xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

35dd454f599a074448689edb5da0e63cda2e0bab

85

py

Python

standard/configparser/main.py

gwaysoft/python

a74a0b553dfca9606083a41ab6d03801e67d2467

[ "Apache-2.0" ]

null

standard/configparser/main.py

gwaysoft/python

a74a0b553dfca9606083a41ab6d03801e67d2467

[ "Apache-2.0" ]

null

standard/configparser/main.py

gwaysoft/python

a74a0b553dfca9606083a41ab6d03801e67d2467

[ "Apache-2.0" ]

null

from gspackage.utils import config print(config.getValue(section="log", key="log1"))

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5

ea10ad9e30c7d5be37b12bdf92997de856db1b73

93

py

Python

tests/test_fixtures.py

carver/ensauction

4f30d64ea7c470a53d133e7c8e43d8228b62732c

[ "MIT" ]

37

2017-07-25T19:25:00.000Z

2021-12-24T04:47:52.000Z

tests/test_fixtures.py

carver/ensauction

4f30d64ea7c470a53d133e7c8e43d8228b62732c

[ "MIT" ]

12

2017-07-24T16:42:22.000Z

2017-08-22T22:56:39.000Z

tests/test_fixtures.py

carver/ensauction

4f30d64ea7c470a53d133e7c8e43d8228b62732c

[ "MIT" ]

7

2017-07-24T01:16:31.000Z

2022-02-09T00:32:32.000Z

def test_fake_hash(fake_hash): assert fake_hash(b'rainstorms') == b"HASH(brainstorms)"

18.6

59

0.731183

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0.571429

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5

ea249efaa02a502e9fe20537925a3f636801aa2a

378

py

Python

core/messenger/security/permissions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

1

2018-11-30T21:56:14.000Z

core/messenger/security/permissions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

null

core/messenger/security/permissions.py

anthill-arch/platform

ff45dc71b2f3141bbd95baaf4da7ff1d2ac24ca0

[ "MIT" ]

null

class BasePermission: def __init__(self, user): self.user = user def has_permission(self, action): raise NotImplementedError class AllowAny: def has_permission(self, action): return True class IsAuthenticated(BasePermission): def has_permission(self, action): return self.user.id is not None and self.user.is_authenticated()

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378

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null

0

1

0

1

0

5

ea29f74fda33ec9cf310e295b7eeb3f893ff3ad0

133

py

Python

tests/test_main.py

alysivji/coveralls-example

325a5935b5f165ed791ab2c206d1ce42602f36ea

[ "MIT" ]

null

tests/test_main.py

alysivji/coveralls-example

325a5935b5f165ed791ab2c206d1ce42602f36ea

[ "MIT" ]

null

tests/test_main.py

alysivji/coveralls-example

325a5935b5f165ed791ab2c206d1ce42602f36ea

[ "MIT" ]

null

from app.main import add, subtract def test_add(): assert add(2, 3) == 5 def test_subtract(): assert subtract(5, 3) == 2

13.3

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5

ea5822f51010f616dae4f72ba2f35ff1f51a4406

25,459

py

Python

connector/python/tests/test_pyspark_riak.py

nikolaypavlov/spark-riak-connector

84859aa4d82dd7234fb5c3c21789108d3a6c1094

[ "Apache-2.0" ]

63

2015-09-12T04:10:58.000Z

2022-03-20T16:35:27.000Z

connector/python/tests/test_pyspark_riak.py

nikolaypavlov/spark-riak-connector

84859aa4d82dd7234fb5c3c21789108d3a6c1094

[ "Apache-2.0" ]

83

2015-09-11T13:30:50.000Z

2018-11-24T11:13:06.000Z

connector/python/tests/test_pyspark_riak.py

nikolaypavlov/spark-riak-connector

84859aa4d82dd7234fb5c3c21789108d3a6c1094

[ "Apache-2.0" ]

34

2015-09-10T15:52:54.000Z

2018-07-03T10:33:43.000Z

from __future__ import print_function import pytest import sys from operator import add import findspark findspark.init() from pyspark import SparkContext, SparkConf, SQLContext, Row import os, subprocess, json, riak, time import pyspark_riak import timeout_decorator import datetime import tzlocal import pytz import math from pyspark_tests_fixtures import * from random import randint #### Notes #### ''' Saving ints to riak ts preserves the value of the timestamp. Querying ints using riak client is, in this case, simple, just query the int range Saving datetimes to riak ts, the datetimes will be treated as local time, converted then to gmt time. You can query with riak client by int only, so in this case you must convert your local datetime to utc int. If you do ts_get, you can use local datetime to query. The query will be converted automatically to utc before query. Reading datetime from ts using spark timestamp option will convert datetime back to local datetime. ''' ###### FUNCTIONS ####### def setup_table(client): riak_ts_table_name = 'spark-riak-%d' % int(time.time()) riak_ts_table = client.table(riak_ts_table_name) create_sql = """CREATE TABLE %(table_name)s ( field1 varchar not null, field2 varchar not null, datetime timestamp not null, data sint64, PRIMARY KEY ((field1, field2, quantum(datetime, 24, h)), field1, field2, datetime)) """ % ({'table_name': riak_ts_table_name}) return riak_ts_table_name, create_sql, riak_ts_table def setup_kv_obj(client, bucket_name, key, content_type, data): bucket = client.bucket(bucket_name) obj = riak.RiakObject(client, bucket, key) obj.content_type = content_type obj.data = data return obj def setup_ts_obj(ts_table, data): return ts_table.new(data) def unix_time_seconds(dt): td = dt - datetime.datetime.utcfromtimestamp(0) return int(td.total_seconds()) def unix_time_millis(dt): td = unix_time_seconds(dt) return int(td * 1000.0) def make_data_long(start_date, N, M): data = [] one_second = datetime.timedelta(seconds=1) one_day = datetime.timedelta(days=1) for i in range(M): for j in range(N): data.append(['field1_val', 'field2_val', unix_time_millis(start_date + i*one_day + j*one_second), i+j]) end_date = start_date + (M-1)*one_day + (N-1)*one_second return data, start_date, end_date def make_data_timestamp(start_date, N, M): timestamp_data = [] long_data = [] one_second = datetime.timedelta(seconds=1) one_day = datetime.timedelta(days=1) local_start_date = convert_to_local_dt(start_date) for i in range(M): for j in range(N): cur_local_timestamp = local_start_date + i*one_day + j*one_second timestamp_data.append(['field1_val', 'field2_val', cur_local_timestamp, i+j]) long_data.append(['field1_val', 'field2_val', unix_time_millis(convert_dt_to_gmt_dt(cur_local_timestamp)), i+j]) start_timestamp = convert_dt_to_gmt_dt(timestamp_data[0][2]) end_timestamp = convert_dt_to_gmt_dt(timestamp_data[-1][2]) start_long = long_data[0][2] end_long = long_data[-1][2] return timestamp_data, start_timestamp, end_timestamp, long_data, start_long, end_long def convert_dt_to_gmt_dt(dt): gmt_dt_with_tzinfo = pytz.utc.normalize(dt) year = gmt_dt_with_tzinfo.year month = gmt_dt_with_tzinfo.month day = gmt_dt_with_tzinfo.day hour = gmt_dt_with_tzinfo.hour minute = gmt_dt_with_tzinfo.minute second = gmt_dt_with_tzinfo.second gmt_dt = datetime.datetime(year, month, day, hour, minute, second) return gmt_dt def convert_to_local_dt(dt): # local_tz = tzlocal.get_localzone() local_tz = pytz.utc local_dt = local_tz.localize(dt) return local_dt def convert_local_dt_to_gmt_dt(dt): local_dt = convert_to_local_dt(dt) return convert_dt_to_gmt_dt(local_dt) def make_table_with_data(N, M, useLong, spark_context, riak_client): riak_ts_table_name, create_sql, riak_ts_table = setup_table(riak_client) riak_ts_table.query(create_sql) seed_date = datetime.datetime(2016, 1, 1, 12, 0, 0) if useLong: test_data, start, end = make_data_long(seed_date, N, M) test_rdd = spark_context.parallelize(test_data) else: timestamp_data, start_timestamp, end_timestamp, long_data, start_long, end_long = make_data_timestamp(seed_date, N, M) test_rdd = spark_context.parallelize(timestamp_data) test_df = test_rdd.toDF(['field1', 'field2', 'datetime', 'data']) test_df.write.format('org.apache.spark.sql.riak').mode('Append').save(riak_ts_table_name) if useLong: return start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table else: return start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table def make_kv_data(N, spark_context): source_data = [] test_data = [] keys = [] bad_keys = [] for i in range(N): keys.append(str(u'key'+str(i))) source_data.append({str(u'key'+str(i)) : {u'data' : i}}) test_data.append( (str(u'key'+str(i)),{u'data' : i})) bad_keys.append(str(i)) source_rdd = spark_context.parallelize(source_data) return source_rdd, source_data, test_data, keys, bad_keys def make_kv_data_2i(N, test_bucket_name, riak_client): bucket = riak_client.bucket_type('default').bucket(test_bucket_name) test_data = [] string2i = [] integer2i = [] partitions = [] bad_partitions = [] for i in range(N): obj = riak.RiakObject(riak_client, bucket, str(u'key'+str(i))) obj.content_type = 'application/json' obj.data = {u'data' : i} obj.add_index('string_index_bin', 'string_val_'+str(i)) obj.add_index('integer_index_int', i) obj.store() test_data.append((str('key'+str(i)),{u'data' : i})) string2i.append('string_val_'+str(i)) integer2i.append(i) partitions.append((i,i)) bad_partitions.append((N+i,N+i)) return test_data, string2i, integer2i, partitions, bad_partitions def make_filter(useLong, start, end): if useLong: temp_filter = """datetime >= %(start_date)s AND datetime <= %(end_date)s AND field1 = '%(field1)s' AND field2 = '%(field2)s' """ % ({'start_date': unix_time_millis(start), 'end_date': unix_time_millis(end), 'field1': 'field1_val', 'field2': 'field2_val'}) else: temp_filter = """datetime >= CAST(%(start_date)s AS TIMESTAMP) AND datetime <= CAST(%(end_date)s AS TIMESTAMP) AND field1 = '%(field1)s' AND field2 = '%(field2)s' """ % ({'start_date': start, 'end_date': end, 'field1': 'field1_val', 'field2': 'field2_val'}) return temp_filter def make_ts_query(riak_ts_table_name, start, end): fmt = """ select * from {table_name} where datetime >= {start_date} AND datetime <= {end_date} AND field1 = '{field1}' AND field2 = '{field2}' """ query = fmt.format(table_name=riak_ts_table_name, start_date=unix_time_millis(start), end_date=unix_time_millis(end), field1='field1_val', field2='field2_val') return query ###### TESTS ####### # def _test_connection(spark_context, riak_client, sql_context): # # riak_client.ping() # # obj = setup_kv_obj(riak_client, 'temp_bucket', 'temp_key', 'text/plain', 'temp_data') # # obj.store() # # result = riak_client.bucket('temp_bucket').get('temp_key') # # assert result.data == 'temp_data' # # riak_ts_table_name, create_sql, riak_ts_table = setup_table(riak_client) # # riak_ts_table.query(create_sql) # # time.sleep(5) # # ts_obj = setup_ts_obj(riak_ts_table, [['field1_val', 'field2_val', unix_time_millis(datetime.datetime(2015, 1, 1, 12, 0, 0)), 0]]) # # ts_obj.store() # # result = riak_client.ts_get(riak_ts_table_name, ['field1_val', 'field2_val', unix_time_millis(datetime.datetime(2015, 1, 1, 12, 0, 0))]) # # assert result.rows == [['field1_val', 'field2_val', unix_time_millis(datetime.datetime(2015, 1, 1, 12, 0, 0)), 0]] ###### Riak TS Test ####### def _test_spark_df_ts_write_use_long(N, M, spark_context, riak_client, sql_context): useLong=True start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) query = make_ts_query(riak_ts_table_name, start, end) result = riak_ts_table.query(query) assert sorted(result.rows, key=lambda x: x[2]) == sorted(test_rdd.collect(), key=lambda x: x[2]) def _test_spark_df_ts_write_use_timestamp(N, M, spark_context, riak_client, sql_context): useLong=False start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) query = make_ts_query(riak_ts_table_name, start_timestamp, end_timestamp) result = riak_ts_table.query(query) assert sorted(result.rows, key=lambda x: x[2]) == sorted(spark_context.parallelize(long_data).collect(), key=lambda x: x[2]) def _test_spark_df_ts_read_use_long(N, M, spark_context, riak_client, sql_context): useLong=True start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, start, end) result = sql_context.read.format("org.apache.spark.sql.riak").option("spark.riakts.bindings.timestamp", "useLong").load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) def _test_spark_df_ts_read_use_long_ts_quantum(N, M, spark_context, riak_client, sql_context): useLong=True start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, start, end) result = sql_context.read.format("org.apache.spark.sql.riak") \ .option("spark.riakts.bindings.timestamp", "useLong") \ .option("spark.riak.partitioning.ts-quantum", "24h") \ .load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) def _test_spark_df_ts_read_use_timestamp(N, M, spark_context, riak_client, sql_context): useLong=False start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, unix_time_seconds(start_timestamp), unix_time_seconds(end_timestamp)) result = sql_context.read.format("org.apache.spark.sql.riak").option("spark.riakts.bindings.timestamp", "useTimestamp").load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) def _test_spark_df_ts_read_use_timestamp_ts_quantum(N, M, spark_context, riak_client, sql_context): useLong=False start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, unix_time_seconds(start_timestamp), unix_time_seconds(end_timestamp)) result = sql_context.read.format("org.apache.spark.sql.riak").option("spark.riakts.bindings.timestamp", "useTimestamp").option("spark.riak.partitioning.ts-quantum", "24h").load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) def _test_spark_df_ts_range_query_input_split_count_use_long(N, M, S,spark_context, riak_client, sql_context): useLong=True start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) time.sleep(1) temp_filter = make_filter(useLong, start, end) result = sql_context.read.format("org.apache.spark.sql.riak") \ .option("spark.riakts.bindings.timestamp", "useLong") \ .option("spark.riak.input.split.count", str(S)) \ .option("spark.riak.partitioning.ts-range-field-name", "datetime") \ .load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) assert result.rdd.getNumPartitions() == S def _test_spark_df_ts_range_query_input_split_count_use_long_ts_quantum(N, M, S,spark_context, riak_client, sql_context): useLong=True start, end, riak_ts_table_name, test_df, test_rdd, test_data, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, start, end) result = sql_context.read.format("org.apache.spark.sql.riak") \ .option("spark.riakts.bindings.timestamp", "useLong") \ .option("spark.riak.partitioning.ts-quantum", "24h") \ .option("spark.riak.input.split.count", str(S)) \ .option("spark.riak.partitioning.ts-range-field-name", "datetime") \ .load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) assert result.rdd.getNumPartitions() == S def _test_spark_df_ts_range_query_input_split_count_use_timestamp(N, M, S,spark_context, riak_client, sql_context): useLong=False start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, unix_time_seconds(start_timestamp), unix_time_seconds(end_timestamp)) result = sql_context.read.format("org.apache.spark.sql.riak") \ .option("spark.riakts.bindings.timestamp", "useTimestamp") \ .option("spark.riak.input.split.count", str(S)) \ .option("spark.riak.partitioning.ts-range-field-name", "datetime") \ .load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) assert result.rdd.getNumPartitions() == S def _test_spark_df_ts_range_query_input_split_count_use_timestamp_ts_quantum(N, M, S,spark_context, riak_client, sql_context): useLong=False start_timestamp, end_timestamp, riak_ts_table_name, test_df, test_rdd, timestamp_data, long_data, start_long, end_long, riak_ts_table = make_table_with_data(N, M, useLong, spark_context, riak_client) temp_filter = make_filter(useLong, unix_time_seconds(start_timestamp), unix_time_seconds(end_timestamp)) result = sql_context.read.format("org.apache.spark.sql.riak") \ .option("spark.riakts.bindings.timestamp", "useTimestamp") \ .option("spark.riak.partitioning.ts-quantum", "24h") \ .option("spark.riak.input.split.count", str(S)) \ .option("spark.riak.partitioning.ts-range-field-name", "datetime") \ .load(riak_ts_table_name).filter(temp_filter) assert sorted(result.collect(), key=lambda x: x[2]) == sorted(test_df.collect(), key=lambda x: x[2]) assert result.rdd.getNumPartitions() == S ###### Riak KV Tests ###### def _test_spark_rdd_write_kv(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) source_rdd, source_data, test_data, keys, bad_keys = make_kv_data(N, spark_context) source_rdd.saveToRiak(test_bucket_name, "default") test_data = [{x.key: x.data} for x in riak_client.bucket(test_bucket_name).multiget(keys)] assert sorted(source_data) == sorted(test_data) def _test_spark_rdd_kv_read_query_all(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) source_rdd, source_data, test_data, keys, bad_keys = make_kv_data(N, spark_context) source_rdd.saveToRiak(test_bucket_name, "default") result = spark_context.riakBucket(test_bucket_name).queryAll() assert sorted(result.collect(), key=lambda x: x[0]) == sorted(test_data, key=lambda x: x[0]) def _test_spark_rdd_kv_read_query_bucket_keys(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) source_rdd, source_data, test_data, keys, bad_keys = make_kv_data(N, spark_context) source_rdd.saveToRiak(test_bucket_name) result = spark_context.riakBucket(test_bucket_name).queryBucketKeys(*keys) assert sorted(result.collect(), key=lambda x: x[0]) == sorted(test_data, key=lambda x: x[0]) result = spark_context.riakBucket(test_bucket_name).queryBucketKeys(*bad_keys) assert sorted(result.collect(), key=lambda x: x[0]) == sorted([], key=lambda x: x[0]) def _test_spark_rdd_kv_read_query_2i_keys(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) test_data, string2i, integer2i, partitions, bad_partitions = make_kv_data_2i(N, test_bucket_name, riak_client) result = spark_context.riakBucket(test_bucket_name).query2iKeys('string_index', *string2i) assert sorted(result.collect(), key=lambda x: x[0]) == sorted(test_data, key=lambda x: x[0]) result = spark_context.riakBucket(test_bucket_name).query2iKeys('integer_index', *integer2i) assert sorted(result.collect(), key=lambda x: x[0]) == sorted(test_data, key=lambda x: x[0]) def _test_spark_rdd_kv_read_query2iRange(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) test_data, string2i, integer2i, partitions, bad_partitions = make_kv_data_2i(N, test_bucket_name, riak_client) result = spark_context.riakBucket(test_bucket_name).query2iRange('integer_index', integer2i[0], integer2i[-1]) assert sorted(result.collect(), key=lambda x: x[0]) == sorted(test_data, key=lambda x: x[0]) result = spark_context.riakBucket(test_bucket_name).query2iRange('integer_index', N, 2*N) assert sorted(result.collect(), key=lambda x: x[0]) == sorted([], key=lambda x: x[0]) def _test_spark_rdd_kv_read_partition_by_2i_range(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) test_data, string2i, integer2i, partitions, bad_partitions = make_kv_data_2i(N, test_bucket_name, riak_client) result = spark_context.riakBucket(test_bucket_name).partitionBy2iRanges('integer_index', *partitions) assert sorted(result.collect(), key=lambda x: x[0]), sorted(test_data, key=lambda x: x[0]) assert result.getNumPartitions() == N result = spark_context.riakBucket(test_bucket_name).partitionBy2iRanges('integer_index', *bad_partitions) assert sorted(result.collect(), key=lambda x: x[0]) == sorted([], key=lambda x: x[0]) assert result.getNumPartitions() == N def _test_spark_rdd_kv_read_partition_by_2i_keys(N, spark_context, riak_client, sql_context): test_bucket_name = "test-bucket-"+str(randint(0,100000)) test_data, string2i, integer2i, partitions, bad_partitions = make_kv_data_2i(N, test_bucket_name, riak_client) result = spark_context.riakBucket(test_bucket_name).partitionBy2iKeys('string_index', *string2i) assert sorted(result.collect(), key=lambda x: x[0]), sorted(test_data, key=lambda x: x[0]) assert result.getNumPartitions() == N bad_strings = ['no', 'nein', 'net'] result = spark_context.riakBucket(test_bucket_name).partitionBy2iKeys('string_index', *bad_strings) assert sorted(result.collect(), key=lambda x: x[0]) == sorted([], key=lambda x: x[0]) assert result.getNumPartitions() == len(bad_strings) ###### Run Tests ###### # def test_con(spark_context, riak_client, sql_context): # _test_connection(spark_context, riak_client, sql_context) ###### KV Tests ####### @pytest.mark.riakkv def test_kv_write(spark_context, riak_client, sql_context): _test_spark_rdd_write_kv(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_all(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_query_all(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_bucket_keys(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_query_bucket_keys(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_2i_keys(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_query_2i_keys(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_2i_range(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_query2iRange(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_partition_by_2i_range(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_partition_by_2i_range(10, spark_context, riak_client, sql_context) @pytest.mark.riakkv def test_kv_query_partition_by_2i_keys(spark_context, riak_client, sql_context): _test_spark_rdd_kv_read_partition_by_2i_keys(10, spark_context, riak_client, sql_context) # # if object values are JSON objects with more than 4 keys exception happens # https://github.com/basho/spark-riak-connector/issues/206 @pytest.mark.regression @pytest.mark.riakkv def test_read_JSON_value_with_more_then_4_fields(spark_context, riak_client): bucket = riak_client.bucket("test-bucket-"+str(randint(0,100000))) item = bucket.new("test-key") item.data = {'field1': 'abc', 'field2': 'def', 'field3': 'ABC123', 'field4': 'home', 'field5': '10', 'field6': '10.0.0.1', 'field7': '1479398907', 'field8': '1479398907', 'field9': 'DEF456,GHI789', 'field11': 'JKL000', 'field12': 'abc'} item.store() result = spark_context.riakBucket(bucket.name).queryBucketKeys("test-key").collect() # # if object value is a JSON object that contains a List of values, exception raised # https://bashoeng.atlassian.net/browse/SPARK-275 # @pytest.mark.regression @pytest.mark.riakkv def test_read_JSON_value_with_an_empty_list (spark_context, riak_client): bucket = riak_client.bucket("test-bucket-"+str(randint(0,100000))) item = bucket.new("test-key") item.data = {u'client_ip': u'35.185.22.50', u'created_time': 1481562884357, u'event_keys': []} item.store() result = spark_context.riakBucket(bucket.name).queryBucketKeys("test-key").collect() # # if object value is a JSON object that contains a List of values, exception raised # https://bashoeng.atlassian.net/browse/SPARK-275 # @pytest.mark.regression @pytest.mark.riakkv def test_read_JSON_value_with_not_empty_list (spark_context, riak_client): bucket = riak_client.bucket("test-bucket-"+str(randint(0,100000))) item = bucket.new("test-key") item.data = {"session_ids":["t_sess_1401"], "last_active_time":1481562896697, "ecompany":"test.riak.ecompany.com.1"} item.store() result = spark_context.riakBucket(bucket.name).queryBucketKeys("test-key").collect() # # if object value is a JSON object that contains an empty Object, exception raised # https://bashoeng.atlassian.net/browse/SPARK-281 # @pytest.mark.regression @pytest.mark.riakkv def test_read_JSON_value_with_an_empty_map (spark_context, riak_client): bucket = riak_client.bucket("test-bucket-"+str(randint(0,100000))) item = bucket.new("test-key-empty-object") item.data = {u'client_ip': u'35.185.22.50', u'created_time': 1481562884357, u'event_keys': {}} item.store() result = spark_context.riakBucket(bucket.name).queryBucketKeys("test-key-empty-object").collect() ###### TS Tests ####### @pytest.mark.riakts def test_ts_df_write_use_timestamp(spark_context, riak_client, sql_context): _test_spark_df_ts_write_use_timestamp(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_write_use_long(spark_context, riak_client, sql_context): _test_spark_df_ts_write_use_long(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_read_use_timestamp(spark_context, riak_client, sql_context): _test_spark_df_ts_read_use_timestamp(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_read_use_long(spark_context, riak_client, sql_context): _test_spark_df_ts_read_use_long(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_read_use_timestamp_ts_quantum(spark_context, riak_client, sql_context): _test_spark_df_ts_read_use_timestamp_ts_quantum(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_read_use_long_ts_quantum(spark_context, riak_client, sql_context): _test_spark_df_ts_read_use_long_ts_quantum(10, 5, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_range_query_input_split_count_use_timestamp(spark_context, riak_client, sql_context): _test_spark_df_ts_range_query_input_split_count_use_timestamp(10, 5, 3, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_range_query_input_split_count_use_long(spark_context, riak_client, sql_context): _test_spark_df_ts_range_query_input_split_count_use_long(10, 5, 3, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_range_query_input_split_count_use_timestamp_ts_quantum(spark_context, riak_client, sql_context): _test_spark_df_ts_range_query_input_split_count_use_timestamp_ts_quantum(10, 5, 3, spark_context, riak_client, sql_context) @pytest.mark.riakts def test_ts_df_range_query_input_split_count_use_long_ts_quantum(spark_context, riak_client, sql_context): _test_spark_df_ts_range_query_input_split_count_use_long_ts_quantum(10, 5, 3, spark_context, riak_client, sql_context)

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samcli/commands/delete/__init__.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

[ "BSD-2-Clause", "Apache-2.0" ]

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2018-05-08T21:48:56.000Z

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samcli/commands/delete/__init__.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

[ "BSD-2-Clause", "Apache-2.0" ]

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2018-05-08T22:44:28.000Z

2020-08-24T20:19:24.000Z

samcli/commands/delete/__init__.py

torresxb1/aws-sam-cli

d307f2eb6e1a91a476a5e2ca6070f974b0c913f1

[ "BSD-2-Clause", "Apache-2.0" ]

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2018-05-08T22:09:19.000Z

2020-08-17T09:04:37.000Z

""" `sam delete` command """ # Expose the cli object here from .command import cli # noqa

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Python

practice/pythonchallenge/lib.py

mrundle/mrundle-dev

d85bac312df9f2d1eb7f39c483746fe9953e9a68

[ "MIT" ]

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2017-06-22T08:48:12.000Z

2018-10-19T14:04:17.000Z

practice/pythonchallenge/lib.py

mrundle/mrundle-dev

d85bac312df9f2d1eb7f39c483746fe9953e9a68

[ "MIT" ]

null

practice/pythonchallenge/lib.py

mrundle/mrundle-dev

d85bac312df9f2d1eb7f39c483746fe9953e9a68

[ "MIT" ]

null

from urllib.request import urlopen def raw_url(url): return urlopen(url).read()

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py

Python

abilities/__init__.py

jack-skerrett-bluefruit/Python-ScreenPlay

045486bdf441fa3a7a6cde59e7b7e12a7d53fbed

[ "MIT" ]

null

abilities/__init__.py

jack-skerrett-bluefruit/Python-ScreenPlay

045486bdf441fa3a7a6cde59e7b7e12a7d53fbed

[ "MIT" ]

null

abilities/__init__.py

jack-skerrett-bluefruit/Python-ScreenPlay

045486bdf441fa3a7a6cde59e7b7e12a7d53fbed

[ "MIT" ]

null

from .browse_the_web import browse_the_web, browser_for, waiting_browser_for __all__ = [ 'browse_the_web', 'browser_for', 'waiting_browser_for' # browse_the_web.py ]

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py

Python

raybot/util/__init__.py

Zverik/bot_na_rayone

b1586ed2c73b28fab5bb75d70c747a61f573d80d

[ "0BSD" ]

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2021-01-23T17:26:18.000Z

2022-01-19T14:23:57.000Z

raybot/util/__init__.py

Zverik/bot_na_rayone

b1586ed2c73b28fab5bb75d70c747a61f573d80d

[ "0BSD" ]

null

raybot/util/__init__.py

Zverik/bot_na_rayone

b1586ed2c73b28fab5bb75d70c747a61f573d80d

[ "0BSD" ]

5

2021-02-01T13:51:46.000Z

2021-02-16T21:17:20.000Z

from .map import get_map from .util import *

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py

Python

lib/metrics/__init__.py

kisonho/torchmanager

ac01c61a132238bc0d39bf2173dfd37f44dbbf30

[ "BSD-2-Clause" ]

null

lib/metrics/__init__.py

kisonho/torchmanager

ac01c61a132238bc0d39bf2173dfd37f44dbbf30

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null

lib/metrics/__init__.py

kisonho/torchmanager

ac01c61a132238bc0d39bf2173dfd37f44dbbf30

[ "BSD-2-Clause" ]

null

from .accuracy import Accuracy, CategoricalAccuracy, MAE, SparseCategoricalAccuracy from .conf_met import ConfusionMetrics from .iou import InstanceIoU, MeanIoU, MIoU from .metric import Metric, metric

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py

Python

worker/test.py

soltanoff/dj_task_queue

b219756d607d7b42bd33d8d603cad7a425962011

[ "MIT" ]

null

worker/test.py

soltanoff/dj_task_queue

b219756d607d7b42bd33d8d603cad7a425962011

[ "MIT" ]

1

2018-12-03T11:32:52.000Z

worker/test.py

soltanoff/dj_task_queue

b219756d607d7b42bd33d8d603cad7a425962011

[ "MIT" ]

null

import random import time time.sleep(random.randint(0, 10))

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py

Python

01_Data_Types_And_Variables/06_vicious_variables.py

DetainedDeveloper/Pimp-My-Python

8c0749013581bebd65b3df67f92605c4ecd8e685

[ "MIT" ]

1

2021-07-01T10:54:45.000Z

01_Data_Types_And_Variables/06_vicious_variables.py

DetainedDeveloper/Pimp-My-Python

8c0749013581bebd65b3df67f92605c4ecd8e685

[ "MIT" ]

null

01_Data_Types_And_Variables/06_vicious_variables.py

DetainedDeveloper/Pimp-My-Python

8c0749013581bebd65b3df67f92605c4ecd8e685

[ "MIT" ]

null

# x = 3 # print(x, type(x)) # x = 3.14159 # print(x, type(x)) # x = "3" # print(x, type(x)) # x = 3 == 3 # print(x, type(x)) # x = [1, 2, 3] # print(x, type(x)) # x = {"1": "one", "2": "two", "3": "three"} # print(x, type(x)) # START # x = 3 # y = "Jay" # print(x + y) # END # START # x = 3 # y = 6 # print(x + y) # y = "Jay" # print("x is " + str(x) + " and y is " + y) # print("x is {} and y is {}".format(x, y)) # print(f"x is {x} and y is {y}") # END

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py

Python

chokozainerrl/experiments/__init__.py

chokozainer/chokozainerrl

7745e307dba715f11887ccccce8cf1067ecfb99f

[ "BSD-2-Clause" ]

null

chokozainerrl/experiments/__init__.py

chokozainer/chokozainerrl

7745e307dba715f11887ccccce8cf1067ecfb99f

[ "BSD-2-Clause" ]

null

chokozainerrl/experiments/__init__.py

chokozainer/chokozainerrl

7745e307dba715f11887ccccce8cf1067ecfb99f

[ "BSD-2-Clause" ]

null

from chokozainerrl.experiments.collect_demos import collect_demonstrations # NOQA from chokozainerrl.experiments.evaluator import eval_performance # NOQA from chokozainerrl.experiments.hooks import LinearInterpolationHook # NOQA from chokozainerrl.experiments.hooks import StepHook # NOQA from chokozainerrl.experiments.prepare_output_dir import is_under_git_control # NOQA from chokozainerrl.experiments.prepare_output_dir import prepare_output_dir # NOQA from chokozainerrl.experiments.train_agent import train_agent # NOQA from chokozainerrl.experiments.train_agent import train_agent_with_evaluation # NOQA from chokozainerrl.experiments.train_agent_async import train_agent_async # NOQA from chokozainerrl.experiments.train_agent_batch import train_agent_batch # NOQA from chokozainerrl.experiments.train_agent_batch import train_agent_batch_with_evaluation # NOQA

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py

Python

aliyun-python-sdk-sae/aliyunsdksae/__init__.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

aliyun-python-sdk-sae/aliyunsdksae/__init__.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

aliyun-python-sdk-sae/aliyunsdksae/__init__.py

jorsonzen/aliyun-openapi-python-sdk

0afbfa8e5f9e19455695aa799f7dcc1cd853d827

[ "Apache-2.0" ]

null

__version__ = '1.18.15'

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py

Python

merino/__init__.py

cmap/merino

30701866b05b8517e09beaec17a3ea4454c1fd63

[ "BSD-3-Clause" ]

3

2018-10-05T11:38:36.000Z

2019-07-23T16:53:18.000Z

merino/__init__.py

cmap/merino

30701866b05b8517e09beaec17a3ea4454c1fd63

[ "BSD-3-Clause" ]

9

2018-09-17T20:35:24.000Z

2021-07-13T14:04:51.000Z

merino/__init__.py

cmap/merino

30701866b05b8517e09beaec17a3ea4454c1fd63

[ "BSD-3-Clause" ]

1

2018-09-25T14:32:49.000Z

__author__ = 'dlahr' import os.path #default_config_filepath = os.path.expanduser("~/.prism_pipeline.cfg") default_config_filepath = "https://s3.amazonaws.com/analysis.clue.io/vdb/merino/prism_pipeline.cfg"

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aa1a58e97d19be803d606327b96d1f8d0340cf28

113

py

Python

tests/bpyutils/i18n/test_i18n_util.py

boilpy/bpyutils

9f81c544abaf7b3221f3c6783f293c344bd94544

[ "MIT" ]

null

tests/bpyutils/i18n/test_i18n_util.py

boilpy/bpyutils

9f81c544abaf7b3221f3c6783f293c344bd94544

[ "MIT" ]

null

tests/bpyutils/i18n/test_i18n_util.py

boilpy/bpyutils

9f81c544abaf7b3221f3c6783f293c344bd94544

[ "MIT" ]

null

import pytest from bpyutils.i18n.util import ( get_locale ) def test_get_locale(): raise NotImplementedError

14.125

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null

0

1

0

1

0

5

aa368cefb370efa049950320a1e413452fa9b1a1

157

py

Python

otp/speedchat/SCDecoders.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

99

2019-11-02T22:25:00.000Z

2022-02-03T03:48:00.000Z

otp/speedchat/SCDecoders.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

42

2019-11-03T05:31:08.000Z

2022-03-16T22:50:32.000Z

otp/speedchat/SCDecoders.py

TheFamiliarScoot/open-toontown

678313033174ea7d08e5c2823bd7b473701ff547

[ "BSD-3-Clause" ]

57

2019-11-03T07:47:37.000Z

2022-03-22T00:41:49.000Z

from .SCStaticTextTerminal import decodeSCStaticTextMsg from .SCCustomTerminal import decodeSCCustomMsg from .SCEmoteTerminal import decodeSCEmoteWhisperMsg

39.25

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11.833333

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true

0

1

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null

0

1

0

null

0

1

0

1

0

5

a4c64c22f072e8d8e99026148d27d089d907e027

189

py

Python

user/views.py

mallumac/DjangoBlog

d082046f6b072cdad54be6d8fce2223fdde3851e

[ "MIT" ]

1

2020-11-01T03:57:51.000Z

user/views.py

mallumac/DjangoBlog

d082046f6b072cdad54be6d8fce2223fdde3851e

[ "MIT" ]

null

user/views.py

mallumac/DjangoBlog

d082046f6b072cdad54be6d8fce2223fdde3851e

[ "MIT" ]

null

from django.shortcuts import render, HttpResponse # Create your views here. def home(request): return HttpResponse("Welcome") def about(request): return HttpResponse("About page")

23.625

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null

0

1

0

1

0

5

a4e5432afd293310bfc132a3be8df91289b58c53

79

py

Python

XueshuSpider/__init__.py

dilllll/xueshu_spider

48e35e2a5581cc2bb9864ef01cec7dffe9ba5e74

[ "MIT" ]

6

2018-07-05T06:17:17.000Z

2021-05-12T09:09:05.000Z

XueshuSpider/__init__.py

dilllll/xueshu_spider

48e35e2a5581cc2bb9864ef01cec7dffe9ba5e74

[ "MIT" ]

4

2020-10-27T21:18:53.000Z

2022-03-02T14:55:28.000Z

XueshuSpider/__init__.py

dilllll/xueshu_spider

48e35e2a5581cc2bb9864ef01cec7dffe9ba5e74

[ "MIT" ]

3

2019-11-15T12:45:38.000Z

2020-11-24T03:13:14.000Z

""" @Author : dilless @Time : 2018/6/23 0:59 @File : __init__.py.py """

13.166667

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12

79

3.25

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79

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0.886076

0

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0

5

3521aec3e550578743dca147a883e851ff8f7ff1

51

py

Python

sanic_prometheus/exceptions.py

smiralr/sanic-prometheus

19429c18a823527c7b0e99261b8fc54feabaa1ba

[ "MIT" ]

71

2017-04-18T19:26:35.000Z

2021-11-24T23:04:24.000Z

sanic_prometheus/exceptions.py

valerylisay/sanic-prometheus-mon

2292c1d4c48fc6b22074445ef23e052b9d093bba

[ "MIT" ]

32

2017-05-02T22:14:41.000Z

2022-02-08T18:54:35.000Z

sanic_prometheus/exceptions.py

valerylisay/sanic-prometheus-mon

2292c1d4c48fc6b22074445ef23e052b9d093bba

[ "MIT" ]

25

2017-04-28T17:04:43.000Z

2022-01-05T12:13:11.000Z

class SanicPrometheusError(RuntimeError): pass

17

41

0.803922

4

51

10.25

1

0

0.137255

51

2

42

25.5

0.931818

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1

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true

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0.5

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1

0

1

null

0

1

0

null

0

1

0

5

3533ca2fbccbe0afe4cfb8494770431494c9a4f3

546

py

Python

bipbop/client/__init__.py

Adianta/bipbop-python

9963a3bddc41a2c9f099dbbf890eacd9a62402d1

[ "MIT" ]

null

bipbop/client/__init__.py

Adianta/bipbop-python

9963a3bddc41a2c9f099dbbf890eacd9a62402d1

[ "MIT" ]

null

bipbop/client/__init__.py

Adianta/bipbop-python

9963a3bddc41a2c9f099dbbf890eacd9a62402d1

[ "MIT" ]

null

# BIPBOP from bipbop.client.database import Database from bipbop.client.namebycpfcnpj import NameByCPFCNPJ from bipbop.client.table import Table from bipbop.client.field import Field from bipbop.client.exception import Exception from bipbop.client.webservice import WebService from bipbop.client.servicediscovery import ServiceDiscovery from bipbop.client.servicediscoveryjuristek import ServiceDiscoveryJuristek from bipbop.client.push import Push from bipbop.client.pushjuristek import PushJuristek from bipbop.client.receiver import Receiver

39

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546

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0.231092

0.369748

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0.086081

546

13

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0

1

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true

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null

1

0

null

0

1

0

1

0

1

0

5

101d10922ca823661f6646fb62b2891b0062bea2

138

py

Python

main.py

emmlejeail/stockMarketPrediction

e56ee6077fdc727a8029350b99eaf892b18a408c

[ "MIT" ]

null

main.py

emmlejeail/stockMarketPrediction

e56ee6077fdc727a8029350b99eaf892b18a408c

[ "MIT" ]

null

main.py

emmlejeail/stockMarketPrediction

e56ee6077fdc727a8029350b99eaf892b18a408c

[ "MIT" ]

null

import numpy as np import pandas as pd import datetime as dt import math import seaborn as sns import matplotlib.pyplot as plt import glob

19.714286

31

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25

138

4.56

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true

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1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

5

1025d202d48d499125b0b0a20b9622b34b3f912b

149

py

Python

model/dungeon_tile.py

MattJTrueblood/Allies_RL_Prototype

1c7c4360156d0dc5ff53c49401d25026761862df

[ "Unlicense" ]

1

2018-11-19T19:51:49.000Z

model/dungeon_tile.py

MattJTrueblood/Allies_RL_Prototype

1c7c4360156d0dc5ff53c49401d25026761862df

[ "Unlicense" ]

null

model/dungeon_tile.py

MattJTrueblood/Allies_RL_Prototype

1c7c4360156d0dc5ff53c49401d25026761862df

[ "Unlicense" ]

null

class DungeonTile: def __init__(self, canvas_tile, is_obstacle): self.canvas_tile = canvas_tile self.is_obstacle = is_obstacle

21.285714

49

0.704698

19

149

5

0.473684

0.315789

0.294737

0

0.228188

149

6

50

24.833333

0.826087

0

1

0.25

false

0

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1

0

null

1

0

1

0

null

0

1

0

5

103e9e71f5648bdb42bcb90f1ed73a347ed3865a

249

py

Python

Notebooks/SentinelUtilities/SentinelUtils/__init__.py

ms-bnick/Azure-Sentinel

8a25e5cbac6001af10617ba709a580a5a65b9ae0

[ "MIT" ]

1

2019-11-22T12:25:53.000Z

Notebooks/SentinelUtilities/SentinelUtils/__init__.py

ms-bnick/Azure-Sentinel

8a25e5cbac6001af10617ba709a580a5a65b9ae0

[ "MIT" ]

1

2019-09-20T19:56:54.000Z

Notebooks/SentinelUtilities/SentinelUtils/__init__.py

ms-bnick/Azure-Sentinel

8a25e5cbac6001af10617ba709a580a5a65b9ae0

[ "MIT" ]

1

2019-09-20T18:13:21.000Z

""" SentinelUtils: This package provides utility methods in general """ # __init__.py from .config_reader import ConfigReader from .version_management import VersionInformation, ModuleVersionCheck from .obfuscation_utility import ObfuscationUtility

31.125

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249

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true

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1

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1

0

5

106a5c947e2d64cc1813bbe19dde75ba6efa66ad

84

py

Python

avalonplex_core/utils.py

AvalonPlex/avalonplex-core

3b3aa263edf5c22e8dec3430ecafa28504a48435

[ "MIT" ]

null

avalonplex_core/utils.py

AvalonPlex/avalonplex-core

3b3aa263edf5c22e8dec3430ecafa28504a48435

[ "MIT" ]

1

2018-01-22T07:02:27.000Z

2018-01-22T07:02:36.000Z

avalonplex_core/utils.py

AvalonPlex/avalonplex-core

3b3aa263edf5c22e8dec3430ecafa28504a48435

[ "MIT" ]

null

def invert_dict(source: dict) -> dict: return {v: k for k, v in source.items()}

28

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84

3.533333

0.666667

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null

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1

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null

0

1

0

1

0

5

108a31e9190da77c43f8dcf1056083876f329ed7

206

py

Python

models/remote/vote.py

anthill-gaming/anthill-admin

e3c29a9bd7c04d2c6ce29528578a93395adf59e0

[ "MIT" ]

null

models/remote/vote.py

anthill-gaming/anthill-admin

e3c29a9bd7c04d2c6ce29528578a93395adf59e0

[ "MIT" ]

null

models/remote/vote.py

anthill-gaming/anthill-admin

e3c29a9bd7c04d2c6ce29528578a93395adf59e0

[ "MIT" ]

null

from anthill.platform.remote_models import remote_model_factory __all__ = ['Voting', 'VotingMember'] Voting = remote_model_factory('vote.Voting') VotingMember = remote_model_factory('vote.VotingMember')

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0

5

10abe7ec4383ca43124bd8d9c3c98394101fda3a

38,733

py

Python

asynet/training/trainer.py

ibugueno/Event-Camera-Applications

16d62fc548e3abf8b994c0d50f8a1aa3dadc7095

[ "MIT" ]

1

2021-07-06T08:53:26.000Z

asynet/training/trainer.py

ibugueno/Event-Camera-Applications

16d62fc548e3abf8b994c0d50f8a1aa3dadc7095

[ "MIT" ]

null

asynet/training/trainer.py

ibugueno/Event-Camera-Applications

16d62fc548e3abf8b994c0d50f8a1aa3dadc7095

[ "MIT" ]

1

2021-07-06T08:53:26.000Z

import os import abc import tqdm import torch import numpy as np import torch.nn as nn import torch.optim as optim from torch.utils.tensorboard import SummaryWriter import dataloader.dataset from dataloader.loader import Loader from models.facebook_sparse_vgg import FBSparseVGG from models.facebook_sparse_object_det import FBSparseObjectDet from models.dense_VGG import DenseVGG from models.dense_object_det import DenseObjectDet from models.yolo_loss import yoloLoss from models.yolo_detection import yoloDetect from models.yolo_detection import nonMaxSuppression from utils.statistics_pascalvoc import BoundingBoxes, BoundingBox, BBType, VOC_Evaluator, MethodAveragePrecision import utils.visualizations as visualizations class AbstractTrainer(abc.ABC): def __init__(self, settings): self.settings = settings self.model = None self.scheduler = None self.nr_classes = None self.val_loader = None self.train_loader = None self.nr_val_epochs = None self.bounding_boxes =None self.object_classes = None self.nr_train_epochs = None self.model_input_size = None if self.settings.event_representation == 'histogram': self.nr_input_channels = 2 elif self.settings.event_representation == 'event_queue': self.nr_input_channels = 30 self.dataset_builder = dataloader.dataset.getDataloader(self.settings.dataset_name) self.dataset_loader = Loader self.writer = SummaryWriter(self.settings.ckpt_dir) self.createDatasets() self.buildModel() self.optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=self.settings.init_lr) if settings.steps_lr is not None: self.scheduler = torch.optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=settings.steps_lr, gamma=settings.factor_lr) if settings.resume_training: self.loadCheckpoint(self.settings.resume_ckpt_file) self.batch_step = 0 self.epoch_step = 0 self.training_loss = 0 self.val_batch_step = 0 self.validation_loss = 0 self.training_accuracy = 0 self.validation_accuracy = 0 self.max_validation_accuracy = 0 self.val_confusion_matrix = np.zeros([self.nr_classes, self.nr_classes]) # tqdm progress bar self.pbar = None @abc.abstractmethod def buildModel(self): """Model is constructed in child class""" pass def createDatasets(self): """ Creates the validation and the training data based on the lists specified in the config/settings.yaml file. """ train_dataset = self.dataset_builder(self.settings.dataset_path, self.settings.object_classes, self.settings.height, self.settings.width, self.settings.nr_events_window, augmentation=True, mode='training', event_representation=self.settings.event_representation) self.nr_train_epochs = int(train_dataset.nr_samples / self.settings.batch_size) + 1 self.nr_classes = train_dataset.nr_classes self.object_classes = train_dataset.object_classes val_dataset = self.dataset_builder(self.settings.dataset_path, self.settings.object_classes, self.settings.height, self.settings.width, self.settings.nr_events_window, mode='validation', event_representation=self.settings.event_representation) self.nr_val_epochs = int(val_dataset.nr_samples / self.settings.batch_size) + 1 self.train_loader = self.dataset_loader(train_dataset, batch_size=self.settings.batch_size, device=self.settings.gpu_device, num_workers=self.settings.num_cpu_workers, pin_memory=False) self.val_loader = self.dataset_loader(val_dataset, batch_size=self.settings.batch_size, device=self.settings.gpu_device, num_workers=self.settings.num_cpu_workers, pin_memory=False) @staticmethod def denseToSparse(dense_tensor): """ Converts a dense tensor to a sparse vector. :param dense_tensor: BatchSize x SpatialDimension_1 x SpatialDimension_2 x ... x FeatureDimension :return locations: NumberOfActive x (SumSpatialDimensions + 1). The + 1 includes the batch index :return features: NumberOfActive x FeatureDimension """ non_zero_indices = torch.nonzero(torch.abs(dense_tensor).sum(axis=-1)) locations = torch.cat((non_zero_indices[:, 1:], non_zero_indices[:, 0, None]), dim=-1) select_indices = non_zero_indices.split(1, dim=1) features = torch.squeeze(dense_tensor[select_indices], dim=-2) return locations, features def resetValidation(self): """Resets all the validation statistics to zero""" self.val_batch_step = 0 self.validation_loss = 0 self.validation_accuracy = 0 self.val_confusion_matrix = np.zeros([self.nr_classes, self.nr_classes]) def saveValidationStatistics(self): """Saves the recorded validation statistics to an event file (tensorboard)""" self.writer.add_scalar('Validation/Validation_Loss', self.validation_loss, self.epoch_step) self.writer.add_scalar('Validation/Validation_Accuracy', self.validation_accuracy, self.epoch_step) self.val_confusion_matrix = self.val_confusion_matrix / (np.sum(self.val_confusion_matrix, axis=-1, keepdims=True) + 1e-9) plot_confusion_matrix = visualizations.visualizeConfusionMatrix(self.val_confusion_matrix) self.writer.add_image('Validation/Confusion_Matrix', plot_confusion_matrix, self.epoch_step, dataformats='HWC') def storeLossesObjectDetection(self, loss_list): """Writes the different losses to tensorboard""" loss_names = ['Overall_Loss', 'Offset_Loss', 'Shape_Loss', 'Confidence_Loss', 'Confidence_NoObject_Loss', 'Class_Loss'] for i_loss in range(len(loss_list)): loss_value = loss_list[i_loss].data.cpu().numpy() self.writer.add_scalar('TrainingLoss/' + loss_names[i_loss], loss_value, self.batch_step) def saveBoundingBoxes(self, gt_bbox, detected_bbox): """ Saves the bounding boxes in the evaluation format :param gt_bbox: gt_bbox[0, 0, :]: ['u', 'v', 'w', 'h', 'class_id'] :param detected_bbox[0, :]: [batch_idx, u, v, w, h, pred_class_id, pred_class_score, object score] """ image_size = self.model_input_size.cpu().numpy() for i_batch in range(gt_bbox.shape[0]): for i_gt in range(gt_bbox.shape[1]): gt_bbox_sample = gt_bbox[i_batch, i_gt, :] id_image = self.val_batch_step * self.settings.batch_size + i_batch if gt_bbox[i_batch, i_gt, :].sum() == 0: break bb_gt = BoundingBox(id_image, gt_bbox_sample[-1], gt_bbox_sample[0], gt_bbox_sample[1], gt_bbox_sample[2], gt_bbox_sample[3], image_size, BBType.GroundTruth) self.bounding_boxes.addBoundingBox(bb_gt) for i_det in range(detected_bbox.shape[0]): det_bbox_sample = detected_bbox[i_det, :] id_image = self.val_batch_step * self.settings.batch_size + det_bbox_sample[0] bb_det = BoundingBox(id_image, det_bbox_sample[5], det_bbox_sample[1], det_bbox_sample[2], det_bbox_sample[3], det_bbox_sample[4], image_size, BBType.Detected, det_bbox_sample[6]) self.bounding_boxes.addBoundingBox(bb_det) def saveValidationStatisticsObjectDetection(self): """Saves the statistice relevant for object detection""" evaluator = VOC_Evaluator() metrics = evaluator.GetPascalVOCMetrics(self.bounding_boxes, IOUThreshold=0.5, method=MethodAveragePrecision.EveryPointInterpolation) acc_AP = 0 total_positives = 0 for metricsPerClass in metrics: acc_AP += metricsPerClass['AP'] total_positives += metricsPerClass['total positives'] mAP = acc_AP / self.nr_classes self.validation_accuracy = mAP self.writer.add_scalar('Validation/Validation_Loss', self.validation_loss, self.epoch_step) self.writer.add_scalar('Validation/Validation_mAP', self.validation_accuracy, self.epoch_step) def getLearningRate(self): for param_group in self.optimizer.param_groups: return param_group['lr'] def loadCheckpoint(self, filename): if os.path.isfile(filename): print("=> loading checkpoint '{}'".format(filename)) checkpoint = torch.load(filename) self.epoch_step = checkpoint['epoch'] self.model.load_state_dict(checkpoint['state_dict']) self.optimizer.load_state_dict(checkpoint['optimizer']) print("=> loaded checkpoint '{}' (epoch {})".format(filename, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(filename)) def saveCheckpoint(self): file_path = os.path.join(self.settings.ckpt_dir, 'model_step_' + str(self.epoch_step) + '.pth') torch.save({'state_dict': self.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'epoch': self.epoch_step}, file_path) class FBSparseVGGModel(AbstractTrainer): def buildModel(self): """Creates the specified model""" self.model = FBSparseVGG(self.nr_classes, self.nr_input_channels, vgg_12=(self.settings.dataset_name == 'NCars')) self.model.to(self.settings.gpu_device) self.model_input_size = self.model.spatial_size def train(self): """Main training and validation loop""" while True: if (self.epoch_step % 5) == 0: self.validationEpoch() self.trainEpoch() self.epoch_step += 1 self.scheduler.step() def trainEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_train_epochs, unit='Batch', unit_scale=True) self.model = self.model.train() self.training_loss = 0 self.training_accuracy = 0 loss_function = nn.CrossEntropyLoss() for i_batch, sample_batched in enumerate(self.train_loader): _, labels, histogram = sample_batched self.optimizer.zero_grad() # Change size to input size of sparse VGG histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) histogram = histogram.permute(0, 2, 3, 1) locations, features = self.denseToSparse(histogram) model_output = self.model([locations, features, histogram.shape[0]]) loss = loss_function(model_output, target=labels) # Save training statistics predicted_classes = model_output.argmax(1) accuracy = (predicted_classes == labels).float().mean() self.training_accuracy += accuracy.data.cpu().numpy() self.training_loss += loss.data.cpu().numpy() loss.backward() self.optimizer.step() # Visualization # events_sample1 = events[events[:, -1] == 0, :-1] # file_path = os.path.join(self.settings.vis_dir, 'image_' + str(self.batch_step) + '.png') # visualizations.visualizeEventsTime(events_sample1.data.cpu().numpy(), self.settings.height, # self.settings.width, path_name=file_path, last_k_events=None) if self.batch_step % (self.nr_train_epochs * 50) == 0: batch_one_mask = locations[:, -1] == 0 vis_locations = locations[batch_one_mask, :2] features = features[batch_one_mask, :] # file_path = os.path.join(self.settings.vis_dir, 'image_' + str(self.batch_step) + '.png') # visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, # features=features.cpu().numpy(), path_name=file_path) image = visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, features=features.cpu().numpy()) self.writer.add_image('Training/Input Histogram', image, self.epoch_step, dataformats='HWC') self.pbar.set_postfix(TrainLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.batch_step += 1 self.writer.add_scalar('Training/Training_Accuracy', self.training_accuracy / float(self.nr_train_epochs), self.epoch_step) self.writer.add_scalar('Training/Training_Loss', self.training_loss / float(self.nr_train_epochs), self.epoch_step) self.writer.add_scalar('Training/Learning_Rate', self.getLearningRate(), self.epoch_step) self.pbar.close() def validationEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_val_epochs, unit='Batch', unit_scale=True) self.resetValidation() self.model = self.model.eval() loss_function = nn.CrossEntropyLoss() for i_batch, sample_batched in enumerate(self.val_loader): _, labels, histogram = sample_batched # Convert spatial dimension to model input size histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) histogram = histogram.permute(0, 2, 3, 1) locations, features = self.denseToSparse(histogram) with torch.no_grad(): model_output = self.model([locations, features, histogram.shape[0]]) loss = loss_function(model_output, target=labels) # Save validation statistics predicted_classes = model_output.argmax(1) accuracy = (predicted_classes == labels).float().mean() self.validation_loss += loss.data.cpu().numpy() self.validation_accuracy += accuracy.data.cpu().numpy() np.add.at(self.val_confusion_matrix, (predicted_classes.data.cpu().numpy(), labels.data.cpu().numpy()), 1) # Visualization # events_sample1 = events[events[:, -1] == 0, :-1] # file_path = os.path.join(self.settings.vis_dir, 'image_' + str(self.batch_step) + '.png') # visualizations.visualizeEventsTime(events_sample1.data.cpu().numpy(), self.settings.height, # self.settings.width, path_name=file_path, last_k_events=None) self.pbar.set_postfix(ValLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.val_batch_step += 1 self.validation_loss = self.validation_loss / float(self.val_batch_step) self.validation_accuracy = self.validation_accuracy / float(self.val_batch_step) self.saveValidationStatistics() if self.max_validation_accuracy < self.validation_accuracy: self.max_validation_accuracy = self.validation_accuracy self.saveCheckpoint() self.pbar.close() class DenseVGGModel(AbstractTrainer): def buildModel(self): """Creates the specified model""" self.model = DenseVGG(self.nr_classes, in_c=self.nr_input_channels, vgg_12=(self.settings.dataset_name == 'NCars')) self.model.to(self.settings.gpu_device) # Input size is determined by architecture if self.settings.dataset_name == 'NCaltech101': self.model_input_size = [191, 255] elif self.settings.dataset_name == 'NCars': self.model_input_size = [95, 127] def train(self): """Main training and validation loop""" while self.epoch_step <= 6507: if (self.epoch_step % 50) == 0: self.validationEpoch() self.trainEpoch() self.epoch_step += 1 self.scheduler.step() def trainEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_train_epochs, unit='Batch', unit_scale=True) self.model = self.model.train() self.training_loss = 0 self.training_accuracy = 0 loss_function = nn.CrossEntropyLoss() for i_batch, sample_batched in enumerate(self.train_loader): events, labels, histogram = sample_batched self.optimizer.zero_grad() # Change size to input size of sparse VGG histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) model_output = self.model(histogram) loss = loss_function(model_output, target=labels) # Save training statistics predicted_classes = model_output.argmax(1) accuracy = (predicted_classes == labels).float().mean() self.training_accuracy += accuracy.data.cpu().numpy() self.training_loss += loss.data.cpu().numpy() loss.backward() self.optimizer.step() if self.batch_step % (self.nr_train_epochs * 50) == 0: # file_path = os.path.join(self.settings.vis_dir, 'image_' + str(self.batch_step) + '.png') # visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, # features=features.cpu().numpy(), path_name=file_path) image = visualizations.visualizeHistogram(histogram[0, :, :, :].permute(1, 2, 0).cpu().int().numpy()) self.writer.add_image('Training/Input Histogram', image, self.epoch_step, dataformats='HWC') self.pbar.set_postfix(TrainLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.batch_step += 1 self.writer.add_scalar('Training/Training_Accuracy', self.training_accuracy / float(self.nr_train_epochs), self.epoch_step) self.writer.add_scalar('Training/Training_Loss', self.training_loss / float(self.nr_train_epochs), self.epoch_step) self.writer.add_scalar('Training/Learning_Rate', self.getLearningRate(), self.epoch_step) self.pbar.close() def validationEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_val_epochs, unit='Batch', unit_scale=True) self.resetValidation() self.model = self.model.eval() loss_function = nn.CrossEntropyLoss() for i_batch, sample_batched in enumerate(self.val_loader): events, labels, histogram = sample_batched # Convert spatial dimension to model input size histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) with torch.no_grad(): model_output = self.model(histogram) loss = loss_function(model_output, target=labels) # Save validation statistics predicted_classes = model_output.argmax(1) accuracy = (predicted_classes == labels).float().mean() self.validation_loss += loss.data.cpu().numpy() self.validation_accuracy += accuracy.data.cpu().numpy() np.add.at(self.val_confusion_matrix, (predicted_classes.data.cpu().numpy(), labels.data.cpu().numpy()), 1) self.pbar.set_postfix(ValLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.val_batch_step += 1 self.validation_loss = self.validation_loss / float(self.val_batch_step) self.validation_accuracy = self.validation_accuracy / float(self.val_batch_step) self.saveValidationStatistics() if self.max_validation_accuracy < self.validation_accuracy: self.max_validation_accuracy = self.validation_accuracy self.saveCheckpoint() self.pbar.close() class SparseObjectDetModel(AbstractTrainer): def buildModel(self): """Creates the specified model""" self.model = FBSparseObjectDet(self.nr_classes, nr_input_channels=self.nr_input_channels, small_out_map=(self.settings.dataset_name == 'NCaltech101_ObjectDetection')) self.model.to(self.settings.gpu_device) self.model_input_size = self.model.spatial_size # [191, 255] if self.settings.use_pretrained and (self.settings.dataset_name == 'NCaltech101_ObjectDetection' or self.settings.dataset_name == 'Prophesee'): self.loadPretrainedWeights() def loadPretrainedWeights(self): """Loads pretrained model weights""" checkpoint = torch.load(self.settings.pretrained_sparse_vgg) try: pretrained_dict = checkpoint['state_dict'] except KeyError: pretrained_dict = checkpoint['model'] pretrained_dict_short = {} for k, v in pretrained_dict.items(): if 'sparseModel.25' in k: break pretrained_dict_short[k] = v self.model.load_state_dict(pretrained_dict_short, strict=False) def train(self): """Main training and validation loop""" validation_step = 50 - 48 * (self.settings.dataset_name == 'Prophesee') while True: self.trainEpoch() if (self.epoch_step % validation_step) == (validation_step - 1): self.validationEpoch() self.epoch_step += 1 self.scheduler.step() def trainEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_train_epochs, unit='Batch', unit_scale=True) self.model = self.model.train() loss_function = yoloLoss for i_batch, sample_batched in enumerate(self.train_loader): event, bounding_box, histogram = sample_batched self.optimizer.zero_grad() # Change size to input size of sparse VGG histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) histogram = histogram.permute(0, 2, 3, 1) # Change x, width and y, height bounding_box[:, :, [0, 2]] = (bounding_box[:, :, [0, 2]] * self.model_input_size[1].float() / self.settings.width).long() bounding_box[:, :, [1, 3]] = (bounding_box[:, :, [1, 3]] * self.model_input_size[0].float() / self.settings.height).long() locations, features = self.denseToSparse(histogram) # Deep Learning Magic model_output = self.model([locations, features, histogram.shape[0]]) out = loss_function(model_output, bounding_box, self.model_input_size) loss = out[0] # Write losses statistics self.storeLossesObjectDetection(out) loss.backward() self.optimizer.step() if self.batch_step % (self.nr_train_epochs * 50) == 0: batch_one_mask = locations[:, -1] == 0 vis_locations = locations[batch_one_mask, :2] features = features[batch_one_mask, :] with torch.no_grad(): detected_bbox = yoloDetect(model_output, self.model_input_size.to(model_output.device), threshold=0.3).long().cpu().numpy() detected_bbox = detected_bbox[detected_bbox[:, 0] == 0, 1:-2] # Visualization # file_path = os.path.join(self.settings.vis_dir, 'image_' + str(self.batch_step) + '.png') # visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, # features=features.cpu().numpy(), path_name=file_path, # bounding_box=bounding_box[0, :, :].cpu().numpy(), # class_name=[self.object_classes[i] for i in bounding_box[0, :, -1]]) image = visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, features=features.cpu().numpy(), bounding_box=bounding_box[0, :, :].cpu().numpy(), class_name=[self.object_classes[i] for i in bounding_box[0, :, -1]]) image = visualizations.drawBoundingBoxes(image, detected_bbox[:, :-1], class_name=[self.object_classes[i] for i in detected_bbox[:, -1]], ground_truth=False, rescale_image=False) self.writer.add_image('Training/Input Histogram', image, self.epoch_step, dataformats='HWC') self.pbar.set_postfix(TrainLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.batch_step += 1 self.writer.add_scalar('Training/Learning_Rate', self.getLearningRate(), self.epoch_step) self.pbar.close() def validationEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_val_epochs, unit='Batch', unit_scale=True) self.resetValidation() self.model = self.model.eval() self.bounding_boxes = BoundingBoxes() loss_function = yoloLoss # Images are upsampled for visualization val_images = np.zeros([2, int(self.model_input_size[0]*1.5), int(self.model_input_size[1]*1.5), 3]) for i_batch, sample_batched in enumerate(self.val_loader): event, bounding_box, histogram = sample_batched # Convert spatial dimension to model input size histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) histogram = histogram.permute(0, 2, 3, 1) # Change x, width and y, height bounding_box[:, :, [0, 2]] = (bounding_box[:, :, [0, 2]] * self.model_input_size[1].float() / self.settings.width).long() bounding_box[:, :, [1, 3]] = (bounding_box[:, :, [1, 3]] * self.model_input_size[0].float() / self.settings.height).long() locations, features = self.denseToSparse(histogram) with torch.no_grad(): model_output = self.model([locations, features, histogram.shape[0]]) loss = loss_function(model_output, bounding_box, self.model_input_size)[0] detected_bbox = yoloDetect(model_output, self.model_input_size.to(model_output.device), threshold=0.3) detected_bbox = nonMaxSuppression(detected_bbox, iou=0.6) detected_bbox = detected_bbox.cpu().numpy() # Save validation statistics self.saveBoundingBoxes(bounding_box.cpu().numpy(), detected_bbox) if self.val_batch_step % (self.nr_val_epochs - 2) == 0: batch_one_mask = locations[:, -1] == 0 vis_locations = locations[batch_one_mask, :2] features = features[batch_one_mask, :] vis_detected_bbox = detected_bbox[detected_bbox[:, 0] == 0, 1:-2].astype(np.int) image = visualizations.visualizeLocations(vis_locations.cpu().int().numpy(), self.model_input_size, features=features.cpu().numpy(), bounding_box=bounding_box[0, :, :].cpu().numpy(), class_name=[self.object_classes[i] for i in bounding_box[0, :, -1]]) image = visualizations.drawBoundingBoxes(image, vis_detected_bbox[:, :-1], class_name=[self.object_classes[i] for i in vis_detected_bbox[:, -1]], ground_truth=False, rescale_image=False) val_images[int(self.val_batch_step // (self.nr_val_epochs - 2))] = image self.pbar.set_postfix(ValLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.val_batch_step += 1 self.validation_loss += loss self.validation_loss = self.validation_loss / float(self.val_batch_step) self.saveValidationStatisticsObjectDetection() self.writer.add_image('Validation/Input Histogram', val_images, self.epoch_step, dataformats='NHWC') if self.max_validation_accuracy < self.validation_accuracy: self.max_validation_accuracy = self.validation_accuracy self.saveCheckpoint() self.pbar.close() class DenseObjectDetModel(AbstractTrainer): def buildModel(self): """Creates the specified model""" self.model = DenseObjectDet(self.nr_classes, in_c=self.nr_input_channels, small_out_map=(self.settings.dataset_name == 'NCaltech101_ObjectDetection')) self.model.to(self.settings.gpu_device) if self.settings.dataset_name == 'NCaltech101_ObjectDetection': self.model_input_size = torch.tensor([191, 255]) elif self.settings.dataset_name == 'Prophesee': self.model_input_size = torch.tensor([223, 287]) if self.settings.use_pretrained and (self.settings.dataset_name == 'NCaltech101_ObjectDetection' or self.settings.dataset_name == 'Prophesee'): self.loadPretrainedWeights() def loadPretrainedWeights(self): """Loads pretrained model weights""" checkpoint = torch.load(self.settings.pretrained_dense_vgg) try: pretrained_dict = checkpoint['state_dict'] except KeyError: pretrained_dict = checkpoint['model'] pretrained_dict = {k: v for k, v in pretrained_dict.items() if 'conv_layers.' in k and int(k[12]) <= 4} self.model.load_state_dict(pretrained_dict, strict=False) def train(self): """Main training and validation loop""" validation_step = 50 - 40 * (self.settings.dataset_name == 'Prophesee') while True: self.trainEpoch() if (self.epoch_step % validation_step) == (validation_step - 1): self.validationEpoch() self.epoch_step += 1 self.scheduler.step() def trainEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_train_epochs, unit='Batch', unit_scale=True) self.model = self.model.train() loss_function = yoloLoss for i_batch, sample_batched in enumerate(self.train_loader): event, bounding_box, histogram = sample_batched self.optimizer.zero_grad() # Change size to input size of sparse VGG histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) # Change x, width and y, height bounding_box[:, :, [0, 2]] = (bounding_box[:, :, [0, 2]] * self.model_input_size[1].float() / self.settings.width).long() bounding_box[:, :, [1, 3]] = (bounding_box[:, :, [1, 3]] * self.model_input_size[0].float() / self.settings.height).long() # Deep Learning Magic model_output = self.model(histogram) out = loss_function(model_output, bounding_box, self.model_input_size) loss = out[0] # Write losses statistics self.storeLossesObjectDetection(out) loss.backward() self.optimizer.step() if self.batch_step % (self.nr_train_epochs * 50) == 0: with torch.no_grad(): detected_bbox = yoloDetect(model_output, self.model_input_size.to(model_output.device), threshold=0.3).long().cpu().numpy() detected_bbox = detected_bbox[detected_bbox[:, 0] == 0, 1:-2] image = visualizations.visualizeHistogram(histogram[0, :, :, :].permute(1, 2, 0).cpu().int().numpy()) image = visualizations.drawBoundingBoxes(image, bounding_box[0, :, :].cpu().numpy(), class_name=[self.object_classes[i] for i in bounding_box[0, :, -1]], ground_truth=True, rescale_image=True) image = visualizations.drawBoundingBoxes(image, detected_bbox[:, :-1], class_name=[self.object_classes[i] for i in detected_bbox[:, -1]], ground_truth=False, rescale_image=False) self.writer.add_image('Training/Input Histogram', image, self.epoch_step, dataformats='HWC') self.pbar.set_postfix(TrainLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.batch_step += 1 self.writer.add_scalar('Training/Learning_Rate', self.getLearningRate(), self.epoch_step) self.pbar.close() def validationEpoch(self): self.pbar = tqdm.tqdm(total=self.nr_val_epochs, unit='Batch', unit_scale=True) self.resetValidation() self.model = self.model.eval() self.bounding_boxes = BoundingBoxes() loss_function = yoloLoss # Images are upsampled for visualization val_images = np.zeros([2, int(self.model_input_size[0]*1.5), int(self.model_input_size[1]*1.5), 3]) for i_batch, sample_batched in enumerate(self.val_loader): event, bounding_box, histogram = sample_batched # Convert spatial dimension to model input size histogram = torch.nn.functional.interpolate(histogram.permute(0, 3, 1, 2), torch.Size(self.model_input_size)) # Change x, width and y, height bounding_box[:, :, [0, 2]] = (bounding_box[:, :, [0, 2]] * self.model_input_size[1].float() / self.settings.width).long() bounding_box[:, :, [1, 3]] = (bounding_box[:, :, [1, 3]] * self.model_input_size[0].float() / self.settings.height).long() with torch.no_grad(): model_output = self.model(histogram) loss = loss_function(model_output, bounding_box, self.model_input_size)[0] detected_bbox = yoloDetect(model_output, self.model_input_size.to(model_output.device), threshold=0.3) detected_bbox = nonMaxSuppression(detected_bbox, iou=0.6) detected_bbox = detected_bbox.cpu().numpy() # Save validation statistics self.saveBoundingBoxes(bounding_box.cpu().numpy(), detected_bbox) if self.val_batch_step % (self.nr_val_epochs - 2) == 0: vis_detected_bbox = detected_bbox[detected_bbox[:, 0] == 0, 1:-2].astype(np.int) image = visualizations.visualizeHistogram(histogram[0, :, :, :].permute(1, 2, 0).cpu().int().numpy()) image = visualizations.drawBoundingBoxes(image, bounding_box[0, :, :].cpu().numpy(), class_name=[self.object_classes[i] for i in bounding_box[0, :, -1]], ground_truth=True, rescale_image=True) image = visualizations.drawBoundingBoxes(image, vis_detected_bbox[:, :-1], class_name=[self.object_classes[i] for i in vis_detected_bbox[:, -1]], ground_truth=False, rescale_image=False) val_images[int(self.val_batch_step // (self.nr_val_epochs - 2))] = image self.pbar.set_postfix(ValLoss=loss.data.cpu().numpy()) self.pbar.update(1) self.val_batch_step += 1 self.validation_loss += loss self.validation_loss = self.validation_loss / float(self.val_batch_step) self.saveValidationStatisticsObjectDetection() self.writer.add_image('Validation/Input Histogram', val_images, self.epoch_step, dataformats='NHWC') if self.max_validation_accuracy < self.validation_accuracy: self.max_validation_accuracy = self.validation_accuracy self.saveCheckpoint() self.pbar.close()

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10b3882ee2ed13c8cf8c936c1ba9710264020740

208

py

Python

src/seedsigner/models/__init__.py

valerio-vaccaro/seedsigner

d8d5c6286f5e35833630e4e619b9c3b59ac8e532

[ "MIT" ]

null

src/seedsigner/models/__init__.py

valerio-vaccaro/seedsigner

d8d5c6286f5e35833630e4e619b9c3b59ac8e532

[ "MIT" ]

null

src/seedsigner/models/__init__.py

valerio-vaccaro/seedsigner

d8d5c6286f5e35833630e4e619b9c3b59ac8e532

[ "MIT" ]

null

from .wallet import * # base class has to be first from .blue_wallet import * from .generic_ur2_wallet import * from .seed_storage import * from .sparrow_wallet import * from .specter_desktop_wallet import *

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Python

src/meetshaus.commentextender/meetshaus/commentextender/interfaces.py

potzenheimer/meetshaus

73b2b5208109792870c71a40a62889981dcdf645

[ "MIT" ]

null

src/meetshaus.commentextender/meetshaus/commentextender/interfaces.py

potzenheimer/meetshaus

73b2b5208109792870c71a40a62889981dcdf645

[ "MIT" ]

18

2015-02-13T15:09:39.000Z

2019-01-17T21:03:28.000Z

src/meetshaus.commentextender/meetshaus/commentextender/interfaces.py

potzenheimer/meetshaus

73b2b5208109792870c71a40a62889981dcdf645

[ "MIT" ]

null

from plone.theme.interfaces import IDefaultPloneLayer class IMeetshausExtender(IDefaultPloneLayer): """ A marker interface for the package layer """

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Python

tf_idf/run_ans_agnostic.py

ethanjperez/allennlp

e520993f16f0da7e2c40f6e44b8dc56338f46b57

[ "Apache-2.0" ]

24

2019-09-16T00:10:54.000Z

2021-09-08T19:31:51.000Z

tf_idf/run_ans_agnostic.py

ethanjperez/allennlp

e520993f16f0da7e2c40f6e44b8dc56338f46b57

[ "Apache-2.0" ]

null

tf_idf/run_ans_agnostic.py

ethanjperez/allennlp

e520993f16f0da7e2c40f6e44b8dc56338f46b57

[ "Apache-2.0" ]

7

2019-09-16T02:37:31.000Z

2021-09-01T06:06:17.000Z

""" run.py Run TF-IDF Debater and generate debater data for the given debate option. """ from pytorch_pretrained_bert.tokenization import BertTokenizer, BasicTokenizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.metrics.pairwise import cosine_similarity import argparse import json import os import numpy as np import tqdm EOS_TOKENS = ['.', '!', '?'] ANS2IDX = {'A': 0, 'B': 1, 'C': 2, 'D': 3} DEBATE2STR = ['Ⅰ', 'Ⅱ', 'Ⅲ', 'Ⅳ'] def parse_args(): p = argparse.ArgumentParser(description='TF-IDF Runner') p.add_argument("-m", "--mode", required=True, help='Mode in n-sents, q-only') p.add_argument("-t", "--train", required=True, help='Path to raw train data to compute TF-IDF') p.add_argument("-v", "--val", required=True, help='Path to raw valid data to compute TF-IDF') p.add_argument("-d", "--debate_option", default=0, type=int, help='Which MC option to support (I, II, III, IV)') p.add_argument("-s", "--dataset", default='race', help='Dataset to run on') p.add_argument("-p", "--pretrained", default='datasets/bert/uncased_L-12_H-768_A-12/vocab.txt') p.add_argument("-x", "--sort", default=False, help='Whether or not to return all sentences sorted by choice.') p.add_argument("-r", "--prefix", required=True, help='Prefix for file') return p.parse_args() def parse_data(args, tokenizer, basic): # Create Tracking Variables keys, p_a = {}, [] # Iterate through Data for dtype in [args.train, args.val]: levels = [os.path.join(dtype, x) for x in os.listdir(dtype)] for level in levels: passages = [os.path.join(level, x) for x in os.listdir(level)] print('\nProcessing %s...' % level) for p in tqdm.tqdm(passages): # Get Key Stub k = os.path.relpath(p, dtype) # Read File with open(p, 'rb') as f: data = json.load(f) # Create State Variables passage_idx = [] # Tokenize Passage => Tokenize Passage, then Perform Sentence Split context = data['article'] ctx_tokens = basic.tokenize(context) # Iterate through tokens and create new sentence every EOS token ctx_sentence_tokens = [[]] for t in ctx_tokens: if t in EOS_TOKENS: ctx_sentence_tokens[-1].append(t) ctx_sentence_tokens.append([]) else: ctx_sentence_tokens[-1].append(t) # Pop off last empty sentence if necessary if len(ctx_sentence_tokens[-1]) == 0: ctx_sentence_tokens = ctx_sentence_tokens[:-1] # Create Context Sentences by joining each sentence ctx_sentences = [" ".join(x) for x in ctx_sentence_tokens] # Create BERT CTX Sentence Tokens ctx_sentence_tokens = [tokenizer.tokenize(x) for x in ctx_sentences] for sent in ctx_sentence_tokens: passage_idx.append(len(p_a)) p_a.append(sent) # Iterate through each Question for idx in range(len(data['questions'])): # Create Specific Example Key key = os.path.join(k, str(idx)) # Fetch q, ans, options = data['questions'][idx], ANS2IDX[data['answers'][idx]], data['options'][idx] # Tokenize Question question_tokens = tokenizer.tokenize(q) q_idx = len(p_a) p_a.append(question_tokens) # Create Dictionary Entry keys[key] = {'passage': ctx_sentences, 'passage_idx': passage_idx, 'question': q, 'answer': ans, 'options': options, 'question_idx': q_idx} return keys, p_a def parse_dream_data(args, tokenizer, basic): # Create Tracking Variables keys, p_a = {}, [] # Iterate through Data for dfile in [args.train, args.val]: # Load, Iterate through Data with open(dfile, 'rb') as f: data = json.load(f) for i, article in enumerate(data): passage_idx = [] context = " ".join(article[0]) ctx_tokens = basic.tokenize(context) # Iterate through tokens and create new sentence every EOS token ctx_sentence_tokens = [[]] for t in ctx_tokens: if t in EOS_TOKENS: ctx_sentence_tokens[-1].append(t) ctx_sentence_tokens.append([]) else: ctx_sentence_tokens[-1].append(t) # Pop off last empty sentence if necessary if len(ctx_sentence_tokens[-1]) == 0: ctx_sentence_tokens = ctx_sentence_tokens[:-1] # Create Context Sentences by joining each sentence ctx_sentences = [" ".join(x) for x in ctx_sentence_tokens] # Create BERT CTX Sentence Tokens ctx_sentence_tokens = [tokenizer.tokenize(x) for x in ctx_sentences] for sent in ctx_sentence_tokens: passage_idx.append(len(p_a)) p_a.append(sent) # Iterate through each Question for idx in range(len(article[1])): # Create Specific Example Key key = os.path.join(article[2], str(idx)) # Fetch q, ans, options = article[1][idx]['question'], article[1][idx]['answer'], article[1][idx]['choice'] # Tokenize Question question_tokens = tokenizer.tokenize(q) q_idx = len(p_a) p_a.append(question_tokens) # Create Dictionary Entry keys[key] = {'passage': ctx_sentences, 'passage_idx': passage_idx, 'question': q, 'answer': ans, 'options': options, 'question_idx': q_idx} return keys, p_a def compute_tf(p_a): """Given tensor of [ndoc, words], compute Term Frequence (BoW) Representation [ndoc, voc_sz]""" # Compute Vocabulary vocab = set() print('\nCreating Vocabulary...') for doc in tqdm.tqdm(p_a): vocab |= set(doc) vocab = {w: i for i, w in enumerate(list(vocab))} # Compute Count TF Matrix tf = np.zeros((len(p_a), len(vocab)), dtype=int) print('\nComputing TF Matrix...') for i, doc in tqdm.tqdm(enumerate(p_a)): for w in doc: tf[i][vocab[w]] += 1 return tf def dump_debates(args, idf, keys): """Run Single-Turn Debates on validation set, dump to file""" levels = [os.path.join(args.val, x) for x in os.listdir(args.val)] dump_dicts = [{} for _ in range(len(DEBATE2STR))] for level in levels: passages = [os.path.join(level, x) for x in os.listdir(level)] print('\nRunning Debates for %s...' % level) for p in tqdm.tqdm(passages): # Get Key Stub k, cur_question = os.path.relpath(p, args.val), 0 while os.path.join(k, str(cur_question)) in keys: key = os.path.join(k, str(cur_question)) d = keys[key] # Mode in num-sent if args.mode == 'n-sents': for oidx in range(len(DEBATE2STR)): # Get Chosen Sentence if oidx < len(d['passage']): chosen = d['passage'][oidx] else: chosen = "" # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[args.debate_option]], "stances": [], "em": 0, "sentences_chosen": [chosen], "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[oidx][os.path.join('test', key)] = example_dict cur_question += 1 # Mode in q-only: elif args.mode == 'q-only': # Get Q-IDX, Passage-IDX q_idx, passage_idx = d['question_idx'], d['passage_idx'] if (idf[passage_idx].shape[0] == 0) or (idf[q_idx].shape[0] == 0): import IPython IPython.embed() # Compute Scores sent_scores = cosine_similarity(idf[passage_idx], idf[q_idx]).flatten() k_max_ind = sent_scores.argsort()[::-1] if not args.sort: for oidx in range(len(DEBATE2STR)): # Get Chosen Sentence if oidx < len(k_max_ind): chosen = d['passage'][k_max_ind[oidx]] else: chosen = "" # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[args.debate_option]], "stances": [], "em": 0, "sentences_chosen": [chosen], "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[oidx][os.path.join('test', key)] = example_dict cur_question += 1 else: # Return all sentence choices sorted chosen = [d['passage'][k] for k in k_max_ind] # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[0]], "stances": [], "em": 0, "sentences_chosen": chosen, "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[0][os.path.join('test', key)] = example_dict cur_question += 1 # Dump to file if not args.sort: for i, mode in enumerate(DEBATE2STR): file_stub = 'tf_idf/race_test_tfidf_%s_%s' % (args.mode, mode) with open(file_stub + ".json", 'w') as f: json.dump(dump_dicts[i], f) else: file_stub = 'tf_idf/%s_race_test_tfidf_agnostic_all_sorted' % args.prefix with open(file_stub + '.json', 'w') as f: json.dump(dump_dicts[0], f) def dump_dream_debates(args, idf, keys): dump_dicts = [{} for _ in range(3)] with open(args.val, 'rb') as f: data = json.load(f) for i, article in enumerate(data): for idx in range(len(article[1])): # Get Key key = os.path.join(article[2], str(idx)) d = keys[key] # Mode in num-sent if args.mode == 'n-sents': for oidx in range(3): # Get Chosen Sentence if oidx < len(d['passage']): chosen = d['passage'][oidx] else: chosen = "" # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[oidx]], "stances": [], "em": 0, "sentences_chosen": [chosen], "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[oidx][os.path.join('test', key)] = example_dict elif args.mode == 'q-only': # Get Q-IDX, Passage-IDX q_idx, passage_idx = d['question_idx'], d['passage_idx'] if (idf[passage_idx].shape[0] == 0) or (idf[q_idx].shape[0] == 0): import IPython IPython.embed() # Compute Scores sent_scores = cosine_similarity(idf[passage_idx], idf[q_idx]).flatten() k_max_ind = sent_scores.argsort()[::-1] if not args.sort: for oidx in range(3): # Get Chosen Sentence if oidx < len(k_max_ind): chosen = d['passage'][k_max_ind[oidx]] else: chosen = "" # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[args.debate_option]], "stances": [], "em": 0, "sentences_chosen": [chosen], "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[oidx][os.path.join('test', key)] = example_dict else: # Return all sentence choices sorted chosen = [d['passage'][k] for k in k_max_ind] # Assemble Example Dict example_dict = {"passage": " ".join(d['passage']), "question": d['question'], "advantage": 0, "debate_mode": [DEBATE2STR[args.debate_option]], "stances": [], "em": 0, "sentences_chosen": chosen, "answer_index": d['answer'], "prob": 0.0, "options": d['options']} dump_dicts[0][os.path.join('test', key)] = example_dict if not args.sort: for i, mode in enumerate(DEBATE2STR[:3]): file_stub = 'tf_idf/dream_test_tfidf_%s_%s' % (args.mode, mode) with open(file_stub + '.json', 'w') as f: json.dump(dump_dicts[i], f) else: file_stub = 'tf_idf/%s_dream_test_tfidf_agnostic_all_sorted' % args.prefix with open(file_stub + '.json', 'w') as f: json.dump(dump_dicts[0], f) if __name__ == '__main__': # Parse Args arguments = parse_args() # Load BERT Tokenizer bert_tokenizer = BertTokenizer.from_pretrained(arguments.pretrained, do_lower_case=True) basic_tokenizer = BasicTokenizer(do_lower_case=False) # Create Dataset if arguments.dataset == 'race': D, PA = parse_data(arguments, bert_tokenizer, basic_tokenizer) # Compute TF Matrix TF = compute_tf(PA) # Compute TF-IDF Matrix print('\nComputing TF-IDF Matrix...') transformer = TfidfTransformer() TF_IDF = transformer.fit_transform(TF) assert(TF_IDF.shape[0] == len(PA) == len(TF)) # Compute Scoring Matrix print('\nScoring Matrix...') # Dump Debates dump_debates(arguments, TF_IDF, D) else: D, PA = parse_dream_data(arguments, bert_tokenizer, basic_tokenizer) # Compute TF Matrix TF = compute_tf(PA) # Compute TF-IDF Matrix print('\nComputing TF-IDF Matrix...') transformer = TfidfTransformer() TF_IDF = transformer.fit_transform(TF) assert (TF_IDF.shape[0] == len(PA) == len(TF)) # Compute Scoring Matrix print('\nScoring Matrix...') # Dump Debates dump_dream_debates(arguments, TF_IDF, D)

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52f6c731c1450e188b425f2ee01a6fb66294b563

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py

Python

source/pydwf/core/api/__init__.py

sidneycadot/pydwf

cd9eba8b45d990f09095bec62b20115f0757baba

[ "MIT" ]

14

2021-05-10T16:19:45.000Z

2022-03-13T08:30:12.000Z

source/pydwf/core/api/__init__.py

sidneycadot/pydwf

cd9eba8b45d990f09095bec62b20115f0757baba

[ "MIT" ]

22

2021-05-01T09:51:09.000Z

2021-11-13T12:35:36.000Z

source/pydwf/core/api/__init__.py

sidneycadot/pydwf

cd9eba8b45d990f09095bec62b20115f0757baba

[ "MIT" ]

2

2021-05-02T12:13:16.000Z

2022-03-11T21:15:07.000Z

"""This sub-package contains classes that encapsulate DWF API calls intended for one instrument, protocol, etc."""

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5e0c1d7f17ad17f3be64ba32a0d7a7b81a0e71f6

182

py

Python

ask_fandom/intents/semantic_media_wiki/__init__.py

Wikia/ask-fandom

cc1d6f9b73a41c0e67b62512c8c0731d340b3015

[ "MIT" ]

5

2019-03-09T11:25:47.000Z

2021-11-03T09:44:03.000Z

ask_fandom/intents/semantic_media_wiki/__init__.py

Wikia/ask-fandom

cc1d6f9b73a41c0e67b62512c8c0731d340b3015

[ "MIT" ]

11

2019-03-08T17:58:25.000Z

2019-03-24T10:57:26.000Z

ask_fandom/intents/semantic_media_wiki/__init__.py

Wikia/ask-fandom

cc1d6f9b73a41c0e67b62512c8c0731d340b3015

[ "MIT" ]

1

2019-03-18T17:39:55.000Z

""" SemanticMediaWiki based intents """ from .base import SemanticFandomIntent from .tv_series import EpisodeFactIntent, PersonFactIntent from. wowwiki import WoWGroupsMemberIntent

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eab2f46284c1f59f13243c886ac2f65017d6b7c8

89

py

Python

Application Code/covid19/mainApp/admin.py

Cshayan/covid-19-tracker

56bc79c3139591972a016a250fa806905ea29c55

[ "MIT" ]

1

2020-04-08T20:15:10.000Z

Application Code/covid19/mainApp/admin.py

Abhinandan-Purkait/codechef-teslacoil

e032022c2f37b26689976b89bfcc81a5ee3ccfd0

[ "MIT" ]

15

2020-09-26T00:57:17.000Z

2022-03-12T00:24:25.000Z

Application Code/covid19/mainApp/admin.py

Abhinandan-Purkait/covidtracker-teslacoil

c77ad78879aded75d6d810a7a19eaf83019cce41

[ "MIT" ]

2

2020-04-08T20:16:30.000Z

2020-04-08T20:40:19.000Z

from django.contrib import admin from .models import Images admin.site.register(Images)

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179

py

Python

pyspj/models/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

pyspj/models/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

pyspj/models/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

from .base import SPJResult from .continuity import ContinuitySPJResult from .general import load_result, to_continuity, to_simple, ResultType from .simple import SimpleSPJResult

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eacd751d16efd0e8feb9643cacc8aefe9102906d

8,161

py

Python

CIFAR10/cifar10_set/Cifar10_NN_Models.py

ynakaDream/deep-learning

5492816f983244fc89314faf984a8f4de2d5ce96

[ "MIT" ]

null

CIFAR10/cifar10_set/Cifar10_NN_Models.py

ynakaDream/deep-learning

5492816f983244fc89314faf984a8f4de2d5ce96

[ "MIT" ]

null

CIFAR10/cifar10_set/Cifar10_NN_Models.py

ynakaDream/deep-learning

5492816f983244fc89314faf984a8f4de2d5ce96

[ "MIT" ]

null

import torch.nn as nn import torch.nn.functional as F class FCModel1(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) * Accuracy: 55.5 % * Loss: 0.0200 ''' def __init__(self, input_size, output_size): super(FCModel1, self).__init__() self.fc1 = nn.Linear(input_size, 500) self.fc2 = nn.Linear(500, 500) self.fc3 = nn.Linear(500, output_size) self.dropout1 = nn.Dropout2d() self.dropout2 = nn.Dropout2d() def forward(self, x): x = x.view(-1, 32 * 32 * 3) x = F.relu(self.fc1(x)) x = self.dropout1(x) x = F.relu(self.fc2(x)) x = self.dropout2(x) output = self.fc3(x) return output class FCModel2(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) * Accuracy: 55.6 % * Loss: 0.0199 ''' def __init__(self, input_size, output_size): super(FCModel2, self).__init__() self.fc1 = nn.Linear(input_size, 500) self.fc2 = nn.Linear(500, 500) self.fc3 = nn.Linear(500, 500) self.fc4 = nn.Linear(500, 500) self.fc5 = nn.Linear(500, 500) self.fc6 = nn.Linear(500, output_size) self.dropout1 = nn.Dropout2d() self.dropout2 = nn.Dropout2d() self.dropout3 = nn.Dropout2d() self.dropout4 = nn.Dropout2d() self.dropout5 = nn.Dropout2d() def forward(self, x): x = x.view(-1, 32 * 32 * 3) x = F.relu(self.fc1(x)) x = self.dropout1(x) x = F.relu(self.fc2(x)) x = self.dropout2(x) x = F.relu(self.fc3(x)) x = self.dropout3(x) x = F.relu(self.fc4(x)) x = self.dropout4(x) x = F.relu(self.fc5(x)) x = self.dropout5(x) output = self.fc6(x) return output class FCModel3(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) * Accuracy: % * Loss: ''' def __init__(self, input_size, output_size): super(FCModel3, self).__init__() self.fc1 = nn.Linear(input_size, 500) self.batch1 = nn.BatchNorm2d(500) self.fc2 = nn.Linear(500, 500) self.batch2 = nn.BatchNorm2d(500) self.fc3 = nn.Linear(500, output_size) def forward(self, x): x = x.view(-1, 32 * 32 * 3) x = F.relu(self.fc1(x)) x = self.batch1(x) x = F.relu(self.fc2(x)) x = self.batch2(x) output = self.fc3(x) return output class CNNModel1(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) * Accuracy: 55.6 % * Loss: 0.0270 ''' def __init__(self): super(CNNModel1, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, padding=1) self.conv2 = nn.Conv2d(in_channels=20, out_channels=3, kernel_size=3, padding=1) self.fc1 = nn.Linear(3 * 32 * 32, 500) self.fc2 = nn.Linear(500, 500) self.fc3 = nn.Linear(500, 10) self.dropout1 = nn.Dropout2d() self.dropout2 = nn.Dropout2d() def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = x.view(-1, 3 * 32 * 32) x = F.relu(self.fc1(x)) x = self.dropout1(x) x = F.relu(self.fc2(x)) x = self.dropout2(x) output = self.fc3(x) return output class CNNModel2(nn.Module): def __init__(self): super(CNNModel2, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, padding=1) self.batch1 = nn.BatchNorm2d(20) self.conv2 = nn.Conv2d(in_channels=20, out_channels=3, kernel_size=3, padding=1) self.batch2 = nn.BatchNorm2d(3) self.fc1 = nn.Linear(3 * 32 * 32, 500) self.fc2 = nn.Linear(500, 500) self.fc3 = nn.Linear(500, 10) def forward(self, x): x = self.batch1(F.relu(self.conv1(x))) x = self.batch2(F.relu(self.conv2(x))) x = x.view(-1, 3 * 32 * 32) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) output = self.fc3(x) return output class CNNModel3(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) * Accuracy: 56.6 % * Loss: 0.0190 ''' def __init__(self): super(CNNModel3, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, padding=1) self.pool1 = nn.MaxPool2d(kernel_size=3, stride=3) self.conv2 = nn.Conv2d(in_channels=20, out_channels=3, kernel_size=3, padding=1) self.pool2 = nn.MaxPool2d(kernel_size=3, stride=3) self.fc1 = nn.Linear(3 * 3 * 3, 500) self.drop1 = nn.Dropout2d() self.fc2 = nn.Linear(500, 500) self.drop2 = nn.Dropout2d() self.fc3 = nn.Linear(500, 10) def forward(self, x): x = self.pool1(F.relu(self.conv1(x))) x = self.pool2(F.relu(self.conv2(x))) x = x.view(-1, 3 * 3 * 3) x = self.drop1(F.relu(self.fc1(x))) x = self.drop2(F.relu(self.fc2(x))) output = self.fc3(x) return output class CNNModel4(nn.Module): ''' epoch: 50 batch: 64 nn.CrossEntropyLoss() optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=5e-6) * Accuracy: 57.9 % * Loss: 0.0187 ''' def __init__(self): super(CNNModel4, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=20, kernel_size=3, padding=1) self.pool1 = nn.MaxPool2d(kernel_size=3, stride=3) self.conv2 = nn.Conv2d(in_channels=20, out_channels=3, kernel_size=3, padding=1) self.pool2 = nn.MaxPool2d(kernel_size=3, stride=3) self.fc1 = nn.Linear(3 * 3 * 3, 500) self.drop1 = nn.Dropout2d() self.fc2 = nn.Linear(500, 500) self.drop2 = nn.Dropout2d() self.fc3 = nn.Linear(500, 10) def forward(self, x): x = self.pool1(F.relu(self.conv1(x))) x = self.pool2(F.relu(self.conv2(x))) x = x.view(-1, 3 * 3 * 3) x = self.drop1(F.relu(self.fc1(x))) x = self.drop2(F.relu(self.fc2(x))) output = self.fc3(x) return output class AlexNetModel(nn.Module): def __init__(self): super(AlexNetModel, self).__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4) self.lrn1 = nn.LocalResponseNorm(96) self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2) self.conv2 = nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, padding=2) self.lrn2 = nn.LocalResponseNorm(256) self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2) self.conv3 = nn.Conv2d(in_channels=256, out_channels=384, kernel_size=3, padding=1) self.conv4 = nn.Conv2d(in_channels=384, out_channels=384, kernel_size=3, padding=1) self.conv5 = nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1) self.pool5 = nn.MaxPool2d(kernel_size=1, stride=2) self.fc1 = nn.Linear(256, 4096) self.drop1 = nn.Dropout() self.fc2 = nn.Linear(4096, 4096) self.drop2 = nn.Dropout() self.fc3 = nn.Linear(4096, 10) def forward(self, x): x = self.pool1(F.relu(self.lrn1(self.conv1(x)))) x = self.pool2(F.relu(self.lrn2(self.conv2(x)))) x = F.relu(self.conv3(x)) x = F.relu(self.conv4(x)) x = self.pool5(F.relu(self.conv5(x))) x = x.view(-1, 256) x = self.drop1(F.relu(self.fc1(x))) x = self.drop2(F.relu(self.fc2(x))) output = self.fc3(x) return output

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eace3f07f213bba5669af4d93316a7e06568efee

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py

Python

src/drone_control/common/__init__.py

Adrien4193/drone_control

4455b0e510003a176ce301c9600cd929e42ecd42

[ "BSD-2-Clause" ]

null

src/drone_control/common/__init__.py

Adrien4193/drone_control

4455b0e510003a176ce301c9600cd929e42ecd42

[ "BSD-2-Clause" ]

null

src/drone_control/common/__init__.py

Adrien4193/drone_control

4455b0e510003a176ce301c9600cd929e42ecd42

[ "BSD-2-Clause" ]

null

from common import Pose, Attitude, Callback, Timer

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272

py

Python

wewcompiler/utils/__init__.py

nitros12/A-Compiler

8c7c6790a1903b329b71aa1771564deb200c61c3

[ "MIT" ]

8

2018-05-07T15:48:24.000Z

2020-11-27T20:09:56.000Z

wewcompiler/utils/__init__.py

nitros12/A-Compiler

8c7c6790a1903b329b71aa1771564deb200c61c3

[ "MIT" ]

4

2018-03-26T22:53:58.000Z

2018-05-07T16:02:39.000Z

wewcompiler/utils/__init__.py

nitros12/A-Compiler

8c7c6790a1903b329b71aa1771564deb200c61c3

[ "MIT" ]

1

2019-08-22T17:11:54.000Z

from typing import Iterable def add_line_count(lines: Iterable[str], counter: Iterable[int]) -> Iterable[str]: return (f"{next(counter):>3}| {lv}" for lv in lines) def strip_newlines(strs: Iterable[str]) -> Iterable[str]: return (i.rstrip("\n\r") for i in strs)

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d809bb2ca41a652caa9b0c5553ca27f4b8b58c32

229

py

Python

orange3/Orange/canvas/report.py

rgschmitz1/BioDepot-workflow-builder

f74d904eeaf91ec52ec9b703d9fb38e9064e5a66

[ "MIT" ]

54

2017-01-08T17:21:49.000Z

2021-11-02T08:46:07.000Z

orange3/Orange/canvas/report.py

Synthia-3/BioDepot-workflow-builder

4ee93abe2d79465755e82a145af3b6a6e1e79fd4

[ "MIT" ]

22

2017-03-28T06:03:14.000Z

2021-07-28T05:43:55.000Z

orange3/Orange/canvas/report.py

Synthia-3/BioDepot-workflow-builder

4ee93abe2d79465755e82a145af3b6a6e1e79fd4

[ "MIT" ]

21

2017-01-26T21:12:09.000Z

2022-01-31T21:34:59.000Z

import sys import warnings import Orange.widgets.report warnings.warn( "'Orange.canvas.report' was moved to 'Orange.widgets.report'", DeprecationWarning, stacklevel=2, ) sys.modules[__name__] = Orange.widgets.report

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1

0

5

d80f6133835f316773bb8e56852262a5f10aeb3b

43

py

Python

tests/unit/__init__.py

deepakpesumalnai/Tornado_API_Python_Development_Testing

008347d1d961a9e8d8199550d721a10f701c6f77

[ "MIT" ]

46

2019-07-12T04:14:25.000Z

2022-02-22T16:39:03.000Z

tests/unit/__init__.py

deepakpesumalnai/Tornado_API_Python_Development_Testing

008347d1d961a9e8d8199550d721a10f701c6f77

[ "MIT" ]

2

2019-12-04T18:14:44.000Z

2020-06-20T11:16:19.000Z

tests/unit/__init__.py

deepakpesumalnai/Tornado_API_Python_Development_Testing

008347d1d961a9e8d8199550d721a10f701c6f77

[ "MIT" ]

20

2019-09-15T04:26:24.000Z

2021-03-23T16:14:15.000Z

21.5

42

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1

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1

0

null

0

1

0

5

d834053aca59305751be2b528124067f163cb1e5

160

py

Python

python/rabbit/example.py

kindrabbit/programming

2c9b7e24e33ecc174c2efb51727b3886ebc00acf

[ "Apache-2.0" ]

1

2021-01-24T02:07:34.000Z

python/rabbit/example.py

kindrabbit/programming

2c9b7e24e33ecc174c2efb51727b3886ebc00acf

[ "Apache-2.0" ]

null

python/rabbit/example.py

kindrabbit/programming

2c9b7e24e33ecc174c2efb51727b3886ebc00acf

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- if __name__=='__main__': print("Hello every body, I am Thomas.Li. I will be study hard for algorithms excercise")

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1

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1

0

5

d83985516cab68f92fe23ab335c8f3b28702f2fc

302

py

Python

tests/conftest.py

glemaitre/hexrd

b68b1ba72e0f480d29bdaae2adbd6c6e2380cc7c

[ "BSD-3-Clause" ]

27

2020-02-18T12:15:08.000Z

2022-03-24T17:53:46.000Z

tests/conftest.py

glemaitre/hexrd

b68b1ba72e0f480d29bdaae2adbd6c6e2380cc7c

[ "BSD-3-Clause" ]

259

2020-02-02T22:18:29.000Z

2022-03-30T19:59:58.000Z

tests/conftest.py

glemaitre/hexrd

b68b1ba72e0f480d29bdaae2adbd6c6e2380cc7c

[ "BSD-3-Clause" ]

11

2020-02-18T12:14:44.000Z

2022-03-04T16:19:11.000Z

import os from pathlib import Path import pytest @pytest.fixture def example_repo_path(): if 'HEXRD_EXAMPLE_REPO_PATH' not in os.environ: pytest.fail('Environment varable HEXRD_EXAMPLE_REPO_PATH not set!') repo_path = os.environ['HEXRD_EXAMPLE_REPO_PATH'] return Path(repo_path)

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4.822222

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0.211982

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25.166667

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0.111111

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0.333333

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0.555556

0

null

1

0

null

0

1

0

1

0

5

dca810656bb61d4e224e0402305dea2711b7342e

2,045

py

Python

vcentershell_driver/driver.py

doppleware/vCenterShell_test

c91870169c5780e5c561b2ae682991af20257c4f

[ "Apache-2.0" ]

null

vcentershell_driver/driver.py

doppleware/vCenterShell_test

c91870169c5780e5c561b2ae682991af20257c4f

[ "Apache-2.0" ]

null

vcentershell_driver/driver.py

doppleware/vCenterShell_test

c91870169c5780e5c561b2ae682991af20257c4f

[ "Apache-2.0" ]

null

from vCenterShell.commands.command_orchestrator import CommandOrchestrator class VCenterShellDriver: def __init__(self): """ ctor must be without arguments, it is created with reflection at run time """ self.command_orchestrator = None # type: CommandOrchestrator def initialize(self, context): self.command_orchestrator = CommandOrchestrator(context) def ApplyConnectivityChanges(self, context, request): return self.command_orchestrator.connect_bulk(context, request) def disconnect_all(self, context, ports): return self.command_orchestrator.disconnect_all(context, ports) def disconnect(self, context, ports, network_name): return self.command_orchestrator.disconnect(context, ports, network_name) def remote_destroy_vm(self, context, ports): return self.command_orchestrator.destroy_vm(context, ports) def remote_refresh_ip(self, context, ports): return self.command_orchestrator.refresh_ip(context, ports) def PowerOff(self, context, ports): return self.command_orchestrator.power_off(context, ports) # the name is by the Qualisystems conventions def PowerOn(self, context, ports): """ Powers off the remote vm :param models.QualiDriverModels.ResourceRemoteCommandContext context: the context the command runs on :param list[string] ports: the ports of the connection between the remote resource and the local resource, NOT IN USE!!! """ return self.command_orchestrator.power_on(context, ports) # the name is by the Qualisystems conventions def PowerCycle(self, context, ports, delay): return self.command_orchestrator.power_cycle(context, ports, delay) def deploy_from_template(self, context, deploy_data): return self.command_orchestrator.deploy_from_template(context, deploy_data) def deploy_from_image(self, context, deploy_data): return self.command_orchestrator.deploy_from_image(context, deploy_data)

40.9

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6.095833

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0.15311

0

0.190709

2,045

49

129

41.734694

0.883988

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0

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0.461538

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0

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1

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null

0

1

0

1

0

5

dcb5e862435d727ba1e47c7e74b4d977f6a6c02d

67

py

Python

inquire/extraction/location/__init__.py

rebeccabilbro/inquire

ff47ff46add727a10f14d801ea924d4b0ece6805

[ "MIT" ]

null

inquire/extraction/location/__init__.py

rebeccabilbro/inquire

ff47ff46add727a10f14d801ea924d4b0ece6805

[ "MIT" ]

null

inquire/extraction/location/__init__.py

rebeccabilbro/inquire

ff47ff46add727a10f14d801ea924d4b0ece6805

[ "MIT" ]

1

2019-05-06T13:38:56.000Z

# answer extraction: location from extractors import get_extractor

22.333333

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37

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true

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1

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1

0

5

f4986a17cac186036b27caa735bf679e3e14f309

84

py

Python

inkpy/__init__.py

facelesspanda/inkpy

f21949b856f19f0b4781fa878760a7d1a03d3623

[ "MIT" ]

null

inkpy/__init__.py

facelesspanda/inkpy

f21949b856f19f0b4781fa878760a7d1a03d3623

[ "MIT" ]

null

inkpy/__init__.py

facelesspanda/inkpy

f21949b856f19f0b4781fa878760a7d1a03d3623

[ "MIT" ]

null

from .story import Story from .inklist import InkList from .error import StoryError

21

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null

0

1

0

1

0

1

0

5

f4c8b33aa875e2750f9d828d84ddd69dff775b18

253

py

Python

especifico/apps/__init__.py

athenianco/especifico

af8b97868390ba23a2c5e3e8506bd5215ee0084a

[ "Apache-2.0" ]

null

especifico/apps/__init__.py

athenianco/especifico

af8b97868390ba23a2c5e3e8506bd5215ee0084a

[ "Apache-2.0" ]

null

especifico/apps/__init__.py

athenianco/especifico

af8b97868390ba23a2c5e3e8506bd5215ee0084a

[ "Apache-2.0" ]

null

""" This module defines Especifico applications. A Especifico App wraps a specific framework application and exposes a standardized interface for users to create and configure their Especifico application. """ from .abstract import AbstractApp # NOQA

31.625

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1

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1

0

5

f4eedd25e4828da6af98cb43ead8e48bb6ca8920

292

py

Python

hutoolpy/file/__init__.py

twocucao/hutoolpy

84f3de287a7a0793b169b3a83eac60b34254b26a

[ "MIT" ]

null

hutoolpy/file/__init__.py

twocucao/hutoolpy

84f3de287a7a0793b169b3a83eac60b34254b26a

[ "MIT" ]

null

hutoolpy/file/__init__.py

twocucao/hutoolpy

84f3de287a7a0793b169b3a83eac60b34254b26a

[ "MIT" ]

null

""" read by line """ def get_path(): raise NotImplementedError def get_absolute_path(): raise NotImplementedError def get_canonical_path(): raise NotImplementedError def get_cwd_path(): raise NotImplementedError def is_absolute_path(): raise NotImplementedError

12.166667

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null

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null

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f4f277b4ef0edc1b15c9a1351aa4cf6d2c3a17d2

58

py

Python

gym-sweden/gym_sweden/envs/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

1

2021-02-26T10:09:15.000Z

gym-sweden/gym_sweden/envs/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

gym-sweden/gym_sweden/envs/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

from gym_sweden.envs.swedenworld_env import SwedenWorldEnv

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76116a05382daaa6c40ad3b7854cabe2e8015de7

159,150

py

Python

fn_exchange/fn_exchange/util/customize.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2020-08-25T03:43:07.000Z

fn_exchange/fn_exchange/util/customize.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

1

2019-07-08T16:57:48.000Z

fn_exchange/fn_exchange/util/customize.py

rudimeyer/resilient-community-apps

7a46841ba41fa7a1c421d4b392b0a3ca9e36bd00

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Generate the Resilient customizations required for fn_exchange""" from __future__ import print_function from resilient_circuits.util import * def codegen_reload_data(): """Parameters to codegen used to generate the fn_exchange package""" reload_params = {"package": u"fn_exchange", "incident_fields": [], "action_fields": [u"exchange_delete_if_no_subfolders", u"exchange_destination_folder_path", u"exchange_email", u"exchange_email_ids", u"exchange_emails", u"exchange_end_date", u"exchange_folder_path", u"exchange_get_email", u"exchange_hard_delete", u"exchange_has_attachments", u"exchange_meeting_body", u"exchange_meeting_end_time", u"exchange_meeting_start_time", u"exchange_meeting_subject", u"exchange_message_body", u"exchange_message_subject", u"exchange_num_emails", u"exchange_optional_attendees", u"exchange_order_by_recency", u"exchange_required_attendees", u"exchange_search_subfolders", u"exchange_sender", u"exchange_start_date"], "function_params": [u"exchange_delete_if_no_subfolders", u"exchange_destination_folder_path", u"exchange_email", u"exchange_email_ids", u"exchange_emails", u"exchange_end_date", u"exchange_folder_path", u"exchange_get_email", u"exchange_hard_delete", u"exchange_has_attachments", u"exchange_meeting_body", u"exchange_meeting_end_time", u"exchange_meeting_start_time", u"exchange_meeting_subject", u"exchange_message_body", u"exchange_message_subject", u"exchange_num_emails", u"exchange_optional_attendees", u"exchange_order_by_recency", u"exchange_required_attendees", u"exchange_search_subfolders", u"exchange_sender", u"exchange_start_date"], "datatables": [], "message_destinations": [u"fn_exchange"], "functions": [u"exchange_create_meeting", u"exchange_delete_emails", u"exchange_find_emails", u"exchange_get_mailbox_info", u"exchange_move_emails", u"exchange_move_folder_contents_and_delete_folder", u"exchange_send_email"], "phases": [], "automatic_tasks": [], "scripts": [], "workflows": [u"example_of_exchange_create_meeting", u"example_of_exchange_delete_emails", u"example_of_exchange_find_emails", u"example_of_exchange_get_mailbox_info", u"example_of_exchange_move_emails", u"example_of_exchange_send_email", u"exchange_move_and_delete_folder"], "actions": [u"Exchange Create Meeting", u"Exchange Delete Emails", u"Exchange Find Emails", u"Exchange Get Mailbox Info", u"Exchange Move Emails", u"Exchange Move Folder Contents and Delete Folder", u"Exchange Send Email"] } return reload_params def customization_data(client=None): """Produce any customization definitions (types, fields, message destinations, etc) that should be installed by `resilient-circuits customize` """ # This import data contains: # Action fields: # exchange_delete_if_no_subfolders # exchange_destination_folder_path # exchange_email # exchange_email_ids # exchange_emails # exchange_end_date # exchange_folder_path # exchange_get_email # exchange_hard_delete # exchange_has_attachments # exchange_meeting_body # exchange_meeting_end_time # exchange_meeting_start_time # exchange_meeting_subject # exchange_message_body # exchange_message_subject # exchange_num_emails # exchange_optional_attendees # exchange_order_by_recency # exchange_required_attendees # exchange_search_subfolders # exchange_sender # exchange_start_date # Function inputs: # exchange_delete_if_no_subfolders # exchange_destination_folder_path # exchange_email # exchange_email_ids # exchange_emails # exchange_end_date # exchange_folder_path # exchange_get_email # exchange_hard_delete # exchange_has_attachments # exchange_meeting_body # exchange_meeting_end_time # exchange_meeting_start_time # exchange_meeting_subject # exchange_message_body # exchange_message_subject # exchange_num_emails # exchange_optional_attendees # exchange_order_by_recency # exchange_required_attendees # exchange_search_subfolders # exchange_sender # exchange_start_date # Message Destinations: # fn_exchange # Functions: # exchange_create_meeting # exchange_delete_emails # exchange_find_emails # exchange_get_mailbox_info # exchange_move_emails # exchange_move_folder_contents_and_delete_folder # exchange_send_email # Workflows: # example_of_exchange_create_meeting # example_of_exchange_delete_emails # example_of_exchange_find_emails # example_of_exchange_get_mailbox_info # example_of_exchange_move_emails # example_of_exchange_send_email # exchange_move_and_delete_folder # Rules: # Exchange Create Meeting # Exchange Delete Emails # Exchange Find Emails # Exchange Get Mailbox Info # Exchange Move Emails # Exchange Move Folder Contents and Delete Folder # Exchange Send Email yield ImportDefinition(u""" eyJ0YXNrX29yZGVyIjogW10sICJ3b3JrZmxvd3MiOiBbeyJ1dWlkIjogImRlMGZiNDNhLTdlNzAt NDIwOC04NDljLTNhZTcwZDk0ODdjZiIsICJkZXNjcmlwdGlvbiI6ICJRdWVyeSBlbWFpbHMgYW5k IHRoZW4gY3JlYXRlIGFydGlmYWN0cyBmcm9tIHJlc3VsdHMuIiwgIm9iamVjdF90eXBlIjogImFy dGlmYWN0IiwgImV4cG9ydF9rZXkiOiAiZXhhbXBsZV9vZl9leGNoYW5nZV9maW5kX2VtYWlscyIs 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870cb9d473da96adc5cbc70f8b3f11e9fb41e074

57

py

Python

03 - Luigi and Kubernetes/02 - Combine kubernetes and luigi/src/preprocessing/__main__.py

falknerdominik/luigi_with_kubernetes_summary

0f28a7498edc9154c981b8609fd836c44c17fa55

[ "MIT" ]

1

2020-10-07T11:46:13.000Z

03 - Luigi and Kubernetes/02 - Combine kubernetes and luigi/src/preprocessing/__main__.py

falknerdominik/luigi_with_kubernetes_summary

0f28a7498edc9154c981b8609fd836c44c17fa55

[ "MIT" ]

3

2021-02-08T20:42:17.000Z

2021-04-30T21:08:47.000Z

03 - Luigi and Kubernetes/02 - Combine kubernetes and luigi/src/preprocessing/__main__.py

falknerdominik/luigi_with_kubernetes_summary

0f28a7498edc9154c981b8609fd836c44c17fa55

[ "MIT" ]

null

from .simple_workflow import run_pipeline run_pipeline()

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5

8711b454f9bfde5d98efe06b6201cdfafa8a9aa7

111

py

Python

py_tdlib/constructors/passport_element_passport.py

Mr-TelegramBot/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

24

2018-10-05T13:04:30.000Z

2020-05-12T08:45:34.000Z

py_tdlib/constructors/passport_element_passport.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

3

2019-06-26T07:20:20.000Z

2021-05-24T13:06:56.000Z

py_tdlib/constructors/passport_element_passport.py

MrMahdi313/python-tdlib

2e2d21a742ebcd439971a32357f2d0abd0ce61eb

[ "MIT" ]

5

2018-10-05T14:29:28.000Z

2020-08-11T15:04:10.000Z

from ..factory import Type class passportElementPassport(Type): passport = None # type: "identityDocument"

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87578fca02e543e7f8b1474e23953a54f5bca218

112

py

Python

resources/mgltools_x86_64Linux2_1.5.6/lib/python2.5/site-packages/numpy/testing/__init__.py

J-E-J-S/aaRS-Pipeline

43f59f28ab06e4b16328c3bc405cdddc6e69ac44

[ "MIT" ]

null

resources/mgltools_x86_64Linux2_1.5.6/lib/python2.5/site-packages/numpy/testing/__init__.py

J-E-J-S/aaRS-Pipeline

43f59f28ab06e4b16328c3bc405cdddc6e69ac44

[ "MIT" ]

null

resources/mgltools_x86_64Linux2_1.5.6/lib/python2.5/site-packages/numpy/testing/__init__.py

J-E-J-S/aaRS-Pipeline

43f59f28ab06e4b16328c3bc405cdddc6e69ac44

[ "MIT" ]

null

from info import __doc__ from numpytest import * from utils import * from parametric import ParametricTestCase

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5e42f987f62aab6694cde27bc2ef80bf9e34e021

159

py

Python

{{cookiecutter.project_slug}}/app/models/__init__.py

andredias/cookiecutter-fastapi

b5df6f80908d924e51a02fba34d1a01ccf4fc265

[ "MIT" ]

8

2021-06-12T10:11:03.000Z

2022-03-23T13:27:23.000Z

{{cookiecutter.project_slug}}/app/models/__init__.py

andredias/cookiecutter-fastapi

b5df6f80908d924e51a02fba34d1a01ccf4fc265

[ "MIT" ]

12

2021-06-08T22:58:30.000Z

2022-02-22T15:43:10.000Z

{{cookiecutter.project_slug}}/app/models/__init__.py

andredias/cookiecutter-fastapi

b5df6f80908d924e51a02fba34d1a01ccf4fc265

[ "MIT" ]

2

2021-08-10T22:55:49.000Z

2021-08-10T23:05:29.000Z

from secrets import randbelow from sqlalchemy import MetaData metadata = MetaData() MAX_ID = 2 ** 31 def random_id() -> int: return randbelow(MAX_ID)

13.25

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159

5.090909

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null

0

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5

5e4ccfbb1a6b0a9395ddf778241ee4e18794436b

72

py

Python

pub_data_visualization/outages/load/rte/__init__.py

cre-os/pub-data-visualization

e5ec45e6397258646290836fc1a3b39ad69bf266

[ "MIT" ]

10

2020-10-08T11:35:49.000Z

2021-01-22T16:47:59.000Z

pub_data_visualization/outages/load/rte/__init__.py

l-leo/pub-data-visualization

68eea00491424581b057495a7f0f69cf74e16e7d

[ "MIT" ]

3

2021-03-15T14:26:43.000Z

2021-12-02T15:27:49.000Z

pub_data_visualization/outages/load/rte/__init__.py

cre-dev/pub-data-visualization

229bb7a543684be2cb06935299345ce3263da946

[ "MIT" ]

1

2021-01-22T16:47:10.000Z

""" Module to load outages data from RTE. """ from .load import *

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5e683b110e311588740ff1d9107f31fb4652118a

10,262

py

Python

idaes/generic_models/properties/core/reactions/equilibrium_constant.py

eyoung55/idaes-pse

01a6d795d484155923baabcfc2878d5285b8c9b1

[ "RSA-MD" ]

null

idaes/generic_models/properties/core/reactions/equilibrium_constant.py

eyoung55/idaes-pse

01a6d795d484155923baabcfc2878d5285b8c9b1

[ "RSA-MD" ]

3

2021-07-20T20:12:59.000Z

2022-03-09T21:06:40.000Z

idaes/generic_models/properties/core/reactions/equilibrium_constant.py

eyoung55/idaes-pse

01a6d795d484155923baabcfc2878d5285b8c9b1

[ "RSA-MD" ]

1

2021-08-13T15:20:31.000Z

################################################################################# # The Institute for the Design of Advanced Energy Systems Integrated Platform # Framework (IDAES IP) was produced under the DOE Institute for the # Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021 # by the software owners: The Regents of the University of California, through # Lawrence Berkeley National Laboratory, National Technology & Engineering # Solutions of Sandia, LLC, Carnegie Mellon University, West Virginia University # Research Corporation, et al. All rights reserved. # # Please see the files COPYRIGHT.md and LICENSE.md for full copyright and # license information. ################################################################################# """ Methods for calculating equilibrium constants """ from pyomo.environ import exp, Var, units as pyunits, value from idaes.generic_models.properties.core.generic.generic_reaction import \ ConcentrationForm from .dh_rxn import constant_dh_rxn from idaes.core.util.misc import set_param_from_config from idaes.core.util.constants import Constants as c from idaes.core.util.exceptions import BurntToast, ConfigurationError # ----------------------------------------------------------------------------- # Constant Keq class ConstantKeq(): @staticmethod def build_parameters(rblock, config): parent = rblock.parent_block() units = parent.get_metadata().derived_units c_form = config.concentration_form if c_form is None: raise ConfigurationError( "{} concentration_form configuration argument was not set. " "Please ensure that this argument is included in your " "configuration dict.".format(rblock.name)) elif (c_form == ConcentrationForm.moleFraction or c_form == ConcentrationForm.massFraction): e_units = None else: order = 0 try: # This will work for Reaction Packages pc_set = parent.config.property_package._phase_component_set except AttributeError: # Need to allow for inherent reactions in Property Packages if not parent._electrolyte: # In most cases ,should have _phase_component_set pc_set = parent._phase_component_set else: # However, for electrolytes need true species set pc_set = parent.true_phase_component_set for p, j in pc_set: order += rblock.reaction_order[p, j].value if (c_form == ConcentrationForm.molarity or c_form == ConcentrationForm.activity): c_units = units["density_mole"] elif c_form == ConcentrationForm.molality: c_units = units["amount"]*units["mass"]**-1 elif c_form == ConcentrationForm.partialPressure: c_units = units["pressure"] else: raise BurntToast( "{} get_concentration_term received unrecognised " "ConcentrationForm ({}). This should not happen - please " "contact the IDAES developers with this bug." .format(rblock.name, c_form)) e_units = c_units**order rblock.k_eq_ref = Var( doc="Equilibrium constant at reference state", units=e_units) set_param_from_config(rblock, param="k_eq_ref", config=config) @staticmethod def return_expression(b, rblock, r_idx, T): return rblock.k_eq_ref @staticmethod def calculate_scaling_factors(b, rblock): return 1/value(rblock.k_eq_ref) # ----------------------------------------------------------------------------- # van t'Hoff equation (constant dh_rxn) class van_t_hoff(): @staticmethod def build_parameters(rblock, config): parent = rblock.parent_block() units = parent.get_metadata().derived_units c_form = config.concentration_form if c_form is None: raise ConfigurationError( "{} concentration_form configuration argument was not set. " "Please ensure that this argument is included in your " "configuration dict.".format(rblock.name)) elif (c_form == ConcentrationForm.moleFraction or c_form == ConcentrationForm.massFraction): e_units = None else: order = 0 try: # This will work for Reaction Packages pc_set = parent.config.property_package._phase_component_set except AttributeError: # Need to allow for inherent reactions in Property Packages if not parent._electrolyte: # In most cases ,should have _phase_component_set pc_set = parent._phase_component_set else: # However, for electrolytes need true species set pc_set = parent.true_phase_component_set for p, j in pc_set: order += rblock.reaction_order[p, j].value if (c_form == ConcentrationForm.molarity or c_form == ConcentrationForm.activity): c_units = units["density_mole"] elif c_form == ConcentrationForm.molality: c_units = units["amount"]*units["mass"]**-1 elif c_form == ConcentrationForm.partialPressure: c_units = units["pressure"] else: raise BurntToast( "{} get_concentration_term received unrecognised " "ConcentrationForm ({}). This should not happen - please " "contact the IDAES developers with this bug." .format(rblock.name, c_form)) e_units = c_units**order rblock.k_eq_ref = Var( doc="Equilibrium constant at reference state", units=e_units) set_param_from_config(rblock, param="k_eq_ref", config=config) rblock.T_eq_ref = Var( doc="Reference temperature for equilibrium constant", units=units["temperature"]) set_param_from_config(rblock, param="T_eq_ref", config=config) @staticmethod def return_expression(b, rblock, r_idx, T): units = rblock.parent_block().get_metadata().derived_units return rblock.k_eq_ref * exp( -(b.dh_rxn[r_idx] / pyunits.convert(c.gas_constant, to_units=units["gas_constant"])) * (1/T - 1/rblock.T_eq_ref)) @staticmethod def calculate_scaling_factors(b, rblock): return 1/value(rblock.k_eq_ref) # ----------------------------------------------------------------------------- # Constant dh_rxn and ds_rxn class gibbs_energy(): @staticmethod def build_parameters(rblock, config): parent = rblock.parent_block() units = parent.get_metadata().derived_units c_form = config.concentration_form if c_form is None: raise ConfigurationError( "{} concentration_form configuration argument was not set. " "Please ensure that this argument is included in your " "configuration dict.".format(rblock.name)) elif (c_form == ConcentrationForm.molarity or c_form == ConcentrationForm.activity): order = 0 try: # This will work for Reaction Packages pc_set = parent.config.property_package._phase_component_set except AttributeError: # Need to allow for inherent reactions in Property Packages if not parent._electrolyte: # In most cases ,should have _phase_component_set pc_set = parent._phase_component_set else: # However, for electrolytes need true species set pc_set = parent.true_phase_component_set for p, j in pc_set: order += rblock.reaction_order[p, j].value rblock._keq_units = (pyunits.convert(1*pyunits.mol/pyunits.L, units["density_mole"]))**order else: raise ConfigurationError( "{} calculation of equilibrium constant based on Gibbs energy " "is only supported for molarity or activity forms. " "Currently selected form: {}".format(rblock.name, c_form)) # Check that heat of reaction is constant if config.heat_of_reaction is not constant_dh_rxn: raise ConfigurationError( "{} calculating equilibrium constants from Gibbs energy " "assumes constant heat of reaction. Please ensure you are " "using the constant_dh_rxn method for this reaction" .format(rblock.name)) rblock.ds_rxn_ref = Var( doc="Specific molar entropy of reaction at reference state", units=units["entropy_mole"]) set_param_from_config(rblock, param="ds_rxn_ref", config=config) rblock.T_eq_ref = Var( doc="Reference temperature for equilibrium constant", units=units["temperature"]) set_param_from_config(rblock, param="T_eq_ref", config=config) @staticmethod def return_expression(b, rblock, r_idx, T): units = rblock.parent_block().get_metadata().derived_units R = pyunits.convert(c.gas_constant, to_units=units["gas_constant"]) return (exp( (-rblock.dh_rxn_ref / (R*T)) + (rblock.ds_rxn_ref / R)) * rblock._keq_units) @staticmethod def calculate_scaling_factors(b, rblock): units = rblock.parent_block().get_metadata().derived_units R = pyunits.convert(c.gas_constant, to_units=units["gas_constant"]) keq_val = value(exp(-rblock.dh_rxn_ref/(R*rblock.T_eq_ref) + rblock.ds_rxn_ref/R) * rblock._keq_units) return 1/keq_val

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5e7a85c211b8b5c6d6c67ff3fdcc80747f44d2a7

84

py

Python

10_Math2/Step09/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

10_Math2/Step09/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

3

2020-11-04T05:38:53.000Z

2021-03-02T02:15:19.000Z

10_Math2/Step09/gamjapark.py

StudyForCoding/BEAKJOON

84e1c5e463255e919ccf6b6a782978c205420dbf

[ "MIT" ]

null

import math R = int(input()) print("%.4f"%((R**2) * math.pi)) print("%.4f"%(R*R*2))

16.8

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1

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5

5ea0b9ee530ca1a43000bc48742672827bd203eb

51

py

Python

p2ner/components/plugin/tcpbandwidthclient/tcpbandwidthclient/__init__.py

schristakidis/p2ner

46694a41e8c1ebdc28f520b58c126da8785f3eed

[ "Apache-2.0" ]

2

2015-06-01T22:04:34.000Z

2017-07-06T09:35:00.000Z

p2ner/components/plugin/tcpbandwidthclient/tcpbandwidthclient/__init__.py

schristakidis/p2ner

46694a41e8c1ebdc28f520b58c126da8785f3eed

[ "Apache-2.0" ]

null

p2ner/components/plugin/tcpbandwidthclient/tcpbandwidthclient/__init__.py

schristakidis/p2ner

46694a41e8c1ebdc28f520b58c126da8785f3eed

[ "Apache-2.0" ]

1

2019-11-26T10:22:35.000Z

from TCPClient import Client as TCPBandwidthClient

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0d6fdee8a3aeeb4a98a9b4f5a65ea6f3937971a1

84

py

Python

numerical_analysis/integration/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

numerical_analysis/integration/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

numerical_analysis/integration/__init__.py

iagerogiannis/numerical-analysis

ae6f10b70cd8f60e746d897d861e48253df1063a

[ "BSD-3-Clause" ]

null

from .integration import trapezoid, simpson1_3, simpson3_8, romberg, gauss_legendre

42

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6.181818

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1

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0

5

0d95ef2ca1f11631bab0001c9ff0af1aac5b5196

116

py

Python

tape/models/__init__.py

nickbhat/tape-1

96778d1a3bb35acf966c6b32b4df1d738fa19ffd

[ "MIT" ]

42

2019-12-09T12:44:45.000Z

2022-03-13T19:46:41.000Z

tape/models/__init__.py

nickbhat/tape-1

96778d1a3bb35acf966c6b32b4df1d738fa19ffd

[ "MIT" ]

11

2019-12-16T08:59:56.000Z

2022-01-26T21:13:25.000Z

tape/models/__init__.py

nickbhat/tape-1

96778d1a3bb35acf966c6b32b4df1d738fa19ffd

[ "MIT" ]

7

2019-12-16T09:58:42.000Z

2020-12-04T06:58:19.000Z

from .ModelBuilder import ModelBuilder # noqa: F401 from .AbstractTapeModel import AbstractTapeModel # noqa: F401

38.666667

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0

1

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1

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5

0d9974ce049c49b25459e702ee8510d375db7bf5

128

py

Python

output.py

AashikSharif/Histopathological_Cancer_Detector

aba35991c9ac52335e64d6672c8546f053aad1fa

[ "MIT" ]

1

2021-06-04T08:24:44.000Z

output.py

AashikSharif/Histopathological_Cancer_Detector

aba35991c9ac52335e64d6672c8546f053aad1fa

[ "MIT" ]

null

output.py

AashikSharif/Histopathological_Cancer_Detector

aba35991c9ac52335e64d6672c8546f053aad1fa

[ "MIT" ]

null

version https://git-lfs.github.com/spec/v1 oid sha256:424162a32534391049617cdeeb9b9a7eb844f848253eadfa3245def08c39acc5 size 876

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null

1

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1

0

5

217341d85a4a5a9480a909dc11ed380ed9f167d6

49

py

Python

__init__.py

cgrima/sharad

efa882e8e067362eeac879d4a72d2f02856b231d

[ "MIT" ]

1

2018-06-20T02:36:50.000Z

__init__.py

cgrima/sharad

efa882e8e067362eeac879d4a72d2f02856b231d

[ "MIT" ]

null

__init__.py

cgrima/sharad

efa882e8e067362eeac879d4a72d2f02856b231d

[ "MIT" ]

1

2020-11-13T15:40:56.000Z

""" Author: C. Grima (cyril.grima@gmail.com) """

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1

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null

0

1

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5

219ae6ebf76aa038e1b67f419eb312165a67652b

118

py

Python

game/admin.py

apkallum/django-magnetic-cave

377e1cdcc2cb30c93bd431bbed304e34f62cf8ec

[ "MIT" ]

null

game/admin.py

apkallum/django-magnetic-cave

377e1cdcc2cb30c93bd431bbed304e34f62cf8ec

[ "MIT" ]

null

game/admin.py

apkallum/django-magnetic-cave

377e1cdcc2cb30c93bd431bbed304e34f62cf8ec

[ "MIT" ]

null

from django.contrib import admin from .models import Game, Move admin.site.register(Game) admin.site.register(Move)

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21a60653687df0531276af7490af113c91e6fbd4

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py

Python

CodeWars/7 Kyu/Alien Accent.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Alien Accent.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

CodeWars/7 Kyu/Alien Accent.py

anubhab-code/Competitive-Programming

de28cb7d44044b9e7d8bdb475da61e37c018ac35

[ "MIT" ]

null

def convert(st): return st.replace('o','u').replace('a','o')

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1d019b75ed831494b95dbc1c9629ce56ed97e032

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py

Python

ndscheduler/tools/__init__.py

palto42/ndscheduler

dc875b5c32192cb1fe2d658f320f81215cb1d9f5

[ "BSD-2-Clause" ]

9

2021-05-03T07:31:22.000Z

2022-03-19T23:07:36.000Z

ndscheduler/tools/__init__.py

palto42/ndscheduler

dc875b5c32192cb1fe2d658f320f81215cb1d9f5

[ "BSD-2-Clause" ]

2

2021-05-01T14:40:21.000Z

2021-06-20T16:50:37.000Z

ndscheduler/tools/__init__.py

palto42/ndscheduler

dc875b5c32192cb1fe2d658f320f81215cb1d9f5

[ "BSD-2-Clause" ]

3

2021-10-12T04:10:10.000Z

2022-03-19T23:28:32.000Z

"""Utilities to be used by multiple jobs"""

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df23849e32afc7d08880cf20eb7536527463116c

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py

Python

saau/sections/foreign/noncitizen.py

Mause/statistical_atlas_of_au

9a1e46cdb1075f993086640827dabb0f4df4fd17

[ "MIT" ]

null

saau/sections/foreign/noncitizen.py

Mause/statistical_atlas_of_au

9a1e46cdb1075f993086640827dabb0f4df4fd17

[ "MIT" ]

null

saau/sections/foreign/noncitizen.py

Mause/statistical_atlas_of_au

9a1e46cdb1075f993086640827dabb0f4df4fd17

[ "MIT" ]

null

# foreign-noncitizen

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df352f12774f12139a6f05b9c471df607c3ccde5

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py

Python

_solutions/design-patterns/oop/oop_abstract_b.py

sages-pl/2022-01-pythonsqlalchemy-aptiv

1d6d856608e9dbe25b139e8968c48b7f46753b84

[ "MIT" ]

null

_solutions/design-patterns/oop/oop_abstract_b.py

sages-pl/2022-01-pythonsqlalchemy-aptiv

1d6d856608e9dbe25b139e8968c48b7f46753b84

[ "MIT" ]

null

_solutions/design-patterns/oop/oop_abstract_b.py

sages-pl/2022-01-pythonsqlalchemy-aptiv

1d6d856608e9dbe25b139e8968c48b7f46753b84

[ "MIT" ]

null

class IrisAbstract(metaclass=ABCMeta): @abstractmethod def mean(self) -> float: ... @abstractmethod def sum(self) -> float: ... @abstractmethod def len(self) -> int: ...

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py

Python

programing/may_leetcode.py

meobach/Algorithms

3e502132924ac9e647216bfef5b5f105e8c09b5b

[ "MIT" ]

null

programing/may_leetcode.py

meobach/Algorithms

3e502132924ac9e647216bfef5b5f105e8c09b5b

[ "MIT" ]

null

programing/may_leetcode.py

meobach/Algorithms

3e502132924ac9e647216bfef5b5f105e8c09b5b

[ "MIT" ]

null

data="meobach" #data[0]="a" print(data[0])

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py

Python

FaceSwap-master/PRNet-master/demo_texture.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

1

2020-06-21T13:45:26.000Z

FaceSwap-master/PRNet-master/demo_texture.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

null

FaceSwap-master/PRNet-master/demo_texture.py

CSID-DGU/-2020-1-OSSP1-ninetynine-2

b1824254882eeea0ee44e4e60896b72c51ef1d2c

[ "MIT" ]

3

2020-09-02T03:18:45.000Z

2021-01-27T08:24:05.000Z

version https://git-lfs.github.com/spec/v1 oid sha256:29953b7e089f913eeb234bb479a8a3f545d8ffa1599ad79dee37aa0b3adcf391 size 4177

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10dd8cc74620abdc900c4c8ecc958bcc9391850b

12,274

py

Python

resultsapp/views.py

ad-software/bestenliste_laufen

d6fa739001c83db4cdbcdf974d8387ec3d652410

[ "MIT" ]

1

2021-04-29T08:33:20.000Z

resultsapp/views.py

ad-software/bestenliste_laufen

d6fa739001c83db4cdbcdf974d8387ec3d652410

[ "MIT" ]

1

2020-12-23T14:26:54.000Z

2020-12-25T11:53:03.000Z

resultsapp/views.py

ad-software/bestenliste_laufen

d6fa739001c83db4cdbcdf974d8387ec3d652410

[ "MIT" ]

1

2020-10-26T17:44:45.000Z

from django.shortcuts import render, get_object_or_404 from .forms import EventsForm from .helper import Helper from .models import Club, DisciplineDistance, DisciplineTime, Event, ResultDistance, ResultTime import logging logger = logging.getLogger('console_file') def annual_records_m_view(request, year): logger.debug('create annual record list male for year ' + str(year)) discipline_distance_queryset = DisciplineDistance.objects.all() records_distance = [] for discipline in discipline_distance_queryset: discipline_object = DisciplineDistance.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultDistance.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "m": event = result_item.event_id if str(year) == Helper.get_year_from_result_event(event): records_distance.append(result_item) logger.debug('added result_item: ' + str(result_item)) break discipline_time_queryset = DisciplineTime.objects.all() records_time = [] for discipline in discipline_time_queryset: discipline_object = DisciplineTime.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultTime.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "m": event = result_item.event_id if str(year) == Helper.get_year_from_result_event(event): records_time.append(result_item) logger.debug('added result_item: ' + str(result_item)) break years = Helper.get_years_with_events() context = { "result_distance_object_list": records_distance, "result_time_object_list": records_time, "year_list": years, "year": year } return render(request, "resultsapp/annual_record_list_m.html", context) def annual_records_w_view(request, year): logger.debug('create annual record list male for year ' + str(year)) discipline_distance_queryset = DisciplineDistance.objects.all() records_distance = [] for discipline in discipline_distance_queryset: discipline_object = DisciplineDistance.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultDistance.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "w": event = result_item.event_id if str(year) == Helper.get_year_from_result_event(event): records_distance.append(result_item) logger.debug('added result_item: ' + str(result_item)) break discipline_time_queryset = DisciplineTime.objects.all() records_time = [] for discipline in discipline_time_queryset: discipline_object = DisciplineTime.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultTime.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "w": event = result_item.event_id if str(year) == Helper.get_year_from_result_event(event): records_time.append(result_item) logger.debug('added result_item: ' + str(result_item)) break years = Helper.get_years_with_events() context = { "result_distance_object_list": records_distance, "result_time_object_list": records_time, "year_list": years, "year": year } return render(request, "resultsapp/annual_record_list_w.html", context) def about_view(request, *args, **kwargs): return render(request, "about.html", {}) def club_view(request): obj = Club.objects.all() context = { "object": obj } return render(request, "resultsapp/club_view.html", context) def discipline_detail_view(request, id): obj = get_object_or_404(DisciplineDistance, id=id) obj.min = Helper.convert_from_seconds(obj.min) obj.max = Helper.convert_from_seconds(obj.max) context = { "object": obj } return render(request, "resultsapp/discipline_detail.html", context) def discipline_list_view(request): queryset_distance = DisciplineDistance.objects.all() queryset_time = DisciplineTime.objects.all() context = { "queryset_distance": queryset_distance, "queryset_time": queryset_time } return render(request, "resultsapp/discipline_list.html", context) def event_create_view(request): form = EventsForm(request.POST or None) if form.is_valid(): form.save() form = EventsForm() context = { 'form': form } return render(request, "resultsapp/event_create.html", context) def event_for_year_list_view(request, year): queryset = Event.objects.filter(date__iregex=r"{}.*".format(year)).order_by('-date') years = Helper.get_years_with_events() context = { "object_list": queryset, "year_list": years, } return render(request, "resultsapp/event_for_year_list.html", context) def event_detail_view(request, id): obj = get_object_or_404(Event, id=id) context = { "object": obj } return render(request, "resultsapp/event_details.html", context) def home_view(request, *args, **kwargs): print(args, kwargs) print(request.user) return render(request, "home.html", {}) def record_list_m_view(request): logger.debug('create record list male') discipline_distance_queryset = DisciplineDistance.objects.all() records_distance = [] for discipline in discipline_distance_queryset: discipline_object = DisciplineDistance.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultDistance.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "m": records_distance.append(result_item) break logger.debug('result_item: ' + str(result_item)) discipline_time_queryset = DisciplineTime.objects.all() records_time = [] for discipline in discipline_time_queryset: discipline_object = DisciplineTime.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultTime.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "m": records_time.append(result_item) break logger.debug('result_item: ' + str(result_item)) context = { "result_distance_object_list": records_distance, "result_time_object_list": records_time } return render(request, "resultsapp/record_list_m.html", context) def record_list_w_view(request): logger.debug('create record list female') discipline_distance_queryset = DisciplineDistance.objects.all() records_distance = [] for discipline in discipline_distance_queryset: discipline_object = DisciplineDistance.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultDistance.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "w": records_distance.append(result_item) break logger.debug('result_item: ' + str(result_item)) discipline_time_queryset = DisciplineTime.objects.all() records_time = [] for discipline in discipline_time_queryset: discipline_object = DisciplineTime.objects.get(name=discipline) logger.debug('discipline.id: ' + str(discipline_object.id)) result_queryset = ResultTime.objects.filter(discipline_id=discipline_object.id).order_by('result_value') for result_item in result_queryset: member = result_item.member_id logger.debug('member: ' + str(member)) sex = Helper.get_sex_from_result_member(member) if sex == "w": records_time.append(result_item) break logger.debug('result_item: ' + str(result_item)) context = { "result_distance_object_list": records_distance, "result_time_object_list": records_time } return render(request, "resultsapp/record_list_w.html", context) def statistics_view(request): logger.debug('show statistic') statistics = [] event_count = Event.objects.all().count() statistics.append("Number of events: " + str(event_count)) years = Helper.get_years_with_events() for year in years: event_count = Event.objects.filter(date__iregex=r"{}.*".format(year)).count() statistics.append("Number of events in " + str(year) + ": " + str(event_count)) result_count_distance = ResultDistance.objects.all().count() result_count_time = ResultTime.objects.all().count() result_count = int(result_count_distance) + int(result_count_time) statistics.append("Number of results: " + str(result_count)) result_distance_queryset = ResultDistance.objects.all() result_time_queryset = ResultTime.objects.all() for year in years: logger.debug("year: " + str(year)) result_counter = 0 for result in result_distance_queryset: logger.debug(str(result.event_id)) if str(year) == Helper.get_year_from_result_event(result.event_id): result_counter = result_counter + 1 for result in result_time_queryset: logger.debug(str(result.event_id)) if str(year) == Helper.get_year_from_result_event(result.event_id): result_counter = result_counter + 1 statistics.append("Number of results in " + str(year) + ": " +str(result_counter)) context = { "object_list": statistics, } return render(request, "resultsapp/statistics.html", context) def years_with_annual_records_m_view(request): years = Helper.get_years_with_events() context = { "year_list": years } return render(request, "resultsapp/annual_records_m_filter.html", context) def years_with_annual_records_w_view(request): years = Helper.get_years_with_events() context = { "year_list": years } return render(request, "resultsapp/annual_records_w_filter.html", context) def years_with_events_view(request): years = Helper.get_years_with_events() context = { "year_list": years } return render(request, "resultsapp/event_year_filter.html", context)

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5

10e9f2d457a66bbd3c9852a7d65637f6808eb1c9

3,575

py

Python

tests/satosa/micro_services/test_attribute_authorization.py

SURFscz/SATOSA

d0f5552258cf027d6965303e84035984e3bba2a1

[ "Apache-2.0" ]

null

tests/satosa/micro_services/test_attribute_authorization.py

SURFscz/SATOSA

d0f5552258cf027d6965303e84035984e3bba2a1

[ "Apache-2.0" ]

1

2020-03-18T18:13:14.000Z

tests/satosa/micro_services/test_attribute_authorization.py

SURFscz/SATOSA

d0f5552258cf027d6965303e84035984e3bba2a1

[ "Apache-2.0" ]

1

2018-04-26T11:11:49.000Z

from satosa.internal_data import InternalResponse, AuthenticationInformation from satosa.micro_services.attribute_authorization import AttributeAuthorization from satosa.exception import SATOSAAuthenticationError from satosa.context import Context class TestAttributeAuthorization: def create_authz_service(self, attribute_allow, attribute_deny): authz_service = AttributeAuthorization(config=dict(attribute_allow=attribute_allow,attribute_deny=attribute_deny), name="test_authz", base_url="https://satosa.example.com") authz_service.next = lambda ctx, data: data return authz_service def test_authz_allow_success(self): attribute_allow = { "": { "default": {"a0": ['.+@.+']} } } attribute_deny = {} authz_service = self.create_authz_service(attribute_allow, attribute_deny) resp = InternalResponse(AuthenticationInformation(None, None, None)) resp.attributes = { "a0": ["test@example.com"], } try: ctx = Context() ctx.state = dict() authz_service.process(ctx, resp) except SATOSAAuthenticationError as ex: assert False def test_authz_allow_fail(self): attribute_allow = { "": { "default": {"a0": ['foo1','foo2']} } } attribute_deny = {} authz_service = self.create_authz_service(attribute_allow, attribute_deny) resp = InternalResponse(AuthenticationInformation(None, None, None)) resp.attributes = { "a0": ["bar"], } try: ctx = Context() ctx.state = dict() authz_service.process(ctx, resp) assert False except SATOSAAuthenticationError as ex: assert True def test_authz_allow_second(self): attribute_allow = { "": { "default": {"a0": ['foo1','foo2']} } } attribute_deny = {} authz_service = self.create_authz_service(attribute_allow, attribute_deny) resp = InternalResponse(AuthenticationInformation(None, None, None)) resp.attributes = { "a0": ["foo2","kaka"], } try: ctx = Context() ctx.state = dict() authz_service.process(ctx, resp) except SATOSAAuthenticationError as ex: assert False def test_authz_deny_success(self): attribute_deny = { "": { "default": {"a0": ['foo1','foo2']} } } attribute_allow = {} authz_service = self.create_authz_service(attribute_allow, attribute_deny) resp = InternalResponse(AuthenticationInformation(None, None, None)) resp.attributes = { "a0": ["foo2"], } try: ctx = Context() ctx.state = dict() authz_service.process(ctx, resp) assert False except SATOSAAuthenticationError as ex: assert True def test_authz_deny_fail(self): attribute_deny = { "": { "default": {"a0": ['foo1','foo2']} } } attribute_allow = {} authz_service = self.create_authz_service(attribute_allow, attribute_deny) resp = InternalResponse(AuthenticationInformation(None, None, None)) resp.attributes = { "a0": ["foo3"], } try: ctx = Context() ctx.state = dict() authz_service.process(ctx, resp) except SATOSAAuthenticationError as ex: assert False

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8031ebd36418987ff6074b0e0b9dc78aea30daaa

186

py

Python

tests/test_basic.py

pran1990/home-to-work

5f80a0658aa9922ec594c136607d849e5007eed3

[ "Apache-2.0" ]

null

tests/test_basic.py

pran1990/home-to-work

5f80a0658aa9922ec594c136607d849e5007eed3

[ "Apache-2.0" ]

null

tests/test_basic.py

pran1990/home-to-work

5f80a0658aa9922ec594c136607d849e5007eed3

[ "Apache-2.0" ]

null

from .context import sample import unittest class BasicTest(unittest.TestCase): def setUp(self): pass def test_something(self): self.assertEquals(2, 2)

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80555f9b653a3c7fef621b2e18578fe3f8d02f8f

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py

Python

Week2/exercise1a.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

Week2/exercise1a.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

Week2/exercise1a.py

tb2010/pynet

bb206d7ff0d183f62ca8549b596011de6a28b3d4

[ "MIT" ]

null

#!/usr/bin/env python import my_func1 my_func1.hw()

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1

0

1

0

null

0

1

0

1

0

5

33837365a7dec75e59caf9bbff70980bf898a14c

89

py

Python

onadata/libs/exceptions.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

38

2017-02-28T05:39:40.000Z

2019-01-16T04:39:04.000Z

onadata/libs/exceptions.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

48

2019-03-18T09:26:31.000Z

2019-05-27T08:12:03.000Z

onadata/libs/exceptions.py

BuildAMovement/whistler-kobocat

7f61dd0761bb0aa5b27c909bcff8c29453d3311d

[ "BSD-2-Clause" ]

5

2017-02-22T12:25:19.000Z

2019-01-15T11:16:40.000Z

class NoRecordsFoundError(Exception): pass class J2XException(Exception): pass

12.714286

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0.752809

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89

8.375

0.625

0.38806

0

0.013699

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38

14.833333

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0

0.5

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0

null

1

0

1

0

null

0

1

0

5

33bbd281c990c5456312e1b34c6a2409d0550f60

71

py

Python

modules/__init__.py

tobias-z/4-sem-python

35c0a73f0a2085f2dc539c8ec8761c26675aa078

[ "MIT" ]

null

modules/__init__.py

tobias-z/4-sem-python

35c0a73f0a2085f2dc539c8ec8761c26675aa078

[ "MIT" ]

null

modules/__init__.py

tobias-z/4-sem-python

35c0a73f0a2085f2dc539c8ec8761c26675aa078

[ "MIT" ]

null

# this is an empty file to make python understand that this is a module

71

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1

0

1

0

1

0

null

0

1

0

5

33d51e5f97da143c8011d2118684208e84be9c43

532

py

Python

copying-list/copyingList.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

copying-list/copyingList.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

copying-list/copyingList.py

anmolpal1999/python-for-beginners

738d73006cf21206cd10ea89d9796669fc141df3

[ "MIT" ]

null

print('-------------------------------------------------------------------------') family=['me','sis','Papa','Mummy','Chacha'] print('Now, we will copy family list to a another list') for i in range(len(family)): cpy_family=family[1:4] print(family) print('--------------------------------------') print(cpy_family) print('--------------------------------------') cpy_family.append('Chachi') print(cpy_family) print('thank you') print('-------------------------------------------------------------------------')

35.466667

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0.379699

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0.212121

0.191919

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null

0

1

0

5

1d256abb23c95253a8c15fab3e97559b5d9128cb

7,929

py

Python

tests/Polarizability/polarizablesystem_periodic_test.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

null

tests/Polarizability/polarizablesystem_periodic_test.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

1

2018-01-03T12:08:41.000Z

tests/Polarizability/polarizablesystem_periodic_test.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Apr 23 12:31:50 2018 @author: Vladislav Sláma """ from QChemTool import Structure from QChemTool.Development.polarizablesytem_periodic import PolarizableSystem from QChemTool import energy_units from QChemTool.QuantumChem.Fluorographene.fluorographene import orientFG import numpy as np parameters_type_manual = False system = "2perylene" # "anthanthrene", "perylene", "2perylene" if not parameters_type_manual: # Automatic definition of parameters # Set parameters of the system FG_charges = "ESPfit" params_polar={"VinterFG": True,"coarse_grain": "plane", "charge_type": FG_charges,"approximation": 1.1, "symm": True} # Load FG structure struc = Structure() if system == "perylene": struc.load_xyz("FGrph_1perylene_2dist_ser_TDDFT-wB97XD_geom_BLYP-landl2dz_symm.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines #struc.center(72,73,86) struc = orientFG(struc) elif system == "anthanthrene": struc.load_xyz("FGrph_1anthranthrene_1dist_par_TDDFT-wB97XD_geom_BLYP-landl2dz_symm_7x11.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines # struc.center(41,43,133) struc = orientFG(struc) elif system == "2perylene": struc.load_xyz("FGrph_2perylene_1dist_par_TDDFT-wB97XD_geom_BLYP-landl2dz_symm_9x12.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines # struc.center(58,57,83) struc = orientFG(struc) struc.output_to_xyz("FGrph_2perylene_1dist_par_reorient.xyz") # Initialize the system elstat = {"structure": struc,"charge": FG_charges} diel = {"structure": struc,"polar": params_polar} params = {"energy_type": "QC","permivity": 1.0,"order": 2} system = PolarizableSystem(diel = diel, elstat = elstat, params = params) # identify defects - separated because now changes can be made to the database system.identify_defects() # Calculate energies in the system Ndef = len(system.defects) HH = np.zeros((Ndef,Ndef),dtype='f8') for ii in range(Ndef): dAVA = system.get_elstat_energy(ii,"excited-ground") Eshift, res_Energy, TrDip = system.get_SingleDefectProperties(ii) E01_vacuum = system.defects[ii].get_transition_energy() HH[ii,ii] = E01_vacuum._value + Eshift._value with energy_units("1/cm"): # print(system.defects[0].name,dAVA.value) print(system.defects[ii].name,ii+1,"energy shift:",Eshift.value) print(system.defects[ii].name,ii+1,"transition dipole:",TrDip) for ii in range(Ndef): for jj in range(ii+1,Ndef): J_inter, res = system.get_HeterodimerProperties(ii, jj, EngA = HH[ii,ii], EngB = HH[jj,jj], approx=1.1) #J_inter, res = system.get_HeterodimerProperties(ii, jj, approx=1.1) HH[ii,jj] = J_inter._value HH[jj,ii] = HH[ii,jj] with energy_units("1/cm"): print(system.defects[ii].name,ii+1,"-",system.defects[jj].name,jj+1,"interaction E:",J_inter.value) else: # Set fluorographene charges # manual definition CF_charge = -0.0522 CF2_charge = 2*CF_charge FG_charges={'CF': CF_charge,'CF2': CF2_charge,'CD': 0.0,'C': 0.0} FG_charges['FC'] = -FG_charges['CF'] FG_charges['F2C'] = -FG_charges["CF2"]/2.0 # set fluorographene atomic polarizabilities # manual definition #------------------------------------------------------------------------------ # polxy polz amp per phase # CF_AE_params = [7.53538330517, 0.0000, 1.0326577124, 2, 0.0] # CF_A_E_params = [0.505521019116, 0.000000, 0.4981493, 2, np.pi/2] # CF_BE_params = [0.129161747387, 0.0000, 0.05876077, 2, 0.0] # CF_Ast_params = [2.30828107, 0.0000000, 0.08196599, 2, 0.0] #[2.30828107, 0.00000, 0.081966, 2] CF_AE_params = [8.94690348, 4.50738195, 1.65097606, 3, 0.0] CF_A_E_params = [0.39013017, 2.09784509, 0.59003868, 3, 0.0] CF_BE_params = [0.57543444, 3.98822098, 0.63754235, 3, 0.0] CF_Ast_params = [5.17064221/2, 4.99791421/2, 0.25093473/2, 3, 0.0] VinterFG = 0.0 C_params = [0.00000000, 0.0000000, 0.0, 0, 0.0] FC_AE_params = [0.00000000, 0.0000000, 0.0, 0, 0.0] FC_A_E_params = [0.0000000, 0.0000000, 0.0, 0, 0.0] FC_BE_params = [0.00000000, 0.0000000, 0.0, 0, 0.0] FC_Ast_params = [0.0000000, 0.0000000, 0.0, 0, 0.0] polar = {'AlphaE': {"CF": CF_AE_params, "FC": FC_AE_params, "C": C_params}} polar['Alpha_E'] = {"CF": CF_A_E_params, "FC": FC_A_E_params, "C": C_params} polar['BetaEE'] = {"CF": CF_BE_params, "FC": FC_BE_params, "C": C_params} polar['Alpha_st'] = {"CF": CF_Ast_params, "FC": FC_Ast_params, "C": C_params} params_polar={"VinterFG": 0.0,"coarse_grain": "C", "polarizability": polar,"approximation": 1.1} # Load FG structure struc = Structure() if system == "perylene": struc.load_xyz("FGrph_1perylene_2dist_ser_TDDFT-wB97XD_geom_BLYP-landl2dz_symm.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines #struc.center(72,73,86) struc = orientFG(struc) elif system == "anthanthrene": struc.load_xyz("FGrph_1anthranthrene_1dist_par_TDDFT-wB97XD_geom_BLYP-landl2dz_symm_7x11.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines # struc.center(41,43,133) struc = orientFG(struc) elif system == "2perylene": struc.load_xyz("FGrph_2perylene_1dist_par_TDDFT-wB97XD_geom_BLYP-landl2dz_symm_9x12.xyz") # For practical calculation also reorient sheet in propper direction (plane) and carbons has to be before fluorines # struc.center(58,57,83) struc = orientFG(struc) struc.output_to_xyz("FGrph_2perylene_1dist_par_reorient.xyz") # Initialize the system elstat = {"structure": struc,"charge": FG_charges} diel = {"structure": struc,"polar": params_polar} params = {"energy_type": "QC","permivity": 1.0,"order": 2} system = PolarizableSystem(diel = diel, elstat = elstat, params = params) # identify defects - separated because now changes can be made to the database system.identify_defects() # Calculate energies in the system Ndef = len(system.defects) HH = np.zeros((Ndef,Ndef),dtype='f8') for ii in range(Ndef): dAVA = system.get_elstat_energy(ii,"excited-ground") Eshift, res_Energy, TrDip = system.get_SingleDefectProperties(ii) E01_vacuum = system.defects[ii].get_transition_energy() HH[ii,ii] = E01_vacuum._value + Eshift._value with energy_units("1/cm"): # print(system.defects[0].name,dAVA.value) print(system.defects[ii].name,ii+1,"energy shift:",Eshift.value) print(system.defects[ii].name,ii+1,"transition dipole:",TrDip) for ii in range(Ndef): for jj in range(ii+1,Ndef): J_inter, res = system.get_HeterodimerProperties(ii, jj, EngA = HH[ii,ii], EngB = HH[jj,jj], approx=1.1) #J_inter, res = system.get_HeterodimerProperties(ii, jj, approx=1.1) HH[ii,jj] = J_inter._value HH[jj,ii] = HH[ii,jj] with energy_units("1/cm"): print(system.defects[ii].name,ii+1,"-",system.defects[jj].name,jj+1,"interaction E:",J_inter.value)

50.503185

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0

null

0

1

0

null

0

5

1d2a63c7cd51385546389c2eee6d366e8e52d06b

136

py

Python

budgets/admin.py

scott-currie/budget_tool

4be65e7a205178a6c362467e3835bc3289123c8d

[ "MIT" ]

null

budgets/admin.py

scott-currie/budget_tool

4be65e7a205178a6c362467e3835bc3289123c8d

[ "MIT" ]

null

budgets/admin.py

scott-currie/budget_tool

4be65e7a205178a6c362467e3835bc3289123c8d

[ "MIT" ]

null

from django.contrib import admin from .models import Budget, Transaction admin.site.register(Budget) admin.site.register(Transaction)

19.428571

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0.823529

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136

6.222222

0.555556

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0

1

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true

0

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null

0

1

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1

0

5

1d3a21717d50a8bd589ad1b6ee2392e2d539b3ff

159

py

Python

gui/docks/__init__.py

Battle-Of-Two-K/aeflot_front

83e1f77fd95f3be7b0262cc9de25adbdb568d82e

[ "MIT" ]

null

gui/docks/__init__.py

Battle-Of-Two-K/aeflot_front

83e1f77fd95f3be7b0262cc9de25adbdb568d82e

[ "MIT" ]

10

2021-06-26T17:29:15.000Z

2021-07-06T22:04:02.000Z

gui/docks/__init__.py

Battle-Of-Two-K/aeflot_front

83e1f77fd95f3be7b0262cc9de25adbdb568d82e

[ "MIT" ]

3

2021-06-25T09:15:59.000Z

2021-06-27T07:25:39.000Z

from ._dock_area import AeflotFrontDockArea, Docks, AddDockCommand from ._axonometric_dock import AxonometricDock from ._projection_dock import ProjectionDock

39.75

66

0.880503

17

159

7.882353

0.647059

0.149254

0

0.08805

159

3

67

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0.924138

0

1

0

true

0

1

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null

0

1

0

1

0

1

0

5

1d68f887276189c21cf41b8dfdf3757ffb2116c7

600

py

Python

tests/conftest.py

lig/genekey

51f27dac22f6856125a2bda73e93414d80ac2ad9

[ "MIT" ]

null

tests/conftest.py

lig/genekey

51f27dac22f6856125a2bda73e93414d80ac2ad9

[ "MIT" ]

null

tests/conftest.py

lig/genekey

51f27dac22f6856125a2bda73e93414d80ac2ad9

[ "MIT" ]

null

import pathlib import pytest @pytest.fixture(scope='session') def tests_dir(): return pathlib.Path(__file__).parent.absolute() @pytest.fixture(scope='session') def fixtures_dir(tests_dir): return tests_dir.joinpath('fixtures') @pytest.fixture(scope='session') def fixture_hamlet_info(fixtures_dir): return fixtures_dir.joinpath('hamlet.info') @pytest.fixture(scope='session') def fixture_hamlet_txt(fixtures_dir): return fixtures_dir.joinpath('hamlet.txt') @pytest.fixture(scope='session') def fixture_empty_txt(fixtures_dir): return fixtures_dir.joinpath('empty.txt')

20.689655

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0

null

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1

0

null

0

1

0

1

0

5

1d6b978de5f72ef11122c69cfaa77fd7713963ee

50

py

Python

post_grades_main.py

Saivivek-Peddi/canvas_automated_grading

03a28663938996aed571789870cbec9900f80800

[ "MIT" ]

1

2022-03-03T07:58:30.000Z

post_grades_main.py

Saivivek-Peddi/canvas_automated_grading

03a28663938996aed571789870cbec9900f80800

[ "MIT" ]

null

post_grades_main.py

Saivivek-Peddi/canvas_automated_grading

03a28663938996aed571789870cbec9900f80800

[ "MIT" ]

null

from post_grades import Post_Grades Post_Grades()

16.666667

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null

0

1

0

1

0

5

1d7905d6914b426fee3da0172cf291de3e4c91eb

167

py

Python

sublime_api/api_on_close.py

ekazyam/study

bd4d6bae8624c7b6e166881c898afa0afd3b0c70

[ "MIT" ]

null

sublime_api/api_on_close.py

ekazyam/study

bd4d6bae8624c7b6e166881c898afa0afd3b0c70

[ "MIT" ]

null

sublime_api/api_on_close.py

ekazyam/study

bd4d6bae8624c7b6e166881c898afa0afd3b0c70

[ "MIT" ]

null

import sublime import sublime_plugin class EventDump(sublime_plugin.EventListener): def on_close(self, view): sublime.message_dialog("file is closed.")

18.555556

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null

0

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1

0

5

1d9d1220bee01bdd1589a97b642375ce48fbcba5

756

py

Python

python/kyu-7/mumbling/test_mumbling.py

ledwindra/codewars

0552669a69e801cfe5f9a3696a4d98be63a96951

[ "WTFPL" ]

1

2020-11-13T16:55:04.000Z

python/kyu-7/mumbling/test_mumbling.py

ledwindra/codewars

0552669a69e801cfe5f9a3696a4d98be63a96951

[ "WTFPL" ]

1

2020-01-28T15:48:17.000Z

python/kyu-7/mumbling/test_mumbling.py

ledwindra/codewars

0552669a69e801cfe5f9a3696a4d98be63a96951

[ "WTFPL" ]

null

from mumbling import accum class TestMumbling: def test_0(self): assert accum("ZpglnRxqenU") == "Z-Pp-Ggg-Llll-Nnnnn-Rrrrrr-Xxxxxxx-Qqqqqqqq-Eeeeeeeee-Nnnnnnnnnn-Uuuuuuuuuuu" def test_1(self): assert accum("NyffsGeyylB") == "N-Yy-Fff-Ffff-Sssss-Gggggg-Eeeeeee-Yyyyyyyy-Yyyyyyyyy-Llllllllll-Bbbbbbbbbbb" def test_2(self): assert accum("MjtkuBovqrU") == "M-Jj-Ttt-Kkkk-Uuuuu-Bbbbbb-Ooooooo-Vvvvvvvv-Qqqqqqqqq-Rrrrrrrrrr-Uuuuuuuuuuu" def test_3(self): assert accum("EvidjUnokmM") == "E-Vv-Iii-Dddd-Jjjjj-Uuuuuu-Nnnnnnn-Oooooooo-Kkkkkkkkk-Mmmmmmmmmm-Mmmmmmmmmmm" def test_4(self): assert accum("HbideVbxncC") == "H-Bb-Iii-Dddd-Eeeee-Vvvvvv-Bbbbbbb-Xxxxxxxx-Nnnnnnnnn-Cccccccccc-Ccccccccccc"

42

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0.138889

756

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null

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1

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1

0

5

d51dd60d9b0ae98c5f8618aca0daaa439f4adac7

35

py

Python

cvstudio/view/forms/repo_form/__init__.py

haruiz/PytorchCvStudio

ccf79dd0cc0d61f3fd01b1b5d96f7cda7b681eef

[ "MIT" ]

32

2019-10-31T03:10:52.000Z

2020-12-23T11:50:53.000Z

cvstudio/view/forms/repo_form/__init__.py

haruiz/CvStudio

ccf79dd0cc0d61f3fd01b1b5d96f7cda7b681eef

[ "MIT" ]

19

2019-10-31T15:06:05.000Z

2020-06-15T02:21:55.000Z

cvstudio/view/forms/repo_form/__init__.py

haruiz/PytorchCvStudio

ccf79dd0cc0d61f3fd01b1b5d96f7cda7b681eef

[ "MIT" ]

8

2019-10-31T03:32:50.000Z

2020-07-17T20:47:37.000Z

from .repo_form import NewRepoForm

17.5

34

0.857143

5

35

5.8

1

0

0.114286

35

1

35

0.935484

0

1

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true

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1

0

1

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null

0

1

0

null

0

1

0

1

0

5

d5223bf4c64ef3f565590eece1ec55e778ac262b

353

py

Python

Bio/Motif/Applications/__init__.py

eoc21/biopython

c0f8db8f55a506837c320459957a0ce99b0618b6

[ "PostgreSQL" ]

3

2017-10-23T21:53:57.000Z

2019-09-23T05:14:12.000Z

Bio/Motif/Applications/__init__.py

eoc21/biopython

c0f8db8f55a506837c320459957a0ce99b0618b6

[ "PostgreSQL" ]

null

Bio/Motif/Applications/__init__.py

eoc21/biopython

c0f8db8f55a506837c320459957a0ce99b0618b6

[ "PostgreSQL" ]

null

# Copyright 2009 by Bartek Wilczynski. All rights reserved. # Revisions copyright 2009 by Peter Cock. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. """Motif command line tool wrappers.""" from _AlignAce import AlignAceCommandline

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py

Python

build/lib/discovery_imaging_utils/func_denoising.py

erikglee/discovery_imaging_utils

bf2cfa70178ceb603932a62cfcbe57550bcc1078

[ "MIT" ]

null

build/lib/discovery_imaging_utils/func_denoising.py

erikglee/discovery_imaging_utils

bf2cfa70178ceb603932a62cfcbe57550bcc1078

[ "MIT" ]

null

build/lib/discovery_imaging_utils/func_denoising.py

erikglee/discovery_imaging_utils

bf2cfa70178ceb603932a62cfcbe57550bcc1078

[ "MIT" ]

null

import os import glob import json import _pickle as pickle from discovery_imaging_utils import imaging_utils import pandas as pd import numpy as np import matplotlib.pyplot as plt import scipy.interpolate as interp from sklearn.decomposition import PCA import scipy.interpolate as interp def interpolate(timepoint_defined, signal, interp_type, TR): #defined_timepoints should be an array the length of the t with True at timepoints #that are defined and False at timepoints that are not defined. signal should also #be an array of length t. Timepoints at defined as False will be overwritten. This #script supports extrapolation at beginning/end of the time signal. As a quality control #for the spline interpolation, the most positive/negative values observed in the defined #portion of the signal are set as bounds for the interpolated signal #interpolation types supported: #(1) linear - takes closest point before/after undefined timepoint and interpolates. # in end cases, uses the two points before/after #(2) cubic_spline - takes 5 closest time points before/after undefined timepoints #and applies cubic spline to undefined points. Uses defined signal to determine maximum/minimum #bounds for new interpolated points. #(3) spectral - yet to be implemented, will be based off of code from the 2014 Power # paper timepoint_defined = np.array(timepoint_defined) true_inds = np.where(timepoint_defined == True)[0] false_inds = np.where(timepoint_defined == False)[0] signal_copy = np.array(signal) if interp_type == 'linear': #Still need to handle beginning/end cases for temp_timepoint in false_inds: #past_timepoint = true_inds[np.sort(np.where(true_inds < temp_timepoint)[0])[-1]] #future_timepoint = true_inds[np.sort(np.where(true_inds > temp_timepoint)[0])[0]] #Be sure there is at least one future timepoint and one past timepoint. #If there isn't, then grab either two past or two future timepoints and use those #for interpolation. If there aren't even two total past + future timepoints, then #just set the output to 0. Could also set the output to be unadjusted, but this #is a way to make the issue more obvious. temp_past_timepoint = np.sort(np.where(true_inds < temp_timepoint)[0]) temp_future_timepoint = np.sort(np.where(true_inds > temp_timepoint)[0]) #If we don't have enough data to interpolate/extrapolate if len(temp_past_timepoint) + len(temp_future_timepoint) < 2: signal_copy[temp_timepoint] = 0 #If we do have enough data to interpolate/extrapolate else: if len(temp_past_timepoint) == 0: past_timepoint = true_inds[temp_future_timepoint[1]] else: past_timepoint = true_inds[temp_past_timepoint[-1]] if len(temp_future_timepoint) == 0: future_timepoint = true_inds[temp_past_timepoint[-2]] else: future_timepoint = true_inds[temp_future_timepoint[0]] #Find the appopriate past/future values past_value = signal_copy[int(past_timepoint)] future_value = signal_copy[int(future_timepoint)] #Use the interp1d function for interpolation interp_object = interp.interp1d([past_timepoint, future_timepoint], [past_value, future_value], bounds_error=False, fill_value='extrapolate') signal_copy[temp_timepoint] = interp_object(temp_timepoint).item(0) return signal_copy #For cubic spline interpolation, instead of taking the past/future timepoint #we will just take the closest 5 timepoints. If there aren't 5 timepoints, we will #set the output to 0 if interp_type == 'cubic_spline': sorted_good = np.sort(signal_copy[true_inds]) min_bound = sorted_good[0] max_bound = sorted_good[-1] #Continue if there are at least 5 good inds true_inds_needed = 5 if len(true_inds) >= true_inds_needed: for temp_timepoint in false_inds: closest_inds = true_inds[np.argsort(np.absolute(true_inds - temp_timepoint))] closest_vals = signal_copy[closest_inds.astype(int)] interp_object = interp.interp1d(closest_inds, closest_vals, kind = 'cubic', bounds_error=False, fill_value='extrapolate') signal_copy[temp_timepoint.astype(int)] = interp_object(temp_timepoint).item(0) min_bound_exceded = np.where(signal_copy < min_bound)[0] if len(min_bound_exceded) > 0: signal_copy[min_bound_exceded] = min_bound max_bound_exceded = np.where(signal_copy > max_bound)[0] if len(max_bound_exceded) > 0: signal_copy[max_bound_exceded] = max_bound #If there aren't enough good timepoints, then set the bad timepoints = 0 else: signal_copy[false_inds.astype(int)] = 0 return signal_copy if interp_type == 'spectral': signal_copy = spectral_interpolation(timepoint_defined, signal_copy, TR) return signal_copy def load_comps_dict(parc_obj, comps_dict): #Internal function to load a specific dictionary #file used in denoising. Supports multiple levels #of properties such as 'confounds.framewise_displacement' #and in cases where PCA reduction is used, does not include #n_skip_vols in PCA reduction, but pads the beginning of the #PCA reduction output with zeros to cover n_skip_vols. # # example_comps_dict = {'confounds.framewise_displacement' : False, # 'confounds.twelve_motion_regs' : 3, # 'aroma_noise_ics' : 3} # #This dictionary would form an output array <7,n_timepoints> including #framewise displacement, 3 PCs from twelve motion regressors, and #3 PCs from the aroma noise ICs # #In cases where dim0 > 3.5*dim1 for an extracted element, swaps element dimensions if comps_dict == False: return False comps_matrix = [] #Iterate through all key value pairs for key, value in comps_dict.items(): #Load the current attribute of interest #if key has '.' representing multiple levels, #then recursively go through them to get the object if len(key.split('.')) == 1: temp_arr = getattr(parc_obj, key) else: levels = key.split('.') new_obj = getattr(parc_obj, levels[0]) for temp_obj in levels[1:]: new_obj = getattr(new_obj, temp_obj) temp_arr = new_obj #If temp_arr is only 1d, at a second dimension for comparison if len(temp_arr.shape) == 1: temp_arr = np.reshape(temp_arr, (temp_arr.shape[0],1)) #Current fix to reshape the aroma noise ICs... should #be addressing this at the parcel_timeseries object level though if temp_arr.shape[0] > 3.5*temp_arr.shape[1]: temp_arr = np.transpose(temp_arr) #If necessary, use PCA on the temp_arr if value != False: temp_arr = reduce_ics(temp_arr, value, parc_obj.n_skip_vols) #Either start a new array or stack to existing if comps_matrix == []: comps_matrix = temp_arr else: comps_matrix = np.vstack((comps_matrix, temp_arr)) return comps_matrix def reduce_ics(input_matrix, num_dimensions, n_skip_vols): #Takes input_matrix <num_original_dimensions, num_timepoints>. Returns #the num_dimensions top PCs from the input_matrix which are derived excluding #n_skip_vols, but zeros are padded to the beginning of the time series #in place of the n_skip_vols. if input_matrix.shape[0] > input_matrix.shape[1]: raise NameError('Error: input_matrix should have longer dim1 than dim0') if input_matrix.shape[0] <= 1: raise NameError('Error: input matrix must have multiple matrices') input_matrix_transposed = input_matrix.transpose() partial_input_matrix = input_matrix_transposed[n_skip_vols:,:] pca_temp = PCA() pca_temp.fit(partial_input_matrix) transformed_pcs = pca_temp.transform(partial_input_matrix) pca_time_signal = np.zeros((num_dimensions,input_matrix.shape[1])) pca_time_signal[:,n_skip_vols:] = transformed_pcs.transpose()[0:num_dimensions,:] #This section is from old iteration WITH ERROR!!! #good_components_inds = np.linspace(0,num_dimensions - 1, num = num_dimensions).astype(int) #pca_time_signal = np.zeros((num_dimensions, input_matrix.shape[1])) #pca_time_signal[:,n_skip_vols:] = pca_temp.components_[good_components_inds,:] return pca_time_signal def demean_normalize(one_d_array): #Takes a 1d array and subtracts mean, and #divides by standard deviation temp_arr = one_d_array - np.nanmean(one_d_array) return temp_arr/np.nanstd(temp_arr) def find_timepoints_to_scrub(parc_object, scrubbing_dictionary): #This function is an internal function for the main denoising script. #The purpose of this function is to return a array valued true for #volumes to be included in subsequent analyses and a false for volumes #that need to be scrubbed. #This script will also get rid of the n_skip_vols at the beginning of the #scan. And these volumes don't get accounted for in Uniform. #If you don't want to scrub, just set scrubbing_dictionary equal to False, and #this script will only get rid of the initial volumes if type(scrubbing_dictionary) == type(False): if scrubbing_dictionary == False: temp_val = getattr(parc_object.confounds, 'framewise_displacement') good_arr = np.ones(temp_val.shape) good_arr[0:parc_object.n_skip_vols] = 0 return good_arr else: raise NameError ('Error, if scrubbing dictionary is a boolean it must be False') if 'Uniform' in scrubbing_dictionary: amount_to_keep = scrubbing_dictionary.get('Uniform')[0] evaluation_metrics = scrubbing_dictionary.get('Uniform')[1] evaluation_array = [] for temp_metric in evaluation_metrics: if evaluation_array == []: evaluation_array = demean_normalize(getattr(parc_object.confounds, temp_metric)) else: temp_val = np.absolute(demean_normalize(getattr(parc_object.confounds, temp_metric))) evaluation_array = np.add(evaluation_array, temp_val) num_timepoints_to_keep = int(evaluation_array.shape[0]*amount_to_keep) sorted_inds = np.argsort(evaluation_array) good_inds = sorted_inds[0:num_timepoints_to_keep] good_arr = np.zeros(evaluation_array.shape) good_arr[good_inds] = 1 good_arr[0:parc_object.n_skip_vols] = 0 return good_arr #If neither of the first two options were used, we will assume #they dictionary has appropriate key/value pairs describing scrubbing #criteria temp_val = getattr(parc_object.confounds, 'framewise_displacement') good_arr = np.ones(temp_val.shape) good_arr[0:parc_object.n_skip_vols] = 0 #Iterate through all key/value pairs and set the good_arr #value for indices which the nuisance threshold is exceeded #equal to 0 for temp_key, temp_thresh in scrubbing_dictionary.items(): temp_values = getattr(parc_object.confounds, temp_key) bad_inds = np.where(temp_values > temp_thresh)[0] good_arr[bad_inds] = 0 return good_arr def spectral_interpolation(timepoint_defined, signal, TR): good_timepoint_inds = np.where(timepoint_defined == True)[0] bad_timepoint_inds = np.where(timepoint_defined == False)[0] num_timepoints = timepoint_defined.shape[0] signal_copy = signal.copy() t = float(TR)*good_timepoint_inds h = signal[good_timepoint_inds] TH = np.linspace(0,(num_timepoints - 1)*TR,num=num_timepoints) ofac = float(32) hifac = float(1) N = h.shape[0] #Number of timepoints T = np.max(t) - np.min(t) #Total observed timespan #Calculate sampling frequencies f = np.linspace(1/(T*ofac), hifac*N/(2*T), num = int(((hifac*N/(2*T))/((1/(T*ofac))) + 1))) #angular frequencies and constant offsets w = 2*np.pi*f t1 = np.reshape(t,((1,t.shape[0]))) w1 = np.reshape(w,((w.shape[0],1))) tan_a = np.sum(np.sin(np.matmul(w1,t1*2)), axis=1) tan_b = np.sum(np.cos(np.matmul(w1,t1*2)), axis=1) tau = np.divide(np.arctan2(tan_a,tan_b),2*w) #Calculate the spectral power sine and cosine terms cterm = np.cos(np.matmul(w1,t1) - np.asarray([np.multiply(w,tau)]*t.shape[0]).transpose()) sterm = np.sin(np.matmul(w1,t1) - np.asarray([np.multiply(w,tau)]*t.shape[0]).transpose()) D = np.reshape(h,(1,h.shape[0]) )#This already has the correct shape ##C_final = (sum(Cmult,2).^2)./sum(Cterm.^2,2) #This calculation is done speerately for the numerator, denominator, and the division Cmult = np.multiply(cterm, D) numerator = np.sum(Cmult,axis=1) denominator = np.sum(np.power(cterm,2),axis=1) c = np.divide(numerator, denominator) #Repeat the above for sine term Smult = np.multiply(sterm,D) numerator = np.sum(Smult, axis=1) denominator = np.sum(np.power(sterm,2),axis=1) s = np.divide(numerator,denominator) #The inverse function to re-construct the original time series Time = TH T_rep = np.asarray([Time]*w.shape[0]) #already have w defined prod = np.multiply(T_rep, w1) sin_t = np.sin(prod) cos_t = np.cos(prod) sw_p = np.multiply(sin_t,np.reshape(s,(s.shape[0],1))) cw_p = np.multiply(cos_t,np.reshape(c,(c.shape[0],1))) S = np.sum(sw_p,axis=0) C = np.sum(cw_p,axis=0) H = C + S #Normalize the reconstructed spectrum, needed when ofac > 1 Std_H = np.std(H) Std_h = np.std(h) norm_fac = np.divide(Std_H,Std_h) H = np.divide(H,norm_fac) signal_copy[bad_timepoint_inds] = H[bad_timepoint_inds] return signal_copy def flexible_denoise_parc(parc_obj, hpf_before_regression, scrub_criteria_dictionary, interpolation_method, noise_comps_dict, clean_comps_dict, high_pass, low_pass): #Function inputs: #parc_object = a parcellated timeseries object generated from #file "imaging_utility_classes.py" which will contain both an #uncleaned parcellated time series, and other nuisance variables # etc. of interest #hpf_before_regression = the cutoff frequency for an optional high #pass filter that can be applied to the nuisance regressors (noise/clean) and the #uncleaned time signal before any regression or scrubbing occurs. Recommended #value would be 0.01 or False (False for if you want to skip this step) #scrub_criteria_dictionary = a dictionary that describes how scrubbing should be #implemented. Three main options are (1) instead of inputting a dictionary, setting this #variable to False, which will skip scrubbing, (2) {'Uniform' : [AMOUNT_TO_KEEP, ['std_dvars', 'framewise_displacement']]}, #which will automatically only keep the best timepoints (for if you want all subjects to be scrubbed an equivelant amount). #This option will keep every timepoint if AMOUNT_TO_KEEP was 1, and no timepoints if it was 0. The list of confounds following #AMOUNT_TO_KEEP must at least contain one metric (but can be as many as you want) from parc_object.confounds. If more than one #metric is given, they will be z-transformed and their sum will be used to determine which timepoints should be #kept, with larger values being interpreted as noiser (WHICH MEANS THIS OPTION SHOULD ONLY BE USED WITH METRICS WHERE #ZERO OR NEGATIVE BASED VALUES ARE FINE AND LARGE POSITIVE VALUES ARE BAD) - this option could potentially produce #slightly different numbers of timepoints accross subjects still if the bad timepoints overlap to varying degrees with #the number of timepoints that are dropped at the beginning of the scan. (3) {'std_dvars' : 1.2, 'framewise_displacement' : 0.5} - #similar to the "Uniform" option, the input metrics should be found in parc_object.confounds. Here only timepoints #with values below all specified thresholds will be kept for further analyses #interpolation_method: options are 'linear', 'cubic_spline' and (IN FUTURE) 'spectral'. #While scrubbed values are not included to determine any of the weights in the denoising #model, they will still be interpolated over and then "denoised" (have nuisance variance #removed) so that we have values to put into the optional filter at the end of processing. #The interpolated values only have any influence on the filtering proceedure, and will be again #removed from the time signal after filtering and thus not included in the final output. Interpolation #methods will do weird things if there aren't many timepoints after scrubbing. All interpolation #schemes besides spectral are essentially wrappers over scipy's 1d interpolation methods. 'spectral' #interpolation is implemented based on code from Anish Mitra/Jonathan Power #as shown in Power's 2014 NeuroImage paper #noise_comps_dict and clean_comps_dict both have the same syntax. The values #specified by both of these matrices will be used (along with constant and linear trend) #to construct the denoising regression model for the input timeseries, but only the #noise explained by the noise_comps_dict will be removed from the input timeseries ( #plus also the constant and linear trend). Unlike the scrub_criteria_dictionary, the #data specifed here do not need to come from the confounds section of the parc_object, #and because of this, if you want to include something found under parc_object.confounds, #you will need to specify "confounds" in the name. An example of the dictionary can be seen below: # # clean_comps_dict = {'aroma_clean_ics' : False} # # # noise_comps_dict = {'aroma_noise_ics' : 5, # 'confounds.wmcsfgsr' : False # 'confounds.twelve_motion_regs' : False # } # # #The dictionary key should specify an element to be included in the denoising process #and the dictionary value should be False if you don't want to do a PCA reduction on #the set of nuisance variables (this will be the case more often than not), alternatively #if the key represents a grouping of confounds, then you can use the value to specify the #number of principal components to kept from a reduction of the grouping. If hpf_before_regression #is used, the filtering will happen after the PCA. # # # #high_pass, low_pass: Filters to be applied as the last step in processing. #set as False if you don't want to use them, otherwise set equal to the #cutoff frequency # #If any of the input parameters are set to True, they will be treated as if they were #set to False, because True values wouldn't mean anything.... # # # ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# #Create an array with 1s for timepoints to use, and 0s for scrubbed timepointsx good_timepoints = find_timepoints_to_scrub(parc_obj, scrub_criteria_dictionary) #Load the arrays with the data for both the clean and noise components to be used in regression clean_comps_pre_filter = load_comps_dict(parc_obj, clean_comps_dict) noise_comps_pre_filter = load_comps_dict(parc_obj, noise_comps_dict) #Apply an initial HPF to everything if necessary - this does not remove scrubbed timepoints, #but does skips the first n_skip_vols (which will be set to 0 and not used in subsequent steps) if hpf_before_regression != False: b, a = imaging_utils.construct_filter('highpass', [hpf_before_regression], parc_obj.TR, 6) #start with the clean comps matrix if type(clean_comps_pre_filter) != type(False): clean_comps_post_filter = np.zeros(clean_comps_pre_filter.shape) for clean_dim in range(clean_comps_pre_filter.shape[0]): clean_comps_post_filter[clean_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, clean_comps_pre_filter[clean_dim, parc_obj.n_skip_vols:]) #this option for both clean/noise indicates there is no input matrix to filter else: clean_comps_post_filter = False #Move to the noise comps matrix if type(noise_comps_pre_filter) != type(False): noise_comps_post_filter = np.zeros(noise_comps_pre_filter.shape) for noise_dim in range(noise_comps_pre_filter.shape[0]): noise_comps_post_filter[noise_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, noise_comps_pre_filter[noise_dim, parc_obj.n_skip_vols:]) else: noise_comps_post_filter = False #then filter the original time signal filtered_time_series = np.zeros(parc_obj.time_series.shape) for original_ts_dim in range(parc_obj.time_series.shape[0]): filtered_time_series[original_ts_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, parc_obj.time_series[original_ts_dim, parc_obj.n_skip_vols:]) #If you don't want to apply the initial HPF, then #just make a copy of the matrices of interest else: clean_comps_post_filter = clean_comps_pre_filter noise_comps_post_filter = noise_comps_pre_filter filtered_time_series = parc_obj.time_series #Now create the nuisance regression model. Only do this step if #the noise_comps_post_filter isn't false. good_timepoint_inds = np.where(good_timepoints == True)[0] bad_timepoint_inds = np.where(good_timepoints == False)[0] if type(noise_comps_post_filter) == type(False): regressed_time_signal = filtered_time_series else: #Weird thing where I need to swap dimensions here...(implemented correctly) #First add constant/linear trend to the denoising model constant = np.ones((1,filtered_time_series.shape[1])) linear_trend = np.linspace(0,filtered_time_series.shape[1],num=filtered_time_series.shape[1]) linear_trend = np.reshape(linear_trend, (1,filtered_time_series.shape[1]))[0] noise_comps_post_filter = np.vstack((constant, linear_trend, noise_comps_post_filter)) regressed_time_signal = np.zeros(filtered_time_series.shape).transpose() filtered_time_series_T = filtered_time_series.transpose() #If there aren't any clean components, #do a "hard" or "agressive" denosing if type(clean_comps_post_filter) == type(False): noise_comps_post_filter_T_to_be_used = noise_comps_post_filter[:,good_timepoint_inds].transpose() XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(noise_comps_post_filter_T_to_be_used) for temp_time_signal_dim in range(filtered_time_series.shape[0]): regressed_time_signal[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(filtered_time_series_T[good_timepoint_inds,temp_time_signal_dim], XT_X_Neg1_XT, noise_comps_post_filter_T_to_be_used) #If there are clean components, then #do a "soft" denoising else: full_matrix_to_be_used = np.vstack((noise_comps_post_filter, clean_comps_post_filter))[:,good_timepoint_inds].transpose() noise_comps_post_filter_T_to_be_used = noise_comps_post_filter[:,good_timepoint_inds].transpose() XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(full_matrix_to_be_used) for temp_time_signal_dim in range(filtered_time_series.shape[0]): regressed_time_signal[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(filtered_time_series_T[good_timepoint_inds,temp_time_signal_dim], XT_X_Neg1_XT, noise_comps_post_filter_T_to_be_used) #Put back into original dimensions regressed_time_signal = regressed_time_signal.transpose() #Now apply interpolation interpolated_time_signal = np.zeros(regressed_time_signal.shape) if interpolation_method == 'spectral': interpolated_time_signal = spectral_interpolation_fast(good_timepoints, regressed_time_signal, parc_obj.TR) else: for dim in range(regressed_time_signal.shape[0]): interpolated_time_signal[dim,:] = interpolate(good_timepoints, regressed_time_signal[dim,:], interpolation_method, parc_obj.TR) #Now if necessary, apply additional filterign: if high_pass == False and low_pass == False: filtered_time_signal = interpolated_time_signal else: if high_pass != False and low_pass == False: b, a = imaging_utils.construct_filter('highpass', [high_pass], parc_obj.TR, 6) elif high_pass == False and low_pass != False: b, a = imaging_utils.construct_filter('lowpass', [low_pass], parc_obj.TR, 6) elif high_pass != False and low_pass != False: b, a = imaging_utils.construct_filter('bandpass', [high_pass, low_pass], parc_obj.TR, 6) filtered_time_signal = np.zeros(regressed_time_signal.shape) for dim in range(regressed_time_signal.shape[0]): filtered_time_signal[dim,:] = imaging_utils.apply_filter(b,a,regressed_time_signal[dim,:]) #Now set all the undefined timepoints to Nan cleaned_time_signal = filtered_time_signal cleaned_time_signal[:,bad_timepoint_inds] = np.nan return cleaned_time_signal, good_timepoint_inds def flexible_orth_denoise_parc(parc_obj, hpf_before_regression, scrub_criteria_dictionary, interpolation_method, noise_comps_dict, clean_comps_dict, high_pass, low_pass): #THIS FUNCTION IS THE SAME AS FLEXIBLE DENOISE PARC, #EXCEPT FOR HERE, THE REGRESSORS IDENTIFIED BY CLEAN #COMPS DICT ARE REGRESSED FROM THE REGRESSORS IDENTIFIED #BY NOISE COMPS DICT PRIOR TO THE REGRESSORS FROM NOISE COMPS #DICT BEING USED TO CLEAN THE TIMESERIES. THIS MEANS THE MODEL #TO CLEAN THE TIMESERIES WILL ONLY CONTAIN THE ORTHOGONALIZED #NUISANCE VARIABLES (filtering and other options will be applied #as per usual) #Function inputs: #parc_object = a parcellated timeseries object generated from #file "imaging_utility_classes.py" which will contain both an #uncleaned parcellated time series, and other nuisance variables # etc. of interest #hpf_before_regression = the cutoff frequency for an optional high #pass filter that can be applied to the nuisance regressors (noise/clean) and the #uncleaned time signal before any regression or scrubbing occurs. Recommended #value would be 0.01 or False (False for if you want to skip this step) #scrub_criteria_dictionary = a dictionary that describes how scrubbing should be #implemented. Three main options are (1) instead of inputting a dictionary, setting this #variable to False, which will skip scrubbing, (2) {'Uniform' : [AMOUNT_TO_KEEP, ['std_dvars', 'framewise_displacement']]}, #which will automatically only keep the best timepoints (for if you want all subjects to be scrubbed an equivelant amount). #This option will keep every timepoint if AMOUNT_TO_KEEP was 1, and no timepoints if it was 0. The list of confounds following #AMOUNT_TO_KEEP must at least contain one metric (but can be as many as you want) from parc_object.confounds. If more than one #metric is given, they will be z-transformed and their sum will be used to determine which timepoints should be #kept, with larger values being interpreted as noiser (WHICH MEANS THIS OPTION SHOULD ONLY BE USED WITH METRICS WHERE #ZERO OR NEGATIVE BASED VALUES ARE FINE AND LARGE POSITIVE VALUES ARE BAD) - this option could potentially produce #slightly different numbers of timepoints accross subjects still if the bad timepoints overlap to varying degrees with #the number of timepoints that are dropped at the beginning of the scan. (3) {'std_dvars' : 1.2, 'framewise_displacement' : 0.5} - #similar to the "Uniform" option, the input metrics should be found in parc_object.confounds. Here only timepoints #with values below all specified thresholds will be kept for further analyses #interpolation_method: options are 'linear', 'cubic_spline' and (IN FUTURE) 'spectral'. #While scrubbed values are not included to determine any of the weights in the denoising #model, they will still be interpolated over and then "denoised" (have nuisance variance #removed) so that we have values to put into the optional filter at the end of processing. #The interpolated values only have any influence on the filtering proceedure, and will be again #removed from the time signal after filtering and thus not included in the final output. Interpolation #methods will do weird things if there aren't many timepoints after scrubbing. All interpolation #schemes besides spectral are essentially wrappers over scipy's 1d interpolation methods. 'spectral' #interpolation is implemented based on code from Anish Mitra/Jonathan Power #as shown in Power's 2014 NeuroImage paper #noise_comps_dict and clean_comps_dict both have the same syntax. The values #specified by both of these matrices will be used (along with constant and linear trend) #to construct the denoising regression model for the input timeseries, but only the #noise explained by the noise_comps_dict will be removed from the input timeseries ( #plus also the constant and linear trend). Unlike the scrub_criteria_dictionary, the #data specifed here do not need to come from the confounds section of the parc_object, #and because of this, if you want to include something found under parc_object.confounds, #you will need to specify "confounds" in the name. An example of the dictionary can be seen below: # # clean_comps_dict = {'aroma_clean_ics' : False} # # # noise_comps_dict = {'aroma_noise_ics' : 5, # 'confounds.wmcsfgsr' : False # 'confounds.twelve_motion_regs' : False # } # # #The dictionary key should specify an element to be included in the denoising process #and the dictionary value should be False if you don't want to do a PCA reduction on #the set of nuisance variables (this will be the case more often than not), alternatively #if the key represents a grouping of confounds, then you can use the value to specify the #number of principal components to kept from a reduction of the grouping. If hpf_before_regression #is used, the filtering will happen after the PCA. # # # #high_pass, low_pass: Filters to be applied as the last step in processing. #set as False if you don't want to use them, otherwise set equal to the #cutoff frequency # #If any of the input parameters are set to True, they will be treated as if they were #set to False, because True values wouldn't mean anything.... # # # ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# ################################################################################################# #Create an array with 1s for timepoints to use, and 0s for scrubbed timepointsx good_timepoints = find_timepoints_to_scrub(parc_obj, scrub_criteria_dictionary) #Load the arrays with the data for both the clean and noise components to be used in regression clean_comps_pre_filter = load_comps_dict(parc_obj, clean_comps_dict) noise_comps_pre_filter = load_comps_dict(parc_obj, noise_comps_dict) #Apply an initial HPF to everything if necessary - this does not remove scrubbed timepoints, #but does skips the first n_skip_vols (which will be set to 0 and not used in subsequent steps) if hpf_before_regression != False: b, a = imaging_utils.construct_filter('highpass', [hpf_before_regression], parc_obj.TR, 6) #start with the clean comps matrix if type(clean_comps_pre_filter) != type(False): clean_comps_post_filter = np.zeros(clean_comps_pre_filter.shape) for clean_dim in range(clean_comps_pre_filter.shape[0]): clean_comps_post_filter[clean_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, clean_comps_pre_filter[clean_dim, parc_obj.n_skip_vols:]) #this option for both clean/noise indicates there is no input matrix to filter else: clean_comps_post_filter = False #Move to the noise comps matrix if type(noise_comps_pre_filter) != type(False): noise_comps_post_filter = np.zeros(noise_comps_pre_filter.shape) for noise_dim in range(noise_comps_pre_filter.shape[0]): noise_comps_post_filter[noise_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, noise_comps_pre_filter[noise_dim, parc_obj.n_skip_vols:]) else: noise_comps_post_filter = False #then filter the original time signal filtered_time_series = np.zeros(parc_obj.time_series.shape) for original_ts_dim in range(parc_obj.time_series.shape[0]): filtered_time_series[original_ts_dim, parc_obj.n_skip_vols:] = imaging_utils.apply_filter(b, a, parc_obj.time_series[original_ts_dim, parc_obj.n_skip_vols:]) #If you don't want to apply the initial HPF, then #just make a copy of the matrices of interest else: clean_comps_post_filter = clean_comps_pre_filter noise_comps_post_filter = noise_comps_pre_filter filtered_time_series = parc_obj.time_series #Now create the nuisance regression model. Only do this step if #the noise_comps_post_filter isn't false. good_timepoint_inds = np.where(good_timepoints == True)[0] bad_timepoint_inds = np.where(good_timepoints == False)[0] if type(noise_comps_post_filter) == type(False): regressed_time_signal = filtered_time_series else: #Weird thing where I need to swap dimensions here...(implemented correctly) #First add constant/linear trend to the denoising model constant = np.ones((1,filtered_time_series.shape[1])) linear_trend = np.linspace(0,filtered_time_series.shape[1],num=filtered_time_series.shape[1]) linear_trend = np.reshape(linear_trend, (1,filtered_time_series.shape[1]))[0] noise_comps_post_filter = np.vstack((constant, linear_trend, noise_comps_post_filter)) regressed_time_signal = np.zeros(filtered_time_series.shape).transpose() filtered_time_series_T = filtered_time_series.transpose() #If there aren't any clean components, #do a "hard" or "agressive" denosing if type(clean_comps_post_filter) == type(False): noise_comps_post_filter_T_to_be_used = noise_comps_post_filter[:,good_timepoint_inds].transpose() XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(noise_comps_post_filter_T_to_be_used) for temp_time_signal_dim in range(filtered_time_series.shape[0]): regressed_time_signal[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(filtered_time_series_T[good_timepoint_inds,temp_time_signal_dim], XT_X_Neg1_XT, noise_comps_post_filter_T_to_be_used) #If there are clean components, then #do a "soft" denoising... ########################################################################### ########################################################################### ####THIS CHUNK OF CODE IS THE ONLY THING TO BE CHANGED BETWEEN############# ####THE ORIGINAL FLEXIBLE DENOISE FUNC FUNCTION AND THIS ONE############### ########################################################################### ########################################################################### else: noise_comps_post_filter_T_to_be_used = noise_comps_post_filter[:,good_timepoint_inds].transpose() clean_comps_post_filter_T_to_be_used = clean_comps_post_filter[:,good_timepoint_inds].transpose() orth_noise_comps_post_filter = np.zeros(noise_comps_post_filter.shape).transpose() initial_XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(clean_comps_post_filter_T_to_be_used) for temp_time_signal_dim in range(orth_noise_comps_post_filter.shape[1]): orth_noise_comps_post_filter[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(noise_comps_post_filter_T_to_be_used[:,temp_time_signal_dim], initial_XT_X_Neg1_XT, clean_comps_post_filter_T_to_be_used) noise_comps_post_filter_T_to_be_used = orth_noise_comps_post_filter[good_timepoint_inds,:] XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(noise_comps_post_filter_T_to_be_used) for temp_time_signal_dim in range(filtered_time_series.shape[0]): regressed_time_signal[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(filtered_time_series_T[good_timepoint_inds,temp_time_signal_dim], XT_X_Neg1_XT, noise_comps_post_filter_T_to_be_used) #full_matrix_to_be_used = np.vstack((noise_comps_post_filter, clean_comps_post_filter))[:,good_timepoint_inds].transpose() #noise_comps_post_filter_T_to_be_used = noise_comps_post_filter[:,good_timepoint_inds].transpose() #XT_X_Neg1_XT = imaging_utils.calculate_XT_X_Neg1_XT(full_matrix_to_be_used) #for temp_time_signal_dim in range(filtered_time_series.shape[0]): # regressed_time_signal[good_timepoint_inds,temp_time_signal_dim] = imaging_utils.partial_clean_fast(filtered_time_series_T[good_timepoint_inds,temp_time_signal_dim], XT_X_Neg1_XT, noise_comps_post_filter_T_to_be_used) #Put back into original dimensions regressed_time_signal = regressed_time_signal.transpose() ########################################################################### ########################################################################### ########################################################################### ########################################################################### ########################################################################### #Now apply interpolation interpolated_time_signal = np.zeros(regressed_time_signal.shape) if interpolation_method == 'spectral': interpolated_time_signal = spectral_interpolation_fast(good_timepoints, regressed_time_signal, parc_obj.TR) else: for dim in range(regressed_time_signal.shape[0]): interpolated_time_signal[dim,:] = interpolate(good_timepoints, regressed_time_signal[dim,:], interpolation_method, parc_obj.TR) #Now if necessary, apply additional filterign: if high_pass == False and low_pass == False: filtered_time_signal = interpolated_time_signal else: if high_pass != False and low_pass == False: b, a = imaging_utils.construct_filter('highpass', [high_pass], parc_obj.TR, 6) elif high_pass == False and low_pass != False: b, a = imaging_utils.construct_filter('lowpass', [low_pass], parc_obj.TR, 6) elif high_pass != False and low_pass != False: b, a = imaging_utils.construct_filter('bandpass', [high_pass, low_pass], parc_obj.TR, 6) filtered_time_signal = np.zeros(regressed_time_signal.shape) for dim in range(regressed_time_signal.shape[0]): filtered_time_signal[dim,:] = imaging_utils.apply_filter(b,a,regressed_time_signal[dim,:]) #Now set all the undefined timepoints to Nan cleaned_time_signal = filtered_time_signal cleaned_time_signal[:,bad_timepoint_inds] = np.nan return cleaned_time_signal, good_timepoint_inds def spectral_interpolation_fast(timepoint_defined, signal, TR): good_timepoint_inds = np.where(timepoint_defined == True)[0] bad_timepoint_inds = np.where(timepoint_defined == False)[0] num_timepoints = timepoint_defined.shape[0] signal_copy = signal.copy() t = float(TR)*good_timepoint_inds h = signal[:,good_timepoint_inds] TH = np.linspace(0,(num_timepoints - 1)*TR,num=num_timepoints) ofac = float(8) #Higher than this is slow without good quality improvements hifac = float(1) N = timepoint_defined.shape[0] #Number of timepoints T = np.max(t) - np.min(t) #Total observed timespan #Calculate sampling frequencies f = np.linspace(1/(T*ofac), hifac*N/(2*T), num = int(((hifac*N/(2*T))/((1/(T*ofac))) + 1))) #angular frequencies and constant offsets w = 2*np.pi*f t1 = np.reshape(t,((1,t.shape[0]))) w1 = np.reshape(w,((w.shape[0],1))) tan_a = np.sum(np.sin(np.matmul(w1,t1*2)), axis=1) tan_b = np.sum(np.cos(np.matmul(w1,t1*2)), axis=1) tau = np.divide(np.arctan2(tan_a,tan_b),2*w) a1 = np.matmul(w1,t1) b1 = np.asarray([np.multiply(w,tau)]*t.shape[0]).transpose() cs_input = a1 - b1 #Calculate the spectral power sine and cosine terms cterm = np.cos(cs_input) sterm = np.sin(cs_input) cos_denominator = np.sum(np.power(cterm,2),axis=1) sin_denominator = np.sum(np.power(sterm,2),axis=1) #The inverse function to re-construct the original time series pt. 1 Time = TH T_rep = np.asarray([Time]*w.shape[0]) #already have w defined prod = np.multiply(T_rep, w1) sin_t = np.sin(prod) cos_t = np.cos(prod) for i in range(h.shape[0]): ##C_final = (sum(Cmult,2).^2)./sum(Cterm.^2,2) #This calculation is done speerately for the numerator, denominator, and the division Cmult = np.multiply(cterm, h[i,:]) numerator = np.sum(Cmult,axis=1) c = np.divide(numerator, cos_denominator) #Repeat the above for sine term Smult = np.multiply(sterm,h[i,:]) numerator = np.sum(Smult, axis=1) s = np.divide(numerator,sin_denominator) #The inverse function to re-construct the original time series pt. 2 sw_p = np.multiply(sin_t,np.reshape(s,(s.shape[0],1))) cw_p = np.multiply(cos_t,np.reshape(c,(c.shape[0],1))) S = np.sum(sw_p,axis=0) C = np.sum(cw_p,axis=0) H = C + S #Normalize the reconstructed spectrum, needed when ofac > 1 Std_H = np.std(H) Std_h = np.std(h) norm_fac = np.divide(Std_H,Std_h) H = np.divide(H,norm_fac) signal_copy[i,bad_timepoint_inds] = H[bad_timepoint_inds] return signal_copy

46.343373

243

0.649053

6,230

46,158

4.569984

0.098555

0.027748

0.031084

0.030206

0.771487

0.751607

0.726634

0.722454

0.71132

0.706544

0

0.00885

0.241128

46,158

995

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0.091644

0

null

0

1

0

null

0

5

d57086974dfdc1cdc4703c6c166fa6a75615171b

623

py

Python

CheckPermutation/TestSolution.py

quyentruong/InterviewQuestion_Python

ce56dd9476de9f44b92d7fcb0160743f3a78c25a

[ "MIT" ]

null

CheckPermutation/TestSolution.py

quyentruong/InterviewQuestion_Python

ce56dd9476de9f44b92d7fcb0160743f3a78c25a

[ "MIT" ]

null

CheckPermutation/TestSolution.py

quyentruong/InterviewQuestion_Python

ce56dd9476de9f44b92d7fcb0160743f3a78c25a

[ "MIT" ]

null

# Quyen Truong # 11/16/2016 from Solution import check import unittest class MyTestCase(unittest.TestCase): def test_same_length(self): self.assertEqual(check("dog", "god"), True) self.assertEqual(check("Happy", "appHy"), True) def test_different_length(self): self.assertEqual(check("Dokoko", "kokokodo"), False) self.assertEqual(check("Hajimemas", "Hamejimask"), False) def test_case_sensitive(self): self.assertEqual(check("NEWYEAR", "newyear"), False) self.assertEqual(check("HOLIDAY", "dayholi"), False) if __name__ == '__main__': unittest.main()

25.958333

65

0.672552

71

623

5.704225

0.535211

0.222222

0.296296

0.177778

0.148148

0

0.015686

0.18138

623

23

66

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0.778431

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false

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null

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null

0

1

0

1

0

5

d59014a61b1f50d7a4a7d66ce2dd74bf3065965c

140

py

Python

setup.py

Anaphory/online_cognacy_ident

7a986af497e42871ac208d773a009bd844bc10d8

[ "MIT" ]

null

setup.py

Anaphory/online_cognacy_ident

7a986af497e42871ac208d773a009bd844bc10d8

[ "MIT" ]

null

setup.py

Anaphory/online_cognacy_ident

7a986af497e42871ac208d773a009bd844bc10d8

[ "MIT" ]

null

#!/usr/bin/env python from distutils.core import setup setup(name='online_cognacy_ident', packages=['online_cognacy_ident'], )

17.5

40

0.714286

18

140

5.333333

0.777778

0.270833

0.375

0

0.157143

140

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0.813559

0.142857

0

0.336134

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1

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true

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null

1

0

1

0

null

0

1

0

5

d59f5d6bbb372b363e999a40c9be636d90af5798

558

py

Python

ejercicio4.py

Javifdz12/ejercicios_agregacion_y_composicionn

72010cbd530945702654c0a5b010b2e23e5dffcf

[ "Apache-2.0" ]

1

2022-03-27T17:29:34.000Z

ejercicio4.py

emherraiz/ejercicios_agregacion_y_composicionn

cda40b33f0cd7578c1f6af1eca2f68c60db1d4e2

[ "Apache-2.0" ]

null

ejercicio4.py

emherraiz/ejercicios_agregacion_y_composicionn

cda40b33f0cd7578c1f6af1eca2f68c60db1d4e2

[ "Apache-2.0" ]

1

2022-03-22T12:01:50.000Z

class zoo: def __init__(self,stock,cuidadores,animales): pass class cuidador: def __init__(self,animales,vacaciones): pass class animal: def __init__(self,dieta): pass class vacaciones: def __init__(self): pass class comida: def __init__(self,dieta): pass class stock: def __init__(self,comida): pass class carnivoros: def __init__(self): pass class herbivoros: def __init__(self): pass class insectivoros: def __init__(self): pass print("puerca")

18

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64

558

4.96875

0.296875

0.198113

0.311321

0.188679

0.345912

0.157233

0

0.27957

558

30

50

18.6

0.791045

0

0.535714

0

0.010772

0

1

0.321429

false

0.321429

0

0.642857

0.035714

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d5af0869e43ed4942961d12e3dde20debc931e8b

70

py

Python

smtp_project/send_all_emails.py

SilasPDJ/autoesk

df0a7457de4795a76887f682f0515431c903ee86

[ "MIT" ]

null

smtp_project/send_all_emails.py

SilasPDJ/autoesk

df0a7457de4795a76887f682f0515431c903ee86

[ "MIT" ]

1

2020-09-24T20:29:05.000Z

2021-12-24T05:00:52.000Z

smtp_project/send_all_emails.py

SilasPDJ/autoesk

df0a7457de4795a76887f682f0515431c903ee86

[ "MIT" ]

null

from smtp_project import EmailExecutor # ainda vou fazer com django

14

38

0.814286

10

70

5.6

1

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70

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17.5

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0.371429

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true

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null

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null

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0

5

6381479a356d75650892a35fa677203d36c14c75

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py

Python

test/install_and_test_tool_shed_repositories/functional/__init__.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

null

test/install_and_test_tool_shed_repositories/functional/__init__.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

null

test/install_and_test_tool_shed_repositories/functional/__init__.py

bopopescu/phyG

023f505b705ab953f502cbc55e90612047867583

[ "CC-BY-3.0" ]

1

2020-07-25T21:03:18.000Z

'''Tests'''

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63a295f6a0037de44c8184fde6c1336cc5542095

179

py

Python

DelibeRating/DelibeRating/deliberating-env/Lib/site-packages/resources/tests.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

null

DelibeRating/DelibeRating/deliberating-env/Lib/site-packages/resources/tests.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

null

DelibeRating/DelibeRating/deliberating-env/Lib/site-packages/resources/tests.py

Severose/DelibeRating

5d227f35c071477ce3fd6fbf3ab13a44d13f6e08

[ "MIT" ]

null

# django imports from django.test import TestCase from django.test.client import Client class CSSTestCase(TestCase): """ """ def setUp(self): """ """

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5

892dae7ec41fb7c9c45e207d0c488ef73a4ee9bf

623

py

Python

Lib/site-packages/node/testing/env.py

Dr8Ninja/ShareSpace

7b445783a313cbdebb1938e824e98370a42def5f

[ "MIT" ]

11

2015-04-02T17:47:44.000Z

2020-10-26T20:27:43.000Z

Lib/site-packages/node/testing/env.py

Dr8Ninja/ShareSpace

7b445783a313cbdebb1938e824e98370a42def5f

[ "MIT" ]

5

2017-01-18T11:05:42.000Z

2019-03-30T06:19:21.000Z

Lib/site-packages/node/testing/env.py

Dr8Ninja/ShareSpace

7b445783a313cbdebb1938e824e98370a42def5f

[ "MIT" ]

2

2015-09-15T06:50:22.000Z

2016-12-01T11:12:01.000Z

# -*- coding: utf-8 -*- from node.behaviors import Adopt from node.behaviors import AsAttrAccess from node.behaviors import DefaultInit from node.behaviors import NodeChildValidate from node.behaviors import Nodify from node.behaviors import OdictStorage from node.interfaces import INode from plumber import plumbing from zope.interface import implementer @implementer(INode) class MockupNode(object): __name__ = None __parent__ = None class NoNode(object): pass @plumbing( NodeChildValidate, DefaultInit, Adopt, AsAttrAccess, Nodify, OdictStorage) class MyNode(object): pass

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null

0

1

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1

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5

893248e44e566b8f51288a234c1d036b8303743b

208

py

Python

scripts/build_mpfr.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

scripts/build_mpfr.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

scripts/build_mpfr.py

1byte2bytes/SydChain

ac1fffd9f87c2afa6e2f6a0540d69dad0815ef4f

[ "MIT" ]

null

# Copyright (c) Sydney Erickson 2017 import buildlib import buildsettings import build_autoconf import build_gmp buildlib.build_configure("mpfr-4.0.0.tar.gz", "--with-gmp={}".format(buildsettings.builddir))

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null

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1

0

1

0

5

897df60facf49a89f49c8bf84849ad60a7d627b6

45

py

Python

service.py

Markman-B/kodi-addon-soundcloud

f48d5097601ff48515f53f1af46d2e270e6fa76f

[ "MIT" ]

null

service.py

Markman-B/kodi-addon-soundcloud

f48d5097601ff48515f53f1af46d2e270e6fa76f

[ "MIT" ]

null

service.py

Markman-B/kodi-addon-soundcloud

f48d5097601ff48515f53f1af46d2e270e6fa76f

[ "MIT" ]

null

from resources import service service.run()

11.25

29

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null

0

1

0

1

0

5

897fef4e9c6caeeee7b8e5e1778e615280fda76e

142

py

Python

flog/testing/__init__.py

rice0208/Flog

d56be0b814a0c6ca1fe4abd2c764552121698a94

[ "MIT" ]

14

2020-09-20T01:23:01.000Z

2022-02-09T09:11:10.000Z

flog/testing/__init__.py

rice0208/Flog

d56be0b814a0c6ca1fe4abd2c764552121698a94

[ "MIT" ]

15

2020-12-23T13:19:46.000Z

2022-01-22T08:38:22.000Z

flog/testing/__init__.py

rice0208/Flog

d56be0b814a0c6ca1fe4abd2c764552121698a94

[ "MIT" ]

4

2021-03-14T01:49:30.000Z

2021-11-25T08:31:55.000Z

14.2

43

0.739437

19

142

5.263158

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0.32

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0.033333

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142

9

44

15.777778

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false

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0.666667

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null

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0

1

0

null

0

1

0

1

0

5

89aaed927d4e80e25cd50b0f11d9c982c1d6e058

150

py

Python

team_builder/accounts/admin.py

squadran2003/Social-Team-Builder

08fdab7cf176de0daf38078cd9fcd5f17501cef8

[ "MIT" ]

null

team_builder/accounts/admin.py

squadran2003/Social-Team-Builder

08fdab7cf176de0daf38078cd9fcd5f17501cef8

[ "MIT" ]

null

team_builder/accounts/admin.py

squadran2003/Social-Team-Builder

08fdab7cf176de0daf38078cd9fcd5f17501cef8

[ "MIT" ]

null

from django.contrib import admin from django.conf import settings # Register your models here. from .models import User admin.site.register(User)

15

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150

5.409091

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150

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true

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null

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1

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null

0

1

0

1

0

1

0

5

89b84ae5907fffefe1d368e48142350dceb7d284

98

py

Python

tests/database.py

roor0/dispatch

12c4f567096411abe62abaf61c7c124496764346

[ "Apache-2.0" ]

3,417

2020-02-23T22:54:47.000Z

2022-03-31T13:01:01.000Z

tests/database.py

roor0/dispatch

12c4f567096411abe62abaf61c7c124496764346

[ "Apache-2.0" ]

607

2020-02-24T14:27:02.000Z

2022-03-30T19:15:39.000Z

tests/database.py

roor0/dispatch

12c4f567096411abe62abaf61c7c124496764346

[ "Apache-2.0" ]

359

2020-02-24T19:04:43.000Z

2022-03-29T06:48:12.000Z

from sqlalchemy.orm import scoped_session, sessionmaker Session = scoped_session(sessionmaker())

24.5

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11

98

7.272727

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null

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982e92b8db668220d68d5d85b3f61ea4ba86e366

92

py

Python

naucse_render/__init__.py

Kobzol/naucse_render

20509c2b9f20fb727116325a847df2e428a430b9

[ "MIT" ]

3

2019-01-19T02:56:21.000Z

2019-01-21T12:41:54.000Z

naucse_render/__init__.py

Kobzol/naucse_render

20509c2b9f20fb727116325a847df2e428a430b9

[ "MIT" ]

23

2019-01-29T14:18:32.000Z

2022-02-15T13:37:56.000Z

naucse_render/__init__.py

Kobzol/naucse_render

20509c2b9f20fb727116325a847df2e428a430b9

[ "MIT" ]

5

2019-01-18T13:17:13.000Z

2021-12-01T13:47:10.000Z

from .course import get_course from .lesson import get_lessons from .compile import compile

23

31

0.836957

14

92

5.357143

0.5

0.24

0

0.130435

92

3

32

30.666667

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0

1

0

true

0

1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

984d23737469f50ccf515c0aba578a9b2d7d3d11

148

py

Python

reportservice/report/__init__.py

mbp76/restful-flask

63f9c5b157e245bacc99d0fc8d638cf1e834c752

[ "Apache-2.0" ]

1

2019-04-15T09:33:30.000Z

reportservice/report/__init__.py

mbp76/restful-flask

63f9c5b157e245bacc99d0fc8d638cf1e834c752

[ "Apache-2.0" ]

null

reportservice/report/__init__.py

mbp76/restful-flask

63f9c5b157e245bacc99d0fc8d638cf1e834c752

[ "Apache-2.0" ]

1

2017-10-23T17:56:50.000Z

from flask import Blueprint report = Blueprint('report', __name__, template_folder='templates', static_folder='static') from . import views

16.444444

67

0.75

17

148

6.176471

0.647059

0.285714

0

0.148649

148

8

68

18.5

0.833333

0

0.141892

0

1

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false

0

0.5

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0.5

1

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1

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0

null

0

1

0

1

0

5

986bcf0fd9f8f9c946e29cc9aac787b9a1d8386e

129

py

Python

maio/models/__init__.py

jonmsawyer/maio

468fe495189d970ccc6ec01665865bbf2c6ec578

[ "MIT" ]

null

maio/models/__init__.py

jonmsawyer/maio

468fe495189d970ccc6ec01665865bbf2c6ec578

[ "MIT" ]

5

2016-09-22T23:17:40.000Z

2018-04-05T22:36:37.000Z

maio/models/__init__.py

jonmsawyer/maio

468fe495189d970ccc6ec01665865bbf2c6ec578

[ "MIT" ]

null

from .File import File from .Caption import Caption from .Media import Media from .Playlist import Playlist from .Tag import Tag

21.5

30

0.806202

20

129

5.2

0.35

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0.155039

129

5

31

25.8

0.954128

0

1

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true

0

1

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

5

9889b77282e13fbeca2ce7fccebff634bef4a48b

267

py

Python

py_tea_code/3.mypro-modules/b/module_B1.py

qq4215279/study_python

b0eb9dedfc4abb2fd6c024a599e7375869c3d77a

[ "Apache-2.0" ]

null

py_tea_code/3.mypro-modules/b/module_B1.py

qq4215279/study_python

b0eb9dedfc4abb2fd6c024a599e7375869c3d77a

[ "Apache-2.0" ]

null

py_tea_code/3.mypro-modules/b/module_B1.py

qq4215279/study_python

b0eb9dedfc4abb2fd6c024a599e7375869c3d77a

[ "Apache-2.0" ]

null

#import a.aa.module_AA # a.aa.module_AA.fun_AA() # from a.aa import module_AA # module_AA.fun_AA() # from a.aa.module_AA import fun_AA # fun_AA() # import a # import math # print(a.math.pi) # print(id(math)) # print(id(a.math)) from a import * module_A.fun_A()

13.35

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0.685393

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267

3.245283

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0.255814

0

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19

36

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true

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