.message_user(request, "Filing is being processed.") return HttpResponseRedirect(".") return super().response_change(request, obj) class HoldingAdmin(admin.ModelAdmin): class Meta: model = Holding # raw_id_fields = ["filing"] list_select_related = ( "filing", "cusip", "filing__cik", "filing__filing_list", ) readonly_fields = ("filing",) list_display = ( "id", "cik", "filing_link", "filing", "date_filed", "nameOfIssuer", "titleOfClass", "cusip", "value", "sshPrnamt", "sshPrnamtType", "investmentDiscretion", "putCall", "otherManager", "sole", "shared", "nonee", ) def cik(self, obj=None): return format_html( f'<a target="_blank" href="/cik/{obj.filing.cik}">{obj.filing.cik}</a>' # noqa ) def date_filed(self, obj=None): return obj.filing.date_filed # https://stackoverflow.com/questions/2168475/django-admin-how-to-sort-by-one-of-the-custom-list-display-fields-that-has-no-d date_filed.admin_order_field = "filing__date_filed" def filing_link(self, obj=None): return format_html( f'<a target="_blank" href="/admin/filing/filing/{obj.filing.id}/change/">Link</a>' # noqa ) search_fields = ( "nameOfIssuer", "cusip__cusip_number", "cusip__company_name", ) admin.site.register(Holding, HoldingAdmin) admin.site.register(FilingList, FilingListAdmin) admin.site.register(Filing, FilingAdmin) admin.site.register(Cik, CikAdmin) admin.site.register(CikObservation, CikObservationAdmin) admin.site.register(Cusip, CusipAdmin) admin.site.register(CusipObservation, CusipObservationAdmin)

27.981308

129

0.644122

671

5,988

5.530551

0.223547

0.056589

0.037726

0.045271

0.271625

0.158448

0.145783

0.137968

0.113177

0

0.005861

0.230628

5,988

213

130

28.112676

0.799653

0.06513

0

0.202614

0

0.026144

0.217508

0.065879

0

1

0.071895

false

0

0.052288

0.03268

0.437909

0

null

0

null

0

1

0

0996f3d1f1ac8a9ea6f99a214f2486805b79d23f

3,742

py

Python

__init__.py

FabienBasset/evolucare-skill

4ecce1615cb11d72196ea745d2753fec19117b12

[ "Apache-2.0" ]

null

__init__.py

FabienBasset/evolucare-skill

4ecce1615cb11d72196ea745d2753fec19117b12

[ "Apache-2.0" ]

null

__init__.py

FabienBasset/evolucare-skill

4ecce1615cb11d72196ea745d2753fec19117b12

[ "Apache-2.0" ]

null

# TODO: Add an appropriate license to your skill before publishing. See # the LICENSE file for more information. # Below is the list of outside modules you'll be using in your skill. # They might be built-in to Python, from mycroft-core or from external # libraries. If you use an external library, be sure to include it # in the requirements.txt file so the library is installed properly # when the skill gets installed later by a user. from adapt.intent import IntentBuilder from mycroft.skills.core import MycroftSkill, intent_handler from mycroft.util.log import LOG from mycroft.skills.context import adds_context, removes_context class EvolucareSkill(MycroftSkill): def __init__(self): super(EvolucareSkill, self).__init__(name="EvolucareSkill") self.last_tension = 0 @intent_handler(IntentBuilder("TensionMesure") .require("mesure") .require("tension")) @adds_context('TensionProtocol') def handle_tension_question_mesure(self, message): self.speak_dialog('tension.mesure.protocol') @intent_handler(IntentBuilder("TensionQuestion") .require("tension")) @adds_context('TensionContext') def handle_tension_question(self, message): self.speak_dialog('tension.question', expect_response=True) @intent_handler(IntentBuilder("TensionQuestionDecline") .require("negation") .require("TensionContext") .build()) @removes_context('TensionContext') def handle_tension_question_decline(self, message): self.speak_dialog('tension.question.decline') @intent_handler(IntentBuilder("TensionProtocolIntent") .require("acceptation") .require("TensionContext") .build()) @adds_context('TensionProtocol') @removes_context('TensionContext') def handle_tension_question_accept(self, message): self.speak_dialog('tension.mesure.protocol') @intent_handler(IntentBuilder("TensionCalculIntent") .require("pret") .require("TensionProtocol") .optionally("negation") .build()) def handle_tension_calcul_intent(self, message): neg = message.data.get("negation") if not neg: self.TensionCalulate() else: self.speak_dialog("tension.protocol.wait") @removes_context('TensionProtocol') def TensionCalulate(self): self.speak_dialog('tension.calcul') # TO DO : calculate and return tension self.last_tension = 0 self.speak_dialog("tension.response", data={"tension": self.last_tension} ) #@intent_handler(IntentBuilder("")) #def handle_default_intent(self, message): #self.speak_dialog("response", data={"response": message.data["utterance"]}) #@intent_handler(IntentBuilder("").require("Count").require("Dir")) #def handle_count_intent(self, message): #if message.data["Dir"] == "up": #self.count += 1 #else: # assume "down" #self.count -= 1 #self.speak_dialog("count.is.now", data={"count": self.count}) # The "stop" method defines what Mycroft does when told to stop during # the skill's execution. In this case, since the skill's functionality # is extremely simple, there is no need to override it. If you DO # need to implement stop, you should return True to indicate you handled # it. # # def stop(self): # return False def create_skill(): return EvolucareSkill()

33.711712

84

0.638696

401

3,742

5.820449

0.366584

0.034704

0.057841

0.065981

0.172665

0.161525

0.142245

0.062554

0

0.001442

0.258685

3,742

110

85

34.018182

0.839942

0.312667

0

0.288462

0

0.171777

0.052673

0

0.009091

0

1

0.153846

false

0

0.076923

0.019231

0.269231

0

null

0

null

0

1

0

0997cd0a89022a9406ffd19fb23a90e8f3cec543

300

py

Python

web/searching.py

Kabanosk/JAVRIS

f3fac115eb537e689c59bd093da34e7f0b34a035

[ "MIT" ]

null

web/searching.py

Kabanosk/JAVRIS

f3fac115eb537e689c59bd093da34e7f0b34a035

[ "MIT" ]

null

web/searching.py

Kabanosk/JAVRIS

f3fac115eb537e689c59bd093da34e7f0b34a035

[ "MIT" ]

null

import webbrowser as web from bs4 import BeautifulSoup STARTING_URL = 'https://www.google.com/search?q=' def get_first_website(phrase): phrase_split = phrase.split() phrase_url = '+'.join(phrase_split) search_url = STARTING_URL + phrase_url web.open_new_tab(search_url)

25

50

0.716667

42

300

4.833333

0.595238

0.162562

0.167488

0

0.004082

0.183333

300

11

51

27.272727

0.82449

0

0.114583

0

1

0.125

false

0

0.25

0

0.375

0

null

0

null

0

1

0

099c6d4626feec61b7b00c6c857042abd77c6c2a

2,039

py

Python

ai_script_writer.py

FLWL/aoc-ai-parser

2e08fc7b0909579aced5a84bda3645dbe8834d39

[ "MIT" ]

10

2019-03-17T00:48:35.000Z

2022-02-06T18:15:48.000Z

ai_script_writer.py

FLWL/aoc-ai-parser

2e08fc7b0909579aced5a84bda3645dbe8834d39

[ "MIT" ]

null

ai_script_writer.py

FLWL/aoc-ai-parser

2e08fc7b0909579aced5a84bda3645dbe8834d39

[ "MIT" ]

1

2022-01-16T12:38:52.000Z

from ai_constants import * import ai_generator def get_tab_string(tabment): return '\t' * tabment def express_node(cur_node, tabment = 0): child_nodes = cur_node.children if cur_node.type == 'DEFRULE': return "(defrule\n" \ + express_node(child_nodes[0], tabment + 1) \ + "=>\n" \ + express_node(child_nodes[1], tabment + 1) \ + ")" elif cur_node.type == 'CONDITIONS' or cur_node.type == 'ACTIONS': variable_amount_return = "" for child_node in child_nodes: variable_amount_return += str(express_node(child_node, tabment)) return variable_amount_return elif cur_node.value in FLOW: variable_amount_return = get_tab_string(tabment) + "(" + str(cur_node.value) + "\n" for child_node in child_nodes: variable_amount_return += express_node(child_node, tabment + 1) variable_amount_return += get_tab_string(tabment) + ")\n" return variable_amount_return elif cur_node.type == 'FACT*' or cur_node.type == 'ACTION*': variable_amount_return = get_tab_string(tabment) + "(" + str(cur_node.value) for child_node in child_nodes: variable_amount_return += " " + str(express_node(child_node, tabment)) variable_amount_return += ")\n" return variable_amount_return return cur_node.value def express_script(script_tree): script_text = "" for rule in script_tree: script_text += express_node(rule) script_text += "\n\n" return script_text def write_script(script_tree, file_path): with open(file_path, 'w') as f: f.write(express_script(script_tree)) f.flush() if __name__ == '__main__': # generate and express a rule rule_tree = ai_generator.generate_rule() rule_script = express_node(rule_tree) print(rule_script) # generate and write a script script_tree = ai_generator.generate_script() write_script(script_tree, "random_script.per")

30.432836

91

0.650809

262

2,039

4.698473

0.221374

0.062551

0.178716

0.061738

0.415922

0.3355

0.243704

0.207961

0

0.003901

0.245709

2,039

66

92

30.893939

0.796489

0.026974

0

0.130435

0

0.047451

0

1

0.086957

false

0

0.043478

0.021739

0.282609

0.021739

0

null

0

null

0

1

0

099d1dd35cd095ae208ec87e7df60676dd935b0a

1,316

py

Python

euler-148.py

simonolander/euler

4d7c4cd9333201cd0065419a511f111b6d75d90c

[ "MIT" ]

null

euler-148.py

simonolander/euler

4d7c4cd9333201cd0065419a511f111b6d75d90c

[ "MIT" ]

null

euler-148.py

simonolander/euler

4d7c4cd9333201cd0065419a511f111b6d75d90c

[ "MIT" ]

null

import numpy as np from tabulate import tabulate np.set_printoptions(linewidth=400, threshold=100000) def product(gen): ans = 1 for g in gen: ans *= g + 1 return ans def count_divs_pow(p): if p == 0 or p == 1: return 0 else: full_size = 7**(p-1) * (7**(p-1) - 1) // 2 fulls = 21 * full_size smalls = 28 * count_divs_pow(p-1) return fulls + smalls def base7(n): ans = [] while n > 0: ans.append(n % 7) n //= 7 return ans def num_not_divisible(i): return product(base7(i)) def pascal(n): pascal = np.zeros((n, n)) for x, y in np.ndindex(n, n): if x == 0 or y == 0: pascal[x, y] = 1 else: pascal[x, y] = (pascal[x-1, y] + pascal[x, y-1]) % 7 print(pascal) def pascal_zeroes(n): row = [1] zeroes = [[0, 0, 0]] for i in range(1, n): row = [1] + [(a + b) % 7 for a, b in zip(row, row[1::])] + [1] count = len([r for r in row if r == 0]) zeroes.append([i, count, count - zeroes[i-1][1]]) return tabulate(zeroes, ['Row index', 'Count Zeros']) def c(n): ans = 0 for i in range(n): ans += num_not_divisible(i) if i % 1000000 == 0: print(i, ans) return ans print(c(1000000000))

19.939394

70

0.506839

215

1,316

3.046512

0.293023

0.042748

0.036641

0.039695

0.036641

0

0.076923

0.338146

1,316

65

71

20.246154

0.675086

0

0.104167

0

0.015198

0

1

0.145833

false

0

0.041667

0.020833

0.333333

0.083333

0

null

0

null

0

1

0

099e3f2b24bd01bfd5b7e1350533a5d17bf7ffdd

1,695

py

Python

abandoned-ideas/yml-generator.py

HenryZheng1/sengrep-cli-py

89d2ffad813706a534290f248220f0d32aeb4c3c

[ "Apache-2.0" ]

null

abandoned-ideas/yml-generator.py

HenryZheng1/sengrep-cli-py

89d2ffad813706a534290f248220f0d32aeb4c3c

[ "Apache-2.0" ]

null

abandoned-ideas/yml-generator.py

HenryZheng1/sengrep-cli-py

89d2ffad813706a534290f248220f0d32aeb4c3c

[ "Apache-2.0" ]

2

2021-07-23T16:46:16.000Z

2021-07-30T02:59:43.000Z

from csv import reader import yaml import json def splitrow(row, DELIMETER): x = row.split(DELIMETER) return ([] if row == '' else x) def get_data_from_csv(settings, DELIMETER = '|'): rules = [] with open(settings['CSV_FILENAME'], 'r') as csv_file: csv_reader = reader(csv_file) for row in csv_reader: bug_id = row[0] pattern_either = splitrow(row[1], DELIMETER) pattern_inside = splitrow(row[2], DELIMETER) pattern_not_inside = splitrow(row[3], DELIMETER) languages = splitrow(row[4], DELIMETER) message = row[5] severity = row[6] patterns = { "pattern-either": {"pattern":patt for patt in pattern_either} , "pattern-not-inside" : { "pattern": patt for patt in pattern_not_inside }, "pattern-inside" : {"pattern" : patt for patt in pattern_inside}, } patterns = {k: v for k, v in patterns.items() if v} single_rule_obj = { "id" : bug_id, "patterns" : patterns, "message" : message, "languages" : languages, "severity" : severity } rules.append(single_rule_obj) return {"rules" : rules} def convert_json_to_yaml(yml_dict, settings): with open(settings['OUTPUT_FILENAME'], 'w') as ymlfile: yaml.dump(yml_dict, ymlfile, allow_unicode=True) def go(config_filename = 'yml-generator-config.json'): with open(config_filename, 'r') as json_file: settings = json.load(json_file) yml_dict = get_data_from_csv(settings, settings['DELIMETER']) convert_json_to_yaml(yml_dict, settings) go()

35.3125

150

0.60118

207

1,695

4.724638

0.323672

0.056237

0.04908

0.055215

0.205521

0.160532

0.132924

0

0.005752

0.282006

1,695

47

151

36.06383

0.797864

0

0.100945

0.014758

0

1

0.114286

false

0

0.085714

0

0.257143

0

null

0

null

0

1

0

099e6785d5350ed75115c74f1a4e9bf333839d99

4,018

py

Python

linearizer/utils.py

Max1993Liu/Linearizer

739c47c0d98d262a0bc962a450729bcf83c61212

[ "MIT" ]

null

linearizer/utils.py

Max1993Liu/Linearizer

739c47c0d98d262a0bc962a450729bcf83c61212

[ "MIT" ]

null

linearizer/utils.py

Max1993Liu/Linearizer

739c47c0d98d262a0bc962a450729bcf83c61212

[ "MIT" ]

null

import numpy as np import pandas as pd from types import FunctionType import warnings from .transform import BaseTransformer def drop_na(x, y, according='both'): """ Drop the values in both x and y if the element in `according` is missing ex. drop_na([1, 2, np.nan], [1, 2, 3], 'x') => [1, 2], [1, 2] """ if according == 'x': valid_index = ~np.isnan(x) elif according == 'y': valid_index = ~np.isnan(y) elif according == 'both': valid_index = (~np.isnan(x)) & (~np.isnan(y)) else: raise ValueError('According should be one of {}'.format(['x', 'y', 'both'])) return np.array(x)[valid_index], np.array(y)[valid_index] def check_binary_label(y): """ Make sure the label contains only 0 and 1 """ if set(y) != set([0, 1]): raise ValueError('The label must be binary 0 or 1.') def check_numerical(x): if isinstance(x, list): x = x[0] if not pd.api.types.is_numeric_dtype(x): raise ValueError('The input must be a numerical array.') def as_positive_rate(x, y, bins, interval_value='mean'): """ Group numerical variable x into several bins and calculate the positive rate within each bin :param bins: Integer or a sequence of values as cutoff points :param interval_value: One of ['left', 'right', 'mean'], how the interval is converted to a scalar """ if isinstance(x, list): x = np.array(x) check_numerical(x) check_binary_label(y) if len(set(x)) <= bins: pos_pct = pd.Series(y).groupby(x).mean() else: intervals = pd.cut(x, bins) if interval_value == 'left': intervals = [i.left for i in intervals] elif interval_value == 'right': intervals = [i.right for i in intervals] elif interval_value == 'mean': intervals = [(i.left + i.right) / 2.0 for i in intervals] else: raise ValueError('Only {} is supported.'.format(['left', 'right', 'mean'])) pos_pct = pd.Series(y).groupby(intervals).mean() return pos_pct.index.values, pos_pct.values EPILSON = 1e-15 def _odds(p): p = np.clip(p, EPILSON, 1 - EPILSON) return p / (1 - p) def _logodds(p): return np.log(_odds(p)) _TRANSFORMS = { 'odds': _odds, 'logodds': _logodds } def preprocess(x, y, binary_label=True, bins=50, transform_y=None, interval_value='mean', ignore_na=True): """ Preprocess the input before finding the best transformations :param binary_label: Whether the label is binary (0, 1), in other words. whether the problem is classification or regression. :param transform_y: Transformation applied to y, can either be a string within ['odds', 'logodds'], or a function :param bins: Integer or a sequence of values as cutoff points :param interval_value: One of ['left', 'right', 'mean'], how the interval is converted to a scalar :ignore_na: Whether to ignore NA """ if binary_label: x, y = as_positive_rate(x, y, bins, interval_value) if transform_y is not None: # make sure y is an array y = np.array(y) if isinstance(transform_y, str): if transform_y not in _TRANSFORMS: raise ValueError('Only {} is supported.'.format(_TRANSFORMS.keys())) y = _TRANSFORMS[transform_y](y) elif isinstance(transform_y, FunctionType): y = transform_y(y) else: raise ValueError('Only string and function is supported for `transform_y`.') if ignore_na: x, y = drop_na(x, y, according='both') return x, y def _check_complexity(): cpl = {} for cls in BaseTransformer.__subclasses__(): complexity = cls.complexity if complexity in cpl: warnings.warn('{} and {} has the same complexity {}.'.\ format(cls.__name__, cpl[complexity].__name__, complexity)) cpl[complexity] = cls

31.637795

106

0.612245

561

4,018

4.26025

0.253119

0.007531

0.020084

0.021339

0.250209

0.220084

0.153975

0.127197

0.099582

0

0.009174

0.267546

4,018

126

107

31.888889

0.802922

0.245893

0

0.078947

0

0.100952

0

1

0.105263

false

0

0.065789

0.013158

0.236842

0

null

0

null

0

1

0

09a082c5b766d52ac4bd284843a07b1bfbf38eba

325

py

Python

testings.py

GYosifov88/Python-Fundamentals

b46ba2822bd2dac6ff46830c6a520e559b448442

[ "MIT" ]

null

testings.py

GYosifov88/Python-Fundamentals

b46ba2822bd2dac6ff46830c6a520e559b448442

[ "MIT" ]

null

testings.py

GYosifov88/Python-Fundamentals

b46ba2822bd2dac6ff46830c6a520e559b448442

[ "MIT" ]

null

class Object: def __init__(self, type): self.type = type def square(self, a, b): if self.type == 'square': return a * b if self.type == 'triangle': return (a * b) / 2 vid = input() object = Object(vid) a = int(input()) b = int(input()) print(f'{object.square(a,b)}')

18.055556

35

0.52

46

325

3.586957

0.391304

0.193939

0.048485

0.09697

0.145455

0

0.004464

0.310769

325

17

36

19.117647

0.732143

0

0.104615

0

1

0.153846

false

0

0.384615

0.076923

0

null

0

null

0

1

0

09a0e0698bb4f209bcf75379d2f58d655b33426a

809

py

Python

argdeco/__main__.py

klorenz/python-argdeco

eb614d63430c5da68a972bdc40f8a1541070089d

[ "MIT" ]

null

argdeco/__main__.py

klorenz/python-argdeco

eb614d63430c5da68a972bdc40f8a1541070089d

[ "MIT" ]

null

argdeco/__main__.py

klorenz/python-argdeco

eb614d63430c5da68a972bdc40f8a1541070089d

[ "MIT" ]

null

from .main import Main from .arguments import arg from textwrap import dedent main = Main() command = main.command @command('install-bash-completions', arg('--dest', help="destination file. Typically ~/.bashrc or ~/.profile", default="~/.bashrc"), arg('script_name'), ) def install_bash_completions(dest, script_name): main.install_bash_completion(dest=dest, script_name=script_name) print(dedent(""" To activate bash completions of script_name run: . %s """ % dest)) @command('uninstall-bash-completions', arg('--dest', help="destination file. Typically ~/.bashrc or ~/.profile", default="~/.bashrc"), arg('script_name'), ) def uninstall_bash_completions(dest, script_name): main.uninstall_bash_completion(dest=dest, script_name=script_name) main()

28.892857

100

0.700865

100

809

5.5

0.31

0.163636

0.101818

0.08

0.614545

0.494545

0.341818

0

0.15204

809

27

101

29.962963

0.801749

0

0.181818

0

0.352287

0.061805

0

1

0.090909

false

0

0.136364

0

0.227273

0.045455

0

null

0

null

0

1

0

09a25fafbbc8341875cf49512a6963ebb67af9a9

1,092

py

Python

working-with-data/part1/reading-and-writing-text-files.py

LucasHelal/data-science

9b243be1dea23a521e6ebb49dc358708a9b17dbd

[ "MIT" ]

null

working-with-data/part1/reading-and-writing-text-files.py

LucasHelal/data-science

9b243be1dea23a521e6ebb49dc358708a9b17dbd

[ "MIT" ]

null

working-with-data/part1/reading-and-writing-text-files.py

LucasHelal/data-science

9b243be1dea23a521e6ebb49dc358708a9b17dbd

[ "MIT" ]

null

import sys import pandas as pd # Can open csv files as a dataframe dframe = pd.read_csv('lec25.csv') # Can also use read_table with ',' as a delimiter dframe = pd.read_table('lec25.csv', sep=',') # If we dont want the header to be the first row dframe = pd.read_csv('lec25.csv', header=None) # We can also indicate a particular number of rows to be read pd.read_csv('lec25.csv', header=None, nrows=2) # Now let's see how we can write DataFrames out to text files dframe.to_csv('mytextdata_out.csv') # You'll see this file where you're ipython Notebooks are saved (Usually # under my documents) # We can also use other delimiters # we'll import sys to see the output # Use sys.stdout to see the output directly and not save it dframe.to_csv(sys.stdout, sep='_') # Just to make sure we understand the delimiter dframe.to_csv(sys.stdout, sep='?') # We can also choose to write only a specific subset of columns dframe.to_csv(sys.stdout, columns=[0, 1, 2]) # You should also checkout pythons built-in csv reader and writer fot more info # https://docs.python.org/2/library/csv.html

28.736842

79

0.739011

199

1,092

4

0.492462

0.030151

0.055276

0.052764

0.187186

0.16206

0.067839

0

0.014254

0.164835

1,092

37

80

29.513514

0.858553

0.636447

0

0.149215

0

1

0

false

0

0.2

0

0.2

0

null

0

null

0

1

0

09a2b27d6e9143175c3eaab5d912857ca2f60085

1,428

py

Python

comments/urls.py

ggetzie/greaterdebater

fb1739f3db42717f3d63fe6c9dbf0c2402fb1fd5

[ "MIT" ]

null

comments/urls.py

ggetzie/greaterdebater

fb1739f3db42717f3d63fe6c9dbf0c2402fb1fd5

[ "MIT" ]

1

2020-05-02T02:03:08.000Z

comments/urls.py

ggetzie/greaterdebater

fb1739f3db42717f3d63fe6c9dbf0c2402fb1fd5

[ "MIT" ]

null

from django.conf.urls import patterns from comments.views import CommentDebateList # This url file is included from items.urls with the prefix /comments/ urlpatterns = patterns('', # Add a comment to a topic (r'^(?P<topic_id>\d+)/add/$', 'comments.views.add'), # Edit a comment (r'^(?P<topic_id>\d+)/edit/$', 'comments.views.edit'), # View a single comment on a page by itself (r'^(?P<comment_id>\d+)/?$', 'comments.views.comment_detail'), # Delete a comment (r'[delete|undelete]/$', 'comments.views.delete'), # View all arguments associated with a comment (r'^(?P<comment_id>\d+)/arguments/?(?P<page>\d+)?/?$', CommentDebateList.as_view(paginate_by=10, template_name='comments/comment_args.html', context_object_name='args_list')), # Flag a comment as spam (r'^flag/$', 'comments.views.flag'), # Follow or unfollow a topic or comment for # updates when new replies are made (r'^follow/$', 'comments.views.toggle_follow'), )

42

93

0.464286

142

1,428

4.577465

0.450704

0.14

0.041538

0.027692

0.067692

0

0.00241

0.418768

1,428

33

94

43.272727

0.780723

0.217787

0

0.293852

0.203436

0

1

0

false

0

0.142857

0

0.142857

0

null

0

null

0

1

0

09a4876d8faaee60c05b563c48e7b9207133b300

2,022

py

Python

src/engine/app.py

vxlk/stoinks

afea92824a21d203098dd41137957f2343ec363d

[ "MIT" ]

1

2020-12-30T23:54:58.000Z

src/engine/app.py

vxlk/stoinks

afea92824a21d203098dd41137957f2343ec363d

[ "MIT" ]

null

src/engine/app.py

vxlk/stoinks

afea92824a21d203098dd41137957f2343ec363d

[ "MIT" ]

null

import sys from threading import Thread from PyQt5.QtWidgets import * from PyQt5.QtGui import * from PyQt5.QtCore import * from pyqtconsole.console import PythonConsole from view.console import Console from view.gui_dock import * from util.logger import * from model.engine import * # clear logs logger.ClearLogs() # make Qapp app = QApplication([]) app.setApplicationName("Stoinks Alpha") window = QMainWindow() console = PythonConsole() logConsole = Console() # stop debug console from resizing logConsoleFrame = QScrollArea() logConsoleFrame.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn) logConsoleFrame.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn) logConsoleFrame.setWidgetResizable(True) logConsoleFrame.setWidget(logConsole) #temp for now gui = finance_tab_container() consoleContainer = QDockWidget("Input") consoleContainer.setAllowedAreas(Qt.LeftDockWidgetArea) consoleContainer.setWidget(console) logConsoleContainer = QDockWidget("Output") logConsoleContainer.setAllowedAreas(Qt.RightDockWidgetArea) logConsoleContainer.setWidget(logConsoleFrame) guiContainer = QDockWidget("GUI View") guiContainer.setAllowedAreas(Qt.TopDockWidgetArea) guiContainer.setWidget(gui) window.addDockWidget(Qt.LeftDockWidgetArea, consoleContainer) window.addDockWidget(Qt.RightDockWidgetArea, logConsoleContainer) window.addDockWidget(Qt.TopDockWidgetArea, guiContainer) #console.show() add dock widget calls show on its widget i think console.eval_in_thread() # let the input terminal go # make an engine engine.connectConsole(console) engine.connectDebugConsole(logConsole) # Force the style to be the same on all OSs: app.setStyle("Fusion") # Now use a palette to switch to dark colors: palette = QPalette() palette.setColor(QPalette.Window, QColor(53, 53, 53)) palette.setColor(QPalette.WindowText, Qt.white) app.setPalette(palette) window.setMinimumSize(820, 800) window.show() app.exec_() # sys.exit(app.exec_()) engine.stop()

27.69863

67

0.787834

218

2,022

7.275229

0.481651

0.031526

0.039723

0

0.008479

0.125124

2,022

73

68

27.69863

0.888072

0.137488

0

0.022864

0

1

0

false

0

0.217391

0

0.217391

0

null

0

null

0

1

0

09a65708ef251f13b9781f1e9b250a16f7eb5521

8,710

py

Python

Agents/agent.py

TylerJamesMalloy/bullet3

e357853815c1e0297683218273de79e586b574c8

[ "Zlib" ]

null

Agents/agent.py

TylerJamesMalloy/bullet3

e357853815c1e0297683218273de79e586b574c8

[ "Zlib" ]

null

Agents/agent.py

TylerJamesMalloy/bullet3

e357853815c1e0297683218273de79e586b574c8

[ "Zlib" ]

null

import logging, os, time, multiprocessing, sys, signal logging.disable(logging.WARNING) os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" import tensorflow as tf import gym import pybullet, pybullet_envs, pybullet_data import numpy as np import pandas as pd from stable_baselines.sac.policies import MlpPolicy from stable_baselines.clac.policies import MlpPolicy as CLAC_MlpPolicy from stable_baselines.common.vec_env import DummyVecEnv from stable_baselines import SAC, CLAC #from tensorflow.python.client import device_lib #print(device_lib.list_local_devices()) # ENVIRONMENT_NAMES Walker2DBulletEnv-v0, Robots/AntBulletEnv-v0 , HopperBulletEnv-v0 , HumanoidBulletEnv-v0, HalfCheetahBulletEnv-v0 FOLDER = "Results/InvertedDoublePendulumBulletEnv" NUM_RESAMPLES = 50 NUM_TRAINING_STEPS = 100000 NUM_TESTING_STEPS = 50000 ENVIRONMENT_NAME = "InvertedDoublePendulumBulletEnv-v0" if(not os.path.exists(FOLDER + '/Extreme/results')): os.mkdir(FOLDER + '/Extreme/results') if(not os.path.exists(FOLDER + '/Generalization/results')): os.mkdir(FOLDER + '/Generalization/results') if(not os.path.exists(FOLDER + '/Training/results')): os.mkdir(FOLDER + '/Training/results') if(not os.path.exists(FOLDER + '/Training/models')): os.mkdir(FOLDER + '/Training/models') CLAC_COEFS = [2.0] SAC_COEFS = [2.0] def eval_model(model, env, model_name, coef, testing_timesteps, training_timestep, agent_step, resample_step, randomization): obs = env.reset() states = None reward_sum = 0 Data = pd.DataFrame() all_rewards = [] allPlayedCards = [] if(randomization > 0): env.env_method("randomize", randomization) for test_time in range(testing_timesteps): action, states = model.predict(obs, states) obs, rewards, dones, infos = env.step(action) reward_sum += rewards[0] if(dones[0]): d = {"Model": model_name, "Reward": reward_sum, "Timestep": training_timestep, "Coef": coef, "Randomization": randomization, "AgentID": agent_step, "Resample": resample_step} Data = Data.append(d, ignore_index=True) all_rewards.append(reward_sum) reward_sum = 0 if(randomization > 0): env.env_method("randomize", randomization) Avg = np.mean(all_rewards) return Data def test_agent(agent_step): now = time.time() for coef_index in range(len(CLAC_COEFS)): mut_coef = CLAC_COEFS[coef_index] ent_coef = SAC_COEFS[coef_index] training_timestep = 0 clac_env = gym.make(ENVIRONMENT_NAME) clac_env = DummyVecEnv([lambda: clac_env]) clac_model = CLAC(CLAC_MlpPolicy, clac_env, mut_inf_coef=mut_coef, verbose=1) sac_env = gym.make(ENVIRONMENT_NAME) sac_env = DummyVecEnv([lambda: sac_env]) sac_model = SAC(MlpPolicy, sac_env, ent_coef=ent_coef, verbose=1) mirl_env = gym.make(ENVIRONMENT_NAME) mirl_env = DummyVecEnv([lambda: mirl_env]) mirl_model = CLAC(CLAC_MlpPolicy, mirl_env, mut_inf_coef=mut_coef, coef_schedule=3.3e-3, verbose=1) for resample_step in range(0, NUM_RESAMPLES): features = pd.DataFrame() if(agent_step == 1): print(mut_coef, " ", ent_coef, " ", NUM_TRAINING_STEPS, " ", ENVIRONMENT_NAME, " ", FOLDER, " ", resample_step) (clac_model, learning_results) = clac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000) (sac_model, learning_results) = sac_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000) (mirl_model, learning_results) = mirl_model.learn(total_timesteps=NUM_TRAINING_STEPS, log_interval=1000) # Save models clac_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step)) sac_model.save(FOLDER + "/Training/models/CLAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step)) mirl_model.save(FOLDER + "/Training/models/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step)) training_timestep += NUM_TRAINING_STEPS # Test Normal eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0) eval_results.to_pickle(FOLDER + "/Training/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Training/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 0) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Training/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") # Test generalization eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Generalization/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Generalization/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 1) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Generalization/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") # Test generalization Extreme eval_results = eval_model(clac_model, clac_env, "CLAC", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Extreme/results/CLAC_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(sac_model, sac_env, "SAC", ent_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Extreme/results/SAC_" + str(ent_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") eval_results = eval_model(mirl_model, mirl_env, "MIRL", mut_coef, NUM_TESTING_STEPS, training_timestep, agent_step, resample_step, 2) eval_results['AgentID'] = agent_step eval_results.to_pickle(FOLDER + "/Extreme/results/MIRL_" + str(mut_coef).replace(".", "p") + "_" + str(agent_step) + "_" + str(resample_step) + ".pkl") clac_env.env_method("reset_features") sac_env.env_method("reset_features") mirl_env.env_method("reset_features") del sac_model del sac_env del clac_model del clac_env del mirl_model del mirl_env later = time.time() difference = int(later - now) print("Tested Agent Time: ", difference) def main(): Agents = [1, 2] print("Initializng workers: ", Agents) original_sigint_handler = signal.signal(signal.SIGINT, signal.SIG_IGN) pool = multiprocessing.Pool(processes=len(Agents)) signal.signal(signal.SIGINT, original_sigint_handler) try: print("Starting jobs") res = pool.map_async(test_agent, Agents) print("Waiting for results") #res.get(1000000) # Without the timeout this blocking call ignores all signals. except KeyboardInterrupt: print("Caught Keyboard Interrupt, terminating workers") pool.terminate() pool.join() else: print("Normal termination") pool.close() pool.join() if __name__ == "__main__": main()

46.57754

186

0.666475

1,083

8,710

5.036934

0.171745

0.054445

0.026398

0.032997

0.520257

0.492759

0.474427

0.458845

0.426581

0

0.010124

0.2062

8,710

186

187

46.827957

0.778855

0.042939

0

0.120301

0

0.115301

0.048523

0

1

0.022556

false

0

0.075188

0

0.105263

0.052632

0

null

0

null

0

1

0

09a9810fc20f0e86e48fafc4f5dbb9adb6c5702a

1,274

py

Python

ToDoApp/todo/urls.py

akmcinto/ToDoApp

2176294c1cfc33a2e651f613f23922a2c8879a84

[ "Apache-2.0" ]

null

ToDoApp/todo/urls.py

akmcinto/ToDoApp

2176294c1cfc33a2e651f613f23922a2c8879a84

[ "Apache-2.0" ]

null

ToDoApp/todo/urls.py

akmcinto/ToDoApp

2176294c1cfc33a2e651f613f23922a2c8879a84

[ "Apache-2.0" ]

null

""" Copyright 2016 Andrea McIntosh 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 django.conf.urls import url, include from . import views app_name = 'todo' urlpatterns = [ url(r'^$', views.index_view, name='index'), url(r'^(?P<pk>[0-9]+)/$', views.list_details, name='detail'), url(r'^(?P<pk>[0-9]+)/newitem/$', views.new_item, name='new_item'), url(r'^newlist/$', views.new_list, name='new_list'), url(r'^register/$', views.register, name='register'), url(r'^accounts/login/$', 'django.contrib.auth.views.login', name='login'), url(r'^accounts/logout/$', views.user_logout, name='logout'), url(r'^accounts/viewlists/$', views.view_lists, name='viewlists'), url(r'^accounts/', include('django.contrib.auth.urls')), ]

39.8125

79

0.689953

188

1,274

4.62766

0.515957

0.041379

0.055172

0.036782

0.02069

0

0.011194

0.158556

1,274

31

80

41.096774

0.800373

0.435636

0

0.360294

0.148529

0

1

0

false

0

0.142857

0

0.142857

0

null

0

null

0

1

0

09a9fcdb559137e2907018a24bc26f28eb5ecd69

81,886

py

Python

froi/main.py

sunshineDrizzle/FreeROI

e2bae1a19835667988e9dbe4a1a88e5b2778d819

[ "BSD-3-Clause" ]

13

2016-02-12T05:10:23.000Z

2021-01-13T01:40:12.000Z

froi/main.py

sunshineDrizzle/FreeROI

e2bae1a19835667988e9dbe4a1a88e5b2778d819

[ "BSD-3-Clause" ]

14

2015-05-04T05:56:45.000Z

2021-01-24T11:49:13.000Z

froi/main.py

sunshineDrizzle/FreeROI

e2bae1a19835667988e9dbe4a1a88e5b2778d819

[ "BSD-3-Clause" ]

8

2016-03-07T06:29:51.000Z

2017-10-30T13:59:27.000Z

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: """Graphic User Interface.""" import sys import os import glob import ConfigParser from PyQt4.QtCore import * from PyQt4.QtGui import * from version import __version__ from algorithm.imtool import label_edge_detection as vol_label_edge_detection from algorithm.imtool import inverse_transformation from algorithm.meshtool import label_edge_detection as surf_label_edge_detection from core.labelconfig import LabelConfig from utils import get_icon_dir from widgets.listwidget import LayerView from widgets.gridwidget import GridView from widgets.orthwidget import OrthView from widgets.datamodel import VolumeListModel from widgets.drawsettings import PainterStatus, ViewSettings, MoveSettings from widgets.binarizationdialog import VolBinarizationDialog, SurfBinarizationDialog from widgets.intersectdialog import VolIntersectDialog, SurfIntersectDialog from widgets.localmaxdialog import LocalMaxDialog from widgets.no_gui_tools import gen_label_color from widgets.smoothingdialog import SmoothingDialog from widgets.growdialog import GrowDialog, VolumeRGDialog from widgets.watersheddialog import WatershedDialog from widgets.slicdialog import SLICDialog from widgets.clusterdialog import SurfClusterDialog, VolClusterDialog from widgets.regularroidialog import RegularROIDialog from widgets.regularroifromcsvfiledialog import RegularROIFromCSVFileDialog from widgets.roi2gwmidialog import Roi2gwmiDialog from widgets.roimergedialog import ROIMergeDialog from widgets.opendialog import OpenDialog from widgets.labelmanagedialog import LabelManageDialog from widgets.labelconfigcenter import LabelConfigCenter from widgets.roidialog import VolROIDialog, SurfROIDialog from widgets.atlasdialog import AtlasDialog from widgets.binaryerosiondialog import VolBinErosionDialog, SurfBinErosionDialog from widgets.binarydilationdialog import VolBinDilationDialog, SurfBinDilationDialog from widgets.greydilationdialog import GreydilationDialog from widgets.greyerosiondialog import GreyerosionDialog from widgets.meants import MeanTSDialog from widgets.voxelstatsdialog import VoxelStatsDialog from widgets.registervolume import RegisterVolumeDialog from widgets.treemodel import TreeModel from widgets.surfacetreewidget import SurfaceTreeView from widgets.surfaceview import SurfaceView from widgets.scribingdialog import ScribingDialog from widgets.surfaceRGdialog import SurfaceRGDialog from widgets.prob_map_dialog import SurfProbMapDialog from widgets.concatenate_dialog import SurfConcatenateDialog class BpMainWindow(QMainWindow): """Class BpMainWindow provides UI interface of FreeROI. Example: -------- >>> from PyQt4.QtGui import QApplication >>> import main >>> app = QApplication([]) >>> win = main.BpMainWindow() ...... >>> win.show() >>> app.exec_() """ def __init__(self, parent=None): """Initialize an instance of BpMainWindow.""" # Inherited from QMainWindow if sys.platform == 'darwin': # Workaround for Qt issue on OSX that causes QMainWindow to # hide when adding QToolBar, see # https://bugreports.qt-project.org/browse/QTBUG-4300 super(BpMainWindow, self).__init__(parent, Qt.MacWindowToolBarButtonHint) else: super(BpMainWindow, self).__init__(parent) # temporary variable self._save_dir = None self._temp_dir = None self.is_save_configure = False # pre-define model variables, one for volume dataset, another # for suface dataset self.volume_model = None self.surface_model = None self.tabWidget = None self.volume_actions_status = {} self.surface_actions_status = {} self.volume_view = None self.surface_view = None self.list_view = None self.surface_tree_view = None self.painter_status = PainterStatus(ViewSettings()) def config_extra_settings(self, data_dir): """Set data directory and update some configurations.""" # load data directory configuration self.label_path = data_dir self.label_config_dir = os.path.join(self.label_path, 'labelconfig') self.label_config_suffix = 'lbl' # set icon configuration self._icon_dir = get_icon_dir() self.setWindowTitle('FreeROI') self.setWindowIcon(QIcon(os.path.join(self._icon_dir, 'logo.png'))) self._init_configuration() self.center() self._create_actions() self._create_menus() def center(self): """Display main window in the center of screen.""" qr = self.frameGeometry() cp = QDesktopWidget().availableGeometry().center() qr.moveCenter(cp) self.move(qr.topLeft()) def _init_configuration(self): """Load configuration for GUI.""" config_file = os.path.expanduser('~/.froi.conf') if os.path.exists(config_file): config = ConfigParser.RawConfigParser() config.read(config_file) self.window_width = config.getint('width', 'int') self.window_height = config.getint('height', 'int') self.orth_scale_factor = config.getint('orth_scale', 'int') self.grid_scale_factor = config.getint('grid_scale', 'int') self.window_xpos = config.getint('xpos', 'int') self.window_ypos = config.getint('ypos', 'int') self.resize(self.window_width, self.window_height) self.move(self.window_xpos, self.window_ypos) self.default_orth_scale_factor = float(self.orth_scale_factor) / 100 self.default_grid_scale_factor = float(self.grid_scale_factor) / 100 else: # self.setWindowState(Qt.WindowMaximized) screen_geo = QDesktopWidget().screenGeometry() self.setMinimumSize(screen_geo.width()*2/3, screen_geo.height()*8/9) self.default_orth_scale_factor = 1.0 self.default_grid_scale_factor = 2.0 def _init_tab_widget(self): # set tab widget self.tabWidget = QTabWidget() self.tabWidget.setTabShape(QTabWidget.Rounded) self.tabWidget.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Expanding) self.tabWidget.setMaximumWidth(280) self.tabWidget.currentChanged.connect(self._tabwidget_index_changed) # set central widget central_widget = QWidget() layout = QHBoxLayout() central_widget.setLayout(layout) central_widget.layout().addWidget(self.tabWidget) self.setCentralWidget(central_widget) # add tool bar self._add_toolbar() # self.setUnifiedTitleAndToolBarOnMac(True) # change actions status self._actions['add_image'].setEnabled(True) self._actions['new_image'].setEnabled(True) self._actions['save_image'].setEnabled(True) self._actions['close'].setEnabled(True) def _init_vol_actions(self): self._actions['duplicate_image'].setEnabled(True) # self._actions['ld_lbl'].setEnabled(True) # self._actions['ld_glbl'].setEnabled(True) self._actions['orth_view'].setEnabled(True) self._actions['cross_hover_view'].setEnabled(True) self._actions['original_view'].setEnabled(True) self._actions['remove_image'].setEnabled(False) self._actions['undo'].setEnabled(False) self._actions['redo'].setEnabled(False) self._vol_func_module_set_enabled(True) self._actions['binarization'].setEnabled(True) self._actions['binaryerosion'].setEnabled(True) self._actions['binarydilation'].setEnabled(True) self._actions['edge_dete'].setEnabled(True) self._actions['inverse'].setEnabled(True) self._actions['label_management'].setEnabled(True) self._actions['cluster'].setEnabled(True) self._actions['intersect'].setEnabled(True) if not self.volume_model.is_mni_space(): self._actions['atlas'].setEnabled(False) def _init_surf_actions(self): self._actions['duplicate_image'].setEnabled(True) self._actions['undo'].setEnabled(False) self._actions['redo'].setEnabled(False) self._spinbox.setEnabled(False) self._surf_func_module_set_enabled(True) self._actions['binarization'].setEnabled(True) self._actions['binaryerosion'].setEnabled(True) self._actions['binarydilation'].setEnabled(True) self._actions['edge_dete'].setEnabled(True) self._actions['inverse'].setEnabled(True) self._actions['label_management'].setEnabled(True) self._actions['cluster'].setEnabled(True) self._actions['intersect'].setEnabled(True) def _save_configuration(self): """Save GUI configuration to a file.""" config_file = os.path.expanduser('~/.freeroi.conf') config = ConfigParser.RawConfigParser() config.add_section('width') config.add_section('height') config.add_section('orth_scale') config.add_section('grid_scale') config.add_section('xpos') config.add_section('ypos') config.set('width', 'int', self.width()) config.set('height', 'int', self.height()) config.set('xpos', 'int', self.x()) config.set('ypos', 'int', self.y()) if hasattr(self, 'volume_model') and isinstance(self.volume_model, VolumeListModel): config.set('orth_scale', 'int', int(self.volume_model.get_scale_factor('orth')*100)) config.set('grid_scale', 'int', int(self.volume_model.get_scale_factor('grid')*100)) else: config.set('orth_scale', 'int', int(self.default_orth_scale_factor * 100)) config.set('grid_scale', 'int', int(self.default_grid_scale_factor * 100)) with open(config_file, 'wb') as conf: config.write(conf) def closeEvent(self, e): if self.is_save_configure: self._save_configuration() e.accept() def _create_actions(self): """Create actions.""" # create a dictionary to store actions info self._actions = {} # Open template action self._actions['add_template'] = QAction(QIcon(os.path.join( self._icon_dir, 'open.png')), self.tr("&Open standard template"), self) self._actions['add_template'].setShortcut(self.tr("Ctrl+O")) self._actions['add_template'].triggered.connect(self._add_template) self._actions['add_template'].setEnabled(True) # Add a new volume image action self._actions['add_volume_image'] = QAction(QIcon(os.path.join( self._icon_dir, 'add.png')), self.tr("&Add volume file ... "), self) self._actions['add_volume_image'].triggered.connect(self._add_volume_image) self._actions['add_volume_image'].setEnabled(True) # Add a new surface image action self._actions['add_surface_image'] = QAction(QIcon(os.path.join( self._icon_dir, 'add.png')), self.tr("&Add surface file ... "), self) self._actions['add_surface_image'].triggered.connect(self._add_surface_image) self._actions['add_surface_image'].setEnabled(True) # Add a new image action self._actions['add_image'] = QAction(QIcon(os.path.join(self._icon_dir, 'add.png')), self.tr("&Add image ... "), self) self._actions['add_image'].setShortcut(self.tr("Ctrl+A")) self._actions['add_image'].triggered.connect(self._add_image) self._actions['add_image'].setEnabled(False) # Remove an image self._actions['remove_image'] = QAction(QIcon(os.path.join( self._icon_dir, 'remove.png')), self.tr("&Remove image"), self) self._actions['remove_image'].setShortcut(self.tr("Ctrl+R")) self._actions['remove_image'].triggered.connect(self._remove_image) self._actions['remove_image'].setEnabled(False) # New image self._actions['new_image'] = QAction(QIcon(os.path.join( self._icon_dir, 'create.png')), self.tr("&New image"), self) self._actions['new_image'].setShortcut(self.tr("Ctrl+N")) self._actions['new_image'].triggered.connect(self._new_image) self._actions['new_image'].setEnabled(False) # Duplicate image self._actions['duplicate_image'] = QAction(self.tr("Duplicate"), self) self._actions['duplicate_image'].triggered.connect( self._duplicate_image) self._actions['duplicate_image'].setEnabled(False) # Save image self._actions['save_image'] = QAction(QIcon(os.path.join( self._icon_dir, 'save.png')), self.tr("&Save image as..."), self) self._actions['save_image'].setShortcut(self.tr("Ctrl+S")) self._actions['save_image'].triggered.connect(self._save_image) self._actions['save_image'].setEnabled(False) ## Load Label Config #self._actions['ld_lbl'] = QAction('Load Label', self) #self._actions['ld_lbl'].triggered.connect(self._ld_lbl) #self._actions['ld_lbl'].setEnabled(False) ## Load Global Label Config #self._actions['ld_glbl'] = QAction('Load Global Label', self) #self._actions['ld_glbl'].triggered.connect(self._ld_glbl) #self._actions['ld_glbl'].setEnabled(False) # Close display self._actions['close'] = QAction(self.tr("Close tab"), self) self._actions['close'].setShortcut(self.tr("Ctrl+W")) self._actions['close'].triggered.connect(self._close_display) self._actions['close'].setEnabled(False) # Quit action self._actions['quit'] = QAction(QIcon(os.path.join( self._icon_dir, 'quit.png')), self.tr("&Quit"), self) self._actions['quit'].setShortcut(self.tr("Ctrl+Q")) self._actions['quit'].triggered.connect(self.close) # Grid view action self._actions['grid_view'] = QAction(QIcon(os.path.join( self._icon_dir, 'gridview.png')), self.tr("Lightbox"), self) self._actions['grid_view'].triggered.connect(self._grid_view) self._actions['grid_view'].setEnabled(False) # Orth view action self._actions['orth_view'] = QAction(QIcon(os.path.join( self._icon_dir, 'orthview.png')), self.tr("Orthographic"), self) self._actions['orth_view'].triggered.connect(self._orth_view) self._actions['orth_view'].setEnabled(False) # return original size self._actions['original_view'] = QAction(QIcon(os.path.join( self._icon_dir, 'original_size.png')), self.tr("Reset view"), self) self._actions['original_view'].triggered.connect(self._reset_view) self._actions['original_view'].setEnabled(False) # whether display the cross hover self._actions['cross_hover_view'] = QAction(QIcon(os.path.join( self._icon_dir, 'cross_hover_enable.png')), self.tr("Disable cross hover"), self) self._actions['cross_hover_view'].triggered.connect(self._display_cross_hover) self._actions['cross_hover_view'].setEnabled(False) # Binarization view action self._actions['binarization'] = QAction(QIcon(os.path.join( self._icon_dir, 'binarization.png')), self.tr("Binarization"), self) self._actions['binarization'].triggered.connect(self._binarization) self._actions['binarization'].setEnabled(False) # Intersection action self._actions['intersect'] = QAction(QIcon(os.path.join( self._icon_dir, 'intersect.png')), self.tr("Intersection"), self) self._actions['intersect'].triggered.connect(self._intersect) self._actions['intersect'].setEnabled(False) # Extract mean time course self._actions['meants'] = QAction(QIcon(os.path.join( self._icon_dir, 'voxel_curve.png')), self.tr("Extract Mean Time Course"), self) self._actions['meants'].triggered.connect(self._meants) self._actions['meants'].setEnabled(False) # Voxel Stats self._actions['voxelstats'] = QAction(self.tr("Voxel number stats"), self) self._actions['voxelstats'].triggered.connect(self._voxelstats) self._actions['voxelstats'].setEnabled(False) # Local Max action self._actions['localmax'] = QAction(QIcon(os.path.join( self._icon_dir, 'localmax.png')), self.tr("Local Max"), self) self._actions['localmax'].triggered.connect(self._local_max) self._actions['localmax'].setEnabled(False) # Inversion action self._actions['inverse'] = QAction(QIcon(os.path.join( self._icon_dir, 'inverse.png')), self.tr("Inversion"), self) self._actions['inverse'].triggered.connect(self._inverse) self._actions['inverse'].setEnabled(False) # Smoothing action self._actions['smoothing'] = QAction(QIcon(os.path.join( self._icon_dir, 'smoothing.png')), self.tr("Smoothing"), self) self._actions['smoothing'].triggered.connect(self._smooth) self._actions['smoothing'].setEnabled(False) # Concatenate overlays to one overlay self._actions['concatenate'] = QAction(self.tr('Concatenate'), self) self._actions['concatenate'].triggered.connect(self._concatenate) self._actions['concatenate'].setEnabled(False) # Calculate probability map action self._actions['probability_map'] = QAction(self.tr('ProbabilityMap'), self) self._actions['probability_map'].triggered.connect(self._prob_map) self._actions['probability_map'].setEnabled(False) # Region Growing action self._actions['region_grow'] = QAction(QIcon(os.path.join( self._icon_dir, 'grow.png')), self.tr("Region Growing"), self) self._actions['region_grow'].triggered.connect(self._region_grow) self._actions['region_grow'].setEnabled(False) # Lable Management action self._actions['label_management'] = QAction(self.tr("Label Management"), self) self._actions['label_management'].triggered.connect(self._label_manage) self._actions['label_management'].setEnabled(False) # Snapshot self._actions['snapshot'] = QAction(self.tr("Snapshot"), self) self._actions['snapshot'].triggered.connect(self._snapshot) self._actions['snapshot'].setEnabled(False) # Watershed action self._actions['watershed'] = QAction(QIcon(os.path.join( self._icon_dir, 'watershed.png')), self.tr("Watershed"), self) self._actions['watershed'].triggered.connect(self._watershed) self._actions['watershed'].setEnabled(False) # SLIC action self._actions['slic'] = QAction(QIcon(os.path.join( self._icon_dir, 'slic.png')), self.tr("SLIC"), self) self._actions['slic'].triggered.connect(self._slic) self._actions['slic'].setEnabled(False) # Cluster action self._actions['cluster'] = QAction(QIcon(os.path.join( self._icon_dir, 'cluster.png')), self.tr("Cluster"), self) self._actions['cluster'].triggered.connect(self._cluster) self._actions['cluster'].setEnabled(False) # Opening self._actions['opening'] = QAction(self.tr("Opening"), self) self._actions['opening'].triggered.connect(self._opening) self._actions['opening'].setEnabled(False) # Binary_erosion view action self._actions['binaryerosion'] = QAction(self.tr("Binary Erosion"), self) self._actions['binaryerosion'].triggered.connect(self._binaryerosion) self._actions['binaryerosion'].setEnabled(False) # Binary_dilation view action self._actions['binarydilation'] = QAction(self.tr("Binary Dilation"), self) self._actions['binarydilation'].triggered.connect(self._binarydilation) self._actions['binarydilation'].setEnabled(False) # grey_erosion view action self._actions['greyerosion'] = QAction(self.tr("Grey Erosion"), self) self._actions['greyerosion'].triggered.connect(self._greyerosion) self._actions['greyerosion'].setEnabled(False) # grey_dilation view action self._actions['greydilation'] = QAction(self.tr("Grey Dilation"), self) self._actions['greydilation'].triggered.connect(self._greydilation) self._actions['greydilation'].setEnabled(False) # About software self._actions['about_freeroi'] = QAction(self.tr("About FreeROI"), self) self._actions['about_freeroi'].triggered.connect(self._about_freeroi) # About Qt self._actions['about_qt'] = QAction(QIcon(os.path.join( self._icon_dir, 'qt.png')), self.tr("About Qt"), self) self._actions['about_qt'].triggered.connect(qApp.aboutQt) # Hand self._actions['hand'] = QAction(QIcon(os.path.join( self._icon_dir, 'hand.png')), self.tr("Hand"), self) self._actions['hand'].triggered.connect(self._hand_enable) self._actions['hand'].setCheckable(True) self._actions['hand'].setChecked(False) self._actions['hand'].setEnabled(False) # Cursor self._actions['cursor'] = QAction(QIcon(os.path.join( self._icon_dir, 'cursor.png')), self.tr("Cursor"), self) self._actions['cursor'].triggered.connect(self._cursor_enable) self._actions['cursor'].setCheckable(True) self._actions['cursor'].setChecked(True) self._actions['cursor'].setEnabled(True) # Edit self._actions['edit'] = QAction(QIcon(os.path.join( self._icon_dir, 'edit.png')), self.tr("Edit"), self) self._actions['edit'].triggered.connect(self._roidialog_enable) self._actions['edit'].setCheckable(True) self._actions['edit'].setChecked(False) # Undo self._actions['undo'] = QAction(QIcon(os.path.join( self._icon_dir, 'undo.png')), self.tr("Undo"), self) self._actions['undo'].triggered.connect(self._undo) # Redo self._actions['redo'] = QAction(QIcon(os.path.join( self._icon_dir, 'redo.png')), self.tr("Redo"), self) self._actions['redo'].triggered.connect(self._redo) # sphere and cube roi self._actions['regular_roi'] = QAction(QIcon(os.path.join( self._icon_dir, 'sphere_and_cube.png')), self.tr("Regular ROI"), self) self._actions['regular_roi'].triggered.connect(self._regular_roi) self._actions['regular_roi'].setEnabled(False) # sphere and cube roi from csv file self._actions['regular_roi_from_csv'] = QAction(QIcon(os.path.join( self._icon_dir, 'sphere_and_cube.png')), self.tr("Regular ROI From CSV File"), self) self._actions['regular_roi_from_csv'].triggered.connect(self._regular_roi_from_csv_file) self._actions['regular_roi_from_csv'].setEnabled(False) # ROI to Interface self._actions['r2i'] = QAction(QIcon(os.path.join( self._icon_dir, 'r2i.png')), self.tr("ROI2Interface"), self) self._actions['r2i'].triggered.connect(self._r2i) self._actions['r2i'].setEnabled(False) # Edge detection for ROI self._actions['edge_dete'] = QAction(QIcon(os.path.join( self._icon_dir, 'edge_detection.png')), self.tr("Edge Detection"), self) self._actions['edge_dete'].triggered.connect(self._label_edge_detection) self._actions['edge_dete'].setEnabled(False) # Atlas information self._actions['atlas'] = QAction(QIcon(os.path.join( self._icon_dir, 'atlas.png')), self.tr("Candidate Label"), self) self._actions['atlas'].triggered.connect(self._atlas_dialog) self._actions['atlas'].setEnabled(False) # ROI Merging self._actions['roi_merge'] = QAction(QIcon(os.path.join( self._icon_dir, 'merging.png')), self.tr("ROI Merging"), self) self._actions['roi_merge'].triggered.connect(self._roi_merge) self._actions['roi_merge'].setEnabled(False) # ROI scribing self._actions['scribing'] = QAction(self.tr("scribing"), self) self._actions['scribing'].triggered.connect(self._roi_scribing) self._actions['scribing'].setEnabled(False) # surface region grow self._actions['surf_region_grow'] = QAction(self.tr("surf_RG"), self) self._actions['surf_region_grow'].triggered.connect(self._surf_rg) self._actions['surf_region_grow'].setEnabled(False) def _surf_rg(self): new_dialog = SurfaceRGDialog(self.surface_model, self.surface_view, self) new_dialog.show() def _roi_scribing(self): new_dialog = ScribingDialog(self.surface_view, self) new_dialog.show() def _add_toolbar(self): """Add toolbar.""" # Initialize a spinbox for zoom-scale selection self._spinbox = QSpinBox() self._spinbox.setMaximum(500) self._spinbox.setMinimum(50) self._spinbox.setSuffix('%') self._spinbox.setSingleStep(10) self._spinbox.setValue(self.default_grid_scale_factor * 100) self._spinbox.valueChanged.connect(self._set_scale_factor) # Add a toolbar self._toolbar = self.addToolBar("Tools") #self._toolbar.setIconSize(QSize(38,38)) # Add file actions self._toolbar.addAction(self._actions['add_image']) self._toolbar.addAction(self._actions['remove_image']) self._toolbar.addAction(self._actions['new_image']) self._toolbar.addAction(self._actions['save_image']) # Add view actions self._toolbar.addSeparator() self._toolbar.addAction(self._actions['grid_view']) self._toolbar.addAction(self._actions['orth_view']) self._toolbar.addAction(self._actions['original_view']) self._toolbar.addAction(self._actions['cross_hover_view']) # Add cursor status self._toolbar.addSeparator() self._toolbar.addAction(self._actions['hand']) self._toolbar.addAction(self._actions['cursor']) self._toolbar.addAction(self._actions['edit']) # Add undo redo self._toolbar.addSeparator() self._toolbar.addAction(self._actions['undo']) self._toolbar.addAction(self._actions['redo']) self._toolbar.addSeparator() self._toolbar.addWidget(self._spinbox) def _set_scale_factor(self, value): """Set scale factor.""" value = float(value) / 100 self.volume_model.set_scale_factor(value, self.volume_view.display_type()) def _add_template(self): """Open a dialog window and select a template file.""" template_dir = os.path.join(self.label_path, 'standard', 'MNI152_T1_2mm_brain.nii.gz') template_name = QFileDialog.getOpenFileName( self, 'Open standard file', template_dir, 'Nifti files (*.nii.gz *.nii)') if not template_name == '': if sys.platform == 'win32': template_path = unicode(template_name).encode('gb2312') else: template_path = str(template_name) self._add_volume_img(template_path) def _add_image(self): if self.tabWidget.currentWidget() == self.list_view: self._add_volume_image() else: self._add_surface_image() def _add_volume_image(self): """Add new item.""" if self._temp_dir == None: temp_dir = QDir.currentPath() else: temp_dir = self._temp_dir file_name = QFileDialog.getOpenFileName(self, 'Add new volume file', temp_dir, "Nifti files (*.nii *.nii.gz)") if file_name != '': if sys.platform == 'win32': file_path = unicode(file_name).encode('gb2312') else: file_path = str(file_name) self._add_volume_img(file_path) def _add_surface_image(self): """Add new surface image.""" if self._temp_dir is None: temp_dir = QDir.currentPath() else: temp_dir = self._temp_dir file_name = QFileDialog.getOpenFileName(self, 'Add new surface file', temp_dir) if file_name != '': if sys.platform == 'win32': file_path = unicode(file_name).encode('gb2312') else: file_path = str(file_name) self._add_surface_img(file_path) def _duplicate_image(self): """Duplicate image.""" if self.tabWidget.currentWidget() is self.list_view: index = self.volume_model.currentIndex() dup_img = self.volume_model._data[index.row()].duplicate() self.volume_model.insertRow(0, dup_img) self.list_view.setCurrentIndex(self.volume_model.index(0)) # change button status self._actions['remove_image'].setEnabled(True) elif self.tabWidget.currentWidget() is self.surface_tree_view: index = self.surface_model.current_index() depth = self.surface_model.index_depth(index) if depth != 2: QMessageBox.warning(self, 'Warning!', 'Get overlay failed!\nYou may have not selected any overlay!', QMessageBox.Yes) return self.surface_model.add_item(index, source=self.surface_model.data(index, Qt.UserRole + 5).copy(), vmin=self.surface_model.data(index, Qt.UserRole), vmax=self.surface_model.data(index, Qt.UserRole + 1), colormap=self.surface_model.data(index, Qt.UserRole + 3), alpha=self.surface_model.data(index, Qt.UserRole + 2), visible=self.surface_model.data(index, Qt.UserRole + 8), islabel=self.surface_model.data(index, Qt.UserRole + 7), name=self.surface_model.data(index, Qt.DisplayRole)) def _add_volume_img(self, source, name=None, header=None, view_min=None, view_max=None, alpha=255, colormap='gray'): """ Add image.""" # If model is NULL, then re-initialize it. if not self.volume_model: self._vol_label_config_center = self._init_label_config_center() self._vol_label_config_center.size_edit.setRange(1, 10) self._vol_label_config_center.size_edit.setValue(4) self.volume_model = VolumeListModel([], self._vol_label_config_center) self.volume_model.set_scale_factor(self.default_grid_scale_factor, 'grid') self.volume_model.set_scale_factor(self.default_orth_scale_factor, 'orth') self._init_vol_roidialog(self.volume_model) # Save previous opened directory (except `standard` directory) file_path = source if sys.platform == 'win32': temp_dir = os.path.dirname(unicode(file_path, 'gb2312')) if not os.stat(temp_dir) == os.stat(os.path.join(self.label_path, 'standard')): self._temp_dir = temp_dir else: temp_dir = os.path.dirname(file_path) if not os.path.samefile(temp_dir, os.path.join(self.label_path, 'standard')): self._temp_dir = temp_dir if self.volume_model.addItem(file_path, None, name, header, view_min, view_max, alpha, colormap): # If only one data in VolumeList, then initialize views. if self.volume_model.rowCount() == 1: # initialize views self.list_view = LayerView(self._vol_label_config_center) self.list_view.setModel(self.volume_model) self.volume_view = GridView(self.volume_model, self.painter_status) # connect signals with slots self.list_view.current_changed.connect(self._update_undo) self.list_view.current_changed.connect(self._update_redo) self.list_view._list_view.selectionModel().currentChanged.connect(self.vol_roidialog.clear_rois) self.volume_model.rowsInserted.connect(self._update_remove_image) self.volume_model.undo_stack_changed.connect(self._update_undo) self.volume_model.redo_stack_changed.connect(self._update_redo) # set current volume index self.list_view.setCurrentIndex(self.volume_model.index(0)) # set crosshair as the center of the data self.volume_model.set_cross_pos([self.volume_model.getY()/2, self.volume_model.getX()/2, self.volume_model.getZ()/2]) # Enable cursor tracking # self.list_view._list_view.selectionModel().currentChanged.connect( # self._switch_cursor_status) if not self.tabWidget: self._init_tab_widget() if self.tabWidget.count() == 0: self.tabWidget.addTab(self.list_view, "Volume") self._init_vol_actions() elif self.tabWidget.count() == 1 and self.tabWidget.currentWidget() != self.list_view: self.tabWidget.addTab(self.list_view, "Volume") self.tabWidget.setCurrentIndex(1) self._init_vol_actions() elif self.tabWidget.count() == 2 and self.tabWidget.currentWidget() != self.list_view: self.tabWidget.setCurrentIndex(self.tabWidget.count() - self.tabWidget.currentIndex() - 1) if self.centralWidget().layout().indexOf(self.volume_view) == -1: # Could not find the self.volume_view if self.centralWidget().layout().indexOf(self.surface_view) != -1: self.centralWidget().layout().removeWidget(self.surface_view) self.surface_view.setParent(None) self.centralWidget().layout().addWidget(self.volume_view) if self.volume_model.rowCount() > 1: self._actions['remove_image'].setEnabled(True) # set current volume index self.list_view.setCurrentIndex(self.volume_model.index(0)) self.is_save_configure = True else: ret = QMessageBox.question(self, 'FreeROI', 'Cannot load ' + file_path + ': due to mismatch data size.\nNeed registration?', QMessageBox.Cancel, QMessageBox.Yes) if ret == QMessageBox.Yes: register_volume_dialog = RegisterVolumeDialog(self.volume_model, file_path) register_volume_dialog.exec_() def _add_surface_img(self, source, index=None, offset=None, vmin=None, vmax=None, colormap='jet', alpha=1.0, visible=True, islabel=False): """ Add surface image.""" # If model is NULL, then re-initialize it. if not self.surface_model: self._surf_label_config_center = self._init_label_config_center() self._surf_label_config_center.size_edit.setRange(0, 10) self._surf_label_config_center.size_edit.setValue(1) self.surface_model = TreeModel([]) self.surface_tree_view = SurfaceTreeView(self.surface_model, self._surf_label_config_center) self.surface_tree_view_control = self.surface_tree_view.get_treeview() self._init_surf_roidialog(self.surface_model) if index is None: index = self.surface_tree_view_control.currentIndex() # Save previous opened directory (except `standard` directory) file_path = source if sys.platform == 'win32': temp_dir, basename = os.path.split(unicode(file_path, 'gb2312')) if not os.stat(temp_dir) == os.stat(os.path.join(self.label_path, 'standard')): self._temp_dir = temp_dir else: temp_dir, basename = os.path.split(file_path) if not os.path.samefile(temp_dir, os.path.join(self.label_path, 'standard')): self._temp_dir = temp_dir ends = basename.split('.')[-1] if len(self.surface_model.get_data()) == 0 and ends not in ('pial', 'white', 'inflated', 'gii'): QMessageBox.warning(self, 'Warning', 'You must choose the brain surface file first!', QMessageBox.Yes) elif self.surface_model.add_item(index, file_path, vmin=vmin, vmax=vmax, alpha=alpha, colormap=colormap, visible=visible, islabel=islabel): # Initial the tabwidget. if not self.tabWidget: self._init_tab_widget() if self.tabWidget.count() == 0: self.tabWidget.addTab(self.surface_tree_view, "Surface") self._init_surf_actions() elif self.tabWidget.count() == 1 and self.tabWidget.currentWidget() != self.surface_tree_view: self.tabWidget.addTab(self.surface_tree_view, "Surface") self.tabWidget.setCurrentIndex(1) self._init_surf_actions() elif self.tabWidget.count() == 2 and self.tabWidget.currentWidget() != self.surface_tree_view: self.tabWidget.setCurrentIndex(self.tabWidget.count() - self.tabWidget.currentIndex() - 1) # Initial surface_view if not self.surface_view: self.surface_view = SurfaceView() self.surface_view.set_model(self.surface_model) self.surface_view.set_painter_status(self.painter_status) if self.centralWidget().layout().indexOf(self.surface_view) == -1: # Could not find the self.surface_view if self.centralWidget().layout().indexOf(self.volume_view) != -1: self.centralWidget().layout().removeWidget(self.volume_view) self.volume_view.setParent(None) self.centralWidget().layout().addWidget(self.surface_view) self._actions['remove_image'].setEnabled(True) else: QMessageBox.question(self, 'FreeROI', 'Cannot load ' + file_path + ' !', QMessageBox.Yes) def _save_actions_status(self, actions_status): actions_status['grid_view'] = self._actions['grid_view'].isEnabled() actions_status['orth_view'] = self._actions['orth_view'].isEnabled() actions_status['hand'] = self._actions['hand'].isEnabled() actions_status['snapshot'] = self._actions['snapshot'].isEnabled() actions_status['orth_view'] = self._actions['orth_view'].isEnabled() actions_status['cross_hover_view'] = self._actions['cross_hover_view'].isEnabled() actions_status['original_view'] = self._actions['original_view'].isEnabled() actions_status['remove_image'] = self._actions['remove_image'].isEnabled() actions_status['undo'] = self._actions['undo'].isEnabled() actions_status['redo'] = self._actions['redo'].isEnabled() # actions_status['functional_module_set_enabled'] = self._actions['binarization'].isEnabled() actions_status['atlas'] = self._actions['atlas'].isEnabled() def _disable_vol_actions(self): # set enabled status volume-specific actions self._actions['grid_view'].setEnabled(False) self._actions['orth_view'].setEnabled(False) self._actions['hand'].setEnabled(False) self._actions['snapshot'].setEnabled(False) self._actions['orth_view'].setEnabled(False) self._actions['cross_hover_view'].setEnabled(False) self._actions['original_view'].setEnabled(False) self._actions['undo'].setEnabled(False) self._actions['redo'].setEnabled(False) self._vol_func_module_set_enabled(False) self._spinbox.setEnabled(False) def _disable_surf_actions(self): # Disable surface-specific actions for volume self._surf_func_module_set_enabled(False) def _restore_actions_status(self, actions_status): # Restore all toolbar controls if actions_status: self._actions['grid_view'].setEnabled(actions_status['grid_view']) self._actions['hand'].setEnabled(actions_status['hand']) self._actions['snapshot'].setEnabled(actions_status['snapshot']) self._actions['orth_view'].setEnabled(actions_status['orth_view']) self._actions['cross_hover_view'].setEnabled(actions_status['cross_hover_view']) self._actions['original_view'].setEnabled(actions_status['original_view']) self._actions['remove_image'].setEnabled(actions_status['remove_image']) self._actions['undo'].setEnabled(actions_status['undo']) self._actions['redo'].setEnabled(actions_status['redo']) if actions_status == self.volume_actions_status: self._vol_func_module_set_enabled(True) self._spinbox.setEnabled(True) if not self.volume_model.is_mni_space(): self._actions['atlas'].setEnabled(actions_status['atlas']) else: self._surf_func_module_set_enabled(True) def _tabwidget_index_changed(self): if self.tabWidget.count() == 2: if self.tabWidget.currentWidget() == self.list_view: self.centralWidget().layout().removeWidget(self.surface_view) self.surface_view.setParent(None) self.centralWidget().layout().addWidget(self.volume_view) self._save_actions_status(self.surface_actions_status) self._disable_surf_actions() self._restore_actions_status(self.volume_actions_status) else: self.centralWidget().layout().removeWidget(self.volume_view) self.volume_view.setParent(None) self.centralWidget().layout().addWidget(self.surface_view) self._save_actions_status(self.volume_actions_status) self._disable_vol_actions() self._restore_actions_status(self.surface_actions_status) self._roidialog_disable() def _new_image(self): """Create new image.""" if self.tabWidget.currentWidget() == self.list_view: self.new_volume_image() else: self.new_surface_image() def _update_remove_image(self): """Update the display after removing an image.""" if self.volume_model.rowCount() == 1: self._actions['remove_image'].setEnabled(False) else: self._actions['remove_image'].setEnabled(True) def new_volume_image(self, data=None, name=None, colormap=None): """Create a new volume for brain parcellation.""" if colormap is None: colormap = self._vol_label_config_center.get_first_label_config() self.volume_model.new_image(data, name, None, colormap) self.list_view.setCurrentIndex(self.volume_model.index(0)) # change button status self._actions['remove_image'].setEnabled(True) def new_surface_image(self): self.surface_model.add_item(self.surface_tree_view_control.currentIndex()) def new_image_action(self): """Change the related status of other actions after creating an image.""" self._actions['remove_image'].setEnabled(True) def _remove_image(self): """Remove current image.""" if self.tabWidget.currentWidget() == self.list_view: self._remove_volume_image() else: self._remove_surface_image() def _remove_volume_image(self): row = self.list_view.currentRow() self.volume_model.delItem(row) if self.volume_model.rowCount() == 1: self._actions['remove_image'].setEnabled(False) def _remove_surface_image(self): self.surface_model.del_item(self.surface_tree_view_control.currentIndex()) if self.surface_model.rowCount(QModelIndex()) == 0: self._actions['remove_image'].setEnabled(False) def _save_image(self): """Save overlay as a file.""" if self._save_dir is not None: temp_dir = self._save_dir else: temp_dir = str(QDir.currentPath()) if self._temp_dir is None else self._temp_dir if self.tabWidget.currentWidget() == self.list_view: index = self.volume_model.currentIndex() file_types = "Compressed NIFTI file(*.nii.gz);;NIFTI file(*.nii)" file_path = os.path.join(temp_dir, str(self.volume_model.data(index, Qt.DisplayRole))) overlay = self.volume_model._data[index.row()] else: index = self.surface_tree_view_control.currentIndex() if not index.isValid(): QMessageBox.warning(self, 'Error', 'You have not specified a overlay!', QMessageBox.Yes) return else: parent = index.parent() if not parent.isValid(): QMessageBox.warning(self, 'Error', 'You have not specified a overlay!', QMessageBox.Yes) return file_types = "Compressed NIFTI file(*.nii.gz);;NIFTI file(*.nii);;FS label(*.label)" file_path = os.path.join(temp_dir, str(self.surface_model.data(index, Qt.DisplayRole))) overlay = index.internalPointer() path, filter = QFileDialog.getSaveFileNameAndFilter(self, 'Save image as...', file_path, file_types) if str(path) != '': if sys.platform == 'win32': path = unicode(path).encode('gb2312') self._temp_dir = os.path.dirname(unicode(path, 'gb2312')) else: path = str(path) self._temp_dir = os.path.dirname(path) if filter == 'FS label(*.label)': index = self.surface_model.get_surface_index() # FIXME coordinates in freesurfer-style label file should come from '.white' file coords = self.surface_model.data(index, Qt.UserRole + 6).coords overlay.save2label(path, hemi_coords=coords) else: overlay.save2nifti(path) def _close_display(self): """Close current display.""" old_index = self.tabWidget.currentIndex() if self.tabWidget.count() == 1: self.setCentralWidget(QWidget()) self.removeToolBar(self._toolbar) if self.tabWidget.currentWidget() == self.list_view: self._set_scale_factor(self.default_grid_scale_factor) self.volume_model = None self.volume_view = None self.volume_actions_status.clear() else: self.surface_model = None self.surface_view = None self.surface_actions_status.clear() self._actions['add_image'].setEnabled(False) self._actions['remove_image'].setEnabled(False) self._actions['new_image'].setEnabled(False) self._actions['save_image'].setEnabled(False) #self._actions['ld_glbl'].setEnabled(False) #self._actions['ld_lbl'].setEnabled(False) self._actions['close'].setEnabled(False) self._disable_vol_actions() self._disable_surf_actions() elif self.tabWidget.count() == 2 and self.tabWidget.currentWidget() == self.list_view: self.tabWidget.setCurrentIndex(self.tabWidget.count() - old_index - 1) self.tabWidget.removeTab(old_index) self._set_scale_factor(self.default_grid_scale_factor) self.volume_model = None self.volume_view = None self.volume_actions_status.clear() elif self.tabWidget.count() == 2 and self.tabWidget.currentWidget() == self.surface_tree_view: self.tabWidget.setCurrentIndex(self.tabWidget.count() - old_index - 1) self.tabWidget.removeTab(old_index) self.surface_model = None self.surface_view = None self.surface_actions_status.clear() def _about_freeroi(self): """ About software.""" QMessageBox.about(self, self.tr("About FreeROI"), self.tr("<p><b>FreeROI</b> is a versatile image " "processing software developed for " "neuroimaging data.</p>" "<p>Its goal is to provide a user-friendly " "interface for neuroimaging researchers " "to visualize and analyze their data, " "especially in defining region of interest " "(ROI) for ROI analysis.</p>" "<p>Version: " + __version__ + "</p>" "<p>Written by: Lijie Huang, Zetian Yang, " "Guangfu Zhou, Zhaoguo Liu, Xiaobin Dang, " "Xiangzhen Kong, Xu Wang, and Zonglei Zhen." "</p>" "<p><b>FreeROI</b> is under Revised BSD " "License.</p>" "<p>Copyright(c) 2012-2015 " "Neuroinformatic Team in LiuLab " "from Beijing Normal University</p>" "<p></p>" "<p>Please join and report bugs to:</p>" "<p><b>nitk-user@googlegroups.com</b></p>")) def _create_menus(self): """Create menus.""" self.file_menu = self.menuBar().addMenu(self.tr("File")) self.file_menu.addAction(self._actions['add_volume_image']) self.file_menu.addAction(self._actions['add_template']) self.file_menu.addSeparator() self.file_menu.addAction(self._actions['add_surface_image']) self.file_menu.addSeparator() self.file_menu.addAction(self._actions['new_image']) self.file_menu.addAction(self._actions['remove_image']) self.file_menu.addAction(self._actions['duplicate_image']) self.file_menu.addAction(self._actions['save_image']) #self.file_menu.addAction(self._actions['ld_lbl']) #self.file_menu.addAction(self._actions['ld_glbl']) self.file_menu.addSeparator() self.file_menu.addAction(self._actions['close']) self.file_menu.addAction(self._actions['quit']) #self.volume_menu = self.menuBar().addMenu(self.tr("Volume")) #self.volume_menu.addAction(self._actions['new_image']) #self.volume_menu.addAction(self._actions['remove_image']) self.view_menu = self.menuBar().addMenu(self.tr("View")) self.view_menu.addAction(self._actions['grid_view']) self.view_menu.addAction(self._actions['orth_view']) self.view_menu.addAction(self._actions['original_view']) self.view_menu.addAction(self._actions['cross_hover_view']) self.tool_menu = self.menuBar().addMenu(self.tr("Tools")) # Basic tools basic_tools = self.tool_menu.addMenu(self.tr("Basic Tools")) basic_tools.addAction(self._actions['binarization']) basic_tools.addAction(self._actions['intersect']) basic_tools.addAction(self._actions['localmax']) basic_tools.addAction(self._actions['inverse']) basic_tools.addAction(self._actions['smoothing']) basic_tools.addAction(self._actions['concatenate']) basic_tools.addAction(self._actions['probability_map']) basic_tools.addAction(self._actions['meants']) basic_tools.addAction(self._actions['voxelstats']) # Segment tools segment_tools = self.tool_menu.addMenu(self.tr("Segmentation")) segment_tools.addAction(self._actions['region_grow']) segment_tools.addAction(self._actions['watershed']) segment_tools.addAction(self._actions['slic']) segment_tools.addAction(self._actions['cluster']) segment_tools.addAction(self._actions['surf_region_grow']) # ROI tools roi_tools = self.tool_menu.addMenu(self.tr("ROI Tools")) roi_tools.addAction(self._actions['edge_dete']) roi_tools.addAction(self._actions['roi_merge']) roi_tools.addAction(self._actions['regular_roi']) roi_tools.addAction(self._actions['regular_roi_from_csv']) roi_tools.addAction(self._actions['r2i']) roi_tools.addAction(self._actions['scribing']) # Morphological tools morphological_tools = self.tool_menu.addMenu( self.tr("Morphological Processing")) morphological_tools.addAction(self._actions['opening']) morphological_tools.addAction(self._actions['binarydilation']) morphological_tools.addAction(self._actions['binaryerosion']) morphological_tools.addAction(self._actions['greydilation']) morphological_tools.addAction(self._actions['greyerosion']) # label management self.tool_menu.addAction(self._actions['atlas']) self.tool_menu.addAction(self._actions['label_management']) self.tool_menu.addAction(self._actions['snapshot']) self.help_menu = self.menuBar().addMenu(self.tr("Help")) self.help_menu.addAction(self._actions['about_freeroi']) self.help_menu.addAction(self._actions['about_qt']) def _cursor_enable(self): """Cursor enabled.""" if self._actions['cursor'].isChecked(): self._actions['cursor'].setChecked(True) if self.tabWidget.currentWidget() is self.list_view: if isinstance(self.volume_view, OrthView): self._actions['hand'].setChecked(False) self.volume_view.set_cursor(Qt.ArrowCursor) self.volume_view.set_label_mouse_tracking(True) self._roidialog_disable() self.painter_status.set_draw_settings(ViewSettings()) else: self._actions['cursor'].setChecked(True) def _voxel_edit_enable(self): """Voxel brush enabled.""" self._vol_label_config_center.set_is_roi_edit(False) self.painter_status.set_draw_settings(self._vol_label_config_center) self.volume_view.set_cursor(Qt.CrossCursor) self.volume_view.set_label_mouse_tracking(False) def _vertex_edit_enable(self): """Vertex brush enabled.""" self._surf_label_config_center.set_is_roi_edit(False) self.painter_status.set_draw_settings(self._surf_label_config_center) def _vol_roi_edit_enable(self): """Volume ROI brush enabled.""" self._vol_label_config_center.set_is_roi_edit(True) self.painter_status.set_draw_settings(self._vol_label_config_center) self.volume_view.set_cursor(Qt.CrossCursor) self.volume_view.set_label_mouse_tracking(False) def _surf_roi_edit_enable(self): """Surface ROI brush enabled.""" self._surf_label_config_center.set_is_roi_edit(True) self.painter_status.set_draw_settings(self._surf_label_config_center) def _vol_roi_batch_enable(self): """Volume ROI batch enabled.""" self.volume_view.set_label_mouse_tracking(False) self._vol_label_config_center.set_is_roi_edit(False) self.painter_status.set_draw_settings(self.vol_roidialog) def _surf_roi_batch_enable(self): """Surface ROI batch enabled.""" self._surf_label_config_center.set_is_roi_edit(False) self.painter_status.set_draw_settings(self.surf_roidialog) def _roidialog_enable(self): """ROI dialog enabled.""" if self._actions['edit'].isChecked(): self._actions['cursor'].setChecked(False) self._actions['edit'].setChecked(True) if self.tabWidget.currentWidget() is self.list_view: if isinstance(self.volume_view, OrthView): self._actions['hand'].setChecked(False) self.vol_roidialog.show_dialog() elif self.tabWidget.currentWidget() is self.surface_tree_view: self.surf_roidialog.show_dialog() else: self._actions['edit'].setChecked(True) def _roidialog_disable(self): """Disable the roi dialog.""" if hasattr(self, "vol_roidialog"): if self.vol_roidialog.isVisible(): self.vol_roidialog.hide_dialog() if hasattr(self, "surf_roidialog"): if self.surf_roidialog.isVisible(): self.surf_roidialog.hide_dialog() self._actions['edit'].setChecked(False) def _atlas_dialog(self): """Atlas information dialog.""" if 'atlasdialog' in self.__dict__: self.atlasdialog.show() else: self.atlasdialog = AtlasDialog(self.volume_model, self) self.atlasdialog.show() def _hand_enable(self): """Hand enabled.""" if self._actions['hand'].isChecked(): self._actions['cursor'].setChecked(False) self._actions['hand'].setChecked(True) self._roidialog_disable() self.painter_status.set_draw_settings(MoveSettings()) self.volume_view.set_cursor(Qt.OpenHandCursor) self.volume_view.set_label_mouse_tracking(True) else: self._actions['hand'].setChecked(True) def _switch_cursor_status(self): """Change the cursor status.""" self._actions['cursor'].setChecked(True) self._cursor_enable() def _update_undo(self): """Update the undo status.""" if self.volume_model.current_undo_available(): self._actions['undo'].setEnabled(True) else: self._actions['undo'].setEnabled(False) def _update_redo(self): """Update the redo status.""" if self.volume_model.current_redo_available(): self._actions['redo'].setEnabled(True) else: self._actions['redo'].setEnabled(False) def _init_vol_roidialog(self, model): """Initialize volume ROI Dialog.""" self.vol_roidialog = VolROIDialog(model, self._vol_label_config_center, self) self.vol_roidialog.vx_edit_enabled.connect(self._voxel_edit_enable) self.vol_roidialog.roi_edit_enabled.connect(self._vol_roi_edit_enable) self.vol_roidialog.roi_batch_enabled.connect(self._vol_roi_batch_enable) def _init_surf_roidialog(self, model): """Initialize Surface ROI Dialog.""" self.surf_roidialog = SurfROIDialog(model, self._surf_label_config_center, self) self.surf_roidialog.vx_edit_enabled.connect(self._vertex_edit_enable) self.surf_roidialog.roi_edit_enabled.connect(self._surf_roi_edit_enable) self.surf_roidialog.roi_batch_enabled.connect(self._surf_roi_batch_enable) def _init_label_config_center(self): """Initialize LabelConfigCenter.""" lbl_path = os.path.join(self.label_config_dir, '*.' + self.label_config_suffix) label_configs = glob.glob(lbl_path) self.label_configs = map(LabelConfig, label_configs) self._list_view_model = QStandardItemModel() # _list_view_model.appendRow(QStandardItem("None")) for x in self.label_configs: self._list_view_model.appendRow(QStandardItem(x.get_name())) self._label_models = [] for item in self.label_configs: model = QStandardItemModel() indexs = sorted(item.get_index_list()) for index in indexs: text_index_icon_item = QStandardItem(gen_label_color(item.get_label_color(item.get_index_label(index))), str(index) + ' ' + item.get_index_label(index)) model.appendRow(text_index_icon_item) self._label_models.append(model) return LabelConfigCenter(self.label_configs, self._list_view_model, self._label_models) def _get_label_config(self, file_path): """Get label config file.""" # Get label config file dir = os.path.dirname(file_path) file = os.path.basename(file_path) split_list = file.split('.') nii_index = split_list.index('nii') file = ''.join(split_list[:nii_index]) config_file = os.path.join(file, 'lbl') if os.path.isfile(config_file): label_config = LabelConfig(config_file, False) else: label_config = self.label_config return label_config def _undo(self): """The undo action.""" self.volume_model.undo_current_image() def _redo(self): """The redo action.""" self.volume_model.redo_current_image() def _regular_roi(self): """Generate regular(cube, sphere, etc.) roi dialog.""" regular_roi_dialog = RegularROIDialog(self.volume_model) regular_roi_dialog.exec_() def _regular_roi_from_csv_file(self): """Generate regular(cube, sphere, etc.) roi from csv file.""" regular_roi_from_csv_file = RegularROIFromCSVFileDialog(self.volume_model) regular_roi_from_csv_file.exec_() def _label_edge_detection(self): """edge detection for labels""" if self.tabWidget.currentWidget() is self.list_view: # get information from the model index = self.volume_model.currentIndex() data = self.volume_model.data(index, Qt.UserRole + 6) name = self.volume_model.data(index, Qt.DisplayRole) new_name = "edge_" + name # detect edges new_data = vol_label_edge_detection(data) # save result as a new overlay self.volume_model.addItem(new_data, None, new_name, self.volume_model.data(index, Qt.UserRole + 11), None, None, 255, 'green') elif self.tabWidget.currentWidget() is self.surface_tree_view: # get information from the model index = self.surface_model.current_index() depth = self.surface_model.index_depth(index) if depth != 2: QMessageBox.warning(self, 'Warning!', 'Get overlay failed!\nYou may have not selected any overlay!', QMessageBox.Yes) return if not self.surface_model.data(index, Qt.UserRole + 7): QMessageBox.warning(self, 'Warning!', "Current overlay isn't for ROIs.\nThis tool should be used for ROIs", QMessageBox.Yes) return data = self.surface_model.data(index, Qt.UserRole + 10) name = self.surface_model.data(index, Qt.DisplayRole) new_name = "edge_" + name # detect the edges new_data = surf_label_edge_detection(data, self.surface_model.data(index.parent(), Qt.UserRole + 6).faces) # save result as a new overlay self.surface_model.add_item(index, source=new_data.astype(int), colormap=self.surface_model.data(index, Qt.UserRole + 3), islabel=True, name=new_name) else: return def _roi_merge(self): """ROI merge dialog.""" new_dialog = ROIMergeDialog(self.volume_model) new_dialog.exec_() def _r2i(self): """ROI to gwmi dialog.""" new_dialog = Roi2gwmiDialog(self.volume_model) new_dialog.exec_() def _opening(self): """Opening Dialog which using the opening algorithm to process the image.""" new_dialog = OpenDialog(self.volume_model) new_dialog.exec_() def _voxelstats(self): """Voxel statistical analysis dialog.""" new_dialog = VoxelStatsDialog(self.volume_model, self) new_dialog.show() def _label_manage(self): """Label management dialog.""" self.label_manage_dialog = LabelManageDialog(self.label_configs, self._list_view_model, self._label_models, self.label_config_dir, self.label_config_suffix, self) self.label_manage_dialog.exec_() def _ld_lbl(self): """Local label config file.""" file_name = QFileDialog.getOpenFileName(self, 'Load Label File', QDir.currentPath(), "Label files (*.lbl)") if file_name: label_config = LabelConfig(str(file_name), False) self.volume_model.set_cur_label(label_config) def _ld_glbl(self): """Local global label config file.""" file_name = QFileDialog.getOpenFileName(self, 'Load Label File', QDir.currentPath(), "Label files (*.lbl)") if file_name: label_config = LabelConfig(str(file_name), True) self.volume_model.set_global_label(label_config) def _grid_view(self): """Grid view option.""" self._actions['grid_view'].setEnabled(False) self._actions['orth_view'].setEnabled(True) self._actions['hand'].setEnabled(False) self._actions['snapshot'].setEnabled(False) self._actions['cursor'].trigger() self.centralWidget().layout().removeWidget(self.volume_view) self.volume_view.set_display_type('grid') self.volume_model.scale_changed.disconnect() self.volume_model.repaint_slices.disconnect() self.volume_model.cross_pos_changed.disconnect(self.volume_view.update_cross_pos) self.volume_view.deleteLater() self._spinbox.setValue(100 * self.volume_model.get_scale_factor('grid')) self.volume_view = GridView(self.volume_model, self.painter_status, self._gridview_vertical_scrollbar_position) self.centralWidget().layout().addWidget(self.volume_view) def _orth_view(self): """Orth view option.""" self._actions['orth_view'].setEnabled(False) self._actions['grid_view'].setEnabled(True) self._actions['snapshot'].setEnabled(True) self._actions['hand'].setEnabled(True) self._actions['cursor'].trigger() self._gridview_vertical_scrollbar_position = \ self.volume_view.get_vertical_srollbar_position() self.centralWidget().layout().removeWidget(self.volume_view) self.volume_view.set_display_type('orth') self.volume_model.scale_changed.disconnect() self.volume_model.repaint_slices.disconnect() self.volume_model.cross_pos_changed.disconnect(self.volume_view.update_cross_pos) self.volume_view.deleteLater() self._spinbox.setValue(100 * self.volume_model.get_scale_factor('orth')) self.volume_view = OrthView(self.volume_model, self.painter_status) self.centralWidget().layout().addWidget(self.volume_view) def _display_cross_hover(self): """Display the cross hover on the image.""" if self.volume_model._display_cross: self.volume_model.set_cross_status(False) self._actions['cross_hover_view'].setText('Enable cross hover') self._actions['cross_hover_view'].setIcon(QIcon(os.path.join(self._icon_dir,'cross_hover_disable.png'))) else: self.volume_model.set_cross_status(True) self._actions['cross_hover_view'].setText('Disable cross hover') self._actions['cross_hover_view'].setIcon(QIcon(os.path.join(self._icon_dir,'cross_hover_enable.png'))) def _reset_view(self): """Reset view parameters.""" if self.volume_view.display_type() == 'orth': if not self.volume_model.get_scale_factor('orth') == \ self.default_orth_scale_factor: self._spinbox.setValue(100 * self.default_orth_scale_factor) self.volume_view.reset_view() elif self.volume_view.display_type() == 'grid': if not self.volume_model.get_scale_factor('grid') == \ self.default_grid_scale_factor: self._spinbox.setValue(100 * self.default_grid_scale_factor) def _binarization(self): """Image binarization dialog.""" if self.tabWidget.currentWidget() is self.list_view: binarization_dialog = VolBinarizationDialog(self.volume_model) elif self.tabWidget.currentWidget() is self.surface_tree_view: binarization_dialog = SurfBinarizationDialog(self.surface_model) else: return binarization_dialog.exec_() def _binaryerosion(self): """Image binary erosion dialog.""" if self.tabWidget.currentWidget() is self.list_view: binaryerosion_dialog = VolBinErosionDialog(self.volume_model) elif self.tabWidget.currentWidget() is self.surface_tree_view: binaryerosion_dialog = SurfBinErosionDialog(self.surface_model) else: return binaryerosion_dialog.exec_() def _binarydilation(self): """Image binarydilation dialog.""" if self.tabWidget.currentWidget() is self.list_view: binarydilation_dialog = VolBinDilationDialog(self.volume_model) elif self.tabWidget.currentWidget() is self.surface_tree_view: binarydilation_dialog = SurfBinDilationDialog(self.surface_model) else: return binarydilation_dialog.exec_() def _greyerosion(self): """Image greyerosion dialog.""" greyerosiondialog = GreyerosionDialog(self.volume_model) greyerosiondialog.exec_() def _greydilation(self): """Image greydilation dialog.""" greydilation_dialog = GreydilationDialog(self.volume_model) greydilation_dialog.exec_() def _intersect(self): """Image intersect dialog.""" if self.tabWidget.currentWidget() is self.list_view: intersect_dialog = VolIntersectDialog(self.volume_model) elif self.tabWidget.currentWidget() is self.surface_tree_view: intersect_dialog = SurfIntersectDialog(self.surface_model) else: return intersect_dialog.exec_() def _meants(self): """Image meants dialog.""" new_dialog = MeanTSDialog(self.volume_model) new_dialog.exec_() def _local_max(self): """Compute image local max value dialog.""" new_dialog = LocalMaxDialog(self.volume_model, self) new_dialog.exec_() def _inverse(self): """Inverse the given image.""" if self.tabWidget.currentWidget() is self.list_view: index = self.volume_model.currentIndex() data = self.volume_model.data(index, Qt.UserRole + 6) name = self.volume_model.data(index, Qt.DisplayRole) # inverse process new_data = inverse_transformation(data) new_name = 'inv_' + name # save result as a new image self.volume_model.addItem(new_data, None, new_name, self.volume_model.data(index, Qt.UserRole + 11)) elif self.tabWidget.currentWidget() is self.surface_tree_view: index = self.surface_model.current_index() depth = self.surface_model.index_depth(index) if depth != 2: QMessageBox.warning(self, 'Warning!', 'Get overlay failed!\nYou may have not selected any overlay!', QMessageBox.Yes) return data = self.surface_model.data(index, Qt.UserRole + 10) name = self.surface_model.data(index, Qt.DisplayRole) new_data = inverse_transformation(data) new_name = "inv_" + name # save result as a new overlay self.surface_model.add_item(index, source=new_data, name=new_name) else: return def _smooth(self): """Image smooth dialog.""" new_dialog = SmoothingDialog(self.volume_model) new_dialog.exec_() def _prob_map(self): """Calculate probability map""" dialog = SurfProbMapDialog(self.surface_model) dialog.exec_() def _concatenate(self): dialog = SurfConcatenateDialog(self.surface_model) dialog.exec_() def _region_grow(self): """Image region grow dialog.""" # new_dialog = GrowDialog(self.volume_model, self) new_dialog = VolumeRGDialog(self.volume_model) new_dialog.exec_() def _watershed(self): """Image watershed dialog.""" new_dialog = WatershedDialog(self.volume_model, self) new_dialog.exec_() def _slic(self): """Image supervoxel segmentation dialog.""" new_dialog = SLICDialog(self.volume_model, self) new_dialog.exec_() def _cluster(self): """Image cluster dialog.""" if self.tabWidget.currentWidget() is self.list_view: cluster_dialog = VolClusterDialog(self.volume_model) elif self.tabWidget.currentWidget() is self.surface_tree_view: cluster_dialog = SurfClusterDialog(self.surface_model) else: return cluster_dialog.exec_() def _vol_func_module_set_enabled(self, status): """ set enabled status for actions of volume functional module. """ self._actions['meants'].setEnabled(status) self._actions['voxelstats'].setEnabled(status) self._actions['localmax'].setEnabled(status) self._actions['smoothing'].setEnabled(status) self._actions['atlas'].setEnabled(status) self._actions['region_grow'].setEnabled(status) self._actions['watershed'].setEnabled(status) self._actions['slic'].setEnabled(status) self._actions['opening'].setEnabled(status) self._actions['greydilation'].setEnabled(status) self._actions['greyerosion'].setEnabled(status) self._actions['regular_roi'].setEnabled(status) self._actions['regular_roi_from_csv'].setEnabled(status) self._actions['r2i'].setEnabled(status) self._actions['roi_merge'].setEnabled(status) def _surf_func_module_set_enabled(self, status): """ set enabled status for actions of surface functional module. """ self._actions['scribing'].setEnabled(status) self._actions['surf_region_grow'].setEnabled(status) self._actions['concatenate'].setEnabled(status) self._actions['probability_map'].setEnabled(status) def _snapshot(self): """Capture images from OrthView.""" self.volume_view.save_image() def set_save_dir(self, path): self._save_dir = path

46.97992

120

0.591078

8,508

81,886

5.415609

0.084391

0.084751

0.030602

0.013369

0.546532

0.449822

0.367198

0.31218

0.251519

0.219919

0

0.003673

0.301737

81,886

1,742

121

47.006889

0.802162

0.07624

0

0.338685

0

0.087442

0.002332

0

0.000574

0

1

0.070336

false

0

0.037462

0

0.120031

0

null

0

null

0

1

0

09abc457e5bd1caa1d8046d6ee92bbfdae5edefe

1,475

py

Python

backend/naki/naki/model/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

null

backend/naki/naki/model/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

6

2021-03-08T23:32:15.000Z

2022-02-26T08:11:38.000Z

backend/naki/naki/model/digital_item.py

iimcz/emod

432094c020247597a94e95f76cc524c20b68b685

[ "MIT" ]

null

import colander from sqlalchemy import Column, ForeignKey from sqlalchemy.types import DateTime, Integer, Unicode, UnicodeText from naki.model.meta import Base class DigitalItem(Base): __tablename__ = "tDigitalItem" id_item = Column('sID_Item', Unicode(64), primary_key = True, info={'colanderalchemy': {'missing': None}}) mime = Column('sMime', Unicode(64)) created = Column('dCreated', DateTime) description = Column('sDescription', UnicodeText, info={'colanderalchemy': {'missing': ''}}) id_user = Column('sAuthor', Unicode(64)) rights = Column('sRights', Integer, info={'colanderalchemy': {'missing': 0}}) def __init__(self, id_item, mime, created, description, id_user, rights): self.id_item = id_item self.mime = mime self.created = created self.description = description self.id_user = id_user self.rights = rights def get_dict(self): return ({ 'id_item': self.id_item, 'mime': self.mime, 'created': str(self.created), 'description': self.description, 'id_user': self.id_user, 'rights': self.rights, }) def set_from_dict(self, d): #self.id_item = d['id_item'] self.mime = d['mime'] #self.created = d['created'] self.description = d['description'] self.id_user = d['id_user'] self.rights = d['rights']

34.302326

110

0.602034

165

1,475

5.206061

0.30303

0.055879

0.046566

0.032596

0

0.006452

0.264407

1,475

42

111

35.119048

0.785253

0.03661

0

0.137518

0

1

0.090909

false

0

0.121212

0.030303

0.484848

0

null

0

null

0

1

0

09ae724cdc803309af6a236723605a5ad5b9d098

4,389

py

Python

z3/labeled_dice.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

279

2015-01-10T09:55:35.000Z

2022-03-28T02:34:03.000Z

z3/labeled_dice.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

10

2017-10-05T15:48:50.000Z

2021-09-20T12:06:52.000Z

z3/labeled_dice.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

83

2015-01-20T03:44:00.000Z

2022-03-13T23:53:06.000Z

#!/usr/bin/python -u # -*- coding: latin-1 -*- # # Labeled dice and Building block problems in Z3 # # * Labeled dice # # From Jim Orlin 'Colored letters, labeled dice: a logic puzzle' # http://jimorlin.wordpress.com/2009/02/17/colored-letters-labeled-dice-a-logic-puzzle/ # ''' # My daughter Jenn bough a puzzle book, and showed me a cute puzzle. There # are 13 words as follows: BUOY, CAVE, CELT, FLUB, FORK, HEMP, JUDY, # JUNK, LIMN, QUIP, SWAG, VISA, WISH. # # There are 24 different letters that appear in the 13 words. The question # is: can one assign the 24 letters to 4 different cubes so that the # four letters of each word appears on different cubes. (There is one # letter from each word on each cube.) It might be fun for you to try # it. I'll give a small hint at the end of this post. The puzzle was # created by Humphrey Dudley. # ''' # # Also, see Jim Orlin's followup 'Update on Logic Puzzle': # http://jimorlin.wordpress.com/2009/02/21/update-on-logic-puzzle/ # # # * Building Blocks puzzle (Dell Logic Puzzles) in MiniZinc. # # From http://brownbuffalo.sourceforge.net/BuildingBlocksClues.html # """ # Each of four alphabet blocks has a single letter of the alphabet on each # of its six sides. In all, the four blocks contain every letter but # Q and Z. By arranging the blocks in various ways, you can spell all of # the words listed below. Can you figure out how the letters are arranged # on the four blocks? # # BAKE ONYX ECHO OVAL # # GIRD SMUG JUMP TORN # # LUCK VINY LUSH WRAP # """ # # This Z3 model was written by Hakan Kjellerstrand (hakank@gmail.com) # See also my Z3 page: http://hakank.org/z3/ # # from __future__ import print_function from z3_utils_hakank import * def labeled_dice(): print("Labeled dice\n") # # data # n = 4 m = 24 A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, Y = ( list(range(m))) letters = [A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, Y] letters_s = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "Y"] num_words = 13 words = [ [B,U,O,Y], [C,A,V,E], [C,E,L,T], [F,L,U,B], [F,O,R,K], [H,E,M,P], [J,U,D,Y], [J,U,N,K], [L,I,M,N], [Q,U,I,P], [S,W,A,G], [V,I,S,A], [W,I,S,H] ] solve_it(n,m,letters,letters_s,num_words,words) def building_blocks(): print("Building blocks\n") n = 4 m = 24 A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, R, S, T, U, V, W, X, Y = ( list(range(m))) letters = [A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, R, S, T, U, V, W, X, Y] letters_s = ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X","Y"] num_words = 12 words = [ [B,A,K,E], [O,N,Y,X], [E,C,H,O], [O,V,A,L], [G,I,R,D], [S,M,U,G], [J,U,M,P], [T,O,R,N], [L,U,C,K], [V,I,N,Y], [L,U,S,H], [W,R,A,P] ] solve_it(n,m,letters,letters_s,num_words,words) def solve_it(n,m,letters,letters_s,num_words,words): sol = Solver() # # declare variables # dice = [makeIntVar(sol, "dice[%i]" % i, 0, n - 1) for i in range(m)] # constraints # the letters in a word must be on a different die for i in range(num_words): sol.add(Distinct([dice[words[i][j]] for j in range(n)])) # there must be exactly 6 letters of each die for i in range(n): sol.add(Sum([If(dice[j] == i,1,0) for j in range(m)]) == 6) # # solution and search num_solutions = 0 while sol.check() == sat: num_solutions += 1 mod = sol.model() for d in range(n): print("die %i:" % d, end=' ') for i in range(m): if mod.eval(dice[i]) == d: print(letters[i], end=' ') print() print("The words with the cube label:") for i in range(num_words): for j in range(n): print("%s (%i)" % (letters_s[words[i][j]], mod.eval(dice[words[i][j]]).as_long()), end=' ') print() sol.add(Or([dice[i] != mod.eval(dice[i]) for i in range(m)])) print() print() print("num_solutions:", num_solutions) if __name__ == "__main__": labeled_dice() print("\n\n\n") building_blocks()

26.281437

99

0.549784

790

4,389

3.003797

0.275949

0.029499

0.007585

0.010114

0.245259

0.212811

0.196797

0.170249

0.13485

0

0.015077

0.259512

4,389

166

100

26.439759

0.715077

0.398724

0

0.216867

0

0.063154

0

1

0.036145

false

0

0.024096

0

0.060241

0.156627

0

null

0

null

0

1

0

09b071406342703275a6a5f8df9c8ce73299146c

1,602

py

Python

scripts/ai/mle/test_ai.py

AlexGustafsson/word-frequencies

21a73dc1e56770f5563f928b7e3943874c995bd9

[ "Unlicense" ]

null

scripts/ai/mle/test_ai.py

AlexGustafsson/word-frequencies

21a73dc1e56770f5563f928b7e3943874c995bd9

[ "Unlicense" ]

null

scripts/ai/mle/test_ai.py

AlexGustafsson/word-frequencies

21a73dc1e56770f5563f928b7e3943874c995bd9

[ "Unlicense" ]

null

import pickle import random from argparse import ArgumentParser # Requires NLTK to be installed: # python3 -m pip install nltk # python3 -c 'import nltk;nltk.download("punkt")' # May be slow at first start due to NLTK preparing its dependencies from nltk.tokenize.treebank import TreebankWordDetokenizer from nltk.lm import MLE detokenize = TreebankWordDetokenizer().detokenize def generate_sentence(model: MLE, length: int, seed=random.randint(0, 1e10)): content = [] for token in model.generate(length, random_seed=seed): if token == '<s>': continue if token == '</s>': break content.append(token) return detokenize(content) def main() -> None: """Main entrypoint.""" # Create an argument parser for parsing CLI arguments parser = ArgumentParser(description="A tool to train an AI to predict the probability of a word in a sentence") # Add parameters for the server connection parser.add_argument("-i", "--input", required=True, type=str, help="The serialized model previously trained") parser.add_argument("-w", "--word", required=True, type=str, help="The word to check the probability for") parser.add_argument("-c", "--context", required=True, type=str, help="The context / sentence for the word") # Parse the arguments options = parser.parse_args() model = None with open(options.input, "rb") as file: model = pickle.load(file) print(model.logscore(options.word, options.context.split())) print(generate_sentence(model, 10)) if __name__ == '__main__': main()

32.693878

115

0.692884

210

1,602

5.214286

0.495238

0.024658

0.046575

0.052055

0.071233

0

0.006197

0.194132

1,602

48

116

33.375

0.841983

0.189139

0

0.177156

0

1

0.071429

false

0

0.178571

0

0.285714

0.071429

0

null

0

null

0

1

0

09b38cadc8a5b66d765f9f62596709fa7325c773

7,529

py

Python

lib/common/render_utils.py

YuliangXiu/ICON

ece5a09aa2d56aec28017430e65a0352622a0f30

[ "Intel" ]

486

2021-12-16T03:13:31.000Z

2022-03-30T04:26:48.000Z

lib/common/render_utils.py

YuliangXiu/ICON

ece5a09aa2d56aec28017430e65a0352622a0f30

[ "Intel" ]

33

2021-12-30T07:28:10.000Z

2022-03-30T08:04:06.000Z

lib/common/render_utils.py

YuliangXiu/ICON

ece5a09aa2d56aec28017430e65a0352622a0f30

[ "Intel" ]

38

2021-12-17T10:55:01.000Z

2022-03-30T23:25:39.000Z

# -*- coding: utf-8 -*- # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is # holder of all proprietary rights on this computer program. # You can only use this computer program if you have closed # a license agreement with MPG or you get the right to use the computer # program from someone who is authorized to grant you that right. # Any use of the computer program without a valid license is prohibited and # liable to prosecution. # # Copyright©2019 Max-Planck-Gesellschaft zur Förderung # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute # for Intelligent Systems. All rights reserved. # # Contact: ps-license@tuebingen.mpg.de import torch from torch import nn import trimesh import math from typing import NewType from pytorch3d.structures import Meshes from pytorch3d.renderer.mesh import rasterize_meshes Tensor = NewType('Tensor', torch.Tensor) def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor: ''' Compute solid angle between the input points and triangles Follows the method described in: The Solid Angle of a Plane Triangle A. VAN OOSTEROM AND J. STRACKEE IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. BME-30, NO. 2, FEBRUARY 1983 Parameters ----------- points: BxQx3 Tensor of input query points triangles: BxFx3x3 Target triangles thresh: float float threshold Returns ------- solid_angles: BxQxF A tensor containing the solid angle between all query points and input triangles ''' # Center the triangles on the query points. Size should be BxQxFx3x3 centered_tris = triangles[:, None] - points[:, :, None, None] # BxQxFx3 norms = torch.norm(centered_tris, dim=-1) # Should be BxQxFx3 cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1) # Should be BxQxF numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1) del cross_prod dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1) dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1) dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1) del centered_tris denominator = (norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] + dot12 * norms[:, :, :, 0]) del dot01, dot12, dot02, norms # Should be BxQ solid_angle = torch.atan2(numerator, denominator) del numerator, denominator torch.cuda.empty_cache() return 2 * solid_angle def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor: ''' Uses winding_numbers to compute inside/outside Robust inside-outside segmentation using generalized winding numbers Alec Jacobson, Ladislav Kavan, Olga Sorkine-Hornung Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018 Gavin Barill NEIL G. Dickson Ryan Schmidt David I.W. Levin and Alec Jacobson Parameters ----------- points: BxQx3 Tensor of input query points triangles: BxFx3x3 Target triangles thresh: float float threshold Returns ------- winding_numbers: BxQ A tensor containing the Generalized winding numbers ''' # The generalized winding number is the sum of solid angles of the point # with respect to all triangles. return 1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1) def batch_contains(verts, faces, points): B = verts.shape[0] N = points.shape[1] verts = verts.detach().cpu() faces = faces.detach().cpu() points = points.detach().cpu() contains = torch.zeros(B, N) for i in range(B): contains[i] = torch.as_tensor( trimesh.Trimesh(verts[i], faces[i]).contains(points[i])) return 2.0 * (contains - 0.5) def dict2obj(d): # if isinstance(d, list): # d = [dict2obj(x) for x in d] if not isinstance(d, dict): return d class C(object): pass o = C() for k in d: o.__dict__[k] = dict2obj(d[k]) return o def face_vertices(vertices, faces): """ :param vertices: [batch size, number of vertices, 3] :param faces: [batch size, number of faces, 3] :return: [batch size, number of faces, 3, 3] """ bs, nv = vertices.shape[:2] bs, nf = faces.shape[:2] device = vertices.device faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None] vertices = vertices.reshape((bs * nv, vertices.shape[-1])) return vertices[faces.long()] class Pytorch3dRasterizer(nn.Module): """ Borrowed from https://github.com/facebookresearch/pytorch3d Notice: x,y,z are in image space, normalized can only render squared image now """ def __init__(self, image_size=224): """ use fixed raster_settings for rendering faces """ super().__init__() raster_settings = { 'image_size': image_size, 'blur_radius': 0.0, 'faces_per_pixel': 1, 'bin_size': None, 'max_faces_per_bin': None, 'perspective_correct': True, 'cull_backfaces': True, } raster_settings = dict2obj(raster_settings) self.raster_settings = raster_settings def forward(self, vertices, faces, attributes=None): fixed_vertices = vertices.clone() fixed_vertices[..., :2] = -fixed_vertices[..., :2] meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long()) raster_settings = self.raster_settings pix_to_face, zbuf, bary_coords, dists = rasterize_meshes( meshes_screen, image_size=raster_settings.image_size, blur_radius=raster_settings.blur_radius, faces_per_pixel=raster_settings.faces_per_pixel, bin_size=raster_settings.bin_size, max_faces_per_bin=raster_settings.max_faces_per_bin, perspective_correct=raster_settings.perspective_correct, ) vismask = (pix_to_face > -1).float() D = attributes.shape[-1] attributes = attributes.clone() attributes = attributes.view(attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]) N, H, W, K, _ = bary_coords.shape mask = pix_to_face == -1 pix_to_face = pix_to_face.clone() pix_to_face[mask] = 0 idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D) pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D) pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2) pixel_vals[mask] = 0 # Replace masked values in output. pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2) pixel_vals = torch.cat( [pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1) return pixel_vals

33.914414

79

0.592376

919

7,529

4.723613

0.311208

0.045151

0.014513

0.003686

0.156876

0.130615

0.10182

0.096752

0.075098

0

0.023652

0.298048

7,529

221

80

34.067873

0.79754

0.321424

0

0.037037

0

0.021409

0

1

0.064815

false

0.009259

0.064815

0

0.212963

0

null

0

null

0

1

0

09b41443c1ba334ee6ad9dc77418ea29db20354e

456

py

Python

merge_string.py

mrillusi0n/compete

ac798e2b1ff27abddd8bebf113d079228f038e56

[ "MIT" ]

null

merge_string.py

mrillusi0n/compete

ac798e2b1ff27abddd8bebf113d079228f038e56

[ "MIT" ]

null

merge_string.py

mrillusi0n/compete

ac798e2b1ff27abddd8bebf113d079228f038e56

[ "MIT" ]

null

######################### AABCAAADA from collections import OrderedDict def remove_duplicates(block): """ >>> remove_duplicates('AAB') >>> 'AB' """ freq = OrderedDict() for c in block: freq[c] = freq.get(c, 0) + 1 return ''.join(freq.keys()) def solve(text, block_size): return '\n'.join(map(remove_duplicates, [text[i:i+block_size] for i in range(0, len(text), block_size)])) print(solve('AABCAAADA', 3))

18.24

65

0.58114

59

456

4.389831

0.525424

0.185328

0.100386

0

0.011019

0.203947

456

24

66

19

0.702479

0.105263

0

0.03022

0

1

0.2

false

0

0.1

0.5

0.1

0

null

0

null

0

1

0

09b5d250f780316cd9c06c021e66be29bc76a8ed

884

py

Python

tests/views/test_is_component_field_model_or_unicorn_field.py

nerdoc/django-unicorn

e512b8f64f5c276a78127db9a05d9d5c042232d5

[ "MIT" ]

1

2021-12-21T16:20:49.000Z

tests/views/test_is_component_field_model_or_unicorn_field.py

teury/django-unicorn

5e9142b8a7e13b862ece419d567e805cc783b517

[ "MIT" ]

null

tests/views/test_is_component_field_model_or_unicorn_field.py

teury/django-unicorn

5e9142b8a7e13b862ece419d567e805cc783b517

[ "MIT" ]

1

2022-02-10T07:47:01.000Z

from django_unicorn.components import UnicornView from django_unicorn.views.utils import _is_component_field_model_or_unicorn_field from example.coffee.models import Flavor class TypeHintView(UnicornView): model: Flavor = None class ModelInstanceView(UnicornView): model = Flavor() def test_type_hint(): component = TypeHintView(component_name="asdf", component_id="hjkl") name = "model" actual = _is_component_field_model_or_unicorn_field(component, name) assert actual assert component.model is not None assert type(component.model) == Flavor def test_model_instance(): component = ModelInstanceView(component_name="asdf", component_id="hjkl") name = "model" actual = _is_component_field_model_or_unicorn_field(component, name) assert actual assert component.model is not None assert type(component.model) == Flavor

27.625

81

0.7681

110

884

5.890909

0.3

0.067901

0.074074

0.097222

0.566358

0.512346

0

0.154977

884

31

82

28.516129

0.86747

0

0.47619

0

0.029412

0

0.285714

1

0.095238

false

0

0.142857

0

0.428571

0

null

0

null

0

1

0

09bb144937911126f0899a4f90a8ca7646246b73

431

py

Python

data_wrangling/data_manipulation/check_if_even.py

dkedar7/Notes

08a9e710a774fd46ec525e0041c1cbd67fbe6c20

[ "MIT" ]

3

2021-05-28T09:00:56.000Z

2021-12-21T01:12:20.000Z

data_wrangling/data_manipulation/check_if_even.py

dkedar7/Notes

08a9e710a774fd46ec525e0041c1cbd67fbe6c20

[ "MIT" ]

null

data_wrangling/data_manipulation/check_if_even.py

dkedar7/Notes

08a9e710a774fd46ec525e0041c1cbd67fbe6c20

[ "MIT" ]

null

import pytest testdata = [ (2, True), (3, False), (4, True), (5, True) # We expect this test to fail ] def check_if_even(a): """ Returns True if 'a' is an even number """ return a % 2 == 0 @pytest.mark.parametrize('sample, expected_output', testdata) def test_check_if_even(sample, expected_output): """ Define test cases """ assert check_if_even(sample) == expected_output

17.958333

61

0.612529

59

431

4.305085

0.576271

0.082677

0.129921

0.133858

0.244094

0

0.01875

0.257541

431

23

62

18.73913

0.775

0.194896

0

0.073016

0

0.083333

1

0.166667

false

0

0.083333

0

0.333333

0

null

0

null

0

1

0

09c0c79a0b5cfaa45266d9d9675a6a0f9435dae8

6,234

py

Python

orwell/agent/main.py

dchilot/agent-server-game-python

ce8db9560047a06960343cc66a9eddb11e77f5a1

[ "BSD-3-Clause" ]

null

orwell/agent/main.py

dchilot/agent-server-game-python

ce8db9560047a06960343cc66a9eddb11e77f5a1

[ "BSD-3-Clause" ]

null

orwell/agent/main.py

dchilot/agent-server-game-python

ce8db9560047a06960343cc66a9eddb11e77f5a1

[ "BSD-3-Clause" ]

null

import logging import sys import socket from cliff.app import App from cliff.command import Command from cliff.commandmanager import CommandManager class RegisteredCommand(Command): def __init__(self, app, app_args): super(RegisteredCommand, self).__init__(app, app_args) @classmethod def register_to(klass, command_manager): command_manager.add_command(klass._command_name, klass) class SingleCommand(RegisteredCommand): def take_action(self, parsed_args): self.app.send(self._command_name + ' ' + parsed_args.object[0]) class List(SingleCommand): "List something." log = logging.getLogger(__name__) port = None host = socket.gethostbyname(socket.getfqdn()) def take_action(self, parsed_args): self.app.send( ' '.join(( self._command_name, List.host, List.port))) message = self.app.receive() self.log.info(message) class ListPlayer(List): "List all players." log = logging.getLogger(__name__) _command_name = 'list player' class ListRobot(List): "List all robots." log = logging.getLogger(__name__) _command_name = 'list robot' class Add(SingleCommand): "Add something." log = logging.getLogger(__name__) def get_parser(self, prog_name): parser = super(Add, self).get_parser(prog_name) parser.add_argument( 'object', nargs=1) return parser class AddPlayer(Add): "Add a player." log = logging.getLogger(__name__) _command_name = 'add player' class AddRobot(Add): "Add a robot." log = logging.getLogger(__name__) _command_name = 'add robot' class Remove(SingleCommand): "Remove something." log = logging.getLogger(__name__) def get_parser(self, prog_name): parser = super(Remove, self).get_parser(prog_name) parser.add_argument( 'object', nargs=1) return parser class RemovePlayer(Remove): "Remove a player." log = logging.getLogger(__name__) _command_name = 'remove player' class RemoveRobot(Remove): "Remove a robot." log = logging.getLogger(__name__) _command_name = 'remove robot' class Start(SingleCommand): "Start something." log = logging.getLogger(__name__) _command_name = 'start' def get_parser(self, prog_name): parser = super(Start, self).get_parser(prog_name) parser.add_argument( 'object', nargs=1, choices=('game',)) return parser class Stop(SingleCommand): "Stop something." log = logging.getLogger(__name__) _command_name = 'stop' def get_parser(self, prog_name): parser = super(Stop, self).get_parser(prog_name) parser.add_argument( 'object', nargs=1, choices=('application', 'game')) return parser class AgentApp(App): log = logging.getLogger(__name__) def __init__(self): command = CommandManager('orwell.agent') super(AgentApp, self).__init__( description='Orwell agent.', version='0.0.1', command_manager=command, ) Start.register_to(command) Stop.register_to(command) ListPlayer.register_to(command) ListRobot.register_to(command) AddPlayer.register_to(command) AddRobot.register_to(command) RemovePlayer.register_to(command) RemoveRobot.register_to(command) self._zmq_context = None self._zmq_publish_socket = None self._zmq_pull_socket = None def build_option_parser( self, description, version, argparse_kwargs=None): parser = super(AgentApp, self).build_option_parser( description, version, argparse_kwargs) parser.add_argument( '-p', '--port', type=int, default=9003, help='The port to send commands to.') parser.add_argument( '-a', '--address', type=str, default='127.0.0.1', help='The address to send commands to.') parser.add_argument( '-l', '--listen', type=int, default=9004, help='The port to listen to for replies.') return parser def initialize_app(self, argv): self.log.debug('initialize_app') import zmq self._zmq_context = zmq.Context() self.log.debug('created context = %s' % self._zmq_context) self._zmq_publish_socket = self._zmq_context.socket(zmq.PUB) self.log.debug( 'created publish socket = %s' % self._zmq_publish_socket) self._zmq_publish_socket.setsockopt(zmq.LINGER, 1) self._zmq_publish_socket.connect("tcp://%s:%i" % ( self.options.address, self.options.port)) self._zmq_pull_socket = self._zmq_context.socket(zmq.PULL) self.log.debug('created pull socket = %s' % self._zmq_pull_socket) self._zmq_pull_socket.setsockopt(zmq.LINGER, 1) self._zmq_pull_socket.bind("tcp://0.0.0.0:%i" % self.options.listen) List.port = str(self.options.listen) import time time.sleep(0.001) def send(self, command): self.log.debug('send command "%s"' % command) self.log.debug('call socket.send("%s")' % command) self._zmq_publish_socket.send(command) def receive(self): self.log.debug('try to receive a message') message = self._zmq_pull_socket.recv() self.log.debug('received: %s', message) return message def prepare_to_run_command(self, cmd): self.log.debug('prepare_to_run_command %s', cmd.__class__.__name__) def clean_up(self, cmd, result, err): self.log.debug('clean_up %s', cmd.__class__.__name__) if err: self.log.debug('got an error: %s', err) def main(argv=sys.argv[1:]): myapp = AgentApp() return myapp.run(argv) if ("__main__" == __name__): sys.exit(main(sys.argv[1:])) # pragma: no coverage

26.193277

76

0.611806

722

6,234

4.984765

0.185596

0.033065

0.063351

0.076688

0.331759

0.315643

0.278411

0.19172

0.127258

0.106141

0

0.007539

0.276548

6,234

237

77

26.303797

0.790466

0.031601

0

0.24581

0

0.111862

0.003541

0

1

0.089385

false

0

0.044693

0

0.374302

0

null

0

null

0

1

0

09c267a3cb1cc17f6f5bb5ef69492d09f87a64fa

1,475

py

Python

tests/test_models.py

jimimvp/CausalProb

900527725ad43eac258df2b16ef93fd1643deb3a

[ "MIT" ]

3

2021-11-04T16:37:45.000Z

2022-03-08T10:24:19.000Z

tests/test_models.py

jimimvp/CausalProb

900527725ad43eac258df2b16ef93fd1643deb3a

[ "MIT" ]

13

2021-11-07T11:11:54.000Z

2021-11-20T10:40:39.000Z

tests/test_models.py

jimimvp/CausalProb

900527725ad43eac258df2b16ef93fd1643deb3a

[ "MIT" ]

1

2021-11-17T21:40:49.000Z

from causalprob import CausalProb import unittest import jax.numpy as jnp import numpy as np class TestNFConfounderModel(unittest.TestCase): def test_is_inverse_function(self): from models.nf_confounder_model import define_model dim = 2 model = define_model(dim=dim) cp = CausalProb(model=model) theta = {k: cp.init_params[k](i) for i, k in enumerate(cp.init_params)} u, v = cp.fill({k: cp.draw_u[k](1, theta, seed) for seed, k in enumerate(cp.draw_u)}, {}, theta, cp.draw_u.keys()) for rv in cp.f: assert jnp.allclose(cp.finv[rv](cp.f[rv](u[rv], theta, v), theta, v), u[rv]) def test_determinant(self): from models.nf_confounder_model import define_model dim = 2 model = define_model(dim=dim) cp = CausalProb(model=model) theta = {k: cp.init_params[k](i) for i, k in enumerate(cp.init_params)} u, v = cp.fill({k: cp.draw_u[k](1, theta, seed) for seed, k in enumerate(cp.draw_u)}, {}, theta, cp.draw_u.keys()) for rv in cp.ldij: assert jnp.allclose(jnp.round(cp.ldij[rv](v[rv], theta, v).squeeze(), 4), jnp.round( jnp.log( jnp.abs( jnp.linalg.det( cp.dfinvv_dv(rv, {k: _v.squeeze(0) for k, _v in v.items()}, theta)))), 4))

39.864865

122

0.547119

213

1,475

3.671362

0.276995

0.046036

0.053708

0.071611

0.567775

0

0.007035

0.325424

1,475

36

123

40.972222

0.778894

0

0.428571

0

0.071429

1

0.071429

false

0

0.214286

0

0.321429

0

null

0

null

0

1

0

09c9a15d0f7a17f53680be679c2a6066d5b21c97

1,335

py

Python

tools/prepare_data.py

xrick/CTC_DySpeechCommands

d92cb97f7344fb5acdb6aa3fc3dfb7c022fffc6e

[ "MIT" ]

74

2018-05-05T18:43:28.000Z

2022-03-21T13:00:14.000Z

tools/prepare_data.py

xrick/CTC_DySpeechCommands

d92cb97f7344fb5acdb6aa3fc3dfb7c022fffc6e

[ "MIT" ]

5

2018-07-20T16:18:57.000Z

2021-01-26T11:52:31.000Z

tools/prepare_data.py

xrick/CTC_DySpeechCommands

d92cb97f7344fb5acdb6aa3fc3dfb7c022fffc6e

[ "MIT" ]

21

2018-06-18T07:21:19.000Z

2021-04-11T06:49:03.000Z

"""Downloads the training dataset and removes bad samples. """ import csv import os import urllib.request import tarfile import glob DATA_URL = 'http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz' TRAIN_DIR = '../dataset/train/audio/' FILE_BAD = 'bad_samples.txt' def maybe_download(data_url, dest_directory): """Download and extract data set tar file. """ if not os.path.exists(dest_directory): os.makedirs(dest_directory) filename = data_url.split('/')[-1] filepath = os.path.join(dest_directory, filename) if not os.path.exists(filepath): print('Downloading %s ...' % filename) filepath, _ = urllib.request.urlretrieve(data_url, filepath) tarfile.open(filepath, 'r:gz').extractall(dest_directory) print('Successfully unzipped %s' % filename) def remove_bad(f_bad, train_dir): """Deletes bad samples in the dataset. """ num_bad = 0 with open(f_bad, 'r') as fp: for wav in csv.reader(fp, delimiter=','): try: os.remove(train_dir + wav[0]) num_bad += 1 except FileNotFoundError: pass print('bad_training_samples removed: %d' % num_bad) wav_paths = glob.glob(os.path.join(train_dir, '*', '*nohash*.wav')) print('num_training_samples = %d' % len(wav_paths)) maybe_download(DATA_URL, TRAIN_DIR) remove_bad(FILE_BAD, TRAIN_DIR)

26.7

77

0.698876

191

1,335

4.691099

0.413613

0.053571

0.037946

0.044643

0.037946

0

0.006272

0.164045

1,335

49

78

27.244898

0.796595

0.102622

0

0.18713

0.019475

0

1

0.0625

false

0.03125

0.15625

0

0.21875

0.125

0

null

0

null

0

1

0

09caab87d8b63d185ae16695cb5079d8b60078ed

4,232

py

Python

Dashboard_Relay/tests/unit/api/test_authorization.py

weiwa6/SecValidation

e899b7aa3f46ded3b39aeb6a1eeab95cc8dc21b5

[ "BSD-3-Clause" ]

null

Dashboard_Relay/tests/unit/api/test_authorization.py

weiwa6/SecValidation

e899b7aa3f46ded3b39aeb6a1eeab95cc8dc21b5

[ "BSD-3-Clause" ]

null

Dashboard_Relay/tests/unit/api/test_authorization.py

weiwa6/SecValidation

e899b7aa3f46ded3b39aeb6a1eeab95cc8dc21b5

[ "BSD-3-Clause" ]

null

from http import HTTPStatus from authlib.jose import jwt from pytest import fixture from .utils import get_headers from api.errors import AUTH_ERROR def routes(): yield '/health' yield '/deliberate/observables' yield '/observe/observables' yield '/refer/observables' yield '/respond/observables' yield '/respond/trigger' @fixture(scope='module', params=routes(), ids=lambda route: f'POST {route}') def route(request): return request.param @fixture(scope='module') def wrong_jwt_structure(): return 'wrong_jwt_structure' @fixture(scope='module') def wrong_payload_structure_jwt(client): header = {'alg': 'HS256'} payload = {'not_key': 'something'} secret_key = client.application.secret_key return jwt.encode(header, payload, secret_key).decode('ascii') @fixture(scope='session') def invalid_jwt(valid_jwt): header, payload, signature = valid_jwt.split('.') def jwt_decode(s: str) -> dict: from authlib.common.encoding import urlsafe_b64decode, json_loads return json_loads(urlsafe_b64decode(s.encode('ascii'))) def jwt_encode(d: dict) -> str: from authlib.common.encoding import json_dumps, urlsafe_b64encode return urlsafe_b64encode(json_dumps(d).encode('ascii')).decode('ascii') payload = jwt_decode(payload) # Corrupt the valid JWT by tampering with its payload. payload['superuser'] = True payload = jwt_encode(payload) return '.'.join([header, payload, signature]) @fixture(scope='module') def authorization_errors_expected_payload(route): def _make_payload_message(message): payload = { 'errors': [{ 'code': AUTH_ERROR, 'message': f'Authorization failed: {message}', 'type': 'fatal'}] } return payload return _make_payload_message def test_call_with_authorization_header_failure( route, client, authorization_errors_expected_payload ): response = client.post(route) assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( 'Authorization header is missing' ) def test_call_with_wrong_authorization_type( route, client, valid_jwt, authorization_errors_expected_payload ): response = client.post( route, headers=get_headers(valid_jwt, auth_type='wrong_type') ) assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( 'Wrong authorization type' ) def test_call_with_wrong_jwt_structure( route, client, wrong_jwt_structure, authorization_errors_expected_payload ): response = client.post(route, headers=get_headers(wrong_jwt_structure)) assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( 'Wrong JWT structure' ) def test_call_with_jwt_encoded_by_wrong_key( route, client, invalid_jwt, authorization_errors_expected_payload ): response = client.post(route, headers=get_headers(invalid_jwt)) assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( 'Failed to decode JWT with provided key' ) def test_call_with_wrong_jwt_payload_structure( route, client, wrong_payload_structure_jwt, authorization_errors_expected_payload ): response = client.post(route, headers=get_headers(wrong_payload_structure_jwt)) assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( 'Wrong JWT payload structure' ) def test_call_with_missed_secret_key( route, client, valid_jwt, authorization_errors_expected_payload ): right_secret_key = client.application.secret_key client.application.secret_key = None response = client.post(route, headers=get_headers(valid_jwt)) client.application.secret_key = right_secret_key assert response.status_code == HTTPStatus.OK assert response.json == authorization_errors_expected_payload( '<SECRET_KEY> is missing' )

27.660131

79

0.712193

493

4,232

5.821501

0.204868

0.086063

0.1223

0.154007

0.479443

0.416028

0.366551

0.322648

0.303136

0

0.00324

0.197779

4,232

152

80

27.842105

0.842121

0.012287

0

0.268519

0

0.107707

0.005505

0

0.111111

1

0.138889

false

0

0.064815

0.018519

0.277778

0

null

0

null

0

1

0

09cbcab75f8e35ba54cb7a9b30b5581da605210d

1,562

py

Python

ops-implementations/ads-ml-service/app/gunicorn.init.py

IBM/open-prediction-service-hub

8b7db98f46a81b731d0dddfde8e3fb6f91ebc71a

[ "Apache-2.0" ]

1

2021-09-14T18:40:33.000Z

ops-implementations/ads-ml-service/app/gunicorn.init.py

IBM/open-prediction-service-hub

8b7db98f46a81b731d0dddfde8e3fb6f91ebc71a

[ "Apache-2.0" ]

7

2021-04-23T13:41:39.000Z

2021-08-12T09:33:10.000Z

ops-implementations/ads-ml-service/app/gunicorn.init.py

IBM/open-prediction-service-hub

8b7db98f46a81b731d0dddfde8e3fb6f91ebc71a

[ "Apache-2.0" ]

5

2020-12-10T14:27:23.000Z

2022-03-29T08:44:22.000Z

#!/usr/bin/env python3 # # Copyright 2020 IBM # 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.IBM Confidential # import os from multiprocessing import cpu_count TRUE = ('TRUE', 'True', 'true', '1') use_ssl = True if os.getenv('ENABLE_SSL') in TRUE else False settings = os.getenv('SETTINGS') # Gunicorn config variables workers = int(os.getenv('GUNICORN_WORKER_NUM')) \ if os.getenv('GUNICORN_WORKER_NUM') and int(os.getenv('GUNICORN_WORKER_NUM')) > 0 \ else cpu_count() * 2 + 1 # Gunicorn needs to store its temporary file in memory (e.g. /dev/shm) worker_tmp_dir = '/dev/shm' # Container schedulers typically expect logs to come out on stdout/stderr, thus gunicorn is configured to do so log_file = '-' ssl_version = 'TLSv1_2' bind = ':8080' ca_certs = f'{settings}/ca.crt' if use_ssl else None certfile = f'{settings}/server.crt' if use_ssl else None keyfile = f'{settings}/server.key' if use_ssl else None timeout = int(os.getenv('GUNICORN_TIMEOUT')) \ if os.getenv('GUNICORN_TIMEOUT') and int(os.getenv('GUNICORN_TIMEOUT')) > 0 \ else 30

33.956522

111

0.734955

247

1,562

4.558704

0.518219

0.056838

0.085258

0.067496

0.171403

0.083481

0

0.01673

0.158131

1,562

45

112

34.711111

0.839544

0.50064

0

0.283465

0.055118

0

1

0

false

0

0.111111

0

0.111111

0

null

0

null

0

1

0

09ce6912201c289c5c7f36b054105384590d7dc8

2,143

py

Python

graphics/place_camera.py

bdemin/M113_Visualization

bf863af9dfc2902ae9123afeae8d5bd413a4bedb

[ "MIT" ]

null

graphics/place_camera.py

bdemin/M113_Visualization

bf863af9dfc2902ae9123afeae8d5bd413a4bedb

[ "MIT" ]

null

graphics/place_camera.py

bdemin/M113_Visualization

bf863af9dfc2902ae9123afeae8d5bd413a4bedb

[ "MIT" ]

null

import numpy as np def place_camera(time, data, camera, camera_distance, view): # Define camera parameters camera.SetViewUp([0,0,1]) if view == 1: # General view chs_pos = data[0][0].path_loc[time] # Chassis CG @ time cam_d = 12 # [m] cam_h = 4.5 # [m] chs2cam = [2 , -cam_d, cam_h] # vector from chassis to camera position chs_fix = [0,0,0] camera_pos = chs_pos + chs2cam cam_focal_point = chs_pos elif view == 2: # Rear view chassis_pos = data[0][0].path_loc[time] # Chassis CG @ time chs2cam = [-7,0,-0.5] # camera_pos = chassis_pos + chs2cam # Cam direction is locked on the chassis chassis_dir = data[0][0].path_dir[time] cam_d = 10 camera_pos = chassis_pos + [-cam_d*np.cos(chassis_dir[2]), -cam_d*np.sin(chassis_dir[2]), cam_d*np.sin(chassis_dir[1]) + 1.5] camera.Roll(np.rad2deg(chassis_dir[0])) cam_focal_point = chassis_pos elif view == 3: # Wheel view wheel_pos = data[1][7].path_loc[time] # Wheel #7 CG @ time # Cam direction is locked on the wheel wheel_dir = data[1][7].path_dir[time] cam_d = 1.5 camera_pos = wheel_pos + [cam_d*np.sin(wheel_dir[2]), -cam_d*np.cos(wheel_dir[2]), -np.sin(wheel_dir[0]) + 0.2] cam_focal_point = wheel_pos # camera_pos = wheel_pos + [0,-1.6,0.1] elif view == 4: # Top view # NEED TO FIX cam_d = 10 cam_focal_point = [0,0,0] camera_pos = [30,4,60] elif view == 5: # Cool side view test chassis_pos = data[0][0].path_loc[time] # Chassis CG @ time chs2cam = [-7,0,-0.5] camera_pos = chassis_pos + chs2cam # Cam direction is locked on the chassis chassis_dir = data[0][0].path_dir[time] cam_d = 7 cam_focal_point = chassis_pos + [cam_d*np.sin(chassis_dir[2]), -cam_d*np.cos(chassis_dir[2]), -np.sin(chassis_dir[0]) + 0.2] # Place camera and set focal point: camera.SetPosition(camera_pos) camera.SetFocalPoint(cam_focal_point)

31.985075

133

0.585161

340

2,143

3.476471

0.2

0.023689

0.035533

0.042301

0.471235

0.380711

0.348562

0.329103

0.308799

0

0.053665

0.286981

2,143

67

134

31.985075

0.719895

0.20532

0

0.210526

0

1

0.026316

false

0

0.026316

0

0.052632

0

null

0

null

0

1

0

09cf011d62fddefba9f4507356da24e66db71898

16,405

py

Python

src/tools/api_compiler/compiler.py

facade-technologies-inc/facile

4c9134dced71734641fed605e152880cd9ddefe3

[ "MIT" ]

2

2020-09-17T20:51:18.000Z

2020-11-03T15:58:10.000Z

src/tools/api_compiler/compiler.py

facade-technologies-inc/facile

4c9134dced71734641fed605e152880cd9ddefe3

[ "MIT" ]

97

2020-08-26T05:07:08.000Z

2022-03-28T16:01:49.000Z

src/tools/api_compiler/compiler.py

facade-technologies-inc/facile

4c9134dced71734641fed605e152880cd9ddefe3

[ "MIT" ]

null

""" .. /------------------------------------------------------------------------------\ | -- FACADE TECHNOLOGIES INC. CONFIDENTIAL -- | |------------------------------------------------------------------------------| | | | Copyright [2019] Facade Technologies Inc. | | All Rights Reserved. | | | | NOTICE: All information contained herein is, and remains the property of | | Facade Technologies Inc. and its suppliers if any. The intellectual and | | and technical concepts contained herein are proprietary to Facade | | Technologies Inc. and its suppliers and may be covered by U.S. and Foreign | | Patents, patents in process, and are protected by trade secret or copyright | | law. Dissemination of this information or reproduction of this material is | | strictly forbidden unless prior written permission is obtained from Facade | | Technologies Inc. | | | \------------------------------------------------------------------------------/ This file contains the Compiler class - the part of Facile that interprets a user's work in the gui, and converts it into the desired API. """ import os import sys import json from subprocess import check_call, DEVNULL, STDOUT, check_output from shutil import copyfile, rmtree from PySide2.QtCore import QObject, Signal from PySide2.QtWidgets import QApplication import data.statemachine as sm from data.compilationprofile import CompilationProfile from tools.api_compiler.copy_file_manifest import compilation_copy_files from libs.logging import compiler_logger as logger from libs.logging import log_exceptions import libs.env as env from multiprocessing.pool import ThreadPool curPath = os.path.abspath(os.path.join(env.FACILE_DIR, "tools/api_compiler/compiler.py")) dir, filename = os.path.split(curPath) def nongui(fun): """Decorator running the function in non-gui thread while processing the gui events.""" def wrap(*args, **kwargs): pool = ThreadPool(processes=1) a_sync = pool.apply_async(fun, args, kwargs) while not a_sync.ready(): a_sync.wait(0.01) QApplication.processEvents() return a_sync.get() return wrap class Compiler(QObject): stepStarted = Signal(str) stepComplete = Signal() finished = Signal() def __init__(self, compProf: 'CompilationProfile' = None) -> None: """ Initializes the compiler with required information. :return: None """ logger.debug("Instantiating compiler") QObject.__init__(self) self.statem = sm.StateMachine.instance self._compProf = compProf self._name = self.statem._project.getName() self._apiName = self.statem._project.getAPIName() self._backend = self.statem._project.getBackend() self._exeLoc = self.statem._project.getExecutableFile() self._opts = compProf.compResOpts self._apim = self.statem._project.getAPIModel() self._tguim = self.statem._project.getTargetGUIModel() # Save Folders self._saveFolder = os.path.join(compProf.apiFolderDir, self._name + '_API_Files') self._srcFolder = os.path.join(self._saveFolder, self._apiName) self._docFolder = os.path.join(self._srcFolder, 'Documentation') # Make all save folders if they don't exist if not os.path.exists(self._saveFolder): # If the user enters a path that doesn't exist, it is created os.mkdir(self._saveFolder) # TODO: Should notify them of this in compiler dialog if not os.path.exists(self._srcFolder): os.mkdir(self._srcFolder) if not os.path.exists(self._docFolder): os.mkdir(self._docFolder) self._necessaryFiles = ['apicore.pyd'] # THIS IS WHEN OBFUSCATING ALL FILES INDEPENDENTLY # # if sys.executable.endswith('facile.exe'): # self._necessaryFiles = [filepath + 'd' for tmp, filepath in compilation_copy_files] # # # baseapplication is out of place when we make facile into an executable # for filepath in self._necessaryFiles: # if filepath.endswith('baseapplication.pyd'): # self._necessaryFiles.remove(filepath) # self._necessaryFiles.append('baseapplication.pyd') # break # # else: # self._necessaryFiles = [filepath for tmp, filepath in compilation_copy_files] @nongui def _dev_generateAPICore(self): """ Makes the api core file and places it in facile's root directory NOTE: Should only ever be called in a development setting, never by a facile executable. """ msg = 'Generating API core file, this will take a while' logger.info(msg) self.stepStarted.emit(msg) os.chdir(os.path.abspath(os.path.join(env.FACILE_DIR, '..', 'scripts', 'obfuscation'))) exit_code = check_call([sys.executable, "obfuscate_files.py"], stdout=DEVNULL, stderr=STDOUT) if exit_code != 0: logger.critical("File compilation was unsuccessful, which will cause the API not to work.") raise Exception("File compilation was unsuccessful, which will cause the API not to work.") copyfile(os.path.abspath(os.path.join('compiled', 'apicore.pyd')), os.path.join(env.FACILE_DIR, 'apicore.pyd')) rmtree('compiled') os.chdir(dir) logger.info("Finished compiling api core and moving it to facile directory.") self.stepComplete.emit() def generateCustomApp(self) -> None: """ Creates the custom application class/file. :return: None """ msg = "Generating custom application driver" logger.info(msg) self.stepStarted.emit(msg) with open(os.path.join(self._srcFolder, "application.py"), "w+") as f: # TODO: The Facade Tech watermark thing is a little intense when the user needs # to use it for their own purposes and may want to share their generated API online. # Could make a custom tag. I put the original in for the moment though. logger.debug("Reading application-unfilled.py") try: with open(os.path.join(dir, 'application-template.py'), 'r') as g: appStr = g.read() except Exception as e: appStr = 'There was an error generating your API.\n' logger.exception(e) logger.debug("Generating options set") optStr = '{' for opt in self._opts: optStr += str(opt) + ', ' optStr = optStr[:-2] + '}' logger.debug("Generating str of required compIDs") alreadyWritten = [] aps, cas = self._apim.getActionsByType() compIDs = '[' for action in cas: alreadyWritten.append(action.getTargetComponent().getId()) compIDs += str(action.getTargetComponent().getId()) + ', ' # We also want the visibilitybehaviors' triggeractions' components' IDs vbs = self._tguim.getVisibilityBehaviors() for id in vbs: vb = vbs[id] name = vb.methodName triggerAction = vb.getTriggerAction() if name not in alreadyWritten and triggerAction is not None: compIDs += str(triggerAction.getTargetComponent().getId()) + ', ' compIDs = compIDs[:-2] + ']' # remove the final ", " and close bracket logger.debug("Format BaseApp superclass call with necessary info") try: appStr = appStr.format(exeLoc="'" + self._exeLoc + "'", options=optStr, name="'" + self._name + "'", backend="'" + self._backend + "'", reqCompIDs=compIDs) except Exception as e: logger.exception(e) logger.debug("Writing BaseApp") f.write(appStr) logger.debug("Writing methods generated from actions that are used in action pipelines.") alreadyWritten = [] for action in cas: alreadyWritten.append(action.getMethodName()) f.write(action.getMethod()) logger.debug("Writing methods generated from actions that are used by visibility behaviors.") for id in vbs: vb = vbs[id] name = vb.methodName triggerAction = vb.getTriggerAction() if name not in alreadyWritten and triggerAction is not None: f.write(triggerAction.getMethod()) logger.debug("Writing methods generated from action pipelines.") for ap in aps: f.write(ap.getMethod()) logger.info("Finished generating custom application driver.") self.stepComplete.emit() def copyNecessaryFiles(self) -> None: """ Adds all necessary files for compiler to work into created directory :return: None """ self.stepStarted.emit("Copying necessary files") # Only necessary when using multiple files # # make necessary directories before copying files # targetDirs = ['data', 'data/tguim', 'tguiil', 'libs'] # 'data/apim', # for tdir in targetDirs: # tdir = os.path.join(self._srcFolder, tdir) # if not os.path.exists(tdir): # os.mkdir(tdir) for path in self._necessaryFiles: src = os.path.abspath(os.path.join(env.FACILE_SRC_DIR, path)) dest = os.path.abspath(os.path.join(self._srcFolder, path)) logger.info(f"Copying file: {src} -> {dest}") try: copyfile(src, dest) except Exception as e: logger.critical("Unable to copy file.") logger.exception(e) self.stepComplete.emit() def saveTGUIM(self): """ Saves the tguim in the API folder. Saves project as well. :return: None """ msg = "Saving target GUI model" self.stepStarted.emit(msg) logger.info(msg) self.statem._project.save() with open(os.path.join(self._srcFolder, "tguim.json"), "w+") as f: f.write(json.dumps(self._tguim.asDict())) self.stepComplete.emit() def generateSetupFile(self): """ Generates the setup file for installing the API """ # Create setup.py so user can install install API as a package with pip. msg = "Generating setup.py file" self.stepStarted.emit(msg) logger.info(msg) setupTempFile = open(os.path.join(dir, "setup-template.txt"), 'r') setupStr = setupTempFile.read().format(projectName=self.statem._project.getAPIName(), projectVersion='0.1.0') # TODO Add versioning setupTempFile.close() setupFile = open(os.path.join(self._saveFolder, 'setup.py'), 'w') setupFile.write(setupStr) setupFile.close() self.stepComplete.emit() def generateInitFile(self): """ Generates the init file so the package can be installed as an API """ # Create __init__.py so API is a package. msg = "Generating __init__.py file" self.stepStarted.emit(msg) logger.info(msg) with open(os.path.join(dir, "__init__template.txt"), 'r') as initTempFile: targetAppName = self.statem._project.getExecutableFile().split('/')[-1].split('.')[0] # '/app.exe' -> 'app' targetAppName = targetAppName[0].upper() + targetAppName[1:] # 'app' -> 'App' initStr = initTempFile.read().format(targetApplicationName=targetAppName) with open(os.path.join(self._srcFolder, '__init__.py'), 'w') as initFile: initFile.write(initStr) self.stepComplete.emit() def installAPI(self): """ Installs the generated API to PATH """ msg = "Installing as python package" self.stepStarted.emit(msg) logger.info(msg) os.chdir(self._saveFolder) os.system(self._compProf.interpExeDir + " -m pip install . 1>install.log 2>&1") rmtree('setup.py') # Delete setup.py after it's used logger.info("Finished installing python package") self.stepComplete.emit() def copyHelpFiles(self): """ Generates files that give the basic structure and outline of a functional script. Will only write them if they do not yet exist, to avoid overwriting any existing work in the automate.py file. """ msg = "Copying help files" self.stepStarted.emit(msg) logger.info(msg) if not os.path.exists(os.path.join(self._saveFolder, "automate.py")): with open(os.path.join(self._saveFolder, "automate.py"), "w+") as f: with open(os.path.join(dir, 'automate-template.txt'), 'r') as g: autoStr = g.read() targetAppName = self.statem._project.getExecutableFile().split('/')[-1].split('.')[0] targetAppName = targetAppName[0].upper() + targetAppName[1:] # 'app' -> 'App' f.write(autoStr.format(name=self._name, targetapp=targetAppName)) # Remove run script and rewrite every time so that interpreter gets written to it if os.path.exists(os.path.join(self._saveFolder, "run-script.bat")): os.remove(os.path.join(self._saveFolder, "run-script.bat")) with open(os.path.join(self._saveFolder, "run-script.bat"), "w+") as f: with open(os.path.join(dir, "run-script-template.bat"), 'r') as g: rsStr = g.read() f.write(rsStr.format(interpreterLocation=self._compProf.interpExeDir)) self.stepComplete.emit() @nongui def installRequirements(self): """ Installs the necessary requirements to the chosen python interpreter, if they aren't already installed. """ # Get currently installed packages in a list current = check_output([self._compProf.interpExeDir, '-m', 'pip', 'freeze']) installed = [r.decode().split('==')[0] for r in current.split()] # Get necessary packages in a list with open(os.path.join(dir, "api_requirements.txt"), 'r') as f: reqFile = f.read() required = [r.split('==')[0] for r in reqFile.split()] # Check for each package and install the missing ones diff = set(required) - set(installed) for package in diff: msg = "Installing package: " + package self.stepStarted.emit(msg) logger.info(msg) check_call([self._compProf.interpExeDir, '-m', 'pip', 'install', package], stdout=DEVNULL, stderr=STDOUT) self.stepComplete.emit() @log_exceptions(logger=logger) def compileAPI(self): """ Generates the functional API: the final result of compilation. """ logger.info("Compiling API") self.installRequirements() if not sys.executable.endswith('facile.exe'): self._dev_generateAPICore() self.copyNecessaryFiles() self.saveTGUIM() if self._compProf.installApi: self.generateSetupFile() self.generateInitFile() # We want this regardless of installing the api or not self.generateCustomApp() if self._compProf.installApi: self.installAPI() self.copyHelpFiles() if not sys.executable.endswith('facile.exe'): os.remove(os.path.join(env.FACILE_DIR, 'apicore.pyd')) self.finished.emit() logger.info("Finished compiling API")

40.208333

120

0.585431

1,801

16,405

5.262077

0.247085

0.024058

0.027435

0.019204

0.271288

0.22887

0.188773

0.128522

0.082093

0.048328

0

0.002521

0.298689

16,405

407

121

40.307125

0.821208

0.271381

0

0.255507

0

0.134869

0.010355

0

0.007371

0

1

0.057269

false

0

0.061674

0

0.145374

0

null

0

null

0

1

0

09d4d5139b90907a08147b1f476920cdd503f04c

15,484

py

Python

src/testing/functionaltests/webtest.py

pgecsenyi/piepy

37bf6cb5bc8c4f9da3f695216beda7353d79fb29

[ "MIT" ]

1

2018-03-26T22:39:36.000Z

src/testing/functionaltests/webtest.py

pgecsenyi/piepy

37bf6cb5bc8c4f9da3f695216beda7353d79fb29

[ "MIT" ]

null

src/testing/functionaltests/webtest.py

pgecsenyi/piepy

37bf6cb5bc8c4f9da3f695216beda7353d79fb29

[ "MIT" ]

null

""" Web unit tests """ # pylint: disable=too-many-public-methods import time import unittest import requests from testing.communicationhelper import get_json, put_json from testing.functions import are_expected_items_in_list, are_expected_kv_pairs_in_list, \ get_item_from_embedded_dictionary from testing.servermanager import ServerManager from testing.testhelper import TestHelper from testing.videotestenvironment import VideoTestEnvironment class WebTest(unittest.TestCase): #################################################################################################################### # Initialization and cleanup. #################################################################################################################### @classmethod def setUpClass(cls): # Set private static attributes. cls._episode_title_id = 0 cls._file_id = 0 cls._language_id = 0 cls._main_executable = 'main.py' cls._parent_id = 0 cls._playlist_id = 0 cls._quality_id = 0 # Create TestHelper. cls._helper = TestHelper() cls._helper.add_environment(VideoTestEnvironment()) # Create test configuration and files. cls._helper.create_configuration() cls._helper.create_files() # Create Server Manager and start the server. cls._server_manager = ServerManager(cls._main_executable, cls._helper.config_path) cls._server_manager.start() if not cls._server_manager.wait_for_initialization(cls._helper.test_service_base_url): print('The service is unavailable.') cls.tearDownClass() @classmethod def tearDownClass(cls): cls._server_manager.stop() cls._helper.clean() #################################################################################################################### # Real test methods. #################################################################################################################### def test_1_rebuild(self): # Arrange. rebuild_url = WebTest._helper.build_url('rebuild') status_url = WebTest._helper.build_url('status') # Act. requests.get(rebuild_url) # Wait until database is building. Poll status in every 2 seconds. number_of_retries = 0 result = '' while number_of_retries < 10: data = get_json(status_url) result = data['status']['synchronization'] if result == 'not running': break number_of_retries += 1 time.sleep(2) # Assert. self.assertEqual(result, 'not running', 'Rebuild failed.') def test_2_categories(self): # Arrange. url = WebTest._helper.build_url('categories') # Act. data = get_json(url) # Assert. are_expected_items_in_list(self, data, 'categories') are_expected_items_in_list(self, data['categories'], 'audio', 'image', 'video') def test_3_video_languages(self): # Arrange. url = WebTest._helper.build_url('video/languages') # Act. data = get_json(url) # Assert. expected_languages = ['(Uncategorized)', 'English', 'Finnish', 'German', 'Greek', 'Hindi', 'Hungarian'] are_expected_items_in_list(self, data, 'languages') are_expected_kv_pairs_in_list(self, data['languages'], 'language', expected_languages) WebTest._language_id = get_item_from_embedded_dictionary( data['languages'], 'language', 'Greek', 'id') def test_4_video_qualities(self): # Arrange. url = WebTest._helper.build_url('video/qualities') # Act. data = get_json(url) # Assert. expected_qualities = ['(Uncategorized)', 'LQ', 'HQ', 'HD (720p)', 'HD (1080p)'] are_expected_items_in_list(self, data, 'qualities') are_expected_kv_pairs_in_list(self, data['qualities'], 'quality', expected_qualities) WebTest._quality_id = get_item_from_embedded_dictionary( data['qualities'], 'quality', 'HD (720p)', 'id') def test_5_01_video_titles(self): # Arrange. url = WebTest._helper.build_url('video/titles') # Act. data = get_json(url) # Assert. expected_titles = ['(Uncategorized)', 'Triple Payback', 'Battle of Impact', 'Double Prejudice', 'Screw driver 4 (1975)', 'Compressor Head (2014)', 'Family'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) WebTest._parent_id = get_item_from_embedded_dictionary( data['titles'], 'title', 'Compressor Head (2014)', 'id') def test_5_02_video_titles_by_l(self): """ Query video titles by language. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}'.format(WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Battle of Impact', 'Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_03_video_titles_by_p(self): """ Query video titles by parent. """ # Arrange. url = WebTest._helper.build_url('video/titles?parent={}'.format(WebTest._parent_id)) # Act. data = get_json(url) # Assert. expected_titles = [ 'Compressor Head [1x01] Variable Length Codes', 'Compressor Head [1x03] Markov Chain Compression', 'Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_04_video_titles_by_q(self): """ Query video titles by quality. """ # Arrange. url = WebTest._helper.build_url('video/titles?quality={}'.format(WebTest._quality_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Triple Payback', 'Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_05_video_titles_by_l_p(self): """ Query video titles by language and parent. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&parent={}'.format( WebTest._language_id, WebTest._parent_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_06_video_titles_by_l_q(self): """ Query video titles by language and quality. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&quality={}'.format( WebTest._language_id, WebTest._quality_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_07_video_titles_by_p_q(self): """ Query video titles by parent and quality. """ # Arrange. url = WebTest._helper.build_url( 'video/titles?parent={}&quality={}'.format(WebTest._parent_id, WebTest._quality_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) WebTest._episode_title_id = get_item_from_embedded_dictionary( data['titles'], 'title', 'Compressor Head [1x01] Variable Length Codes', 'id') def test_5_08_video_titles_by_sl(self): """ Query video titles by subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?subtitle={}'.format(WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_09_video_titles_by_l_sl(self): """ Query video titles by language and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&subtitle={}'.format( WebTest._language_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_10_video_titles_by_p_sl(self): """ Query video titles by parent and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?parent={}&subtitle={}'.format( WebTest._parent_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_11_video_titles_by_q_sl(self): """ Query video titles by quality and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?quality={}&subtitle={}'.format( WebTest._quality_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_12_video_titles_by_l_p_sl(self): """ Query video titles by language, parent and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&parent={}&subtitle={}'.format( WebTest._language_id, WebTest._parent_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_13_video_titles_by_l_q_sl(self): """ Query video titles by language, quality and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&quality={}&subtitle={}'.format( WebTest._language_id, WebTest._quality_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head (2014)'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_5_14_video_titles_by_l_p_q_sl(self): """ Query video titles by language, parent, quality and subtitle language. """ # Arrange. url = WebTest._helper.build_url('video/titles?language={}&parent={}&quality={}&subtitle={}'.format( WebTest._language_id, WebTest._parent_id, WebTest._quality_id, WebTest._language_id)) # Act. data = get_json(url) # Assert. expected_titles = ['Compressor Head [1x01] Variable Length Codes'] are_expected_items_in_list(self, data, 'titles') are_expected_kv_pairs_in_list(self, data['titles'], 'title', expected_titles) def test_6_search(self): # Arrange. url = WebTest._helper.build_url('search/family') # Act. data = get_json(url) # Assert. expected_titles = ['Family', 'Family [01] Intro'] are_expected_items_in_list(self, data, 'videos') are_expected_kv_pairs_in_list(self, data['videos'], 'title', expected_titles) def test_7_details(self): # Arrange. url = WebTest._helper.build_url('video/details/{}'.format(WebTest._episode_title_id)) # Act. data = get_json(url) # Assert. are_expected_items_in_list(self, data['details'], 'id', 'files', 'subtitles', 'title') self.assertEqual('Compressor Head [1x01] Variable Length Codes', data['details']['title'], 'Wrong title.') are_expected_kv_pairs_in_list( self, data['details']['files'], 'language', ['Finnish', 'Greek', 'Greek']) are_expected_kv_pairs_in_list( self, data['details']['files'], 'quality', ['HD (720p)', 'HD (720p)', 'LQ']) are_expected_kv_pairs_in_list( self, data['details']['subtitles'], 'language', ['English', 'Greek', 'Greek', 'Hungarian']) WebTest._file_id = data['details']['files'][0]['id'] def test_8_01_playlist_add(self): # Arrange. url = WebTest._helper.build_url('playlist/add') payload = { 'title' : 'Test playlist', 'tracks': [ {'category' : 'video', 'file' : WebTest._file_id}]} # Act. data = put_json(url, payload) # Assert. self.assertEqual('Test playlist', data['playlist']['title'], 'Wrong title for the playlist.') WebTest._playlist_id = data['playlist']['id'] def test_8_02_playlist_add_track(self): # Arrange. url = WebTest._helper.build_url('playlist/add-track') payload = {'playlist' : WebTest._playlist_id, 'category' : 'video', 'file' : WebTest._file_id} # Act. data = put_json(url, payload) # Assert. self.assertEqual('video', data['track']['category'], 'Wrong category.') self.assertEqual('Compressor Head [1x01] Variable Length Codes', data['track']['title'], 'Wrong title.') def test_8_03_playlists(self): # Arrange. url = WebTest._helper.build_url('playlist/all') # Act. data = get_json(url) # Assert. self.assertNotEqual(None, data['playlists'], 'There are no playlists in the response.') self.assertEqual(1, len(data['playlists']), 'Incorrect number of playlists.') self.assertEqual('Test playlist', data['playlists'][0]['title'], 'Incorrect playlist title.')

32.529412

120

0.58822

1,703

15,484

5.018203

0.11744

0.05406

0.046806

0.065528

0.685116

0.647905

0.623684

0.55956

0.497192

0.444184

0

0.014447

0.266856

15,484

475

121

32.597895

0.738372

0.091837

0

0.429167

0

0.191525

0.034052

0

0.033333

1

0.104167

false

0

0.033333

0

0.141667

0.004167

0

null

0

null

0

1

0

09d79f0d227847749db1ddb7eb6acbb60326e8b8

862

py

Python

10_Name_Card_Detection/pytorch-faster-rcnn/lib/datasets/factory.py

ZeroWeight/Pattern-Recognize

ce18ab7d218840978f546a94d02d4183c9dc1aac

[ "MIT" ]

4

2018-07-30T01:46:22.000Z

2019-04-09T12:23:52.000Z

10_Name_Card_Detection/pytorch-faster-rcnn/lib/datasets/factory.py

ZeroWeight/Pattern-Recognize

ce18ab7d218840978f546a94d02d4183c9dc1aac

[ "MIT" ]

null

10_Name_Card_Detection/pytorch-faster-rcnn/lib/datasets/factory.py

ZeroWeight/Pattern-Recognize

ce18ab7d218840978f546a94d02d4183c9dc1aac

[ "MIT" ]

1

2020-02-25T05:09:06.000Z

# -------------------------------------------------------- # Fast R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick # -------------------------------------------------------- """Factory method for easily getting imdbs by name.""" __sets = {} from datasets.name_card import name_card import numpy as np for split in ['trainval', 'test']: name = 'name_card_real_{}'.format(split) __sets[name] = (lambda split=split: name_card(split,'NameCardReal')) __sets['name_card_fake_train'] = (lambda: name_card('trainval','NameCardFake')) def get_imdb(name): """Get an imdb (image database) by name.""" if name not in __sets: raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]() def list_imdbs(): """List all registered imdbs.""" return list(__sets.keys())

29.724138

79

0.612529

106

862

4.754717

0.575472

0.095238

0.055556

0

0.005355

0.133411

862

28

80

30.785714

0.669344

0.406032

0

0.203666

0

1

0.153846

false

0

0.153846

0

0.461538

0

null

0

null

0

1

0

09dbc482da6f2620a0ec95d44dab6ffbe0c052f9

4,439

py

Python

monopoly.py

michaelhutton/monopoly

d3adcf524dfb015dbdaaadf905ca8cc4396fde3e

[ "MIT" ]

null

monopoly.py

michaelhutton/monopoly

d3adcf524dfb015dbdaaadf905ca8cc4396fde3e

[ "MIT" ]

null

monopoly.py

michaelhutton/monopoly

d3adcf524dfb015dbdaaadf905ca8cc4396fde3e

[ "MIT" ]

null

import random squares = [ "Go", "Mediterranean Ave.", "Community Chest", "Baltic Ave.", "Income Tax", "Reading Railroad", "Oriental Ave.", "Chance", "Vermont Ave.", "Connecticut Ave.", "Jail", "St. Charles Place", "Electric Company", "States Ave.", "Virginia Ave.", "Pennsylvania Railroad", "St. James Place", "Community Chest", "Tennessee Ave.", "New York Ave.", "Free Parking", "Kentucky Ave.", "Chance", "Indiana Ave.", "Illinois Ave.", "B. & O. Railroad", "Atlantic Ave.", "Ventnor Ave.", "Water Works", "Marvin Gardens", "Go To Jail", "Pacific Ave.", "North Carolina Ave.", "Community Chest", "Pennsylvania Ave.", "Short Line Railroad", "Chance", "Park Place", "Luxury Tax", "Boardwalk" ] SQUARES_LENGTH = len(squares) chance_cards = [ "Advance to Go", "Advance to Illinois Ave.", "Advance to St. Charles Place", "Advance token to nearest Utility", "Advance token to the nearest Railroad", "Bank pays you dividend of $50", "Get out of Jail Free Card", "Go Back 3 Spaces", "Go to Jail", "Make general repairs on all your property", "Pay poor tax of $15", "Take a trip to Reading Railroad", "Take a walk on the Boardwalk", "You have been elected Chairman of the Board", "Your building loan matures - Collect $150" ] community_chest_cards = [ "Advance to Go", "Bank error in your favor - Collect $200", "Doctor's fees - Pay $50", "From sale of stock you get $50", "Get Out of Jail Free Card", "Go to Jail", "Grand Opera Night - Collect $50 from every player for opening night seats", "Holiday Fund matures - Receive $100", "Income tax refund - Collect $20", "Life insurance matures - Collect $100", "Pay hospital fees of $100", "Pay school fees of $150", "Receive $25 consultancy fee", "You are assessed for street repairs - $40 per house - $115 per hotel", "You have won second prize in a beauty contest - Collect $10", "You inherit $100" ] def roll_dice(): return [random.randint(1,6),random.randint(1,6)] def pick_card(player, deck): # Take a random card from either the chance or cc deck # and return players new position last_card = len(deck)-1 choice = random.randint(0,last_card) card = deck[choice] print("Started at: " + str(player["pos"])) if(card == "Advance to Go"): player["pos"] = 0 elif(card == "Advance to Illinois Ave."): player["pos"] = 24 elif(card == "Advance to St. Charles Place"): player["pos"] = 11 elif(card == "Advance token to nearest Utility"): if(player["pos"] == 7): player["pos"] = 12 # Electric Company else: # Pos 22 and 36 go to the same place player["pos"] = 28 # Water Works elif(card == "Advance token to the nearest Railroad"): if(player["pos"] == 7): player["pos"] = 5 # Reading elif(player["pos"] == 22): player["pos"] = 25 # B and O elif(player["pos"] == 36): player["pos"] = 35 # Short Line elif(card == "Go Back 3 Spaces"): player["pos"] = player["pos"] - 3 elif(card == "Go to Jail"): player["pos"] = 10 player["in_jail"] = True elif(card == "Take a trip to Reading Railroad"): player["pos"] = 5 elif(card == "Take a walk on the Boardwalk"): player["pos"] = 39 print("Received card: " + card) print("Ended at: " + str(player["pos"])) return player player1 = { "pos": 0, "doubles_in_a_row": 0, "in_jail": False } for turn in range(1,100): dice = roll_dice() print(dice) if(dice[0] == dice[1]): player1["doubles_in_a_row"] = player1["doubles_in_a_row"] + 1 else: player1["doubles_in_a_row"] = 0 # TODO: if the player has rolled 3 doubles, go to jail! player1["pos"] = (player1["pos"] + dice[0] + dice[1]) % SQUARES_LENGTH # TODO: Check if its a go to jail space if(squares[player1["pos"]] == "Chance"): print("chance!") print(player1) pick_card(player1, chance_cards) print(player1) if(squares[player1["pos"]] == "Community Chest"): print("CC!") pick_card(player1, community_chest_cards) print("Turn " + str(turn) + ": " + squares[player1["pos"]])

29.593333

80

0.581437

586

4,439

4.354949

0.331058

0.067006

0.018809

0.020376

0.185345

0.098746

0.018809

0

0.032807

0.272133

4,439

149

81

29.791946

0.757041

0.060149

0

0.130435

0

0.457823

0

0.006711

0

1

0.014493

false

0

0.007246

0.036232

0.065217

0

null

0

null

0

1

0

09ddae526c3cd9bcfe820b2b4ae3706b5e1e7c32

7,769

py

Python

coinzdense/app.py

pibara/coinzdense-python

f051770b71fa0afe935eb0d2079dab21eea9432d

[ "BSD-3-Clause" ]

null

coinzdense/app.py

pibara/coinzdense-python

f051770b71fa0afe935eb0d2079dab21eea9432d

[ "BSD-3-Clause" ]

null

coinzdense/app.py

pibara/coinzdense-python

f051770b71fa0afe935eb0d2079dab21eea9432d

[ "BSD-3-Clause" ]

null

#!/usr/bin/python3 from coinzdense.signing import SigningKey as _SigningKey from coinzdense.validation import ValidationEnv as _ValidationEnv from coinzdense.wallet import create_wallet as _create_wallet from coinzdense.wallet import open_wallet as _open_wallet def _keys_per_signature(hashlen, otsbits): return 2*(((hashlen*8-1) // otsbits)+1) def _sub_sub_keyspace_usage(hashlen, otsbits, height): return 1 + _keys_per_signature(hashlen, otsbits) * (1 << height) def _sub_keyspace_usage(hashlen, otsbits, heights): usage = _sub_sub_keyspace_usage(hashlen, otsbits,heights[0]) if len(heights) > 1: usage += (1 << heights[0]) * _sub_keyspace_usage(hashlen, otsbits, heights[1:]) return usage def _keyspace_usage(hashlen, otsbits, keyspace): usage = (1 << sum(keyspace[0]["heights"])) + _sub_keyspace_usage(hashlen, otsbits, keyspace[0]["heights"]) if len(keyspace) > 1: usage += (1 << keyspace[0]["reserve"]) * _keyspace_usage(hashlen, otsbits, keyspace[1:]) return usage class KeySpace: def __init__(self, hashlen, otsbits, keyspace, offset=0, size=1<<64, state=None): self.hashlen = hashlen self.otsbits = otsbits self.keyspace = keyspace if state is None: self.state = {} self.state["offset"] = offset self.state["stack"] = size reserve_bits = keyspace[0].get("reserve", None) if reserve_bits is None: self.state["heap_start"] = offset self.state["heap"] = offset self.state["has_reserved"] = False self.state["reserved_heap_start"] = offset self.state["reserved_heap"] = offset else: reserved = (1 << reserve_bits) * _keyspace_usage(hashlen, otsbits, keyspace[1:]) self.state["heap_start"] = offset + reserved self.state["heap"] = offset + reserved self.state["has_reserved"] = True self.state["reserved_heap_start"] = offset self.state["reserved_heap"] = offset self.state["own_offset"] = self.state["heap"] self.state["heap"] += (1 << sum(keyspace[0]["heights"])) + _sub_keyspace_usage(hashlen, otsbits, keyspace[0]["heights"]) else: self.state = state def own_offset(self): return self.state["own_offset"] def allocate_subspace(self): keyspace_size = _keyspace_usage(hashlen, otsbits, keyspace[1:]) self.state["stack"] -= keyspace_size return KeySpace(self.hashlen, self.otsbits, self.keyspace[1:], self.state["stack"], keyspace_size) def get_state(self): return self.state class BlockChainEnv: def __init__(self, conf): assert "appname" in conf, "Please run coinzdense-lint on your blockchain RC" assert "hashlen" in conf, "Please run coinzdense-lint on your blockchain RC" assert "otsbits" in conf, "Please run coinzdense-lint on your blockchain RC" assert "keyspace" in conf, "Please run coinzdense-lint on your blockchain RC" self.appname = conf["appname"] self.hashlen = conf["hashlen"] self.otsbits = conf["otsbits"] self.keyspace = conf["keyspace"] if "hierarchy" in conf: self.hierarchy = conf["hierarchy"] else: self.hierarchy = {} if "sub_path" in conf: self.subpath = conf["sub_path"] else: self.subpath = [] assert isinstance(self.appname, str), "Please run coinzdense-lint on your blockchain RC" assert isinstance(self.hashlen, int), "Please run coinzdense-lint on your blockchain RC" assert isinstance(self.otsbits, int), "Please run coinzdense-lint on your blockchain RC" assert isinstance(self.keyspace, list), "Please run coinzdense-lint on your blockchain RC" assert isinstance(self.hierarchy, dict), "Please run coinzdense-lint on your blockchain RC" assert isinstance(self.subpath, list), "Please run coinzdense-lint on your blockchain RC" assert self.hashlen > 15 assert self.hashlen < 65 assert self.otsbits > 3 assert self.otsbits < 17 self.depth = 0 self._check_hierarchy() for idx, val in enumerate(self.keyspace): assert isinstance(val, dict), "Please run coinzdense-lint on your blockchain RC" total_height = 0 assert "heights" in val, "Please run coinzdense-lint on your blockchain RC" assert isinstance(val["heights"], list), "Please run coinzdense-lint on your blockchain RC" assert len(val["heights"]) > 1, "Please run coinzdense-lint on your blockchain RC" assert len(val["heights"]) < 33, "Please run coinzdense-lint on your blockchain RC" for idx2,height in enumerate(val["heights"]): assert isinstance(height, int), "Please run coinzdense-lint on your blockchain RC" assert height > 2, "Please run coinzdense-lint on your blockchain RC" assert height < 17, "Please run coinzdense-lint on your blockchain RC" total_height += height if idx < len(self.keyspace) -1: assert "reserve" in val, "Please run coinzdense-lint on your blockchain RC" assert isinstance(val["reserve"], int), "Please run coinzdense-lint on your blockchain RC" assert val["reserve"] > 1, "Please run coinzdense-lint on your blockchain RC" assert val["reserve"] < total_height - 1, "Please run coinzdense-lint on your blockchain RC" else: assert "reserve" not in val, "Please run coinzdense-lint on your blockchain RC" for subpath_part in self.subpath: assert isinstance(subpath_part, str), "Please run coinzdense-lint on your blockchain RC" total = _keyspace_usage(self.hashlen, self.otsbits, self.keyspace) assert total.bit_length() < 65, "Please run coinzdense-lint on your blockchain RC" def _check_hierarchy(self, sub_hierarchy=None, depth=0): if sub_hierarchy is not None: my_hierarchy = sub_hierarchy else: my_hierarchy = self.hierarchy my_depth = depth + 1 if my_depth > self.depth: self.depth = my_depth for key, val in my_hierarchy.items(): assert isinstance(val, dict), "Please run coinzdense-lint on your blockchain RC" self._check_hierarchy(val, my_depth) def __getitem__(self, key): if key in self.hierarchy: subconf = {} subconf["appname"] = self.appname subconf["hashlen"] = self.hashlen subconf["otsbits"] = self.otsbits subconf["keyspace"] = self.keyspace[1:] subconf["hierarchy"] = self.hierarchy[key] subconf["sub_path"] = self.subpath[:] + [key] return BlockChainEnv(subconf) else: raise KeyError("No sub-key hierarchy named " + key) def get_signing_key(self, wallet, idx=0, idx2=0, backup=None): path = [self.appname] + self.subpath return _SigningKey(self.hashlen, self.otsbits, self.keyspace, path, self.hierarchy, wallet, idx, idx2, backup) def get_validator(self): path = [self.appname] + self.subpath return _ValidationEnv(self.hashlen, self.otsbits, self.keyspace, path, self.hierarchy) def create_wallet(self, salt, key, password): path = [self.appname] + self.subpath return _create_wallet(salt, key, password, path) def open_wallet(self, wdata, password): path = [self.appname] + self.subpath return _open_wallet(wdata, password, path)

50.122581

132

0.635603

945

7,769

5.10582

0.113228

0.048497

0.102383

0.123938

0.52

0.490363

0.426736

0.399378

0.372228

0.319378

0

0.010256

0.259493

7,769

154

133

50.448052

0.828437

0.002188

0

0.134752

0

0.218036

0

0.212766

1

0.106383

false

0.028369

0.234043

0

null

0

null

0

1

0

09de00e54d3860203b7729e1854754335ac141d7

1,296

py

Python

src/asyncdataflow/inspector.py

tomaszkingukrol/async-data-flow

1572ef101cb0e6a0f27a77401538a4620ee9939f

[ "Apache-2.0" ]

null

src/asyncdataflow/inspector.py

tomaszkingukrol/async-data-flow

1572ef101cb0e6a0f27a77401538a4620ee9939f

[ "Apache-2.0" ]

null

src/asyncdataflow/inspector.py

tomaszkingukrol/async-data-flow

1572ef101cb0e6a0f27a77401538a4620ee9939f

[ "Apache-2.0" ]

null

from collections.abc import Iterable from typing import Callable, Tuple import inspect from .definition import DataFlowInspector from .exceptions import DataFlowFunctionArgsError, DataFlowNotCallableError, DataFlowEmptyError, DataFlowNotTupleError class DataFlowInspect(DataFlowInspector): ''' Function inspection defined in DataFlow ''' def check_dataflow_args(self, dataflow: tuple): if isinstance(dataflow, tuple): if dataflow: for task in dataflow: if isinstance(task, Iterable): self.check_dataflow_args(task) elif isinstance(task, Callable): _check_positional_or_keyword_args(task) else: raise DataFlowNotCallableError(task) else: raise DataFlowEmptyError() else: raise DataFlowNotTupleError(dataflow) def _check_positional_or_keyword_args(func: Callable) -> bool: ''' Check that function has only POSITIONAL_OR_KEYWORD arguments. ''' inspect_args = inspect.signature(func).parameters.values() for arg in inspect_args: if str(arg.kind) != 'POSITIONAL_OR_KEYWORD': raise DataFlowFunctionArgsError(func.__name__, arg)

35.027027

118

0.655864

120

1,296

6.883333

0.416667

0.058111

0.09201

0.058111

0.067797

0

0.280093

1,296

36

119

36

0.885316

0.08179

0

0.12

0

0.017918

0

1

0.08

false

0

0.2

0

0.32

0

null

0

null

0

1

0

09e5ab892fd8685aedec11f8378615ed2931fa1c

891

py

Python

processing_pipeline/extractionless_registration.py

SijRa/Brain-Image-Analysis-using-Deep-Learning

a35411bda6e39eff57f715a695b7fb6a30997706

[ "MIT" ]

2

2022-01-04T16:54:20.000Z

2022-01-24T03:01:14.000Z

processing_pipeline/extractionless_registration.py

SijRa/Brain-Image-Analysis-using-Deep-Learning

a35411bda6e39eff57f715a695b7fb6a30997706

[ "MIT" ]

null

processing_pipeline/extractionless_registration.py

SijRa/Brain-Image-Analysis-using-Deep-Learning

a35411bda6e39eff57f715a695b7fb6a30997706

[ "MIT" ]

1

2020-07-05T09:30:11.000Z

from ants import registration, image_read, image_write, resample_image, crop_image from os import listdir mri_directory = "ADNI_baseline_raw/" template_loc = "MNI152_2009/mni_icbm152_t1_tal_nlin_sym_09a.nii" template = image_read(template_loc) template = resample_image(template, (192, 192, 160), True, 4) #template = crop_image(template) for scan in listdir(mri_directory): id = scan.split('.')[0] filename = "ADNI_original_registered/" + id + ".nii" img_path = mri_directory + scan image = image_read(img_path, reorient=True) if image.shape[1] != 192: print("- Resampling -") image = resample_image(image, (192, 192, 160), True, 4) registered_dict = registration(fixed=template, moving=image, type_of_transform="SyNRA") #img = crop_image(registered_dict['warpedmovout']) image_write(registered_dict['warpedmovout'], filename=filename) print("Registered:",scan)

40.5

89

0.751964

122

891

5.213115

0.467213

0.042453

0.059748

0.040881

0.044025

0

0.048593

0.122334

891

22

90

40.5

0.764706

0.089787

0

0.169136

0.088889

0

1

0

false

0

0.117647

0

0.117647

0

null

0

null

0

1

0

09e7e9329ecb594a1ce5f26cf6f1dcdac3d78aef

15,237

py

Python

sp_api/api/finances/models/shipment_item.py

lionsdigitalsolutions/python-amazon-sp-api

7374523ebc65e2e01e37d03fc4009a44fabf2c3b

[ "MIT" ]

null

sp_api/api/finances/models/shipment_item.py

lionsdigitalsolutions/python-amazon-sp-api

7374523ebc65e2e01e37d03fc4009a44fabf2c3b

[ "MIT" ]

null

sp_api/api/finances/models/shipment_item.py

lionsdigitalsolutions/python-amazon-sp-api

7374523ebc65e2e01e37d03fc4009a44fabf2c3b

[ "MIT" ]

null

# coding: utf-8 """ Selling Partner API for Finances The Selling Partner API for Finances helps you obtain financial information relevant to a seller's business. You can obtain financial events for a given order, financial event group, or date range without having to wait until a statement period closes. You can also obtain financial event groups for a given date range. # noqa: E501 OpenAPI spec version: v0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class ShipmentItem(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'seller_sku': 'str', 'order_item_id': 'str', 'order_adjustment_item_id': 'str', 'quantity_shipped': 'int', 'item_charge_list': 'ChargeComponentList', 'item_charge_adjustment_list': 'ChargeComponentList', 'item_fee_list': 'FeeComponentList', 'item_fee_adjustment_list': 'FeeComponentList', 'item_tax_withheld_list': 'TaxWithheldComponentList', 'promotion_list': 'PromotionList', 'promotion_adjustment_list': 'PromotionList', 'cost_of_points_granted': 'Currency', 'cost_of_points_returned': 'Currency' } attribute_map = { 'seller_sku': 'SellerSKU', 'order_item_id': 'OrderItemId', 'order_adjustment_item_id': 'OrderAdjustmentItemId', 'quantity_shipped': 'QuantityShipped', 'item_charge_list': 'ItemChargeList', 'item_charge_adjustment_list': 'ItemChargeAdjustmentList', 'item_fee_list': 'ItemFeeList', 'item_fee_adjustment_list': 'ItemFeeAdjustmentList', 'item_tax_withheld_list': 'ItemTaxWithheldList', 'promotion_list': 'PromotionList', 'promotion_adjustment_list': 'PromotionAdjustmentList', 'cost_of_points_granted': 'CostOfPointsGranted', 'cost_of_points_returned': 'CostOfPointsReturned' } def __init__(self, seller_sku=None, order_item_id=None, order_adjustment_item_id=None, quantity_shipped=None, item_charge_list=None, item_charge_adjustment_list=None, item_fee_list=None, item_fee_adjustment_list=None, item_tax_withheld_list=None, promotion_list=None, promotion_adjustment_list=None, cost_of_points_granted=None, cost_of_points_returned=None): # noqa: E501 """ShipmentItem - a model defined in Swagger""" # noqa: E501 self._seller_sku = None self._order_item_id = None self._order_adjustment_item_id = None self._quantity_shipped = None self._item_charge_list = None self._item_charge_adjustment_list = None self._item_fee_list = None self._item_fee_adjustment_list = None self._item_tax_withheld_list = None self._promotion_list = None self._promotion_adjustment_list = None self._cost_of_points_granted = None self._cost_of_points_returned = None self.discriminator = None if seller_sku is not None: self.seller_sku = seller_sku if order_item_id is not None: self.order_item_id = order_item_id if order_adjustment_item_id is not None: self.order_adjustment_item_id = order_adjustment_item_id if quantity_shipped is not None: self.quantity_shipped = quantity_shipped if item_charge_list is not None: self.item_charge_list = item_charge_list if item_charge_adjustment_list is not None: self.item_charge_adjustment_list = item_charge_adjustment_list if item_fee_list is not None: self.item_fee_list = item_fee_list if item_fee_adjustment_list is not None: self.item_fee_adjustment_list = item_fee_adjustment_list if item_tax_withheld_list is not None: self.item_tax_withheld_list = item_tax_withheld_list if promotion_list is not None: self.promotion_list = promotion_list if promotion_adjustment_list is not None: self.promotion_adjustment_list = promotion_adjustment_list if cost_of_points_granted is not None: self.cost_of_points_granted = cost_of_points_granted if cost_of_points_returned is not None: self.cost_of_points_returned = cost_of_points_returned @property def seller_sku(self): """Gets the seller_sku of this ShipmentItem. # noqa: E501 The seller SKU of the item. The seller SKU is qualified by the seller's seller ID, which is included with every call to the Selling Partner API. # noqa: E501 :return: The seller_sku of this ShipmentItem. # noqa: E501 :rtype: str """ return self._seller_sku @seller_sku.setter def seller_sku(self, seller_sku): """Sets the seller_sku of this ShipmentItem. The seller SKU of the item. The seller SKU is qualified by the seller's seller ID, which is included with every call to the Selling Partner API. # noqa: E501 :param seller_sku: The seller_sku of this ShipmentItem. # noqa: E501 :type: str """ self._seller_sku = seller_sku @property def order_item_id(self): """Gets the order_item_id of this ShipmentItem. # noqa: E501 An Amazon-defined order item identifier. # noqa: E501 :return: The order_item_id of this ShipmentItem. # noqa: E501 :rtype: str """ return self._order_item_id @order_item_id.setter def order_item_id(self, order_item_id): """Sets the order_item_id of this ShipmentItem. An Amazon-defined order item identifier. # noqa: E501 :param order_item_id: The order_item_id of this ShipmentItem. # noqa: E501 :type: str """ self._order_item_id = order_item_id @property def order_adjustment_item_id(self): """Gets the order_adjustment_item_id of this ShipmentItem. # noqa: E501 An Amazon-defined order adjustment identifier defined for refunds, guarantee claims, and chargeback events. # noqa: E501 :return: The order_adjustment_item_id of this ShipmentItem. # noqa: E501 :rtype: str """ return self._order_adjustment_item_id @order_adjustment_item_id.setter def order_adjustment_item_id(self, order_adjustment_item_id): """Sets the order_adjustment_item_id of this ShipmentItem. An Amazon-defined order adjustment identifier defined for refunds, guarantee claims, and chargeback events. # noqa: E501 :param order_adjustment_item_id: The order_adjustment_item_id of this ShipmentItem. # noqa: E501 :type: str """ self._order_adjustment_item_id = order_adjustment_item_id @property def quantity_shipped(self): """Gets the quantity_shipped of this ShipmentItem. # noqa: E501 The number of items shipped. # noqa: E501 :return: The quantity_shipped of this ShipmentItem. # noqa: E501 :rtype: int """ return self._quantity_shipped @quantity_shipped.setter def quantity_shipped(self, quantity_shipped): """Sets the quantity_shipped of this ShipmentItem. The number of items shipped. # noqa: E501 :param quantity_shipped: The quantity_shipped of this ShipmentItem. # noqa: E501 :type: int """ self._quantity_shipped = quantity_shipped @property def item_charge_list(self): """Gets the item_charge_list of this ShipmentItem. # noqa: E501 :return: The item_charge_list of this ShipmentItem. # noqa: E501 :rtype: ChargeComponentList """ return self._item_charge_list @item_charge_list.setter def item_charge_list(self, item_charge_list): """Sets the item_charge_list of this ShipmentItem. :param item_charge_list: The item_charge_list of this ShipmentItem. # noqa: E501 :type: ChargeComponentList """ self._item_charge_list = item_charge_list @property def item_charge_adjustment_list(self): """Gets the item_charge_adjustment_list of this ShipmentItem. # noqa: E501 :return: The item_charge_adjustment_list of this ShipmentItem. # noqa: E501 :rtype: ChargeComponentList """ return self._item_charge_adjustment_list @item_charge_adjustment_list.setter def item_charge_adjustment_list(self, item_charge_adjustment_list): """Sets the item_charge_adjustment_list of this ShipmentItem. :param item_charge_adjustment_list: The item_charge_adjustment_list of this ShipmentItem. # noqa: E501 :type: ChargeComponentList """ self._item_charge_adjustment_list = item_charge_adjustment_list @property def item_fee_list(self): """Gets the item_fee_list of this ShipmentItem. # noqa: E501 :return: The item_fee_list of this ShipmentItem. # noqa: E501 :rtype: FeeComponentList """ return self._item_fee_list @item_fee_list.setter def item_fee_list(self, item_fee_list): """Sets the item_fee_list of this ShipmentItem. :param item_fee_list: The item_fee_list of this ShipmentItem. # noqa: E501 :type: FeeComponentList """ self._item_fee_list = item_fee_list @property def item_fee_adjustment_list(self): """Gets the item_fee_adjustment_list of this ShipmentItem. # noqa: E501 :return: The item_fee_adjustment_list of this ShipmentItem. # noqa: E501 :rtype: FeeComponentList """ return self._item_fee_adjustment_list @item_fee_adjustment_list.setter def item_fee_adjustment_list(self, item_fee_adjustment_list): """Sets the item_fee_adjustment_list of this ShipmentItem. :param item_fee_adjustment_list: The item_fee_adjustment_list of this ShipmentItem. # noqa: E501 :type: FeeComponentList """ self._item_fee_adjustment_list = item_fee_adjustment_list @property def item_tax_withheld_list(self): """Gets the item_tax_withheld_list of this ShipmentItem. # noqa: E501 :return: The item_tax_withheld_list of this ShipmentItem. # noqa: E501 :rtype: TaxWithheldComponentList """ return self._item_tax_withheld_list @item_tax_withheld_list.setter def item_tax_withheld_list(self, item_tax_withheld_list): """Sets the item_tax_withheld_list of this ShipmentItem. :param item_tax_withheld_list: The item_tax_withheld_list of this ShipmentItem. # noqa: E501 :type: TaxWithheldComponentList """ self._item_tax_withheld_list = item_tax_withheld_list @property def promotion_list(self): """Gets the promotion_list of this ShipmentItem. # noqa: E501 :return: The promotion_list of this ShipmentItem. # noqa: E501 :rtype: PromotionList """ return self._promotion_list @promotion_list.setter def promotion_list(self, promotion_list): """Sets the promotion_list of this ShipmentItem. :param promotion_list: The promotion_list of this ShipmentItem. # noqa: E501 :type: PromotionList """ self._promotion_list = promotion_list @property def promotion_adjustment_list(self): """Gets the promotion_adjustment_list of this ShipmentItem. # noqa: E501 :return: The promotion_adjustment_list of this ShipmentItem. # noqa: E501 :rtype: PromotionList """ return self._promotion_adjustment_list @promotion_adjustment_list.setter def promotion_adjustment_list(self, promotion_adjustment_list): """Sets the promotion_adjustment_list of this ShipmentItem. :param promotion_adjustment_list: The promotion_adjustment_list of this ShipmentItem. # noqa: E501 :type: PromotionList """ self._promotion_adjustment_list = promotion_adjustment_list @property def cost_of_points_granted(self): """Gets the cost_of_points_granted of this ShipmentItem. # noqa: E501 :return: The cost_of_points_granted of this ShipmentItem. # noqa: E501 :rtype: Currency """ return self._cost_of_points_granted @cost_of_points_granted.setter def cost_of_points_granted(self, cost_of_points_granted): """Sets the cost_of_points_granted of this ShipmentItem. :param cost_of_points_granted: The cost_of_points_granted of this ShipmentItem. # noqa: E501 :type: Currency """ self._cost_of_points_granted = cost_of_points_granted @property def cost_of_points_returned(self): """Gets the cost_of_points_returned of this ShipmentItem. # noqa: E501 :return: The cost_of_points_returned of this ShipmentItem. # noqa: E501 :rtype: Currency """ return self._cost_of_points_returned @cost_of_points_returned.setter def cost_of_points_returned(self, cost_of_points_returned): """Sets the cost_of_points_returned of this ShipmentItem. :param cost_of_points_returned: The cost_of_points_returned of this ShipmentItem. # noqa: E501 :type: Currency """ self._cost_of_points_returned = cost_of_points_returned def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(ShipmentItem, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, ShipmentItem): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

35.270833

377

0.671458

1,881

15,237

5.1042

0.095694

0.083116

0.09749

0.089366

0.695136

0.595563

0.505156

0.431622

0.369441

0.262993

0

0.013961

0.257268

15,237

431

378

35.352668

0.834408

0.376386

0

0.091398

0

0.106636

0.053739

0

1

0.172043

false

0

0.016129

0

0.306452

0.010753

0

null

0

null

0

1

0

09e89b2450d77d8cea8acdf70dfa8deb4095af90

3,370

py

Python

my_plugins/youcompleteme/python/ycm/tests/diagnostic_interface_test.py

VirtualLG/vimrc

33f961b0e465b852753479bc4aa0a32a6ff017cf

[ "MIT" ]

null

my_plugins/youcompleteme/python/ycm/tests/diagnostic_interface_test.py

VirtualLG/vimrc

33f961b0e465b852753479bc4aa0a32a6ff017cf

[ "MIT" ]

null

my_plugins/youcompleteme/python/ycm/tests/diagnostic_interface_test.py

VirtualLG/vimrc

33f961b0e465b852753479bc4aa0a32a6ff017cf

[ "MIT" ]

null

# Copyright (C) 2015-2018 YouCompleteMe contributors # # This file is part of YouCompleteMe. # # YouCompleteMe is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # YouCompleteMe is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with YouCompleteMe. If not, see <http://www.gnu.org/licenses/>. from ycm import diagnostic_interface from ycm.tests.test_utils import VimBuffer, MockVimModule, MockVimBuffers from hamcrest import assert_that, contains_exactly, has_entries, has_item from unittest import TestCase MockVimModule() def SimpleDiagnosticToJson( start_line, start_col, end_line, end_col ): return { 'kind': 'ERROR', 'location': { 'line_num': start_line, 'column_num': start_col }, 'location_extent': { 'start': { 'line_num': start_line, 'column_num': start_col }, 'end': { 'line_num': end_line, 'column_num': end_col } }, 'ranges': [ { 'start': { 'line_num': start_line, 'column_num': start_col }, 'end': { 'line_num': end_line, 'column_num': end_col } } ] } def YcmTextPropertyTupleMatcher( start_line, start_col, end_line, end_col ): return has_item( contains_exactly( start_line, start_col, 'YcmErrorProperty', has_entries( { 'end_col': end_col, 'end_lnum': end_line } ) ) ) class DiagnosticInterfaceTest( TestCase ): def test_ConvertDiagnosticToTextProperties( self ): for diag, contents, result in [ # Error in middle of the line [ SimpleDiagnosticToJson( 1, 16, 1, 23 ), [ 'Highlight this error please' ], YcmTextPropertyTupleMatcher( 1, 16, 1, 23 ) ], # Error at the end of the line [ SimpleDiagnosticToJson( 1, 16, 1, 21 ), [ 'Highlight this warning' ], YcmTextPropertyTupleMatcher( 1, 16, 1, 21 ) ], [ SimpleDiagnosticToJson( 1, 16, 1, 19 ), [ 'Highlight unicøde' ], YcmTextPropertyTupleMatcher( 1, 16, 1, 19 ) ], # Non-positive position [ SimpleDiagnosticToJson( 0, 0, 0, 0 ), [ 'Some contents' ], YcmTextPropertyTupleMatcher( 1, 1, 1, 1 ) ], [ SimpleDiagnosticToJson( -1, -2, -3, -4 ), [ 'Some contents' ], YcmTextPropertyTupleMatcher( 1, 1, 1, 1 ) ], ]: with self.subTest( diag = diag, contents = contents, result = result ): current_buffer = VimBuffer( 'foo', number = 1, contents = [ '' ] ) target_buffer = VimBuffer( 'bar', number = 2, contents = contents ) with MockVimBuffers( [ current_buffer, target_buffer ], [ current_buffer, target_buffer ] ): actual = diagnostic_interface._ConvertDiagnosticToTextProperties( target_buffer.number, diag ) print( actual ) assert_that( actual, result )

32.403846

77

0.625223

373

3,370

5.50134

0.380697

0.035088

0.011696

0.02924

0.23538

0.22271

0.195419

0.119396

0.103314

0.068226

0

0.025809

0.275668

3,370

103

78

32.718447

0.81483

0.230861

0

0.272727

0

0.106143

0

0.025974

1

0.038961

false

0

0.051948

0.025974

0.12987

0.012987

0

null

0

null

0

1

0

09edfb321e8839956c0dd18d657713402150647f

2,043

py

Python

examples/design_studies/ihm_fingergait/check_progress.py

cbteeple/somo

53a1a94f7d9d624bc4c43e582c80f24a0e98df24

[ "MIT" ]

null

examples/design_studies/ihm_fingergait/check_progress.py

cbteeple/somo

53a1a94f7d9d624bc4c43e582c80f24a0e98df24

[ "MIT" ]

null

examples/design_studies/ihm_fingergait/check_progress.py

cbteeple/somo

53a1a94f7d9d624bc4c43e582c80f24a0e98df24

[ "MIT" ]

null

# Be sure to run this file from the "palm_sweeps" folder # cd examples/palm_sweeps import os import sys from datetime import datetime path = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")) sys.path.insert(0, path) from somo.sweep import iter_utils config_file = "sweeps/grid_diam_height.yaml" todo_file = "_runs_todo.yaml" num_files_per_folder_end = 5 num_files_per_folder_start = 1 time_per_run = 25 # seconds avg_size = 40 # MB parallel_cores = 4 # Get data from config files config = iter_utils.load_yaml(config_file) todo = iter_utils.load_yaml(todo_file) total_runs = len(todo) # Calculate the time total_time_min = (time_per_run / 60.0) * total_runs / parallel_cores total_time_hr = total_time_min / 60.0 total_time_day = total_time_hr / 24.0 # Calculate total data size total_size_GB = float(avg_size) * total_runs / 1000.0 # Calculate the percent complete folder_to_count = iter_utils.get_group_folder(config) cpt = sum([len(files) for r, d, files in os.walk(folder_to_count)]) total_files_expected_end = total_runs * num_files_per_folder_end total_files_expected_start = total_runs * num_files_per_folder_start progress = (cpt - total_files_expected_start) / ( total_files_expected_end - total_files_expected_start ) eta_min = total_time_min * (1.0 - progress) eta_hr = eta_min / 60.0 eta_day = eta_hr / 24.0 # Print info print("") print("Current time: " + datetime.now().strftime("%H:%M:%S")) print("=================================") print("Number of runs to complete: %d" % (total_runs)) print( "Estimated total data saved @ %0.1f MB per run: %0.2f GB" % (avg_size, total_size_GB) ) print( "Estimated total time @ %0.1f sec per run with %d cores: %0.1f min, %0.2f hrs, %0.3f days" % (time_per_run, parallel_cores, total_time_min, total_time_hr, total_time_day) ) print("---------------------------------") print("Percent Complete: %0.3f %%" % (progress * 100)) print( "Estimated time left: %0.1f min, %0.2f hrs, %0.3f days" % (eta_min, eta_hr, eta_day) ) print("")

29.185714

94

0.708762

335

2,043

4.008955

0.298507

0.067014

0.050633

0.216679

0.067014

0.028295

0

0.030251

0.142438

2,043

69

95

29.608696

0.736301

0.101322

0

0.104167

0

0.041667

0.211939

0.051479

0

1

0

false

0

0.083333

0

0.083333

0.208333

0

null

0

null

0

1

0

09f226a5810e82fde46ce6d76eb7db7321ca355b

3,998

py

Python

Projects/Project 1/Handin/program.py

ymirthor/T-215-STY1

b888da1e88c5aa16eac03353f525e9e0b9d901df

[ "MIT" ]

null

Projects/Project 1/Handin/program.py

ymirthor/T-215-STY1

b888da1e88c5aa16eac03353f525e9e0b9d901df

[ "MIT" ]

null

Projects/Project 1/Handin/program.py

ymirthor/T-215-STY1

b888da1e88c5aa16eac03353f525e9e0b9d901df

[ "MIT" ]

null

from collections import deque as LL class Process: def __init__(self, parent, priority): self.state = 1 # State: 1=ready / 0=blocked self.parent = parent self.children = LL() self.resources = LL() self.priority = priority self.blocked_on = None class Resource: def __init__(self): self.state = 1 # State: 1=ready / 0=allocated self.waitlist = LL() class PCB: def __init__(self, size=16): self.size = size # Nr of processses in PCB self.priorities = 3 # Nr of priorties for RL self.resources = 4 # Nr of resources for RCB self.RL = [LL() for _ in range(3)] # RL with n priorities self.RCB = [Resource() for _ in range(4)] # RCB with n resources self.PCB = [None] * self.size # Empty PCB self.running = 0 # Running process, starts on 0 self.PCB[0] = Process(None, 0) self.RL[0].append(0) def create(self, priority): for idx, process in enumerate(self.PCB): if process == None: self.PCB[idx] = Process(parent=self.running, priority=priority) self.PCB[self.running].children.append(idx) self.RL[priority].append(idx) self.scheduler() return f'process {idx} created' def scheduler(self): for priority in reversed(self.RL): if priority: self.running = priority[0] break def _destroy_recur(self, index): count = 1 # Recur destroy children for child in list(self.PCB[index].children): count += self._destroy_recur(child) # Release all resources for resource in list(self.PCB[index].resources): self.release(resource, index) # Remove from ready list or from waitlist try: pri = self.PCB[index].priority self.RL[pri].remove(index) except ValueError: resource = self.PCB[index].blocked_on self.RCB[resource].waitlist.remove(index) # Remove parent parent = self.PCB[self.PCB[index].parent] parent.children.remove(index) self.PCB[index] = None return count def destroy(self, index): count = self._destroy_recur(index) self.scheduler() return f'{count} processes destroyed' def timeout(self): i = self.running ready_list = self.RL[self.PCB[i].priority] ready_list.remove(i) ready_list.append(i) self.scheduler() return f'process {self.running} running' def request(self, index_resource): resource = self.RCB[index_resource] running_process = self.PCB[self.running] if index_resource in running_process.resources: return f'process {self.running} already has resource' ready_list = self.RL[running_process.priority] if resource.state == 1: resource.state = 0 running_process.resources.append(index_resource) return f'resource {index_resource} allocated' else: running_process.state = 0 running_process.blocked_on = index_resource ready_list.remove(self.running) resource.waitlist.append(self.running) self.scheduler() return f'process {self.running} blocked' def release(self, index_resource, index=None): curr_process = self.PCB[index or self.running] resource = self.RCB[index_resource] curr_process.resources.remove(index_resource) if len(resource.waitlist) == 0: resource.state = 1 else: index_process = resource.waitlist.popleft() process = self.PCB[index_process] self.RL[process.priority].append(index_process) process.state = 1 process.resources.append(index_resource) return f'resource {index_resource} released'

35.070175

79

0.598049

477

3,998

4.907757

0.171908

0.047843

0.041008

0.034173

0.166595

0.105083

0.053823

0

0.009369

0.305903

3,998

114

80

35.070175

0.834234

0.076538

0

0.106383

0

0.059799

0

1

0.106383

false

0

0.010638

0

0.234043

0

null

0

null

0

1

0

09f240acbe9b8fa80d51945cdcc670845719d41c

2,394

py

Python

pg_methods/interfaces/state_processors.py

zafarali/policy-gradient-methods

f0d83a80ddc772dcad0c851aac9bfd41d436c274

[ "MIT" ]

28

2018-06-12T21:37:20.000Z

2021-12-27T15:13:14.000Z

pg_methods/interfaces/state_processors.py

zafarali/policy-gradient-methods

f0d83a80ddc772dcad0c851aac9bfd41d436c274

[ "MIT" ]

3

2018-05-10T16:33:05.000Z

2018-06-19T18:17:37.000Z

pg_methods/interfaces/state_processors.py

zafarali/policy-gradient-methods

f0d83a80ddc772dcad0c851aac9bfd41d436c274

[ "MIT" ]

7

2018-05-08T04:13:21.000Z

2021-04-02T12:31:55.000Z

import gym import torch import numpy as np from pg_methods.interfaces import common_interfaces as common class SimpleStateProcessor(common.Interface): """ Allows one to interface states between a single instance of gym """ def __init__(self, environment_observation_space, one_hot=False, use_cuda=False, normalize=False): self.observation_space = environment_observation_space if isinstance(environment_observation_space, gym.spaces.Box): # continous environment self.continous = True self.state_size = environment_observation_space.shape if len(self.state_size) == 1: self.state_size = self.state_size[0] self.one_hot = False self.normalize = False else: self.continous = False self.one_hot = one_hot if self.one_hot: self.state_size = environment_observation_space.n self.normalize = False self.max_obs = environment_observation_space.n else: self.normalize = normalize self.state_size = 1 self.max_obs = environment_observation_space.n self.use_cuda = use_cuda def state2pytorch(self, state_idx): if self.one_hot and not self.continous: state = np.zeros(self.state_size) state[self.state_idx] = 1 state = torch.from_numpy(state.reshape(1, -1)) if self.use_cuda: return state.float().cuda() else: return state.float() else: state = None if not self.continous: state = torch.from_numpy(np.array([state_idx]).reshape(1, -1)) else: state = torch.from_numpy(np.array(state_idx).reshape(1, -1)) if self.normalize: state = state / self.max_obs if self.use_cuda: return state.float().cuda() else: return state.float() def pytorch2state(self, tensor): if self.continous: return common.pytorch2list(tensor) else: list_state = list(map(int, common.pytorch2list(tensor))) if self.state_size == 1: return list_state[0] else: return list_state

36.272727

102

0.575188

269

2,394

4.921933

0.245353

0.067976

0.07855

0.031722

0.305136

0.26284

0.202417

0.145015

0

0.010237

0.347118

2,394

66

103

36.272727

0.836852

0.035923

0

0.321429

0

1

0.053571

false

0

0.071429

0

0.267857

0

null

0

null

0

1

0

09f69dea9d9541fb1a471fe9f8d7ffca1d756933

3,935

py

Python

tests/test_emlib.py

mjpekala/faster-membranes

f203fc8608603bc7b16a1abeac324d52e9dfe96a

[ "Apache-2.0" ]

null

tests/test_emlib.py

mjpekala/faster-membranes

f203fc8608603bc7b16a1abeac324d52e9dfe96a

[ "Apache-2.0" ]

null

tests/test_emlib.py

mjpekala/faster-membranes

f203fc8608603bc7b16a1abeac324d52e9dfe96a

[ "Apache-2.0" ]

null

"""Unit test for emlib.py To run: PYTHONPATH=../src python test_emlib.py """ __author__ = "Mike Pekala" __copyright__ = "Copyright 2015, JHU/APL" __license__ = "Apache 2.0" import unittest import numpy as np from sklearn.metrics import precision_recall_fscore_support as smetrics import emlib class TestEmlib(unittest.TestCase): def test_metrics(self): Y = np.random.randint(0,2,size=(2,5,5)) Yhat = np.random.randint(0,2,size=(2,5,5)) C,acc,prec,recall,f1 = emlib.metrics(Y, Yhat, display=False) prec2, recall2, f12, supp = smetrics(np.reshape(Y, (Y.size,)), np.reshape(Yhat, (Yhat.size,))) self.assertAlmostEqual(prec, prec2[1]) self.assertAlmostEqual(recall, recall2[1]) self.assertAlmostEqual(f1, f12[1]) def test_mirror_edges(self): X = np.random.rand(10,3,3); b = 2 # b := border size Xm = emlib.mirror_edges(X,b) # make sure the result has the proper size assert(Xm.shape[0] == X.shape[0]); assert(Xm.shape[1] == X.shape[1]+2*b); assert(Xm.shape[2] == X.shape[2]+2*b); # make sure the data looks reasonable self.assertTrue(np.all(Xm[:,:,b-1] == Xm[:,:,b])) self.assertTrue(np.all(Xm[:, b:-b, b:-b] == X)) def test_interior_pixel_generator(self): b = 10 # b := border size Z = np.zeros((2,100,100), dtype=np.int32) for idx, pct in emlib.interior_pixel_generator(Z,b,30): Z[idx[:,0],idx[:,1],idx[:,2]] += 1 self.assertTrue(np.all(Z[:,b:-b,b:-b]==1)) Z[:,b:-b,b:-b] = 0 self.assertTrue(np.all(Z==0)) def test_stratified_interior_pixel_generator(self): b = 10 # b := border size #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # For a 50/50 split of pixels in the interior, the generator # should reproduce the entire interior. #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Y = np.zeros((2,100,100)) Y[:,0:50,:] = 1 Z = np.zeros(Y.shape) for idx,pct in emlib.stratified_interior_pixel_generator(Y,b,30): Z[idx[:,0],idx[:,1],idx[:,2]] += 1 self.assertTrue(np.all(Z[:,b:-b,b:-b]==1)) Z[:,b:-b,b:-b] = 0 self.assertTrue(np.all(Z==0)) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # For a random input, should see a 50/50 split of class # labels, but not necessarily hit the entire interior. #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Y = np.random.rand(2,100,100) > 0.5 nOne=0; nZero=0; for idx,pct in emlib.stratified_interior_pixel_generator(Y,b,30): slices = idx[:,0]; rows = idx[:,1]; cols = idx[:,2] nOne += np.sum(Y[slices,rows,cols] == 1) nZero += np.sum(Y[slices,rows,cols] == 0) self.assertTrue(nOne == nZero) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # For an input tensor with "no-ops", the sampler should only # return pixels with a positive or negative label. #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Y = np.zeros((2,100,100)) Y[:,0:20,0:20] = 1 Y[:,50:70,50:70] = -1 Z = np.zeros(Y.shape) nPos=0; nNeg=0; nTotal=0; for idx,pct in emlib.stratified_interior_pixel_generator(Y,0,10,omitLabels=[0]): slices = idx[:,0]; rows = idx[:,1]; cols = idx[:,2] Z[slices,rows,cols] = Y[slices,rows,cols] nPos += np.sum(Y[slices,rows,cols] == 1) nNeg += np.sum(Y[slices,rows,cols] == -1) nTotal += len(slices) self.assertTrue(nPos == 20*20*2); self.assertTrue(nNeg == 20*20*2); self.assertTrue(nTotal == 20*20*2*2); self.assertTrue(np.all(Y == Z)) if __name__ == "__main__": unittest.main() # vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4

33.067227

88

0.519695

550

3,935

3.634545

0.26

0.015008

0.066533

0.395198

0.330665

0.283642

0.252126

0.235118

0.145073

0

0.057074

0.265311

3,935

118

89

33.347458

0.634383

0.205083

0

0.264706

0

0.016753

0

0.25

1

0.058824

false

0

0.058824

0

0.132353

0

null

0

null

0

1

0

09f91afeaca4a61947c025a6985fde971a2433a0

727

py

Python

app/core/bluetooth/models.py

FHellmann/MLWTF

582c3505d638907a848d5a6c739ee99981300f17

[ "Apache-2.0" ]

null

app/core/bluetooth/models.py

FHellmann/MLWTF

582c3505d638907a848d5a6c739ee99981300f17

[ "Apache-2.0" ]

null

app/core/bluetooth/models.py

FHellmann/MLWTF

582c3505d638907a848d5a6c739ee99981300f17

[ "Apache-2.0" ]

null

#!/usr/bin/python """ Author: Fabio Hellmann <info@fabio-hellmann.de> """ from attr import s, ib from attr.validators import instance_of @s(frozen=True) class BLEDevice(object): """ Device MAC address (as a hex string separated by colons). """ addr = ib(validator=instance_of(str), type=str) """ The name which is set """ name = ib(validator=instance_of(str), type=str) """ Received Signal Strength Indication for the last received broadcast from the device. This is an integer value measured in dB, where 0 dB is the maximum (theoretical) signal strength, and more negative numbers indicate a weaker signal. """ rssi = ib(validator=instance_of(int), type=int)

27.961538

114

0.68088

103

727

4.76699

0.640777

0.081466

0.11609

0.12831

0.126273

0

0.001748

0.213205

727

25

115

29.08

0.856643

0.167813

0

1

0

false

0

0.285714

0

0.857143

0

null

0

null

0

1

0

09f949d20672656308f4b25b2fb52c7d29555163

1,511

py

Python

Algorithms_medium/1102. Path With Maximum Minimum Value.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

4

2020-08-11T20:45:15.000Z

2021-03-12T00:33:34.000Z

Algorithms_medium/1102. Path With Maximum Minimum Value.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

null

Algorithms_medium/1102. Path With Maximum Minimum Value.py

VinceW0/Leetcode_Python_solutions

09e9720afce21632372431606ebec4129eb79734

[ "Xnet", "X11" ]

null

""" 1102. Path With Maximum Minimum Value Medium Given a matrix of integers A with R rows and C columns, find the maximum score of a path starting at [0,0] and ending at [R-1,C-1]. The score of a path is the minimum value in that path. For example, the value of the path 8 → 4 → 5 → 9 is 4. A path moves some number of times from one visited cell to any neighbouring unvisited cell in one of the 4 cardinal directions (north, east, west, south). Example 1: Input: [[5,4,5],[1,2,6],[7,4,6]] Output: 4 Explanation: The path with the maximum score is highlighted in yellow. Example 2: Input: [[2,2,1,2,2,2],[1,2,2,2,1,2]] Output: 2 Example 3: Input: [[3,4,6,3,4],[0,2,1,1,7],[8,8,3,2,7],[3,2,4,9,8],[4,1,2,0,0],[4,6,5,4,3]] Output: 3 Note: 1 <= R, C <= 100 0 <= A[i][j] <= 10^9 """ class Solution: def maximumMinimumPath(self, A: List[List[int]]) -> int: dire = [(0, 1), (1, 0), (0, -1), (-1, 0)] R, C = len(A), len(A[0]) maxHeap = [(-A[0][0], 0, 0)] seen = [[0 for _ in range(C)] for _ in range(R)] while maxHeap: val, x, y = heapq.heappop(maxHeap) # seen[x][y] = 1 # got TLE if x == R - 1 and y == C - 1: return -val for dx, dy in dire: nx, ny = x + dx, y + dy if 0 <= nx < R and 0 <= ny < C and not seen[nx][ny]: seen[nx][ny] = 1 # passed heapq.heappush(maxHeap, (max(val, -A[nx][ny]), nx, ny)) return -1

26.982143

154

0.536069

287

1,511

2.825784

0.337979

0.014797

0.011097

0.014797

0

0.093809

0.294507

1,511

56

155

26.982143

0.664165

0.547981

0

1

0.066667

false

0

0.2

0

null

0

null

0

1

0

09f9da8e8fb3a2cb6c40b0627a6fdbf5844460e0

1,436

py

Python

tests/extractor/test_factory.py

albertteoh/data_pipeline

a99f1c7412375b3e9f4115108fcdde517b2e71a6

[ "Apache-2.0" ]

null

tests/extractor/test_factory.py

albertteoh/data_pipeline

a99f1c7412375b3e9f4115108fcdde517b2e71a6

[ "Apache-2.0" ]

null

tests/extractor/test_factory.py

albertteoh/data_pipeline

a99f1c7412375b3e9f4115108fcdde517b2e71a6

[ "Apache-2.0" ]

null

import pytest import data_pipeline.db.factory as db_factory import data_pipeline.extractor.factory as extractor_factory import tests.unittest_utils as utils import data_pipeline.constants.const as const from pytest_mock import mocker from data_pipeline.db.exceptions import UnsupportedDbTypeError @pytest.fixture() def setup(tmpdir, mocker): mockargv_config = utils.get_default_argv_config(tmpdir) mockargv = mocker.Mock(**mockargv_config) pc_config = {'insert.return_value': None, 'update.return_value': None} mock_pc = mocker.Mock(**pc_config) af_config = {'build_process_control.return_value': mock_pc} mock_audit_factory = mocker.Mock(**af_config) utils.mock_build_kafka_producer(mocker) yield (mockargv, mock_audit_factory) @pytest.mark.parametrize("dbtype, expect_class", [ (const.ORACLE, "OracleCdcExtractor"), (const.MSSQL, "MssqlCdcExtractor"), ]) def test_build(dbtype, expect_class, setup): (mockargv, mock_audit_factory) = setup mode = const.CDCEXTRACT db = db_factory.build(dbtype) extractor = extractor_factory.build(mode, db, mockargv, mock_audit_factory) assert type(extractor).__name__ == expect_class def test_build_unsupported(setup): (mockargv, mock_audit_factory) = setup with pytest.raises(ImportError): db = db_factory.build("AnUnsupportedDatabase") extractor = extractor_factory.build(db, mockargv, mock_audit_factory)

34.190476

79

0.766017

181

1,436

5.78453

0.337017

0.051576

0.091691

0.114613

0.064947

0

0.137187

1,436

41

80

35.02439

0.845036

0

0.0625

0

0.103064

0.038301

0

0.03125

1

0.09375

false

0

0.25

0

0.34375

0

null

0

null

0

1

0

09fa1379267ff36d7eaf0c8f04ba9a7c23bd124b

3,424

py

Python

suremco/tracker.py

modsim/SurEmCo

71fc0cfc62f8733de93ee2736421574a154e3db3

[ "BSD-2-Clause" ]

null

suremco/tracker.py

modsim/SurEmCo

71fc0cfc62f8733de93ee2736421574a154e3db3

[ "BSD-2-Clause" ]

null

suremco/tracker.py

modsim/SurEmCo

71fc0cfc62f8733de93ee2736421574a154e3db3

[ "BSD-2-Clause" ]

null

# SurEmCo - C++ tracker wrapper import ctypes from enum import IntEnum import sys import os import numpy import numpy.ctypeslib class Tracker(object): class Mode(IntEnum): MOVING = 0 STATIC = 1 class Strategy(IntEnum): BRUTE_FORCE = 0 KD_TREE = 1 track_input_type = {'dtype': [ ('x', 'float64'), ('y', 'float64'), ('precision', 'float64'), ('frame', 'int64'), ('index', 'intp'), ('label', 'int64'), ('square_displacement', 'float64') ]} debug = False def __init__(self, debug=False): self.debug = debug file = os.path.join(os.path.dirname(os.path.abspath(__file__)), '_tracker.' + ('so' if sys.platform == 'linux' else 'dll')) old_cwd = os.getcwd() os.chdir(os.path.dirname(file)) _track_so = ctypes.CDLL(file) os.chdir(old_cwd) _track_so.track.argtypes = ( numpy.ctypeslib.ndpointer(**self.track_input_type), # , flags='C_CONTIGUOUS'), ctypes.c_size_t, ctypes.c_float, ctypes.c_int32, ctypes.c_int32, ctypes.c_int32 ) _track_so.track.restype = None _track_so.msd.argtypes = ( numpy.ctypeslib.ndpointer(**self.track_input_type), # , flags='C_CONTIGUOUS'), ctypes.c_size_t, ctypes.c_float, ctypes.c_float ) _track_so.msd.restype = ctypes.c_float self._track_so = _track_so self._track = _track_so.track self._msd = _track_so.msd if self.debug: _track_so.getBuildDate.restype = ctypes.c_char_p # noinspection PyProtectedMember print("Loaded %s compiled at %s" % (_track_so._name, _track_so.getBuildDate().decode(),)) def track(self, transfer, maximum_displacement=1.0, memory=0, mode=None, strategy=None): if mode is None: mode = self.Mode.MOVING if strategy is None: strategy = self.Strategy.BRUTE_FORCE if len(transfer) == 0: raise RuntimeError('Empty data!') if self.debug: from tempfile import NamedTemporaryFile with NamedTemporaryFile(prefix='track_dataset', delete=False) as tf: transfer.tofile(tf) print("track(\"%s\", %d, %f, %d, %d, %d)" % ( tf.name, len(transfer), maximum_displacement, memory, mode, strategy )) return self._track(transfer, len(transfer), maximum_displacement, memory, mode, strategy) def msd(self, transfer, micron_per_pixel=1.0, frames_per_second=1.0): # the MSD calculation was not thoroughly verified if len(transfer) == 0: raise RuntimeError('Empty data!') return self._msd(transfer, len(transfer), micron_per_pixel, frames_per_second) def __del__(self): if not self.debug: return # noinspection PyProtectedMember _handle = self._track_so._handle del self._track_so if sys.platform == 'linux': dl = ctypes.CDLL('libdl.so') dl.dlclose.argtypes = [ctypes.c_void_p] dl.dlclose(_handle) # elif # handle windows? @classmethod def empty_track_input_type(cls, count): return numpy.zeros(count, **cls.track_input_type)

29.517241

101

0.580023

396

3,424

4.775253

0.323232

0.051824

0.037017

0.015865

0.231095

0.209942

0.194606

0.143839

0.101534

0

0.013036

0.305491

3,424

115

102

29.773913

0.78217

0.061916

0

0.166667

0

0.065876

0

1

0.059524

false

0

0.083333

0.011905

0.25

0.02381

0

null

0

null

0

1

0

09fb1d9a7c357f2bc49fb2f43274b073bfff333e

4,026

py

Python

foreign_languages/anderson.py

ds-modules/NESTUD-190A

54ca1cd9a8f369f48946147f72377f874738f7d5

[ "MIT" ]

6

2017-11-06T03:18:12.000Z

2019-10-02T19:41:06.000Z

foreign_languages/anderson.py

admndrsn/NESTUD-190A

54ca1cd9a8f369f48946147f72377f874738f7d5

[ "MIT" ]

null

foreign_languages/anderson.py

admndrsn/NESTUD-190A

54ca1cd9a8f369f48946147f72377f874738f7d5

[ "MIT" ]

2

2018-02-09T01:04:58.000Z

2019-06-19T17:45:34.000Z

from IPython.core.display import display, HTML import translation class translate(object): id_start = 0 def __init__(self, column_types, language_to='en'): self.num_of_columns = len(column_types) + 1 column_types.insert(0, 'original text') self.column_types = column_types self.language_to = language_to self.funcs = {'original text':self.original_text_pls, 'translate':self.tranlate_pls, 'parts of speech':self.polyglot_pos, 'language':self.polyglot_languages} self.header = {'original text':'Original Text:', 'translate':'Translation:', 'parts of speech':'Parts of Speech:', 'language':'Language(s) Detected:'} self.fonttype = 'Courier New' self.additionalcss = '' # these are the functions that will go within the body calls # need to fill in these functions so that we get the right things def tranlate_pls(self, txt): return translation.bing(txt, dst = self.language_to) def original_text_pls(self, txt): return txt def parts_of_speech_pls(self, txt): import nltk tokenized = nltk.word_tokenize(txt) return nltk.pos_tag(tokenized) def polyglot_languages(self, txt): from polyglot.detect import Detector langs = Detector(txt, quiet=True).languages selected_items = [(x.name, x.confidence) for x in langs] # converting to readable from Detector objects stringy_list = ['Name: ' + str(x) + ' Confidence: ' +str(y) for x,y in selected_items] return '<br><br>'.join(stringy_list) def polyglot_pos(self, txt): from polyglot.text import Text return Text(txt).pos_tags # make a function for part of speech counts # and names in the text # maybe name counts # make it so that we can try different translating services # a google integration may be necessary :( # incrementing the ids so that the css of a new one doesn't change an old one def increment_ids(self): strt_id = translate.id_start translate.id_start += self.num_of_columns return range(strt_id, strt_id + self.num_of_columns) # creating the divs and the content that will go in them def create_body(self, id_numbers, txt): # setting up all of the divs that will be there base_column = '<div id="{}">{}<br>{}</div>' blank_divs = base_column * self.num_of_columns # calling the functions specified in our constructor on our body of text content = [self.funcs[col](txt) for col in self.column_types] headers = [self.header[col] for col in self.column_types] # zipping them together so we can make a string in the correct order, then flattening nested_order = list(zip(id_numbers, headers, content)) unnested = [item for sublist in nested_order for item in sublist] return '<div id="wrapper">' + blank_divs.format(*(unnested)) + '</div>' def create_css(self, id_numbers): # picking alternating colors for columns clrs = ['#e6f3f7', 'lightgray'] def alternate(): while True: yield 0 yield 1 gen = alternate() clr_list = [clrs[next(gen)] for i in range(self.num_of_columns)] # width evenly divided by number of columns width = "width:{}%;".format(str(100 / self.num_of_columns)) # setting up for all different css that will be there base_css = "#{} {{background-color: {};" + width + "float:left;padding: .5vw;border-right: solid black 1.5px;}}" blank_csss = base_css * self.num_of_columns # zipping them together so we can make a string in the correct order, then flattening nested_order = list(zip(id_numbers, clr_list)) unnested = [item for sublist in nested_order for item in sublist] final_css = blank_csss.format(*(unnested)) wrapper = "{} #wrapper {{width:100%;clear:both;display: flex;font-family:{};}}".format(self.additionalcss, self.fonttype) return wrapper + final_css def create(self, initial_text): id_list = self.increment_ids() string_ids = ['d'+str(x) for x in id_list] display(HTML('<style>{}</style> <body>{}</body>'.format(self.create_css(string_ids), self.create_body(string_ids, initial_text)))) # Add a return statement so that the values are accessible

31.209302

132

0.721063

611

4,026

4.607201

0.327332

0.025577

0.02238

0.039787

0.13357

0.120071

0.10373

0

0.00507

0.167163

4,026

128

133

31.453125

0.834477

0.23696

0

0.03125

0

0.15498

0.024902

0

1

0.171875

false

0

0.078125

0.03125

0.40625

0

null

0

null

0

1

0

61cb92b7eff849f550f556cfcf71f302f039dac7

1,315

py

Python

landdox/core.py

natefduncan/landdox

58908554034577cc20c6f89ee6056da90cbfbd4e

[ "MIT" ]

1

2019-12-13T16:19:56.000Z

landdox/core.py

natefduncan/landdox

58908554034577cc20c6f89ee6056da90cbfbd4e

[ "MIT" ]

null

landdox/core.py

natefduncan/landdox

58908554034577cc20c6f89ee6056da90cbfbd4e

[ "MIT" ]

null

import requests import json import pandas as pd import os from .endpoints import * class Client: endpoints = { "contacts" : contacts, "leases" : leases, "units" : units, "wells" : wells, "custom" : custom, "tracts" : tracts, "payments" : payments } def __init__(self, client_id, client_secret): self.client_id = client_id self.client_secret = client_secret self.authorize() def __getattr__(self, name): endpoint = self.endpoints.get(name) endpoint.access_token = self.access_token return endpoint def authorize(self): payload = { "client_id" : self.client_id, "client_secret" : self.client_secret, "audience" : "api.landdox.com", "grant_type" : "client_credentials" } url = "https://landdox.auth0.com/oauth/token" response = requests.post(url, data=payload) if response.status_code != 200: raise ValueError("{error}".format(error=response)) else: response = response.json() self.access_token = response.get("access_token") self.expires_in = response.get("expires_in") self.expires_in = response.get("token_type")

26.3

62

0.580989

137

1,315

5.364964

0.416058

0.068027

0.04898

0.073469

0.157823

0.092517

0

0.004415

0.311027

1,315

49

63

26.836735

0.806843

0

0.14688

0

1

0.076923

false

0

0.128205

0

0.282051

0

null

0

null

0

1

0

61cd1b7623e09e8563a60f3d87a7caf270f2faa2

589

py

Python

src/signalplotter/qt/makePyUI.py

jowanpittevils/Databasemanager_Signalplotter

993152ad15793054df2acf386eb1c9a76610b789

[ "BSD-3-Clause" ]

null

src/signalplotter/qt/makePyUI.py

jowanpittevils/Databasemanager_Signalplotter

993152ad15793054df2acf386eb1c9a76610b789

[ "BSD-3-Clause" ]

null

src/signalplotter/qt/makePyUI.py

jowanpittevils/Databasemanager_Signalplotter

993152ad15793054df2acf386eb1c9a76610b789

[ "BSD-3-Clause" ]

null

#%% def makeUI(uiNames): import sys, os print('Check the pwd first, It must be at .../SignalPlotter/qt.') print(os.getcwd()) p0 = os.path.dirname(sys.executable) for uiName in (uiNames): print('===== for: ',uiName,' ======') p1 = '"'+p0+'\Scripts\pyuic5.exe'+'" ' p1 += ' -x "' + uiName + '.ui"' p1 += ' -o "' + uiName + '.py"' print(p1) import subprocess res = subprocess.call(p1) != 0 print('Done.') print('Is there any error: ', res) uiNames = ['plotter_uiDesign'] makeUI(uiNames) # %%

21.035714

69

0.50764

68

589

4.382353

0.647059

0.087248

0

0.02148

0.288625

589

27

70

21.814815

0.689737

0.008489

0

0.266323

0.036082

0.058824

0

1

0.058824

false

0

0.117647

0

0.176471

0.352941

0

null

0

null

0

1

0

61cea84c27bf7df9b0289ed47ffee2781ddbdc17

3,148

py

Python

mpcontribs-users/mpcontribs/users/swf/pre_submission.py

josuav1/MPContribs

3cbf0e83ba6cd749dd4fc988c9f6ad076b05f935

[ "MIT" ]

1

2019-07-03T04:38:58.000Z

mpcontribs-users/mpcontribs/users/swf/pre_submission.py

josuav1/MPContribs

3cbf0e83ba6cd749dd4fc988c9f6ad076b05f935

[ "MIT" ]

null

mpcontribs-users/mpcontribs/users/swf/pre_submission.py

josuav1/MPContribs

3cbf0e83ba6cd749dd4fc988c9f6ad076b05f935

[ "MIT" ]

1

2019-07-03T04:39:04.000Z

from mpcontribs.config import mp_level01_titles from mpcontribs.io.core.recdict import RecursiveDict from mpcontribs.io.core.utils import clean_value, get_composition_from_string from mpcontribs.users.utils import duplicate_check def round_to_100_percent(number_set, digit_after_decimal=1): unround_numbers = [ x / float(sum(number_set)) * 100 * 10**digit_after_decimal for x in number_set ] decimal_part_with_index = sorted([ (index, unround_numbers[index] % 1) for index in range(len(unround_numbers)) ], key=lambda y: y[1], reverse=True) remainder = 100 * 10**digit_after_decimal - sum(map(int, unround_numbers)) index = 0 while remainder > 0: unround_numbers[decimal_part_with_index[index][0]] += 1 remainder -= 1 index = (index + 1) % len(number_set) return [int(x)/float(10**digit_after_decimal) for x in unround_numbers] @duplicate_check def run(mpfile, **kwargs): import pymatgen import pandas as pd from mpcontribs.users.swf.rest.rester import SwfRester # load data from google sheet google_sheet = mpfile.document[mp_level01_titles[0]].pop('google_sheet') google_sheet += '/export?format=xlsx' df_dct = pd.read_excel(google_sheet, sheet_name=None) # rename sheet columns elements = ['Fe', 'V', 'Co'] df_dct['IP Energy Product'].columns = ['IP_Energy_product'] + elements df_dct['total'].columns = elements df_dct['MOKE'].columns = elements + ['thickness', 'MOKE_IP_Hc'] df_dct['VSM'].columns = elements + ['thickness', 'VSM_IP_Hc'] df_dct['formula'].columns = elements df_dct['Kondorsky'].columns = ['angle', 'Kondorsky_Model', 'Experiment'] # round all compositions to 100% for sheet, df in df_dct.items(): if sheet != 'Kondorsky': for idx, row in df.iterrows(): df.loc[idx:idx, elements] = round_to_100_percent(row[elements]) row5 = df_dct['formula'].iloc[0] formula5 = get_composition_from_string( pymatgen.Composition(10*row5).formula.replace(' ', '') ) dct = dict((k, clean_value(v, '%')) for k,v in row5.to_dict().items()) mpfile.add_hierarchical_data({'data': dct}, identifier=formula5) mpfile.add_data_table( formula5, df_dct['Kondorsky'], name='Angular Dependence of Switching Field' ) for sheet, df in df_dct.items(): if sheet == 'formula' or sheet == 'Kondorsky' or sheet == 'total': continue for idx, row in df.iterrows(): composition = pymatgen.Composition(row[elements]*10) formula = get_composition_from_string(composition.formula.replace(' ', '')) dct = dict((k, clean_value(v, '%')) for k,v in row[elements].to_dict().items()) mpfile.add_hierarchical_data({'data': dct}, identifier=formula) columns = [x for x in row.index if x not in elements] if columns: data = row[columns].round(decimals=1) dct = dict((k, clean_value(v)) for k,v in data.to_dict().items()) mpfile.add_hierarchical_data({'data': dct}, identifier=formula)

43.123288

91

0.656607

422

3,148

4.699052

0.303318

0.027736

0.034291

0.036309

0.228946

0.216339

0.195159

0.169945

0.140696

0

0.019013

0.21474

3,148

72

92

43.722222

0.783172

0.025095

0

0.098361

0

0.086162

0

1

0.032787

false

0

0.114754

0

0.163934

0

null

0

null

0

1

0

61cf7342efb940a3f5d7c9b44e90c3d3f4d12610

21,205

py

Python

src/trails/flow_model.py

BenDickens/trails

a89a1a901c7be38cdcb7a59339587e518ab8f14d

[ "MIT" ]

4

2020-09-14T07:20:19.000Z

2021-04-22T14:23:04.000Z

src/trails/flow_model.py

BenDickens/trails

a89a1a901c7be38cdcb7a59339587e518ab8f14d

[ "MIT" ]

5

2021-03-17T17:02:27.000Z

2021-08-31T10:09:38.000Z

src/trails/flow_model.py

BenDickens/trails

a89a1a901c7be38cdcb7a59339587e518ab8f14d

[ "MIT" ]

3

2020-09-07T07:35:28.000Z

2021-04-22T14:23:39.000Z

import os,sys import numpy as np import pandas as pd import geopandas as gpd import matplotlib.pyplot as plt import pygeos from osgeo import gdal from tqdm import tqdm import igraph as ig import contextily as ctx from rasterstats import zonal_stats import time import pylab as pl from IPython import display import seaborn as sns import subprocess import shutil from multiprocessing import Pool,cpu_count import pathlib code_path = (pathlib.Path(__file__).parent.absolute()) gdal.SetConfigOption("OSM_CONFIG_FILE", os.path.join(code_path,'..','..',"osmconf.ini")) from shapely.wkb import loads data_path = os.path.join('..','data') from simplify import * from extract import railway,ferries,mainRoads,roads from population_OD import create_bbox,create_grid pd.options.mode.chained_assignment = None def closest_node(node, nodes): """[summary] Args: node ([type]): [description] nodes ([type]): [description] Returns: [type]: [description] """ dist_2 = np.sum((nodes - node)**2, axis=1) return np.argmin(dist_2) def load_network(osm_path,mainroad=True): """[summary] Args: osm_path ([type]): [description] mainroad (bool, optional): [description]. Defaults to True. Returns: [type]: [description] """ if mainroad: df = mainRoads(osm_path) else: df = roads(osm_path) net = Network(edges=df) net = clean_roundabouts(net) net = split_edges_at_nodes(net) net = add_endpoints(net) net = add_ids(net) net = add_topology(net) net = drop_hanging_nodes(net) net = merge_edges(net) net = reset_ids(net) net = add_distances(net) net = merge_multilinestrings(net) net = fill_attributes(net) net = add_travel_time(net) return net def make_directed(edges): save_edges = [] for ind,edge in edges.iterrows(): if edge.oneway == 'yes': save_edges.append(edge) else: edge.oneway = 'yes' edge.lanes = np.round(edge.lanes/2,0) save_edges.append(edge) edge2 = edge.copy() from_id = edge.from_id to_id = edge.to_id edge2.from_id = to_id edge2.to_id = from_id save_edges.append(edge2) new_edges = pd.DataFrame(save_edges).reset_index(drop=True) new_edges.id = new_edges.index return new_edges def get_gdp_values(gdf,data_path): """[summary] Args: gdf ([type]): [description] Returns: [type]: [description] """ world_pop = os.path.join(data_path,'global_gdp','GDP_2015.tif') gdf['geometry'] = gdf.geometry.apply(lambda x: loads(pygeos.to_wkb(x))) gdp = list(item['sum'] for item in zonal_stats(gdf.geometry,world_pop, stats="sum")) gdp = [x if x is not None else 0 for x in gdp] gdf['geometry'] = pygeos.from_shapely(gdf.geometry) return gdp def country_grid_gdp_filled(trans_network,country,data_path,rough_grid_split=100,from_main_graph=False): """[summary] Args: trans_network ([type]): [description] rough_grid_split (int, optional): [description]. Defaults to 100. Returns: [type]: [description] """ if from_main_graph==True: node_df = trans_network.copy() envelop = pygeos.envelope(pygeos.multilinestrings(node_df.geometry.values)) height = np.sqrt(pygeos.area(envelop)/rough_grid_split) else: node_df = trans_network.nodes.copy() node_df.geometry,approximate_crs = convert_crs(node_df) envelop = pygeos.envelope(pygeos.multilinestrings(node_df.geometry.values)) height = np.sqrt(pygeos.area(envelop)/rough_grid_split) gdf_admin = pd.DataFrame(create_grid(create_bbox(node_df),height),columns=['geometry']) #load data and convert to pygeos country_shape = gpd.read_file(os.path.join(data_path,'GADM','gadm36_levels.gpkg'),layer=0) country_shape = pd.DataFrame(country_shape.loc[country_shape.GID_0==country]) country_shape.geometry = pygeos.from_shapely(country_shape.geometry) gdf_admin = pygeos.intersection(gdf_admin,country_shape.geometry) gdf_admin = gdf_admin.loc[~pygeos.is_empty(gdf_admin.geometry)] gdf_admin['centroid'] = pygeos.centroid(gdf_admin.geometry) gdf_admin['km2'] = area(gdf_admin) gdf_admin['gdp'] = get_gdp_values(gdf_admin,data_path) gdf_admin = gdf_admin.loc[gdf_admin.gdp > 0].reset_index() gdf_admin['gdp_area'] = gdf_admin.gdp/gdf_admin['km2'] return gdf_admin def convert_crs(gdf,current_crs="epsg:4326"): """[summary] Args: gdf ([type]): [description] Returns: [type]: [description] """ if current_crs == "epsg:4326": lat = pygeos.geometry.get_y(pygeos.centroid(gdf['geometry'].iloc[0])) lon = pygeos.geometry.get_x(pygeos.centroid(gdf['geometry'].iloc[0])) # formula below based on :https://gis.stackexchange.com/a/190209/80697 approximate_crs = "epsg:" + str(int(32700-np.round((45+lat)/90,0)*100+np.round((183+lon)/6,0))) else: approximate_crs = "epsg:4326" #from pygeos/issues/95 geometries = gdf['geometry'] coords = pygeos.get_coordinates(geometries) transformer=pyproj.Transformer.from_crs(current_crs, approximate_crs,always_xy=True) new_coords = transformer.transform(coords[:, 0], coords[:, 1]) result = pygeos.set_coordinates(geometries.copy(), np.array(new_coords).T) return result,approximate_crs def area(gdf,km=True): """[summary] Args: gdf ([type]): [description] km (bool, optional): [description]. Defaults to True. Returns: [type]: [description] """ if km: return pygeos.area(convert_crs(gdf)[0])/1e6 else: return pygeos.area(convert_crs(gdf)[0]) def get_basetable(country,data_path): io_data_path = os.path.join(data_path,'country_IO_tables') df = pd.read_csv(os.path.join(io_data_path,'IO_{}_2015_BasicPrice.txt'.format(country)), sep='\t', skiprows=1,header=[0,1,2],index_col = [0,1,2,3], skipfooter=2617,engine='python') basetable = df.iloc[:26,:26] return basetable.astype(int) def create_OD(gdf_admin,country_name,data_path): """[summary] Args: gdf_admin ([type]): [description] country_name ([type]): [description] Returns: [type]: [description] """ # create list of sectors sectors = [chr(i).upper() for i in range(ord('a'),ord('o')+1)] # add a region column if not existing yet. if 'NAME_1' not in gdf_admin.columns: gdf_admin['NAME_1'] = ['reg'+str(x) for x in list(gdf_admin.index)] # prepare paths to downscale a country. We give a country its own directory # to allow for multiple unique countries running at the same time downscale_basepath = os.path.join(code_path,'..','..','downscale_od') downscale_countrypath = os.path.join(code_path,'..','..','run_downscale_od_{}'.format(country_name)) # copy downscaling method into the country directory shutil.copytree(downscale_basepath,downscale_countrypath) # save national IO table as basetable for downscaling get_basetable(country_name,data_path).to_csv(os.path.join(downscale_countrypath,'basetable.csv'), sep=',',header=False,index=False) # create proxy table with GDP values per region/area proxy_reg = pd.DataFrame(gdf_admin[['NAME_1','gdp_area']]) proxy_reg['year'] = 2016 proxy_reg = proxy_reg[['year','NAME_1','gdp_area']] proxy_reg.columns = ['year','id','gdp_area'] proxy_reg.to_csv(os.path.join(downscale_countrypath,'proxy_reg.csv'),index=False) indices = pd.DataFrame(sectors,columns=['sector']) indices['name'] = country_name indices = indices.reindex(['name','sector'],axis=1) indices.to_csv(os.path.join(downscale_countrypath,'indices.csv'),index=False,header=False) # prepare yaml file yaml_file = open(os.path.join(downscale_countrypath,"settings_basic.yml"), "r") list_of_lines = yaml_file.readlines() list_of_lines[6] = ' - id: {}\n'.format(country_name) list_of_lines[8] = ' into: [{}] \n'.format(','.join(['reg'+str(x) for x in list(gdf_admin.index)])) yaml_file = open(os.path.join(downscale_countrypath,"settings_basic.yml"), "w") yaml_file.writelines(list_of_lines) yaml_file.close() # run libmrio p = subprocess.Popen([os.path.join(downscale_countrypath,'mrio_disaggregate'), 'settings_basic.yml'], cwd=os.path.join(downscale_countrypath)) p.wait() # create OD matrix from libmrio results OD = pd.read_csv(os.path.join(downscale_countrypath,'output.csv'),header=None) OD.columns = pd.MultiIndex.from_product([gdf_admin.NAME_1,sectors]) OD.index = pd.MultiIndex.from_product([gdf_admin.NAME_1,sectors]) OD = OD.groupby(level=0,axis=0).sum().groupby(level=0,axis=1).sum() OD = (OD*5)/365 OD_dict = OD.stack().to_dict() gdf_admin['import'] = list(OD.sum(axis=1)) gdf_admin['export'] = list(OD.sum(axis=0)) gdf_admin = gdf_admin.rename({'NAME_1': 'name'}, axis='columns') # and remove country folder again to avoid clutter in the directory shutil.rmtree(downscale_countrypath) return OD,OD_dict,sectors,gdf_admin def prepare_network_routing(transport_network): """[summary] Args: transport_network ([type]): [description] Returns: [type]: [description] """ gdf_roads = make_directed(transport_network.edges) gdf_roads = gdf_roads.rename(columns={"highway": "infra_type"}) gdf_roads['GC'] = gdf_roads.apply(gc_function,axis=1) gdf_roads['max_flow'] = gdf_roads.apply(set_max_flow,axis=1) gdf_roads['flow'] = 0 gdf_roads['wait_time'] = 0 return gdf_roads def create_graph(gdf_roads): """[summary] Args: gdf_roads ([type]): [description] Returns: [type]: [description] """ gdf_in = gdf_roads.reindex(['from_id','to_id'] + [x for x in list(gdf_roads.columns) if x not in ['from_id','to_id']],axis=1) g = ig.Graph.TupleList(gdf_in.itertuples(index=False), edge_attrs=list(gdf_in.columns)[2:],directed=True) sg = g.clusters().giant() gdf_in.set_index('id',inplace=True) return sg,gdf_in def nearest_network_node_list(gdf_admin,gdf_nodes,sg): """[summary] Args: gdf_admin ([type]): [description] gdf_nodes ([type]): [description] sg ([type]): [description] Returns: [type]: [description] """ gdf_nodes = gdf_nodes.loc[gdf_nodes.id.isin(sg.vs['name'])] gdf_nodes.reset_index(drop=True,inplace=True) nodes = {} for admin_ in gdf_admin.itertuples(): nodes[admin_.name] = gdf_nodes.iloc[pygeos.distance((admin_.centroid),gdf_nodes.geometry).idxmin()].id return nodes def set_max_flow(segment): """[summary] Args: segment ([type]): [description] Returns: [type]: [description] """ empty_trip_correction = 0.7 #available capacity for freight reduces # standard lane capacity = 1000 passenger vehicles per lane per hour # trunk and motorway correct by factor 4 # primary correct by factor 2 # secondary correct by factor 1 # tertiary correct factor 0.5 # other roads correct factor 0.5 # passenger vehicle equvalent for trucks: 3.5 # average truck load: 8 tonnes # 30 % of trips are empty # median value per ton: 2,000 USD # median truck value: 8*2000 = 16,000 USD standard_max_flow = 1000/3.5*16000*empty_trip_correction if (segment.infra_type == 'trunk') | (segment.infra_type == 'trunk_link'): return standard_max_flow*4 elif (segment.infra_type == 'motorway') | (segment.infra_type == 'motorway_link'): return standard_max_flow*4 elif (segment.infra_type == 'primary') | (segment.infra_type == 'primary_link'): return standard_max_flow*2 elif (segment.infra_type == 'secondary') | (segment.infra_type == 'secondary_link'): return standard_max_flow*1 elif (segment.infra_type == 'tertiary') | (segment.infra_type == 'tertiary_link'): return standard_max_flow*0.5 else: return standard_max_flow*0.5 def gc_function(segment): """[summary] Args: segment ([type]): [description] Returns: [type]: [description] """ # GC = α ∗ WaitT + β ∗ TrvlT + μ ∗ Trate + γ ∗ stddev Wait_time = 0 if segment.infra_type in ['primary','primary_link']: Trate = 0.5 return 0.57*Wait_time+0.49*segment['time']+1*Trate+0.44*1 elif segment.infra_type in ['secondary','secondary_link']: Trate = 1 return 0.57*Wait_time+0.49*segment['time']+1*Trate+0.44*1 elif segment.infra_type in ['tertiary','tertiary_link']: Trate = 1.5 return 0.57*Wait_time+0.49*segment['time']+1*Trate+0.44*1 else: Trate = 2 return 0.57*Wait_time+0.49*segment['time']+1*Trate+0.44*1 def update_gc_function(segment): """[summary] Args: segment ([type]): [description] Returns: [type]: [description] """ # GC = α ∗ WaitT + β ∗ TrvlT + μ ∗ Trate + γ ∗ stddev if segment['flow'] > segment['max_flow']: segment['wait_time'] += 1 elif segment['wait_time'] > 0: segment['wait_time'] - 1 else: segment['wait_time'] = 0 if segment['infra_type'] in ['primary','primary_link']: Trate = 0.5 return 0.57*segment['wait_time']+0.49*segment['time']+1*Trate+0.44*1 elif segment['infra_type'] in ['secondary','secondary_link']: Trate = 1 return 0.57*segment['wait_time']+0.49*segment['time']+1*Trate+0.44*1 elif segment['infra_type'] in ['tertiary','tertiary_link']: Trate = 1.5 return 0.57*segment['wait_time']+0.49*segment['time']+1*Trate+0.44*1 else: Trate = 2 return 0.57*segment['wait_time']+0.49*segment['time']+1*Trate+0.44*1 def run_flow_analysis(country,transport_network,gdf_admin,OD_dict,notebook=False): """[summary] Args: transport_network ([type]): [description] gdf_admin ([type]): [description] Returns: [type]: [description] """ plt.rcParams['figure.figsize'] = [5, 5] gdf_roads = prepare_network_routing(transport_network) sg,gdf_in = create_graph(gdf_roads) nearest_node = nearest_network_node_list(gdf_admin,transport_network.nodes,sg) dest_nodes = [sg.vs['name'].index(nearest_node[x]) for x in list(nearest_node.keys())] # this is where the iterations goes iterator = 0 optimal = False max_iter = 100 save_fits = [] if not notebook: plt.ion() ## Note this correction # run flow optimization model while optimal == False: #update cost function per segment, dependent on flows from previous iteration. sg.es['GC'] = [(lambda segment: update_gc_function(segment))(segment) for segment in list(sg.es)] sg.es['flow'] = 0 #(re-)assess shortest paths between all regions for admin_orig in (list(gdf_admin.name)): paths = sg.get_shortest_paths(sg.vs[sg.vs['name'].index(nearest_node[admin_orig])],dest_nodes,weights='GC',output="epath") for path,admin_dest in zip(paths,list(gdf_admin.name)): flow_value = OD_dict[(admin_orig,admin_dest)] sg.es[path]['flow'] = [x + flow_value for x in sg.es[path]['flow']] fitting_edges = (sum([x<y for x,y in zip(sg.es['flow'],sg.es['max_flow'])])/len(sg.es)) save_fits.append(fitting_edges) # if at least 99% of roads are below max flow, we say its good enough if (sum([x<y for x,y in zip(sg.es['flow'],sg.es['max_flow'])])/len(sg.es)) > 0.99: optimal = True iterator += 1 # when running the code in a notebook, the figure updates instead of a new figure each iteration if notebook: pl.plot(save_fits) display.display(pl.gcf()) display.clear_output(wait=True) else: plt.plot(save_fits) plt.xlabel('# iteration') plt.ylabel('Share of edges below maximum flow') plt.show() plt.pause(0.0001) #Note this correction if iterator == max_iter: break # save output plt.savefig(os.path.join(code_path,'..','..','figures','{}_flow_modelling.png'.format(country))) gdf_in['flow'] = pd.DataFrame(sg.es['flow'],columns=['flow'],index=sg.es['id']) gdf_in['max_flow'] = pd.DataFrame(sg.es['max_flow'],columns=['max_flow'],index=sg.es['id']) gdf_in['wait_time'] = pd.DataFrame(sg.es['wait_time'],columns=['wait_time'],index=sg.es['id']) gdf_in['overflow'] = gdf_in['flow'].div(gdf_in['max_flow']) return gdf_in def plot_OD_matrix(OD): """[summary] Args: OD ([type]): [description] """ plt.rcParams['figure.figsize'] = [20, 15] sns.heatmap(OD,vmin=0,vmax=1e5,cmap='Reds') def plot_results(gdf_in): """[summary] Args: gdf_in ([type]): [description] """ gdf_in['geometry'] = gdf_in.geometry.apply(lambda x : loads(pygeos.to_wkb(x))) gdf_plot = gpd.GeoDataFrame(gdf_in) gdf_plot.crs = 4326 gdf_plot = gdf_plot.to_crs(3857) plt.rcParams['figure.figsize'] = [20, 10] fig, axes = plt.subplots(1, 2) for iter_,ax in enumerate(axes.flatten()): if iter_ == 0: gdf_plot.loc[gdf_plot.flow>1].plot(ax=ax,column='flow',legend=False,cmap='Reds',linewidth=3) #loc[gdf_plot.flow>1] ctx.add_basemap(ax, source=ctx.providers.Stamen.TonerLite,zoom=15) ax.set_axis_off() ax.set_title('Flows along the network') else: pd.DataFrame(gdf_in.loc[gdf_in.max_flow>1].groupby( 'infra_type').sum()['distance']/gdf_in.groupby('infra_type').sum()['distance']).dropna().sort_values( by='distance',ascending=False).plot(type='bar',color='red',ax=ax) ax.set_ylabel('Percentage of edges > max flow') ax.set_xlabel('Road type') #plt.show(block=True) def country_run(country,data_path=os.path.join('C:\\','Data'),plot=False,save=True): """[summary] Args: country ([type]): [description] plot (bool, optional): [description]. Defaults to True. """ osm_path = os.path.join(data_path,'country_osm','{}.osm.pbf'.format(country)) transport_network = load_network(osm_path) print('NOTE: Network created') gdf_roads = prepare_network_routing(transport_network) sg = create_graph(gdf_roads)[0] main_graph = pd.DataFrame(list(sg.es['geometry']),columns=['geometry']) gdf_admin = country_grid_gdp_filled(main_graph,country,data_path,rough_grid_split=100,from_main_graph=True) print('NOTE: GDP values extracted') # OD,OD_dict,sectors,gdf_admin = create_OD(gdf_admin,country,data_path) # print('NOTE: OD created') # gdf_out = run_flow_analysis(country,transport_network,gdf_admin,OD_dict) # print('NOTE: Flow analysis finished') # if save: # gdf_admin['geometry'] = gdf_admin.geometry.apply(lambda x: loads(pygeos.to_wkb(x))) # gdf_out = gdf_out.loc[~gdf_out.max_flow.isna()].reset_index(drop=True) # gdf_out_save = gdf_out.copy() # gdf_out_save['geometry'] = gdf_out_save.geometry.apply(lambda x: loads(pygeos.to_wkb(x))) # gpd.GeoDataFrame(gdf_admin.drop('centroid',axis=1)).to_file( # os.path.join(code_path,'..','..','data', # '{}.gpkg'.format(country)),layer='grid',driver='GPKG') # gpd.GeoDataFrame(gdf_out_save).to_file(os.path.join('..','..','data', # '{}.gpkg'.format(country)),layer='network',driver='GPKG') # if plot: # plot_results(gdf_out) if __name__ == '__main__': #country_run(sys.argv[1],os.path.join('C:\\','Data'),plot=False) #country_run(sys.argv[1],os.path.join(code_path,'..','..','Data'),plot=False) #data_path = os.path.join('C:\\','Data') if (len(sys.argv) > 1) & (len(sys.argv[1]) == 3): country_run(sys.argv[1]) elif (len(sys.argv) > 1) & (len(sys.argv[1]) > 3): glob_info = pd.read_excel(os.path.join('/scistor','ivm','eks510','projects','trails','global_information.xlsx')) glob_info = glob_info.loc[glob_info.continent==sys.argv[1]] countries = list(glob_info.ISO_3digit) if len(countries) == 0: print('FAILED: Please write the continents as follows: Africa, Asia, Central-America, Europe, North-America,Oceania, South-America') with Pool(cpu_count()) as pool: pool.map(country_run,countries,chunksize=1) else: print('FAILED: Either provide an ISO3 country name or a continent name')

35.400668

145

0.636831

2,919

21,205

4.438164

0.188421

0.029641

0.019298

0.022076

0.343342

0.274334

0.209031

0.183404

0.163952

0.149904

0

0.021345

0.217873

21,205

599

146

35.400668

0.759301

0.212497

0

0.126183

0

0.003155

0.107006

0.005622

0

1

0.059937

false

0

0.07571

0

0.223975

0.012618

0

null

0

null

0

1

0

61cffba0eebf31780c12f21faf032f94e065f6a5

1,238

py

Python

offsite/core/utils.py

wh1te909/backup-offsite

694f773583eb825b44ff20c51598ac9e1106cd32

[ "MIT" ]

4

2021-01-20T15:45:35.000Z

2021-07-09T02:15:31.000Z

offsite/core/utils.py

wh1te909/backup-offsite

694f773583eb825b44ff20c51598ac9e1106cd32

[ "MIT" ]

6

2020-08-02T23:31:07.000Z

2021-09-22T19:19:50.000Z

offsite/core/utils.py

wh1te909/backup-offsite

694f773583eb825b44ff20c51598ac9e1106cd32

[ "MIT" ]

null

from channels.auth import AuthMiddlewareStack from knox.auth import TokenAuthentication from django.contrib.auth.models import AnonymousUser from channels.db import database_sync_to_async @database_sync_to_async def get_user(access_token): try: auth = TokenAuthentication() token = access_token.decode().split("access_token=")[1] user = auth.authenticate_credentials(token.encode()) except Exception: return AnonymousUser() else: return user[0] class KnoxAuthMiddlewareInstance: """ https://github.com/django/channels/issues/1399 """ def __init__(self, scope, middleware): self.middleware = middleware self.scope = dict(scope) self.inner = self.middleware.inner async def __call__(self, receive, send): q = self.scope["query_string"] self.scope["user"] = await get_user(q) inner = self.inner(self.scope) return await inner(receive, send) class KnoxAuthMiddleware: def __init__(self, inner): self.inner = inner def __call__(self, scope): return KnoxAuthMiddlewareInstance(scope, self) KnoxAuthMiddlewareStack = lambda inner: KnoxAuthMiddleware(AuthMiddlewareStack(inner))

25.265306

86

0.697092

136

1,238

6.132353

0.411765

0.064748

0.046763

0.045564

0

0.006129

0.209208

1,238

48

87

25.791667

0.845761

0.037157

0

0.02466

0

1

0.133333

false

0

0.133333

0.033333

0.466667

0

null

0

null

0

1

0

61d14e7bc92cdd86e7f3f92f3039ee396ac2a457

6,841

py

Python

unik/indexing.py

balbasty/unik

7b8b2a0989495eec7bc0db6c672ce904cbcb1063

[ "MIT" ]

null

unik/indexing.py

balbasty/unik

7b8b2a0989495eec7bc0db6c672ce904cbcb1063

[ "MIT" ]

null

unik/indexing.py

balbasty/unik

7b8b2a0989495eec7bc0db6c672ce904cbcb1063

[ "MIT" ]

null

"""Access / change tensor shape.""" import tensorflow as tf import numpy as np from .magik import tensor_compat from .alloc import zeros_like from .types import has_tensor, as_tensor, cast, dtype from .shapes import shape, reshape, flatten, transpose, unstack from ._math_for_indexing import cumprod, minimum, maximum from ._utils import pop @tensor_compat def gather(input, indices, validate_indices=None, axis=None, batch_dims=0, name=None): """Gather / Take values from a tensor / array along an axis.""" if tf.is_tensor(input) or tf.is_tensor(indices) \ or tf.is_tensor(axis) or tf.is_tensor(batch_dims): return tf.gather(input, indices, validate_indices, axis, batch_dims, name) else: if batch_dims > 0: raise NotImplementedError() return np.take(input, indices, axis=axis, mode='raise') @tensor_compat def scatter(indices, updates, *args, **kwargs): """Scatter `updates` at `indices` into a tensor. Signatures ---------- scatter(indices, updates, shape, mode='new', axis=0, name=None) scatter(indices, updates, input, mode, axis=0, name=None) Parameters ---------- indices - (*ind_shape, L) tensor_like[int] ND-indices in which to place the `updates`. The last dimension maps to dimensions of the output tensor. updates - (*up_shape, *slice_shape) tensor_like or scalar Values to place in the tensor. shape - vector_like[int], if mode == 'new' Shape of the output tensor. input - (*shape) tensor_like, if mode != 'new' Tensor in which to place `updates`. mode - {'new', 'update', 'add', 'sub', 'min', 'max'}, default='new' Scatter mode. name - str, optional A name for the operation. Returns ------- output - (*shape) tensor or array Tensor with updated values. """ # Parse arguments args = list(args) kwargs = dict(kwargs) mode = pop(args, 1) if len(args) > 1 else kwargs.pop('mode', 'new') if mode == 'new': input = [] _shape = pop(args, 0) if len(args) > 0 else kwargs.pop('shape', None) else: input = pop(args, 0) if len(args) > 0 else kwargs.pop('input', None) _shape = shape(input) name = pop(args, 0) if len(args) > 0 else kwargs.pop('name', None) # Ensure tensors if has_tensor([indices, updates, _shape, input], 'tf'): updates = as_tensor(updates, 'tf') indices = as_tensor(indices, 'tf') elif has_tensor([indices, updates, _shape, input], 'np'): updates = as_tensor(updates, 'np') indices = as_tensor(indices, 'np') else: updates = as_tensor(updates) indices = as_tensor(indices) if mode == 'new': # Mode new: allocate tensor and populate if has_tensor([indices, updates, _shape], 'tf'): print(indices.dtype) return tf.scatter_nd(indices, updates, _shape, name=name) else: # np.put works with linear indices only. # NOTE: with this implementation, ind_shape and up_shape # must be exactly equal, not just broadcastable. output = zeros_like(updates, shape=_shape) indices = reshape(indices, [-1, shape(indices)[-1]]) indices = sub2ind(transpose(indices), _shape) updates = flatten(updates) np.put(output, indices, updates) return output else: if has_tensor([indices, updates, input], 'tf'): if mode == 'update': scatter_fn = tf.tensor_scatter_nd_update elif mode == 'add': scatter_fn = tf.tensor_scatter_nd_add elif mode == 'sub': scatter_fn = tf.tensor_scatter_nd_sub elif mode == 'min': scatter_fn = tf.tensor_scatter_nd_min elif mode == 'max': scatter_fn = tf.tensor_scatter_nd_max else: raise ValueError('Unknown operation {}'.format(mode)) updates = cast(updates, dtype(input)) return scatter_fn(input, indices, updates, name=name) else: # If mode != 'update', equivalent to: # 0) the left-hand side is the input tensor # 1) generate right-hand side using mode scatter with mode 'new' # 2) apply op(LHS, RHS), if mode == 'update': output = input.copy() indices = reshape(indices, [-1, shape(indices)[-1]]) indices = sub2ind(transpose(indices), _shape) updates = flatten(updates) np.put(output, indices, updates) return output elif mode == 'add': op = lambda x, y: x + y elif mode == 'sub': op = lambda x, y: x - y elif mode == 'min': op = lambda x, y: minimum(x, y) elif mode == 'max': op = lambda x, y: maximum(x, y) else: raise ValueError('Unknown operation {}'.format(mode)) updates = scatter(indices, updates, shape=_shape, mode='new') return op(input, updates) @tensor_compat def sub2ind(subs, shape): """Convert sub indices (i, j, k) into linear indices. The rightmost dimension is the most rapidly changing one -> if shape == [D, H, W], the strides are therefore [H*W, W, 1] Parameters ---------- subs : (D, *shape) tensor_like List of sub-indices. The first dimension is the number of dimension. Each element should have the same number of elements and shape. shape : (D,) vector_like Size of each dimension. Its length should be the same as the first dimension of ``subs``. Returns ------- ind : (*shape) tensor or array Linear indices """ *subs, ind = unstack(subs) stride = cumprod(shape[1:], reverse=True) for i, s in zip(subs, stride): ind = ind + as_tensor(i) * s return ind @tensor_compat def where(cond, x=None, y=None, name=None): """Select values from two tensors based on a condition.""" if has_tensor([cond, x, y], 'tf'): return tf.where(cond, x, y, name) else: if x is None and y is None: return np.where(cond) else: return np.where(cond, x, y) @tensor_compat def boolean_mask(input, mask, axis=0, name='boolean_mask'): """Gather elements from a tensor / array using a mask.""" input = as_tensor(input) if has_tensor([input, mask], 'tf'): return tf.boolean_mask(input, mask, axis=axis, name=name) else: axis = axis or 0 slices = (slice(None, None),) * axis + (mask,) + (Ellipsis,) return input.__getitem__(slices)

35.262887

77

0.582517

874

6,841

4.45881

0.217391

0.04311

0.029253

0.021812

0.220683

0.185014

0.126508

0.089556

0

0.005427

0.299664

6,841

193

78

35.445596

0.807973

0.2887

0

0.357143

0

0.030647

0

1

0.044643

false

0

0.071429

0

0.232143

0.008929

0

null

0

null

0

1

0

61d192d69ecdae0462072ff12464ac90f01f69d0

1,478

py

Python

aleph/views/alerts_api.py

adikadashrieq/aleph

acc03197c10e511a279ae3a05120187223f173d2

[ "MIT" ]

1

2019-06-18T21:35:59.000Z

aleph/views/alerts_api.py

heartofstone/aleph

d66b6615d2bfa10c291c63754f53b468de8bebde

[ "MIT" ]

null

aleph/views/alerts_api.py

heartofstone/aleph

d66b6615d2bfa10c291c63754f53b468de8bebde

[ "MIT" ]

null

from flask import Blueprint, request from aleph.core import db from aleph.model import Alert from aleph.search import DatabaseQueryResult from aleph.views.forms import AlertSchema from aleph.views.serializers import AlertSerializer from aleph.views.util import require, obj_or_404 from aleph.views.util import parse_request from aleph.views.context import tag_request blueprint = Blueprint('alerts_api', __name__) @blueprint.route('/api/2/alerts', methods=['GET']) def index(): require(request.authz.logged_in) query = Alert.by_role_id(request.authz.id) result = DatabaseQueryResult(request, query) return AlertSerializer.jsonify_result(result) @blueprint.route('/api/2/alerts', methods=['POST', 'PUT']) def create(): require(request.authz.session_write) data = parse_request(AlertSchema) alert = Alert.create(data, request.authz.id) db.session.commit() tag_request(alert_id=alert.id) return AlertSerializer.jsonify(alert) @blueprint.route('/api/2/alerts/<int:alert_id>', methods=['GET']) def view(alert_id): require(request.authz.logged_in) alert = obj_or_404(Alert.by_id(alert_id, role_id=request.authz.id)) return AlertSerializer.jsonify(alert) @blueprint.route('/api/2/alerts/<int:alert_id>', methods=['DELETE']) def delete(alert_id): require(request.authz.session_write) alert = obj_or_404(Alert.by_id(alert_id, role_id=request.authz.id)) alert.delete() db.session.commit() return ('', 204)

31.446809

71

0.750338

207

1,478

5.198068

0.2657

0.066915

0.065056

0.066915

0.472119

0.288104

0.230483

0

0.012327

0.121786

1,478

46

72

32.130435

0.816641

0

0.277778

0

0.075101

0.037889

0

1

0.111111

false

0

0.25

0

0.472222

0.166667

0

null

0

null

0

1

0

61d210a06894e407303586520efa2e44fe445461

11,283

py

Python

run.py

Acforest/LogPrompt

199766cea9988bc6e8b1c71352b090da68bbb71d

[ "Apache-2.0" ]

null

run.py

Acforest/LogPrompt

199766cea9988bc6e8b1c71352b090da68bbb71d

[ "Apache-2.0" ]

null

run.py

Acforest/LogPrompt

199766cea9988bc6e8b1c71352b090da68bbb71d

[ "Apache-2.0" ]

null

# 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. """ This script can be used to train and evaluate either a regular supervised model or a PET/iPET model on one of the supported tasks and datasets. """ import os import log import pet import torch import argparse from pet.config import load_configs from pet.tasks import PROCESSORS, UNLABELED_SET, TRAIN_SET, DEV_SET, TEST_SET, METRICS, DEFAULT_METRICS, load_examples from pet.utils import eq_div from pet.wrapper import WRAPPER_TYPES, MODEL_CLASSES logger = log.get_logger('root') def main(): parser = argparse.ArgumentParser(description="Command line interface for PET/iPET") # Required parameters parser.add_argument("--data_dir", default=None, type=str, required=True, help="The input data directory, which should contain the data files for the task") parser.add_argument("--model_type", default=None, type=str, required=True, choices=MODEL_CLASSES.keys(), help="The type of the pretrained language model to use") parser.add_argument("--model_name_or_path", default=None, type=str, required=True, help="Path to the pre-trained model or shortcut name") parser.add_argument("--task_name", default=None, type=str, required=True, choices=PROCESSORS.keys(), help="The name of the task to train/evaluate on") parser.add_argument("--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written") # PET-specific optional parameters parser.add_argument("--wrapper_type", default="mlm", choices=WRAPPER_TYPES, help="The wrapper type. Set this to 'mlm' for a masked language model like BERT or to 'plm' " "for a permuted language model like XLNet") parser.add_argument("--pattern_ids", default=[0], type=int, nargs='+', help="The ids of the PVPs to be used") parser.add_argument("--lm_training", action='store_true', help="Whether to use language modeling as auxiliary task") parser.add_argument("--alpha", default=0.9999, type=float, help="Weighting term for the auxiliary language modeling task") parser.add_argument("--temperature", default=2, type=float, help="Temperature used for combining PVPs") parser.add_argument("--verbalizer_file", default=None, help="The path to a file to override default verbalizers") parser.add_argument("--reduction", default='wmean', choices=['wmean', 'mean'], help="Reduction strategy for merging predictions from multiple PET models. Select either " "uniform weighting (mean) or weighting based on train set accuracy (wmean)") parser.add_argument("--decoding_strategy", default='default', choices=['default', 'ltr', 'parallel'], help="The decoding strategy with multiple masks") parser.add_argument("--no_distillation", action='store_true', help="If set to true, no distillation is performed") parser.add_argument("--repetitions", default=3, type=int, help="The number of times to repeat training and testing with different seeds") parser.add_argument("--max_seq_length", default=256, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded") parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation") parser.add_argument("--per_gpu_unlabeled_batch_size", default=8, type=int, help="Batch size per GPU/CPU for auxiliary language modeling examples") parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass") parser.add_argument("--num_train_epochs", default=3, type=float, help="Total number of training epochs to perform") parser.add_argument("--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform, override num_train_epochs") # Other optional parameters parser.add_argument("--train_examples", default=-1, type=int, help="The total number of train examples to use, where -1 equals all examples") parser.add_argument("--eval_examples", default=-1, type=int, help="The total number of evaluation examples to use, where -1 equals all examples") parser.add_argument("--dev_examples", default=-1, type=int, help="The total number of development examples to use, where -1 equals all examples") parser.add_argument("--unlabeled_examples", default=-1, type=int, help="The total number of unlabeled examples to use, where -1 equals all examples") parser.add_argument("--split_examples_evenly", action='store_true', help="If true, train examples are not chosen randomly, but split evenly across all labels") parser.add_argument("--cache_dir", default="pretrained", type=str, help="Where to store the pre-trained models downloaded") parser.add_argument("--learning_rate", default=1e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--early_stop_epochs", default=5, type=int, help="Threshold epochs for early stop") parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps") parser.add_argument("--no_cuda", action='store_true', help="Avoid using CUDA when available") parser.add_argument("--overwrite_output_dir", action='store_true', help="Overwrite the content of the output directory") parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument("--do_train", action='store_true', help="Whether to perform training") parser.add_argument("--do_eval", action='store_true', help="Whether to perform evaluation") parser.add_argument("--priming", action='store_true', help="Whether to use priming for evaluation") parser.add_argument("--eval_set", choices=['dev', 'test'], default='test', help="Whether to perform evaluation on the dev set or the test set") parser.add_argument("--embed_size", default=128, type=int, help="The embedding size of prompt") parser.add_argument("--prompt_encoder_type", type=str, default="lstm", choices=['lstm', 'mlp', 'manual'], help="The type of encoder") parser.add_argument("--eval_every_step", default=20, type=int, help="Evaluate between two `eval_every_step` steps") args = parser.parse_args() logger.info("Parameters: {}".format(args)) if os.path.exists(args.output_dir) and os.listdir(args.output_dir) \ and args.do_train and not args.overwrite_output_dir: raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir)) assert args.do_train or args.do_eval, "`do_train` and `do_eval` should be at least true for one" # Setup CUDA, GPU & distributed training args.device = "cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" args.n_gpu = torch.cuda.device_count() # Prepare task if args.task_name not in PROCESSORS: raise ValueError("Task '{}' not found".format(args.task_name)) processor = PROCESSORS[args.task_name]() args.label_list = processor.get_labels() train_ex_per_label, eval_ex_per_label, dev_ex_per_label = None, None, None train_ex, eval_ex, dev_ex = args.train_examples, args.eval_examples, args.dev_examples if args.split_examples_evenly: train_ex_per_label = eq_div(args.train_examples, len(args.label_list)) if args.train_examples != -1 else -1 eval_ex_per_label = eq_div(args.eval_examples, len(args.label_list)) if args.eval_examples != -1 else -1 dev_ex_per_label = eq_div(args.dev_examples, len(args.label_list)) if args.dev_examples != -1 else -1 train_ex, eval_ex, dev_ex = None, None, None eval_set = TEST_SET if args.eval_set == 'test' else DEV_SET train_data = load_examples( args.task_name, args.data_dir, TRAIN_SET, num_examples=train_ex, num_examples_per_label=train_ex_per_label) eval_data = load_examples( args.task_name, args.data_dir, eval_set, num_examples=eval_ex, num_examples_per_label=eval_ex_per_label) dev_data = load_examples( args.task_name, args.data_dir, DEV_SET, num_examples=dev_ex, num_examples_per_label=dev_ex_per_label) unlabeled_data = load_examples( args.task_name, args.data_dir, UNLABELED_SET, num_examples=args.unlabeled_examples) args.metrics = METRICS.get(args.task_name, DEFAULT_METRICS) pet_model_cfg, pet_train_cfg, pet_eval_cfg = load_configs(args) pet.train_pet(train_data=train_data, eval_data=eval_data, dev_data=dev_data, unlabeled_data=unlabeled_data, model_config=pet_model_cfg, train_config=pet_train_cfg, eval_config=pet_eval_cfg, do_train=args.do_train, do_eval=args.do_eval, pattern_ids=args.pattern_ids, output_dir=args.output_dir, repetitions=args.repetitions, reduction=args.reduction, no_distillation=args.no_distillation, seed=args.seed) if __name__ == "__main__": main()

58.46114

119

0.660197

1,498

11,283

4.781709

0.215621

0.056541

0.106799

0.02122

0.226721

0.17744

0.160128

0.107078

0.074131

0

0.006516

0.238323

11,283

192

120

58.765625

0.826972

0.070637

0

0.006667

0.329542

0.016821

0

0.006667

1

0.006667

false

0.006667

0.06

0

0.066667

0

null

0

null

0

1

0

61d24122d7792980c0b72c95b9dc3ec6c9efd631

2,282

py

Python

data/external/repositories_2to3/253384/national_data_science_bowl_2-master/alexcode/code/model.py

Keesiu/meta-kaggle

87de739aba2399fd31072ee81b391f9b7a63f540

[ "MIT" ]

null

data/external/repositories_2to3/253384/national_data_science_bowl_2-master/alexcode/code/model.py

Keesiu/meta-kaggle

87de739aba2399fd31072ee81b391f9b7a63f540

[ "MIT" ]

null

data/external/repositories_2to3/253384/national_data_science_bowl_2-master/alexcode/code/model.py

Keesiu/meta-kaggle

87de739aba2399fd31072ee81b391f9b7a63f540

[ "MIT" ]

1

2019-12-04T08:23:33.000Z

from keras.models import Sequential from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D from keras.layers.core import Activation, Dense, Flatten, Dropout from keras.optimizers import Adam from keras.regularizers import l2 from keras import backend as K def center_normalize(x): """ Custom activation for online sample-wise center and std. normalization """ return (x - K.mean(x)) / K.std(x) def get_model(): model = Sequential() model.add(Activation(activation=center_normalize, input_shape=(45, 64, 64))) model.add(Convolution2D(64, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(64, 3, 3, border_mode='valid')) model.add(Activation('relu')) model.add(ZeroPadding2D(padding=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(96, 3, 3, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(96, 3, 3, border_mode='valid')) model.add(Activation('relu')) model.add(ZeroPadding2D(padding=(1, 1))) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(128, 2, 2, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(128, 2, 2, border_mode='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(256, 2, 2, border_mode='same')) model.add(Activation('relu')) model.add(Convolution2D(256, 2, 2, border_mode='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(512, 2, 2, border_mode='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(1024, W_regularizer=l2(3e-3))) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) adam = Adam(lr=0.0001) model.compile(optimizer=adam, loss='rmse') return model

34.575758

82

0.659509

318

2,282

4.672956

0.226415

0.193809

0.133244

0.148048

0.631225

0.611036

0.583445

0

0.064293

0.175285

2,282

65

83

35.107692

0.725292

0.030675

0

0.44898

0

0.039455

0

1

0.040816

false

0

0.122449

0

0.204082

0

null

0

null

0

1

0

61d29e48cb817ece86e476bffbf91b00d5532c33

8,685

py

Python

BuildDeb.py

KOLANICH/GraalVM_deb_packages_CI

f41786b4daa11efebe24402f5000111137365b4f

[ "Apache-2.0", "Unlicense" ]

null

BuildDeb.py

KOLANICH/GraalVM_deb_packages_CI

f41786b4daa11efebe24402f5000111137365b4f

[ "Apache-2.0", "Unlicense" ]

null

BuildDeb.py

KOLANICH/GraalVM_deb_packages_CI

f41786b4daa11efebe24402f5000111137365b4f

[ "Apache-2.0", "Unlicense" ]

null

#!/usr/bin/env python3 import sys import struct import re import os from itertools import chain import warnings import tarfile import sh from tqdm import tqdm from pydebhelper import * from getLatestVersionAndURLWithGitHubAPI import getTargets def genGraalProvides(start=6, end=8): # java 12 still not supported yet graalvmProvides = ["default-jre", "default-jre-headless", "java-compiler"] for i in range(start, end + 1): si = str(i) graalvmProvides += ["openjdk-" + si + "-jre", "openjdk-" + si + "-jre-headless", "java" + si + "-runtime", "java" + si + "-runtime-headless", "java" + si + "-sdk-headless"] return graalvmProvides config = OrderedDict() config["llvm"] = { "descriptionLong": "LLVM engine for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/languages/llvm/", "rip": { "bin": ["lli"], "other": ["jre/languages/llvm"] } } config["js"] = { "descriptionLong": "JavaScript engine & node.js runtime for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/languages/js/", "rip": { "bin": ["js", "node", "npm"], "other": ["jre/languages/js", "jre/lib/graalvm/graaljs-launcher.jar"] } } config["python"] = { "descriptionLong": "python runtime for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/languages/python/", "rip": { "bin": ["graalpython"], "other": ["jre/languages/python", "jre/lib/graalvm/graalpython-launcher.jar", "LICENSE_GRAALPYTHON", "jre/languages/python/LICENSE_GRAALPYTHON"] } } config["ruby"] = { "descriptionLong": "ruby runtime for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/languages/ruby/", "rip": { "bin": ["truffleruby", "ruby", "bundle", "bundler", "gem", "irb", "rake", "rdoc", "ri"], "other": ["jre/languages/ruby", "jre/lib/boot/truffleruby-services.jar", "jre/lib/graalvm/truffleruby-launcher.jar", "LICENSE_TRUFFLERUBY.md", "3rd_party_licenses_truffleruby.txt"] } } config["r"] = { "descriptionLong": "R runtime for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/languages/R/", "rip": { "bin": ["R", "Rscript"], "other": ["jre/languages/R", "LICENSE_FASTR", "3rd_party_licenses_fastr.txt"] } } config["gu"] = { "descriptionLong": "Package manager for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/graal-updater/", "rip": { "bin": ["gu"], "other": ["jre/lib/installer", "bin/gu"] } } config["polyglot"] = { "descriptionLong": "Polyglot for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/polyglot/", "rip": { "bin": ["polyglot"], "other": ["jre/lib/polyglot"] } } config["samples"] = { "descriptionLong": "Example code for GraalVM", "homepage": "https://www.graalvm.org/", "rip": { "other": ["sample"] } } config["visualvm"] = { "descriptionLong": "VisualVM for GraalVM", "homepage": "https://www.graalvm.org/docs/reference-manual/tools/#heap-viewer", "rip": { "bin": ["jvisualvm"], "other": ["lib/visualvm"] } } def removeUnneededSources(unpackedDir): for f in chain(unpackedDir.glob("**/src.zip"), unpackedDir.glob("**/*.src.zip")): f.unlink() def ripGraalPackage(unpackedDir, packagesDir, version, maintainer, builtDir): mainPackageName = "graalvm" systemPrefix = "usr/lib/jvm/graalvm-ce-amd64" removeUnneededSources(unpackedDir) results = [] for pkgPostfix, pkgCfg in config.items(): pkgCfg = type(pkgCfg)(pkgCfg) rip = pkgCfg["rip"] del pkgCfg["rip"] with Package(mainPackageName + "-" + pkgPostfix, packagesDir, version=version, section="java", maintainer=maintainer, builtDir=builtDir, **pkgCfg) as pkg: if "other" in rip: for el in rip["other"]: pkg.rip(unpackedDir / el, systemPrefix + "/" + el) if "bin" in rip: for el in rip["bin"]: a = "bin/" + el aUnp = unpackedDir / a if aUnp.exists() or aUnp.is_symlink(): pkg.rip(aUnp, systemPrefix + "/" + a) else: warnings.warn(str(aUnp) + " doesn't exist") b = "jre/" + a bUnp = unpackedDir / b if aUnp.exists() or aUnp.is_symlink(): pkg.rip(bUnp, systemPrefix + "/" + b) else: warnings.warn(str(bUnp) + " doesn't exist") results.append(pkg) with Package(mainPackageName, packagesDir, version=version, section="java", homepage="https://github.com/oracle/graal/releases", provides=genGraalProvides(), descriptionShort="graalvm", descriptionLong="GraalVM is a high-performance, embeddable, polyglot virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Kotlin, and LLVM-based languages such as C and C++. \nAdditionally, GraalVM allows efficient interoperability between programming languages and compiling Java applications ahead-of-time into native executables for faster startup time and lower memory overhead.", maintainer=maintainer, builtDir=builtDir) as graalVM: graalVM.rip(unpackedDir, systemPrefix) results.append(graalVM) return results def isSubdir(parent: Path, child: Path) -> bool: parent = parent.absolute().resolve() child = child.absolute().resolve().relative_to(parent) for p in child.parts: if p == "..": return False return True def unpack(archPath, extrDir): extrDir = extrDir.resolve() packedSize = archPath.stat().st_size with archPath.open("rb") as arch: arch.seek(packedSize - 4) unpackedSize = struct.unpack("<I", arch.read(4))[0] with tarfile.open(archPath, "r:gz") as arch: with tqdm(total=unpackedSize, unit="B", unit_divisor=1024, unit_scale=True) as pb: for f in arch: fp = (extrDir / f.name).absolute() if isSubdir(extrDir, fp): if fp.is_file() or fp.is_symlink(): fp.unlink() fp.parent.mkdir(parents=True, exist_ok=True) arch.extract(f, extrDir, set_attrs=True) pb.set_postfix(file=str(fp.relative_to(extrDir)), refresh=False) pb.update(f.size) currentProcFileDescriptors = Path("/proc") / str(os.getpid()) / "fd" fj = sh.firejail.bake(noblacklist=str(currentProcFileDescriptors), _fg=True) aria2c = fj.aria2c.bake(_fg=True, **{"continue": "true", "check-certificate": "true", "enable-mmap": "true", "optimize-concurrent-downloads": "true", "j": 16, "x": 16, "file-allocation": "falloc"}) def download(targets): args = [] for dst, uri in targets.items(): args += [uri, linesep, " ", "out=", str(dst), linesep] pO, pI = os.pipe() with os.fdopen(pI, "w") as pIF: pIF.write("".join(args)) pIF.flush() try: aria2c(**{"input-file": str(currentProcFileDescriptors / str(pO))}) finally: os.close(pO) try: os.close(pI) except: pass vmTagRx = re.compile("^vm-((?:\\d+\\.){2}\\d+(?:-rc\\d+))?$") vmTitleMarker = "GraalVM Community Edition .+$" platformMarker = "linux-amd64" versionFileNameMarker = "[\\w\\.-]+" releaseFileNameMarker = versionFileNameMarker + "-" + platformMarker def getLatestGraalVMRelease(): downloadFileNameRx = re.compile("^" + releaseFileNameMarker + "\\.tar\\.gz$") return max(getTargets("oracle/graal", re.compile("^" + vmTitleMarker), vmTagRx, downloadFileNameRx)) def getLatestGraalRuntimeRelease(repoPath): downloadFileNameRx = re.compile(".+installable-ce-" + releaseFileNameMarker + "\\.jar$") return max(getTargets(repoPath, re.compile(".+- " + vmTitleMarker), vmTagRx, downloadFileNameRx)) def doBuild(): thisDir = Path(".") downloadDir = Path(thisDir / "downloads") archPath = Path(downloadDir / "graalvm-github.tar.gz") unpackDir = thisDir / "graalvm-unpacked" packagesRootsDir = thisDir / "packagesRoots" builtDir = thisDir / "packages" repoDir = thisDir / "public" / "repo" selT = getLatestGraalVMRelease() print("Selected release:", selT, file=sys.stderr) runtimesRepos = {"python": "graalvm/graalpython", "ruby": "oracle/truffleruby", "R": "oracle/fastr"} runtimeReleases = {k: getLatestGraalRuntimeRelease(v) for k, v in runtimesRepos.items()} runtimeFiles = {(downloadDir / (k + ".jar")): v.uri for k, v in runtimeReleases.items()} downloadTargets = {archPath: selT.uri, **runtimeFiles} download(downloadTargets) unpack(archPath, unpackDir) graalUnpackedRoot = unpackDir / ("graalvm-ce-" + selT.version) guCmd = fj.bake(str(graalUnpackedRoot / "bin/gu"), _fg=True) guCmd("-L", "install", *runtimeFiles.keys()) builtDir.mkdir(parents=True, exist_ok=True) maintainer = Maintainer() pkgs = ripGraalPackage(graalUnpackedRoot, packagesRootsDir, selT.version, maintainer=maintainer, builtDir=builtDir) for pkg in pkgs: pkg.build() with Repo(root=repoDir, descr=maintainer.name+"'s repo for apt with GraalVM binary packages, built from the official builds on GitHub") as r: for pkg in pkgs: r += pkg print(r.packages2add) if __name__ == "__main__": doBuild()

32.773585

691

0.687737

1,041

8,685

5.705091

0.331412

0.021889

0.027277

0.034854

0.146658

0.134534

0.103553

0.097491

0.086378

0

0.003736

0.137018

8,685

264

692

32.897727

0.788659

0.006102

0

0.081731

0

0.004808

0.338355

0.045423

0

1

0.043269

false

0.004808

0.052885

0

0.125

0.009615

0

null

0

null

0

1

0

61d2ae9ec01343c7273afc66fcb5912f5895801a

6,267

py

Python

mergify_engine/utils.py

Madhu-1/mergify-engine

9ca4f4697cc825230b1584f5587f10393cabc971

[ "Apache-2.0" ]

null

mergify_engine/utils.py

Madhu-1/mergify-engine

9ca4f4697cc825230b1584f5587f10393cabc971

[ "Apache-2.0" ]

null

mergify_engine/utils.py

Madhu-1/mergify-engine

9ca4f4697cc825230b1584f5587f10393cabc971

[ "Apache-2.0" ]

null

# -*- encoding: utf-8 -*- # # Copyright © 2017 Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import contextlib import datetime import hashlib import hmac import logging import shutil import subprocess import sys import tempfile from billiard import current_process import celery.app.log import daiquiri import github import redis from mergify_engine import config LOG = daiquiri.getLogger(__name__) global REDIS_CONNECTION_CACHE REDIS_CONNECTION_CACHE = None def get_redis_for_cache(): global REDIS_CONNECTION_CACHE if REDIS_CONNECTION_CACHE is None: REDIS_CONNECTION_CACHE = redis.StrictRedis.from_url( config.STORAGE_URL, decode_responses=True, ) p = current_process() REDIS_CONNECTION_CACHE.client_setname("cache:%s" % p.name) return REDIS_CONNECTION_CACHE global REDIS_CONNECTION_HTTP_CACHE REDIS_CONNECTION_HTTP_CACHE = None def get_redis_for_http_cache(): global REDIS_CONNECTION_HTTP_CACHE if REDIS_CONNECTION_HTTP_CACHE is None: REDIS_CONNECTION_HTTP_CACHE = redis.StrictRedis.from_url(config.HTTP_CACHE_URL) p = current_process() REDIS_CONNECTION_HTTP_CACHE.client_setname("http-cache:%s" % p.name) return REDIS_CONNECTION_HTTP_CACHE def utcnow(): return datetime.datetime.now(tz=datetime.timezone.utc) def unicode_truncate(s, length, encoding="utf-8"): """Truncate a string to length in bytes. :param s: The string to truncate. :param length: The length in number of bytes — not characters.""" return s.encode(encoding)[:length].decode(encoding, errors="ignore") class CustomFormatter( daiquiri.formatter.ColorExtrasFormatter, celery.app.log.TaskFormatter ): pass CELERY_EXTRAS_FORMAT = ( "%(asctime)s [%(process)d] %(color)s%(levelname)-8.8s " "[%(task_id)s] " "%(name)s%(extras)s: %(message)s%(color_stop)s" ) def GithubPullRequestLog(self): return daiquiri.getLogger( __name__, gh_owner=self.base.user.login, gh_repo=self.base.repo.name, gh_private=self.base.repo.private, gh_branch=self.base.ref, gh_pull=self.number, gh_pull_url=self.html_url, gh_pull_state=("merged" if self.merged else (self.mergeable_state or "none")), ) github.PullRequest.PullRequest.log = property(GithubPullRequestLog) def setup_logging(): outputs = [] if config.LOG_STDOUT: outputs.append( daiquiri.output.Stream( sys.stdout, formatter=CustomFormatter(fmt=CELERY_EXTRAS_FORMAT), level=config.LOG_STDOUT_LEVEL, ) ) if config.LOG_DATADOG: outputs.append(daiquiri.output.Datadog()) daiquiri.setup( outputs=outputs, level=(logging.DEBUG if config.DEBUG else logging.INFO), ) daiquiri.set_default_log_levels( [ ("celery", "INFO"), ("kombu", "WARN"), ("github.Requester", "WARN"), ("urllib3.connectionpool", "WARN"), ("urllib3.util.retry", "WARN"), ("vcr", "WARN"), ("httpx", "WARN"), ("cachecontrol", "WARN"), ] ) config.log() def compute_hmac(data): mac = hmac.new( config.WEBHOOK_SECRET.encode("utf8"), msg=data, digestmod=hashlib.sha1 ) return str(mac.hexdigest()) def get_github_pulls_from_sha(repo, sha): try: return list( github.PaginatedList.PaginatedList( github.PullRequest.PullRequest, repo._requester, "%s/commits/%s/pulls" % (repo.url, sha), None, headers={"Accept": "application/vnd.github.groot-preview+json"}, ) ) except github.GithubException as e: if e.status in [404, 422]: return [] raise class Gitter(object): def __init__(self): self.tmp = tempfile.mkdtemp(prefix="mergify-gitter") LOG.info("working in: %s", self.tmp) def __call__(self, *args, **kwargs): # pragma: no cover LOG.info("calling: %s", " ".join(args)) kwargs["cwd"] = self.tmp kwargs["stderr"] = subprocess.STDOUT try: return subprocess.check_output(["git"] + list(args), **kwargs) except subprocess.CalledProcessError as e: LOG.info("output: %s", e.output) raise def cleanup(self): LOG.info("cleaning: %s", self.tmp) try: self("credential-cache", "--socket=%s/.git/creds/socket" % self.tmp, "exit") except subprocess.CalledProcessError: # pragma: no cover LOG.warning("git credential-cache exit fail") shutil.rmtree(self.tmp) def configure(self): self("config", "user.name", "%s-bot" % config.CONTEXT) self("config", "user.email", config.GIT_EMAIL) # Use one git cache daemon per Gitter self("config", "credential.useHttpPath", "true") self( "config", "credential.helper", "cache --timeout=300 --socket=%s/.git/creds/socket" % self.tmp, ) def add_cred(self, username, password, path): domain = config.GITHUB_DOMAIN self( "credential", "approve", input=( "url=https://%s:%s@%s/%s\n\n" % (username, password, domain, path) ).encode("utf8"), ) @contextlib.contextmanager def ignore_client_side_error(): try: yield except github.GithubException as e: if 400 <= e.status < 500: return raise def Github(*args, **kwargs): kwargs["base_url"] = "https://api.%s" % config.GITHUB_DOMAIN return github.Github(*args, **kwargs)

27.977679

88

0.6322

749

6,267

5.142857

0.368491

0.054517

0.036345

0.043614

0.126947

0.095535

0.031153

0

0.006811

0.250359

6,267

223

89

28.103139

0.812686

0.1251

0

0.107595

0

0.129677

0.035583

0

1

0.094937

false

0.018987

0.094937

0.012658

0.272152

0

null

0

null

0

1

0

61d440e6d71c032e6b0102e0319c9ad174f35ff4

1,750

py

Python

milefrienddb/models/vehicles.py

jcrjaci/mil_test

ed54f55c5aacd8ffd110b7c173422dbd0cac631f

[ "MIT" ]

null

milefrienddb/models/vehicles.py

jcrjaci/mil_test

ed54f55c5aacd8ffd110b7c173422dbd0cac631f

[ "MIT" ]

null

milefrienddb/models/vehicles.py

jcrjaci/mil_test

ed54f55c5aacd8ffd110b7c173422dbd0cac631f

[ "MIT" ]

null

"""Vehicle's app models.""" import uuid from django.db import models from .clients import Client class Vehicle(models.Model): """Model representing a vehicle.""" road_worthiness_path = 'vehicles/certs/road_worthiness' ownership_path = 'vehicles/certs/ownership' photo_path = 'vehicles/photos' id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False) client = models.ForeignKey(Client, null=True) make = models.CharField(max_length=20) model = models.CharField(max_length=20) year = models.IntegerField(null=True) license_plate_number = models.CharField(max_length=20) tracker_id = models.CharField(max_length=64) car_value = models.FloatField(null=True) cert_road_worthiness = models.FileField(upload_to=road_worthiness_path) cert_ownership = models.FileField(upload_to=ownership_path) policy_number = models.CharField(max_length=255) photo = models.FileField(upload_to=photo_path) date_insurance = models.DateTimeField(null=True) premium_paid = models.FloatField(null=True) bonus_paid = models.FloatField(null=True) net_premium = models.FloatField(null=True) driven_meters = models.IntegerField(default=0) driven_minutes = models.IntegerField(default=0) total_fuel_consumption = models.FloatField(null=True, blank=True) car_health = models.TextField(null=True) date_created = models.DateTimeField(auto_now_add=True) date_updated = models.DateTimeField(auto_now=True) def __str__(self): """String representation of the object.""" return "{0}, {1}, {2}".format(self.make, self.model, self.license_plate_number) class Meta: db_table = 'vehicles_vehicle' app_label = 'milefrienddb'

38.043478

87

0.737714

222

1,750

5.594595

0.418919

0.057971

0.072464

0.096618

0.136876

0

0.011494

0.154857

1,750

45

88

38.888889

0.828262

0.050286

0

0.066829

0.032807

0

1

0.029412

false

0

0.088235

0

0.941176

0

null

0

null

0

1

0

61d6182a3cde9be8c7c4791931417d4e0d9e7b55

187

py

Python

ejercicio_4.py

Laurardila440/taller-de-secuencias

9db216d2431661e0777273fc5b8360a316d7dbd2

[ "Apache-2.0" ]

null

ejercicio_4.py

Laurardila440/taller-de-secuencias

9db216d2431661e0777273fc5b8360a316d7dbd2

[ "Apache-2.0" ]

null

ejercicio_4.py

Laurardila440/taller-de-secuencias

9db216d2431661e0777273fc5b8360a316d7dbd2

[ "Apache-2.0" ]

null

""" Entradas compra-->int-->c salidas Descuento-->flot-->d """ c=float(input("digite compra")) #caja negra d=(c*0.15) total=(c-d) #Salidas print("el total a pagar es de :"+str(total))

14.384615

44

0.641711

32

187

3.75

0.71875

0.033333

0

0.018293

0.122995

187

12

45

15.583333

0.713415

0.395722

0

0.359223

0

1

0

false

0

0.25

0

null

0

null

0

1

0

61d6aa3833e84422d5bd54157900ea1d35ffca0b

878

py

Python

429.py

geethakamath18/Leetcode

8e55e0a47ee35ed100b30dda6682c7ce1033d4b2

[ "MIT" ]

null

429.py

geethakamath18/Leetcode

8e55e0a47ee35ed100b30dda6682c7ce1033d4b2

[ "MIT" ]

null

429.py

geethakamath18/Leetcode

8e55e0a47ee35ed100b30dda6682c7ce1033d4b2

[ "MIT" ]

null

#LeetCode problem 429: N-ary Tree Level Order Traversal """ # Definition for a Node. class Node: def __init__(self, val=None, children=None): self.val = val self.children = children """ class Solution: def levelOrder(self, root: 'Node') -> List[List[int]]: res=[] h=self.getHeight(root) for i in range(1,h+1): a=[] self.getLevelOrder(root,a,i) res.append(a) return res def getHeight(self, root:'Node')->int: if root is None: return 0 m=1 for child in root.children: m=max(self.getHeight(child)+1,m) return m def getLevelOrder(self, root: 'Node', l:List, level:int): if level==1: l.append(root.val) for child in root.children: self.getLevelOrder(child,l,level-1)

26.606061

61

0.544419

116

878

4.086207

0.362069

0.050633

0.075949

0.059072

0.092827

0

0.017094

0.333713

878

32

62

27.4375

0.793162

0.223235

0

0.095238

0

0.017831

0

1

0.142857

false

0

0.333333

0

null

0

null

0

1

0

61d7cc4850de782acf97ce8fd6bae60d5d5eb06f

544

py

Python

PyhonServer/app-client.py

sixfourtwo/auhack19

65b94c6cbdbfbd50e355c12b8ca2792b3b086321

[ "Apache-2.0" ]

null

PyhonServer/app-client.py

sixfourtwo/auhack19

65b94c6cbdbfbd50e355c12b8ca2792b3b086321

[ "Apache-2.0" ]

null

PyhonServer/app-client.py

sixfourtwo/auhack19

65b94c6cbdbfbd50e355c12b8ca2792b3b086321

[ "Apache-2.0" ]

null

# importing the requests library import requests import json # api-endpoint URL = "http://127.0.0.1:80/water_mark" # defining a params dict for the parameters to be sent to the API # data is picture data # tagString is the text to embed into picture. data = { "data":"This is the original text", "tagString":" Yesyesyes" } PARAMS = json.dumps(data) rPost = requests.post(url = URL, data = PARAMS) # kør det med JSON data1 = json.loads(rPost.text) #print("waterMarked data: " + rPost.text ) print("DATA: \n" + data1["data"])

22.666667

66

0.6875

83

544

4.493976

0.554217

0.058981

0.075067

0

0.022624

0.1875

544

23

67

23.652174

0.821267

0.430147

0

0.298013

0

1

0

false

0

0.181818

0

0.181818

0.090909

0

null

0

null

0

1

0

61d90f523acdcf1af2ba8df7242ffe2e2fdeac93

9,827

py

Python

memnet.py

404akhan/memnet

a8cf9e0a480575d9d36de6fa3357f667d64e0b05

[ "BSD-3-Clause" ]

1

2018-02-01T05:17:13.000Z

memnet.py

404akhan/memnet

a8cf9e0a480575d9d36de6fa3357f667d64e0b05

[ "BSD-3-Clause" ]

null

memnet.py

404akhan/memnet

a8cf9e0a480575d9d36de6fa3357f667d64e0b05

[ "BSD-3-Clause" ]

null

import torch import torch.nn.functional as F import torch.nn.init as init from torch import nn, autograd from torch.utils.data import DataLoader from babi import BabiDataset, pad_collate from torch.nn.utils import clip_grad_norm torch.backends.cudnn.benchmark = True torch.backends.cudnn.fastest = True class MemoryCell(nn.Module): def __init__(self, num_mem_slots, embed_dim): super(MemoryCell, self).__init__() self.num_mem_slots = num_mem_slots self.embed_dim = embed_dim # Memory update linear layers. self.U = nn.Linear(embed_dim, embed_dim) self.V = nn.Linear(embed_dim, embed_dim, bias=False) self.W = nn.Linear(embed_dim, embed_dim, bias=False) self.prelu_memory = nn.PReLU(init=1) init.xavier_normal(self.U.weight) init.xavier_normal(self.V.weight) init.xavier_normal(self.W.weight) def forward(self, inputs, keys): memories = keys memory_inputs = inputs for index, sentence in enumerate(memory_inputs): # Compute memory updates. sentence = sentence.unsqueeze(1).repeat(1, self.num_mem_slots, 1) sentence = sentence.view_as(memories) memory_gates = F.sigmoid((sentence * (memories + keys)).sum(dim=-1)) memory_gates = memory_gates.expand_as(memories) candidate_memories = self.prelu_memory(self.U(memories) + self.V(sentence) + self.W(keys)) updated_memories = memories + memory_gates * candidate_memories updated_memories = updated_memories / ( updated_memories.norm(p=2, dim=-1).expand_as(updated_memories) + 1e-12) memories = updated_memories return memories class RecurrentEntityNetwork(nn.Module): def __init__(self, hidden_dim, max_num_sentences=150, vocab_size=50): super(RecurrentEntityNetwork, self).__init__() self.max_num_sentences = max_num_sentences self.embed_dim = hidden_dim self.num_mem_slots = 20 self.vocab_size = vocab_size self.memory_mask = nn.Parameter(torch.randn(max_num_sentences, 1)) self.question_mask = nn.Parameter(torch.randn(max_num_sentences, 1)) self.embedding = nn.Embedding(vocab_size + self.num_mem_slots, hidden_dim, padding_idx=0) init.uniform(self.embedding.weight, a=-(3 ** 0.5), b=3 ** 0.5) self.cell = MemoryCell(self.num_mem_slots, hidden_dim) # Fully connected linear layers. self.C = nn.Linear(hidden_dim, hidden_dim) self.H = nn.Linear(hidden_dim, hidden_dim, bias=False) self.Z = nn.Linear(hidden_dim, vocab_size, bias=False) self.prelu_outputs = nn.ReLU() # Initialize weights. init.xavier_normal(self.C.weight) init.xavier_normal(self.H.weight) init.xavier_normal(self.Z.weight) self.memory_mask.data.fill_(1) self.question_mask.data.fill_(1) def forward(self, contexts, questions): batch_size, context_length, context_num_words = contexts.size() _, question_length = questions.size() # List of sentence embeddings for every story in a batch. (num. sentences, batch size, encoder dim.) contexts = self.embedding(contexts.view(batch_size, -1)) contexts = contexts.view(batch_size, context_length, context_num_words, -1) questions = self.embedding(questions) memory_mask = self.memory_mask[:context_length].unsqueeze(0).unsqueeze(2).expand(*contexts.size()) question_mask = self.question_mask[:question_length].unsqueeze(0).expand(*questions.size()) memory_inputs = torch.sum(contexts * memory_mask, dim=2).squeeze().t() question_inputs = torch.sum(questions * question_mask, dim=1).squeeze() # Compute memory updates. keys = torch.arange(self.vocab_size, self.vocab_size + self.num_mem_slots) keys = torch.autograd.Variable(keys.unsqueeze(0).expand(batch_size, self.num_mem_slots).long().cuda()) keys = self.embedding(keys).view(batch_size * self.num_mem_slots, -1) network_graph = self.cell(memory_inputs, keys) network_graph = self.C(network_graph).view(batch_size, self.num_mem_slots, self.embed_dim) # Apply attention to the entire acyclic graph using the questions. attention_energies = network_graph * question_inputs.unsqueeze(1).expand_as(network_graph) attention_energies = attention_energies.sum(dim=-1) attention_weights = F.softmax(attention_energies).expand_as(network_graph) attended_network_graph = (network_graph * attention_weights).sum(dim=1).squeeze() # Condition the fully-connected layer using the questions. outputs = self.prelu_outputs(question_inputs + self.H(attended_network_graph)) outputs = self.Z(outputs) return outputs HIDDEN_DIM = 100 BATCH_SIZE = 100 NUM_EPOCHS = 250 LOG_FILE = "memnet.txt" if __name__ == '__main__': dataset = BabiDataset() vocab_size = len(dataset.QA.VOCAB) criterion = nn.CrossEntropyLoss(size_average=False) model = RecurrentEntityNetwork(HIDDEN_DIM, 130, vocab_size) model.cuda() early_stopping_counter = 0 best_accuracy = 0 optimizer = torch.optim.Adam(model.parameters(), lr=0.005) for epoch in range(NUM_EPOCHS): dataset.set_mode('train') train_loader = DataLoader( dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn=pad_collate ) model.train() if early_stopping_counter < 20: total_accuracy = 0 num_batches = 0 for batch_idx, data in enumerate(train_loader): optimizer.zero_grad() contexts, questions, answers = data contexts = autograd.Variable(contexts.long().cuda()) questions = autograd.Variable(questions.long().cuda()) answers = autograd.Variable(answers.cuda()) outputs = model(contexts, questions) l2_loss = 0 for name, param in model.named_parameters(): l2_loss += 0.001 * torch.sum(param * param) loss = criterion(outputs, answers) + l2_loss predictions = F.softmax(outputs).data.max(1)[1] correct = predictions.eq(answers.data).cpu().sum() acc = correct * 100. / len(contexts) loss.backward() clip_grad_norm(model.parameters(), 40) total_accuracy += acc num_batches += 1 if batch_idx % 20 == 0: print('[Epoch %d] [Training] loss : %f, acc : %f, batch_idx : %d' % ( epoch, loss.data[0], total_accuracy / num_batches, batch_idx )) optimizer.step() dataset.set_mode('valid') valid_loader = DataLoader( dataset, batch_size=BATCH_SIZE, shuffle=False, collate_fn=pad_collate ) model.eval() total_accuracy = 0 num_batches = 0 for batch_idx, data in enumerate(valid_loader): contexts, questions, answers = data contexts = autograd.Variable(contexts.long().cuda()) questions = autograd.Variable(questions.long().cuda()) answers = autograd.Variable(answers.cuda()) outputs = model(contexts, questions) l2_loss = 0 for name, param in model.named_parameters(): l2_loss += 0.001 * torch.sum(param * param) loss = criterion(outputs, answers) + l2_loss predictions = F.softmax(outputs).data.max(1)[1] correct = predictions.eq(answers.data).cpu().sum() acc = correct * 100. / len(contexts) total_accuracy += acc num_batches += 1 total_accuracy = total_accuracy / num_batches if total_accuracy > best_accuracy: best_accuracy = total_accuracy best_state = model.state_dict() early_stopping_counter = 0 else: early_stopping_counter += 1 print('[Epoch %d] [Validate] Accuracy : %f' % (epoch, total_accuracy)) with open(LOG_FILE, 'a') as fp: fp.write('[Epoch %d] [Validate] Accuracy : %f' % (epoch, total_accuracy) + '\n') if total_accuracy == 1.0: break else: print('Early Stopping at Epoch %d, Valid Accuracy : %f' % (epoch, best_accuracy)) break dataset.set_mode('test') test_loader = DataLoader( dataset, batch_size=BATCH_SIZE, shuffle=False, collate_fn=pad_collate ) test_acc = 0 num_batches = 0 for batch_idx, data in enumerate(test_loader): contexts, questions, answers = data contexts = autograd.Variable(contexts.long().cuda()) questions = autograd.Variable(questions.long().cuda()) answers = autograd.Variable(answers.cuda()) model.state_dict().update(best_state) outputs = model(contexts, questions) l2_loss = 0 for name, param in model.named_parameters(): l2_loss += 0.001 * torch.sum(param * param) loss = criterion(outputs, answers) + l2_loss predictions = F.softmax(outputs).data.max(1)[1] correct = predictions.eq(answers.data).cpu().sum() acc = correct * 100. / len(contexts) test_acc += acc num_batches += 1 print('[Epoch %d] [Test] Accuracy : %f' % (epoch, test_acc / num_batches)) with open(LOG_FILE, 'a') as fp: fp.write('[Epoch %d] [Test] Accuracy : %f' % (epoch, test_acc / num_batches) + '\n')

37.083019

110

0.623792

1,189

9,827

4.936081

0.176619

0.021469

0.020617

0.025558

0.427671

0.381496

0.335492

0.313341

0.292213

0.279605

0

0.01646

0.270479

9,827

265

111

37.083019

0.802204

0.035413

0

0.302703

0

0.005405

0.02893

0

1

0.021622

false

0

0.037838

0

0.081081

0.021622

0

null

0

null

0

1

0

61d93349709f00bb603d8566d8afdb83080026fb

3,444

py

Python

tests/test_tba.py

StanfordAHA/Lake

34df001db107e1a0824b7fdb05b9f2145bf49a3e

[ "BSD-3-Clause" ]

11

2019-10-14T02:05:38.000Z

2022-03-10T14:10:22.000Z

tests/test_tba.py

StanfordAHA/Lake

34df001db107e1a0824b7fdb05b9f2145bf49a3e

[ "BSD-3-Clause" ]

29

2019-09-02T05:49:40.000Z

2022-02-26T00:57:54.000Z

tests/test_tba.py

StanfordAHA/Lake

34df001db107e1a0824b7fdb05b9f2145bf49a3e

[ "BSD-3-Clause" ]

1

2021-04-16T20:26:13.000Z

from lake.models.tba_model import TBAModel from lake.modules.transpose_buffer_aggregation import TransposeBufferAggregation from lake.passes.passes import lift_config_reg import magma as m from magma import * import fault import tempfile import kratos as k import random as rand import pytest def test_tba(word_width=16, fetch_width=4, num_tb=1, tb_height=1, max_range=5, max_range_inner=5): model_tba = TBAModel(word_width, fetch_width, num_tb, tb_height, max_range, max_range_inner) new_config = {} new_config["range_outer"] = 5 new_config["range_inner"] = 3 new_config["stride"] = 2 new_config["indices"] = [0, 1, 2] new_config["dimensionality"] = 2 new_config["tb_height"] = 1 new_config["starting_addr"] = 0 model_tba.set_config(new_config=new_config) dut = TransposeBufferAggregation(word_width, fetch_width, num_tb, tb_height, max_range, max_range_inner, max_stride=5, tb_iterator_support=2) lift_config_reg(dut.internal_generator) magma_dut = k.util.to_magma(dut, flatten_array=True, check_flip_flop_always_ff=False) tester = fault.Tester(magma_dut, magma_dut.clk) # configuration registers tester.circuit.tb_0_indices_0 = 0 tester.circuit.tb_0_indices_1 = 1 tester.circuit.tb_0_indices_2 = 2 tester.circuit.tb_0_range_outer = 5 tester.circuit.tb_0_range_inner = 3 tester.circuit.tb_0_stride = 2 tester.circuit.tb_0_dimensionality = 2 tester.circuit.tb_0_tb_height = 1 tester.circuit.tb_0_starting_addr = 0 tester.circuit.clk = 0 tester.circuit.rst_n = 1 tester.step(2) tester.circuit.rst_n = 0 tester.step(2) tester.circuit.tba_ren = 1 tester.circuit.rst_n = 1 rand.seed(0) num_iters = 100 for i in range(num_iters): data = [] for j in range(fetch_width): data.append(rand.randint(0, 2**word_width - 1)) for j in range(fetch_width): setattr(tester.circuit, f"SRAM_to_tb_data_{j}", data[j]) valid_data = rand.randint(0, 1) tester.circuit.valid_data = valid_data mem_valid_data = rand.randint(0, 1) tester.circuit.mem_valid_data = mem_valid_data tb_index_for_data = 0 tester.circuit.tb_index_for_data = tb_index_for_data ack_in = valid_data tester.circuit.ack_in = ack_in model_data, model_valid = \ model_tba.tba_main(data, valid_data, ack_in, tb_index_for_data, 1, mem_valid_data) tester.eval() tester.circuit.tb_to_interconnect_valid.expect(model_valid) if model_valid: tester.circuit.tb_to_interconnect_data.expect(model_data[0]) tester.step(2) with tempfile.TemporaryDirectory() as tempdir: tester.compile_and_run(target="verilator", directory=tempdir, magma_output="verilog", flags=["-Wno-fatal"]) if __name__ == "__main__": test_tba()

29.947826

94

0.594948

440

3,444

4.313636

0.259091

0.143836

0.094837

0.075869

0.310327

0.114858

0.092729

0.055848

0

0.026237

0.324913

3,444

114

95

30.210526

0.790108

0.006678

0

0.170455

0

0.036268

0

1

0.011364

false

0.011364

0.113636

0

0.125

0

null

0

null

0

1

0

61da398102287561106f2583dbf3dd6a0d400ea3

1,442

py

Python

2018/02/py/run.py

Bigsby/aoc

409fefbb0467628fa298288064acb622bb53ee58

[ "CC0-1.0" ]

1

2021-06-11T17:24:05.000Z

2018/02/py/run.py

Bigsby/aoc

409fefbb0467628fa298288064acb622bb53ee58

[ "CC0-1.0" ]

null

2018/02/py/run.py

Bigsby/aoc

409fefbb0467628fa298288064acb622bb53ee58

[ "CC0-1.0" ]

null

#! /usr/bin/python3 import sys, os, time from typing import List, Tuple from itertools import combinations def part1(ids: List[str]) -> int: twice_count = 0 thrice_count = 0 for id in ids: id_counts = { id.count(c) for c in id } twice_count += 2 in id_counts thrice_count += 3 in id_counts return twice_count * thrice_count def part2(ids: List[str]) -> str: for id1, id2 in combinations(ids, 2): differences = [ i for i in range(len(id1)) if id1[i] != id2[i] ] if len(differences) == 1: diferent_index = differences[0] return id1[:diferent_index] + id1[diferent_index + 1:] raise Exception("Ids differencing 1 not found") def solve(ids: List[str]) -> Tuple[int,str]: return ( part1(ids), part2(ids) ) def get_input(file_path: str) -> List[str]: if not os.path.isfile(file_path): raise FileNotFoundError(file_path) with open(file_path, "r") as file: return [ line.strip() for line in file.readlines() ] def main(): if len(sys.argv) != 2: raise Exception("Please, add input file path as parameter") start = time.perf_counter() part1_result, part2_result = solve(get_input(sys.argv[1])) end = time.perf_counter() print("P1:", part1_result) print("P2:", part2_result) print() print(f"Time: {end - start:.7f}") if __name__ == "__main__": main()

25.75

72

0.615811

207

1,442

4.135266

0.36715

0.046729

0.035047

0

0.027959

0.255895

1,442

56

73

25.75

0.769804

0.012483

0

0.074438

0

1

0.125

false

0

0.075

0.025

0.3

0.1

0

null

0

null

0

1

0

61df694948c2ba5c7d34c79e97268eab5f090a30

3,272

py

Python

palette/core/color_transfer.py

SuziKim/PaletteSelection

cfc0052996b5c8dc1da2d6e30798dd1fed138ebe

[ "MIT" ]

23

2015-08-25T12:31:44.000Z

2021-12-15T03:18:12.000Z

palette/core/color_transfer.py

SuziKim/PaletteSelection

cfc0052996b5c8dc1da2d6e30798dd1fed138ebe

[ "MIT" ]

null

palette/core/color_transfer.py

SuziKim/PaletteSelection

cfc0052996b5c8dc1da2d6e30798dd1fed138ebe

[ "MIT" ]

7

2017-07-27T10:57:36.000Z

2022-02-22T06:51:44.000Z

# -*- coding: utf-8 -*- ## @package palette.core.color_transfer # # Color transfer. # @author tody # @date 2015/09/16 import numpy as np from scipy.interpolate import Rbf import matplotlib.pyplot as plt from palette.core.lab_slices import LabSlice, LabSlicePlot, Lab2rgb_py ## Color transfer for ab coordinates. class ABTransfer: ## Constructor # @param abs_original original ab coordinates. # @param abs_edited edited ab coordinates. def __init__(self, abs_original, abs_edited): abs_original = np.array(abs_original) abs_edited = np.array(abs_edited) rbf_a = Rbf(abs_original[:, 0], abs_original[:, 1], abs_edited[:, 0]) rbf_b = Rbf(abs_original[:, 0], abs_original[:, 1], abs_edited[:, 1]) self._rbf = [rbf_a, rbf_b] ## Color transfer for ab coordinates. def transfer(self, ab_original): abs_edited = [rbf(ab_original[0], ab_original[1]) for rbf in self._rbf] abs_edited = np.array(abs_edited) return abs_edited ## Simple plotter for ABTransfer. class ABTransferPlot: ## Constructor # @param abs_original original ab coordinates. # @param abs_edited edited ab coordinates. # @param L target L coordinate. # @param abs_animation list of ab coordinates for plot animation. def __init__(self, abs_original, abs_edited, L=50, abs_animation=[]): self._slice = LabSlice(func=Lab2rgb_py) self._slice_plot = LabSlicePlot(self._slice) self._slice_plot.plot(L) self._abs_original = abs_original self._abs_edited = abs_edited self._abs_animation = abs_animation self._transfer = ABTransfer(abs_original, abs_edited) self._plot() ## Animation function for matplot. def animationFunc(self, step, *args): ab_id = step % len(self._abs_animation) ab_original = self._abs_animation[ab_id] xy_original, xy_edited = self._blendResult(ab_original) self._setArrow(self._blend_plot, xy_original, xy_edited) return self._blend_plot def _plot(self): xys_original = [self._slice.ab2xy(ab_original) for ab_original in self._abs_original] xys_edited = [self._slice.ab2xy(ab_edited) for ab_edited in self._abs_edited] for xy_original, xy_edited in zip(xys_original, xys_edited): self._arrow(xy_original, xy_edited) xy_original, xy_edited = self._blendResult(self._abs_animation[0]) self._blend_plot = self._arrow(xy_original, xy_edited, color=[0.7, 0.5, 0.4]) def _arrow(self, ps, pe, color=[1, 1, 1]): xs = [ps[0], pe[0]] ys = [ps[1], pe[1]] return [plt.plot(xs, ys, '-', color=color, linewidth=2, alpha=0.8)[0], plt.plot(ps[0], ps[1], 'o', color=color, linewidth=2, alpha=0.8)[0]] def _setArrow(self, arrow_plot, ps, pe): xs = [ps[0], pe[0]] ys = [ps[1], pe[1]] arrow_plot[0].set_data(xs, ys) arrow_plot[1].set_data(ps[0], ps[1]) def _blendResult(self, ab_original): ab_edited = self._transfer.transfer(ab_original) xy_original = self._slice.ab2xy(ab_original) xy_edited = self._slice.ab2xy(ab_edited) return xy_original, xy_edited

37.181818

93

0.652812

457

3,272

4.38512

0.201313

0.071856

0.063872

0.062874

0.365269

0.336327

0.191617

0.160679

0.132735

0.096806

0

0.022673

0.231663

3,272

87

94

37.609195

0.774463

0.17665

0

0.113208

0

0.00075

0

1

0.150943

false

0

0.075472

0

0.339623

0

null

0

null

0

1

0

61dfafddb5a99f013e5962a29c6779ac49a5f150

1,447

py

Python

CursoEmVideoPython/desafio95.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

null

CursoEmVideoPython/desafio95.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

1

2020-07-04T16:27:25.000Z

CursoEmVideoPython/desafio95.py

miguelabreuss/scripts_python

cf33934731a9d1b731672d4309aaea0a24ae151a

[ "MIT" ]

null

scoult = dict() gols = list() time = list() temp = 0 while True: scoult['Jogador'] = str(input('Qual o nome do jogador: ')) scoult['Número partidas'] = int(input('Quantas partidas foram jogadas? ')) for i in range(0,scoult['Número partidas']): gols.append(int(input(f'Quantos gols foram marcados na partida {i+1} de {scoult["Jogador"]}? '))) scoult['Gols'] = gols[:] for i in range(0,scoult['Número partidas']): if i == 0: scoult['Total de gols'] = gols[i] else: scoult['Total de gols'] += gols[i] time.append(scoult.copy()) gols.clear() if str(input('Deseja continuar [S/N]? ')) in 'Nn': break print('-' * 50) print('-' * 50) print('{:^50}'.format('TABELO PERFORMANCE')) print('-' * 50) print('{:<5}{:<15}{:<25}{:<5}'.format('cod', 'Jogador', 'Gols', 'Total')) for e in time: print('{:<5}{:<15}{:<25}{:<5}'.format(temp, e['Jogador'], str(e['Gols']), e['Total de gols'])) temp += 1 print('-' * 50) while True: temp = int(input('De aual jogador você deseja mais detalhes? [cod] 999 p/ sair. ')) if temp == 999: break else: print(f'-- Performance do jogador: {time[temp]["Jogador"]}') for i in range(0,time[temp]["Número partidas"]): print(f' => Na partida {i+1} {time[temp]["Jogador"]} marcou {time[temp]["Gols"][i]} vez(es).') print(f'Foi um total de {time[temp]["Total de gols"]} gols')

38.078947

109

0.561852

206

1,447

3.946602

0.325243

0.04305

0.054121

0.04059

0.189422

0.174662

0.078721

0

0.031858

0.219074

1,447

37

110

39.108108

0.687611

0

0.324324

0

0.027027

0.422944

0.078093

0

1

0

false

0

0.27027

0

null

0

null

0

1

0

61e1ff665914cfb40790ee569edb6f9cb201dad5

3,668

py

Python

Algorithms/On-Policy/A2C/DISCOVER_A2C.py

baturaysaglam/DISCOVER

423158c84a5935ca5755ccad06ea5fe20fb57d76

[ "MIT" ]

null

Algorithms/On-Policy/A2C/DISCOVER_A2C.py

baturaysaglam/DISCOVER

423158c84a5935ca5755ccad06ea5fe20fb57d76

[ "MIT" ]

null

Algorithms/On-Policy/A2C/DISCOVER_A2C.py

baturaysaglam/DISCOVER

423158c84a5935ca5755ccad06ea5fe20fb57d76

[ "MIT" ]

null

import numpy as np import torch import torch.nn as nn import torch.optim as optim from utils import init class Explorer(nn.Module): def __init__(self, state_dim, max_action, exp_regularization): super(Explorer, self).__init__() init_ = lambda m: init(m, nn.init.orthogonal_, lambda x: nn.init.constant_(x, 0), np.sqrt(2)) self.l1 = init_(nn.Linear(state_dim, 64)) self.l2 = init_(nn.Linear(64, 64)) self.l3 = init_(nn.Linear(64, state_dim)) self.max_action = max_action self.exp_regularization = exp_regularization def forward(self, state): a = torch.tanh(self.l1(state)) a = torch.tanh(self.l2(a)) return self.max_action * torch.tanh(self.l3(a)) * self.exp_regularization ** 2 class DISCOVER_A2C(): def __init__(self, state_dim, max_action, exp_regularization, policy, value_loss_coef, entropy_coef, learning_rate=None, adam_eps=None, alpha=None, max_grad_norm=None, device=None): self.policy = policy self.explorer = Explorer(state_dim, max_action, exp_regularization).to(device) self.value_loss_coef = value_loss_coef self.entropy_coef = entropy_coef self.max_grad_norm = max_grad_norm self.optimizer = optim.RMSprop(policy.parameters(), learning_rate, eps=adam_eps, alpha=alpha) self.explorer_optimizer = optim.Adam(self.explorer.parameters(), lr=learning_rate, eps=adam_eps) def explore(self, inputs): return self.explorer(inputs) def update_parameters(self, rollouts): obs_shape = rollouts.obs.size()[2:] action_shape = rollouts.actions.size()[-1] num_steps, num_processes, _ = rollouts.rewards.size() values, action_log_probs, dist_entropy, _ = self.policy.evaluate_actions( rollouts.obs[:-1].view(-1, *obs_shape), rollouts.exploration_directions.view(-1, *obs_shape), rollouts.recurrent_hidden_states[0].view(-1, self.policy.recurrent_hidden_state_size), rollouts.masks[:-1].view(-1, 1), rollouts.actions.view(-1, action_shape)) values = values.view(num_steps, num_processes, 1) action_log_probs = action_log_probs.view(num_steps, num_processes, 1) advantages = rollouts.returns[:-1] - values value_loss = advantages.pow(2).mean() action_loss = -(advantages.detach() * action_log_probs).mean() self.optimizer.zero_grad() (value_loss * self.value_loss_coef + action_loss - dist_entropy * self.entropy_coef).backward() nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) self.optimizer.step() # Compute the explorer loss values, action_log_probs, dist_entropy, _ = self.policy.evaluate_actions( rollouts.obs[:-1].view(-1, *obs_shape), self.explorer(rollouts.obs[:-1].view(-1, *obs_shape)), rollouts.recurrent_hidden_states[0].view(-1, self.policy.recurrent_hidden_state_size), rollouts.masks[:-1].view(-1, 1), rollouts.actions.view(-1, action_shape)) values = values.view(num_steps, num_processes, 1) advantages = rollouts.returns[:-1] - values value_loss = advantages.pow(2).mean() self.explorer_optimizer.zero_grad() (-value_loss * self.value_loss_coef).backward() nn.utils.clip_grad_norm_(self.explorer.parameters(), self.max_grad_norm) self.explorer_optimizer.step()

37.050505

104

0.638768

464

3,668

4.771552

0.206897

0.022584

0.029359

0.036134

0.503162

0.45122

0.409666

0.370822

0.291328

0

0.016619

0.245365

3,668

98

105

37.428571

0.783237

0.006816

0

0.222222

0

1

0.069444

false

0

0.069444

0.013889

0.194444

0

null

0

null

0

1

0

61e3abea3e991562a75549fe727c93817d1999de

3,400

py

Python

user/beaninfo_Global.py

dvdrm/gd

c004724344577bb608fa0611d10c16b211995f72

[ "Apache-2.0" ]

null

user/beaninfo_Global.py

dvdrm/gd

c004724344577bb608fa0611d10c16b211995f72

[ "Apache-2.0" ]

null

user/beaninfo_Global.py

dvdrm/gd

c004724344577bb608fa0611d10c16b211995f72

[ "Apache-2.0" ]

null

from telethon import events, Button from .login import user from .. import jdbot from ..bot.utils import cmd, TASK_CMD,split_list, press_event from ..diy.utils import read, write import asyncio import re @user.on(events.NewMessage(pattern=r'^setbd', outgoing=True)) async def SetBeanDetailInfo(event): try: msg_text= event.raw_text.split(' ') if len(msg_text) == 2: text = msg_text[-1] else: text = None if text==None: await event.edit('请输入正确的格式: setbd 屏蔽京豆数量') return key="BOTShowTopNum" kv=f'{key}="{text}"' change="" configs = read("str") if kv not in configs: if key in configs: configs = re.sub(f'{key}=("|\').*("|\')', kv, configs) write(configs) else: configs = read("str") configs += f'export {key}="{text}"\n' write(configs) change = f'已替换屏蔽京豆数为{text}' else: change = f'设定没有改变,想好再来.' await event.edit(change) except Exception as e: title = "【💥错误💥】" name = "文件名：" + os.path.split(__file__)[-1].split(".")[0] function = "函数名：" + e.__traceback__.tb_frame.f_code.co_name details = "错误详情：第 " + str(e.__traceback__.tb_lineno) + " 行" tip = '建议百度/谷歌进行查询' await jdbot.send_message(chat_id, f"{title}\n\n{name}\n{function}\n错误原因：{str(e)}\n{details}\n{traceback.format_exc()}\n{tip}") logger.error(f"错误--->{str(e)}") @user.on(events.NewMessage(pattern=r'^bd', outgoing=True)) async def CCBeanDetailInfo(event): msg_text= event.raw_text.split(' ') if len(msg_text) == 2: text = msg_text[-1] else: text = None if text==None: await event.edit('请指定要查询的账号,格式: cb 1 或 cb ptpin') return key="BOTCHECKCODE" kv=f'{key}="{text}"' change="" configs = read("str") intcount=0 if kv not in configs: if key in configs: configs = re.sub(f'{key}=("|\').*("|\')', kv, configs) change += f"【替换】环境变量:`{kv}`\n" write(configs) else: configs = read("str") configs += f'export {key}="{text}"\n' change += f"【新增】环境变量:`{kv}`\n" write(configs) await event.edit('开始查询账号'+text+'的资产，请稍后...') cmdtext="task /ql/data/scripts/jk_script/bot_jd_bean_info_QL.js now" p = await asyncio.create_subprocess_shell( cmdtext, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) res_bytes, res_err = await p.communicate() res = res_bytes.decode('utf-8') txt=res.split('\n') strReturn="" await event.delete() if res: for line in txt: if "【" in line and "🔔" not in line: strReturn=strReturn+line+'\n' if intcount==100: intcount=0 if strReturn: await user.send_message(event.chat_id, strReturn) strReturn="" else: await user.send_message(event.chat_id,'查询失败!') if strReturn: await user.send_message(event.chat_id, strReturn)

33.009709

134

0.516471

406

3,400

4.219212

0.362069

0.024518

0.032691

0.035026

0.38704

0.371862

0.336836

0.318739

0.283713

0

0.005809

0.341765

3,400

103

135

33.009709

0.758266

0

0.516854

0

0.142017

0.040282

0

1

0

false

0

0.078652

0

0.101124

0

null

0

null

0

1

0

61e6fadc19dca2b7aaa1c0e67b41806d94ed6219

12,263

py

Python

pyemits/core/ml/regression/trainer.py

thompson0012/PyEmits

9cb6fbf27ca7e8952ed5aca26118055e04492c23

[ "Apache-2.0" ]

6

2021-10-21T14:13:25.000Z

2021-12-26T12:22:51.000Z

pyemits/core/ml/regression/trainer.py

thompson0012/PyEmits

9cb6fbf27ca7e8952ed5aca26118055e04492c23

[ "Apache-2.0" ]

null

pyemits/core/ml/regression/trainer.py

thompson0012/PyEmits

9cb6fbf27ca7e8952ed5aca26118055e04492c23

[ "Apache-2.0" ]

null

from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor from sklearn.neural_network import MLPRegressor from sklearn.linear_model import ElasticNet, Ridge, Lasso, BayesianRidge, HuberRegressor from xgboost import XGBRegressor from lightgbm import LGBMRegressor from pyemits.core.ml.base import BaseTrainer, BaseWrapper, NeuralNetworkWrapperBase from pyemits.common.config_model import BaseConfig, KerasSequentialConfig, TorchLightningSequentialConfig from pyemits.common.data_model import RegressionDataModel from pyemits.common.py_native_dtype import SliceableDeque from pyemits.common.validation import raise_if_value_not_contains from typing import List, Dict, Optional, Union, Any from pyemits.core.ml.regression.nn import TorchLightningWrapper RegModelContainer = { 'RF': RandomForestRegressor, 'GBDT': GradientBoostingRegressor, # 'HGBDT': HistGradientBoostingRegressor, 'AdaBoost': AdaBoostRegressor, 'MLP': MLPRegressor, 'ElasticNet': ElasticNet, 'Ridge': Ridge, 'Lasso': Lasso, 'BayesianRidge': BayesianRidge, 'Huber': HuberRegressor, 'XGBoost': XGBRegressor, 'LightGBM': LGBMRegressor } def _get_reg_model(algo_or_wrapper: Union[str, BaseWrapper]): if isinstance(algo_or_wrapper, str): return RegModelContainer[algo_or_wrapper] # return wrapper model elif isinstance(algo_or_wrapper, BaseWrapper): return algo_or_wrapper def fill_algo_config_clf(clf_or_wrapper, algo_config: Optional[BaseConfig] = None): # nn wrapper if isinstance(clf_or_wrapper, NeuralNetworkWrapperBase): # have algo config if algo_config is not None: # if keras model object if isinstance(algo_config, KerasSequentialConfig): for i in algo_config.layer: clf_or_wrapper.model_obj.add(i) clf_or_wrapper.model_obj.compile(**algo_config.compile) return clf_or_wrapper elif isinstance(algo_config, TorchLightningSequentialConfig): clf_or_wrapper: TorchLightningWrapper for nos, layer in enumerate(algo_config.layer, 1): clf_or_wrapper.add_layer2blank_model(str(nos), layer) return clf_or_wrapper # not support pytorch, mxnet model right now raise TypeError('now only support KerasSequentialConfig') # no algo config return clf_or_wrapper # sklearn clf path if algo_config is None: return clf_or_wrapper() # activate else: return clf_or_wrapper(**dict(algo_config)) def fill_fit_config_clf(clf_or_wrapper, X, y, fit_config: Optional[Union[BaseConfig, Dict]] = None, ): from pyemits.core.ml.regression.nn import torchlighting_data_helper # nn wrapper if isinstance(clf_or_wrapper, NeuralNetworkWrapperBase): dl_train, dl_val = torchlighting_data_helper(X, y) if fit_config is None: # pytorch_lightning path if isinstance(clf_or_wrapper, TorchLightningWrapper): return clf_or_wrapper.fit(dl_train, dl_val) # keras path return clf_or_wrapper.fit(X, y) if isinstance(fit_config, BaseConfig): if isinstance(clf_or_wrapper, TorchLightningWrapper): return clf_or_wrapper.fit(dl_train, dl_val, **dict(fit_config)) # keras path return clf_or_wrapper.fit(X, y, **dict(fit_config)) elif isinstance(fit_config, Dict): if isinstance(clf_or_wrapper, TorchLightningWrapper): return clf_or_wrapper.fit(dl_train, dl_val, **fit_config) # keras path return clf_or_wrapper.fit(X, y, **fit_config) # sklearn/xgboost/lightgbm clf else: if fit_config is None: return clf_or_wrapper.fit(X, y) else: assert isinstance(fit_config, BaseConfig), "fig_config type not matched" return clf_or_wrapper.fit(X, y, **dict(fit_config)) class RegTrainer(BaseTrainer): def __init__(self, algo: List[Union[str, Any]], algo_config: List[Optional[BaseConfig]], raw_data_model: RegressionDataModel, other_config: Dict[str, Union[List, BaseConfig, Any]] = {}): """ universal class for regression model training, all-in-one training including sklearn, xgboost, lightgbm, keras, pytorch_lightning you are not required to fill the algo config if you have idea on algo_config the algo config is designed for people to config their model based on the configuration that provided in config_model so that people can easily config their model during creation for Pytorch_lightning user, pls configured your model before use this. at that moment, no algo_config is Parameters ---------- algo: List[str] the machine learning algorithm, any machine learning model that have fit/predict can used in here algo_config: List[BaseConfig] or List[None] the respective config model of algo raw_data_model: RegressionDataModel data model obj, stores data and meta data other_config: BaseConfig other global config, shall be used in its sub-class """ super(RegTrainer, self).__init__(algo, algo_config) # raise_if_value_not_contains(algo, list(RegModelContainer.keys())) self.raw_data_model = raw_data_model self.other_config = other_config self.clf_models = SliceableDeque() self._is_algo_valid() self._is_algo_config_valid() def _is_algo_valid(self): for item in self._algo: if not isinstance(item, (str, NeuralNetworkWrapperBase)): raise TypeError('must be str or WrapperBase') if isinstance(item, str): raise_if_value_not_contains([item], list(RegModelContainer.keys())) def _is_algo_config_valid(self): for item in self._algo_config: if item is None: continue # skip to next loop if not isinstance(item, (BaseConfig, Dict)): raise TypeError('Only accept ConfigBase or Dict as input') # no checking when model is object, which directly passing it def is_config_exists(self, config_key: str): config_item = self.other_config.get(config_key, None) if config_item is None: return False return True def get_fill_fit_config(self): fit_config = self.other_config.get('fit_config', None) if isinstance(fit_config, list): assert len(fit_config) == len(self._algo), 'length not matched' return fit_config elif fit_config is None: fit_config_ = [] # rename variable for i in range(len(self._algo)): fit_config_.append(None) fit_config = fit_config_ # pointer, return fit_config else: raise TypeError('fit config not a list type') def _fit(self): X = self.raw_data_model.X_data y = self.raw_data_model.y_data # make sure y is 1D array in RegTrainer fit_config = self.get_fill_fit_config() for n, (algo, algo_config) in enumerate(zip(self._algo, self._algo_config)): clf = fill_algo_config_clf(_get_reg_model(algo), algo_config) fill_fit_config_clf(clf, X, y, fit_config[n]) self.clf_models.append((str(algo), clf)) return class ParallelRegTrainer(RegTrainer): def __init__(self, algo: List[str], algo_config: List[BaseConfig], raw_data_model: RegressionDataModel, other_config: Dict[str, Union[List, BaseConfig, Any]] = {}): """ handy function to realize parallel training Parameters ---------- algo: List[str] the machine learning algorithm, any machine learning model that have fit/predict can used in here algo_config: List[BaseConfig] or List[None] the respective config model of algo raw_data_model: RegressionDataModel data model obj, stores data and meta data other_config: BaseConfig other global config, shall be used in its sub-class """ super(ParallelRegTrainer, self).__init__(algo, algo_config, raw_data_model, other_config) def _fit(self): from joblib import Parallel, delayed parallel = Parallel(n_jobs=-1) def _get_fitted_trainer(algo: List, algo_config: List[BaseConfig], raw_data_model: RegressionDataModel, other_config: Dict[str, BaseConfig] = {}): trainer = RegTrainer(algo, algo_config, raw_data_model, other_config) trainer.fit() # fit config auto filled by RegTrainer, no need to handle return trainer out: List[RegTrainer] = parallel( delayed(_get_fitted_trainer)([algo_], [algo_config_], self.raw_data_model, self.other_config) for algo_, algo_config_ in zip(self._algo, self._algo_config)) for obj in out: self.clf_models.append(obj.clf_models) return def fit(self): return self._fit() class MultiOutputRegTrainer(RegTrainer): """ machine learning based multioutput regression trainer bring forecasting power into machine learning model, forecasting is not only the power of deep learning """ def __init__(self, algo: List[Union[str, Any]], algo_config: List[Optional[BaseConfig]], raw_data_model: RegressionDataModel, other_config: Dict[str, Union[List, BaseConfig, Any]] = {}, parallel_n_jobs: int = -1): super(MultiOutputRegTrainer, self).__init__(algo, algo_config, raw_data_model, other_config) self.parallel_n_jobs = parallel_n_jobs def _fit(self): fit_config = self.get_fill_fit_config() from sklearn.multioutput import MultiOutputRegressor X = self.raw_data_model.X_data y = self.raw_data_model.y_data for n, (algo, algo_config) in enumerate(zip(self._algo, self._algo_config)): clf = fill_algo_config_clf(_get_reg_model(algo), algo_config) # clf already activated clf = MultiOutputRegressor(estimator=clf, n_jobs=self.parallel_n_jobs) fill_fit_config_clf(clf, X, y, fit_config[n]) self.clf_models.append((str(algo), clf)) return class KFoldCVTrainer(RegTrainer): def __init__(self, algo: List[Union[str, Any]], algo_config: List[Optional[BaseConfig]], raw_data_model: RegressionDataModel, other_config: Dict[str, Union[List, BaseConfig, Any]] = {}, ): super(KFoldCVTrainer, self).__init__(algo, algo_config, raw_data_model, other_config) def _fit(self): from pyemits.core.ml.cross_validation import KFoldCV kfold_config = self.other_config.get('kfold_config', None) if kfold_config is not None: kfold_cv = KFoldCV(self.raw_data_model, kfold_config) else: kfold_cv = KFoldCV(self.raw_data_model) splitted_kfold = kfold_cv.split() for n, item in enumerate(splitted_kfold): self._meta_data_model.add_meta_data('kfold_record', [item]) train_idx = item[0] test_idx = item[1] X_ = self.raw_data_model.X_data[train_idx] y_ = self.raw_data_model.y_data[train_idx] sliced_data_model = RegressionDataModel(X_, y_) trainer = ParallelRegTrainer(self._algo, self._algo_config, sliced_data_model, other_config=self.other_config) trainer.fit() self.clf_models.append((f'kfold_{n}', trainer.clf_models)) return

40.471947

125

0.644133

1,455

12,263

5.160825

0.171821

0.057265

0.038354

0.031163

0.408443

0.368491

0.352777

0.306299

0.28619

0.281928

0

0.000793

0.279785

12,263

302

126

40.60596

0.849411

0.166925

0

0.338308

0

0.02892

0.002116

0

0.00995

1

0.084577

false

0

0.079602

0.004975

0.308458

0

null

0

null

0

1

0

61e7231e5da397e138846e32322894665e310b28

7,092

py

Python

network_core/network_graph.py

markusgl/SocialCompanion

e816af21c600b33dbcac25d088d4d75957d0349a

[ "MIT" ]

2

2018-12-21T12:55:21.000Z

2019-05-29T06:35:58.000Z

network_core/network_graph.py

markusgl/SocialCompanion

e816af21c600b33dbcac25d088d4d75957d0349a

[ "MIT" ]

8

2019-12-16T21:08:36.000Z

2021-03-31T18:58:35.000Z

network_core/network_graph.py

markusgl/SocialCompanion

e816af21c600b33dbcac25d088d4d75957d0349a

[ "MIT" ]

null

""" knowledge graph representation using neo4j this class uses py2neo with will be the final version """ import os import json from py2neo import Graph, Relationship, NodeMatcher, Node from network_core.ogm.node_objects import Me, Contact, Misc USERTYPE = "User" CONTACTTYPE = "Contact" ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) relationships = {'freund': 'FRIEND', 'schwester': 'SISTER', 'bruder': 'BROTHER', 'mutter': 'MOTHER', 'vater': 'FATHER', 'tochter': 'DAUGHTER', 'sohn': 'SON', 'enkel': 'GRANDCHILD', 'enkelin': 'GRANDCHILD'} class NetworkGraph: def __init__(self): path = os.path.realpath(ROOT_DIR + '/neo4j_creds.json') with open(path) as f: data = json.load(f) username = data['username'] password = data['password'] self.graph = Graph(host="localhost", username=username, password=password) def add_node_by_name(self, name, age=None, gender=None, node_type="PERSON"): if name == 'USER': node_type = 'user' node = Node(node_type, name=name, age=age, gender=gender) self.graph.create(node) return node def get_node_by_name(self, name): matcher = NodeMatcher(self.graph) node = matcher.match(name=name).first() return node def add_relationship(self, node1, node2, rel_type='KNOWS'): first_node = self.get_node_by_name(node1) second_node = self.get_node_by_name(node2) if not first_node: first_node = self.add_node_by_name(node1) if not second_node: second_node = self.add_node_by_name(node2) self.graph.create(Relationship(first_node, rel_type, second_node)) def add_rel_tuple(self, ent1, ent2): """ Pushes a new central user 'Me' to the graph Gets a username, creats an Me object and pushes it to the graph :param username: string username :return: me object (see ogm pkg) """ # define nodes node1 = Misc() node1.name = ent1 node2 = Misc() node2.name = ent2 # add relationship to nodes node1.related_ent.add(node2) node2.related_ent.add(node1) # save to neo4j self.graph.create(node1) self.graph.create(node2) def search_node_by_name(self, node_name): # replace white spaces _node_name = node_name.replace(" ", "") query = 'MATCH (n) WHERE n.name={node_name} RETURN n;' result = self.graph.run(query, node_name=_node_name, ).data() if result: node = result[0]['n.name'] else: node = None return node def add_me_w_firstname(self, username, age="", gender=""): """ Pushes a new central user 'Me' to the graph Gets a username, creats an Me object and pushes it to the graph :param username: string username :return: me object (see ogm pkg) """ # OGM me = Me() me.firstname = username.title() me.lastname = "" me.age = age me.gender = gender self.graph.push(me) return me def add_me_w_lastname(self, username, age="", gender=""): """ Pushes a new central user 'Me' to the graph Gets a username, creats an Me object and pushes it to the graph :param username: string username :return: me object (see ogm pkg) """ # OGM me = Me() me.firstname = "" me.lastname = username.title() me.age = age me.gender = gender self.graph.push(me) return me def get_me_by_firstname(self, me_name): """ return me object by firstname :param me_name: string with firstname of me :return: me object """ result = self.graph.run('MATCH (n:Me) WHERE n.firstname="' + me_name.title() + '" RETURN n.firstname').data() me = Me() if result: me.firstname = result[0]['n.firstname'] return me else: return None def get_me_by_lastname(self, me_name): """ return me object by firstname :param me_name: string with firstname of me :return: me object """ result = self.graph.run('MATCH (n:Me) WHERE n.lastname="' + me_name.title() + '" RETURN n.lastname').data() me = Me() if result: me.firstname = result[0]['n.lastname'] return me else: return None def add_contact(self, me_name, contactname, relationship): """ adds a new contact to the central user i.e. 'Me' in graph :param me: name of the centraluser object :param contact: string will be converted to contact object :param relationship: string will be converted to object property :return: """ # select central user 'Me' me = self.get_me_by_firstname(me_name) contact = Contact() contact.firstname = contactname relationship = relationships[relationship] if relationship == 'freund': me.friend.add(contact) contact.friend.add(me) elif relationship == 'bruder': me.brother.add(contact) contact.brother.add(me) elif relationship == 'schwester': me.sister.add(contact) contact.sister.add(me) elif relationship == 'mutter': me.mother.add(contact) elif relationship == 'vater': me.father.add(contact) elif relationship == 'sohn': me.son.add(contact) elif relationship == 'tocher': me.daughter.add(contact) #TODO other relationships self.graph.push(me) def search_relationship_by_contactname(self, me_name, contact_name): mename = me_name.replace(" ", "") contactname = contact_name.replace(" ", "") query = 'MATCH (n:Me)-[r]->(c:Contact) WHERE n.firstname={me_name} AND c.firstname={contactname} RETURN type(r);' result = self.graph.run(query, me_name=mename, contactname=contactname ).data() if result: relationship = result[0]['type(r)'] else: relationship = None return relationship def search_contactname_by_relationship(self, me_name, relationship): relationship = relationships[relationship] if relationship: result = self.graph.run('MATCH (u:Me)-[:'+relationship+']->(c:Contact) RETURN c.firstname;', rel=relationship).data() else: return None if result: contactname = result[0]['c.firstname'] else: contactname = None return contactname

30.437768

129

0.563593

820

7,092

4.756098

0.181707

0.032308

0.017949

0.023077

0.351282

0.258205

0.223846

0

0.006321

0.330795

7,092

232

130

30.568966

0.815424

0.164834

0

0.269504

0

0.007092

0.102432

0.012249

0

0.00431

0

1

0.092199

false

0.014184

0.028369

0

0.212766

0

null

0

null

0

1

0

61ea28b84ee81d7761635919c06d71cde4b781c4

2,355

py

Python

src/train_and_evaluate.py

rajeevteejwal/mlops_wine_quality

970ce27712932ca535309230da69fc5c29d82c38

[ "MIT" ]

null

src/train_and_evaluate.py

rajeevteejwal/mlops_wine_quality

970ce27712932ca535309230da69fc5c29d82c38

[ "MIT" ]

null

src/train_and_evaluate.py

rajeevteejwal/mlops_wine_quality

970ce27712932ca535309230da69fc5c29d82c38

[ "MIT" ]

null

import os import pandas as pd from sklearn.linear_model import ElasticNet from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error import argparse import numpy as np import json import joblib from get_data import read_config def evaluate_metrics(actual, pred): r2 = r2_score(actual,pred) mae = mean_absolute_error(actual,pred) rmse = np.sqrt(mean_squared_error(actual,pred)) return r2, rmse, mae def train_and_evaluate(config_path): config = read_config(config_path) train_data_path = config["split_data"]["train_path"] test_data_path = config["split_data"]["test_path"] output_col = config["base"]["target_col"] random_state = config["base"]["random_state"] train_dataset = pd.read_csv(train_data_path,sep=",", encoding="utf-8") test_dataset = pd.read_csv(test_data_path,sep=",", encoding="utf-8") y_train = train_dataset[[output_col]] x_train = train_dataset.drop([output_col],axis=1) y_test = test_dataset[[output_col]] x_test = test_dataset.drop([output_col],axis=1) alpha = config["estimators"]["ElasticNet"]["params"]["alpha"] l1_ratio = config["estimators"]["ElasticNet"]["params"]["l1_ratio"] lr = ElasticNet(alpha=alpha,l1_ratio=l1_ratio,random_state=random_state) lr.fit(x_train,y_train) prediction = lr.predict(x_test) r2, rmse, mae = evaluate_metrics(y_test,prediction) print(f"ElasticNet model (alpha: {alpha}, l1_ratio: {l1_ratio}") print(f" RMSE: {rmse}") print(f" MAE: {mae}") print(f" R2 Score: {r2}") scores_file = config["reports"]["scores"] params_file = config["reports"]["params"] with open(scores_file,"w") as f: scores = { "r2":r2, "rmse":rmse, "mae":mae } json.dump(scores,f,indent=4) with open(params_file,"w") as f: params = { "alpha":alpha, "l1_ratio":l1_ratio } json.dump(params,f,indent=4) model_dir = config["model_dir"] os.makedirs(model_dir,exist_ok=True) model_path = os.path.join(model_dir,"model.joblib") joblib.dump(lr,model_path) if __name__ == '__main__': args = argparse.ArgumentParser() args.add_argument("--config",default="params.yaml") parsed_args = args.parse_args() train_and_evaluate(config_path=parsed_args.config)

31.824324

77

0.675159

331

2,355

4.52568

0.280967

0.037383

0.032043

0.034045

0.17757

0.11215

0

0.011992

0.185563

2,355

73

78

32.260274

0.76903

0

0.143161

0

1

0.033333

false

0

0.15

0

0.2

0.066667

0

null

0

null

0

1

0

61ebdb6920b4b4c3e3a8b0b2f9c1a74ed61083fb

961

py

Python

examples/plot_magnitudes.py

zsiciarz/pygcvs

ed5522ab9cf9237592a6af7a0bc8cad079afeb67

[ "MIT" ]

null

examples/plot_magnitudes.py

zsiciarz/pygcvs

ed5522ab9cf9237592a6af7a0bc8cad079afeb67

[ "MIT" ]

null

examples/plot_magnitudes.py

zsiciarz/pygcvs

ed5522ab9cf9237592a6af7a0bc8cad079afeb67

[ "MIT" ]

null

""" Visualisation of maximum/minimum magnitude for GCVS stars. """ import sys import matplotlib.pyplot as plot from pygcvs import read_gcvs if __name__ == '__main__': try: gcvs_file = sys.argv[1] except IndexError: print('Usage: python plot_magnitudes.py <path to iii.dat>') else: min_magnitudes = [] max_magnitudes = [] for star in read_gcvs(gcvs_file): if star['min_magnitude'] and star['max_magnitude']: min_magnitudes.append(star['min_magnitude']) max_magnitudes.append(star['max_magnitude']) plot.title('GCVS variable star magnitudes') plot.plot(min_magnitudes, max_magnitudes, 'ro') plot.xlabel('Min magnitude') plot.ylabel('Max magnitude') # invert axes because brightest stars have lowest magnitude value plot.gca().invert_xaxis() plot.gca().invert_yaxis() plot.savefig('magnitudes.png')

30.03125

73

0.64204

115

961

5.147826

0.513043

0.065878

0.054054

0.087838

0

0.001389

0.25078

961

31

74

31

0.820833

0.127992

0

0.218072

0

1

0

false

0

0.136364

0

0.136364

0.045455

0

null

0

null

0

1

0

61ed3298ce258d1708cb601b97ca2bb3d32448c9

18,023

py

Python

netor/tinydb/scripts/netorconf.py

aegiacometti/neto

4169a93a4d789facfe9a41d214b1a6c15e8f2fb9

[ "Apache-2.0" ]

1

2020-01-02T04:31:11.000Z

netor/tinydb/scripts/netorconf.py

aegiacometti/neto

4169a93a4d789facfe9a41d214b1a6c15e8f2fb9

[ "Apache-2.0" ]

null

netor/tinydb/scripts/netorconf.py

aegiacometti/neto

4169a93a4d789facfe9a41d214b1a6c15e8f2fb9

[ "Apache-2.0" ]

1

2021-02-23T04:34:48.000Z

#!/usr/bin/env python3 import os import sys import configparser import fileinput import netorlogging import datetime from shutil import copyfile def _netor_config(): """ It is used for updating the Neto home directory in the configuration files and scripts. This is useful, if you want to have 2 working installations of Neto in completely independent directories. It will update the ``NETOR_HOME_DIRECTORY`` variable in the ``netor.conf`` file, and also in the following Neto python scripts which then works with the TinyDB: # netor/tinydb/scripts/listdb.py # netor/tinydb/scripts/pushcustdb.py # netor/tinydb/scripts/worker.py # netor/tinydb/scripts/switchdb.py Later it will also update the ``hosts_file`` variable in the following bash scripts: # bin/netor-ping # bin/netor-traceroute :return: nothing """ _NETOR_HOME_DIRECTORY = os.getenv('NETOR') config = configparser.ConfigParser(interpolation=configparser.ExtendedInterpolation()) netor_config_path_name = _NETOR_HOME_DIRECTORY + "netor/netor.config" config.read(netor_config_path_name) if os.path.isdir(_NETOR_HOME_DIRECTORY): answer = input("\nDefault \"$NETOR/netor\" directory found at:\n" + str(_NETOR_HOME_DIRECTORY) + "\nDo you want to keep it (y/n): ").lower() if answer == "y": print("Keeping same configuration\n") try: config['Netor']['netor_home_directory'] = _NETOR_HOME_DIRECTORY except KeyError: print("\nConfiguration files do no exist, clone the previous directory before start the changes\n") sys.exit(1) with open(netor_config_path_name, 'w') as configfile: config.write(configfile) _update_ansible(_NETOR_HOME_DIRECTORY) tinydb_log_file = config['TinyDB']['tinydb_log_file'] _update_config(tinydb_log_file, __file__, _NETOR_HOME_DIRECTORY) sys.exit() elif answer == "n": print('If you want to change the $NETOR directory, you must first update the $NETOR environment variable') print('Set $NETOR environment value by adding/changing the line at the end of the file /etc/environment') print('NETOR=\"/my/dir/netor/\"') print('Restart the system and execute this script again') else: print("Invalid option/n") sys.exit() else: print("\nDefault \"$NETOR/netor\" NOT found") print('Set $NETOR environment value by adding/changing the line at the end of the file /etc/environment') print('NETOR=\"/my/dir/netor/\"') print('Restart the system and execute this script again') def _update_ansible(netor_home_directory): """ Update Ansible configuration files. :param netor_home_directory: Neto home directory to used for updating the configuration files :return: nothing """ ansible_config_file = os.environ['HOME'] + '/.ansible.cfg' replace_static_vars_scripts(ansible_config_file, '#inventory ', '= ' + netor_home_directory + 'netor/ansible/hosts', '', '') replace_static_vars_scripts(ansible_config_file, 'transport', ' = paramiko', '', '') replace_static_vars_scripts(ansible_config_file, 'host_key_auto_add', ' = True', '', '') replace_static_vars_scripts(ansible_config_file, 'host_key_checking', ' = False', '', '') replace_static_vars_scripts(ansible_config_file, 'inventory = ', netor_home_directory + 'netor/ansible/hosts', '', '') print('\nNetor home directory replaced in Ansible.') def _backup_filename(new_netor_home_directory, filename): """ Create a backup of the specified configuration file :param new_netor_home_directory: it is the actual new Neto home directory to be updated on files :param filename: file name to backup :return: nothing """ print('\nBacking up ' + filename + ' to ' + new_netor_home_directory + 'netor/salt/backup/') source = new_netor_home_directory + 'netor/salt/config/' + filename destination = new_netor_home_directory + 'netor/salt/backup/' + filename + "_" + \ datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S') copyfile(source, destination) def _create_master_config_file(new_netor_home_directory, filename): """ Create new Salt master configuration file. :param new_netor_home_directory: it is the actual new Neto home directory to be updated on files :param filename: filename to backup :return: nothing """ full_path_filename = new_netor_home_directory + 'netor/salt/config/' + filename file = open(full_path_filename, '+w') file.write('# for salt-sproxy\n') file.write('use_existing_proxy: true\n') file.write('##### Large-scale tuning settings #####\n') file.write('##########################################\n') file.write('#max_open_files: 100000\n') file.write('\n') file.write('##### Security settings #####\n') file.write('# Enable "open mode", this mode still maintains encryption, but turns off\n') file.write('# authentication, this is only intended for highly secure environments or for\n') file.write('# the situation where your keys end up in a bad state. If you run in open mode\n') file.write('# you do so at your own risk!\n') file.write('open_mode: True\n') file.write('\n') file.write('# Enable auto_accept, this setting will automatically accept all incoming\n') file.write('# public keys from the minions. Note that this is insecure.\n') file.write('auto_accept: True\n') file.write('\n') file.write('# The path to the master\'s configuration file.\n') file.write('conf_file: ' + new_netor_home_directory + 'netor/salt/config/master\n') file.write('\n') file.write('# Directory used to store public key data:\n') file.write('pki_dir: ' + new_netor_home_directory + 'netor/salt/config/pki/master\n') file.write('\n') file.write('##### File Server settings #####\n') file.write('file_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/states/\n') file.write('\n') file.write('##### Pillar settings #####\n') file.write('pillar_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/states/\n') file.write('engines:\n') file.write(' - slack:\n') file.write(' token: YOUR-TOKEN-GOES-HERE\n') file.write(' control: true\n') file.write(' fire_all: False\n') file.write('######## CREATE YOUR OWN POLICIES FOR COMMAND PERMISSIONS ########\n') file.write(' groups:\n') file.write(' default:\n') file.write(' users:\n') file.write(' - \'*\'\n') file.write(' commands:\n') file.write(' - \'*\'\n') file.close() def _update_master_config_file(new_netor_home_directory, filename): """ Update Salt master configuration file. :param new_netor_home_directory: Location where the file is located :param filename: file name :return: nothing """ _backup_filename(new_netor_home_directory, filename) # pending to develop update of the file with the new directory _create_master_config_file(new_netor_home_directory, filename) def _create_minion_config_file(new_netor_home_directory, filename): """ Create Salt minion configuration file. :param new_netor_home_directory: Location where the file will be located :param filename: file name :return: nothing """ full_path_filename = new_netor_home_directory + 'netor/salt/config/' + filename file = open(full_path_filename, '+w') file.write('##### Primary configuration settings #####\n') file.write('master: localhost\n') file.write('\n') file.write('# The path to the minion\'s configuration file.\n') file.write('conf_file: ' + new_netor_home_directory + 'netor/salt/config/minion\n') file.write('# The directory to store the pki information in\n') file.write('pki_dir: ' + new_netor_home_directory + 'netor/salt/config/pki/minion\n') file.write('\n') file.write('##### File Directory Settings #####\n') file.write('file_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write(' - ' + new_netor_home_directory + 'neto/salt/config/pillar/states/\n') file.write('\n') file.write('pillar_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/ states /\n') file.write('\n') file.write('###### Security settings #####\n') file.write('# Enable "open mode", this mode still maintains encryption, but turns off\n') file.write('# authentication, this is only intended for highly secure environments or for\n') file.write('# the situation where your keys end up in a bad state. If you run in open mode\n') file.write('# you do so at your own risk!\n') file.write('open_mode: True\n') file.close() def _update_minion_config_file(new_netor_home_directory, filename): """ Update Salt minion configuration file. :param new_netor_home_directory: Location where the file is located :param filename: file name :return: """ _backup_filename(new_netor_home_directory, filename) # pending to develop update of the file with the new directory _create_minion_config_file(new_netor_home_directory, filename) def _create_proxy_config_file(new_netor_home_directory, filename): """ Create Salt proxy configuration file. :param new_netor_home_directory: Location where the file will be located :param filename: file name :return: """ full_path_filename = new_netor_home_directory + 'netor/salt/config/' + filename file = open(full_path_filename, '+w') file.write('##### Primary configuration settings #####\n') file.write('\n') file.write('master: localhost\n') file.write('conf_file: ' + new_netor_home_directory + 'netor/salt/config/proxy\n') file.write('mine_enabled: true # not required, but nice to have\n') file.write('mine_functions:\n') file.write(' net.ipaddrs: []\n') file.write(' net.lldp: []\n') file.write(' net.mac: []\n') file.write(' net.arp: []\n') file.write(' net.interfaces: []\n') file.write('mine_interval: 5\n') file.write('\n') file.write('###### Thread settings #####\n') file.write('multiprocessing: false\n') file.write('\n') file.write('##### File Directory Settings #####\n') file.write('file_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write('pillar_roots:\n') file.write(' base:\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/\n') file.write(' - ' + new_netor_home_directory + 'netor/salt/config/pillar/\n') file.write('\n') file.write('###### Security settings #####\n') file.write('###########################################\n') file.write('# Enable "open mode", this mode still maintains encryption, but turns off\n') file.write('# authentication, this is only intended for highly secure environments or for\n') file.write('# the situation where your keys end up in a bad state. If you run in open mode\n') file.write('# you do so at your own risk!\n') file.write('open_mode: True\n') file.write('# The directory to store the pki information in\n') file.write('pki_dir: ' + new_netor_home_directory + 'netor/salt/config/pki/proxy # not required - this separates ' 'the proxy keys into a different directory\n') file.close() def _update_proxy_config_file(new_netor_home_directory, filename): """ Update Salt proxy configuration file. :param new_netor_home_directory: Directory where the file is located :param filename: file name :return: """ _backup_filename(new_netor_home_directory, filename) # pending to develop update of the file with the new directory _create_proxy_config_file(new_netor_home_directory, filename) def _file_update_redirect(new_netor_home_directory, filename): """ Update the configuration files. :param new_netor_home_directory: Directory where the files are located :param filename: file name to update :return: nothing """ if 'master' in filename: _update_master_config_file(new_netor_home_directory, filename) elif 'minion' in filename: _update_minion_config_file(new_netor_home_directory, filename) elif 'proxy' in filename: _update_proxy_config_file(new_netor_home_directory, filename) else: print('\nError while checking Salt master, minion and proxy configuration files') sys.exit(1) def _file_create_redirect(new_netor_home_directory, filename): """ Create the configuration files. :param new_netor_home_directory: it is the actual new Neto home directory where to create the file :param filename: file name to create :return: nothing """ if 'master' in filename: _create_master_config_file(new_netor_home_directory, filename) elif 'minion' in filename: _create_minion_config_file(new_netor_home_directory, filename) elif 'proxy' in filename: _create_proxy_config_file(new_netor_home_directory, filename) else: print('\nError while checking Salt master, minion and proxy configuration files') sys.exit(1) def _create_update_master_minion_proxy(new_netor_home_directory, filename): """ Update or create (if do not exists) Salt configuration files. :param new_netor_home_directory: it is the actual new Neto home directory to used in the process :param filename: file name to update :return: nothing """ full_salt_config_filename = new_netor_home_directory + 'netor/salt/' + filename if os.path.isfile(full_salt_config_filename): _file_update_redirect(new_netor_home_directory, filename) else: _file_create_redirect(new_netor_home_directory, filename) def _update_config(tinydb_log_file, __file__, new_netor_home_directory): """ Execute the actual updates in the files. Salt master, minion and proxy. :param tinydb_log_file: the filename to send the logging message after the operation is completed :param __file__: script name who is sending the message to log :param new_netor_home_directory: it is the actual new Neto home directory to be updated on files :return: nothing """ _create_update_master_minion_proxy(new_netor_home_directory, 'master') _create_update_master_minion_proxy(new_netor_home_directory, 'minion') _create_update_master_minion_proxy(new_netor_home_directory, 'proxy') print('\nNetor home directory replaced in salt master, minion and proxy.') print("\nAdd or modified if necessary " + new_netor_home_directory + "bin to your .profile") print(" vi $HOME/.profile") print(" PATH=\"$PATH:" + new_netor_home_directory + "bin\n") print("\nAdd or modified if necessary " + new_netor_home_directory + " to /etc/environment") print(" sudo vi /etc/environment") print(" NETOR=\"$PATH:" + new_netor_home_directory) print("\nLogoff session or restart system, and login again.") print("\nATTENTION: If you are using Salt restart the daemons with \"netor-salt-restart\"\n") netorlogging.log_msg(tinydb_log_file, __file__, "Netconf executed. Neto.config and static vars in scripts updated. ") def replace_static_vars_scripts(filename, search, replace, delimiter, extra): """ Replace line by line the ``NETOR_HOME_DIRECTORY`` static variable in scripts. :param filename: filename to review :param search: search pattern to look for :param replace: patter to replace :param delimiter: to add a delimiter surrounding the path names :param extra: add extra path information :return: nothing """ try: for line in fileinput.input(filename, inplace=True): if search in line: print((search + delimiter + replace + extra + delimiter), end="\n") else: print(line, end="") except FileNotFoundError: print("\nERROR File not found " + filename) print("Manually find systemd folder and file " + filename.split("/")[-1] + " and modify the parameter \"" + search + "\" in the file to point to " + replace + "\n") except PermissionError: print("\nERROR Permission denied to modify file " + filename) print("Manually modify the parameter -\"" + search + "\" in the file to point to " + replace) def check_netor_config(netor_home_directory): """ Verifies if the ``netor.config`` file exists in the file tree. :param netor_home_directory: to verify if the netor home directory and file exists :return: nothing """ if (os.path.isdir(netor_home_directory)) and (os.path.isfile((netor_home_directory + "netor/netor.config"))): return else: print("Neto home directory or config file not found.\nRun configuration script (netor-config).") sys.exit(1) if __name__ == '__main__': _netor_config() print()

42.011655

119

0.668146

2,364

18,023

4.887902

0.140017

0.082562

0.089139

0.123583

0.648897

0.623107

0.59524

0.563479

0.525314

0.44855

0

0.000983

0.209455

18,023

428

120

42.109813

0.810008

0.195972

0

0.453815

0

0.02008

0.378009

0.045221

0

1

0.060241

false

0

0.028112

0

0.092369

0.128514

0

null

0

null

0

1

0

61edb2c25c99c318b707a55fcdfcaaf007b47999

4,780

py

Python

test/api/mutations/test_check_repository_by_commit.py

uliana291/the-zoo

a15a4162c39553abe91224f4feff5d3b66f9413e

[ "MIT" ]

null

test/api/mutations/test_check_repository_by_commit.py

uliana291/the-zoo

a15a4162c39553abe91224f4feff5d3b66f9413e

[ "MIT" ]

null

test/api/mutations/test_check_repository_by_commit.py

uliana291/the-zoo

a15a4162c39553abe91224f4feff5d3b66f9413e

[ "MIT" ]

null

import pytest from zoo.auditing.models import Issue from zoo.auditing.check_discovery import Effort, Kind, Severity pytestmark = pytest.mark.django_db @pytest.fixture def scenario(mocker, repository_factory, issue_factory, check_factory, fake_path): owner, name, sha = "games", "lemmings", "GINLNNIIJL" repository = repository_factory(id=42, owner=owner, name=name, remote_id=3) kinds = {} for namespace, id, status, severity, effort in [ ("A", "new", Issue.Status.NEW, Severity.UNDEFINED, Effort.UNDEFINED), ("A", "fixed", Issue.Status.FIXED, Severity.ADVICE, Effort.LOW), ("A", "wontfix", Issue.Status.WONTFIX, Severity.WARNING, Effort.MEDIUM), ("A", "not-found", Issue.Status.NOT_FOUND, Severity.CRITICAL, Effort.HIGH), ("A", "reopened", Issue.Status.REOPENED, Severity.UNDEFINED, Effort.UNDEFINED), ("B", "new", Issue.Status.NEW, Severity.ADVICE, Effort.LOW), ("B", "fixed", Issue.Status.FIXED, Severity.WARNING, Effort.MEDIUM), ("B", "wontfix", Issue.Status.WONTFIX, Severity.CRITICAL, Effort.HIGH), ("B", "not-found", Issue.Status.NOT_FOUND, Severity.ADVICE, Effort.LOW), ("B", "reopened", Issue.Status.REOPENED, Severity.UNDEFINED, Effort.HIGH), ("C", "is-found", Issue.Status.NEW, Severity.CRITICAL, Effort.HIGH), ("C", "not-found", Issue.Status.NOT_FOUND, Severity.WARNING, Effort.LOW), ]: kind = Kind( category="tests", namespace=namespace, id=id, severity=severity, effort=effort, title=f"Title for {namespace}:{id}", description=f"Description for {namespace}:{id} | Status: {{was}} -> {{is}}", ) kinds[kind.key] = kind if namespace != "C": issue_factory(repository=repository, kind_key=kind.key, status=status.value) checks = [ # known issues, found check_factory("A:new", True, {"was": "new", "is": "known"}), check_factory("A:fixed", True, {"was": "fixed", "is": "reopened"}), check_factory("A:wontfix", True, {"was": "wontfix", "is": "wontfix"}), check_factory("A:not-found", True, {"was": "not-found", "is": "new"}), check_factory("A:reopened", True, {"was": "reopened", "is": "known"}), # known issues, not found check_factory("B:new", False, {"was": "new", "is": "fixed"}), check_factory("B:fixed", False, {"was": "fixed", "is": "not-found"}), check_factory("B:wontfix", False, {"was": "wontfix", "is": "fixed"}), check_factory("B:not-found", False, {"was": "not-found", "is": "not-found"}), check_factory("B:reopened", False, {"was": "reopened", "is": "fixed"}), # new issues check_factory("C:is-found", True), check_factory("C:not-found", False), ] mocker.patch("zoo.api.mutations.CHECKS", checks) mocker.patch("zoo.auditing.check_discovery.KINDS", kinds) m_download_repository = mocker.patch( "zoo.api.mutations.download_repository", return_value=fake_path ) yield repository, sha m_download_repository.assert_called_once_with(repository, mocker.ANY, sha=sha) query = """ mutation test ($input: CheckRepositoryByCommitInput!) { checkRepositoryByCommit (input: $input) { allCheckResults { isFound kindKey status details severity effort title description } } } """ def test_unknown_repository(snapshot, call_api): input = {"owner": "games", "name": "doom", "sha": "IDKFA"} response = call_api(query, input) snapshot.assert_match(response) def test_all_results(scenario, snapshot, call_api): repository, sha = scenario input = {"owner": repository.owner, "name": repository.name, "sha": sha} response = call_api(query, input) snapshot.assert_match(response) def test_only_found(scenario, snapshot, call_api): repository, sha = scenario input = { "owner": repository.owner, "name": repository.name, "sha": sha, "onlyFound": True, } response = call_api(query, input) snapshot.assert_match(response) def test_with_repository(scenario, snapshot, call_api): repository, sha = scenario query = """ mutation test ($input: CheckRepositoryByCommitInput!) { checkRepositoryByCommit (input: $input) { repository { id owner name url remoteId } } } """ input = {"owner": repository.owner, "name": repository.name, "sha": sha} response = call_api(query, input) snapshot.assert_match(response)

34.142857

88

0.604393

526

4,780

5.385932

0.19962

0.039534

0.022944

0.028239

0.43911

0.326509

0.310272

0.222379

0.163784

0

0.000826

0.240167

4,780

139

89

34.388489

0.779185

0.011297

0

0.192661

0

0.262389

0.042567

0

0.045872

1

0.045872

false

0

0.027523

0

0.073395

0

null

0

null

0

1

0

61f65e88bb74b76264401d01893c2004742b5044

1,919

py

Python

build.py

micklenguyen/hw2-scripting

3603a2c4d7518890eacc4f071f347f90dd295ee6

[ "MIT" ]

null

build.py

micklenguyen/hw2-scripting

3603a2c4d7518890eacc4f071f347f90dd295ee6

[ "MIT" ]

null

build.py

micklenguyen/hw2-scripting

3603a2c4d7518890eacc4f071f347f90dd295ee6

[ "MIT" ]

null

def main(): content_pages = auto_populate_content_files() for page in content_pages: filepath = page['filepath'] output = page['output'] title = page['title'] # Read content of html pages content = open(filepath).read() # Invoke function to return finished_page (base.html with filled in content) finshed_page = apply_template(content, title, content_pages) write_html(output, finshed_page) def auto_populate_content_files(): import glob import os # Loop through files in the content/ directory and save paths as a list all_html_files = glob.glob("content/*.html") #print(all_html_files) # Loop through the all_html_files list, modify and extract file_name and name_only from the path pages = [] for file_path in all_html_files: # Saving the path to a varaible (ex. content/resume.html) file_path = file_path # Removes the file path from the file name (ex. content/resume.html -> resume.html) file_name = os.path.basename(file_path) # Removes the file path from the file name (ex. content/resume.html -> resume.html) file_name = os.path.basename(file_path) #print(file_name) # Split the name from the file extention (ex. resume.html -> resume) name_only, extension = os.path.splitext(file_name) # Build a list with dicts of content information pages.append({ "filepath": file_path, "title": name_only, "output": "docs/" + file_name, "filename": file_name }) return pages def apply_template(content, title, pages): from jinja2 import Template # Read base.html and save to template template_html = open("templates/base.html").read() new_template = Template(template_html) finished_page = new_template.render( title=title, content=content, pages=pages, ) return finished_page def write_html(output, finshed_page): # Writes complete html files open(output, "w+").write(finshed_page) if __name__ == "__main__": main()

24.922078

97

0.730589

281

1,919

4.790036

0.266904

0.053492

0.035661

0.042348

0.181278

0.142645

0

0.000628

0.16988

1,919

77

98

24.922078

0.844319

0.364773

0

0.05

0

0.077944

0

1

0.1

false

0

0.075

0

0.225

0

null

0

null

0

1

0

61f94a0bece7deb448882a08f6a458e64ef93c8e

35,113

py

Python

src/jote/jote.py

InformaticsMatters/data-manager-job-tester

f8915e005f16685d159535a2455628eb1d7ac518

[ "MIT" ]

null

src/jote/jote.py

InformaticsMatters/data-manager-job-tester

f8915e005f16685d159535a2455628eb1d7ac518

[ "MIT" ]

1

2022-01-28T10:06:28.000Z

2022-01-31T14:51:52.000Z

src/jote/jote.py

InformaticsMatters/data-manager-job-tester

f8915e005f16685d159535a2455628eb1d7ac518

[ "MIT" ]

null

#!/usr/bin/env python """Informatics Matters Job Tester (JOTE). Get help running this utility with 'jote --help' """ import argparse import os import shutil import stat from stat import S_IRGRP, S_IRUSR, S_IWGRP, S_IWUSR import subprocess import sys from typing import Any, Dict, List, Optional, Tuple from munch import DefaultMunch import yaml from yamllint import linter from yamllint.config import YamlLintConfig from decoder import decoder from .compose import get_test_root, INSTANCE_DIRECTORY, DEFAULT_TEST_TIMEOUT_M from .compose import Compose # Where can we expect to find Job definitions? _DEFINITION_DIRECTORY: str = "data-manager" # What's the default manifest file? _DEFAULT_MANIFEST: str = os.path.join(_DEFINITION_DIRECTORY, "manifest.yaml") # Where can we expect to find test data? _DATA_DIRECTORY: str = "data" # Our yamllint configuration file # from the same directory as us. _YAMLLINT_FILE: str = os.path.join(os.path.dirname(__file__), "jote.yamllint") # Read the version file _VERSION_FILE: str = os.path.join(os.path.dirname(__file__), "VERSION") with open(_VERSION_FILE, "r", encoding="utf-8") as file_handle: _VERSION = file_handle.read().strip() # Job image types (lower-case) _IMAGE_TYPE_SIMPLE: str = "simple" _IMAGE_TYPE_NEXTFLOW: str = "nextflow" _DEFAULT_IMAGE_TYPE: str = _IMAGE_TYPE_SIMPLE # User HOME directory. # Used to check for netflow files if nextflow is executed. # The user CANNOT have any pf their own nextflow config. _USR_HOME: str = os.environ.get("HOME", "") def _print_test_banner(collection: str, job_name: str, job_test_name: str) -> None: print(" ---") print(f"+ collection={collection} job={job_name} test={job_test_name}") def _lint(definition_filename: str) -> bool: """Lints the provided job definition file.""" if not os.path.isfile(_YAMLLINT_FILE): print(f"! The yamllint file ({_YAMLLINT_FILE}) is missing") return False with open(definition_filename, "rt", encoding="UTF-8") as definition_file: errors = linter.run(definition_file, YamlLintConfig(file=_YAMLLINT_FILE)) if errors: # We're given a 'generator' and we don't know if there are errors # until we iterator over it. So here we print an initial error message # on the first error. found_errors: bool = False for error in errors: if not found_errors: print(f'! Job definition "{definition_file}" fails yamllint:') found_errors = True print(error) if found_errors: return False return True def _validate_schema(definition_filename: str) -> bool: """Checks the Job Definition against the decoder's schema.""" with open(definition_filename, "rt", encoding="UTF-8") as definition_file: job_def: Optional[Dict[str, Any]] = yaml.load( definition_file, Loader=yaml.FullLoader ) assert job_def # If the decoder returns something there's been an error. error: Optional[str] = decoder.validate_job_schema(job_def) if error: print( f'! Job definition "{definition_filename}"' " does not comply with schema" ) print("! Full response follows:") print(error) return False return True def _validate_manifest_schema(manifest_filename: str) -> bool: """Checks the Manifest against the decoder's schema.""" with open(manifest_filename, "rt", encoding="UTF-8") as definition_file: job_def: Optional[Dict[str, Any]] = yaml.load( definition_file, Loader=yaml.FullLoader ) assert job_def # If the decoder returns something there's been an error. error: Optional[str] = decoder.validate_manifest_schema(job_def) if error: print(f'! Manifest "{manifest_filename}"' " does not comply with schema") print("! Full response follows:") print(error) return False return True def _check_cwd() -> bool: """Checks the execution directory for sanity (cwd). Here we must find a data-manager directory """ expected_directories: List[str] = [_DEFINITION_DIRECTORY, _DATA_DIRECTORY] for expected_directory in expected_directories: if not os.path.isdir(expected_directory): print(f'! Expected directory "{expected_directory}"' " but it is not here") return False return True def _load(manifest_filename: str, skip_lint: bool) -> Tuple[List[DefaultMunch], int]: """Loads definition files listed in the manifest and extracts the definitions that contain at least one test. The definition blocks for those that have tests (ignored or otherwise) are returned along with a count of the number of tests found (ignored or otherwise). If there was a problem loading the files an empty list and -ve count is returned. """ # Prefix manifest filename with definition directory if required... manifest_path: str = ( manifest_filename if manifest_filename.startswith(f"{_DEFINITION_DIRECTORY}/") else os.path.join(_DEFINITION_DIRECTORY, manifest_filename) ) if not os.path.isfile(manifest_path): print(f'! The manifest file is missing ("{manifest_path}")') return [], -1 if not _validate_manifest_schema(manifest_path): return [], -1 with open(manifest_path, "r", encoding="UTF-8") as manifest_file: manifest: Dict[str, Any] = yaml.load(manifest_file, Loader=yaml.FullLoader) if manifest: manifest_munch: DefaultMunch = DefaultMunch.fromDict(manifest) # Iterate through the named files... job_definitions: List[DefaultMunch] = [] num_tests: int = 0 for jd_filename in manifest_munch["job-definition-files"]: # Does the definition comply with the dschema? # No options here - it must. jd_path: str = os.path.join(_DEFINITION_DIRECTORY, jd_filename) if not _validate_schema(jd_path): return [], -1 # YAML-lint the definition? if not skip_lint: if not _lint(jd_path): return [], -2 with open(jd_path, "r", encoding="UTF-8") as jd_file: job_def: Dict[str, Any] = yaml.load(jd_file, Loader=yaml.FullLoader) if job_def: jd_munch: DefaultMunch = DefaultMunch.fromDict(job_def) for jd_name in jd_munch.jobs: if jd_munch.jobs[jd_name].tests: num_tests += len(jd_munch.jobs[jd_name].tests) if num_tests: jd_munch.definition_filename = jd_filename job_definitions.append(jd_munch) return job_definitions, num_tests def _copy_inputs(test_inputs: List[str], project_path: str) -> bool: """Copies all the test files into the test project directory.""" # The files are assumed to reside in the repo's 'data' directory. print(f'# Copying inputs (from "${{PWD}}/{_DATA_DIRECTORY}")...') expected_prefix: str = f"{_DATA_DIRECTORY}/" for test_input in test_inputs: print(f"# + {test_input}") if not test_input.startswith(expected_prefix): print("! FAILURE") print(f'! Input file {test_input} must start with "{expected_prefix}"') return False if not os.path.isfile(test_input): print("! FAILURE") print(f"! Missing input file {test_input} ({test_input})") return False # Looks OK, copy it shutil.copy(test_input, project_path) print("# Copied") return True def _check_exists(name: str, path: str, expected: bool, fix_permissions: bool) -> bool: exists: bool = os.path.exists(path) if expected and not exists: print(f"# exists ({expected}) [FAILED]") print("! FAILURE") print(f'! Check exists "{name}" (does not exist)') return False if not expected and exists: print(f"# exists ({expected}) [FAILED]") print("! FAILURE") print(f'! Check does not exist "{name}" (exists)') return False # File exists or does not exist, as expected. # If it exists we check its 'user' and 'group' read and write permission. # # If 'fix_permissions' is True (i.e. the DM is expected to fix (group) permissions) # the group permissions are expected to be incorrect. If False # then the group permissions are expected to be correct/ if exists: stat_info: os.stat_result = os.stat(path) # Check user permissions file_mode: int = stat_info.st_mode if file_mode & S_IRUSR == 0 or file_mode & S_IWUSR == 0: print("! FAILURE") print( f'! "{name}" exists but has incorrect user permissions' f" ({stat.filemode(file_mode)})" ) return False # Check group permissions if file_mode & S_IRGRP == 0 or file_mode & S_IWGRP == 0: # Incorrect permissions. if not fix_permissions: # And not told to fix them! print("! FAILURE") print( f'! "{name}" exists but has incorrect group permissions (fix-permissions=False)' f" ({stat.filemode(file_mode)})" ) return False else: # Correct group permissions. if fix_permissions: # But told to fix them! print("! FAILURE") print( f'! "{name}" exists but has correct group permissions (fix-permissions=True)' f" ({stat.filemode(file_mode)})" ) return False print(f"# exists ({expected}) [OK]") return True def _check_line_count(name: str, path: str, expected: int) -> bool: line_count: int = 0 with open(path, "rt", encoding="UTF-8") as check_file: for _ in check_file: line_count += 1 if line_count != expected: print(f"# lineCount ({line_count}) [FAILED]") print("! FAILURE") print(f"! Check lineCount {name}" f" (found {line_count}, expected {expected})") return False print(f"# lineCount ({line_count}) [OK]") return True def _check( t_compose: Compose, output_checks: DefaultMunch, fix_permissions: bool ) -> bool: """Runs the checks on the Job outputs. We currently support 'exists' and 'lineCount'. If 'fix_permissions' is True we error if the permissions are correct, if False we error if the permissions are not correct. """ assert t_compose assert isinstance(t_compose, Compose) assert output_checks assert isinstance(output_checks, List) print("# Checking...") for output_check in output_checks: output_name: str = output_check.name print(f"# - {output_name}") expected_file: str = os.path.join( t_compose.get_test_project_path(), output_name ) for check in output_check.checks: check_type: str = list(check.keys())[0] if check_type == "exists": if not _check_exists( output_name, expected_file, check.exists, fix_permissions ): return False elif check_type == "lineCount": if not _check_line_count(output_name, expected_file, check.lineCount): return False else: print("! FAILURE") print(f"! Unknown output check type ({check_type})") return False print("# Checked") return True def _run_nextflow( command: str, project_path: str, timeout_minutes: int = DEFAULT_TEST_TIMEOUT_M ) -> Tuple[int, str, str]: """Runs nextflow in the project directory returning the exit code, stdout and stderr. """ assert command assert project_path # The user cannot have a nextflow config in their home directory. # Nextflow looks here and any config will be merged with the test config. if _USR_HOME: home_config: str = os.path.join(_USR_HOME, ".nextflow", "config") if os.path.exists(home_config) and os.path.isfile(home_config): print("! FAILURE") print( "! A nextflow test but" f" you have your own config file ({home_config})" ) print("! You cannot test Jobs and have your own config file") return 1, "", "" print('# Executing the test ("nextflow")...') print(f'# Execution directory is "{project_path}"') cwd = os.getcwd() os.chdir(project_path) try: test = subprocess.run( command, shell=True, check=False, capture_output=True, timeout=timeout_minutes * 60, ) finally: os.chdir(cwd) return test.returncode, test.stdout.decode("utf-8"), test.stderr.decode("utf-8") def _test( args: argparse.Namespace, filename: str, collection: str, job: str, job_definition: DefaultMunch, ) -> Tuple[int, int, int, int]: """Runs the tests for a specific Job definition returning the number of tests passed, skipped (due to run-level), ignored and failed. """ assert job_definition assert isinstance(job_definition, DefaultMunch) # The test status, assume success tests_passed: int = 0 tests_skipped: int = 0 tests_ignored: int = 0 tests_failed: int = 0 if args.image_tag: print(f"W Replacing image tag. Using '{args.image_tag}'") job_image: str = f"{job_definition.image.name}:{args.image_tag}" else: job_image = f"{job_definition.image.name}:{job_definition.image.tag}" job_image_memory: str = job_definition.image["memory"] if job_image_memory is None: job_image_memory = "1Gi" job_image_cores: int = job_definition.image["cores"] if job_image_cores is None: job_image_cores = 1 job_project_directory: str = job_definition.image["project-directory"] job_working_directory: str = job_definition.image["working-directory"] if "type" in job_definition.image: job_image_type: str = job_definition.image["type"].lower() else: job_image_type = _DEFAULT_IMAGE_TYPE # Does the image need the (group write) permissions # of files it creates fixing? Default is 'no'. # If 'yes' (true) the DM is expected to fix the permissions of the # generated files once the job has finished. job_image_fix_permissions: bool = False if "fix-permissions" in job_definition.image: job_image_fix_permissions = job_definition.image["fix-permissions"] for job_test_name in job_definition.tests: # If a job test has been named, # skip this test if it doesn't match. # We do not include this test in the count. if args.test and not args.test == job_test_name: continue _print_test_banner(collection, job, job_test_name) # The status changes to False if any # part of this block fails. test_status: bool = True print(f"> definition filename={filename}") # Does the test have an 'ignore' declaration? # Obey it unless the test is named explicitly - # i.e. if th user has named a specific test, run it. if "ignore" in job_definition.tests[job_test_name]: if args.test: print("W Ignoring the ignore: property (told to run this test)") else: print('W Ignoring test (found "ignore")') tests_ignored += 1 continue # Does the test have a 'run-level' declaration? # If so, is it higher than the run-level specified? if args.test: print("W Ignoring any run-level check (told to run this test)") else: if "run-level" in job_definition.tests[job_test_name]: run_level = job_definition.tests[job_test_name]["run-level"] print(f"> run-level={run_level}") if run_level > args.run_level: print(f'W Skipping test (test is "run-level: {run_level}")') tests_skipped += 1 continue else: print("> run-level=Undefined") # Render the command for this test. # First extract the variables and values from 'options' # and then 'inputs'. job_variables: Dict[str, Any] = {} for variable in job_definition.tests[job_test_name].options: job_variables[variable] = job_definition.tests[job_test_name].options[ variable ] # If the option variable's declaration is 'multiple' # it must be handled as a list, e.g. it might be declared like this: - # # The double-comment is used # to avoid mypy getting upset by the 'type' line... # # # properties: # # fragments: # # title: Fragment molecules # # multiple: true # # mime-types: # # - chemical/x-mdl-molfile # # type: file # # We only pass the basename of the input to the command decoding # i.e. strip the source directory. # A list of input files (relative to this directory) # We populate this with everything we find declared as an input input_files: List[str] = [] # Process every 'input' if job_definition.tests[job_test_name].inputs: for variable in job_definition.tests[job_test_name].inputs: # Test variable must be known as an input or option. # Is the variable an option (otherwise it's an input) variable_is_option: bool = False variable_is_input: bool = False if variable in job_definition.variables.options.properties: variable_is_option = True elif variable in job_definition.variables.inputs.properties: variable_is_input = True if not variable_is_option and not variable_is_input: print("! FAILURE") print( f"! Test variable ({variable})" + " not declared as input or option" ) # Record but do no further processing tests_failed += 1 test_status = False # Is it declared as a list? value_is_list: bool = False if variable_is_option: if job_definition.variables.options.properties[variable].multiple: value_is_list = True else: if job_definition.variables.inputs.properties[variable].multiple: value_is_list = True # Add each value or just one value # (depending on whether it's a list) if value_is_list: job_variables[variable] = [] for value in job_definition.tests[job_test_name].inputs[variable]: job_variables[variable].append(os.path.basename(value)) input_files.append(value) else: value = job_definition.tests[job_test_name].inputs[variable] job_variables[variable] = os.path.basename(value) input_files.append(value) decoded_command: str = "" test_environment: Dict[str, str] = {} if test_status: # Jote injects Job variables that are expected. # 'DM_' variables are injected by the Data Manager, # other are injected by Jote. # - DM_INSTANCE_DIRECTORY job_variables["DM_INSTANCE_DIRECTORY"] = INSTANCE_DIRECTORY # - CODE_DIRECTORY job_variables["CODE_DIRECTORY"] = os.getcwd() # Has the user defined any environment variables in the test? # If so they must exist, although we don't care about their value. # Extract them here to pass to the test. if "environment" in job_definition.tests[job_test_name]: for env_name in job_definition.tests[job_test_name].environment: env_value: Optional[str] = os.environ.get(env_name, None) if env_value is None: print("! FAILURE") print("! Test environment variable is not defined") print(f"! variable={env_name}") # Record but do no further processing tests_failed += 1 test_status = False break test_environment[env_name] = env_value if test_status: # Get the raw (encoded) command from the job definition... raw_command: str = job_definition.command # Decode it using our variables... decoded_command, test_status = decoder.decode( raw_command, job_variables, "command", decoder.TextEncoding.JINJA2_3_0, ) if not test_status: print("! FAILURE") print("! Failed to render command") print(f"! error={decoded_command}") # Record but do no further processing tests_failed += 1 test_status = False # Create the test directories, docker-compose file # and copy inputs... t_compose: Optional[Compose] = None job_command: str = "" project_path: str = "" if test_status: # The command must not contain new-lines. # So split then join the command. assert decoded_command job_command = "".join(decoded_command.splitlines()) print(f"> image={job_image}") print(f"> image-type={job_image_type}") print(f"> command={job_command}") # Create the project t_compose = Compose( collection, job, job_test_name, job_image, job_image_type, job_image_memory, job_image_cores, job_project_directory, job_working_directory, job_command, test_environment, args.run_as_user, ) project_path = t_compose.create() if input_files: # Copy the data into the test's project directory. # Data's expected to be found in the Job's 'inputs'. test_status = _copy_inputs(input_files, project_path) # Run the container if test_status and not args.dry_run: timeout_minutes: int = DEFAULT_TEST_TIMEOUT_M if "timeout-minutes" in job_definition.tests[job_test_name]: timeout_minutes = job_definition.tests[job_test_name]["timeout-minutes"] exit_code: int = 0 out: str = "" err: str = "" if job_image_type in [_IMAGE_TYPE_SIMPLE]: # Run the image container assert t_compose exit_code, out, err = t_compose.run(timeout_minutes) elif job_image_type in [_IMAGE_TYPE_NEXTFLOW]: # Run nextflow directly assert job_command assert project_path exit_code, out, err = _run_nextflow( job_command, project_path, timeout_minutes ) else: print("! FAILURE") print(f"! unsupported image-type ({job_image_type}") test_status = False if test_status: expected_exit_code: int = job_definition.tests[ job_test_name ].checks.exitCode if exit_code != expected_exit_code: print("! FAILURE") print( f"! exit_code={exit_code}" f" expected_exit_code={expected_exit_code}" ) print("! Test stdout follows...") print(out) print("! Test stderr follows...") print(err) test_status = False if args.verbose: print(out) # Inspect the results # (only if successful so far) if ( test_status and not args.dry_run and job_definition.tests[job_test_name].checks.outputs ): assert t_compose test_status = _check( t_compose, job_definition.tests[job_test_name].checks.outputs, job_image_fix_permissions, ) # Clean-up if test_status and not args.keep_results: assert t_compose t_compose.delete() # Count? if test_status: print("- SUCCESS") tests_passed += 1 else: tests_failed += 1 # Told to stop on first failure? if not test_status and args.exit_on_failure: break return tests_passed, tests_skipped, tests_ignored, tests_failed def _wipe() -> None: """Wipes the results of all tests.""" test_root: str = get_test_root() if os.path.isdir(test_root): shutil.rmtree(test_root) def arg_check_run_level(value: str) -> int: """A type checker for the argparse run-level.""" i_value = int(value) if i_value < 1: raise argparse.ArgumentTypeError("Minimum value is 1") if i_value > 100: raise argparse.ArgumentTypeError("Maximum value is 100") return i_value def arg_check_run_as_user(value: str) -> int: """A type checker for the argparse run-as-user.""" i_value = int(value) if i_value < 0: raise argparse.ArgumentTypeError("Minimum value is 0") if i_value > 65_535: raise argparse.ArgumentTypeError("Maximum value is 65535") return i_value # ----------------------------------------------------------------------------- # main # ----------------------------------------------------------------------------- def main() -> int: """The console script entry-point. Called when jote is executed or from __main__.py, which is used by the installed console script. """ # Build a command-line parser # and process the command-line... arg_parser: argparse.ArgumentParser = argparse.ArgumentParser( description="Data Manager Job Tester", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) arg_parser.add_argument( "-m", "--manifest", help="The manifest file.", default=_DEFAULT_MANIFEST, type=str, ) arg_parser.add_argument( "-c", "--collection", help="The Job collection to test. If not" " specified the Jobs in all collections" " will be candidates for testing.", ) arg_parser.add_argument( "-j", "--job", help="The Job to test. If specified the collection" " is required. If not specified all the Jobs" " that match the collection will be" " candidates for testing.", ) arg_parser.add_argument( "--image-tag", help="An image tag to use rather then the one defined in the job definition.", ) arg_parser.add_argument( "-t", "--test", help="A specific test to run. If specified the job" " is required. If not specified all the Tests" " that match the collection will be" " candidates for testing.", ) arg_parser.add_argument( "-r", "--run-level", help="The run-level of the tests you want to" " execute. All tests at or below this level" " will be executed, a value from 1 to 100", default=1, type=arg_check_run_level, ) arg_parser.add_argument( "-u", "--run-as-user", help="A user ID to run the tests as. If not set" " your user ID is used to run the test" " containers.", type=arg_check_run_as_user, ) arg_parser.add_argument( "-d", "--dry-run", action="store_true", help="Setting this flag will result in jote" " simply parsing the Job definitions" " but not running any of the tests." " It is can be used to check the syntax of" " your definition file and its test commands" " and data.", ) arg_parser.add_argument( "-k", "--keep-results", action="store_true", help="Normally all material created to run each" " test is removed when the test is" " successful", ) arg_parser.add_argument( "-v", "--verbose", action="store_true", help="Displays test stdout" ) arg_parser.add_argument( "--version", action="store_true", help="Displays jote version" ) arg_parser.add_argument( "-x", "--exit-on-failure", action="store_true", help="Normally jote reports test failures but" " continues with the next test." " Setting this flag will force jote to" " stop when it encounters the first failure", ) arg_parser.add_argument( "-s", "--skip-lint", action="store_true", help="Normally jote runs the job definition" " files against the prevailing lint" " configuration of the repository under test." " Using this flag skips that step", ) arg_parser.add_argument( "-w", "--wipe", action="store_true", help="Wipe does nto run any tests, it simply" " wipes the repository clean of jote" " test material. It would be wise" " to run this once you have finished testing." " Using this negates the effect of any other" " option.", ) arg_parser.add_argument( "-a", "--allow-no-tests", action="store_true", help="Normally jote expects to run tests" " and if you have no tests jote will fail." " To prevent jote complaining about the lack" " of tests you can use this option.", ) args: argparse.Namespace = arg_parser.parse_args() # If a version's been asked for act on it and then leave if args.version: print(_VERSION) return 0 if args.test and args.job is None: arg_parser.error("--test requires --job") if args.job and args.collection is None: arg_parser.error("--job requires --collection") if args.wipe and args.keep_results: arg_parser.error("Cannot use --wipe and --keep-results") # Args are OK if we get here. total_passed_count: int = 0 total_skipped_count: int = 0 total_ignore_count: int = 0 total_failed_count: int = 0 # Check CWD if not _check_cwd(): print("! FAILURE") print("! The directory does not look correct") arg_parser.error("Done (FAILURE)") # Told to wipe? # If so wipe, and leave. if args.wipe: _wipe() print("Done [Wiped]") return 0 print(f'# Using manifest "{args.manifest}"') # Load all the files we can and then run the tests. job_definitions, num_tests = _load(args.manifest, args.skip_lint) if num_tests < 0: print("! FAILURE") print("! Definition file has failed yamllint") arg_parser.error("Done (FAILURE)") msg: str = "test" if num_tests == 1 else "tests" print(f"# Found {num_tests} {msg}") if args.collection: print(f'# Limiting to Collection "{args.collection}"') if args.job: print(f'# Limiting to Job "{args.job}"') if args.test: print(f'# Limiting to Test "{args.test}"') if job_definitions: # There is at least one job-definition with a test # Now process all the Jobs that have tests... for job_definition in job_definitions: # If a collection's been named, # skip this file if it's not the named collection collection: str = job_definition.collection if args.collection and not args.collection == collection: continue for job_name in job_definition.jobs: # If a Job's been named, # skip this test if the job does not match if args.job and not args.job == job_name: continue if job_definition.jobs[job_name].tests: num_passed, num_skipped, num_ignored, num_failed = _test( args, job_definition.definition_filename, collection, job_name, job_definition.jobs[job_name], ) total_passed_count += num_passed total_skipped_count += num_skipped total_ignore_count += num_ignored total_failed_count += num_failed # Break out of this loop if told to stop on failures if num_failed > 0 and args.exit_on_failure: break # Break out of this loop if told to stop on failures if num_failed > 0 and args.exit_on_failure: break # Success or failure? # It's an error to find no tests. print(" ---") dry_run: str = "[DRY RUN]" if args.dry_run else "" summary: str = ( f"passed={total_passed_count}" f" skipped={total_skipped_count}" f" ignored={total_ignore_count}" f" failed={total_failed_count}" ) failed: bool = False if total_failed_count: arg_parser.error(f"Done (FAILURE) {summary} {dry_run}") failed = True elif total_passed_count == 0 and not args.allow_no_tests: arg_parser.error( f"Done (FAILURE) {summary}" f" (at least one test must pass)" f" {dry_run}" ) failed = True else: print(f"Done (OK) {summary} {dry_run}") # Automatically wipe. # If there have been no failures # and not told to keep directories. if total_failed_count == 0 and not args.keep_results: _wipe() return 1 if failed else 0 # ----------------------------------------------------------------------------- # MAIN # ----------------------------------------------------------------------------- if __name__ == "__main__": _RET_VAL: int = main() if _RET_VAL != 0: sys.exit(_RET_VAL)

35.183367

100

0.584741

4,265

35,113

4.637749

0.124033

0.034176

0.012235

0.016987

0.277199

0.207634

0.14909

0.108443

0.088827

0.076188

0

0.003386

0.318771

35,113

997

101

35.218656

0.823537

0.183123

0

0.245199

0

0.196027

0.023108

0

0.023634

1

0.023634

false

0.013294

0.022157

0

0.100443

0.134417

0

null

0

null

0

1

0

61f9d61ddf16dfe982de5cd443717f5e39b05a82

7,027

py

Python

transforms/waveform.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

6

2021-02-18T05:18:17.000Z

2022-02-19T02:49:32.000Z

transforms/waveform.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

null

transforms/waveform.py

koukyo1994/kaggle-rfcx

c3573d014d99312b58882e7b939de6c1055129b1

[ "MIT" ]

2

2021-02-18T11:31:50.000Z

2022-02-19T02:49:07.000Z

import colorednoise as cn import librosa import numpy as np def get_waveform_transforms(config: dict, phase: str): transforms = config.get("transforms") if transforms is None: return None else: if transforms[phase] is None: return None trns_list = [] for trns_conf in transforms[phase]: trns_name = trns_conf["name"] trns_params = {} if trns_conf.get("params") is None else trns_conf["params"] if globals().get(trns_name) is not None: trns_cls = globals()[trns_name] trns_list.append(trns_cls(**trns_params)) if len(trns_list) > 0: return Compose(trns_list) else: return None class Compose: def __init__(self, transforms: list): self.transforms = transforms def __call__(self, y: np.ndarray): for trns in self.transforms: y = trns(y) return y class OneOf: def __init__(self, transforms: list): self.transforms = transforms def __call__(self, y: np.ndarray): n_trns = len(self.transforms) trns_idx = np.random.choice(n_trns) trns = self.transforms[trns_idx] y = trns(y) return y class AudioTransform: def __init__(self, always_apply=False, p=0.5): self.always_apply = always_apply self.p = p def __call__(self, y: np.ndarray): if self.always_apply: return self.apply(y) else: if np.random.rand() < self.p: return self.apply(y) else: return y def apply(self, y: np.ndarray): raise NotImplementedError class Normalize: def __call__(self, y: np.ndarray): max_vol = np.abs(y).max() y_vol = y * 1 / max_vol return np.asfortranarray(y_vol) class NewNormalize: def __call__(self, y: np.ndarray): y_mm = y - y.mean() return y_mm / y_mm.abs().max() class LibrosaNormalize: def __call__(self, y: np.ndarray): return librosa.util.normalize(y) class GaussianNoiseSNR(AudioTransform): def __init__(self, always_apply=False, p=0.5, min_snr=5.0, max_snr=20.0, **kwargs): super().__init__(always_apply, p) self.min_snr = min_snr self.max_snr = max_snr def apply(self, y: np.ndarray, **params): snr = np.random.uniform(self.min_snr, self.max_snr) a_signal = np.sqrt(y ** 2).max() a_noise = a_signal / (10 ** (snr / 20)) white_noise = np.random.randn(len(y)) a_white = np.sqrt(white_noise ** 2).max() augmented = (y + white_noise * 1 / a_white * a_noise).astype(y.dtype) return augmented class PinkNoiseSNR(AudioTransform): def __init__(self, always_apply=False, p=0.5, min_snr=5.0, max_snr=20.0, **kwargs): super().__init__(always_apply, p) self.min_snr = min_snr self.max_snr = max_snr def apply(self, y: np.ndarray, **params): snr = np.random.uniform(self.min_snr, self.max_snr) a_signal = np.sqrt(y ** 2).max() a_noise = a_signal / (10 ** (snr / 20)) pink_noise = cn.powerlaw_psd_gaussian(1, len(y)) a_pink = np.sqrt(pink_noise ** 2).max() augmented = (y + pink_noise * 1 / a_pink * a_noise).astype(y.dtype) return augmented class PitchShift(AudioTransform): def __init__(self, always_apply=False, p=0.5, max_steps=5, sr=32000): super().__init__(always_apply, p) self.max_steps = max_steps self.sr = sr def apply(self, y: np.ndarray, **params): n_steps = np.random.randint(-self.max_steps, self.max_steps) augmented = librosa.effects.pitch_shift(y, sr=self.sr, n_steps=n_steps) return augmented class Identity(AudioTransform): def __init__(self, always_apply=False, p=0.5): super().__init__(always_apply=always_apply, p=p) def apply(self, y: np.ndarray, **params): return y class PitchUp(AudioTransform): def __init__(self, always_apply=False, p=0.5, max_steps=5, sr=32000): super().__init__(always_apply=always_apply, p=p) self.max_steps = max_steps self.sr = sr def apply(self, y: np.ndarray, **params): n_steps = np.random.randint(0, self.max_steps) augmented = librosa.effects.pitch_shift(y, sr=self.sr, n_steps=n_steps) return augmented class PitchDown(AudioTransform): def __init__(self, always_apply=False, p=0.5, max_steps=5, sr=32000): super().__init__(always_apply=always_apply, p=p) self.max_steps = max_steps self.sr = sr def apply(self, y: np.ndarray, **params): n_steps = np.random.randint(-self.max_steps, 0) augmented = librosa.effects.pitch_shift(y, sr=self.sr, n_steps=n_steps) return augmented class TimeStretch(AudioTransform): def __init__(self, always_apply=False, p=0.5, max_rate=1.2): super().__init__(always_apply, p) self.max_rate = max_rate def apply(self, y: np.ndarray, **params): rate = np.random.uniform(0, self.max_rate) augmented = librosa.effects.time_stretch(y, rate) return augmented class TimeShift(AudioTransform): def __init__(self, always_apply=False, p=0.5, max_shift_second=2, sr=32000, padding_mode="replace"): super().__init__(always_apply, p) assert padding_mode in ["replace", "zero"], "`padding_mode` must be either 'replace' or 'zero'" self.max_shift_second = max_shift_second self.sr = sr self.padding_mode = padding_mode def apply(self, y: np.ndarray, **params): shift = np.random.randint(-self.sr * self.max_shift_second, self.sr * self.max_shift_second) augmented = np.roll(y, shift) if self.padding_mode == "zero": if shift > 0: augmented[:shift] = 0 else: augmented[shift:] = 0 return augmented class VolumeControl(AudioTransform): def __init__(self, always_apply=False, p=0.5, db_limit=10, mode="uniform"): super().__init__(always_apply, p) assert mode in ["uniform", "fade", "fade", "cosine", "sine"], \ "`mode` must be one of 'uniform', 'fade', 'cosine', 'sine'" self.db_limit = db_limit self.mode = mode def apply(self, y: np.ndarray, **params): db = np.random.uniform(-self.db_limit, self.db_limit) if self.mode == "uniform": db_translated = 10 ** (db / 20) elif self.mode == "fade": lin = np.arange(len(y))[::-1] / (len(y) - 1) db_translated = 10 ** (db * lin / 20) elif self.mode == "cosine": cosine = np.cos(np.arange(len(y)) / len(y) * np.pi * 2) db_translated = 10 ** (db * cosine / 20) else: sine = np.sin(np.arange(len(y)) / len(y) * np.pi * 2) db_translated = 10 ** (db * sine / 20) augmented = y * db_translated return augmented

31.231111

104

0.605237

974

7,027

4.106776

0.13347

0.06875

0.028

0.056

0.56375

0.53925

0.47825

0.45725

0.414

0

0.019654

0.268678

7,027

224

105

31.370536

0.758708

0

0.479042

0

0.028889

0

0.011976

1

0.173653

false

0

0.017964

0.011976

0.407186

0

null

0

null

0

1

0

61fa91668b7e930a4d4c6429b8910bfdb88b86e5

1,095

py

Python

plyplus/test/test_trees.py

rubycandy/test-plyplus

ced9377e6c26dcf308dd9f480411af9c8dbe9c56

[ "MIT" ]

169

2015-01-16T12:48:23.000Z

2021-12-09T16:00:13.000Z

plyplus/test/test_trees.py

rubycandy/test-plyplus

ced9377e6c26dcf308dd9f480411af9c8dbe9c56

[ "MIT" ]

26

2015-01-23T16:30:28.000Z

2018-07-07T09:14:18.000Z

plyplus/test/test_trees.py

rubycandy/test-plyplus

ced9377e6c26dcf308dd9f480411af9c8dbe9c56

[ "MIT" ]

53

2015-01-22T20:20:10.000Z

2021-12-05T13:39:57.000Z

from __future__ import absolute_import import unittest import logging import copy import pickle from plyplus.plyplus import STree logging.basicConfig(level=logging.INFO) class TestSTrees(unittest.TestCase): def setUp(self): self.tree1 = STree('a', [STree(x, y) for x, y in zip('bcd', 'xyz')]) def test_deepcopy(self): assert self.tree1 == copy.deepcopy(self.tree1) def test_parents(self): s = copy.deepcopy(self.tree1) s.calc_parents() for i, x in enumerate(s.tail): assert x.parent() == s assert x.index_in_parent == i def test_pickle(self): s = copy.deepcopy(self.tree1) data = pickle.dumps(s) assert pickle.loads(data) == s def test_pickle_with_parents(self): s = copy.deepcopy(self.tree1) s.calc_parents() data = pickle.dumps(s) s2 = pickle.loads(data) assert s2 == s for i, x in enumerate(s2.tail): assert x.parent() == s2 assert x.index_in_parent == i if __name__ == '__main__': unittest.main()

24.886364

76

0.613699

149

1,095

4.342282

0.315436

0.083462

0.098918

0.12983

0.293663

0.244204

0.139104

0

0.012531

0.271233

1,095

43

77

25.465116

0.798246

0

0.272727

0

0.013699

0

0.212121

1

0.151515

false

0

0.181818

0

0.363636

0

null

0

null

0

1

0

61fae1b5b671ac52f912549b4f9c186cb38b0495

1,563

py

Python

misaligned.py

clean-code-craft-tcq-2/test-failer-in-py-yashaswin-mayya

1861f2db11a508e9c1e2f7ce351d11d87c0c734c

[ "MIT" ]

null

misaligned.py

clean-code-craft-tcq-2/test-failer-in-py-yashaswin-mayya

1861f2db11a508e9c1e2f7ce351d11d87c0c734c

[ "MIT" ]

null

misaligned.py

clean-code-craft-tcq-2/test-failer-in-py-yashaswin-mayya

1861f2db11a508e9c1e2f7ce351d11d87c0c734c

[ "MIT" ]

null

MAJOR_COLORS = ["White", "Red", "Black", "Yellow", "Violet"] MINOR_COLORS = ["Blue", "Orange", "Green", "Brown", "Slate"] def get_color_from_pair_number(pair_number): zero_based_pair_number = pair_number - 1 major_index = zero_based_pair_number // len(MINOR_COLORS) minor_index = zero_based_pair_number % len(MINOR_COLORS) return MAJOR_COLORS[major_index], MINOR_COLORS[minor_index] def print_color_map(): for i in range(5): for j in range(5): pair_number = i * 5 + j +1 #1 is added to account for zero error as list index begins with 0 print(f'{pair_number} | {get_color_from_pair_number(pair_number)[0]} | {get_color_from_pair_number(pair_number)[1]}') print_color_map() def test_color_map(test_paid_number, expected_major_colour, expected_minor_color): assert(get_color_from_pair_number(test_paid_number) == (expected_major_colour, expected_minor_color)) #testing each of 25 color pairs if __name__ == '__main__': print_color_map() test_color_map(1, 'White', 'Blue') test_color_map(2, 'White', 'Orange') test_color_map(3, 'White', 'Green') test_color_map(4, 'White', 'Brown') test_color_map(5, 'White', 'Slate') test_color_map(6, 'Red', 'Blue') test_color_map(7, 'Red', 'Orange') test_color_map(8, 'Red', 'Green') test_color_map(9, 'Red', 'Brown') test_color_map(10, 'Red', 'Slate') test_color_map(11, 'Black', 'Blue') test_color_map(12, 'Black', 'Orange') test_color_map(13, 'Black', 'Green') print("All is well (maybe!)\n")

36.348837

129

0.685861

237

1,563

4.122363

0.303797

0.139202

0.171955

0.065507

0.325486

0.28045

0.18219

0.104401

0

0.021622

0.171465

1,563

42

130

37.214286

0.732819

0.060141

0

0.064516

0

0.209413

0.060027

0

0.032258

1

0.096774

false

0

0.129032

0.16129

0

null

0

null

0

1

0

61fe5553a131ad8494dec157c4505511e27beecb

611

py

Python

examples/embed_cmd.py

bentettmar/discord.py-self_embed

4253ea7977b17972de2e15de3606a183f70b22b0

[ "MIT" ]

2

2022-03-31T04:06:05.000Z

2022-03-31T16:39:40.000Z

examples/embed_cmd.py

bentettmar/discord.py-self_embed

4253ea7977b17972de2e15de3606a183f70b22b0

[ "MIT" ]

3

2022-03-29T11:58:16.000Z

2022-03-31T16:41:13.000Z

examples/embed_cmd.py

bentettmar/discord.py-self_embed

4253ea7977b17972de2e15de3606a183f70b22b0

[ "MIT" ]

null

import discord_self_embed from discord.ext import commands bot = commands.Bot(command_prefix=".", self_bot=True) @bot.event async def on_ready(): print("ready") @bot.command(name="embed") async def embed_cmd(ctx): embed = discord_self_embed.Embed("discord.py-self_embed", description="A way for selfbots to send embeds again.", colour="ff0000", url="https://github.com/bentettmar/discord.py-self_embed") embed.set_author("Ben Tettmar") await ctx.send(embed.generate_url(hide_url=True)) # You can also send the embed converted to a string which will auto hide the url. bot.run("TOKEN_HERE")

33.944444

193

0.749591

97

611

4.57732

0.57732

0.081081

0.072072

0.081081

0

0.007463

0.12275

611

17

194

35.941176

0.820896

0.129296

0

0.283019

0.039623

0

1

0

false

0

0.166667

0

0.166667

0.083333

0

null

0

null

0

1

0

1101b9ca063e23e2fd57ae664425f377c0723f09

8,823

py

Python

analysis.py

liunx/lmms

ea54f64934d90887a38446ef02ed2baed91548db

[ "MIT" ]

null

analysis.py

liunx/lmms

ea54f64934d90887a38446ef02ed2baed91548db

[ "MIT" ]

null

analysis.py

liunx/lmms

ea54f64934d90887a38446ef02ed2baed91548db

[ "MIT" ]

null

import re import copy from operator import itemgetter import music21 as m21 class Core: meter_len = 192 notes = {'C': 60, 'D': 62, 'E': 64, 'F': 65, 'G': 67, 'A': 69, 'B': 71} percussion = { 35: 'AcousticBassDrum', 36: 'BassDrum1', 37: 'SideStick', 38: 'AcousticSnare', 39: 'HandClap', 40: 'ElectricSnare', 41: 'LowFloorTom', 42: 'ClosedHiHat', 43: 'HighFloorTom', 44: 'PedalHi-Hat', 45: 'LowTom', 46: 'OpenHi-Hat', 47: 'Low-MidTom', 48: 'Hi-MidTom', 49: 'CrashCymbal1', 50: 'HighTom', 51: 'RideCymbal1', 52: 'ChineseCymbal', 53: 'RideBell', 54: 'Tambourine', 55: 'SplashCymbal', 56: 'Cowbell', 57: 'CrashCymbal2', 58: 'Vibraslap', 59: 'RideCymbal2', 60: 'HiBongo', 61: 'LowBongo', 62: 'MuteHiConga', 63: 'OpenHiConga', 64: 'LowConga', 65: 'HighTimbale', 66: 'LowTimbale', 67: 'HighAgogo', 68: 'LowAgogo', 69: 'Cabasa', 70: 'Maracas', 71: 'ShortWhistle', 72: 'LongWhistle', 73: 'ShortGuiro', 74: 'LongGuiro', 75: 'Claves', 76: 'HiWoodBlock', 77: 'LowWoodBlock', 78: 'MuteCuica', 79: 'OpenCuica', 80: 'MuteTriangle', 81: 'OpenTriangle'} def __init__(self, staff, data): self.total_len = 0 self.noteset = [] self.roman_numerals = [] self.instructions = {} self.styles = {} self.emotions = {} self.time_signs = {0: staff['timesign']} self.keys = {0: staff['key']} self.analysis(copy.deepcopy(data)) def show_noteset(self): print("==== total notes ====") for i in self.noteset: print(i) def note_midi(self, note): step = note[0].upper() midi = self.notes[step] if note[0].islower(): midi += 12 * note.count("'") else: midi -= 12 * note.count(step) if note.count('-') > 0: alter = note.count('-') midi -= alter elif note.count('#') > 0: alter = note.count('#') midi += alter return midi def note_len(self, note): num = 0 dot = 0 # Rest & Notation m = re.match(r'([a-grA-GR\'#-]+)(\d+)([.]*)', note) if not m: return 0 num = int(m.group(2)) dot = m.group(3).count('.') n1 = self.meter_len / num curr = n1 for _ in range(dot): n1 += curr / 2 curr = curr / 2 return n1 def to_note(self, note, offset): d = {} d['offset'] = offset midi = self.note_midi(note) d['midi'] = midi if note.count('~') > 0: d['tie'] = 1 else: d['tie'] = 0 return d def is_note(self, note): m = re.match(r'[a-grA-GR\'#-]+\d+', note) if not m: return False return True def divide_keyword(self, n, offset): if n.startswith('!!'): d = {'offset': offset, 'instruction': n[2:]} self.instructions[offset] = n[2:] elif n.startswith('$$'): self.styles[offset] = n[2:] elif n.startswith('!'): d = {'offset': offset, 'roman_numeral': n[1:]} self.roman_numerals.append(d) elif n.startswith('*'): self.emotions[offset] = n[1:] else: raise ValueError("Unknown keyword: {}!".format(n)) def to_noteset(self, data): offset = 0 _len = 0 for n in data: # chord | trip if type(n) == list: if n[0] == 'chord': _len = self.note_len(n[-1]) for _n in n[1:]: d = self.to_note(_n, offset) d['len'] = _len self.noteset.append(d) offset += _len elif n[0] == 'tripchord': _len = self.note_len(n[-1]) * 2 / 3 for _n in n[1:]: d = self.to_note(_n, offset) d['len'] = _len self.noteset.append(d) offset += _len elif n[0] == 'trip': _len = self.note_len(n[-1]) * 2 / 3 for _n in n[1:]: if _n[0] != 'r': d = self.to_note(_n, offset) d['len'] = _len self.noteset.append(d) offset += _len else: raise ValueError("Unknown keyword: {}!".format(n[0])) else: # skip keywords if not self.is_note(n): self.divide_keyword(n, offset) continue # skip Rest note if n[0].upper() == 'R': _len = self.note_len(n) offset += _len continue d = self.to_note(n, offset) _len = self.note_len(n) offset += _len d['len'] = _len self.noteset.append(d) self.total_len = offset def _tie(self, nset, i): _len = len(self.noteset) while i < _len: _nset = self.noteset[i] if _nset['midi'] == nset['midi'] and (nset['offset'] + nset['len']) == _nset['offset']: if _nset['tie'] > 0: self._tie(_nset, i) nset['tie'] = 0 nset['len'] += _nset['len'] _nset['drop'] = 1 else: nset['tie'] = 0 nset['len'] += _nset['len'] _nset['drop'] = 1 break i += 1 def update_tie(self): _noteset = [] _noteset_len = len(self.noteset) i = 0 while i < _noteset_len: nset = self.noteset[i] if nset.get('drop'): i += 1 continue if nset['tie'] > 0: self._tie(nset, i) i += 1 for i in self.noteset: if i.get('drop'): continue _noteset.append(i) self.noteset = _noteset def update_roman_numeral(self): # get the total length of notesets if not self.total_len > 0: return _len = len(self.roman_numerals) if _len == 0: return i = 0 while i < _len: rn = self.roman_numerals[i] if rn['roman_numeral'] == 'N': rn['drop'] = 1 i += 1 continue if (i + 1) == _len: rn['len'] = self.total_len - rn['offset'] break _rn = self.roman_numerals[i + 1] rn['len'] = _rn['offset'] - rn['offset'] i += 1 # rm dropped set l = [] for i in self.roman_numerals: if 'drop' in i: continue l.append(i) self.roman_numerals = l def analysis(self, data): raise NotImplementedError class Analysis(Core): def __init__(self, staff, data): super().__init__(staff, data) def reform_roman_numeral(self): d = {} for rn in self.roman_numerals: d[rn['offset']] = rn return d def analysis(self, data): self.to_noteset(data) self.update_tie() self.update_roman_numeral() def get_result(self): d = {} d['noteset'] = self.noteset d['styles'] = self.styles d['roman_numerals'] = self.reform_roman_numeral() d['emotions'] = self.emotions d['instructions'] = self.instructions d['total_len'] = self.total_len d['time_signs'] = self.time_signs d['keys'] = self.keys return d if __name__ == "__main__": data = ['C4~', ['chord', 'E4~', 'G4~'], [ 'chord', 'E4~', 'G4~'], ['chord', 'E4', 'G4']] data2 = ['C4', ['trip', 'C4', 'E4', 'G4']] data3 = ['C4~', 'C4', 'E4~', 'E4'] data4 = ['CC8', 'r8', 'DD8', 'CC8', 'CC8', 'r8', 'DD8', 'r8'] data5 = [ 'c2', '!up', '!good', 'c4.', 'c8', 'c2', '!happy', 'c2', 'c1~', 'c1', 'G2', 'c4.', 'c8', 'c1', 'G2', 'd4.', 'B8', 'c1', 'G2', 'c4.', 'e8', 'g2', 'e4', 'c4', 'd2', 'c4.', 'd8', 'd1', 'G2', 'c4.', 'c8', 'c1', 'G2', 'd4.', 'B8', 'c1', 'G2', 'c4.', 'e8', 'g2', 'e4', 'c4', 'f2', 'e4.', 'd8', 'c1', 'r1', 'r1', 'r1', 'r1'] data6 = ['!I', 'R1', '!II', 'R1', '!III', '!IV', '!V', '!VI', '!VII'] data7 = ['$$pop', 'r1', '!I', 'r1', '*happy', '!IV', '!V7', '!i', '!Isus4', '!!ts_44', '!!to_D'] #rym = Rhythm(data) #bt = Beats(data4) ml = Melody({}, data7) # ml.show_noteset()

33.804598

115

0.438286

999

8,823

3.744745

0.264264

0.028067

0.036354

0.027265

0.262229

0.221866

0.193264

0.139

0.109596

0

0.050727

0.39227

8,823

260

116

33.934615

0.64696

0.017908

0

0.295652

0

0.129752

0

1

0.069565

false

0

0.017391

0

0.152174

0.008696

0

null

0

null

0

1

0

11025303e524cbae387748d4c806d2a09276590a

6,302

py

Python

tests/server/utils.py

csadorf/aiida-optimade

99ee1113cfc109a40a83bb43af8d07ce7e1601e6

[ "MIT" ]

null

tests/server/utils.py

csadorf/aiida-optimade

99ee1113cfc109a40a83bb43af8d07ce7e1601e6

[ "MIT" ]

null

tests/server/utils.py

csadorf/aiida-optimade

99ee1113cfc109a40a83bb43af8d07ce7e1601e6

[ "MIT" ]

null

# pylint: disable=no-name-in-module,too-many-arguments import json import re import typing from urllib.parse import urlparse import warnings from requests import Response from fastapi.testclient import TestClient from pydantic import BaseModel import pytest from starlette import testclient from optimade import __api_version__ from optimade.models import ResponseMeta class OptimadeTestClient(TestClient): """Special OPTIMADE edition of FastAPI's (Starlette's) TestClient This is needed, since `urllib.parse.urljoin` removes paths from the passed `base_url`. So this will prepend any requests with the MAJOR OPTIMADE version path. """ def __init__( self, app: typing.Union[testclient.ASGI2App, testclient.ASGI3App], base_url: str = "http://example.org", raise_server_exceptions: bool = True, root_path: str = "", version: str = "", ) -> None: super(OptimadeTestClient, self).__init__( app=app, base_url=base_url, raise_server_exceptions=raise_server_exceptions, root_path=root_path, ) if version: if not version.startswith("v"): version = f"/v{version}" if re.match(r"v[0-9](.[0-9]){0,2}", version) is None: warnings.warn( f"Invalid version passed to client: '{version}'. " f"Will use the default: '/v{__api_version__.split('.')[0]}'" ) version = f"/v{__api_version__.split('.')[0]}" self.version = version def request( # pylint: disable=too-many-locals self, method: str, url: str, params: testclient.Params = None, data: testclient.DataType = None, headers: typing.MutableMapping[str, str] = None, cookies: testclient.Cookies = None, files: testclient.FileType = None, auth: testclient.AuthType = None, timeout: testclient.TimeOut = None, allow_redirects: bool = None, proxies: typing.MutableMapping[str, str] = None, hooks: typing.Any = None, stream: bool = None, verify: typing.Union[bool, str] = None, cert: typing.Union[str, typing.Tuple[str, str]] = None, json: typing.Any = None, # pylint: disable=redefined-outer-name ) -> Response: if ( re.match(r"/?v[0-9](.[0-9]){0,2}/", url) is None and not urlparse(url).scheme ): while url.startswith("/"): url = url[1:] url = f"{self.version}/{url}" return super().request( method=method, url=url, params=params, data=data, headers=headers, cookies=cookies, files=files, auth=auth, timeout=timeout, allow_redirects=allow_redirects, proxies=proxies, hooks=hooks, stream=stream, verify=verify, cert=cert, json=json, ) class EndpointTests: """Base class for common tests of endpoints""" request_str: str = None response_cls: BaseModel = None response = None json_response = None @pytest.fixture(autouse=True) def get_response(self, client): """Get response from client""" self.response = client.get(self.request_str) self.json_response = self.response.json() yield self.response = None self.json_response = None @staticmethod def check_keys(keys: list, response_subset: typing.Iterable): """Utility function to help validate dict keys""" for key in keys: assert ( key in response_subset ), f"{key} missing from response {response_subset}" def test_response_okay(self): """Make sure the response was successful""" assert self.response.status_code == 200, ( f"Request to {self.request_str} failed: " f"{json.dumps(self.json_response, indent=2)}" ) def test_meta_response(self): """General test for `meta` property in response""" assert "meta" in self.json_response meta_required_keys = ResponseMeta.schema()["required"] meta_optional_keys = list( set(ResponseMeta.schema()["properties"].keys()) - set(meta_required_keys) ) implemented_optional_keys = ["data_available", "implementation"] self.check_keys(meta_required_keys, self.json_response["meta"]) self.check_keys(implemented_optional_keys, meta_optional_keys) self.check_keys(implemented_optional_keys, self.json_response["meta"]) def test_serialize_response(self): """General test for response JSON and pydantic model serializability""" assert self.response_cls is not None, "Response class unset for this endpoint" self.response_cls(**self.json_response) # pylint: disable=not-callable def client_factory(): """Return TestClient for OPTIMADE server""" from aiida_optimade.main import APP def inner( version: str = None, raise_server_exceptions: bool = True ) -> OptimadeTestClient: if version: return OptimadeTestClient( APP, base_url="http://example.org", version=version, raise_server_exceptions=raise_server_exceptions, ) return OptimadeTestClient( APP, base_url="http://example.org", raise_server_exceptions=raise_server_exceptions, ) return inner class NoJsonEndpointTests: """A simplified mixin class for tests on non-JSON endpoints.""" request_str: str = None response_cls: BaseModel = None response: Response = None @pytest.fixture(autouse=True) def get_response(self, client): """Get response from client""" self.response = client.get(self.request_str) yield self.response = None def test_response_okay(self): """Make sure the response was successful""" assert ( self.response.status_code == 200 ), f"Request to {self.request_str} failed: {self.response.content}"

32.65285

86

0.60822

704

6,302

5.292614

0.261364

0.032206

0.045089

0.020934

0.31562

0.254697

0.214707

0.190553

0.164788

0

0.005367

0.290384

6,302

192

87

32.822917

0.827818

0.125674

0

0.209459

0

0.100663

0.0265

0

0.033784

1

0.074324

false

0.006757

0.087838

0

0.256757

0

null

0

null

0

1

0

11026c0c5eee347310533201a00163d72346ee00

3,673

py

Python

super_topic/main.py

susmote/WeiboTools

659232b4525bcbedf350da1127d382ff6c6e9e71

[ "MIT" ]

3

2018-11-11T22:07:23.000Z

2019-03-08T08:20:31.000Z

super_topic/main.py

susmote/WeiboTools

659232b4525bcbedf350da1127d382ff6c6e9e71

[ "MIT" ]

null

super_topic/main.py

susmote/WeiboTools

659232b4525bcbedf350da1127d382ff6c6e9e71

[ "MIT" ]

1

2021-08-31T06:44:54.000Z

# -*- coding: utf-8 -*- """ Created on 2018/11/5 @author: susmote """ import time import requests import json # 查看自己关注的超话 if __name__ == '__main__': username = input("请输入用户名: ") password = input("请输入密码: ") login_url = "https://passport.weibo.cn/sso/login" headers = { "Referer": "https://passport.weibo.cn/signin/login?entry=mweibo&res=wel&wm=3349&r=https%3A%2F%2Fm.weibo.cn%2F" } session = requests.session() login_post_data = { "username": username, "password": password, "savestate": "1", "r": "https://m.weibo.cn/", "ec": "0", "pagerefer": "https://m.weibo.cn/login?backURL=https%253A%252F%252Fm.weibo.cn%252F", "entry": "mweibo", "wentry": "", "loginfrom": "", "client_id": "", "code": "", "qq": "", "mainpageflag": "1", "hff": "", "hfp": "" } login_page_res = session.post(login_url, data=login_post_data, headers=headers) login_page_res_json = json.loads(login_page_res.text) judge_login_res = session.get("https://m.weibo.cn/api/config").text judge_login_res_json = json.loads(judge_login_res) cookie_str = '' if judge_login_res_json["data"]["login"] == True: print(1, "自动登录成功") for key in list(session.cookies.get_dict().keys()): # 遍历字典 cookie_str += (key + '=' + session.cookies.get_dict()[key] + ';') # 将键值对拿出来拼接一下 else: if login_page_res_json["msg"] == "用户名或密码错误": print("用户名或密码错误") exit() else: print(login_page_res_json) print("不能直接登录,需要进行手势验证码验证") exit() followtopic_list = [] url = "https://m.weibo.cn/api/container/getIndex?containerid=100803_-_followsuper" session = requests.session() headers = { "Host": "m.weibo.cn", "Referer": "https://m.weibo.cn", "Cookie": cookie_str } followtopic_res = session.get(url, headers=headers) followtopic_res_json = json.loads(followtopic_res.text) for i in range(0, len(followtopic_res_json["data"]["cards"][0]["card_group"])): if followtopic_res_json["data"]["cards"][0]["card_group"][i]["card_type"] == "8": followtopic_list.append(followtopic_res_json["data"]["cards"][0]["card_group"][i]) if followtopic_res_json["data"]["cardlistInfo"]["since_id"] != "": followtopic_url = url+"&since_id="+ followtopic_res_json["data"]["cardlistInfo"]["since_id"] res = session.get(followtopic_url, headers=headers) res_json = json.loads(res.text) for i in range(0, len(res_json["data"]["cards"][0]["card_group"])-1): if res_json["data"]["cards"][0]["card_group"][i]["card_type"] == "8": followtopic_list.append(res_json["data"]["cards"][0]["card_group"][i]) for i in range(0, len(followtopic_list)): print(followtopic_list[i]["title_sub"]) st_url = "https://m.weibo.cn/api/config" login_data = session.get(st_url, headers=headers).text login_data_json = json.loads(login_data)["data"] postdata = { "st": login_data_json["st"] } if followtopic_list[i]["buttons"][0]["scheme"] == False: continue else: checkin_url = "https://m.weibo.cn"+str(followtopic_list[i]["buttons"][0]["scheme"]) print(checkin_url) res = session.post(checkin_url, data=postdata, headers=headers) res_json = json.loads(res.text) if res_json["ok"] == 1: print("签到成功 "+res_json["data"]["msg"]) else: print("签到失败 "+res_json)

37.865979

118

0.58263

453

3,673

4.505519

0.267108

0.065164

0.053895

0.044586

0.302793

0.292994

0.231259

0.165605

0.079863

0.055855

0

0.018949

0.238497

3,673

97

119

37.865979

0.710762

0.024231

0

0.142857

0

0.02381

0.238109

0

1

0

false

0.047619

0.035714

0

0.035714

0.095238

0

null

0

null

0

1

0

11028d4ec017320409e77b44e5459cd4e2c1cd81

1,163

py

Python

websupportsk_ddns/notifiers.py

JozefGalbicka/websupportsk-ddns

8fe1408121dc5f14f42e6603d9a50bcaa5afabee

[ "MIT" ]

2

2021-07-28T09:09:58.000Z

2021-07-28T10:28:45.000Z

websupportsk_ddns/notifiers.py

JozefGalbicka/websupportsk-ddns

8fe1408121dc5f14f42e6603d9a50bcaa5afabee

[ "MIT" ]

1

2021-11-14T11:31:38.000Z

2021-11-19T22:38:44.000Z

websupportsk_ddns/notifiers.py

JozefGalbicka/websupportsk-ddns

8fe1408121dc5f14f42e6603d9a50bcaa5afabee

[ "MIT" ]

null

import requests import logging logger = logging.getLogger(__name__) def send_notifications(notifiers, message): for notifier in notifiers: notifier.send_notification(message) class Pushover: def __init__(self, api_token, user_key): self.api_token = api_token self.user_key = user_key self.url = "https://api.pushover.net/1/messages.json" def send_notification(self, text): r = requests.post(self.url, data={ "token": self.api_token, "user": self.user_key, "message": text }) logger.debug(f"Pushover notification response: {r.text}") if "errors" in r.text: logger.error(f"Pushover error occured: {r.text}") class Gotify: def __init__(self, url, api_token): self.api_token = api_token self.url = f"http://{url}/message?token={api_token}" def send_notification(self, text): r = requests.post(self.url, data={ "message": text }) logger.debug(f"Gotify notification response: {r.text}") if "error" in r.text: logger.error(f"Gotify error occured: {r.text}")

29.075

65

0.618229

147

1,163

4.70068

0.285714

0.092619

0.069465

0.04631

0.416787

0.272069

0.147612

0

0.001161

0.259673

1,163

40

66

29.075

0.801394

0

0.322581

0

0.216495

0

1

0.16129

false

0

0.064516

0

0.290323

0

null

0

null

0

1

0

11070c63ba36e05b385352144090c398a2ed7415

15,806

py

Python

code/plotting/plot_lsst.py

modichirag/21cm_cleaning

1615fea4e2d617bb6ef00770a49698901227daa8

[ "MIT" ]

1

2019-08-27T10:05:41.000Z

code/plotting/plot_lsst.py

modichirag/21cm_cleaning

1615fea4e2d617bb6ef00770a49698901227daa8

[ "MIT" ]

null

code/plotting/plot_lsst.py

modichirag/21cm_cleaning

1615fea4e2d617bb6ef00770a49698901227daa8

[ "MIT" ]

null

#!/usr/bin/env python3 # # Plots the power spectra and Fourier-space biases for the HI. # import warnings from mpi4py import MPI rank = MPI.COMM_WORLD.rank #warnings.filterwarnings("ignore") if rank!=0: warnings.filterwarnings("ignore") import numpy as np import os, sys import matplotlib.pyplot as plt from pmesh.pm import ParticleMesh from scipy.interpolate import InterpolatedUnivariateSpline as ius from nbodykit.lab import BigFileMesh, BigFileCatalog, FFTPower from nbodykit.cosmology import Planck15, EHPower, Cosmology sys.path.append('../utils/') sys.path.append('../recon/') sys.path.append('../recon/cosmo4d/') from cosmo4d.pmeshengine import nyquist_mask from lab import mapbias as mapp from lab import mapnoise as mapn from lab import report as rp from lab import dg from getbiasparams import getbias import tools # from matplotlib import rc, rcParams, font_manager rcParams['font.family'] = 'serif' fsize = 12-1 fontmanage = font_manager.FontProperties(family='serif', style='normal', size=fsize, weight='normal', stretch='normal') font = {'family': fontmanage.get_family()[0], 'style': fontmanage.get_style(), 'weight': fontmanage.get_weight(), 'size': fontmanage.get_size(), } # import argparse parser = argparse.ArgumentParser() #parser.add_argument('-m', '--model', help='model name to use') parser.add_argument('-a', '--aa', help='scale factor', default=0.5000, type=float) parser.add_argument('-l', '--bs', help='boxsize', default=1024, type=float) parser.add_argument('-n', '--nmesh', help='nmesh', default=256, type=int) parser.add_argument('-t', '--angle', help='angle of the wedge', default=50, type=float) parser.add_argument('-k', '--kmin', help='kmin of the wedge', default=0.03, type=float) parser.add_argument( '--pp', help='upsample', default=1) args = parser.parse_args() figpath = './figs/' dpath = '../../data/' bs, nc, aa = args.bs, args.nmesh, args.aa nc2 = nc*2 zz = 1/aa- 1 kmin = args.kmin ang = args.angle if args.pp: pm = ParticleMesh(BoxSize=bs, Nmesh=[nc2, nc2, nc2]) else: pm = ParticleMesh(BoxSize=bs, Nmesh=[nc, nc, nc]) rank = pm.comm.rank ## def save2dphoto(Nmu=4, numd=1e-2, aa=None, scatter=False): if numd > 1e-2: print('Too high number density') sys.exit() num = int(numd*bs**3) if aa is None: aas = [0.3333, 0.2000, 0.1429] else: aas = [aa] for ia, aa in enumerate(aas): zz = 1/aa-1 sigz = lambda z : 120*((1+z)/5)**-0.5 ## cat = BigFileCatalog('/global/cscratch1/sd/chmodi/m3127/H1mass/highres/10240-9100/fastpm_%0.4f/Hcat-Numd-%04d/'%(aa, 1e-2*1e4)) if scatter: pos = cat['Position'][:num].compute() dz = np.random.normal(0, sigz(zz), size=pos[:, -1].size) pos[:, -1] += dz layout = pm.decompose(pos) hmesh = pm.paint(pos, layout=layout) else: pos = cat['Position'][:num].compute() layout = pm.decompose(pos) hmesh = pm.paint(pos, layout=layout) def tf(k): #Photoz smoothing kmesh = sum(ki ** 2 for ki in k)**0.5 kmesh[kmesh == 0] = 1 mumesh = k[2]/kmesh weights = np.exp(-kmesh**2 * mumesh**2 * sigz(zz)**2/2.) return weights hmesh /= hmesh.cmean() if not scatter: hmeshc = hmesh.r2c() hmeshc.apply(lambda k, v: nyquist_mask(tf(k), v) * v, out=Ellipsis) hmesh = hmeshc.c2r() ph = FFTPower(hmesh, mode='2d', Nmu=Nmu).power # for iw, wopt in enumerate(['opt', 'pess']): #for iw, wopt in enumerate(['opt']): for it, thopt in enumerate(['opt', 'pess', 'reas']): #for it, thopt in enumerate([ 'reas']): if rank == 0: print(aa, wopt, thopt) angle = np.round(mapn.wedge(zz, att=wopt, angle=True), 0) #dpath = '/global/cscratch1/sd/chmodi/m3127/21cm_cleaning/recon/fastpm_%0.4f/wedge_kmin%0.2f_ang%0.1f/'%(aa, 0.03, angle) dpath = '/global/cscratch1/sd/chmodi/m3127/21cm_cleaning/recon/fastpm_%0.4f/wedge_kmin%0.2f_%s/'%(aa, 0.03, wopt) dpath += 'L%04d-N%04d-R//thermal-%s-hex/ZA/opt_s999_h1massA_fourier_rsdpos/'%(bs, nc, thopt) if scatter: ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/photog-Numd%04d-Nmu%d/'%(wopt, thopt, numd*1e4, Nmu) else: ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/photo-Numd%04d-Nmu%d/'%(wopt, thopt, numd*1e4, Nmu) try: os.makedirs(ofolder) except: pass if rank == 0: print(ofolder) if args.pp: datapp = BigFileMesh(dpath+'/dataw_up/', 'mapp').paint() bpaths = [dpath+'upsample2/%d-0.00//best-fit'%nc2] + [dpath + 'upsample2/%d-0.00//%04d/fit_p/'%(nc2,i) for i in range(100, 50, -20)] else: datapp = BigFileMesh(dpath+'/dataw/', 'mapp').paint() bpaths = [dpath+'%d-0.00//best-fit'%nc] + [dpath + '%d-0.00//%04d/fit_p/'%(nc,i) for i in range(100, 50, -20)] for path in bpaths: if os.path.isdir(path): break if rank == 0: print(path) bfit = BigFileMesh(path, 'mapp').paint() pxrh = FFTPower(hmesh, second=bfit, mode='2d', Nmu=Nmu).power pxwh = FFTPower(hmesh, second=datapp, mode='2d', Nmu=Nmu).power fname = ofolder + 'photo-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' np.savetxt(fname, ph['power'].real) fname = ofolder + 'xdataw-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' np.savetxt(fname, pxwh['power'].real) fname = ofolder + 'xrecon-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' np.savetxt(fname, pxrh['power'].real) def make_plot(Nmu=4, wopt='opt', thopt='reas'): sigz = lambda z : 120*((1+z)/5)**-0.5 nbar = 10**-2.5 b = 3.2 Dphoto = lambda k, mu, z: np.exp(-k**2 * mu**2 * sigz(z)**2/2.) kk = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-opt/thermal-reas-hex/Nmu%d/recon-L%04d_%0.4f-up-k.txt'%(Nmu, bs, aa)) try: modes = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-opt/thermal-reas-hex/Nmu%d/recon-L%04d_%0.4f-up-modes.txt'%(Nmu, bs, aa)) except: datap = mapp.Observable.load('/global/cscratch1/sd/chmodi/m3127/21cm_cleaning/recon/fastpm_%0.4f/wedge_kmin0.03_opt/L%04d-N0256-R/thermal-reas-hex/ZA/opt_s999_h1massA_fourier_rsdpos/datap_up/'%(aa, bs)) tmp = FFTPower(datap.mapp, mode='2d', Nmu=Nmu).power modes = tmp['modes'].astype('float64') np.savetxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-modes.txt'%(wopt, thopt, Nmu, bs, aa), modes) pm1 = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-pm1.txt'%(wopt, thopt, Nmu, bs, aa)) pm2 = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-pm2.txt'%(wopt, thopt, Nmu, bs, aa)) xm = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-xm.txt'%(wopt, thopt, Nmu, bs, aa)) xmw = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/dataw-L%04d_%0.4f-up-xm.txt'%(wopt, thopt, Nmu, bs, aa)) pm1w = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/dataw-L%04d_%0.4f-up-pm1.txt'%(wopt, thopt, Nmu, bs, aa)) mubins = np.linspace(0, 1, kk.shape[1]+1) mu = (mubins[1:] + mubins[:-1])*0.5 pkd = np.loadtxt(dpath + '/pklin_%0.4f.txt'%aa) # pk = np.loadtxt(dpath + '/pklin_1.0000.txt') ipkd = ius(pkd[:, 0], pkd[:, 1]) rr = xm/(pm1*pm2)**0.5 rrw = xmw/(pm1w*pm2)**0.5 pkd = ipkd(kk) fac = b**2*Dphoto(kk, mu, zz)**2 *nbar*pkd rhosq = rr**2*fac/(1+fac) rhosqw = rrw**2*fac/(1+fac) fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i in range(mu.size): lbl1, lbl2 = None, None if i < mu.size//2: lbl1 = '$\mu$=%0.3f-%0.3f'%(mubins[i], mubins[i+1]) else: lbl2 = '$\mu$=%0.3f-%0.3f'%(mubins[i], mubins[i+1]) ax[0].plot(kk[:, i], rhosq[:, i], 'C%d'%i, label=lbl1, lw=2) ax[1].plot(kk[:, i], modes[:, i]**-1*(1+rhosq[:, i]**-1), 'C%d'%i, label=lbl2, lw=2) ax[0].plot(kk[:, i], rhosqw[:, i], 'C%d--'%i, alpha=0.5) ax[1].plot(kk[:, i], modes[:, i]**-1*(1+rhosqw[:, i]**-1), 'C%d--'%i, alpha=0.5) ax[0].plot(kk[:, 0], Dphoto(kk[:, 0], mu[i], zz)**2, 'C%d'%i, lw=1, alpha=1, ls=":") ax[1].set_ylim(1e-3, 100) ax[1].set_yscale('log') ax[1].axhline(1, color='k', ls="--") ax[0].set_ylabel(r'$\rho^2$', fontdict=font) #ax[1].set_ylabel(r'$N^{-1}(1+\rho^{-2})$', fontsize=14) ax[1].set_ylabel(r'Var$(P_\times)/P_\times^2$', fontdict=font) ax[0].legend(prop=fontmanage, loc=1) ax[1].legend(prop=fontmanage, loc=4) for axis in ax[:]: axis.set_xlabel(r'$k\quad [h\,{\rm Mpc}^{-1}]$', fontdict=font) for axis in ax.flatten(): #axis.axhline(1, color='k', ls=':') axis.set_xscale('log') axis.grid(which='both', lw=0.2, alpha=0.2, color='gray') # Put on some more labels. for axis in ax.flatten(): for tick in axis.xaxis.get_major_ticks(): tick.label.set_fontproperties(fontmanage) for tick in axis.yaxis.get_major_ticks(): tick.label.set_fontproperties(fontmanage) ##and finish plt.tight_layout(rect=[0, 0, 1, 0.95]) if rank == 0 and not args.pp: plt.savefig(figpath + '/photo_z%d_L%04d-Nmu%d.pdf'%(zz*10, bs, Nmu)) if rank == 0 and args.pp: plt.savefig(figpath + '/photo_z%d_L%04d-Nmu%d-up.pdf'%(zz*10, bs, Nmu)) def make_plot_data(aa, numd, Nmu=8, wopt='opt', thopt='reas', scatter=False): # mubins = np.linspace(0, 1, Nmu+1) mu = (mubins[1:] + mubins[:-1])*0.5 kk = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-opt/thermal-reas-hex/Nmu%d/recon-L%04d_%0.4f-up-k.txt'%(Nmu, bs, aa)) modes = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-opt/thermal-reas-hex/Nmu%d/recon-L%04d_%0.4f-up-modes.txt'%(Nmu, bs, aa)) pr = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-pm1.txt'%(wopt, thopt, Nmu, bs, aa)) pw = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/dataw-L%04d_%0.4f-up-pm1.txt'%(wopt, thopt, Nmu, bs, aa)) pm1 = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-pm1.txt'%(wopt, thopt, Nmu, bs, aa)) pm2 = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-pm2.txt'%(wopt, thopt, Nmu, bs, aa)) xm = np.loadtxt(dpath + '/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/Nmu%d/recon-L%04d_%0.4f-up-xm.txt'%(wopt, thopt, Nmu, bs, aa)) rr = xm/(pm1*pm2)**0.5 pkd = np.loadtxt(dpath + '/pklin_%0.4f.txt'%aa) ipkd = ius(pkd[:, 0], pkd[:, 1]) pkd = ipkd(kk) if scatter : ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/photog-Numd%04d-Nmu%d/'%(wopt, thopt, numd*1e4, Nmu) else: ofolder = '../../data/ZArecon-rsd/kmin-003_wedge-%s/thermal-%s-hex/photo-Numd%04d-Nmu%d/'%(wopt, thopt, numd*1e4, Nmu) print(ofolder) #get data fname = ofolder + 'photo-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' ph = np.loadtxt(fname) ph += 1/numd fname = ofolder + 'xrecon-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' pxrh = np.loadtxt(fname) fname = ofolder + 'xdataw-L%04d_%0.4f.txt'%(bs, aa) if args.pp : fname = fname[:-4] + '-up.txt' pxwh = np.loadtxt(fname) rhosq = pxrh**2/ph/pr rhosqw = pxwh**2/ph/pw #get theory sigz = lambda z : 120*((1+z)/5)**-0.5 Dphoto = lambda k, mu, z: np.exp(-k**2 * mu**2 * sigz(z)**2/2.) nbar = 10**-2.5 b = 3.2 def iget(ii, k=1): yy = rr[ii] mask = ~np.isnan(yy) return ius(mu[mask], yy[mask], k=k) mus = np.linspace(0, 1, 500) rhosqmu = np.zeros((kk.shape[0], mus.size)) for ik, kv in enumerate(kk[:, -1]): fac = b**2*Dphoto(kv, mus, zz)**2 *nbar*ipkd(kv) try: rhosqmu[ik] = iget(ik)(mus)**2*fac/(1+fac) except Exception as e: print(ik, e) rhosqav = np.zeros((kk.shape[0], mu.size)) for i in range(mu.size): mask = (mus > mubins[i]) & (mus < mubins[i+1]) rhosqav[: ,i] = np.trapz(rhosqmu[:, mask], mus[mask])/(mubins[i+1]-mubins[i]) #make figure fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i in range(mu.size): lbl1, lbl2 = None, None if i <= mu.size: lbl1 = '$\mu$=%0.3f-%0.3f'%(mubins[i], mubins[i+1]) #else: lbl2 = '$\mu$=%0.3f-%0.3f'%(mubins[i], mubins[i+1]) if i ==0: lbl2 = r'Recon$_{\rm Sim}$' ax[0].plot(kk[:, i], rhosq[:, i], 'C%d'%i, label=lbl1) ax[1].plot(kk[:, i], modes[:, i]**-1*(1+rhosq[:, i]**-1), 'C%d'%i, label=lbl2) #ax[0].plot(kk[:, i], rhosqw[:, i], 'C%d--'%i, alpha=0.4) if i ==0: lbl2 = r'Noisy$_{\rm Sim}$' ax[1].plot(kk[:, i], modes[:, i]**-1*(1+rhosqw[:, i]**-1), 'C%d:'%i, alpha=1, lw=0.5, label=lbl2) ax[0].plot(kk[:, i], rhosqav[:, i], 'C%d--'%i, alpha=1, lw=1) if i ==0: lbl2 = r'Recon$_{\rm Pred}$' ax[1].plot(kk[:, i], modes[:, i]**-1*(1+rhosqav[:, i]**-1), 'C%d--'%i, alpha=1, lw=1, label=lbl2) if i ==0: lbl0 = r'$D_{\rm photo}^2$' else: lbl0 = None ax[0].plot(kk[:, 0], Dphoto(kk[:, 0], mu[i], zz)**2, 'C%d'%i, lw=0.5, alpha=1, ls=":", label=lbl0) # ax[0].set_ylim(-.05, 1.1) ax[1].set_ylim(9e-4, 100) ax[1].set_yscale('log') ax[1].axhline(1, color='k', ls="--") ax[0].set_ylabel(r'$\rho^2$', fontdict=font) #ax[1].set_ylabel(r'$N^{-1}(1+\rho^{-2})$', fontsize=14) ax[1].set_ylabel(r'Var$(P_\times)/P_\times^2$', fontdict=font) ax[0].legend(prop=fontmanage, loc=1, ncol=1) ax[1].legend(prop=fontmanage, loc=3, ncol=1) for axis in ax[:]: axis.set_xlabel(r'$k\quad [h\,{\rm Mpc}^{-1}]$', fontdict=font) for axis in ax.flatten(): #axis.axhline(1, color='k', ls=':') axis.set_xscale('log') axis.grid(which='both', lw=0.2, alpha=0.2, color='gray') # Put on some more labels. for axis in ax.flatten(): for tick in axis.xaxis.get_major_ticks(): tick.label.set_fontproperties(fontmanage) for tick in axis.yaxis.get_major_ticks(): tick.label.set_fontproperties(fontmanage) # and finish plt.tight_layout(rect=[0, 0, 1, 0.95]) if rank == 0 and not args.pp: plt.savefig(figpath + '/photod_z%d_L%04d-Nmu%d.pdf'%(zz*10, bs, Nmu)) if rank == 0 and args.pp: if scatter : plt.savefig(figpath + '/photodg_z%d_L%04d-Nmu%d-up.pdf'%(zz*10, bs, Nmu)) else : plt.savefig(figpath + '/photod_z%d_L%04d-Nmu%d-up.pdf'%(zz*10, bs, Nmu)) ################ if __name__=="__main__": #save2dphoto(Nmu=4, numd=10**-2.5, aa=0.2000) #save2dphoto(Nmu=8, numd=10**-2.5, aa=0.2000) #save2dphoto(Nmu=4, numd=10**-2.5, aa=0.2000, scatter=True) #save2dphoto(Nmu=8, numd=10**-2.5, aa=0.2000, scatter=True) #make_plot(Nmu=4) #make_plot(Nmu=8) make_plot_data(aa=0.2000, numd=10**-2.5, Nmu=8) make_plot_data(aa=0.2000, numd=10**-2.5, Nmu=8, scatter=True) make_plot_data(aa=0.2000, numd=10**-2.5, Nmu=4) make_plot_data(aa=0.2000, numd=10**-2.5, Nmu=4, scatter=True) #

41.704485

210

0.575161

2,619

15,806

3.412753

0.148148

0.012531

0.011748

0.016447

0.624636

0.571828

0.554486

0.535131

0.521369

0.510517

0

0.06259

0.211565

15,806

378

211

41.814815

0.65463

0.067696

0

0.337165

0

0.084291

0.208203

0.156901

0

1

0.019157

false

0.003831

0.068966

0

0.095785

0.022989

0

null

0

null

0

1

0

1107964a13a8c587e9dedd0f0fb6a2581ecb0887

3,999

py

Python

ndfinance/strategies/basic/__init__.py

gomtinQQ/NDFinance

522bf0486e5f5337c522d0e34b088f386c7c3290

[ "MIT" ]

35

2020-09-26T16:31:45.000Z

2022-01-01T12:12:21.000Z

ndfinance/strategies/basic/__init__.py

gomtinQQ/NDFinance

522bf0486e5f5337c522d0e34b088f386c7c3290

[ "MIT" ]

1

2020-09-27T08:54:23.000Z

ndfinance/strategies/basic/__init__.py

gomtinQQ/NDFinance

522bf0486e5f5337c522d0e34b088f386c7c3290

[ "MIT" ]

8

2020-10-06T23:51:22.000Z

2022-02-16T12:11:10.000Z

from ndfinance.strategies import Strategy, PeriodicRebalancingStrategy from ndfinance.brokers.base import order from ndfinance.brokers.base.order import * from ndfinance.strategies.utils import * class SameWeightBuyHold(Strategy): def __init__(self): super(SameWeightBuyHold, self).__init__() self.ordered = False def _logic(self): if not self.ordered: weight = 1 / len(self.broker.assets) [self.broker.order(order.Weight(asset, self.broker.portfolio.portfolio_value, 1, weight)) for asset in self.broker.assets.values()] self.ordered = True class SameWeightBuynRebalance(PeriodicRebalancingStrategy): def __init__(self, rebalance_period): super(SameWeightBuynRebalance, self).__init__(rebalance_period) def register_engine(self, *args, **kwargs): super(SameWeightBuynRebalance, self).register_engine(*args, **kwargs) weight = 1 / len(self.broker.assets.keys()) self.weights = [weight for _ in self.broker.assets.keys()] return self def _logic(self): self.broker.order(order.Rebalance(tickers=self.broker.assets.keys(), weights=self.weights)) class OscillatorStrategy(Strategy): def __init__(self, breakout_threshold, oversold_threshold, overbought_threshold, osc_label, use_short=False, use_time_cut=False, timecut_params=None, use_n_perc_rule=False, n_perc_params=None, use_stop_loss=False, stop_loss_params=None, *args, **kwargs): super(OscillatorStrategy, self).__init__() self.use_short = use_short self.breakout_threshold = breakout_threshold self.oversold_threshold = oversold_threshold self.overbought_threshold = overbought_threshold self.osc_label = osc_label self.use_time_cut = use_time_cut self.timecut_params = timecut_params self.use_n_perc_rule = use_n_perc_rule self.n_perc_params = n_perc_params self.use_stop_loss = use_stop_loss self.stop_loss_params = stop_loss_params def register_engine(self, *args, **kwargs): super(OscillatorStrategy, self).register_engine(*args, **kwargs) self.ticker = list(self.broker.assets.keys())[0] return self def _logic(self): indicator_ = self.data_provider.get_ohlcvt(self.ticker, self.osc_label, n=2) indicator = indicator_[-1] indicator_prev = indicator_[0] if not self.broker.portfolio.positions: ordered = True value = apply_n_percent_rule(self.broker.portfolio.portfolio_value, **self.n_perc_params) \ if self.use_n_perc_rule else self.broker.portfolio.portfolio_value if (indicator >= self.breakout_threshold) & (indicator_prev < self.breakout_threshold): self.broker.order(Weight(self.broker.assets[self.ticker], value, 1, 1)) elif (((indicator <= self.breakout_threshold) & (indicator_prev > self.breakout_threshold)) & self.use_short): self.broker.order(Weight(self.broker.assets[self.ticker], value, -1, 1)) else: ordered = False if ordered & self.use_time_cut: self.broker.order(TimeCutClose(self.broker.assets[self.ticker], self.indexer.timestamp, **self.timecut_params)) if ordered & self.use_stop_loss: self.broker.order(StopLoss(self.broker.assets[self.ticker], **self.stop_loss_params)) elif self.broker.portfolio.positions[self.ticker].side == 1: if (indicator <= self.overbought_threshold) & (indicator_prev > self.overbought_threshold): self.broker.order(Close(self.broker.assets[self.ticker])) elif self.broker.portfolio.positions[self.ticker].side == -1: if (indicator >= self.oversold_threshold) & (indicator_prev < self.oversold_threshold): self.broker.order(Close(self.broker.assets[self.ticker]))

44.932584

127

0.68017

473

3,999

5.490486

0.17759

0.100116

0.073931

0.053908

0.363496

0.253369

0.210243

0.182518

0

0.004147

0.216054

3,999

88

128

45.443182

0.824242

0

0.132353

0

1

0.117647

false

0

0.058824

0

0.25

0

null

0

null

0

1

0

110af0aa9cc468fbee2f90b29540e3ee61251308

1,975

py

Python

daemon.py

hletrd/TRIPOL_polarizer

124d202bf876635bd402306fb5d7572fd45ce599

[ "MIT" ]

null

daemon.py

hletrd/TRIPOL_polarizer

124d202bf876635bd402306fb5d7572fd45ce599

[ "MIT" ]

null

daemon.py

hletrd/TRIPOL_polarizer

124d202bf876635bd402306fb5d7572fd45ce599

[ "MIT" ]

null

from flask import Flask, render_template, send_from_directory import serial import serial.tools.list_ports import threading app = Flask(__name__) def run_server(): app.run(host=bind_ip, debug=True, port=bind_port) @app.route('/') def index(): return render_template('_basic.html', ports=serialhandler.get_port_list()) @app.route('/get/angle/now') def get_angle(): return str(serialhandler.angle_now) @app.route('/get/angle/to') def get_angle_to(): return str(serialhandler.angle_to) @app.route('/open/<path:port>') def open_serial(port): serialhandler.connect(port[1:]) return '1' @app.route('/move/<string:angle>') def move_angle(angle): if (360 >= float(angle) >= 0): serialhandler.move_angle(str(float(angle))) return '1' return '0' @app.route('/static/<path:path>') def send_static(path): return send_from_directory('static', path, as_attachment=False) class SerialHandler(object): def __init__(self): self.Serial = serial.Serial() self.Serial.baudrate = 115200 self.Serial.timeout = 0.1 self.angle_now = 0.0 self.angle_to = '0.0' self.q = '' def get_port_list(self): result = serial.tools.list_ports.comports() return result def connect(self, port): self.Serial.port = port self.Serial.open() threading.Timer(0.2, self.read_serial).start() def move_angle(self, angle): self.Serial.write(angle.encode('utf-8')) self.angle_to = angle def read_serial(self): threading.Timer(0.2, self.read_serial).start() try: while self.Serial.in_wating > 0: self.q += self.Serial.read().decode('utf-8') except: while self.Serial.inWaiting() > 0: self.q += self.Serial.read(1).decode('utf-8') splitted = self.q.split('\n\n') last = splitted[len(splitted)-1] if 'angpos:' in last and 'speed:' in last: self.q = '' self.angle_now = (float) (last.split('angpos:')[1].split('\n')[0]) if __name__ == '__main__': bind_ip = '127.0.0.1' bind_port = 8000 serialhandler = SerialHandler() run_server()

23.511905

75

0.698228

301

1,975

4.401993

0.27907

0.075472

0.013585

0.030189

0.083019

0.05283

0

0.025088

0.132152

1,975

84

76

23.511905

0.747958

0

0.092308

0

0.083502

0

1

0.184615

false

0

0.061538

0.384615

0

null

0

null

0

1

0

110ec99e58e5ce9d328a5556af8ee117cc5ebd9a

3,304

py

Python

src/utils.py

senadkurtisi/neural-style-transfer

0048d8b184959de095f0821f63205c8ce3ff2dff

[ "MIT" ]

null

src/utils.py

senadkurtisi/neural-style-transfer

0048d8b184959de095f0821f63205c8ce3ff2dff

[ "MIT" ]

null

src/utils.py

senadkurtisi/neural-style-transfer

0048d8b184959de095f0821f63205c8ce3ff2dff

[ "MIT" ]

null

from PIL import Image import numpy as np import torch import torchvision.transforms.transforms as transforms import os from config import cfg def preprocess_img(img_path): """ Loads the desired image and prepares it for VGG19 model. Parameters: img_path: path to the image Returns: processed: loaded image after preprocessing """ prep = transforms.Compose([transforms.Resize((cfg.IMG_SIZE, cfg.IMG_SIZE)), transforms.ToTensor(), transforms.Lambda(lambda x: x[torch.LongTensor([2, 1, 0])]), transforms.Normalize(mean=[0.40760392, 0.45795686, 0.48501961], std=[1, 1, 1]), transforms.Lambda(lambda x: x.mul_(255)), ]) img = Image.open(img_path) processed = prep(img) if cfg.cuda: processed = processed.cuda() return processed.unsqueeze(0) def get_init_img(mode='noise', source_img=None): """ Constructs the initial image for the NST algorithm. Parameters: mode: how to initialize the image? {'noise', 'other'} source_img: image used for initialization of @mode is set to 'other' Returns: opt_image: initialized image """ assert mode in ['noise', 'other'], f"{mode} is and illegal initialization mode!" if mode == 'style' or mode == 'other': assert (source_img is not None), f"Can't initialize from {mode}!" if mode == 'noise': if cfg.cuda: opt_image = np.random.normal(loc=0, scale=90., size=(1, 3, cfg.IMG_SIZE, cfg.IMG_SIZE)).astype(np.float32) opt_image = torch.from_numpy(opt_image).float().cuda() else: pass else: opt_image = (source_img.detach()).clone() # Make sure that gradients are being calculated for this image # During forward pass opt_image.requires_grad = True return opt_image def gram_matrix(x): """ Calculates the Gram matrix for the feature maps contained in x. Parameters: x: feature maps Returns: G: gram matrix """ b, c, h, w = x.size() F = x.view(b, c, h * w) G = torch.bmm(F, F.transpose(1, 2)) G.div_(h * w) return G def postprocess(img): """ Prepares the image for display and saving. """ postp = transforms.Compose([transforms.Lambda(lambda x: x.mul_(1. / 255)), transforms.Normalize(mean=[-0.40760392, -0.45795686, -0.48501961], std=[1, 1, 1]), transforms.Lambda(lambda x: x[torch.LongTensor([2, 1, 0])]), # turn to RGB ]) img = postp(img) # In order to have more visually appealing images # We need to clip the pixel values img[img > 1] = 1 img[img < 0] = 0 img = transforms.ToPILImage()(img) return img def get_file_name(path): """ Extracts only the filename from the given path. Extension is removed as well. """ base = os.path.basename(path) return os.path.splitext(base)[0]

29.765766

106

0.553874

408

3,304

4.414216

0.384804

0.031094

0.02221

0.051083

0.161022

0.122154

0

0.041265

0.339891

3,304

110

107

30.036364

0.784503

0.251211

0

0.181818

0

0.042979

0

0.036364

1

0.090909

false

0.018182

0.109091

0

0.290909

0

null

0

null

0

1

0