hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a35a828455d482461bb4c57f6960bee94f7748ea
| 139
|
py
|
Python
|
task_01.py
|
emelianovss/ypb-good-develop-01
|
5350e3e2ce258a641e1dc3afa8cde9b7de4d3139
|
[
"MIT"
] | null | null | null |
task_01.py
|
emelianovss/ypb-good-develop-01
|
5350e3e2ce258a641e1dc3afa8cde9b7de4d3139
|
[
"MIT"
] | null | null | null |
task_01.py
|
emelianovss/ypb-good-develop-01
|
5350e3e2ce258a641e1dc3afa8cde9b7de4d3139
|
[
"MIT"
] | null | null | null |
from typing import Optional
def function(value_1: int, value_2: Optional[int]) -> int:
return value_1 + value_2
function(10.0, 10)
| 15.444444
| 58
| 0.71223
| 23
| 139
| 4.130435
| 0.565217
| 0.126316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078947
| 0.179856
| 139
| 8
| 59
| 17.375
| 0.754386
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
a365b32c946ee26d6eee678a2d9a3841f0cbb52f
| 6,873
|
py
|
Python
|
ee_ppipe/taskreport.py
|
samapriya/Planet-GEE-Pipeline-GUI-Ex
|
aee6fc76c17cf6c6ce225572465f876fd14b0079
|
[
"Apache-2.0"
] | 21
|
2017-05-06T13:36:27.000Z
|
2020-03-02T12:47:00.000Z
|
ee_ppipe/taskreport.py
|
samapriya/Planet-GEE-Pipeline-GUI-Ex
|
aee6fc76c17cf6c6ce225572465f876fd14b0079
|
[
"Apache-2.0"
] | null | null | null |
ee_ppipe/taskreport.py
|
samapriya/Planet-GEE-Pipeline-GUI-Ex
|
aee6fc76c17cf6c6ce225572465f876fd14b0079
|
[
"Apache-2.0"
] | 8
|
2017-10-08T01:01:58.000Z
|
2020-02-22T14:19:47.000Z
|
import ee
import ee.mapclient
import subprocess
import csv
from datetime import datetime
import time
import datetime
import re
ee.Initialize()
def genreport(report):
with open(report+'/Tasks_failed.csv','wb') as failed:
writer=csv.DictWriter(failed,fieldnames=["Task ID","Task Type", "Start Date","Start Time","End Date","End Time","Task Description", "Error Message","Source Script", "Output State"],delimiter=',')
writer.writeheader()
with open(report+'/Tasks_completed.csv','wb') as completed:
writer=csv.DictWriter(completed,fieldnames=["Task ID","Task Type", "Start Date","Start Time","End Date","End Time","Task Description", "Output Url", "Output State"],delimiter=',')
writer.writeheader()
with open(report+'/Tasks_canceled.csv','wb') as canceled:
writer=csv.DictWriter(canceled,fieldnames=["Task ID","Task Type", "Start Date","Start Time","End Date","End Time","Task Description", "Source Script", "Output State"],delimiter=',')
writer.writeheader()
try:
for line in subprocess.check_output("earthengine task list",shell=True).split('\n'):
tsk=line.split(' ')[0]
ur=ee.data.getTaskStatus(tsk)
error=str(ur).split('state')[1].split(',')[0].strip("': u'.")
mode = error
if mode == 'FAILED':
tsktype=str(ur).split('task_type')[1].split(',')[0].strip("': u'.")
tskdesc=str(ur).split("'description'")[1].split(',')[0].strip("': u'.")
outurl=str(ur).split('source_url')[1].split(',')[0].strip("': u'.")
strttime=str(ur).split('start_timestamp_ms')[1].split(',')[0].strip("': u'.L")
endtime=str(ur).split('update_timestamp_ms')[1].split(',')[0].strip("': u'.L")
errmsg=str(ur).split('error_message')[1].split(',')[0].strip("': u'.")
state=str(ur).split('state')[1].split(',')[0].strip("': u'.")
tskid=str(ur).split("'id'")[1].split(',')[0].strip("': u'.'}]")
v=int(strttime)/1000
w=int(endtime)/1000
start=datetime.datetime.fromtimestamp(v).strftime('%Y-%m-%d %H:%M:%S.%f')
startdate=start.split(' ')[0]
starttime=start.split(' ')[1].split('.')[0]
end=datetime.datetime.fromtimestamp(w).strftime('%Y-%m-%d %H:%M:%S.%f')
enddate=end.split(' ')[0]
endtime=end.split(' ')[1].split('.')[0]
print(tsktype.title())
print(tskdesc)
print(outurl)
print(start)
print(end)
print(errmsg)
print(tskid)
print(state)
with open(report+'/Tasks_failed.csv','a') as failed:
writer=csv.writer(failed,delimiter=',',lineterminator='\n')
writer.writerow([tskid,tsktype,startdate,starttime,enddate,endtime,tskdesc,errmsg,outurl,state])
elif mode == 'CANCELED':
tsktype=str(ur).split('task_type')[1].split(',')[0].strip("': u'.")
tskdesc=str(ur).split("'description'")[1].split(':')[1].split(',')[0].strip("': u'.")
outurl=str(ur).split('source_url')[1].split(',')[0].strip("[': u'.]")
strttime=str(ur).split('start_timestamp_ms')[1].split(',')[0].strip("': u'.L")
endtime=str(ur).split('update_timestamp_ms')[1].split(',')[0].strip("': u'.L")
state=str(ur).split('state')[1].split(',')[0].strip("': u'.")
tskid=str(ur).split("'id'")[1].split(',')[0].strip("': u'.'}]")
v=int(strttime)/1000
w=int(endtime)/1000
start=datetime.datetime.fromtimestamp(v).strftime('%Y-%m-%d %H:%M:%S.%f')
startdate=start.split(' ')[0]
starttime=start.split(' ')[1].split('.')[0]
end=datetime.datetime.fromtimestamp(w).strftime('%Y-%m-%d %H:%M:%S.%f')
enddate=end.split(' ')[0]
endtime=end.split(' ')[1].split('.')[0]
print(tsktype.title())
print(tskdesc)
print(outurl)
print(start)
print(end)
print(tskid)
print(state)
with open(report+'/Tasks_canceled.csv','a') as canceled:
writer=csv.writer(canceled,delimiter=',',lineterminator='\n')
writer.writerow([tskid,tsktype,startdate,starttime,enddate,endtime,tskdesc,outurl,state])
elif mode == 'COMPLETED':
tsktype=str(ur).split('task_type')[1].split(',')[0].strip("': u'.")
tskdesc=str(ur).split("'description'")[1].split(':')[1].split(',')[0].strip("': u'.")
outurl=str(ur).split('output_url')[1].split(',')[0].strip("[': u'.]")
strttime=str(ur).split('start_timestamp_ms')[1].split(',')[0].strip("': u'.L")
endtime=str(ur).split('update_timestamp_ms')[1].split(',')[0].strip("': u'.L")
state=str(ur).split('state')[1].split(',')[0].strip("': u'.")
tskid=str(ur).split("'id'")[1].split(',')[0].strip("': u'.'}]")
v=int(strttime)/1000
w=int(endtime)/1000
start=datetime.datetime.fromtimestamp(v).strftime('%Y-%m-%d %H:%M:%S.%f')
startdate=start.split(' ')[0]
starttime=start.split(' ')[1].split('.')[0]
end=datetime.datetime.fromtimestamp(w).strftime('%Y-%m-%d %H:%M:%S.%f')
enddate=end.split(' ')[0]
endtime=end.split(' ')[1].split('.')[0]
print(tsktype.title())
print(tskdesc)
print(outurl)
print(start)
print(end)
print(tskid)
print(state)
with open(report+'/Tasks_completed.csv','a') as completed:
writer=csv.writer(completed,delimiter=',',lineterminator='\n')
writer.writerow([tskid,tsktype,startdate,starttime,enddate,endtime,tskdesc,outurl,state])
completed.close()
failed.close()
canceled.close()
except Exception:
with open(report+'/Errorlog.csv','wb') as csvfile:
writer=csv.writer(csvfile,delimiter=',')
writer.writerow([tskid])
csvfile.close()
| 60.289474
| 203
| 0.485814
| 741
| 6,873
| 4.470985
| 0.133603
| 0.065198
| 0.061274
| 0.083308
| 0.775128
| 0.771204
| 0.746152
| 0.727739
| 0.715967
| 0.67371
| 0
| 0.01928
| 0.313255
| 6,873
| 113
| 204
| 60.823009
| 0.682627
| 0
| 0
| 0.59292
| 0
| 0
| 0.15379
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.00885
| false
| 0
| 0.070796
| 0
| 0.079646
| 0.19469
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a36d38b08171f2effdafaf1709a5e7c70a01961f
| 77
|
py
|
Python
|
rasa_chinese_service/core/__init__.py
|
lhr0909/rasa_chinese_service
|
3ea96c3f2b5af94aeb06a47621ca3a4c3c3368a9
|
[
"Apache-2.0"
] | null | null | null |
rasa_chinese_service/core/__init__.py
|
lhr0909/rasa_chinese_service
|
3ea96c3f2b5af94aeb06a47621ca3a4c3c3368a9
|
[
"Apache-2.0"
] | null | null | null |
rasa_chinese_service/core/__init__.py
|
lhr0909/rasa_chinese_service
|
3ea96c3f2b5af94aeb06a47621ca3a4c3c3368a9
|
[
"Apache-2.0"
] | 1
|
2021-10-04T05:52:43.000Z
|
2021-10-04T05:52:43.000Z
|
from rasa_chinese_service.core.policies import StackedBilstmTensorFlowPolicy
| 38.5
| 76
| 0.922078
| 8
| 77
| 8.625
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051948
| 77
| 1
| 77
| 77
| 0.945205
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a378c8fb7c2dd6b3b42c1ae496cde091e9b4ff29
| 2,656
|
py
|
Python
|
tests/test_3_boxplot_visualisation.py
|
weifanjiang/CSSPy
|
361d18d7b9c08bcff11a18524a718b3522c48786
|
[
"MIT"
] | 3
|
2018-10-04T14:00:50.000Z
|
2021-12-11T08:57:26.000Z
|
tests/test_3_boxplot_visualisation.py
|
weifanjiang/CSSPy
|
361d18d7b9c08bcff11a18524a718b3522c48786
|
[
"MIT"
] | null | null | null |
tests/test_3_boxplot_visualisation.py
|
weifanjiang/CSSPy
|
361d18d7b9c08bcff11a18524a718b3522c48786
|
[
"MIT"
] | null | null | null |
# This is a visualization script for the CSSP results on real datasets.
# The visualization is available for the following subsampling functions:
## * Projection DPPs
## * Volume sampling
## * Pivoted QR
## * Double Phase
## * Largest leverage scores
import sys
sys.path.insert(0, '..')
from CSSPy.dataset_tools import *
from CSSPy.visualization_tools import *
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
# Importing the dataset
dataset_name = "leukemia"
exp_number = 50
k = 10
# Load the results from a txt file
savefile_name = "results/test_2/boxplots/"+dataset_name+"_boosting_allalgos_"+str(exp_number)+"samples_k_"+str(k)+".txt"
boosting_error_fro_aggregated_list_load = np.loadtxt(savefile_name)
boosting_error_fro_aggregated_list_load_to_list = []
for i in list(range(5)):
boosting_error_fro_aggregated_list_load_to_list.append(boosting_error_fro_aggregated_list_load[i])
# Plot the comparison of boosting of the algorithms
plt.figure(figsize=(10, 8))
plt.xticks(fontsize=22)
plt.yticks(fontsize=16)
ax = plt.subplot(111)
box_2 = plt.boxplot(boosting_error_fro_aggregated_list_load_to_list, showfliers=False)
plt.setp(box_2['medians'], color='red', linewidth=3)
plt.ylabel(r'$\mathrm{\|\| X- \pi_{S}^{Fr} X \|\| _{Fr}}$', fontsize=18)
plt.xticks(rotation=45)
plt.gca().xaxis.set_ticklabels(["Volume S.","Projection DPP","Largest lvs","Pivoted QR","Double Phase"])
# Save the figure on a pdf file
figfile_name= "results/test_2/"+dataset_name+"_boosting_allalgos_"+str(exp_number)+"samples_k_"+str(k)+".pdf"
plt.savefig(figfile_name)
plt.show()
### The boosting of the algorithms
# Load the results from a txt file
savefile_name = "results/test_2/boxplots/"+dataset_name+"_allalgos_"+str(exp_number)+"samples_k_"+str(k)+".txt"
error_fro_aggregated_list_load = np.loadtxt(savefile_name)
error_fro_aggregated_list_load_to_list = []
for i in list(range(5)):
error_fro_aggregated_list_load_to_list.append(error_fro_aggregated_list_load[i])
# Plot the comparison of boosting of the algorithms
plt.figure(figsize=(10, 8))
plt.xticks(fontsize=22)
plt.yticks(fontsize=16)
ax = plt.subplot(111)
box_2 = plt.boxplot(error_fro_aggregated_list_load_to_list, showfliers=False)
plt.setp(box_2['medians'], color='red', linewidth=3)
plt.ylabel(r'$\mathrm{\|\| X- \pi_{S}^{Fr} X \|\| _{Fr}}$', fontsize=18)
plt.xticks(rotation=45)
plt.gca().xaxis.set_ticklabels(["Volume S.","Projection DPP","Largest lvs","Pivoted QR","Double Phase"])
# Save the figure on a pdf file
figfile_name= "results/test_2/"+dataset_name+"_allalgos_"+str(exp_number)+"samples_k_"+str(k)+".pdf"
plt.savefig(figfile_name)
plt.show()
| 34.947368
| 120
| 0.761295
| 419
| 2,656
| 4.565632
| 0.286396
| 0.041819
| 0.094093
| 0.115003
| 0.785154
| 0.785154
| 0.77679
| 0.77679
| 0.724516
| 0.672243
| 0
| 0.018789
| 0.098268
| 2,656
| 76
| 121
| 34.947368
| 0.779958
| 0.192771
| 0
| 0.5
| 0
| 0
| 0.198869
| 0.02262
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.136364
| 0
| 0.136364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a38065fcce176280d73584d73ddd5255f08bfa92
| 263
|
py
|
Python
|
odm360/__init__.py
|
schuyler/odm360
|
cd33688db91443b5db3451d2800a465d9fef7931
|
[
"MIT"
] | 15
|
2020-06-29T12:14:15.000Z
|
2021-12-23T12:41:01.000Z
|
odm360/__init__.py
|
schuyler/odm360
|
cd33688db91443b5db3451d2800a465d9fef7931
|
[
"MIT"
] | 126
|
2020-06-16T08:21:49.000Z
|
2020-12-27T15:09:13.000Z
|
odm360/__init__.py
|
schuyler/odm360
|
cd33688db91443b5db3451d2800a465d9fef7931
|
[
"MIT"
] | 2
|
2020-09-15T07:32:06.000Z
|
2021-04-28T07:40:01.000Z
|
# -*- coding: utf-8 -*-
# TODO add version
from odm360 import ublox
from odm360 import log
from odm360 import camera360gphoto
from odm360 import workflows
from odm360 import timer
from odm360 import camera360rig
from odm360 import dbase
from odm360 import states
| 23.909091
| 34
| 0.806084
| 38
| 263
| 5.578947
| 0.447368
| 0.377358
| 0.603774
| 0
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| 0
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| 0
| 0
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| 0.13964
| 0.155894
| 263
| 10
| 35
| 26.3
| 0.815315
| 0.144487
| 0
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| 0.1
| 0
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| 0
| true
| 0
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| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
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| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
6e8046fb5baf4dbebb0f8c68cb85c4896c14307b
| 886
|
py
|
Python
|
Python/flatten/flatten.py
|
CNHume/Samples
|
2fdaa7a3193f19a882d0adebffaaf56af0984654
|
[
"MIT"
] | null | null | null |
Python/flatten/flatten.py
|
CNHume/Samples
|
2fdaa7a3193f19a882d0adebffaaf56af0984654
|
[
"MIT"
] | null | null | null |
Python/flatten/flatten.py
|
CNHume/Samples
|
2fdaa7a3193f19a882d0adebffaaf56af0984654
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# 2018-10-24 CNHume Created File
def main():
print flatten([])
print flatten([[0, 1, [4, 5]], [2, 3]])
print flatten([0])
print flatten([0, 1])
print flatten([[0, 1]])
print flatten([0, [0, 1]])
print flatten([[0, 1], [], 2])
print flatten([[0, 1], [2, 3]])
print flatten([[0, 1, [4, 5], 6], [2, 3]])
print flatten([[0, 1, [4, 5], 6], [[0, 1], 2, 3]])
pass
def flatten(elements):
return flatten2(elements)
def flatten1(elements):
return [item for element in elements for item in flat_list(element)]
def flat_list(element):
return flatten1(element) if isinstance(element, list) else [element]
def flatten2(elements):
result = []
for element in elements:
if isinstance(element, list):
result.extend(flatten2(element))
else:
result.append(element)
return result
if __name__ == '__main__':
main()
pass
| 23.315789
| 70
| 0.617381
| 130
| 886
| 4.130769
| 0.292308
| 0.223464
| 0.217877
| 0.182495
| 0.26257
| 0.206704
| 0.147114
| 0.070764
| 0.070764
| 0
| 0
| 0.071629
| 0.196388
| 886
| 37
| 71
| 23.945946
| 0.682584
| 0.058691
| 0
| 0.068966
| 0
| 0
| 0.009627
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.068966
| 0
| null | null | 0.344828
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
6eca93c58b63a1c7b945c38f5f3701a7629ed44e
| 226
|
py
|
Python
|
app/schemas/ws_events.py
|
NewShadesDAO/api
|
1e66336f0ea526f245918ecdc328c9a66280be91
|
[
"CC0-1.0"
] | 1
|
2022-03-21T07:37:02.000Z
|
2022-03-21T07:37:02.000Z
|
app/schemas/ws_events.py
|
NewShadesDAO/api
|
1e66336f0ea526f245918ecdc328c9a66280be91
|
[
"CC0-1.0"
] | 25
|
2022-01-16T13:18:21.000Z
|
2022-03-29T13:08:19.000Z
|
app/schemas/ws_events.py
|
NewShadesDAO/api
|
1e66336f0ea526f245918ecdc328c9a66280be91
|
[
"CC0-1.0"
] | 1
|
2022-01-15T21:42:00.000Z
|
2022-01-15T21:42:00.000Z
|
from datetime import datetime
from typing import List
from pydantic import BaseModel
class EventBase(BaseModel):
pass
class CreateMarkChannelReadEvent(EventBase):
channel_ids: List[str]
last_read_at: datetime
| 16.142857
| 44
| 0.787611
| 27
| 226
| 6.481481
| 0.62963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168142
| 226
| 13
| 45
| 17.384615
| 0.930851
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.125
| 0.375
| 0
| 0.875
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
6e046ea8bdd4ab6c81061765bb7594fa293ccee3
| 96
|
py
|
Python
|
fuzzing_cli/fuzz/ide/__init__.py
|
ConsenSys/diligence-fuzzing
|
74491aa8424752baf338d26d667f5259ef296d89
|
[
"Apache-2.0"
] | 8
|
2021-08-20T10:51:32.000Z
|
2022-03-31T16:26:22.000Z
|
fuzzing_cli/fuzz/ide/__init__.py
|
ConsenSys/diligence-fuzzing
|
74491aa8424752baf338d26d667f5259ef296d89
|
[
"Apache-2.0"
] | 6
|
2021-09-29T05:33:15.000Z
|
2022-02-10T09:01:54.000Z
|
fuzzing_cli/fuzz/ide/__init__.py
|
ConsenSys/diligence-fuzzing
|
74491aa8424752baf338d26d667f5259ef296d89
|
[
"Apache-2.0"
] | 1
|
2021-08-20T11:35:58.000Z
|
2021-08-20T11:35:58.000Z
|
from .brownie import BrownieJob
from .hardhat import HardhatJob
from .truffle import TruffleJob
| 24
| 31
| 0.84375
| 12
| 96
| 6.75
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 96
| 3
| 32
| 32
| 0.964286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6e24c982f6e55fb6df1cccc79726797bc7902172
| 4,779
|
py
|
Python
|
tests/stonith/CallsBase.py
|
svensp/hcloud_ocf
|
aff82e100187f84f04916752ea3c1f95805fc54b
|
[
"MIT"
] | 9
|
2019-06-02T16:37:03.000Z
|
2021-11-25T13:54:59.000Z
|
tests/stonith/CallsBase.py
|
svensp/hcloud_ocf
|
aff82e100187f84f04916752ea3c1f95805fc54b
|
[
"MIT"
] | 9
|
2019-01-03T14:57:39.000Z
|
2021-02-08T12:28:58.000Z
|
tests/stonith/CallsBase.py
|
svensp/hcloud_ocf
|
aff82e100187f84f04916752ea3c1f95805fc54b
|
[
"MIT"
] | 5
|
2019-08-13T16:35:41.000Z
|
2021-11-18T13:43:53.000Z
|
#!/usr/bin/python3
import os
import sys
sys.path.append( os.path.dirname( os.path.realpath(__file__) ) + '/../../stonith' )
import Base
import abc
import hetznercloud
import stonith
import stonith_agent
import time
import unittest
from unittest import mock
from mock import Mock, MagicMock
import stonith_agent
class TestBase(Base.TestBase):
@abc.abstractmethod
def serverAction(self, server):
pass
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_called(self, client, hostFinder):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.takeAction(agent)
self.serverAction(server).assert_called_once()
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_repeatet_after_wait_on_action_server_error(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = \
[hetznercloud.HetznerInternalServerErrorException('502'), MagicMock() ]
self.takeAction(agent)
assert sleep.call_count == 1
assert self.serverAction(server).call_count == 2
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_repeatet_after_wait_on_action_rate_limit_error(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = [hetznercloud.HetznerRateLimitExceeded('502'), \
MagicMock()]
self.takeAction(agent)
assert sleep.call_count == 1
assert self.serverAction(server).call_count == 2
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_repeatet_after_wait_on_action_action_error(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = \
[hetznercloud.HetznerActionException('502'), \
MagicMock()]
self.takeAction(agent)
assert sleep.call_count == 1
assert self.serverAction(server).call_count == 2
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_repeatet_after_wait_on_action_action_error(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = \
[ValueError('json decode error'), MagicMock()]
self.takeAction(agent)
assert sleep.call_count == 1
assert self.serverAction(server).call_count == 2
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_repeatet_after_wait_on_action_action_exception(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
serverAction = MagicMock()
serverAction.wait_until_status_is = \
Mock(side_effect=hetznercloud.HetznerWaitAttemptsExceededException())
self.serverAction(server).side_effect = [serverAction, MagicMock()]
self.takeAction(agent)
assert sleep.call_count == 1
assert self.serverAction(server).call_count == 2
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_is_canceled_after_action_authentication_exception(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = [hetznercloud.HetznerAuthenticationException(), server]
self.takeAction(agent)
assert self.serverAction(server).call_count == 1
@mock.patch('time.sleep')
@mock.patch('shared.HostFinder')
@mock.patch('hetznercloud.HetznerCloudClient')
def test_action_returns_missconfigured_after_action_authentication_exception(self, client, hostFinder, sleep):
server, hostFinder, agent = self.makeBase(client, hostFinder)
agent.client = client
self.serverAction(server).side_effect = [hetznercloud.HetznerAuthenticationException(), server]
returnCode = self.takeAction(agent)
assert returnCode == stonith.ReturnCodes.isMissconfigured
| 42.669643
| 114
| 0.709981
| 512
| 4,779
| 6.447266
| 0.146484
| 0.062708
| 0.093305
| 0.060588
| 0.768858
| 0.759164
| 0.747955
| 0.747955
| 0.747955
| 0.747955
| 0
| 0.005378
| 0.182883
| 4,779
| 111
| 115
| 43.054054
| 0.839949
| 0.003557
| 0
| 0.67
| 0
| 0
| 0.10376
| 0.05209
| 0
| 0
| 0
| 0
| 0.13
| 1
| 0.09
| false
| 0.01
| 0.12
| 0
| 0.22
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6e2e5f2304386d3cf5f0f8adcd53fd975ec41d9f
| 10,338
|
py
|
Python
|
SalmonellaEM.py
|
newby-jay/ptEM
|
1dc40f64ffcd709863652f25cc2ee9c9450a9480
|
[
"Apache-2.0"
] | null | null | null |
SalmonellaEM.py
|
newby-jay/ptEM
|
1dc40f64ffcd709863652f25cc2ee9c9450a9480
|
[
"Apache-2.0"
] | null | null | null |
SalmonellaEM.py
|
newby-jay/ptEM
|
1dc40f64ffcd709863652f25cc2ee9c9450a9480
|
[
"Apache-2.0"
] | null | null | null |
from __future__ import division
from __future__ import print_function
from pylab import *
import pandas as pd
from numba import jit, jitclass
from numba import int64, int32, int16, float64, float32, int16
from EMestimation import *
Salmonella_dtype = [('dim', int64),
('Nh', int64),
('vmag', float64[:]),
('v', float64[:, :])]
@jitclass(Salmonella_dtype)
class ThreeStateModel(EMestimation):
def __init__(self, dim, Nphi):
"""Initialize using the number of velocity states."""
self.dim = dim
Ntheta = Nphi*4
self.Nh = Ntheta*Nphi + 2
Nrun = Ntheta*Nphi
theta = linspace(0, 2*pi, Ntheta+1)[:-1]
phi = linspace(0, 0.5*pi, Nphi+1)[:-1]
THETA = zeros((Nphi, Ntheta))
PHI = zeros((Nphi, Ntheta))
for i in arange(Nphi):
for j in arange(Ntheta):
THETA[i, j] = theta[j]
PHI[i, j] = phi[i]
self.vmag = zeros(self.Nh)
if dim == 2:
v = zeros((self.Nh, 2))
v[2:, 0] = (cos(PHI)*cos(THETA)).flatten()
v[2:, 1] = (cos(PHI)*sin(THETA)).flatten()
self.vmag[2:] = (cos(PHI)**2).flatten()
elif dim == 3:
v = zeros((self.Nh, 3))
v[2:, 0] = (cos(PHI)*cos(THETA)).flatten()
v[2:, 1] = (cos(PHI)*sin(THETA)).flatten()
v[2:, 2] = sin(PHI).flatten()
self.vmag[2:] = ones_like(PHI).flatten()
else:
assert dim == 2 or dim == 3
self.v = v
def pss(self, pars):
k1, k2, k3, k4 = pars[:4]
O = k2*k4 + k1*k4 + k1*k3
p1 = k2*k4/O
p2 = k1*k4/O
p3 = k1*k3/O
return array((p1, p2, p3))
def Pinit(self, Np, pars):
"""Initialize hidden state distribution for each path."""
pinit = self.pss(pars)
p0 = ones((Np, self.Nh))
p0[:, 0] = pinit[0]
p0[:, 1] = pinit[1]
p0[:, 2:] = pinit[2]/(self.Nh - 2)
return p0
def getQ(self, pars):
"""Generate the propagator matrix for the hidden state Markov
process."""
k1, k2, k3, k4 = pars[:4]
A = array(((-k1, k2, 0.), (k1, -k2-k3, k4), (0., k3, -k4)))
Q = eye(3) + A
Ap = dot(A, eye(3))
for n in arange(2, 10):
Ap = dot(Ap, A/n)
Q += Ap
return Q
def UandT(self, dx, pars, Q):
"""Evaluate the observation and hidden state probability matrices for a
given path."""
k1, k2, k3, k4 = pars[:4]
mV, Dtrap, Dtum, Dswim = pars[4:]
Nt, _ = dx.shape
U = zeros((Nt, self.Nh))
Darr = array((Dtrap, Dtum, Dswim))
for n in arange(self.Nh):
r = (dx - mV*self.v[n])**2
D = Darr[min(2, n)]
factor = (4.*pi*D)**(self.dim/2.)
U[:, n] = exp(-psum(r)/(4.*D))/factor
T0 = zeros((self.Nh, self.Nh))
T0[:2, :2] = Q[:2, :2]
T0[1, 2:] = Q[1, 2]
T0[2:, 1] = Q[2, 1]/(self.Nh - 2)
T0[2:, 2:] = eye(self.Nh - 2)*Q[2, 2]
# T = eye(self.Nh) + T0
# Ap = dot(T0, eye(self.Nh))
# for n in arange(2, 10):
# Ap = dot(Ap, T0/n)
# T += Ap
return U, T0
def Mstep(self, DXarray, pInds, S, S2):
"""Extract maximimum likelihood parameters given hidden state
distribution for the E step."""
Np = pInds.size - 1
Npoints, Nh = S.shape
## transition rates
P = psum(S2.reshape(Np, -1).T).reshape(Nh, Nh)
P /= sum(P)
P33 = zeros((3, 3))
P33[:2, :2] = P[:2, :2]
for i in (0, 1):
P33[i, 2] = sum(P[i, 2:])
P33[2, i] = sum(P[2:, i])
P33[2, 2] = sum(P[2:, 2:])
pssFull = psum(S.T)
pssFull /= sum(pssFull)
pss = array((pssFull[0], pssFull[1], sum(pssFull[2:])))
Popt = (P33<=P33.T)*P33 + (P33>P33.T)*P33.T
# Popt = P33
T = Popt/pss
k1, k2, k3, k4 = T[1, 0], T[0, 1], T[2, 1], T[1, 2]
## swim speed and diffusivities
mV, Dtrap, Dtum, Dswim = 0., 0., 0., 0.
NmV, NDtrap, NDtum, NDswim = 0., 0., 0., 0.
for p in arange(Np):
a, b = pInds[p:p+2]
Sp = S[a:b]
dxp = DXarray[a:b]
NmV += sum(Sp*self.vmag)
for n in arange(2, Nh):
mV += sum(Sp[:, n]*psum(self.v[n]*dxp))
mV /= NmV
for p in arange(Np):
a, b = pInds[p:p+2]
Sp = S[a:b]
dxp = DXarray[a:b]
NDtrap += 2.*self.dim*sum(Sp[:, 0])
NDtum += 2.*self.dim*sum(Sp[:, 1])
NDswim += 2.*self.dim*sum(Sp[:, 2:])
Dtrap += sum(Sp[:, 0]*psum(dxp**2))
Dtum += sum(Sp[:, 1]*psum(dxp**2))
for n in arange(2, Nh):
Dswim += sum(Sp[:, n]*psum((dxp - mV*self.v[n])**2))
Dtrap /= NDtrap
Dtum /= NDtum
Dswim /= NDswim
pars = array((k1, k2, k3, k4, mV, Dtrap, Dtum, Dswim))
return pars
################################################################################
################################################################################
fourState_dtype = [('dim', int64),
('Nh', int64),
('vmag', float64[:]),
('v', float64[:, :])]
@jitclass(fourState_dtype)
class FourStateModel(EMestimation):
def __init__(self, dim, Nphi):
"""Initialize using the number of velocity states."""
self.dim = dim
Ntheta = Nphi*4
self.Nh = Ntheta*Nphi + 3
Nrun = Ntheta*Nphi
theta = linspace(0, 2*pi, Ntheta+1)[:-1]
phi = linspace(0, 0.5*pi, Nphi+1)[:-1]
THETA = zeros((Nphi, Ntheta))
PHI = zeros((Nphi, Ntheta))
for i in arange(Nphi):
for j in arange(Ntheta):
THETA[i, j] = theta[j]
PHI[i, j] = phi[i]
self.vmag = zeros(self.Nh)
if dim == 2:
v = zeros((self.Nh, 2))
v[3:, 0] = (cos(PHI)*cos(THETA)).flatten()
v[3:, 1] = (cos(PHI)*sin(THETA)).flatten()
self.vmag[3:] = (cos(PHI)**2).flatten()
elif dim == 3:
v = zeros((self.Nh, 3))
v[3:, 0] = (cos(PHI)*cos(THETA)).flatten()
v[3:, 1] = (cos(PHI)*sin(THETA)).flatten()
v[3:, 2] = sin(PHI).flatten()
self.vmag[3:] = ones_like(PHI).flatten()
else:
assert dim == 2 or dim == 3
self.v = v
def pss(self, pars):
k1, k2, k3, k4, k5, k6 = pars[:6]
A = array(((-k1, k2, 0., 0.), (k1, -k2-k3, k4, 0.),
(0., k3, -k4-k5, k6), (0., 0., k5, -k6)))
u, s, v = svd(A)
pss = v[-1, :]
pss /= sum(pss)
return pss
def Pinit(self, Np, pars):
"""Initialize hidden state distribution for each path."""
# pinit = self.pss(pars)
p0 = ones((Np, self.Nh))/self.Nh
# p0[:, 0] = pinit[0]
# p0[:, 1] = pinit[1]
# p0[:, 2:] = pinit[2]/(self.Nh - 2)
return p0
def getQ(self, pars):
"""Generate the propagator matrix for the hidden state Markov
process."""
k1, k2, k3, k4, k5, k6 = pars[:6]
A = array(((-k1, k2, 0., 0.), (k1, -k2-k3, k4, 0.),
(0., k3, -k4-k5, k6), (0., 0., k5, -k6)))
Q = eye(4) + A
Ap = dot(A, eye(4))
for n in arange(2, 10):
Ap = dot(Ap, A/n)
Q += Ap
return Q
def UandT(self, dx, pars, Q):
"""Evaluate the observation and hidden state probability matrices for a
given path."""
k1, k2, k3, k4, k5, k6 = pars[:6]
mV, Dtrap, Dtum1, Dtum2, Dswim = pars[6:]
Nt, _ = dx.shape
U = zeros((Nt, self.Nh))
Darr = array((Dtrap, Dtum1, Dtum2, Dswim))
for n in arange(self.Nh):
r = (dx - mV*self.v[n])**2
D = Darr[min(3, n)]
factor = (4.*pi*D)**(self.dim/2.)
U[:, n] = exp(-psum(r)/(4.*D))/factor
T0 = zeros((self.Nh, self.Nh))
T0[:3, :3] = Q[:3, :3]
T0[2, 3:] = Q[2, 3]
T0[3:, 2] = Q[3, 2]/(self.Nh - 3)
T0[3:, 3:] = eye(self.Nh - 3)*Q[3, 3]
return U, T0
def Mstep(self, DXarray, pInds, S, S2):
"""Extract maximimum likelihood parameters given hidden state
distribution for the E step."""
Np = pInds.size - 1
Npoints, Nh = S.shape
## transition rates
P = psum(S2.reshape(Np, -1).T).reshape(Nh, Nh)
P /= sum(P)
P44 = zeros((4, 4))
P44[:3, :3] = P[:3, :3]
for i in (0, 1, 2):
P44[i, 3] = sum(P[i, 3:])
P44[3, i] = sum(P[3:, i])
P44[3, 3] = sum(P[3:, 3:])
pssFull = psum(S.T)
pssFull /= sum(pssFull)
pss = array((pssFull[0], pssFull[1], pssFull[2], sum(pssFull[3:])))
Popt = (P44<=P44.T)*P44 + (P44>P44.T)*P44.T
# Popt = P33
T = Popt/pss
k1, k2, k3, k4 = T[1, 0], T[0, 1], T[2, 1], T[1, 2]
k5, k6 = T[3, 2], T[2, 3]
## swim speed and diffusivities
mV, Dtrap, Dtum1, Dtum2, Dswim = 0., 0., 0., 0., 0.
NmV, NDtrap, NDtum1, NDtum2, NDswim = 0., 0., 0., 0., 0.
for p in arange(Np):
a, b = pInds[p:p+2]
Sp = S[a:b]
dxp = DXarray[a:b]
NmV += sum(Sp*self.vmag)
for n in arange(3, Nh):
mV += sum(Sp[:, n]*psum(self.v[n]*dxp))
mV /= NmV
for p in arange(Np):
a, b = pInds[p:p+2]
Sp = S[a:b]
dxp = DXarray[a:b]
NDtrap += 2.*self.dim*sum(Sp[:, 0])
NDtum1 += 2.*self.dim*sum(Sp[:, 1])
NDtum2 += 2.*self.dim*sum(Sp[:, 2])
NDswim += 2.*self.dim*sum(Sp[:, 3:])
Dtrap += sum(Sp[:, 0]*psum(dxp**2))
Dtum1 += sum(Sp[:, 1]*psum(dxp**2))
Dtum2 += sum(Sp[:, 2]*psum(dxp**2))
for n in arange(3, Nh):
Dswim += sum(Sp[:, n]*psum((dxp - mV*self.v[n])**2))
Dtrap /= NDtrap
Dtum1 /= NDtum1
Dtum2 /= NDtum2
Dswim /= NDswim
pars = array((k1, k2, k3, k4, k5, k6, mV, Dtrap, Dtum1, Dtum2, Dswim))
return pars
| 37.053763
| 80
| 0.440704
| 1,496
| 10,338
| 3.028743
| 0.110294
| 0.035754
| 0.017215
| 0.022953
| 0.811962
| 0.784595
| 0.725447
| 0.707791
| 0.689914
| 0.685058
| 0
| 0.074796
| 0.359837
| 10,338
| 278
| 81
| 37.18705
| 0.609852
| 0.09615
| 0
| 0.633745
| 0
| 0
| 0.002206
| 0
| 0
| 0
| 0
| 0
| 0.00823
| 1
| 0.049383
| false
| 0
| 0.028807
| 0
| 0.127572
| 0.004115
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
28145ebc17ab492f77f166e7bc110d6758849f24
| 200
|
py
|
Python
|
tests/extensions/redash-dummy/redash_dummy/extension.py
|
zero1number/redash
|
caabc4afa4e60e273782a46d84099857821c6500
|
[
"BSD-2-Clause"
] | 20,680
|
2015-11-16T15:38:37.000Z
|
2022-03-31T21:43:43.000Z
|
tests/extensions/redash-dummy/redash_dummy/extension.py
|
zero1number/redash
|
caabc4afa4e60e273782a46d84099857821c6500
|
[
"BSD-2-Clause"
] | 3,934
|
2015-11-16T14:46:49.000Z
|
2022-03-31T13:22:31.000Z
|
tests/extensions/redash-dummy/redash_dummy/extension.py
|
zero1number/redash
|
caabc4afa4e60e273782a46d84099857821c6500
|
[
"BSD-2-Clause"
] | 4,147
|
2015-11-17T15:57:23.000Z
|
2022-03-31T11:57:43.000Z
|
module_attribute = "hello!"
def extension(app):
"""This extension will work"""
return "extension loaded"
def assertive_extension(app):
"""This extension won't work"""
assert False
| 16.666667
| 35
| 0.675
| 24
| 200
| 5.541667
| 0.666667
| 0.180451
| 0.240602
| 0.37594
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 200
| 11
| 36
| 18.181818
| 0.83125
| 0.25
| 0
| 0
| 0
| 0
| 0.158273
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.4
| false
| 0
| 0
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
28184bc14f46ca980960a50a4f357dfb72722ebf
| 45
|
py
|
Python
|
src/logging/__init__.py
|
stdevRulo/babysitter_agent
|
ced902190862a31015014628e5566f6fd2610ed7
|
[
"MIT"
] | 1
|
2020-11-17T02:28:49.000Z
|
2020-11-17T02:28:49.000Z
|
src/logging/__init__.py
|
stdevRulo/babysitter_agent
|
ced902190862a31015014628e5566f6fd2610ed7
|
[
"MIT"
] | 2
|
2020-11-11T04:15:11.000Z
|
2020-11-11T20:31:40.000Z
|
src/logging/__init__.py
|
stdevRulo/babysitter_agent
|
ced902190862a31015014628e5566f6fd2610ed7
|
[
"MIT"
] | null | null | null |
from .logging import LoggerFactory as Logger
| 22.5
| 44
| 0.844444
| 6
| 45
| 6.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 45
| 1
| 45
| 45
| 0.974359
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
28362be008db73c4cdf8ab18eb60d2484cd2a936
| 72
|
py
|
Python
|
dowpy/__init__.py
|
JhnBrunelle/dowpy
|
d7f47546c98e2440880664961b0f2d718533a817
|
[
"MIT"
] | null | null | null |
dowpy/__init__.py
|
JhnBrunelle/dowpy
|
d7f47546c98e2440880664961b0f2d718533a817
|
[
"MIT"
] | null | null | null |
dowpy/__init__.py
|
JhnBrunelle/dowpy
|
d7f47546c98e2440880664961b0f2d718533a817
|
[
"MIT"
] | null | null | null |
# Not operational, so protecting against dow.py
from .dow import Dow
| 12
| 47
| 0.75
| 11
| 72
| 4.909091
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.194444
| 72
| 5
| 48
| 14.4
| 0.931034
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
2840493204708afa3f19b4379727b7a673f04313
| 77
|
py
|
Python
|
Python Basics/loops.py
|
NaskoVasilev/Python-Fundamentals
|
7721f7e717061092fa23b7e85ee8e0308c7390cb
|
[
"MIT"
] | 1
|
2020-10-04T13:42:16.000Z
|
2020-10-04T13:42:16.000Z
|
Python Basics/loops.py
|
NaskoVasilev/Python-Fundamentals
|
7721f7e717061092fa23b7e85ee8e0308c7390cb
|
[
"MIT"
] | null | null | null |
Python Basics/loops.py
|
NaskoVasilev/Python-Fundamentals
|
7721f7e717061092fa23b7e85ee8e0308c7390cb
|
[
"MIT"
] | null | null | null |
for i in range(10):
print(i)
i = 1
while i < 6:
print(i)
i += 1
| 9.625
| 19
| 0.467532
| 16
| 77
| 2.25
| 0.5625
| 0.333333
| 0.388889
| 0.444444
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104167
| 0.376623
| 77
| 7
| 20
| 11
| 0.645833
| 0
| 0
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
95c48ca76e0baf16b8d3ab9078e8a4de869acb78
| 128
|
py
|
Python
|
pymps/blockmarker/__init__.py
|
GiggleLiu/pymps
|
c8314581010d68d3fa34af6e87b6af2969fc261d
|
[
"MIT"
] | 4
|
2018-02-17T05:35:54.000Z
|
2021-09-12T10:14:57.000Z
|
pymps/blockmarker/__init__.py
|
GiggleLiu/pymps
|
c8314581010d68d3fa34af6e87b6af2969fc261d
|
[
"MIT"
] | null | null | null |
pymps/blockmarker/__init__.py
|
GiggleLiu/pymps
|
c8314581010d68d3fa34af6e87b6af2969fc261d
|
[
"MIT"
] | null | null | null |
from .blockmatrix import *
from .blockmarker import *
from .blocklib import *
__all__ = ['plotlib', 'spaceconfig', 'autoblock']
| 25.6
| 49
| 0.734375
| 13
| 128
| 6.923077
| 0.692308
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.132813
| 128
| 4
| 50
| 32
| 0.810811
| 0
| 0
| 0
| 0
| 0
| 0.210938
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
95d9e42d5b0dd1006e77bfa59de3aaac210be7c9
| 426
|
py
|
Python
|
inzyme/libc.py
|
nicryptolaus/inzyme
|
8960e8f98cf1b9c0106345e89fd66218aad4d1cd
|
[
"Apache-2.0"
] | 1
|
2018-01-09T02:52:11.000Z
|
2018-01-09T02:52:11.000Z
|
inzyme/libc.py
|
nicryptolaus/inzyme
|
8960e8f98cf1b9c0106345e89fd66218aad4d1cd
|
[
"Apache-2.0"
] | null | null | null |
inzyme/libc.py
|
nicryptolaus/inzyme
|
8960e8f98cf1b9c0106345e89fd66218aad4d1cd
|
[
"Apache-2.0"
] | null | null | null |
import ctypes
class libc:
""" ctypes wrapper for system-calls """
lib = ctypes.cdll.LoadLibrary("libc.so.6")
@classmethod
def inotify_init1(cls, flags):
return libc.lib.inotify_init1(flags)
@classmethod
def inotify_add_watch(cls, fd, pathname, mask):
return libc.lib.inotify_add_watch(fd, pathname, mask)
@classmethod
def inotify_rm_watch(cls, fd, wd):
return libc.lib.inotify_rm_watch(fd, wd)
| 23.666667
| 57
| 0.720657
| 62
| 426
| 4.790323
| 0.435484
| 0.141414
| 0.212121
| 0.20202
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.008403
| 0.161972
| 426
| 17
| 58
| 25.058824
| 0.823529
| 0.07277
| 0
| 0.25
| 0
| 0
| 0.023256
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.083333
| 0.25
| 0.75
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
c2606e79eea16b252fbd7a351e3dfc0bc76d85ee
| 159
|
py
|
Python
|
transformers4rec/data/testing/tabular_data/dataset.py
|
Jwmc999/Transformers4Rec
|
e6cdf13a7c0102303c0258120274f88b2d42c9c2
|
[
"Apache-2.0"
] | 415
|
2021-09-20T20:47:34.000Z
|
2022-03-31T16:51:03.000Z
|
transformers4rec/data/testing/tabular_data/dataset.py
|
Jwmc999/Transformers4Rec
|
e6cdf13a7c0102303c0258120274f88b2d42c9c2
|
[
"Apache-2.0"
] | 128
|
2021-09-21T07:19:38.000Z
|
2022-03-31T15:08:27.000Z
|
transformers4rec/data/testing/tabular_data/dataset.py
|
Jwmc999/Transformers4Rec
|
e6cdf13a7c0102303c0258120274f88b2d42c9c2
|
[
"Apache-2.0"
] | 44
|
2021-09-23T07:25:36.000Z
|
2022-03-29T04:17:53.000Z
|
import pathlib
from transformers4rec.data.dataset import ParquetDataset
tabular_testing_data: ParquetDataset = ParquetDataset(pathlib.Path(__file__).parent)
| 26.5
| 84
| 0.861635
| 17
| 159
| 7.705882
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006803
| 0.075472
| 159
| 5
| 85
| 31.8
| 0.884354
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c266c33da930200075cf94645f2a7e1bb1a6f178
| 194
|
py
|
Python
|
deep_learn/models/sequence/__init__.py
|
ImbesatRizvi/Accio
|
b0ad2d245f4f7c42d85b722db9fad435c0d06a99
|
[
"Apache-2.0"
] | 2
|
2019-07-30T09:39:53.000Z
|
2019-07-30T09:40:06.000Z
|
deep_learn/models/sequence/__init__.py
|
ImbesatRizvi/Accio
|
b0ad2d245f4f7c42d85b722db9fad435c0d06a99
|
[
"Apache-2.0"
] | null | null | null |
deep_learn/models/sequence/__init__.py
|
ImbesatRizvi/Accio
|
b0ad2d245f4f7c42d85b722db9fad435c0d06a99
|
[
"Apache-2.0"
] | 2
|
2018-11-07T22:45:29.000Z
|
2019-10-24T09:53:41.000Z
|
from .MeanFeaturizer import MeanFeaturizer
from .EncoderRNN import EncoderRNN
from .DecoderRNN import DecoderRNN
from .Attention import Attention
from .MultiDimAttention import MultiDimAttention
| 38.8
| 48
| 0.876289
| 20
| 194
| 8.5
| 0.35
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097938
| 194
| 5
| 48
| 38.8
| 0.971429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6c5c4bd293f6c5b556318a657c7b012b296031ca
| 69
|
py
|
Python
|
vivus.py
|
tuguldurs/vivus
|
8d5f4373d1746643b1eee2816e2e5ba9efc926c8
|
[
"MIT"
] | 4
|
2021-05-05T17:01:10.000Z
|
2021-07-09T18:15:32.000Z
|
vivus.py
|
tuguldurs/vivus
|
8d5f4373d1746643b1eee2816e2e5ba9efc926c8
|
[
"MIT"
] | 1
|
2021-11-24T02:11:04.000Z
|
2021-11-24T02:11:04.000Z
|
vivus.py
|
UltraSound-AI/vivus-sdk
|
8d5f4373d1746643b1eee2816e2e5ba9efc926c8
|
[
"MIT"
] | 1
|
2021-05-26T10:52:13.000Z
|
2021-05-26T10:52:13.000Z
|
from sys import argv
from src.main import main as vivus
vivus(argv)
| 13.8
| 34
| 0.782609
| 13
| 69
| 4.153846
| 0.615385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 69
| 4
| 35
| 17.25
| 0.947368
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
6c6da186b9cdaf63760e88d5b76de3f92168bb99
| 55
|
py
|
Python
|
domain/src/presenter/__init__.py
|
python-jacksonsr45/web_services
|
6e37d4f00e9e59a35f06f05ce955ba53242ed9ee
|
[
"MIT"
] | null | null | null |
domain/src/presenter/__init__.py
|
python-jacksonsr45/web_services
|
6e37d4f00e9e59a35f06f05ce955ba53242ed9ee
|
[
"MIT"
] | null | null | null |
domain/src/presenter/__init__.py
|
python-jacksonsr45/web_services
|
6e37d4f00e9e59a35f06f05ce955ba53242ed9ee
|
[
"MIT"
] | null | null | null |
from .client_presenter import ClientPresenterInterface
| 27.5
| 54
| 0.909091
| 5
| 55
| 9.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072727
| 55
| 1
| 55
| 55
| 0.960784
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
66799cee14f0cb61e610ebffaf8786bc2562adc8
| 156
|
py
|
Python
|
common/tests.py
|
rkisdp/rkisdp.django.backend
|
771481cdeea6a101305c4819b06b839266ce6921
|
[
"MIT"
] | null | null | null |
common/tests.py
|
rkisdp/rkisdp.django.backend
|
771481cdeea6a101305c4819b06b839266ce6921
|
[
"MIT"
] | null | null | null |
common/tests.py
|
rkisdp/rkisdp.django.backend
|
771481cdeea6a101305c4819b06b839266ce6921
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# python imports
from __future__ import unicode_literals
# lib imports
from django.test import TestCase
# Create your tests here.
| 17.333333
| 39
| 0.74359
| 21
| 156
| 5.285714
| 0.857143
| 0.198198
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007692
| 0.166667
| 156
| 8
| 40
| 19.5
| 0.846154
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
66e4f0692d8af2976a5fbc01f391b75164a9c5ff
| 171
|
py
|
Python
|
AS-P/Orders/admin.py
|
dhruv289/Team-Shakeshack
|
b354f70c5fef4e6da1af57ec064f46804d550e1b
|
[
"Apache-2.0"
] | 1
|
2018-10-19T04:14:38.000Z
|
2018-10-19T04:14:38.000Z
|
AS-P/Orders/admin.py
|
dhruv289/Team-Shakeshack
|
b354f70c5fef4e6da1af57ec064f46804d550e1b
|
[
"Apache-2.0"
] | null | null | null |
AS-P/Orders/admin.py
|
dhruv289/Team-Shakeshack
|
b354f70c5fef4e6da1af57ec064f46804d550e1b
|
[
"Apache-2.0"
] | null | null | null |
from django.contrib import admin
from .models import Order
from .models import content
# Register your models here.
admin.site.register(Order)
admin.site.register(content)
| 28.5
| 32
| 0.818713
| 25
| 171
| 5.6
| 0.48
| 0.142857
| 0.228571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 171
| 6
| 33
| 28.5
| 0.915033
| 0.152047
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
dd0aac2bed94e6d5f38c8926fd1406379345a2db
| 38,144
|
py
|
Python
|
diet-plan-performance.py
|
windard/leeeeee
|
0107a5f95746592ca4fe78d2b5875cf65b1910e7
|
[
"MIT"
] | null | null | null |
diet-plan-performance.py
|
windard/leeeeee
|
0107a5f95746592ca4fe78d2b5875cf65b1910e7
|
[
"MIT"
] | null | null | null |
diet-plan-performance.py
|
windard/leeeeee
|
0107a5f95746592ca4fe78d2b5875cf65b1910e7
|
[
"MIT"
] | null | null | null |
# coding=utf-8
class Solution(object):
def dietPlanPerformance(self, calories, k, lower, upper):
"""
:type calories: List[int]
:type k: int
:type lower: int
:type upper: int
:rtype: int
"""
index = 0
count = 0
goal = 0
p = k
while p:
count += calories[p-1]
p -= 1
while index < len(calories) - k + 1:
if count > upper:
goal += 1
elif count < lower:
goal -= 1
if index + k < len(calories):
count += calories[index+k]
count -= calories[index]
index += 1
return goal
def _dietPlanPerformance(self, calories, k, lower, upper):
"""
:type calories: List[int]
:type k: int
:type lower: int
:type upper: int
:rtype: int
"""
# Time Limit
index = 0
count = 0
while index < len(calories) - k + 1:
s = k
c = 0
i = index
while i < len(calories) and s:
c += calories[i]
i += 1
s -= 1
if c > upper:
count += 1
elif c < lower:
count -= 1
index += 1
return count
if __name__ == '__main__':
s = Solution()
print s.dietPlanPerformance([6,13,8,7,10,1,12,11], 6, 5, 37)
print s.dietPlanPerformance([1,2,3,4,5], 1, 3, 3)
print s.dietPlanPerformance([3,2], 2, 0, 1)
print s.dietPlanPerformance([6,5,0,0], 2, 1, 5)
print s.dietPlanPerformance([13889,8348,203,4542,11813,6512,6559,6178,6793,5988,5155,5750,13629,952,3495,11178,13537,7996,123,9806,1685,1616,2575,13346,5113,9716,675,390,3901,13208,7575,8474,7857,4428,11675,2718,11349,7200,12863,1938,12473,12948,5155,6950,5885,472,6199,5658,1713,7772,9339,5998,10021,4288,3279,178,3977,5716,1507,13174,13245,2152,9799,7658,12513,13468,2123,10174,12101,560,3935,9346,6633,2996,9619,13958,12493,1032,1367,7322,10449,8572,12187,13806,2853,1232,3151,8546,8265,13684,2675,918,4557,4840,11575,4154,9873,5356,5307,1998,1493,2464,11519,4210,5593,2372,595,6230,9949,8088,12599,1642,10129,9884,4027,9950,2069,8160,9903,7010,5321,6228,10643,4102,6157,6200,7223,4570,13697,3484,875,5966,10453,8320,4159,1794,2697,1780,12563,8598,5133,4056,11047,5066,3585,2124,7275,1054,11925,12640,3407,2306,8793,4596,12883,7318,7309,5560,10471,10778,4385,7244,2843,10558,8261,896,12127,7426,1049,1268,11427,614,7549,7368,7079,5418,3249,2412,10155,7502,517,2400,9791,6562,4505,7700,8931,8502,9965,8232,2487,6554,49,2140,5083,2462,13253,7952,9999,9314,13145,8180,6851,3702,5981,8968,8167,8346,10561,10588,5435,12942,10933,5216,8607,10818,13129,2404,7427,358,13629,13208,6727,4138,5105,300,9659,4702,3845,12192,1439,12530,5081,6772,6931,10166,11517,10440,6199,4490,9121,188,12603,2606,9685,784,4095,7634,10281,5668,11347,10278,5949,1192,4615,2746,5249,1548,907,9086,3589,8720,12703,9474,2937,6644,12451,5926,4717,8376,2455,12410,49,7596,10043,4894,11701,5936,13743,2018,6089,8132,10931,358,7709,13016,4151,9370,49,10140,7579,510,1061,12752,863,1714,10441,12484,12931,10614,2301,11096,7863,1178,8113,9884,6507,13630,1157,5696,4655,11378,13801,7092,7219,1302,6749,7352,87,12666,13729,11884,5579,6302,7237,1024,12943,9586,11945,12004,6931,8134,11709,12824,1111,11514,13137,540,11851,9998,4149,9536,3855,5634,13609,1252,6563,8502,3853,3016,11511,13018,10465,5660,9499,10366,8373,11472,10991,4642,10217,2980,13619,7606,61,8760,6391,4026,6673,12748,1419,2846,5893,4224,9855,8082,1081,10148,6019,7286,3389,6168,5495,5674,12977,9564,7611,2350,5093,9520,2207,10146,7929,10596,5939,11014,317,10696,13639,10842,11153,1292,1275,12718,4693,8896,8431,10706,12873,308,5091,11446,3229,9640,999,8074,12865,9340,13627,1262,11349,880,5375,82,10553,6660,1843,38,13293,10984,103,10666,7569,11852,11243,6254,8341,8992,4495,2834,2873,4644,9395,3430,9805,12568,11735,2410,11873,4796,2875,12526,12437,3600,2079,802,10121,5572,1507,3158,10509,2312,7655,3914,113,10300,1387,10078,1077,8448,79,11282,11385,3396,12947,70,12717,1992,13015,7818,8609,13548,2731,2366,1173,11248,3055,8674,3273,3780,12435,11590,5453,1192,8375,9546,1009,10428,3275,4734,5717,11456,12607,1458,13993,10676,13539,5926,8189,8840,9095,3130,4209,12988,3995,1022,13397,13392,6399,7570,1817,3684,1219,13030,1533,12776,6823,810,7441,8236,2891,9902,6229,10000,207,4514,9753,9556,12595,1670,7485,408,8887,58,1505,1548,8459,558,2549,7456,4511,8722,2057,10484,12019,5478,2429,6888,9095,11834,12497,11980,1739,12862,2357,8944,8415,6322,1209,5092,7059,10150,10189,7960,2871,1354,13825,3468,6667,10942,10805,10496,623,1250,2521,11508,3507,10758,10589,10312,1415,2346,625,11500,7963,2062,13525,112,6703,818,6089,804,7475,2806,11149,6520,11655,12250,1377,11436,6939,10686,8437,4272,7055,6770,12377,2164,7603,54,10685,7240,8215,8026,11169,10860,1419,11886,12341,2432,7103,4898,9625,6644,6131,3878,4925,3096,215,8922,6097,1052,1931,3006,7077,9824,8735,12034,1705,8660,13619,1366,4973,392,11975,4506,2417,10316,7131,6990,11317,2528,1311,5826,12985,2804,2227,9670,189,4408,807,13898,417,11052,379,11134,12981,12640,8751,2498,9497,1476,8131,6006,1885,12920,7975,10138,12417,12348,5819,11630,1413,13472,13407,5106,13929,8596,579,7942,10525,4408,2663,8314,13553,9020,8123,5294,1429,6256,12511,6354,5609,852,1171,7494,5757,13656,11943,13370,11889,12860,4875,6046,1685,2464,3359,1522,2777,7732,3219,3990,893,2776,12823,4762,2145,2284,10139,4190,7125,8976,2687,5138,3001,6836,3795,8822,1853,4904,71,58,9601,7340,6491,462,7733,1280,2255,11970,12937,2772,8083,13094,12637,3224,391,5738,6929,8924,5483,814,13637,7664,6159,4482,4185,7156,6019,1798,6285,9960,6135,7882,4625,4188,9083,13502,6309,752,8639,7938,13785,4756,6419,998,2925,8168,12759,10842,12225,1084,4267,4830,5989,6416,879,12738,11977,4148,4787,2150,12029,13624,9463,13434,10993,3884,3451,10990,10309,4528,3215,9506,1625,1012,8291,7206,2269,1512,1063,9882,12177,12444,12363,2430,11077,9216,4319,7426,1721,12580,6563,3814,7197,3608,11591,3130,145,10812,3547,1166,11541,3136,2911,2546,1513,9012,51,10822,8989,3960,12775,7786,8001,4956,9851,766,13477,4368,12671,3746,5273,10548,323,3222,9933,9832,2743,7402,2865,13852,5531,3118,12597,12272,13551,9026,9177,2756,2675,11014,12845,12856,4135,2346,3153,4828,13179,1054,10981,6784,1711,13970,6441,2372,10403,2518,9149,7352,6727,6970,4049,3292,4907,11966,7525,2608,4783,1058,13305,8788,9587,13174,8333,5880,12167,1882,7265,4428,3611,5102,8533,2640,9871,5058,1993,7956,4435,1558,12829,7128,10423,3418,12833,572,5467,6158,12229,1664,8831,4882,4137,8900,1491,6870,3325,5960,13751,11290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2225, 3148684, 6549511)
| 586.830769
| 36,515
| 0.791999
| 7,197
| 38,144
| 4.196332
| 0.771294
| 0.001391
| 0.004139
| 0.002252
| 0.00841
| 0.00841
| 0.006887
| 0.006887
| 0.006887
| 0.006887
| 0
| 0.789488
| 0.0194
| 38,144
| 64
| 36,516
| 596
| 0.017939
| 0.000603
| 0
| 0.186047
| 0
| 0
| 0.000211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.116279
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
dd0fdd6d3773ab09c3cdba5e847d00c584455bf4
| 99
|
py
|
Python
|
clarify/__init__.py
|
carbonphyber/clarify
|
0529bcbe0671f9394f4ae060fd67e7b4ad9c925c
|
[
"MIT"
] | 26
|
2015-03-11T15:55:38.000Z
|
2022-01-20T23:49:54.000Z
|
clarify/__init__.py
|
carbonphyber/clarify
|
0529bcbe0671f9394f4ae060fd67e7b4ad9c925c
|
[
"MIT"
] | 22
|
2015-11-06T12:34:29.000Z
|
2021-11-03T03:09:09.000Z
|
clarify/__init__.py
|
carbonphyber/clarify
|
0529bcbe0671f9394f4ae060fd67e7b4ad9c925c
|
[
"MIT"
] | 18
|
2016-03-02T00:50:36.000Z
|
2021-02-25T04:33:16.000Z
|
from .version import __version__
from .jurisdiction import Jurisdiction
from .parser import Parser
| 24.75
| 38
| 0.848485
| 12
| 99
| 6.666667
| 0.416667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 99
| 3
| 39
| 33
| 0.91954
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
dd24cfcac58bee0d38da25cd9f55f6fd37dad3bb
| 81
|
py
|
Python
|
kamilcode.py
|
kamilb123/mktyp
|
10a91d5bae714688c2a054fb97bd794c50d9adea
|
[
"MIT"
] | null | null | null |
kamilcode.py
|
kamilb123/mktyp
|
10a91d5bae714688c2a054fb97bd794c50d9adea
|
[
"MIT"
] | null | null | null |
kamilcode.py
|
kamilb123/mktyp
|
10a91d5bae714688c2a054fb97bd794c50d9adea
|
[
"MIT"
] | null | null | null |
def mph2fps(mph):
return mph*5280/3600
def myhello():
print("Konchi_wa")
| 16.2
| 24
| 0.666667
| 12
| 81
| 4.416667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 0.185185
| 81
| 4
| 25
| 20.25
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.25
| 0.75
| 0.25
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
dd5cbe991cee72431e5eafec6cca3cbdf87bdfdf
| 51
|
py
|
Python
|
tests/assets/macros/testwithoutstart.py
|
PinkRoccade-Local-Government-OSS/PinkWave
|
ebbcf71989317f5560f7f7770cb701e6c06a4ac9
|
[
"Apache-2.0"
] | 1
|
2017-11-18T13:34:38.000Z
|
2017-11-18T13:34:38.000Z
|
tests/assets/macros/testwithoutstart.py
|
PinkRoccade-Local-Government-OSS/PinkWave
|
ebbcf71989317f5560f7f7770cb701e6c06a4ac9
|
[
"Apache-2.0"
] | 1
|
2021-06-01T22:02:12.000Z
|
2021-06-01T22:02:12.000Z
|
tests/assets/macros/testwithoutstart.py
|
PinkRoccade-Local-Government-OSS/PinkWave
|
ebbcf71989317f5560f7f7770cb701e6c06a4ac9
|
[
"Apache-2.0"
] | 1
|
2022-01-25T23:01:24.000Z
|
2022-01-25T23:01:24.000Z
|
print "you should not be able to see this message"
| 25.5
| 50
| 0.764706
| 10
| 51
| 3.9
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.196078
| 51
| 1
| 51
| 51
| 0.95122
| 0
| 0
| 0
| 0
| 0
| 0.823529
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
dda2f979f390fdb8004bc7d9ab3ea5178800ee53
| 644
|
py
|
Python
|
wizard/__init__.py
|
IDRISSOUM/hospital_management
|
56a768f29269a77bc890d40479a8aacb90867189
|
[
"Unlicense"
] | null | null | null |
wizard/__init__.py
|
IDRISSOUM/hospital_management
|
56a768f29269a77bc890d40479a8aacb90867189
|
[
"Unlicense"
] | null | null | null |
wizard/__init__.py
|
IDRISSOUM/hospital_management
|
56a768f29269a77bc890d40479a8aacb90867189
|
[
"Unlicense"
] | null | null | null |
# -*- coding: utf-8 -*-
# Part of BrowseInfo. See LICENSE file for full copyright and licensing details.
from . import appointment_start_end_wizard
from . import create_prescription_invoice_wizard
from . import create_prescription_shipment_wizard
from . import medical_appointments_invoice_wizard
from . import medical_bed_transfer_wizard
from . import medical_health_services_invoice_wizard
from . import medical_lab_test_create_wizard
from . import medical_lab_test_invoice_wizard
from . import medical_imaging_test_request_wizard
from . import multiple_test_request_wizard
# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:
| 40.25
| 80
| 0.854037
| 88
| 644
| 5.863636
| 0.488636
| 0.193798
| 0.27907
| 0.267442
| 0.377907
| 0.116279
| 0
| 0
| 0
| 0
| 0
| 0.006873
| 0.096273
| 644
| 15
| 81
| 42.933333
| 0.879725
| 0.254658
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
06d74c2468016349afca0c502eea13626d9a796d
| 24
|
py
|
Python
|
lab99.py
|
upendar245/python
|
ed4d5488b44d385f2d0f75e8558a5b7fc4563d38
|
[
"Apache-2.0"
] | null | null | null |
lab99.py
|
upendar245/python
|
ed4d5488b44d385f2d0f75e8558a5b7fc4563d38
|
[
"Apache-2.0"
] | null | null | null |
lab99.py
|
upendar245/python
|
ed4d5488b44d385f2d0f75e8558a5b7fc4563d38
|
[
"Apache-2.0"
] | null | null | null |
#!/python3/bin/python3
| 8
| 22
| 0.708333
| 3
| 24
| 5.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 0.083333
| 24
| 2
| 23
| 12
| 0.681818
| 0.875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
06ddeff91a0f719f3ceb292039064d7bfe64189b
| 30
|
py
|
Python
|
test/foo.py
|
Guillemdb/flake8-per-file-ignores
|
ab0d3d44764ea834211ca2806bef8694c80cab87
|
[
"MIT"
] | 52
|
2017-10-08T18:20:54.000Z
|
2022-01-08T20:02:10.000Z
|
test/foo.py
|
Guillemdb/flake8-per-file-ignores
|
ab0d3d44764ea834211ca2806bef8694c80cab87
|
[
"MIT"
] | 11
|
2017-10-08T19:09:06.000Z
|
2021-09-13T18:02:50.000Z
|
test/foo.py
|
Guillemdb/flake8-per-file-ignores
|
ab0d3d44764ea834211ca2806bef8694c80cab87
|
[
"MIT"
] | 4
|
2017-11-08T14:12:04.000Z
|
2021-06-20T18:28:45.000Z
|
import sys # noqa
x == None
| 7.5
| 18
| 0.6
| 5
| 30
| 3.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.3
| 30
| 3
| 19
| 10
| 0.857143
| 0.133333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
6640ecae81431e6a8c0efdbb6d7c713e83e9d888
| 19
|
py
|
Python
|
posthog/version.py
|
alx-a/posthog
|
a76959bb2a7640ca8cf367a4d3a0e4ca67f65a5e
|
[
"MIT"
] | 1
|
2021-04-09T09:13:23.000Z
|
2021-04-09T09:13:23.000Z
|
posthog/version.py
|
suryatmodulus/posthog
|
d354305bd05c5e231dced3f1bc33dafcd5875f22
|
[
"MIT"
] | null | null | null |
posthog/version.py
|
suryatmodulus/posthog
|
d354305bd05c5e231dced3f1bc33dafcd5875f22
|
[
"MIT"
] | null | null | null |
VERSION = "1.26.0"
| 9.5
| 18
| 0.578947
| 4
| 19
| 2.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.25
| 0.157895
| 19
| 1
| 19
| 19
| 0.4375
| 0
| 0
| 0
| 0
| 0
| 0.315789
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
9fe3ca92fe73f717654b8b2fdba35307f7af697b
| 253
|
py
|
Python
|
src/lgr_advanced/exceptions.py
|
GuillaumeBlanchet/lgr-django
|
429ca5ddb9311cfb1a7ddc906b32d57780585f40
|
[
"BSD-3-Clause"
] | 1
|
2018-09-19T11:03:11.000Z
|
2018-09-19T11:03:11.000Z
|
src/lgr_advanced/exceptions.py
|
GuillaumeBlanchet/lgr-django
|
429ca5ddb9311cfb1a7ddc906b32d57780585f40
|
[
"BSD-3-Clause"
] | 15
|
2017-06-29T14:05:01.000Z
|
2021-09-22T19:56:23.000Z
|
src/lgr_advanced/exceptions.py
|
GuillaumeBlanchet/lgr-django
|
429ca5ddb9311cfb1a7ddc906b32d57780585f40
|
[
"BSD-3-Clause"
] | 7
|
2017-06-14T17:59:19.000Z
|
2019-08-09T03:16:03.000Z
|
# -*- coding: utf-8 -*-
"""
exceptions.py - Define custom frontend exceptions.
"""
from lgr.exceptions import LGRException
class LGRValidationException(LGRException):
"""
Raised when the XML validation against schema fails.
"""
pass
| 16.866667
| 56
| 0.687747
| 26
| 253
| 6.692308
| 0.884615
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004926
| 0.197628
| 253
| 14
| 57
| 18.071429
| 0.852217
| 0.498024
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
b036bbdcdeff8676d3e15a63048c6d1362e7c902
| 171
|
py
|
Python
|
presentation/code/pedagogical_deltaF_animation.py
|
gchure/phd
|
cf5941e467ee57c6c93c78dda151335cb320f831
|
[
"MIT"
] | 4
|
2020-01-14T01:12:53.000Z
|
2021-11-29T10:33:20.000Z
|
presentation/code/pedagogical_deltaF_animation.py
|
gchure/phd
|
cf5941e467ee57c6c93c78dda151335cb320f831
|
[
"MIT"
] | 1
|
2021-10-13T03:30:26.000Z
|
2021-11-11T18:21:43.000Z
|
presentation/code/pedagogical_deltaF_animation.py
|
gchure/phd
|
cf5941e467ee57c6c93c78dda151335cb320f831
|
[
"MIT"
] | null | null | null |
#%%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
import phd.viz
import phd.thermo
import seaborn as sns
| 21.375
| 60
| 0.824561
| 25
| 171
| 5.64
| 0.64
| 0.12766
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.128655
| 171
| 7
| 61
| 24.428571
| 0.946309
| 0.011696
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b051f128e32e1aa46fe8d6fde179d862428893d5
| 70
|
py
|
Python
|
sonorus/speech/__init__.py
|
imbesat-rizvi/sonorus
|
38698d55b00c67fb3bcff4e4349b6c214a29e6f5
|
[
"MIT"
] | null | null | null |
sonorus/speech/__init__.py
|
imbesat-rizvi/sonorus
|
38698d55b00c67fb3bcff4e4349b6c214a29e6f5
|
[
"MIT"
] | null | null | null |
sonorus/speech/__init__.py
|
imbesat-rizvi/sonorus
|
38698d55b00c67fb3bcff4e4349b6c214a29e6f5
|
[
"MIT"
] | 2
|
2021-01-17T22:53:02.000Z
|
2021-03-03T01:11:43.000Z
|
from .GoogleSTT import GoogleSTT
from .Wav2Vec2STT import Wav2Vec2STT
| 23.333333
| 36
| 0.857143
| 8
| 70
| 7.5
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.064516
| 0.114286
| 70
| 2
| 37
| 35
| 0.903226
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
c67a4fad9017a00ddb4ea1ba5525b8d3521bb2b2
| 155
|
py
|
Python
|
NueralNetwotk.py
|
GnarlyMshtep/MyFirstGraphicalPreceptron
|
471d330c7d0fc11ba65086129e2ddee13f6a740e
|
[
"MIT"
] | null | null | null |
NueralNetwotk.py
|
GnarlyMshtep/MyFirstGraphicalPreceptron
|
471d330c7d0fc11ba65086129e2ddee13f6a740e
|
[
"MIT"
] | null | null | null |
NueralNetwotk.py
|
GnarlyMshtep/MyFirstGraphicalPreceptron
|
471d330c7d0fc11ba65086129e2ddee13f6a740e
|
[
"MIT"
] | null | null | null |
class NueralNetwork:
def __init__(self, numinputSize: int, numHiddenLayers: int, nuronsPerHiddenLayer: list, nuOutputNodes: int) -> None:
pass
| 38.75
| 120
| 0.735484
| 15
| 155
| 7.333333
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.174194
| 155
| 3
| 121
| 51.666667
| 0.859375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0.333333
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
c6ab41ce6d30ff4cc7712b43957ab0706bb226cb
| 46
|
py
|
Python
|
airflow_dbt_python/operators/__init__.py
|
marcusintrohive/airflow-dbt-python
|
d6b8e9e091965ee2319ecc0009f2e2dc00d27819
|
[
"MIT"
] | 37
|
2021-06-15T23:23:28.000Z
|
2022-03-22T08:16:49.000Z
|
airflow_dbt_python/operators/__init__.py
|
marcusintrohive/airflow-dbt-python
|
d6b8e9e091965ee2319ecc0009f2e2dc00d27819
|
[
"MIT"
] | 29
|
2021-06-01T21:03:39.000Z
|
2022-03-12T15:09:33.000Z
|
airflow_dbt_python/operators/__init__.py
|
marcusintrohive/airflow-dbt-python
|
d6b8e9e091965ee2319ecc0009f2e2dc00d27819
|
[
"MIT"
] | 5
|
2021-08-04T08:48:31.000Z
|
2022-02-07T19:14:56.000Z
|
"""Airflow operators for all dbt commands."""
| 23
| 45
| 0.717391
| 6
| 46
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 46
| 1
| 46
| 46
| 0.825
| 0.847826
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
05a124814da88851a4e985081b8f1e4cd7d663a9
| 57
|
py
|
Python
|
modules/__init__.py
|
audioku/cross-accent-maml-asr
|
859a157eb0dd2a3f557b8b9952ece7f16f3509f9
|
[
"MIT"
] | 33
|
2020-04-25T17:25:09.000Z
|
2022-02-11T03:16:11.000Z
|
nn/transformer/__init__.py
|
kefirski/attentive-translation
|
244f42e8e9c6369b6615480fb9b291bf6a0e3eef
|
[
"MIT"
] | 1
|
2020-08-10T07:00:56.000Z
|
2020-09-11T05:28:00.000Z
|
modules/__init__.py
|
audioku/cross-accent-maml-asr
|
859a157eb0dd2a3f557b8b9952ece7f16f3509f9
|
[
"MIT"
] | 4
|
2021-07-19T06:32:29.000Z
|
2022-02-11T03:05:50.000Z
|
from .encoder import Encoder
from .decoder import Decoder
| 28.5
| 28
| 0.842105
| 8
| 57
| 6
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122807
| 57
| 2
| 29
| 28.5
| 0.96
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| 1
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|
0
| 5
|
05c699569713f5e685e46c727632edcecc39c315
| 97
|
py
|
Python
|
src/pos/model/__init__.py
|
cadia-lvl/POS
|
81a146707df82ecc972c28e055cc50b372f5d35b
|
[
"Apache-2.0"
] | 2
|
2020-07-28T14:10:02.000Z
|
2021-08-25T13:28:14.000Z
|
src/pos/model/__init__.py
|
cadia-lvl/POS
|
81a146707df82ecc972c28e055cc50b372f5d35b
|
[
"Apache-2.0"
] | 3
|
2020-07-15T15:17:37.000Z
|
2021-11-08T15:49:02.000Z
|
src/pos/model/__init__.py
|
cadia-lvl/POS
|
81a146707df82ecc972c28e055cc50b372f5d35b
|
[
"Apache-2.0"
] | 1
|
2020-07-15T16:15:05.000Z
|
2020-07-15T16:15:05.000Z
|
"""The ABLTagger."""
from .decoders import *
from .embeddings import *
from .interface import *
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| 25
| 0.71134
| 11
| 97
| 6.272727
| 0.636364
| 0.289855
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| 97
| 5
| 26
| 19.4
| 0.841463
| 0.14433
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|
0
| 5
|
05d75af0b6cc8b7bd983bf4a1c0d29eea6ca7f89
| 224
|
py
|
Python
|
data_generation/test_data_generation.py
|
alexwitt2399/uav-austin
|
ba96063f9c95ae02869fd96b8a611bc8a99513b6
|
[
"MIT"
] | null | null | null |
data_generation/test_data_generation.py
|
alexwitt2399/uav-austin
|
ba96063f9c95ae02869fd96b8a611bc8a99513b6
|
[
"MIT"
] | 3
|
2021-06-08T21:38:01.000Z
|
2022-03-12T00:31:16.000Z
|
data_generation/test_data_generation.py
|
alexwitt23/uav-austin
|
ba96063f9c95ae02869fd96b8a611bc8a99513b6
|
[
"MIT"
] | null | null | null |
""" Collection of unittests to test data generation scripts. """
import unittest
# TODO This replaces the build.py. This would test as many
# of the shape generation utilities as possible, then also
# do end-to-end tests.
| 28
| 64
| 0.754464
| 35
| 224
| 4.828571
| 0.771429
| 0
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| 224
| 7
| 65
| 32
| 0.913514
| 0.861607
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| 0.142857
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| null | 0
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| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
05e916ba9c529fcd41d4cff7a3e902bf313bbd73
| 356
|
py
|
Python
|
dbaas/account/serializers.py
|
didindinn/database-as-a-service
|
747de31ff8546f7874ddd654af860e130afd17a0
|
[
"BSD-3-Clause"
] | 303
|
2015-01-08T10:35:54.000Z
|
2022-02-28T08:54:06.000Z
|
dbaas/account/serializers.py
|
nouraellm/database-as-a-service
|
5e655c9347bea991b7218a01549f5e44f161d7be
|
[
"BSD-3-Clause"
] | 124
|
2015-01-14T12:56:15.000Z
|
2022-03-22T20:45:11.000Z
|
dbaas/account/serializers.py
|
nouraellm/database-as-a-service
|
5e655c9347bea991b7218a01549f5e44f161d7be
|
[
"BSD-3-Clause"
] | 110
|
2015-01-02T11:59:48.000Z
|
2022-02-28T08:54:06.000Z
|
# -*- coding: utf-8 -*-
from __future__ import absolute_import, unicode_literals
from django_services.api import DjangoServiceSerializer
from .models import Team, AccountUser
class TeamSerializer(DjangoServiceSerializer):
class Meta:
model = Team
class UserSerializer(DjangoServiceSerializer):
class Meta:
model = AccountUser
| 20.941176
| 56
| 0.758427
| 35
| 356
| 7.514286
| 0.6
| 0.212928
| 0.243346
| 0.281369
| 0
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| 0
| 0
| 0.003413
| 0.176966
| 356
| 16
| 57
| 22.25
| 0.894198
| 0.058989
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| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
af0dc0464b7e38be7fba200a83cdc29eb37536aa
| 69
|
py
|
Python
|
old_code/src_python/nmpccodegen/controller/constraints/__init__.py
|
kul-forbes/nmpc-codegen
|
0b96da0840504817472b2bdc62c29c98bdf29c8f
|
[
"MIT"
] | 24
|
2017-11-13T02:17:10.000Z
|
2021-03-15T13:47:20.000Z
|
old_code/src_python/nmpccodegen/controller/constraints/__init__.py
|
kul-optec/nmpc-codegen
|
0b96da0840504817472b2bdc62c29c98bdf29c8f
|
[
"MIT"
] | 14
|
2018-01-13T20:20:47.000Z
|
2020-05-12T11:21:12.000Z
|
old_code/src_python/nmpccodegen/controller/constraints/__init__.py
|
kul-optec/nmpc-codegen
|
0b96da0840504817472b2bdc62c29c98bdf29c8f
|
[
"MIT"
] | 5
|
2018-08-14T14:27:41.000Z
|
2020-12-17T08:13:41.000Z
|
from .Constraint import Constraint
from .Input_norm import Input_norm
| 34.5
| 34
| 0.869565
| 10
| 69
| 5.8
| 0.5
| 0.310345
| 0
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| 0.101449
| 69
| 2
| 35
| 34.5
| 0.935484
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| 0
| 0
|
0
| 5
|
af1a32c974284234464dcef39c7b6e78032bf2f2
| 203
|
py
|
Python
|
cogs/__init__.py
|
Potzerus/Dorothy
|
9971c342339e9eb61e5fbf4aa11ae1530555004f
|
[
"MIT"
] | null | null | null |
cogs/__init__.py
|
Potzerus/Dorothy
|
9971c342339e9eb61e5fbf4aa11ae1530555004f
|
[
"MIT"
] | null | null | null |
cogs/__init__.py
|
Potzerus/Dorothy
|
9971c342339e9eb61e5fbf4aa11ae1530555004f
|
[
"MIT"
] | null | null | null |
from .Chuni import Chunii
#from .Drones import Drone
from .Oda import OdaCord
from .Pray import PrayCog
from .Test import TestCog
from .Panopticon import Panopticon
from .Potzscript import PotzScriptCog
| 25.375
| 37
| 0.82266
| 28
| 203
| 5.964286
| 0.535714
| 0
| 0
| 0
| 0
| 0
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| 0.137931
| 203
| 7
| 38
| 29
| 0.954286
| 0.123153
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| 1
| 0
| 1
| 0
|
0
| 5
|
af85614f4ec5c8d93798a8484271dbf1b1dbb7bc
| 230
|
py
|
Python
|
python/multi.py
|
robotlightsyou/test
|
015f13943fc402d8ce86c5f6d2f5a7d032b3340a
|
[
"MIT"
] | 2
|
2019-05-26T15:09:34.000Z
|
2021-09-12T08:01:23.000Z
|
python/multi.py
|
robotlightsyou/test
|
015f13943fc402d8ce86c5f6d2f5a7d032b3340a
|
[
"MIT"
] | null | null | null |
python/multi.py
|
robotlightsyou/test
|
015f13943fc402d8ce86c5f6d2f5a7d032b3340a
|
[
"MIT"
] | 1
|
2021-04-11T20:28:21.000Z
|
2021-04-11T20:28:21.000Z
|
class Multi(object):
ITEMS = (1, 3), (5, 8)
def __getitem__(self, i):
return self.ITEMS[i[0]][i[1]]
def __setitem__(self, i, x):
self.ITEMS[i[0]][i[1]] = x
def __len__(self):
return 2, 2
| 19.166667
| 37
| 0.517391
| 37
| 230
| 2.891892
| 0.486486
| 0.093458
| 0.186916
| 0.205607
| 0.242991
| 0.242991
| 0
| 0
| 0
| 0
| 0
| 0.061728
| 0.295652
| 230
| 11
| 38
| 20.909091
| 0.598765
| 0
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| false
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| 0.875
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| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
afc17716db71c93ed908b2c75be0f95dd37fadae
| 119
|
py
|
Python
|
sfaira/versions/genomes/__init__.py
|
theislab/sfaira
|
77a7b49936047a0cdddc5ace4482186a868c3a7a
|
[
"BSD-3-Clause"
] | 110
|
2020-09-08T07:47:15.000Z
|
2022-03-29T03:33:56.000Z
|
sfaira/versions/genomes/__init__.py
|
theislab/sfaira
|
77a7b49936047a0cdddc5ace4482186a868c3a7a
|
[
"BSD-3-Clause"
] | 405
|
2020-09-15T15:05:46.000Z
|
2022-03-16T14:44:23.000Z
|
sfaira/versions/genomes/__init__.py
|
theislab/sfaira
|
77a7b49936047a0cdddc5ace4482186a868c3a7a
|
[
"BSD-3-Clause"
] | 20
|
2021-03-30T15:30:14.000Z
|
2022-03-07T12:52:58.000Z
|
from .genomes import GenomeContainer, GtfInterface
from .utils import translate_id_to_symbols, translate_symbols_to_id
| 39.666667
| 67
| 0.882353
| 16
| 119
| 6.1875
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
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| 0.084034
| 119
| 2
| 68
| 59.5
| 0.908257
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| 1
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| 1
| 0
|
0
| 5
|
bb7824fa414c9ee61edc49b7dde4d213cd0a19ae
| 179
|
py
|
Python
|
infobip_channels/mms/models/query_parameters/get_inbound_mms_messages.py
|
infobip-community/infobip-api-python-sdk
|
5ffc5ab877ee1748aa29391f991c8c5324387487
|
[
"MIT"
] | null | null | null |
infobip_channels/mms/models/query_parameters/get_inbound_mms_messages.py
|
infobip-community/infobip-api-python-sdk
|
5ffc5ab877ee1748aa29391f991c8c5324387487
|
[
"MIT"
] | null | null | null |
infobip_channels/mms/models/query_parameters/get_inbound_mms_messages.py
|
infobip-community/infobip-api-python-sdk
|
5ffc5ab877ee1748aa29391f991c8c5324387487
|
[
"MIT"
] | null | null | null |
from typing import Optional
from infobip_channels.core.models import QueryParameter
class GetInboundMMSMessagesQueryParameters(QueryParameter):
limit: Optional[int] = None
| 22.375
| 59
| 0.832402
| 18
| 179
| 8.222222
| 0.777778
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
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| 0.117318
| 179
| 7
| 60
| 25.571429
| 0.936709
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bb9c04e4ba6ea179ef1510eb57aa0ec6d0729cf4
| 88
|
py
|
Python
|
PythonDNS/helpers.py
|
endail/PythonDNS
|
21abd703a46e501e491fdb5f8af734c13d787ed8
|
[
"MIT"
] | null | null | null |
PythonDNS/helpers.py
|
endail/PythonDNS
|
21abd703a46e501e491fdb5f8af734c13d787ed8
|
[
"MIT"
] | null | null | null |
PythonDNS/helpers.py
|
endail/PythonDNS
|
21abd703a46e501e491fdb5f8af734c13d787ed8
|
[
"MIT"
] | null | null | null |
def GetDomainNameFromRequest(request):
return str(request.questions[0].qname)[:-1]
| 22
| 47
| 0.75
| 10
| 88
| 6.6
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025316
| 0.102273
| 88
| 3
| 48
| 29.333333
| 0.810127
| 0
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| null | 0
| 0
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| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
bbcbf7679f0f4a8307ad958f04942901c410cc18
| 102
|
py
|
Python
|
sinfer/dataset/__init__.py
|
geoyee/SlideInfer
|
1f790f895f66b6322c5d337ef16a30393cfa41ed
|
[
"Apache-2.0"
] | 4
|
2022-03-28T15:58:34.000Z
|
2022-03-31T16:00:36.000Z
|
sinfer/dataset/__init__.py
|
geoyee/SlideInfer
|
1f790f895f66b6322c5d337ef16a30393cfa41ed
|
[
"Apache-2.0"
] | null | null | null |
sinfer/dataset/__init__.py
|
geoyee/SlideInfer
|
1f790f895f66b6322c5d337ef16a30393cfa41ed
|
[
"Apache-2.0"
] | null | null | null |
from .rasterloader import RasterLoader
from .writer import SegWriter, DetWriter, ClsWriter, GanWriter
| 51
| 62
| 0.843137
| 11
| 102
| 7.818182
| 0.727273
| 0
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| 0
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| 0
| 0.107843
| 102
| 2
| 62
| 51
| 0.945055
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| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bbe9f296b0df9ca75d070b3935e72a25d127fdbe
| 124
|
py
|
Python
|
resultados/admin.py
|
LisandroCanteros/Grupo2_COM06_Info2021
|
86ad9e08db4e8935bf397b6e4db0b3d9d72cb320
|
[
"MIT"
] | null | null | null |
resultados/admin.py
|
LisandroCanteros/Grupo2_COM06_Info2021
|
86ad9e08db4e8935bf397b6e4db0b3d9d72cb320
|
[
"MIT"
] | null | null | null |
resultados/admin.py
|
LisandroCanteros/Grupo2_COM06_Info2021
|
86ad9e08db4e8935bf397b6e4db0b3d9d72cb320
|
[
"MIT"
] | 1
|
2021-09-05T23:29:56.000Z
|
2021-09-05T23:29:56.000Z
|
from django.contrib import admin
from .models import Resultado
# Register your models here.
admin.site.register(Resultado)
| 20.666667
| 32
| 0.814516
| 17
| 124
| 5.941176
| 0.647059
| 0
| 0
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| 0
| 0
| 0.120968
| 124
| 6
| 33
| 20.666667
| 0.926606
| 0.209677
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| 0
| true
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| 0.666667
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
bbeb89c7494603453a577e93908f696240d429b7
| 104
|
py
|
Python
|
src/modeling/trainers/__init__.py
|
akshatha-k/Calibrated_MOPO
|
3b2e675003e9f6d31a0763be2ec784ceeae5099e
|
[
"MIT"
] | null | null | null |
src/modeling/trainers/__init__.py
|
akshatha-k/Calibrated_MOPO
|
3b2e675003e9f6d31a0763be2ec784ceeae5099e
|
[
"MIT"
] | null | null | null |
src/modeling/trainers/__init__.py
|
akshatha-k/Calibrated_MOPO
|
3b2e675003e9f6d31a0763be2ec784ceeae5099e
|
[
"MIT"
] | null | null | null |
__all__ = ["cartpole_trainer", "BNN_trainer"]
from .cartpole_trainer import *
from .BNN_trainer import *
| 34.666667
| 45
| 0.778846
| 13
| 104
| 5.615385
| 0.461538
| 0.410959
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105769
| 104
| 3
| 46
| 34.666667
| 0.784946
| 0
| 0
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| 0
| 0.257143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a5270fa8c410a76396180c91042f0e0b6261d052
| 76
|
py
|
Python
|
src/agent/kubernetes-agent/src/network/__init__.py
|
hyperledger-gerrit-archive/cello
|
52ce6439a391ee65cec76934c1d7b0475543a1e4
|
[
"Apache-2.0"
] | 865
|
2017-01-12T21:51:37.000Z
|
2022-03-26T16:39:16.000Z
|
src/agent/kubernetes-agent/src/network/__init__.py
|
hyperledger-gerrit-archive/cello
|
52ce6439a391ee65cec76934c1d7b0475543a1e4
|
[
"Apache-2.0"
] | 226
|
2017-02-06T08:36:24.000Z
|
2022-03-30T06:13:46.000Z
|
src/agent/kubernetes-agent/src/network/__init__.py
|
hyperledger-gerrit-archive/cello
|
52ce6439a391ee65cec76934c1d7b0475543a1e4
|
[
"Apache-2.0"
] | 506
|
2017-02-08T06:11:18.000Z
|
2022-03-10T04:25:25.000Z
|
#
# SPDX-License-Identifier: Apache-2.0
#
from .fabric import FabricNetwork
| 15.2
| 37
| 0.763158
| 10
| 76
| 5.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029851
| 0.118421
| 76
| 4
| 38
| 19
| 0.835821
| 0.460526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a540a88777ce62bcf3f9334382918c0b4ee14cfe
| 284
|
py
|
Python
|
mplsoccer/__init__.py
|
ElJdP/mplsoccer
|
8a4558c4950bd9e88785ec59e5148af40ab90acf
|
[
"MIT"
] | null | null | null |
mplsoccer/__init__.py
|
ElJdP/mplsoccer
|
8a4558c4950bd9e88785ec59e5148af40ab90acf
|
[
"MIT"
] | null | null | null |
mplsoccer/__init__.py
|
ElJdP/mplsoccer
|
8a4558c4950bd9e88785ec59e5148af40ab90acf
|
[
"MIT"
] | null | null | null |
__version__ = "1.0.0"
from mplsoccer import statsbomb
from mplsoccer.cm import *
from mplsoccer.linecollection import *
from mplsoccer.pitch import *
from mplsoccer.quiver import *
from mplsoccer.radar_chart import *
from mplsoccer.scatterutils import *
from mplsoccer.utils import *
| 28.4
| 38
| 0.809859
| 37
| 284
| 6.081081
| 0.405405
| 0.462222
| 0.506667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012048
| 0.123239
| 284
| 9
| 39
| 31.555556
| 0.891566
| 0
| 0
| 0
| 0
| 0
| 0.017606
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.888889
| 0
| 0.888889
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a545f1cd218946af72d441ee9660f1f3513232ce
| 984
|
py
|
Python
|
Cnn/Test/test1/Lesson1.py
|
thilina27/ProojectRes
|
8342ea354fb52a5f93b178b34e4a712515027590
|
[
"Unlicense"
] | null | null | null |
Cnn/Test/test1/Lesson1.py
|
thilina27/ProojectRes
|
8342ea354fb52a5f93b178b34e4a712515027590
|
[
"Unlicense"
] | null | null | null |
Cnn/Test/test1/Lesson1.py
|
thilina27/ProojectRes
|
8342ea354fb52a5f93b178b34e4a712515027590
|
[
"Unlicense"
] | null | null | null |
Z = [[0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0],
[0, 1, 0, 1, 0, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]]
def compute_neighbours(Z):
rows, cols = len(Z), len(Z[0])
N = [[0, ] * (cols) for i in range(rows)]
for x in range(1, cols - 1):
for y in range(1, rows - 1):
N[y][x] = Z[y - 1][x - 1] + Z[y][x - 1] + Z[y + 1][x - 1] \
+ Z[y - 1][x] + Z[y + 1][x] \
+ Z[y - 1][x + 1] + Z[y][x + 1] + Z[y + 1][x + 1]
return N
def show(Z):
for l in Z[1:-1]: print l[1:-1]
print
def iterate(Z):
rows, cols = len(Z), len(Z[0])
N = compute_neighbours(Z)
for x in range(1, cols - 1):
for y in range(1, rows - 1):
if Z[y][x] == 1 and (N[y][x] < 2 or N[y][x] > 3):
Z[y][x] = 0
elif Z[y][x] == 0 and N[y][x] == 3:
Z[y][x] = 1
return Z
show(Z)
for i in range(4):
iterate(Z)
show(Z)
| 24
| 71
| 0.373984
| 199
| 984
| 1.839196
| 0.140704
| 0.142077
| 0.180328
| 0.196721
| 0.527322
| 0.491803
| 0.491803
| 0.453552
| 0.434426
| 0.306011
| 0
| 0.118928
| 0.393293
| 984
| 40
| 72
| 24.6
| 0.494137
| 0
| 0
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.0625
| 0
| 0
| 1
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
a5701d4dc74336c6faa5726e0ea7cab4550348ed
| 115
|
py
|
Python
|
exchanges/admin.py
|
AcaciaTrading/acacia_main
|
b778aed98894775eeea6b999c10be6c6d7cc5ac9
|
[
"MIT"
] | 25
|
2016-07-14T05:52:07.000Z
|
2021-01-18T19:53:50.000Z
|
exchanges/admin.py
|
AcaciaTrading/acacia_main
|
b778aed98894775eeea6b999c10be6c6d7cc5ac9
|
[
"MIT"
] | null | null | null |
exchanges/admin.py
|
AcaciaTrading/acacia_main
|
b778aed98894775eeea6b999c10be6c6d7cc5ac9
|
[
"MIT"
] | 12
|
2016-07-14T09:29:38.000Z
|
2021-01-18T19:53:58.000Z
|
from django.contrib import admin
from .models import Nonce
# Register your models here.
admin.site.register(Nonce)
| 23
| 32
| 0.808696
| 17
| 115
| 5.470588
| 0.647059
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121739
| 115
| 5
| 33
| 23
| 0.920792
| 0.226087
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
a591e5c05c454753625dae33a189711503970cd9
| 14,929
|
py
|
Python
|
source_code_tokenizer/tokenizer.py
|
matteogabburo/source-code-tokenizer
|
cef43679bb7ff62aed9865261ad1d3f731523946
|
[
"MIT"
] | 1
|
2022-03-14T12:16:17.000Z
|
2022-03-14T12:16:17.000Z
|
source_code_tokenizer/tokenizer.py
|
matteogabburo/source-code-tokenizer
|
cef43679bb7ff62aed9865261ad1d3f731523946
|
[
"MIT"
] | 1
|
2021-01-12T14:50:00.000Z
|
2021-01-12T21:20:14.000Z
|
source_code_tokenizer/tokenizer.py
|
matteogabburo/source-code-tokenizer
|
cef43679bb7ff62aed9865261ad1d3f731523946
|
[
"MIT"
] | null | null | null |
import sys
import abc
import re
import random
from source_code_tokenizer.languages.python.regex import PyRegex
from source_code_tokenizer.languages.c.regex import CRegex
from source_code_tokenizer.languages.cpp.regex import CPPRegex
from source_code_tokenizer.languages.java.regex import JavaRegex
from source_code_tokenizer.languages.js.regex import JSRegex
class CodeTokenizer:
def __init__(self):
self.TOKENIZED_STR = None
self.TOKENIZED = None
self.setup_regex()
def get_groups(self):
r"""Return the list of groups tokenized by the tokenizer."""
return sorted([k for k, v in self.TOKENIZED.groupindex.items()])
def get_regex(self):
return self.TOKENIZED_STR
@abc.abstractmethod
def setup_regex(self):
r"""This is the right place to initialize the self.TOKENIZED regular expression.
example:
self.TOKENIZED = re.compile(PYREGEX, re.MULTILINE)
"""
@abc.abstractmethod
def tokenize(self, text):
"""This method must take a string and return a list of tuples (value, type)"""
@abc.abstractmethod
def get_line_terminators(self, text):
"""This method return the list of line terminators of the language"""
class PythonTokenizer(CodeTokenizer):
def __init__(self):
super().__init__()
self.str_headers = PyRegex().get_str_headers()
def setup_regex(self):
# each regex should be a group
self.TOKENIZED_STR = PyRegex().get_full_regex()
self.TOKENIZED = re.compile(self.TOKENIZED_STR, re.MULTILINE)
def tokenize(self, text):
last_indent_size = 0
tokenized = []
for tok in self.TOKENIZED.finditer(text):
v, k = (tok.group(), tok.lastgroup)
# identation and deidentation
"""
# dynamic tabulation size
if k == "INDENT" and identation_size is None:
if v[0] == '\t':
identation_size = 1
indent_tab = True
elif v[0] == ' ':
identation_size = len(v)
indent_spaces = True
else:
raise ValueError("Identation error. Value not recognized (must be a space or \\t).")
"""
if k == "INDENT":
"""
if v[0] == ' ' and indent_spaces:
pass
elif v[0] == '\t' and indent_tab:
pass
else:
raise Exception("Identation error: only a type of tabulation is allowed (not spaces and tabs in the same program)")
if len(v) % identation_size > 0:
raise Exception("Identation error: the tabulation is inconsistent")
# decide if it is an indentation or a deindentation
if len(v) >= last_indent_size:
k = "INDENT"
else:
k = "DEINDENT"
last_indent_size = len(v)
# add all but one tabs (it will be add at the end of the cycle)
for _ in range(int(len(v) / identation_size)-1):
tokenized.append((v[0] * identation_size, k))
"""
# decide if it is an indentation or a deindentation
if len(v) >= last_indent_size:
k = "INDENT"
else:
k = "DEINDENT"
last_indent_size = len(v)
tokenized.append((v, k))
# replace NEWLINE with \n
if k == "NEWLINE" or k == "NL":
v = "\n"
# check strings errors
def check_strings(s):
if k == "STRING":
# string must be longher than 1 and bos and eos must be in [",'] and equal
# WARNING: a string can also start with (b|r|u|f|br|fr)
to_remove = max(
[len(h) if s.startswith(h) else 0 for h in self.str_headers]
)
v = s[to_remove:]
if not (len(v) > 1 and v[0] == v[-1] and v[0] in ['"', "'"]):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING"
)
if k == "STRING_M":
# string must be longher than 5 and bos and eos must be in [""",'''] and equal
# WARNING: a string can also start with (b|r|u|f|br|fr)
to_remove = max(
[len(h) if s.startswith(h) else 0 for h in self.str_headers]
)
v = s[to_remove:]
if not (len(v) > 5 and v[:3] == v[-3:] and v[:3] in ['"""', "'''"]):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING_M"
)
check_strings(v)
tokenized.append((v, k))
return tokenized
def get_line_terminators(self):
return ["\n"]
class CTokenizer(CodeTokenizer):
def __init__(self):
super().__init__()
self.RM_INDENTATION = re.compile(
CRegex().get_clean_indent_regex(), re.MULTILINE
)
self.RM_SPACES = re.compile(CRegex().get_remove_doublespaces_regex())
self.str_headers = CRegex().get_str_headers()
self.chr_headers = CRegex().get_chr_headers()
def setup_regex(self):
# each regex should be a group
self.TOKENIZED_STR = CRegex().get_full_regex()
self.TOKENIZED = re.compile(self.TOKENIZED_STR, re.MULTILINE)
def tokenize(self, text):
# minify the input removing all the not necessary spaces
def remove_spaces(matchobj):
return " " if matchobj.group(2) is not None else matchobj.group(1)
text = self.RM_INDENTATION.sub(remove_spaces, text)
text = self.RM_SPACES.sub(" ", text).strip()
tokenized = []
for tok in self.TOKENIZED.finditer(text):
v, k = (tok.group(), tok.lastgroup)
# check string and comment errors
def check(s):
if k == "STRING":
# string must be longer than 1 and bos and eos must be equal to "
# WARNING: a string can also start with some prefix
to_remove = max(
[len(h) if s.startswith(h) else 0 for h in self.str_headers]
)
v = s[to_remove:]
if not (len(v) > 1 and v[0] == v[-1] and v[0] == '"'):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING"
)
if k == "CHAR":
# char must be longer than 1 and bos and eos must be equal to '
if not (len(s) > 1 and s[0] == s[-1] and s[0] == "'"):
raise Exception(
"Error on string delimiters occurred while tokenizing CHAR"
)
if k == "COMMENT":
# if comment starts with "/*" len should be higher than 3 and it must finish with "*/"
if len(s) > 1 and s.startswith("/*"):
if not (len(s) > 3 and s[-2:] == "*/"):
raise Exception(
"Error on string delimiters occurred while tokenizing COMMENT"
)
check(v)
tokenized.append((v, k))
return tokenized
def get_line_terminators(self):
return [";"]
class CPPTokenizer(CodeTokenizer):
def __init__(self):
super().__init__()
self.RM_INDENTATION = re.compile(
CPPRegex().get_clean_indent_regex(), re.MULTILINE
)
self.RM_SPACES = re.compile(CPPRegex().get_remove_doublespaces_regex())
self.str_headers = CPPRegex().get_str_headers()
self.chr_headers = CPPRegex().get_chr_headers()
def setup_regex(self):
# each regex should be a group
self.TOKENIZED_STR = CPPRegex().get_full_regex()
self.TOKENIZED = re.compile(self.TOKENIZED_STR, re.MULTILINE)
def tokenize(self, text):
# minify the input removing all the not necessary spaces
def remove_spaces(matchobj):
return " " if matchobj.group(2) is not None else matchobj.group(1)
text = self.RM_INDENTATION.sub(remove_spaces, text)
text = self.RM_SPACES.sub(" ", text).strip()
tokenized = []
for tok in self.TOKENIZED.finditer(text):
v, k = (tok.group(), tok.lastgroup)
# check string and comment errors
def check(s):
if k == "STRING":
# string must be longer than 1 and bos and eos must be equal to "
# WARNING: a string can also start with some prefix
to_remove = max(
[len(h) if s.startswith(h) else 0 for h in self.str_headers]
)
v = s[to_remove:]
if not (len(v) > 1 and v[0] == v[-1] and v[0] == '"'):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING"
)
if k == "CHAR":
# char must be longer than 1 and bos and eos must be equal to '
# WARNING: a char can also start with some prefix
to_remove = max(
[len(h) if s.startswith(h) else 0 for h in self.chr_headers]
)
v = s[to_remove:]
if not (len(v) > 1 and v[0] == v[-1] and v[0] == "'"):
raise Exception(
"Error on string delimiters occurred while tokenizing CHAR"
)
if k == "COMMENT":
# if comment starts with "/*" len should be higher than 3 and it must finish with "*/"
if len(s) > 1 and s.startswith("/*"):
if not (len(s) > 3 and s[-2:] == "*/"):
raise Exception(
"Error on string delimiters occurred while tokenizing COMMENT"
)
check(v)
tokenized.append((v, k))
return tokenized
def get_line_terminators(self):
return [";"]
class JavaTokenizer(CodeTokenizer):
def __init__(self):
super().__init__()
self.RM_INDENTATION = re.compile(
JavaRegex().get_clean_indent_regex(), re.MULTILINE
)
self.RM_SPACES = re.compile(JavaRegex().get_remove_doublespaces_regex())
def setup_regex(self):
# each regex should be a group
self.TOKENIZED_STR = JavaRegex().get_full_regex()
self.TOKENIZED = re.compile(self.TOKENIZED_STR, re.MULTILINE)
def tokenize(self, text):
# minify the input removing all the not necessary spaces
def remove_spaces(matchobj):
return " " if matchobj.group(2) is not None else matchobj.group(1)
text = self.RM_INDENTATION.sub(remove_spaces, text)
text = self.RM_SPACES.sub(" ", text).strip()
tokenized = []
for tok in self.TOKENIZED.finditer(text):
v, k = (tok.group(), tok.lastgroup)
# check string and comment errors
def check(s):
if k == "STRING":
# string must be longer than 1 and bos and eos must be equal to "
if not (len(s) > 1 and s[0] == s[-1] and s[0] == '"'):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING"
)
if k == "CHAR":
# char must be longer than 1 and bos and eos must be equal to '
if not (len(s) > 1 and s[0] == s[-1] and s[0] == "'"):
raise Exception(
"Error on string delimiters occurred while tokenizing CHAR"
)
if k == "COMMENT":
# if comment starts with "/*" len should be higher than 3 and it must finish with "*/"
if len(s) > 1 and s.startswith("/*"):
if not (len(s) > 3 and s[-2:] == "*/"):
raise Exception(
"Error on string delimiters occurred while tokenizing COMMENT"
)
check(v)
tokenized.append((v, k))
return tokenized
def get_line_terminators(self):
return [";"]
class JSTokenizer(CodeTokenizer):
def __init__(self):
super().__init__()
self.RM_INDENTATION = re.compile(
JSRegex().get_clean_indent_regex(), re.MULTILINE
)
self.RM_SPACES = re.compile(JSRegex().get_remove_doublespaces_regex())
def setup_regex(self):
# each regex should be a group
self.TOKENIZED_STR = JSRegex().get_full_regex()
self.TOKENIZED = re.compile(self.TOKENIZED_STR, re.MULTILINE)
def tokenize(self, text):
# minify the input removing all the not necessary spaces
def remove_spaces(matchobj):
return " " if matchobj.group(2) is not None else matchobj.group(1)
text = self.RM_INDENTATION.sub(remove_spaces, text)
text = self.RM_SPACES.sub(" ", text).strip()
tokenized = []
for tok in self.TOKENIZED.finditer(text):
v, k = (tok.group(), tok.lastgroup)
# check string and comment errors
def check(s):
if k == "STRING":
# string must be longer than 1 and bos and eos must be in [", ', `]
if not (len(s) > 1 and s[0] == s[-1] and s[0] in ['"', "'", "`"]):
raise Exception(
"Error on string delimiters occurred while tokenizing STRING"
)
if k == "COMMENT":
# if comment starts with "/*" len should be higher than 3 and it must finish with "*/"
if len(s) > 1 and s.startswith("/*"):
if not (len(s) > 3 and s[-2:] == "*/"):
raise Exception(
"Error on string delimiters occurred while tokenizing COMMENT"
)
check(v)
tokenized.append((v, k))
return tokenized
def get_line_terminators(self):
return [";"]
| 35.630072
| 135
| 0.508942
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| 14,929
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0
| 5
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3c13e9798916dcdde738e3d105f4460ca0af917f
| 31,380
|
py
|
Python
|
Code/Cut-and-Average-Data-Exp2.py
|
nancy-aggarwal/Characterization-of-ARIADNE-source-mass-rotor_Github
|
b415af6dd63fc01aacbbb4293807ae2cab56de65
|
[
"CC-BY-4.0"
] | null | null | null |
Code/Cut-and-Average-Data-Exp2.py
|
nancy-aggarwal/Characterization-of-ARIADNE-source-mass-rotor_Github
|
b415af6dd63fc01aacbbb4293807ae2cab56de65
|
[
"CC-BY-4.0"
] | null | null | null |
Code/Cut-and-Average-Data-Exp2.py
|
nancy-aggarwal/Characterization-of-ARIADNE-source-mass-rotor_Github
|
b415af6dd63fc01aacbbb4293807ae2cab56de65
|
[
"CC-BY-4.0"
] | null | null | null |
# %%
from scipy.io import loadmat
import numpy as np
from datetime import datetime
now = datetime.now
import matplotlib.pyplot as plt
import matplotlib as mpl
import time
import os
import pickle
# import json
# %%
SaveData = True
SaveDataFigs = False
dpiN = 700
dark_plots = True
if dark_plots:
dark='darkbg/'
q = mpl.rc_params_from_file('matplotlibrc_dark')
else:
dark = 'whitebg/'
mpl.rcParams.update(mpl.rcParamsDefault)
SavePlotDir_Exp2 = '../Results/2021-12-18/Exp2/'+dark+'ProcessedData/'
SaveDataDir_Exp2 = '../Results/2021-12-18/Exp2/'+'Pickles/'
if SaveDataFigs:
if not os.path.exists(SavePlotDir_Exp2):
os.makedirs(SavePlotDir_Exp2)
if SaveData:
if not os.path.exists(SaveDataDir_Exp2):
os.makedirs(SaveDataDir_Exp2)
# %%
# %%
if dark_plots:
mpl.rcParams.update(q)
# %matplotlib inline
mpl.rcParams.update({
#'legend.borderpad': 0.3,
#'legend.borderaxespad': 0.25,
# 'legend.columnspacing': 0.6,
# 'legend.handlelength': 0.7,
#'legend.handleheight': 0.4,
#'legend.handletextpad': 0.2,
# 'legend.labelspacing': 0.45,
# 'text.usetex': True,
'font.size':14,
})
else:
# mpl.rcParams.update(mpl.rcParamsDefault)
# %matplotlib inline
font = {
'weight' : 'normal',
'size' : 14,
'family': 'Times New Roman'}
plt.rc('font', **font)
# mpl.rcParams.update({
# 'font.size':30,
# 'font.family':'Times New Roman'
# })
# mpl.rcParams.update({'font.family':'serif'})
# %%
def lighten_color(color, amount=0.5):
"""
Lightens the given color by multiplying (1-luminosity) by the given amount.
Input can be matplotlib color string, hex string, or RGB tuple.
Examples:
>> lighten_color('g', 0.3)
>> lighten_color('#F034A3', 0.6)
>> lighten_color((.3,.55,.1), 0.5)
"""
import matplotlib.colors as mc
import colorsys
try:
c = mc.cnames[color]
except:
c = color
c = colorsys.rgb_to_hls(*mc.to_rgb(c))
return colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2])
# %% [markdown]
# # Load data
# %%
sampling_factor = 6000
f_sample = 500
freq_for_noise_analysis = 5
# %% [markdown]
# ## Experiment 2
# %% [markdown]
# ### Load data ###
# %%
t0 = time.time()
Exp2_data_file = '../Data/Exp2_AxionWeel000.0500.flt.csv'
Exp2_data = np.loadtxt(Exp2_data_file,delimiter= '\t')
print(time.time() - t0)
t0 = time.time()
Exp2_time = Exp2_data[:,0]
Exp2_AW_Z = - Exp2_data[:,1] #(-AW-Z)
Exp2_AW_Y = - Exp2_data[:,2] #(-AW-Y)
Exp2_AV_X = + Exp2_data[:,3] #(+AV-Z)
Exp2_AV_Y = - Exp2_data[:,4] #(-AV-Y)
print(time.time() - t0)
# %% [markdown]
# ### Plot original data ###
# %%
plt.plot(Exp2_time,Exp2_AW_Y,label='AW Y')
plt.plot(Exp2_time,Exp2_AW_Z,label='AW Z')
plt.plot(Exp2_time,Exp2_AV_X,label='AV X')
plt.plot(Exp2_time,Exp2_AV_Y,label='AV Y')
plt.grid()
plt.xlabel('time (s)')
plt.ylabel('Field (pT)')
plt.legend(loc='lower center')
plt.title('Raw data')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_original_data_raw.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ### Times used for deciding various frequencies ###
# %%
# Short times (~4-5 cycles)
Exp2_theta_loc = [ 0,15,325,135,90]
Exp2_Freq = [ 0.1, 0.5, 1, 3, 5]
Exp2_Start_Time = [ 40, 190, 310,390, 475]
Exp2_Stop_Time = [ 81, 199, 315,391.4, 475.9]
ind_freq_analysis = Exp2_Freq.index(freq_for_noise_analysis)
Exp2_theta_loc.append(0)
Exp2_Freq.append(0)
Exp2_Start_Time.append(580)
Exp2_Stop_Time.append(580 + Exp2_Stop_Time[ind_freq_analysis] - Exp2_Start_Time[ind_freq_analysis])
# Intermediate times (~8-10 cycles)
# Exp2_loc = [0,15,325,135,90]
# Exp2_Freq = [0.1, 0.5, 1, 3, 5]
# Exp2_Start_Time = [ 40, 190, 310,390, 475]
# Exp2_Stop_Time = [ 120,206, 318,392.7, 476.7]
# SavePlotDir_Exp2 = '../PythonFigs/FitFigs/Intermediate/Exp2/'
# long times
# Exp2_loc = [0,5,305,110,330]
# Exp2_Freq = [0.1,0.5, 1, 3, 5]
# Exp2_Start_Time = [ 30,170,285,380,450]
# Exp2_Stop_Time = [ 130,240,325,420,500]
# SavePlotDir_Exp2 = '../PythonFigs/FitFigs/Long/Exp2/'
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_time,Exp2_AW_Y,color = "C0")
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_time,Exp2_AW_Z,color = "C1")
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_time,Exp2_AV_Y,color="C2")
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_time,Exp2_AV_X,color="C3")
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
plt.xlabel('time (s)')
# plt.ylabel('Field (pT)')
# plt.legend()
plt.suptitle('raw data (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_original_data_raw_separate_subplots.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ### Plot original data with time cut imposed ###
# %%
fig, splist = plt.subplots(nrows=5,ncols=1,sharex=True)
splist[0].plot(Exp2_time,Exp2_AW_Y,color = "C0")
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_time,Exp2_AW_Z,color = "C1")
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_time,Exp2_AV_Y,color="C2")
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_time,Exp2_AV_X,color="C3")
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
plt.xlabel('time (s)')
ylow = 0
yhigh = 1
ymid=0.5*(ylow+yhigh)
# col=['red','green','magenta']
icol = 4
for i_freq in range(len(Exp2_Freq)):
start = Exp2_Start_Time[i_freq]
stop = Exp2_Stop_Time[i_freq]
h = splist[4].plot([start,start],[ylow,yhigh],color = "C{}".format(icol))
splist[4].plot([start,stop],[ymid,ymid],color=h[0].get_color())
splist[4].plot([stop,stop],[ylow,yhigh],color=h[0].get_color(),label='{} Hz'.format(Exp2_Freq[i_freq]))
icol+=1
splist[4].set_ylim(-1,2)
splist[4].set_yticklabels([])
splist[4].grid()
splist[4].set_ylabel('Cut')
plt.legend(loc=[.8,1.2])
# plt.xlim(168,350)
# plt.ylim(-40,120)
plt.suptitle('Field (pT) and positions of cut')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_original_data_cut_place_old.png',bbox_inches='tight',dpi = dpiN)
# %%
fig, splist = plt.subplots(nrows=5,ncols=1,sharex=True)
splist[0].plot(Exp2_time,Exp2_AW_Y,color = "C0")
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_time,Exp2_AW_Z,color = "C1")
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_time,Exp2_AV_Y,color="C2")
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_time,Exp2_AV_X,color="C3")
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
plt.xlabel('time (s)')
ylow = 0
yhigh = 1
ymid=0.5*(ylow+yhigh)
# col=['red','green','magenta']
icol = 4
for i_freq in range(len(Exp2_Freq)):
start = Exp2_Start_Time[i_freq]
stop = Exp2_Stop_Time[i_freq]
freqtext = '{} Hz'.format(Exp2_Freq[i_freq])
h = splist[4].plot([start,start],[ylow,yhigh]
# ,color = "C{}".format(icol)
,color = [0.3,0.3,0.3]
)
splist[4].plot([start,stop],[ymid,ymid],color=h[0].get_color())
splist[4].plot([stop,stop],[ylow,yhigh],color=h[0].get_color(),label=freqtext)
plt.text((start+stop)/2,ylow-0.3,freqtext,ha='center',va='top')
icol+=1
splist[4].set_ylim(-1.3,1.5)
splist[4].set_yticklabels([])
splist[4].set_yticks([])
splist[4].grid()
splist[4].set_ylabel('Cut')
# plt.legend(ncol=len(Exp2_Freq),mode = "expand",
# loc="lower center",fontsize=10)
# plt.xlim(168,350)
# plt.ylim(-40,120)
plt.suptitle('Field (pT) and positions of cut')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_original_data_cut_place.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ### Error measured by taking data without rotation (0 Hz) ###
# %% [markdown]
# ### Cut data ###
# %%
Exp2_data_cut = {}
Exp2_data_cut['theta']={}
Exp2_data_cut['theta wrap']={}
Exp2_data_cut['time']={}
Exp2_data_cut['AW']={}
Exp2_data_cut['AW']['Z']={}
Exp2_data_cut['AW']['Z wrap'] = {}
Exp2_data_cut['AW']['Y']={}
Exp2_data_cut['AW']['Y wrap'] = {}
Exp2_data_cut['AV']={}
Exp2_data_cut['AV']['X']={}
Exp2_data_cut['AV']['Y']={}
Exp2_data_cut['AV']['Y wrap']={}
Exp2_data_cut['AV']['X wrap']={}
# %%
last_time = 0
for i_freq in range(len(Exp2_Freq)):
freq = Exp2_Freq[i_freq]
Exp2_data_cut['AW']['Z'][freq] = {}
Exp2_data_cut['AW']['Y'][freq] = {}
Exp2_data_cut['AV']['X'][freq] = {}
Exp2_data_cut['AV']['Y'][freq] = {}
Exp2_data_cut['AW']['Z wrap'][freq] = {}
Exp2_data_cut['AW']['Y wrap'][freq] = {}
Exp2_data_cut['AV']['X wrap'][freq] = {}
Exp2_data_cut['AV']['Y wrap'][freq] = {}
if freq==0:
f_new_sample = sampling_factor * freq_for_noise_analysis
else:
f_new_sample = sampling_factor * freq
n_skips = int(np.ceil(f_sample/f_new_sample))
cutbool = (Exp2_time>Exp2_Start_Time[i_freq]) & (Exp2_time<Exp2_Stop_Time[i_freq])
Time_i = Exp2_time[cutbool] - Exp2_Start_Time[i_freq] # reset all clocks to zero
if freq ==0:
Theta_i = 360 * freq_for_noise_analysis * Time_i
else:
Theta_i = 360 * freq * Time_i # degrees
AWZ_i = Exp2_AW_Z[cutbool]
AWY_i = Exp2_AW_Y[cutbool]
AVX_i = Exp2_AV_X[cutbool]
AVY_i = Exp2_AV_Y[cutbool]
new_indices = np.arange(0,len(Time_i),n_skips)
Theta_unwrap = Theta_i[new_indices] - Exp2_theta_loc[i_freq]
Theta_wrap = (Theta_unwrap) % (360)
Exp2_data_cut['theta'][freq] = Theta_unwrap
Exp2_data_cut['time'][freq] = Time_i[new_indices] + last_time
last_time = max(Exp2_data_cut['time'][freq])
Exp2_data_cut['AW']['Z'][freq]['B'] = AWZ_i[new_indices]
Exp2_data_cut['AW']['Y'][freq]['B'] = AWY_i[new_indices]
Exp2_data_cut['AV']['X'][freq]['B'] = AVX_i[new_indices]
Exp2_data_cut['AV']['Y'][freq]['B'] = AVY_i[new_indices]
sort_idx = Theta_wrap.argsort()
Exp2_data_cut['theta wrap'][freq] = Theta_wrap[sort_idx]
Exp2_data_cut['AW']['Z wrap'][freq]['B'] = Exp2_data_cut['AW']['Z'][freq]['B'][sort_idx]
Exp2_data_cut['AW']['Y wrap'][freq]['B'] = Exp2_data_cut['AW']['Y'][freq]['B'][sort_idx]
Exp2_data_cut['AV']['X wrap'][freq]['B'] = Exp2_data_cut['AV']['X'][freq]['B'][sort_idx]
Exp2_data_cut['AV']['Y wrap'][freq]['B'] = Exp2_data_cut['AV']['Y'][freq]['B'][sort_idx]
# %%
fig,[sp1,sp2,sp3,sp4]=plt.subplots(nrows=4,ncols=1,sharex=True,figsize = (7,6))
fig.suptitle('Field as a function of rotation')
plot_freq = [0,0.1,0.5,1,3,5]
i_freq = 0
if 0 in plot_freq:
ncol = len(plot_freq)-1
else:
ncol = len(plot_freq)
alph=1# - i_freq/10
for freq in plot_freq:
if freq ==0:
c = [0,.6,0.3]
else:
if dark_plots:
# cval = 0.8-(ncol-1-i_freq)/ncol
# c = [cval+0.2,cval,cval,alph]
cval = 0.8-i_freq/ncol
c = [cval+0.2,cval,cval,alph]
else:
cval = 0.8-i_freq/ncol
c = [cval+0.2,cval,cval,alph]
i_freq +=1
# print(c)
sp1.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Y'][freq]['B'],label='{} Hz'.format(freq),color = c)
sp2.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Z'][freq]['B'],color = c)
sp3.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['Y'][freq]['B'],color = c)
sp4.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['X'][freq]['B'],color = c)
sp1.set_ylabel('AW Y (pT)')
sp2.set_ylabel('AW Z (pT)')
sp3.set_ylabel('AV Y (pT)')
sp4.set_ylabel('AV X (pT)')
sp4.set_xlabel('$\\theta/\pi$')
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
fig.legend(loc='center right')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_with_0Hz.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,2)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom_with_0Hz.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,4)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom_2_with_0Hz.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,6)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom_3_with_0Hz.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(-2,11)
# %%
fig,[sp1,sp2,sp3,sp4]=plt.subplots(nrows=4,ncols=1,sharex=True,figsize = (7,6))
fig.suptitle('Field as a function of rotation')
plot_freq = [0.1,0.5,1,3,5]
i_freq = 0
if 0 in plot_freq:
ncol = len(plot_freq)-1
else:
ncol = len(plot_freq)
alph = 1# - i_freq/10
for freq in plot_freq:
if freq ==0:
c = [0,.6,0.3]
else:
cval = 0.8-i_freq/ncol
c = [cval+0.2,cval,cval,alph]
i_freq +=1
# print(c)
sp1.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Y'][freq]['B'],label='{} Hz'.format(freq),color = c)
sp2.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Z'][freq]['B'],color = c)
sp3.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['Y'][freq]['B'],color = c)
sp4.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['X'][freq]['B'],color = c)
sp1.set_ylabel('AW Y (pT)')
sp2.set_ylabel('AW Z (pT)')
sp3.set_ylabel('AV Y (pT)')
sp4.set_ylabel('AV X (pT)')
sp4.set_xlabel('$\\theta/\pi$')
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
fig.legend(loc='center right')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,2)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,4)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom_2.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(0,6)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_zoom_3.png',bbox_inches='tight',dpi = dpiN)
sp1.set_xlim(-2,11)
# %%
fig,[sp1,sp2,sp3,sp4]=plt.subplots(nrows=4,ncols=1,sharex=True,figsize = (7,6))
fig.suptitle('Field as a function of rotation')
for freq in Exp2_Freq:
sp1.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AW']['Y wrap'][freq]['B'],label='{} Hz'.format(freq),
alpha = 0.7)
sp2.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AW']['Z wrap'][freq]['B'],
alpha = 0.7)
sp3.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AV']['Y wrap'][freq]['B'],
alpha = 0.7)
sp4.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AV']['X wrap'][freq]['B'],
alpha = 0.7)
sp1.set_ylabel('AW Y')
sp2.set_ylabel('AW Z')
sp3.set_ylabel('AV Y')
sp4.set_ylabel('AV X')
sp4.set_xlabel('$\\theta/\pi$')
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
fig.legend(loc='center right')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_theta_wrap.png',bbox_inches='tight',dpi = dpiN)
# %%
fig,[sp1,sp2,sp3,sp4]=plt.subplots(nrows=4,ncols=1,sharex=True,figsize=(7,6))
for freq in Exp2_Freq:
sp1.plot(Exp2_data_cut['time'][freq],Exp2_data_cut['AW']['Y'][freq]['B'],label='{} Hz'.format(freq))
sp2.plot(Exp2_data_cut['time'][freq],Exp2_data_cut['AW']['Z'][freq]['B'])
sp3.plot(Exp2_data_cut['time'][freq],Exp2_data_cut['AV']['Y'][freq]['B'])
sp4.plot(Exp2_data_cut['time'][freq],Exp2_data_cut['AV']['X'][freq]['B'])
sp1.set_ylabel('AW Y')
sp2.set_ylabel('AW Z')
sp3.set_ylabel('AV Y')
sp4.set_ylabel('AV X')
sp4.set_xlabel('time (s)')
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
fig.legend(loc='center right')
plt.suptitle('Cut data as a function of time')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_time.png',bbox_inches='tight',dpi = dpiN)
# sp1.set_xlim(59,85)
# plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_time_zoom.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# # Error Analysis #
# %% [markdown]
# ### Error measured by taking data without rotation (0 Hz) ###
# %% [markdown]
# ## Standard deviation of cut data ##
# %%
Exp2_data_cut['AW']['Y'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AW']['Y'][0]['B'])
Exp2_data_cut['AW']['Z'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AW']['Z'][0]['B'])
Exp2_data_cut['AV']['Y'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AV']['Y'][0]['B'])
Exp2_data_cut['AV']['X'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AV']['X'][0]['B'])
# %% [markdown]
# ## Plot cut data and its standard deviation as a function of time
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_data_cut['time'][0],Exp2_data_cut['AW']['Y'][0]['B'],color = "C0"
,label = '$\sigma$ = {:.2f} pT'.format(Exp2_data_cut['AW']['Y'][freq_for_noise_analysis]['sigma']))
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_data_cut['time'][0],Exp2_data_cut['AW']['Z'][0]['B'],color = "C1"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AW']['Z'][freq_for_noise_analysis]['sigma'])))
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_data_cut['time'][0],Exp2_data_cut['AV']['Y'][0]['B'],color = "C2"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['Y'][freq_for_noise_analysis]['sigma'])))
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_data_cut['time'][0],Exp2_data_cut['AV']['X'][0]['B'],color = "C3"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['X'][freq_for_noise_analysis]['sigma'])))
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
splist[0].legend(loc = [1,0])
splist[1].legend(loc = [1,0])
splist[2].legend(loc = [1,0])
splist[3].legend(loc = [1,0])
plt.xlabel('time (s)')
# plt.ylabel('Field (pT)')
plt.suptitle('0 Hz data raw (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_noise_raw_data.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Plot cut data and its standard deviation as a function of theta
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_data_cut['theta'][0]/180,Exp2_data_cut['AW']['Y'][0]['B'],color = "C0"
,label = '$\sigma$ = {:.2f} pT'.format(Exp2_data_cut['AW']['Y'][freq_for_noise_analysis]['sigma']))
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_data_cut['theta'][0]/180,Exp2_data_cut['AW']['Z'][0]['B'],color = "C1"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AW']['Z'][freq_for_noise_analysis]['sigma'])))
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_data_cut['theta'][0]/180,Exp2_data_cut['AV']['Y'][0]['B'],color = "C2"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['Y'][freq_for_noise_analysis]['sigma'])))
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_data_cut['theta'][0]/180,Exp2_data_cut['AV']['X'][0]['B'],color = "C3"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['X'][freq_for_noise_analysis]['sigma'])))
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
splist[0].legend(loc = [1,0])
splist[1].legend(loc = [1,0])
splist[2].legend(loc = [1,0])
splist[3].legend(loc = [1,0])
plt.xlabel('$\\theta/\pi$')
# plt.ylabel('Field (pT)')
plt.suptitle('0 Hz data raw (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_noise_raw_data_theta.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Cut data and its standard deviation as a function of theta (wrapped) ##
# %%
Exp2_data_cut['AW']['Y wrap'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AW']['Y wrap'][0]['B'])
Exp2_data_cut['AW']['Z wrap'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AW']['Z wrap'][0]['B'])
Exp2_data_cut['AV']['Y wrap'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AV']['Y wrap'][0]['B'])
Exp2_data_cut['AV']['X wrap'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut['AV']['X wrap'][0]['B'])
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_data_cut['theta wrap'][0]/180,Exp2_data_cut['AW']['Y wrap'][0]['B'],color = "C0"
,label = '$\sigma$ = {:.2f} pT'.format(Exp2_data_cut['AW']['Y wrap'][freq_for_noise_analysis]['sigma']))
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_data_cut['theta wrap'][0]/180,Exp2_data_cut['AW']['Z wrap'][0]['B'],color = "C1"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AW']['Z wrap'][freq_for_noise_analysis]['sigma'])))
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_data_cut['theta wrap'][0]/180,Exp2_data_cut['AV']['Y wrap'][0]['B'],color = "C2"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['Y wrap'][freq_for_noise_analysis]['sigma'])))
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_data_cut['theta wrap'][0]/180,Exp2_data_cut['AV']['X wrap'][0]['B'],color = "C3"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut['AV']['X wrap'][freq_for_noise_analysis]['sigma'])))
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
splist[0].legend(loc = [1,0])
splist[1].legend(loc = [1,0])
splist[2].legend(loc = [1,0])
splist[3].legend(loc = [1,0])
plt.xlabel('$\\theta/\pi$')
# plt.ylabel('Field (pT)')
plt.suptitle('0 Hz data raw (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_noise_raw_data_theta_wrap.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Standard deviation over one cycle without averaging ##
# %%
idx_2pi = np.argmin(np.abs(Exp2_data_cut['theta'][0] - 360))
idx_0pi = np.argmin(np.abs(Exp2_data_cut['theta'][0] - 0))
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_data_cut['theta'][0][idx_0pi:idx_2pi]/180,Exp2_data_cut['AW']['Y'][0]['B'][idx_0pi:idx_2pi]
,color = "C0",label = '$\sigma$ = {:.2f} pT'.format(np.std(Exp2_data_cut['AW']['Y'][0]['B'][idx_0pi:idx_2pi])))
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_data_cut['theta'][0][idx_0pi:idx_2pi]/180,Exp2_data_cut['AW']['Z'][0]['B'][idx_0pi:idx_2pi]
,color = "C1",label = '$\sigma$ = {:.2f} pT'.format(np.std(Exp2_data_cut['AW']['Z'][0]['B'][idx_0pi:idx_2pi])))
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_data_cut['theta'][0][idx_0pi:idx_2pi]/180,Exp2_data_cut['AV']['Y'][0]['B'][idx_0pi:idx_2pi]
,color = "C2",label = '$\sigma$ = {:.2f} pT'.format(np.std(Exp2_data_cut['AV']['Y'][0]['B'][idx_0pi:idx_2pi])))
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_data_cut['theta'][0][idx_0pi:idx_2pi]/180,Exp2_data_cut['AV']['X'][0]['B'][idx_0pi:idx_2pi]
,color = "C3",label = '$\sigma$ = {:.2f} pT'.format(np.std(Exp2_data_cut['AV']['X'][0]['B'][idx_0pi:idx_2pi])))
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
splist[0].legend(loc = [1,0])
splist[1].legend(loc = [1,0])
splist[2].legend(loc = [1,0])
splist[3].legend(loc = [1,0])
plt.xlabel('$\\theta/\pi$')
# plt.ylabel('Field (pT)')
plt.suptitle('0 Hz data raw one cycle (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_noise_raw_data_theta_cut_noavg.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Save to file
# %%
if SaveData:
with open(SaveDataDir_Exp2+'Exp2_cut_data_with_error.pk','wb') as file_obj:
pickle.dump(Exp2_data_cut,file_obj,protocol=pickle.HIGHEST_PROTOCOL)
# %% [markdown]
# # Average
# %%
Exp2_data_cut_avg = {}
Exp2_data_cut_avg['AW'] = {}
Exp2_data_cut_avg['AV'] = {}
# %%
# Now let's try to average
# Since we have timestamps and we know the frequency, we can call a new segment every time we hit 1/freq
# nu = 5
# last_start_time = Exp1_data_cut['time'][nu][0]
Exp2_data_cut_avg['AW']['Z avg']={}
Exp2_data_cut_avg['AW']['Y avg']={}
Exp2_data_cut_avg['AV']['X avg']={}
Exp2_data_cut_avg['AV']['Y avg']={}
Exp2_data_cut_avg['theta avg']={}
for nu in Exp2_Freq:
if nu==0:
n_one_period = int(f_sample/freq_for_noise_analysis)
else:
n_one_period = int(f_sample/nu)
print("averaging at {} Hz".format(nu))
print("Points expected in single cycle = {}".format(n_one_period))
print("Total number of points = {}".format(len(Exp2_data_cut['time'][nu])))
print("Points required for 4 cycles = {}".format(4*n_one_period))
AW_Z_avg = 0
AW_Y_avg = 0
AV_X_avg = 0
AV_Y_avg = 0
i_total = 1
i_segment = 0
i_start = 0
i_end = 0
while i_total<len(Exp2_data_cut['time'][nu]):
if i_total%n_one_period==0:
# print(Exp1_Time_cut[nu][i_total])
i_end = i_total-1
if i_segment ==0:
# time = Exp1_data_cut['time'][nu][i_start:i_end]
theta = Exp2_data_cut['theta'][nu][i_start:i_end]
theta = theta# - theta[0]
AW_Z_avg += Exp2_data_cut['AW']['Z'][nu]['B'][i_start:i_end]
AW_Y_avg += Exp2_data_cut['AW']['Y'][nu]['B'][i_start:i_end]
AV_X_avg += Exp2_data_cut['AV']['X'][nu]['B'][i_start:i_end]
AV_Y_avg += Exp2_data_cut['AV']['Y'][nu]['B'][i_start:i_end]
i_start = i_total
i_segment+=1
i_total+=1
theta_wrap = theta%360
sort_idx = theta_wrap.argsort()
Exp2_data_cut_avg['theta avg'][nu] = theta_wrap[sort_idx]
Exp2_data_cut_avg['AW']['Z avg'][nu] = {}
Exp2_data_cut_avg['AW']['Y avg'][nu] = {}
Exp2_data_cut_avg['AV']['X avg'][nu] = {}
Exp2_data_cut_avg['AV']['Y avg'][nu] = {}
Exp2_data_cut_avg['AW']['Z avg'][nu]['B'] = AW_Z_avg[sort_idx]/i_segment
Exp2_data_cut_avg['AW']['Y avg'][nu]['B'] = AW_Y_avg[sort_idx]/i_segment
Exp2_data_cut_avg['AV']['X avg'][nu]['B'] = AV_X_avg[sort_idx]/i_segment
Exp2_data_cut_avg['AV']['Y avg'][nu]['B'] = AV_Y_avg[sort_idx]/i_segment
print("Number of averages at {} Hz is {}".format(nu,i_segment))
# %%
fig, [sp1,sp2,sp3,sp4] = plt.subplots(nrows=4,ncols=1,sharex=True,figsize=(7,6))
col = []
for freq in Exp2_Freq:
h = sp1.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Z avg'][freq]['B'],visible=False)
dark_col = h[0].get_color()
col.append(dark_col)
light_col = lighten_color(dark_col)
sp1.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AW']['Y wrap'][freq]['B'],color = light_col,alpha=1)
sp2.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AW']['Z wrap'][freq]['B'],color = light_col,alpha =1)
sp3.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AV']['Y wrap'][freq]['B'],color = light_col,alpha=1)
sp4.plot(Exp2_data_cut['theta wrap'][freq]/180,Exp2_data_cut['AV']['X wrap'][freq]['B'],color = light_col,markersize=1,alpha =1)
i_freq = 0
for freq in Exp2_Freq:
dark_col = col[i_freq]
sp1.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Y avg'][freq]['B'],label = '{} Hz'.format(freq)
,color = dark_col)
sp2.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Z avg'][freq]['B'],
color = dark_col)
sp3.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AV']['Y avg'][freq]['B']
,color = dark_col)
sp4.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AV']['X avg'][freq]['B'],
color = dark_col)
i_freq += 1
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
# sp2.legend()
fig.legend(loc='center right')
sp1.set_ylabel('AW Y')
sp2.set_ylabel('AW Z')
sp3.set_ylabel('AV Y')
sp4.set_ylabel('AV X')
sp4.set_xlabel('$\\theta/\pi$')
plt.suptitle('Data average and wrapped')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_avg.png',bbox_inches='tight',dpi = dpiN)
# %%
fig, [sp1,sp2,sp3,sp4] = plt.subplots(nrows=4,ncols=1,sharex=True,figsize=(7,6))
col = []
plot_freq = [.1,.5,1,3,5,0]
for freq in plot_freq:
h = sp1.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Z avg'][freq]['B'],visible=False)
dark_col = h[0].get_color()
col.append(dark_col)
light_col = lighten_color(dark_col)
sp1.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Y'][freq]['B'],color = light_col)
sp2.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AW']['Z'][freq]['B'],color = light_col)
sp3.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['Y'][freq]['B'],color = light_col)
sp4.plot(Exp2_data_cut['theta'][freq]/180,Exp2_data_cut['AV']['X'][freq]['B'],color = light_col)
i_freq = 0
for freq in plot_freq:
dark_col = col[i_freq]
sp1.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Y avg'][freq]['B'],label = '{} Hz'.format(freq)
,color = dark_col)
sp2.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AW']['Z avg'][freq]['B'],
color = dark_col)
sp3.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AV']['Y avg'][freq]['B']
,color = dark_col)
sp4.plot(Exp2_data_cut_avg['theta avg'][freq]/180,Exp2_data_cut_avg['AV']['X avg'][freq]['B'],
color = dark_col)
i_freq += 1
sp1.grid()
sp2.grid()
sp3.grid()
sp4.grid()
# sp2.legend()
fig.legend(loc='center right')
sp1.set_ylabel('AW Y')
sp2.set_ylabel('AW Z')
sp3.set_ylabel('AV Y')
sp4.set_ylabel('AV X')
sp4.set_xlabel('$\\theta/\pi$')
plt.suptitle('Data average and original')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_avg_compare_to_full_set.png',bbox_inches='tight',dpi = dpiN)
plt.xlim(0,4)
plt.savefig(SavePlotDir_Exp2+'Exp2_cut_data_avg_compare_to_1_set.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Standard deviation for data after averaging ##
# %%
Exp2_data_cut_avg['AW']['Y avg'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut_avg['AW']['Y avg'][0]['B'])
Exp2_data_cut_avg['AW']['Z avg'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut_avg['AW']['Z avg'][0]['B'])
Exp2_data_cut_avg['AV']['Y avg'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut_avg['AV']['Y avg'][0]['B'])
Exp2_data_cut_avg['AV']['X avg'][freq_for_noise_analysis]['sigma'] = np.std(Exp2_data_cut_avg['AV']['X avg'][0]['B'])
# %%
fig, splist = plt.subplots(nrows=4,ncols=1,sharex=True)
splist[0].plot(Exp2_data_cut_avg['theta avg'][0]/180,Exp2_data_cut_avg['AW']['Y avg'][0]['B'],color = "C0"
,label = '$\sigma$ = {:.2f} pT'.format(Exp2_data_cut_avg['AW']['Y avg'][freq_for_noise_analysis]['sigma']))
splist[0].set_ylabel('AW Y')
splist[1].plot(Exp2_data_cut_avg['theta avg'][0]/180,Exp2_data_cut_avg['AW']['Z avg'][0]['B'],color = "C1"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut_avg['AW']['Z avg'][freq_for_noise_analysis]['sigma'])))
splist[1].set_ylabel('AW Z')
splist[2].plot(Exp2_data_cut_avg['theta avg'][0]/180,Exp2_data_cut_avg['AV']['Y avg'][0]['B'],color = "C2"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut_avg['AV']['Y avg'][freq_for_noise_analysis]['sigma'])))
splist[2].set_ylabel('AV Y')
splist[3].plot(Exp2_data_cut_avg['theta avg'][0]/180,Exp2_data_cut_avg['AV']['X avg'][0]['B'],color = "C3"
,label = '$\sigma$ = {:.2f} pT'.format((Exp2_data_cut_avg['AV']['X avg'][freq_for_noise_analysis]['sigma'])))
splist[3].set_ylabel('AV X')
splist[0].grid()
splist[1].grid()
splist[2].grid()
splist[3].grid()
splist[0].legend(loc = [1,0])
splist[1].legend(loc = [1,0])
splist[2].legend(loc = [1,0])
splist[3].legend(loc = [1,0])
plt.xlabel('$\\theta/\pi$')
# plt.ylabel('Field (pT)')
plt.suptitle('0 Hz data after averaging (pT)')
if SaveDataFigs:
plt.savefig(SavePlotDir_Exp2+'Exp2_noise_raw_data_theta_avg.png',bbox_inches='tight',dpi = dpiN)
# %% [markdown]
# ## Save to file
# %%
if SaveData:
with open(SaveDataDir_Exp2+'Exp2_cut_averaged_data.pk','wb') as file_obj:
pickle.dump(Exp2_data_cut_avg,file_obj,protocol=pickle.HIGHEST_PROTOCOL)
# %%
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3c39d2aca3680da13fb313cdb5e64d18b872230a
| 67
|
py
|
Python
|
ShadowIntegrators.py
|
Christian-Offen/symplectic-shadow-integration
|
b8d9c2712b9e7d3a10dc8e06d5e26a70842ebc53
|
[
"MIT"
] | 1
|
2022-03-16T14:43:21.000Z
|
2022-03-16T14:43:21.000Z
|
ShadowIntegrators.py
|
Christian-Offen/symplectic-shadow-integration
|
b8d9c2712b9e7d3a10dc8e06d5e26a70842ebc53
|
[
"MIT"
] | null | null | null |
ShadowIntegrators.py
|
Christian-Offen/symplectic-shadow-integration
|
b8d9c2712b9e7d3a10dc8e06d5e26a70842ebc53
|
[
"MIT"
] | null | null | null |
from ShadowSymplecticEuler import *
from ShadowMidpoint import *
| 13.4
| 35
| 0.820896
| 6
| 67
| 9.166667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.149254
| 67
| 4
| 36
| 16.75
| 0.964912
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3c92bac97dd6cd98e0934ccf649d0f0403ff73e4
| 697
|
py
|
Python
|
parsy-backend-v2/flaskApp/conf.py
|
dstambler17/Parsy.io
|
14c4905809f79f191efbbbdfbd0e8d9e838478e7
|
[
"MIT"
] | null | null | null |
parsy-backend-v2/flaskApp/conf.py
|
dstambler17/Parsy.io
|
14c4905809f79f191efbbbdfbd0e8d9e838478e7
|
[
"MIT"
] | null | null | null |
parsy-backend-v2/flaskApp/conf.py
|
dstambler17/Parsy.io
|
14c4905809f79f191efbbbdfbd0e8d9e838478e7
|
[
"MIT"
] | null | null | null |
import os
class OfflineConfiguration:
SQLALCHEMY_DATABASE_URI = f"mysql://{os.environ.get('parsyLocalUser')}:{os.environ.get('parsyLocalPassword')}@localhost/myAssist"
SQLALCHEMY_TRACK_MODIFICATIONS = False
'''class OnlineConfiguration:
SQLALCHEMY_DATABASE_URI = f"mysql://{os.environ.get('parsyProdUser')}:{os.environ.get('parsyProdPassword')}@{os.environ.get('parsyProdServer')}/parsy"
SQLALCHEMY_TRACK_MODIFICATIONS = False'''
class OnlineConfiguration:
SQLALCHEMY_DATABASE_URI = f"mysql://{os.environ.get('parsyDevUser')}:{os.environ.get('parsyDevPassword')}@{os.environ.get('parsyDevServer')}/{os.environ.get('parsyDevDb')}"
SQLALCHEMY_TRACK_MODIFICATIONS = False
| 46.466667
| 176
| 0.763271
| 74
| 697
| 7.027027
| 0.364865
| 0.155769
| 0.207692
| 0.126923
| 0.444231
| 0.444231
| 0.444231
| 0.444231
| 0.369231
| 0.369231
| 0
| 0
| 0.077475
| 697
| 14
| 177
| 49.785714
| 0.808709
| 0
| 0
| 0.285714
| 0
| 0.285714
| 0.520343
| 0.520343
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.285714
| 0.142857
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
3c970a519bc6fa636d014654a2a742248e077e9f
| 244
|
py
|
Python
|
backend/garpix_notify/urls.py
|
Beerhead/garpix_notify
|
a56d17ef278a2e96342e144bc918a647f4cc5d22
|
[
"MIT"
] | 9
|
2021-06-27T16:08:33.000Z
|
2021-12-26T17:33:25.000Z
|
backend/garpix_notify/urls.py
|
Beerhead/garpix_notify
|
a56d17ef278a2e96342e144bc918a647f4cc5d22
|
[
"MIT"
] | 3
|
2022-01-24T11:36:46.000Z
|
2022-02-14T09:46:34.000Z
|
backend/garpix_notify/urls.py
|
Beerhead/garpix_notify
|
a56d17ef278a2e96342e144bc918a647f4cc5d22
|
[
"MIT"
] | 7
|
2021-06-29T15:28:38.000Z
|
2022-01-25T07:40:28.000Z
|
from django.urls import path
from .views import send_webhook, viber_check_webhook
urlpatterns = [
path('send_webhook', send_webhook, name='send_webhook'),
path('viber_check_webhook', viber_check_webhook, name='viber_check_webhook'),
]
| 30.5
| 81
| 0.778689
| 33
| 244
| 5.393939
| 0.363636
| 0.247191
| 0.382022
| 0.269663
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114754
| 244
| 7
| 82
| 34.857143
| 0.824074
| 0
| 0
| 0
| 0
| 0
| 0.254098
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3c9a8dfa730b8c180b131d6531511b2637e34337
| 116
|
py
|
Python
|
rdm/wrappers/__init__.py
|
Alshak/rdm
|
0c969665a4a3c8e6258c3d603de8987bd9639fd1
|
[
"MIT"
] | null | null | null |
rdm/wrappers/__init__.py
|
Alshak/rdm
|
0c969665a4a3c8e6258c3d603de8987bd9639fd1
|
[
"MIT"
] | null | null | null |
rdm/wrappers/__init__.py
|
Alshak/rdm
|
0c969665a4a3c8e6258c3d603de8987bd9639fd1
|
[
"MIT"
] | 1
|
2020-02-29T17:40:32.000Z
|
2020-02-29T17:40:32.000Z
|
from wordification import Wordification
from rsd import RSD
from aleph import Aleph
from treeliker import TreeLiker
| 23.2
| 39
| 0.862069
| 16
| 116
| 6.25
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 116
| 4
| 40
| 29
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b1b3f03aabb207f5df86c48b369edd5a8c2e0a82
| 70
|
py
|
Python
|
tests/test_deprecate_sandbox/test_dep_rel/old.py
|
mikeboers/metatools
|
5aa12e08f03d87c78913b354319abdefac1fe16e
|
[
"BSD-3-Clause"
] | 4
|
2015-03-20T23:37:40.000Z
|
2021-02-05T14:52:57.000Z
|
tests/test_deprecate_sandbox/test_dep_rel/old.py
|
mikeboers/metatools
|
5aa12e08f03d87c78913b354319abdefac1fe16e
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_deprecate_sandbox/test_dep_rel/old.py
|
mikeboers/metatools
|
5aa12e08f03d87c78913b354319abdefac1fe16e
|
[
"BSD-3-Clause"
] | null | null | null |
from metatools.deprecate import module_renamed
module_renamed('.new')
| 23.333333
| 46
| 0.842857
| 9
| 70
| 6.333333
| 0.777778
| 0.45614
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071429
| 70
| 2
| 47
| 35
| 0.876923
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b1bff0a7c07370ce96dfd52fbcdd481a9f23dcc6
| 210
|
py
|
Python
|
074.py
|
souza-joao/cursoemvideo-python3
|
b9f747d1083c1c779630b25b321b291d76611901
|
[
"MIT"
] | null | null | null |
074.py
|
souza-joao/cursoemvideo-python3
|
b9f747d1083c1c779630b25b321b291d76611901
|
[
"MIT"
] | null | null | null |
074.py
|
souza-joao/cursoemvideo-python3
|
b9f747d1083c1c779630b25b321b291d76611901
|
[
"MIT"
] | null | null | null |
from random import randint
tup = (randint(0, 9), randint(0, 9), randint(0, 9), randint(0, 9))
for n in tup:
print(n, end=' ')
print(f'\nO maior valor foi {max(tup)}')
print(f'O menor valor foi {min(tup)}')
| 30
| 66
| 0.633333
| 40
| 210
| 3.325
| 0.525
| 0.240602
| 0.270677
| 0.360902
| 0.270677
| 0.270677
| 0.270677
| 0.270677
| 0.270677
| 0
| 0
| 0.045714
| 0.166667
| 210
| 6
| 67
| 35
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0.280952
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 0.166667
| 0.5
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
b1d67f003e2e911f1eab46dc553939272ad03880
| 19
|
py
|
Python
|
syft/nn/word2vec.py
|
aradhyamathur/PySyft
|
03f73d31b869596978fb779596075ce806afef34
|
[
"Apache-2.0"
] | 1
|
2017-09-22T13:11:01.000Z
|
2017-09-22T13:11:01.000Z
|
syft/nn/word2vec.py
|
aradhyamathur/PySyft
|
03f73d31b869596978fb779596075ce806afef34
|
[
"Apache-2.0"
] | null | null | null |
syft/nn/word2vec.py
|
aradhyamathur/PySyft
|
03f73d31b869596978fb779596075ce806afef34
|
[
"Apache-2.0"
] | 1
|
2020-05-27T10:20:40.000Z
|
2020-05-27T10:20:40.000Z
|
# TOOD: build this
| 9.5
| 18
| 0.684211
| 3
| 19
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 19
| 1
| 19
| 19
| 0.866667
| 0.842105
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b1e606c0d2609e747b70ac36d396bf9c9267b85b
| 52
|
py
|
Python
|
core/src/autogluon/core/scheduler/resource/__init__.py
|
RuohanW/autogluon
|
fa349db5e75a18cd3af7d9d3f1064eb34e92aca1
|
[
"Apache-2.0"
] | 6
|
2020-06-16T19:17:36.000Z
|
2021-07-07T14:50:31.000Z
|
core/src/autogluon/core/scheduler/resource/__init__.py
|
RuohanW/autogluon
|
fa349db5e75a18cd3af7d9d3f1064eb34e92aca1
|
[
"Apache-2.0"
] | null | null | null |
core/src/autogluon/core/scheduler/resource/__init__.py
|
RuohanW/autogluon
|
fa349db5e75a18cd3af7d9d3f1064eb34e92aca1
|
[
"Apache-2.0"
] | 2
|
2021-02-13T04:41:33.000Z
|
2021-07-10T07:14:59.000Z
|
from .resource import *
from .dist_manager import *
| 17.333333
| 27
| 0.769231
| 7
| 52
| 5.571429
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 52
| 2
| 28
| 26
| 0.886364
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b1efc7917d4fc2f36f014d7b8050f070e9c430c3
| 187
|
py
|
Python
|
src/api/domain/models/prediction_model.py
|
DenisioMytnysiano/lodetta
|
b321e14df78e6af129753fa1c1ea9ba2cb72e8db
|
[
"MIT"
] | null | null | null |
src/api/domain/models/prediction_model.py
|
DenisioMytnysiano/lodetta
|
b321e14df78e6af129753fa1c1ea9ba2cb72e8db
|
[
"MIT"
] | null | null | null |
src/api/domain/models/prediction_model.py
|
DenisioMytnysiano/lodetta
|
b321e14df78e6af129753fa1c1ea9ba2cb72e8db
|
[
"MIT"
] | null | null | null |
from pydantic import BaseModel
from api.domain.models.bbox_model import BboxModel
class PredictionModel(BaseModel):
class_name: str
confidence: float
prediction: BboxModel
| 18.7
| 50
| 0.786096
| 22
| 187
| 6.590909
| 0.772727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165775
| 187
| 9
| 51
| 20.777778
| 0.929487
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b1f8e7d417610b08411d943b2f57a1ba17842a2b
| 1,601
|
py
|
Python
|
rsopt/package_data/examples/obj_f_bunch_matching.py
|
radiasoft/rsopt
|
6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3
|
[
"Apache-2.0"
] | 6
|
2020-11-03T16:51:50.000Z
|
2022-02-13T20:40:05.000Z
|
rsopt/package_data/examples/obj_f_bunch_matching.py
|
radiasoft/rsopt
|
6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3
|
[
"Apache-2.0"
] | 97
|
2020-05-18T18:24:49.000Z
|
2022-03-23T15:42:42.000Z
|
rsopt/package_data/examples/obj_f_bunch_matching.py
|
radiasoft/rsopt
|
6d4d123dd61e30c7f562b2f5a28c3ccbbcddbde3
|
[
"Apache-2.0"
] | 4
|
2020-08-18T23:19:55.000Z
|
2021-12-08T20:55:09.000Z
|
import subprocess
import numpy as np
from rsbeams.rsdata.SDDS import readSDDS
def obj_f(_):
analysis_command = ["sddsanalyzebeam", "run_setup.output.sdds", "output.anb"]
subprocess.call(analysis_command)
anb = readSDDS("output.anb")
anb.read()
betax, betax_target = anb.columns['betax'].squeeze(), 10.
betay, betay_target = anb.columns['betay'].squeeze(), 10.
alphax, alphax_target = anb.columns['alphax'].squeeze(), 1.
alphay, alphay_target = anb.columns['alphay'].squeeze(), 1.
obj_val = np.sqrt((betax - betax_target)**2 +
(betay - betay_target)**2 +
(alphax - alphax_target)**2 +
(alphay - alphay_target)**2)
return obj_val
def obj_f_dfols(_):
analysis_command = ["sddsanalyzebeam", "run_setup.output.sdds", "output.anb"]
subprocess.call(analysis_command)
anb = readSDDS("output.anb")
anb.read()
betax, betax_target = anb.columns['betax'].squeeze(), 10.
betay, betay_target = anb.columns['betay'].squeeze(), 10.
alphax, alphax_target = anb.columns['alphax'].squeeze(), 1.
alphay, alphay_target = anb.columns['alphay'].squeeze(), 1.
obj_val = (betax - betax_target)**2 + \
(betay - betay_target)**2 + \
(alphax - alphax_target)**2 + \
(alphay - alphay_target)**2
obj_vec = np.array([betax - betax_target,
betay - betay_target,
alphax - alphax_target,
alphay - alphay_target])
return obj_val, obj_vec
| 32.02
| 81
| 0.592755
| 183
| 1,601
| 4.983607
| 0.20765
| 0.078947
| 0.140351
| 0.072368
| 0.778509
| 0.778509
| 0.778509
| 0.778509
| 0.778509
| 0.778509
| 0
| 0.01705
| 0.267333
| 1,601
| 49
| 82
| 32.673469
| 0.760443
| 0
| 0
| 0.457143
| 0
| 0
| 0.097439
| 0.026234
| 0
| 0
| 0
| 0
| 0
| 1
| 0.057143
| false
| 0
| 0.085714
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5937eaefc28e09a52e36699e6527ac6c7883b943
| 88
|
py
|
Python
|
wsgi.py
|
henryvalbuena/mothership-v2
|
24e7afa73bdfd7f58e1845478338628b1b2a09c8
|
[
"MIT"
] | null | null | null |
wsgi.py
|
henryvalbuena/mothership-v2
|
24e7afa73bdfd7f58e1845478338628b1b2a09c8
|
[
"MIT"
] | null | null | null |
wsgi.py
|
henryvalbuena/mothership-v2
|
24e7afa73bdfd7f58e1845478338628b1b2a09c8
|
[
"MIT"
] | null | null | null |
import os
from src.api import create_app
app = create_app(os.environ["FLASK_CONFIG"])
| 14.666667
| 44
| 0.772727
| 15
| 88
| 4.333333
| 0.666667
| 0.276923
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 88
| 5
| 45
| 17.6
| 0.844156
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
593d0a017e6e4015a1186ebb96881a8b77102c41
| 44
|
py
|
Python
|
utils/training_set_extraction/__init__.py
|
bentocg/sea-ice-deeplearning
|
bd3b302c4d09e34f90128a60428542c9cdb6105d
|
[
"MIT"
] | null | null | null |
utils/training_set_extraction/__init__.py
|
bentocg/sea-ice-deeplearning
|
bd3b302c4d09e34f90128a60428542c9cdb6105d
|
[
"MIT"
] | null | null | null |
utils/training_set_extraction/__init__.py
|
bentocg/sea-ice-deeplearning
|
bd3b302c4d09e34f90128a60428542c9cdb6105d
|
[
"MIT"
] | null | null | null |
from .patch_navigator import PatchNavigator
| 22
| 43
| 0.886364
| 5
| 44
| 7.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
5940df0a48eb93f97375398449ac4f0a1f869b69
| 5,569
|
py
|
Python
|
beancount_mercury/checking_test.py
|
mtlynch/beancount-mercury
|
e767ed9e0925e016526b894b6689a7c13d80541c
|
[
"MIT"
] | 3
|
2022-02-06T10:15:48.000Z
|
2022-03-28T17:39:47.000Z
|
beancount_mercury/checking_test.py
|
mtlynch/beancount-mercury
|
e767ed9e0925e016526b894b6689a7c13d80541c
|
[
"MIT"
] | 1
|
2022-02-06T01:13:16.000Z
|
2022-02-06T01:13:16.000Z
|
beancount_mercury/checking_test.py
|
mtlynch/beancount-mercury
|
e767ed9e0925e016526b894b6689a7c13d80541c
|
[
"MIT"
] | null | null | null |
import io
import textwrap
import pytest # NOQA, pylint: disable=unused-import
from beancount.ingest import extract
from . import CheckingImporter
def _unindent(indented):
return textwrap.dedent(indented).lstrip()
def _stringify_directives(directives):
f = io.StringIO()
extract.print_extracted_entries(directives, f)
return f.getvalue()
def test_identifies_mercury_file(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
02-04-2022,Joe Vendor,-550.00,Sent,Send Money transaction initiated on Mercury,"From Dummy, LLC for bowling balls",
"""))
with mercury_file.open() as f:
assert CheckingImporter(account='Assets:Checking:Mercury').identify(f)
def test_extracts_single_transaction_without_matching_account(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
02-04-2022,Joe Vendor,-550.00,Sent,Send Money transaction initiated on Mercury,"From Dummy, LLC for bowling balls",
"""))
with mercury_file.open() as f:
directives = CheckingImporter(
account='Assets:Checking:Mercury').extract(f)
assert _unindent("""
2022-02-04 * "Joe Vendor" "Send Money transaction initiated on Mercury - From Dummy, LLC for bowling balls"
Assets:Checking:Mercury -550.00 USD
""".rstrip()) == _stringify_directives(directives).strip()
def test_extracts_single_transaction_with_matching_account(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
02-04-2022,Bowlers Paradise,-550.00,Sent,Send Money transaction initiated on Mercury,"From Dummy, LLC for bowling balls",
"""))
with mercury_file.open() as f:
directives = CheckingImporter(
account='Assets:Checking:Mercury',
account_patterns=[
('^Bowlers Paradise$',
'Expenses:Equipment:Bowling-Balls:Bowlers-Paradise')
]).extract(f)
assert _unindent("""
2022-02-04 * "Bowlers Paradise" "Send Money transaction initiated on Mercury - From Dummy, LLC for bowling balls"
Assets:Checking:Mercury -550.00 USD
Expenses:Equipment:Bowling-Balls:Bowlers-Paradise 550.00 USD
""".rstrip()) == _stringify_directives(directives).strip()
def test_matches_transactions_by_priority(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
02-04-2022,Bowlers Paradise,-550.00,Sent,Send Money transaction initiated on Mercury,"From Dummy, LLC for bowling balls",
02-05-2022,Paradise Golf,-150.75,Sent,PARADISE GOLF,,
"""))
with mercury_file.open() as f:
directives = CheckingImporter(
account='Assets:Checking:Mercury',
account_patterns=[
('^Bowlers Paradise$',
'Expenses:Equipment:Bowling-Balls:Bowlers-Paradise'),
('Paradise', 'Expenses:Training:Paradise-Golf')
]).extract(f)
assert _unindent("""
2022-02-04 * "Bowlers Paradise" "Send Money transaction initiated on Mercury - From Dummy, LLC for bowling balls"
Assets:Checking:Mercury -550.00 USD
Expenses:Equipment:Bowling-Balls:Bowlers-Paradise 550.00 USD
2022-02-05 * "Paradise Golf" "PARADISE GOLF"
Assets:Checking:Mercury -150.75 USD
Expenses:Training:Paradise-Golf 150.75 USD
""".rstrip()) == _stringify_directives(directives).strip()
def test_extracts_incoming_transaction(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
01-30-2022,Charlie Customer,694.04,Sent,CHARLIE CUSTOMER,,
"""))
with mercury_file.open() as f:
directives = CheckingImporter(account='Assets:Checking:Mercury',
account_patterns=[
('^Charlie Customer$', 'Income:Sales')
]).extract(f)
assert _unindent("""
2022-01-30 * "Charlie Customer" "CHARLIE CUSTOMER"
Assets:Checking:Mercury 694.04 USD
Income:Sales -694.04 USD
""".rstrip()) == _stringify_directives(directives).strip()
def test_ignores_failed_transaction(tmp_path):
mercury_file = tmp_path / 'transactions-dummy-to-feb052022.csv'
mercury_file.write_text(
_unindent("""
Date,Description,Amount,Status,Bank Description,Reference,Note
01-29-2021,Expensivo's Diamond Emporium,-5876.95,Failed,Expensivo's Diamond Emporium; TRANSACTION_BLOCKED -- C10 -- User is not allowed to send over 5000.0 per 1 day(s).,,
"""))
with mercury_file.open() as f:
directives = CheckingImporter(
account='Assets:Checking:Mercury').extract(f)
assert len(directives) == 0
| 40.948529
| 184
| 0.649129
| 626
| 5,569
| 5.634185
| 0.196486
| 0.059257
| 0.065495
| 0.035724
| 0.759853
| 0.726113
| 0.726113
| 0.721576
| 0.7074
| 0.688687
| 0
| 0.052732
| 0.244029
| 5,569
| 135
| 185
| 41.251852
| 0.785036
| 0.006285
| 0
| 0.67619
| 0
| 0.07619
| 0.538142
| 0.234816
| 0
| 0
| 0
| 0
| 0.057143
| 1
| 0.07619
| false
| 0
| 0.104762
| 0.009524
| 0.2
| 0.009524
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3cbd73749401718b4d4f7e0934c786a2f52e41e9
| 61
|
py
|
Python
|
src/zvit/pb/__init__.py
|
obilaniu/Zvit
|
6a311b7b8e5fd10d09f8f678c86b38d4aa686c17
|
[
"MIT"
] | 8
|
2018-01-23T15:34:02.000Z
|
2020-11-07T02:54:04.000Z
|
src/zvit/pb/__init__.py
|
obilaniu/Zvit
|
6a311b7b8e5fd10d09f8f678c86b38d4aa686c17
|
[
"MIT"
] | null | null | null |
src/zvit/pb/__init__.py
|
obilaniu/Zvit
|
6a311b7b8e5fd10d09f8f678c86b38d4aa686c17
|
[
"MIT"
] | 2
|
2018-01-24T16:28:54.000Z
|
2018-01-31T13:26:41.000Z
|
# -*- coding: utf-8 -*-
#
# Imports
#
from .pebble import *
| 8.714286
| 23
| 0.540984
| 7
| 61
| 4.714286
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021277
| 0.229508
| 61
| 6
| 24
| 10.166667
| 0.680851
| 0.47541
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3cbed78b6545921f5598754408f98c0a396c7771
| 125
|
py
|
Python
|
crud-flask-demo/crud/utils/__init__.py
|
wencan/crud-flask-demo
|
1aa585761be2c7dfde334fe9cfb1658ebdfdc7d5
|
[
"BSD-3-Clause"
] | null | null | null |
crud-flask-demo/crud/utils/__init__.py
|
wencan/crud-flask-demo
|
1aa585761be2c7dfde334fe9cfb1658ebdfdc7d5
|
[
"BSD-3-Clause"
] | null | null | null |
crud-flask-demo/crud/utils/__init__.py
|
wencan/crud-flask-demo
|
1aa585761be2c7dfde334fe9cfb1658ebdfdc7d5
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from .session import scoped_session_maker
__all__ = ("scoped_session_maker")
| 20.833333
| 41
| 0.72
| 17
| 125
| 4.823529
| 0.764706
| 0.317073
| 0.439024
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018182
| 0.12
| 125
| 6
| 42
| 20.833333
| 0.727273
| 0.344
| 0
| 0
| 0
| 0
| 0.246914
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
3ce03acf3985de47041470a6d3248a7d0ebd6442
| 60
|
py
|
Python
|
sites/tests/test_rating.py
|
tc-mwangi/tuzwa-app
|
88875bfc11fb9637ff22f881257d4971cb523b06
|
[
"MIT"
] | null | null | null |
sites/tests/test_rating.py
|
tc-mwangi/tuzwa-app
|
88875bfc11fb9637ff22f881257d4971cb523b06
|
[
"MIT"
] | null | null | null |
sites/tests/test_rating.py
|
tc-mwangi/tuzwa-app
|
88875bfc11fb9637ff22f881257d4971cb523b06
|
[
"MIT"
] | null | null | null |
from django.test import TestCase
from ..models import Rating
| 30
| 32
| 0.833333
| 9
| 60
| 5.555556
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116667
| 60
| 2
| 33
| 30
| 0.943396
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
3ce941f098c152acdbf5c9288df8a0c5fb743cce
| 61
|
py
|
Python
|
py_tdlib/constructors/file_type_none.py
|
Mr-TelegramBot/python-tdlib
|
2e2d21a742ebcd439971a32357f2d0abd0ce61eb
|
[
"MIT"
] | 24
|
2018-10-05T13:04:30.000Z
|
2020-05-12T08:45:34.000Z
|
py_tdlib/constructors/file_type_none.py
|
MrMahdi313/python-tdlib
|
2e2d21a742ebcd439971a32357f2d0abd0ce61eb
|
[
"MIT"
] | 3
|
2019-06-26T07:20:20.000Z
|
2021-05-24T13:06:56.000Z
|
py_tdlib/constructors/file_type_none.py
|
MrMahdi313/python-tdlib
|
2e2d21a742ebcd439971a32357f2d0abd0ce61eb
|
[
"MIT"
] | 5
|
2018-10-05T14:29:28.000Z
|
2020-08-11T15:04:10.000Z
|
from ..factory import Type
class fileTypeNone(Type):
pass
| 10.166667
| 26
| 0.754098
| 8
| 61
| 5.75
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163934
| 61
| 5
| 27
| 12.2
| 0.901961
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
a70889b6b88082a7e1b56ff6e36cac6eab3510e8
| 17,443
|
py
|
Python
|
models/models_statements.py
|
PlusRoss/Hy-Transformer
|
c535f7e7018842ceace04a2079ec9213b0aafbc7
|
[
"MIT"
] | null | null | null |
models/models_statements.py
|
PlusRoss/Hy-Transformer
|
c535f7e7018842ceace04a2079ec9213b0aafbc7
|
[
"MIT"
] | null | null | null |
models/models_statements.py
|
PlusRoss/Hy-Transformer
|
c535f7e7018842ceace04a2079ec9213b0aafbc7
|
[
"MIT"
] | null | null | null |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from typing import Dict
from .gnn_encoder import StarEEncoder, StarEBase
from utils.utils_gcn import get_param, ccorr, rotate, softmax
class StarE_Transformer(StarEEncoder):
model_name = 'StarE_Transformer_Statement'
def __init__(self, kg_graph_repr: Dict[str, np.ndarray], config: dict, id2e: tuple = None):
if id2e is not None:
super(self.__class__, self).__init__(kg_graph_repr, config, id2e[1])
else:
super(self.__class__, self).__init__(kg_graph_repr, config)
self.model_name = 'StarE_Transformer_Statement'
self.hid_drop2 = config['STAREARGS']['HID_DROP2']
self.feat_drop = config['STAREARGS']['FEAT_DROP']
self.num_transformer_layers = config['STAREARGS']['T_LAYERS']
self.num_heads = config['STAREARGS']['T_N_HEADS']
self.num_hidden = config['STAREARGS']['T_HIDDEN']
self.d_model = config['EMBEDDING_DIM']
self.positional = config['STAREARGS']['POSITIONAL']
self.p_option = config['STAREARGS']['POS_OPTION']
self.pooling = config['STAREARGS']['POOLING'] # min / avg / concat
self.hidden_drop = torch.nn.Dropout(self.hid_drop)
self.hidden_drop2 = torch.nn.Dropout(self.hid_drop2)
self.feature_drop = torch.nn.Dropout(self.feat_drop)
encoder_layers = TransformerEncoderLayer(self.d_model, self.num_heads, self.num_hidden, config['STAREARGS']['HID_DROP2'])
self.encoder = TransformerEncoder(encoder_layers, config['STAREARGS']['T_LAYERS'])
self.position_embeddings = nn.Embedding(config['MAX_QPAIRS'] - 1, self.d_model)
self.layer_norm = torch.nn.LayerNorm(self.emb_dim)
if self.pooling == "concat":
self.flat_sz = self.emb_dim * (config['MAX_QPAIRS'] - 1)
self.fc = torch.nn.Linear(self.flat_sz, self.emb_dim)
else:
self.fc = torch.nn.Linear(self.emb_dim, self.emb_dim)
self.LayerNorm = nn.LayerNorm(config['EMBEDDING_DIM'])
def concat(self, e1_embed, rel_embed, qual_rel_embed, qual_obj_embed):
e1_embed = e1_embed.view(-1, 1, self.emb_dim)
rel_embed = rel_embed.view(-1, 1, self.emb_dim)
"""
arrange quals in the conve format with shape [bs, num_qual_pairs, emb_dim]
num_qual_pairs is 2 * (any qual tensor shape[1])
for each datum in bs the order will be
rel1, emb
en1, emb
rel2, emb
en2, emb
"""
quals = torch.cat((qual_rel_embed, qual_obj_embed), 2).view(-1, 2 * qual_rel_embed.shape[1],
qual_rel_embed.shape[2])
stack_inp = torch.cat([e1_embed, rel_embed, quals], 1).transpose(1, 0) # [2 + num_qual_pairs, bs, emb_dim]
return stack_inp
def forward(self, sub, rel, quals, obj=None):
'''
:param sub: bs
:param rel: bs
:param quals: bs*(sl-2) # bs*14
:return:
'''
sub_emb, rel_emb, qual_obj_emb, qual_rel_emb, all_ent, mask = \
self.forward_base(sub, rel, self.hidden_drop, self.feature_drop, quals, True, True, obj)
# bs*emb_dim , ......, bs*6*emb_dim
stk_inp = self.concat(sub_emb, rel_emb, qual_rel_emb, qual_obj_emb)
if self.positional:
positions = torch.arange(stk_inp.shape[0], dtype=torch.long, device=self.device).repeat(stk_inp.shape[1], 1)
pos_embeddings = self.position_embeddings(positions).transpose(1, 0)
stk_inp = stk_inp + pos_embeddings
x = self.encoder(stk_inp, src_key_padding_mask=mask)
if self.pooling == 'concat':
x = x.transpose(1, 0).reshape(-1, self.flat_sz)
elif self.pooling == "avg":
x = torch.mean(x, dim=0)
elif self.pooling == "min":
x, _ = torch.min(x, dim=0)
x = self.fc(x)
if self.config['STAREARGS']['SEP_ENT_EMBEDDING']:
x = torch.mm(x, all_ent[:, :all_ent.shape[1]//2].transpose(1, 0))
else:
x = torch.mm(x, all_ent.transpose(1, 0))
# # jump embed
# x = torch.mm(x, self.init_embed.transpose(1, 0))
score = torch.sigmoid(x)
return score
class StarE_ObjectMask_Transformer(StarEEncoder):
model_name = 'StarE_ObjectMask_Transformer_Statement'
def __init__(self, kg_graph_repr: Dict[str, np.ndarray], config: dict, id2e: tuple = None):
super(self.__class__, self).__init__(kg_graph_repr, config)
self.model_name = 'StarE_Transformer_Statement'
self.hid_drop2 = config['STAREARGS']['HID_DROP2']
self.feat_drop = config['STAREARGS']['FEAT_DROP']
self.num_transformer_layers = config['STAREARGS']['T_LAYERS']
self.num_heads = config['STAREARGS']['T_N_HEADS']
self.num_hidden = config['STAREARGS']['T_HIDDEN']
self.d_model = config['EMBEDDING_DIM']
self.positional = config['STAREARGS']['POSITIONAL']
self.object_mask_emb = torch.nn.Parameter(torch.randn(1, self.emb_dim,dtype=torch.float32),True)
self.hidden_drop = torch.nn.Dropout(self.hid_drop)
self.hidden_drop2 = torch.nn.Dropout(self.hid_drop2)
self.feature_drop = torch.nn.Dropout(self.feat_drop)
encoder_layers = TransformerEncoderLayer(self.d_model, self.num_heads, self.num_hidden, config['STAREARGS']['HID_DROP2'])
self.encoder = TransformerEncoder(encoder_layers, config['STAREARGS']['T_LAYERS'])
self.position_embeddings = nn.Embedding(config['MAX_QPAIRS'], self.d_model)
self.layer_norm = torch.nn.LayerNorm(self.emb_dim)
self.flat_sz = self.emb_dim * (config['MAX_QPAIRS'] - 1)
self.fc = torch.nn.Linear(self.emb_dim, self.emb_dim)
def concat(self, e1_embed, rel_embed, obj_embed, qual_rel_embed, qual_obj_embed):
e1_embed = e1_embed.view(-1, 1, self.emb_dim)
rel_embed = rel_embed.view(-1, 1, self.emb_dim)
obj_embed = obj_embed.view(-1,1, self.emb_dim)
"""
arrange quals in the conve format with shape [bs, num_qual_pairs, emb_dim]
num_qual_pairs is 2 * (any qual tensor shape[1])
for each datum in bs the order will be
rel1, emb
en1, emb
rel2, emb
en2, emb
"""
quals = torch.cat((qual_rel_embed, qual_obj_embed), 2).view(-1, 2 * qual_rel_embed.shape[1],
qual_rel_embed.shape[2])
stack_inp = torch.cat([e1_embed, rel_embed, obj_embed, quals], 1).transpose(1, 0) # [2 + num_qual_pairs, bs, emb_dim]
return stack_inp # 14, 128, 200
def forward(self, sub, rel, quals):
'''
:param sub: bs
:param rel: bs
:param quals: bs*(sl-2) # bs*14
:return:
'''
sub_emb, rel_emb, qual_obj_emb, qual_rel_emb, all_ent, mask = \
self.forward_base(sub, rel, self.hidden_drop, self.feature_drop, quals, True, True)
# bs*emb_dim , ......, bs*6*emb_dim
object_mask = self.object_mask_emb.repeat(sub.shape[0], 1)
ins = torch.zeros((sub.shape), dtype=torch.bool, device=self.device)
mask = torch.cat((mask[:, :2], ins.unsqueeze(1), mask[:, 2:]), axis=1)
stk_inp = self.concat(sub_emb, rel_emb, object_mask, qual_rel_emb, qual_obj_emb)
if self.positional:
positions = torch.arange(stk_inp.shape[0], dtype=torch.long, device=self.device).repeat(stk_inp.shape[1], 1)
pos_embeddings = self.position_embeddings(positions).transpose(1, 0)
stk_inp = stk_inp + pos_embeddings
x = self.encoder(stk_inp, src_key_padding_mask=mask)[2] # to get the object position
x = self.fc(x)
x = torch.mm(x, all_ent.transpose(1, 0))
score = torch.sigmoid(x)
return score
class StarE_Transformer_TripleBaseline(StarEEncoder):
model_name = 'StarE_Transformer_Triple_Baseline'
def __init__(self, kg_graph_repr: Dict[str, np.ndarray], config: dict, id2e: tuple = None):
if id2e is not None:
super(self.__class__, self).__init__(kg_graph_repr, config, id2e[1])
else:
super(self.__class__, self).__init__(kg_graph_repr, config)
self.model_name = 'StarE_Transformer_Statement'
self.hid_drop2 = config['STAREARGS']['HID_DROP2']
self.feat_drop = config['STAREARGS']['FEAT_DROP']
self.num_transformer_layers = config['STAREARGS']['T_LAYERS']
self.num_heads = config['STAREARGS']['T_N_HEADS']
self.num_hidden = config['STAREARGS']['T_HIDDEN']
self.d_model = config['EMBEDDING_DIM']
self.positional = config['STAREARGS']['POSITIONAL']
self.pooling = config['STAREARGS']['POOLING'] # min / avg / concat
self.hidden_drop = torch.nn.Dropout(self.hid_drop)
self.hidden_drop2 = torch.nn.Dropout(self.hid_drop2)
self.feature_drop = torch.nn.Dropout(self.feat_drop)
encoder_layers = TransformerEncoderLayer(self.d_model, self.num_heads, self.num_hidden, config['STAREARGS']['HID_DROP2'])
self.encoder = TransformerEncoder(encoder_layers, config['STAREARGS']['T_LAYERS'])
self.position_embeddings = nn.Embedding(config['MAX_QPAIRS'] - 1, self.d_model)
self.layer_norm = torch.nn.LayerNorm(self.emb_dim)
if self.pooling == "concat":
self.flat_sz = self.emb_dim * (config['MAX_QPAIRS'] - 1)
self.fc = torch.nn.Linear(self.flat_sz, self.emb_dim)
else:
self.fc = torch.nn.Linear(self.emb_dim, self.emb_dim)
def concat(self, e1_embed, rel_embed):
e1_embed = e1_embed.view(-1, 1, self.emb_dim)
rel_embed = rel_embed.view(-1, 1, self.emb_dim)
stack_inp = torch.cat([e1_embed, rel_embed], 1).transpose(1, 0) # [2, bs, emb_dim]
return stack_inp
def forward(self, sub, rel, quals):
'''
:param sub: bs
:param rel: bs
:param quals: bs*(sl-2) # bs*14
:return:
'''
sub_emb, rel_emb, qual_obj_emb, qual_rel_emb, all_ent, mask = \
self.forward_base(sub, rel, self.hidden_drop, self.feature_drop, quals, True, True)
# bs*emb_dim , ......, bs*6*emb_dim
stk_inp = self.concat(sub_emb, rel_emb)
mask = mask[:, :2]
if self.positional:
positions = torch.arange(stk_inp.shape[0], dtype=torch.long, device=self.device).repeat(stk_inp.shape[1], 1)
pos_embeddings = self.position_embeddings(positions).transpose(1, 0)
stk_inp = stk_inp + pos_embeddings
x = self.encoder(stk_inp, src_key_padding_mask=mask)
if self.pooling == 'concat':
x = x.transpose(1, 0).reshape(-1, self.flat_sz)
elif self.pooling == "avg":
x = torch.mean(x, dim=0)
elif self.pooling == "min":
x, _ = torch.min(x, dim=0)
x = self.fc(x)
x = torch.mm(x, all_ent.transpose(1, 0))
score = torch.sigmoid(x)
return score
class Transformer_Statements_mask(StarEBase):
"""Baseline for Transformer decoder only model w/o starE encoder
with well chosen setting
"""
def __init__(self, config: dict):
super().__init__(config)
#self.emb_dim = config['EMBEDDING_DIM']
self.entities = get_param((self.num_ent+1, self.emb_dim), norm=False) # final ind for [MASK]
self.relations = get_param((2 * self.num_rel, self.emb_dim), norm=False)
self.model_name = 'Transformer_Statements_mask'
self.hid_drop2 = config['STAREARGS']['HID_DROP2']
self.feat_drop = config['STAREARGS']['FEAT_DROP']
self.num_transformer_layers = config['STAREARGS']['T_LAYERS']
self.num_heads = config['STAREARGS']['T_N_HEADS']
self.num_hidden = config['STAREARGS']['T_HIDDEN']
self.d_model = config['EMBEDDING_DIM']
self.positional = config['STAREARGS']['POSITIONAL']
self.pooling = config['STAREARGS']['POOLING'] # min / avg / concat
self.device = config['DEVICE']
self.hidden_drop = torch.nn.Dropout(self.hid_drop)
self.hidden_drop2 = torch.nn.Dropout(self.hid_drop2)
self.feature_drop = torch.nn.Dropout(self.feat_drop)
encoder_layers = TransformerEncoderLayer(self.d_model, self.num_heads, self.num_hidden,
config['STAREARGS']['HID_DROP2'])
self.encoder = TransformerEncoder(encoder_layers, config['STAREARGS']['T_LAYERS'])
self.position_embeddings = nn.Embedding(config['MAX_QPAIRS'], self.d_model)
self.layer_norm = torch.nn.LayerNorm(self.emb_dim)
if self.pooling == "concat":
self.flat_sz = self.emb_dim * (config['MAX_QPAIRS'] - 1)
self.fc = torch.nn.Linear(self.flat_sz, self.emb_dim)
else:
self.fc = torch.nn.Linear(self.emb_dim, self.emb_dim)
# added
self.act = torch.tanh if 'ACT' not in config['STAREARGS'].keys() \
else config['STAREARGS']['ACT']
self.bn = torch.nn.BatchNorm1d(config['EMBEDDING_DIM'])
self.LayerNorm = nn.LayerNorm(config['EMBEDDING_DIM'])
self.LayerNorm1 = nn.LayerNorm(config['EMBEDDING_DIM'])
self.LayerNorm_combine = nn.LayerNorm(config['EMBEDDING_DIM'])
def concat(self, e1_embed, rel_embed, obj_embed, qual_rel_embed, qual_obj_embed):
e1_embed = e1_embed.view(-1, 1, self.emb_dim)
rel_embed = rel_embed.view(-1, 1, self.emb_dim)
obj_embed = obj_embed.view(-1,1, self.emb_dim)
quals = torch.cat((qual_rel_embed, qual_obj_embed), 2).view(-1, 2 * qual_rel_embed.shape[1],
qual_rel_embed.shape[2])
stack_inp = torch.cat([e1_embed, rel_embed, obj_embed, quals], 1).transpose(1, 0) # [2 + num_qual_pairs, bs, emb_dim]
return stack_inp
def forward(self, sub, rel, quals, obj=None):
self.score_list = [0., 0.]
entitiy_embeddings = self.entities
entitiy_embeddings = self.LayerNorm(entitiy_embeddings)
entitiy_embeddings = self.feature_drop(entitiy_embeddings)
relation_embeddings = self.relations
relation_embeddings = self.LayerNorm1(relation_embeddings)
sub_emb = torch.index_select(entitiy_embeddings, 0, sub)
rel_emb = torch.index_select(relation_embeddings, 0, rel)
if obj is None:
obj = torch.tensor([self.num_ent]).long().to(sub.device).repeat(sub.shape)
mask_ind = torch.tensor([2]).long().to(sub.device).repeat(sub.shape)
else:
statements = torch.cat([sub.reshape(-1,1),rel.reshape(-1,1),obj.reshape(-1,1),quals], dim=1)
mask_ind = (statements==self.num_ent).nonzero()[:,1]
# if multiple entities are masked:
# ind_aux = torch.arange(statements.shape[1], 0, -1).to(statements.device)
# ind_aux = (statements==self.num_ent) * ind_aux
# mask_ind = torch.argmax(ind_aux, dim=1)
obj_emd = torch.index_select(entitiy_embeddings, 0, obj)
# print(statements==self.num_ent)
quals_ents = quals[:, 1::2].reshape(1, -1).squeeze(0)
quals_rels = quals[:, 0::2].reshape(1, -1).squeeze(0)
qual_obj_emb = torch.index_select(entitiy_embeddings, 0, quals_ents)
qual_rel_emb = torch.index_select(relation_embeddings, 0, quals_rels)
qual_obj_emb = qual_obj_emb.view(sub_emb.shape[0], -1, sub_emb.shape[1])
qual_rel_emb = qual_rel_emb.view(rel_emb.shape[0], -1, rel_emb.shape[1])
# so we first initialize with False
mask = torch.zeros((sub.shape[0], quals.shape[1] + 3)).bool().to(self.device)
# and put True where qual entities and relations are actually padding index 0
mask[:, 3:] = quals == 0
stk_inp = self.concat(sub_emb, rel_emb, obj_emd, qual_rel_emb, qual_obj_emb)
if self.positional:
# positions = torch.arange(stk_inp.shape[0], dtype=torch.long, device=self.device).repeat(stk_inp.shape[1], 1)
qual_ind = 5
positions_main = torch.arange(qual_ind, dtype=torch.long, device=self.device)
positions_qual = torch.arange(qual_ind-2, qual_ind, dtype=torch.long, device=self.device).repeat((stk_inp.shape[0]-qual_ind)//2)
positions = torch.cat([positions_main, positions_qual]).repeat(stk_inp.shape[1], 1)
pos_embeddings = self.position_embeddings(positions).transpose(1, 0)
stk_inp = stk_inp + pos_embeddings
# stk_inp = self.LayerNorm_combine(stk_inp)
# stk_inp = self.hidden_drop2(stk_inp)
x = self.encoder(stk_inp, src_key_padding_mask=mask)
x = x[mask_ind, torch.arange(x.shape[1]).to(x.device)]
x = self.fc(x)
x = torch.mm(x, entitiy_embeddings[:-1].transpose(1, 0)) # final ind for mask embedding
# x = torch.mm(x, self.entities[:-1].transpose(1, 0)) # final ind for mask embedding
# x = torch.mm(x, self.feature_drop(self.LayerNorm(entitiy_embeddings))[:-1].transpose(1, 0)) # final ind for mask embedding
score = torch.sigmoid(x)
return score
| 43.716792
| 140
| 0.629364
| 2,400
| 17,443
| 4.3275
| 0.088333
| 0.025997
| 0.031774
| 0.020797
| 0.789236
| 0.767668
| 0.753129
| 0.74042
| 0.71269
| 0.710187
| 0
| 0.018955
| 0.240842
| 17,443
| 398
| 141
| 43.826633
| 0.765368
| 0.085994
| 0
| 0.699187
| 0
| 0
| 0.075182
| 0.013633
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04878
| false
| 0
| 0.03252
| 0
| 0.142276
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5954a0a07281076e79418e86bc8a5f24f275d42d
| 93
|
py
|
Python
|
kheppy/evocom/commons/__init__.py
|
Ewande/kheppy
|
5c5f6bacbf9020053879947798983547de4c1c79
|
[
"MIT"
] | null | null | null |
kheppy/evocom/commons/__init__.py
|
Ewande/kheppy
|
5c5f6bacbf9020053879947798983547de4c1c79
|
[
"MIT"
] | null | null | null |
kheppy/evocom/commons/__init__.py
|
Ewande/kheppy
|
5c5f6bacbf9020053879947798983547de4c1c79
|
[
"MIT"
] | null | null | null |
from .base import BaseAlgorithm
from .individual import Controller
from .nn import NeuralNet
| 23.25
| 34
| 0.83871
| 12
| 93
| 6.5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 93
| 3
| 35
| 31
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 1
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| 0
| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
596514e8f769452d111da556a04984a9ea84d312
| 184
|
py
|
Python
|
guestbook/views/test_view.py
|
dagothar/django-guestbook
|
ed3e9608f3b973187bd7987557b3b04375cb1549
|
[
"MIT"
] | null | null | null |
guestbook/views/test_view.py
|
dagothar/django-guestbook
|
ed3e9608f3b973187bd7987557b3b04375cb1549
|
[
"MIT"
] | null | null | null |
guestbook/views/test_view.py
|
dagothar/django-guestbook
|
ed3e9608f3b973187bd7987557b3b04375cb1549
|
[
"MIT"
] | null | null | null |
from django.views.generic import View
from django.shortcuts import render
class TestView(View):
def get(self, request):
return render(request, 'guestbook/test.html', {})
| 23
| 57
| 0.722826
| 24
| 184
| 5.541667
| 0.75
| 0.150376
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.168478
| 184
| 7
| 58
| 26.285714
| 0.869281
| 0
| 0
| 0
| 0
| 0
| 0.103261
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0.2
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
597374276eb119c83c92ef89b60ed2cd72c7914d
| 142
|
py
|
Python
|
application/dashboards/callbacks/review.py
|
cnpls/flask-embed-template
|
6f540e1b11874a02d76ae0776a6b204cba94c593
|
[
"Apache-2.0"
] | null | null | null |
application/dashboards/callbacks/review.py
|
cnpls/flask-embed-template
|
6f540e1b11874a02d76ae0776a6b204cba94c593
|
[
"Apache-2.0"
] | null | null | null |
application/dashboards/callbacks/review.py
|
cnpls/flask-embed-template
|
6f540e1b11874a02d76ae0776a6b204cba94c593
|
[
"Apache-2.0"
] | null | null | null |
from dash.dependencies import Input, Output, State
import pandas as pd
import numpy as np
def Initialize_Review_Callbacks(dash_app):
pass
| 23.666667
| 50
| 0.809859
| 22
| 142
| 5.090909
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147887
| 142
| 6
| 51
| 23.666667
| 0.92562
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0.2
| 0.6
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
59b8ff3a04e47ed4e9219cafd55529e2732083b5
| 187
|
py
|
Python
|
accounts/admin.py
|
acounsel/zwazo
|
beeb3591674a8290a89704e3da56aa4d425418e8
|
[
"MIT"
] | null | null | null |
accounts/admin.py
|
acounsel/zwazo
|
beeb3591674a8290a89704e3da56aa4d425418e8
|
[
"MIT"
] | 10
|
2019-11-25T16:54:39.000Z
|
2022-02-10T08:29:51.000Z
|
accounts/admin.py
|
acounsel/zwazo
|
beeb3591674a8290a89704e3da56aa4d425418e8
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from accounts.models import Plan, Organization, Userprofile
admin.site.register(Plan)
admin.site.register(Organization)
admin.site.register(Userprofile)
| 26.714286
| 59
| 0.839572
| 24
| 187
| 6.541667
| 0.5
| 0.171975
| 0.324841
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074866
| 187
| 7
| 60
| 26.714286
| 0.907514
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.4
| 0
| 0.4
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
59dcdf5c14c0ddecee10b4150888c38c01fea829
| 14,387
|
py
|
Python
|
bempp/api/operators/boundary/helmholtz.py
|
ignacia-fp/bempp-cl
|
a65232558826e51e624b1a4f649b6a0ed5a7f551
|
[
"MIT"
] | null | null | null |
bempp/api/operators/boundary/helmholtz.py
|
ignacia-fp/bempp-cl
|
a65232558826e51e624b1a4f649b6a0ed5a7f551
|
[
"MIT"
] | null | null | null |
bempp/api/operators/boundary/helmholtz.py
|
ignacia-fp/bempp-cl
|
a65232558826e51e624b1a4f649b6a0ed5a7f551
|
[
"MIT"
] | null | null | null |
"""Interfaces to Helmholtz operators."""
import numpy as _np
from bempp.api.operators.boundary import common as _common
from bempp.api.assembly.boundary_operator import BoundaryOperator as _BoundaryOperator
def single_layer(
domain,
range_,
dual_to_range,
wavenumber,
parameters=None,
assembler="default_nonlocal",
device_interface=None,
precision=None,
):
"""Assemble the Helmholtz single-layer boundary operator."""
from .modified_helmholtz import single_layer as _modified_single_layer
if _np.real(wavenumber) == 0:
return _modified_single_layer(
domain,
range_,
dual_to_range,
_np.imag(wavenumber),
parameters,
assembler,
device_interface,
precision,
)
return _common.create_operator(
"helmholtz_single_layer_boundary",
domain,
range_,
dual_to_range,
parameters,
assembler,
[_np.real(wavenumber), _np.imag(wavenumber)],
"helmholtz_single_layer",
"default_scalar",
device_interface,
precision,
True,
)
def double_layer(
domain,
range_,
dual_to_range,
wavenumber,
parameters=None,
assembler="default_nonlocal",
device_interface=None,
precision=None,
):
"""Assemble the Helmholtz double-layer boundary operator."""
from .modified_helmholtz import double_layer as _modified_double_layer
if _np.real(wavenumber) == 0:
return _modified_double_layer(
domain,
range_,
dual_to_range,
_np.imag(wavenumber),
parameters,
assembler,
device_interface,
precision,
)
return _common.create_operator(
"helmholtz_double_layer_boundary",
domain,
range_,
dual_to_range,
parameters,
assembler,
[_np.real(wavenumber), _np.imag(wavenumber)],
"helmholtz_double_layer",
"default_scalar",
device_interface,
precision,
True,
)
def adjoint_double_layer(
domain,
range_,
dual_to_range,
wavenumber,
parameters=None,
assembler="default_nonlocal",
device_interface=None,
precision=None,
):
"""Assemble the Helmholtz adj. double-layer boundary operator."""
from .modified_helmholtz import (
adjoint_double_layer as _modified_adjoint_double_layer,
)
if _np.real(wavenumber) == 0:
return _modified_adjoint_double_layer(
domain,
range_,
dual_to_range,
_np.imag(wavenumber),
parameters,
assembler,
device_interface,
precision,
)
return _common.create_operator(
"helmholtz_adjoint_double_layer_boundary",
domain,
range_,
dual_to_range,
parameters,
assembler,
[_np.real(wavenumber), _np.imag(wavenumber)],
"helmholtz_adjoint_double_layer",
"default_scalar",
device_interface,
precision,
True,
)
def hypersingular(
domain,
range_,
dual_to_range,
wavenumber,
parameters=None,
assembler="default_nonlocal",
device_interface=None,
precision=None,
):
"""Assemble the Helmholtz hypersingular boundary operator."""
from .modified_helmholtz import hypersingular as _hypersingular
if domain.shapeset.identifier != "p1_discontinuous":
raise ValueError("Domain shapeset must be of type 'p1_discontinuous'.")
if dual_to_range.shapeset.identifier != "p1_discontinuous":
raise ValueError("Dual to range shapeset must be of type 'p1_discontinuous'.")
if _np.real(wavenumber) == 0:
return _hypersingular(
domain,
range_,
dual_to_range,
_np.imag(wavenumber),
parameters,
assembler,
device_interface,
precision,
)
return _common.create_operator(
"helmholtz_hypersingular_boundary",
domain,
range_,
dual_to_range,
parameters,
assembler,
[_np.real(wavenumber), _np.imag(wavenumber)],
"helmholtz_single_layer",
"helmholtz_hypersingular",
device_interface,
precision,
True,
)
def multitrace_operator(
grid,
wavenumber,
target=None,
space_type="p1",
parameters=None,
assembler="default_nonlocal",
device_interface=None,
precision=None,
):
"""
Simplified version of multitrace operator assembly.
Parameters
----------
grid : Grid
Bempp grid object.
wavenumber : complex
A real or complex wavenumber
target : Grid
The grid for the range spaces. If target is None then
target is set to the input grid (that is the domain
grid).
space_type : string
Currently only "p1" is supported, which means
that the operator is discretised with all P1 basis
functions.
parameters : Parameters
An optional parameters object.
assembler : string
The assembler type.
device_interface : DeviceInterface
The device interface object to be used.
precision : string
Either "single" or "double" for single or
double precision mode.
Output
------
The Helmholtz multitrace operator of the form
[[-dlp, slp], [hyp, adj_dlp]], where
dlp : double layer boundary operator
slp : single layer boundary operator
hyp : hypersingular boundary operator
adj_dlp : adjoint double layer boundary operator.
"""
import bempp.api
from bempp.api.assembly.blocked_operator import BlockedOperator
space = bempp.api.function_space(grid, "P", 1)
if target is not None:
target_space = bempp.api.function_space(target, "P", 1)
else:
target_space = space
slp = single_layer(
space,
target_space,
target_space,
wavenumber,
parameters=parameters,
assembler=assembler,
device_interface=device_interface,
precision=precision,
)
dlp = double_layer(
space,
target_space,
target_space,
wavenumber,
parameters=parameters,
assembler=assembler,
device_interface=device_interface,
precision=precision,
)
hyp = hypersingular(
space,
target_space,
target_space,
wavenumber,
parameters=parameters,
assembler=assembler,
device_interface=device_interface,
precision=precision,
)
adj_dlp = adjoint_double_layer(
space,
target_space,
target_space,
wavenumber,
parameters=parameters,
assembler=assembler,
device_interface=device_interface,
precision=precision,
)
blocked = BlockedOperator(2, 2)
blocked[0, 0] = -dlp
blocked[0, 1] = slp
blocked[1, 0] = hyp
blocked[1, 1] = adj_dlp
return blocked
def osrc_dtn(
space,
wavenumber,
npade=2,
theta=_np.pi / 3.0,
damped_wavenumber=None,
parameters=None,
device_interface=None,
precision=None,
):
"""Assemble the OSRC approximation to the DtN operator."""
if space.shapeset.identifier != "p1_discontinuous":
raise ValueError("Space shapeset must be of type 'p1_discontinuous'.")
return _OsrcDtN(
space,
parameters,
[wavenumber, npade, theta, damped_wavenumber],
device_interface,
precision,
)
class _OsrcDtN(_BoundaryOperator):
"""Implementation of the OSRC DtN operator."""
def __init__(
self, space, parameters, operator_options, device_interface=None, precision=None
):
from bempp.api.operators import OperatorDescriptor
super().__init__(space, space, space, parameters)
self._device_interface = device_interface
self._operator_descriptor = OperatorDescriptor(
"osrc_dtn",
operator_options,
"laplace_beltrami",
"default_sparse",
precision,
True,
None,
1,
)
@property
def descriptor(self):
"""Operator descriptor."""
return self._operator_descriptor
def _assemble(self):
"""Assemble the operator."""
from bempp.api.operators.boundary.sparse import identity
from bempp.api.operators.boundary.sparse import laplace_beltrami
from bempp.api.assembly.discrete_boundary_operator import (
InverseSparseDiscreteBoundaryOperator,
)
space = self._domain
wavenumber, npade, theta, damped_wavenumber = self.descriptor.options
mass = identity(
space,
space,
space,
self._parameters,
self._device_interface,
self.descriptor.precision,
).weak_form()
stiff = laplace_beltrami(
space,
space,
space,
self._parameters,
self._device_interface,
self.descriptor.precision,
).weak_form()
if damped_wavenumber is None:
bbox = space.grid.bounding_box
rad = _np.linalg.norm(bbox[:, 1] - bbox[:, 0]) / 2.0
dk = wavenumber + 0.4j * wavenumber ** (1.0 / 3.0) * rad ** (-2.0 / 3.0)
else:
dk = damped_wavenumber
c0, alpha, beta = _pade_coeffs(npade, theta)
series = c0 * mass
for i in range(npade):
element = (
alpha[i]
/ (dk ** 2)
* stiff
* InverseSparseDiscreteBoundaryOperator(
mass - beta[i] / (dk ** 2) * stiff
)
)
series -= element * mass
operator = 1.0j * wavenumber * series
return operator
def osrc_ntd(
space,
wavenumber,
npade=2,
theta=_np.pi / 3.0,
damped_wavenumber=None,
parameters=None,
device_interface=None,
precision=None,
):
"""Assemble the OSRC approximation to the NtD operator."""
if space.shapeset.identifier != "p1_discontinuous":
raise ValueError("Space shapeset must be of type 'p1_discontinuous'.")
return _OsrcNtD(
space,
parameters,
[wavenumber, npade, theta, damped_wavenumber],
device_interface,
precision,
)
class _OsrcNtD(_BoundaryOperator):
"""Implementation of the OSRC NtD operator."""
def __init__(
self, space, parameters, operator_options, device_interface=None, precision=None
):
from bempp.api.operators import OperatorDescriptor
super().__init__(space, space, space, parameters)
self._device_interface = device_interface
self._operator_descriptor = OperatorDescriptor(
"osrc_ntd",
operator_options,
"laplace_beltrami",
"default_sparse",
precision,
True,
None,
1,
)
@property
def descriptor(self):
"""Operator descriptor."""
return self._operator_descriptor
def _assemble(self):
from bempp.api.operators.boundary.sparse import identity
from bempp.api.operators.boundary.sparse import laplace_beltrami
from bempp.api.assembly.discrete_boundary_operator import (
InverseSparseDiscreteBoundaryOperator,
)
space = self._domain
wavenumber, npade, theta, damped_wavenumber = self.descriptor.options
mass = identity(
space,
space,
space,
self._parameters,
self._device_interface,
self.descriptor.precision,
).weak_form()
stiff = laplace_beltrami(
space,
space,
space,
self._parameters,
self._device_interface,
self.descriptor.precision,
).weak_form()
if damped_wavenumber is None:
bbox = space.grid.bounding_box
rad = _np.linalg.norm(bbox[:, 1] - bbox[:, 0]) / 2.0
dk = wavenumber + 0.4j * wavenumber ** (1.0 / 3.0) * rad ** (-2.0 / 3.0)
else:
dk = damped_wavenumber
c0, alpha, beta = _pade_coeffs(npade, theta)
series = c0 * mass
for i in range(npade):
element = (
alpha[i]
/ (dk ** 2)
* stiff
* InverseSparseDiscreteBoundaryOperator(
mass - beta[i] / (dk ** 2) * stiff
)
)
series -= element * mass
operator = (
1.0
/ (1.0j * wavenumber)
* (
mass
* InverseSparseDiscreteBoundaryOperator(mass - 1.0 / (dk ** 2) * stiff)
* series
)
)
return operator
def _pade_coeffs(n, theta):
"""Compute the coefficients of the Pade series expansion."""
aj = _np.zeros(n)
bj = _np.zeros(n)
for jj in range(1, n + 1):
aj[jj - 1] = 2.0 / (2.0 * n + 1.0) * _np.sin(jj * _np.pi / (2.0 * n + 1.0)) ** 2
bj[jj - 1] = _np.cos(jj * _np.pi / (2.0 * n + 1.0)) ** 2
c0t = _np.exp(1.0j * theta / 2.0) * (
1.0
+ _np.sum(
(aj * (_np.exp(-1j * theta) - 1.0))
/ (1.0 + bj * (_np.exp(-1.0j * theta) - 1.0))
)
)
ajt = (
_np.exp(-1.0j * theta / 2.0)
* aj
/ ((1.0 + bj * (_np.exp(-1.0j * theta) - 1.0)) ** 2)
)
bjt = _np.exp(-1.0j * theta) * bj / (1.0 + bj * (_np.exp(-1.0j * theta) - 1.0))
return c0t, ajt, bjt
| 27.248106
| 89
| 0.558004
| 1,387
| 14,387
| 5.570296
| 0.120404
| 0.071835
| 0.019933
| 0.026404
| 0.775045
| 0.743334
| 0.719001
| 0.707352
| 0.671369
| 0.642635
| 0
| 0.014669
| 0.355599
| 14,387
| 527
| 90
| 27.29981
| 0.818682
| 0.107319
| 0
| 0.741463
| 0
| 0
| 0.060033
| 0.020809
| 0
| 0
| 0
| 0
| 0
| 1
| 0.034146
| false
| 0
| 0.041463
| 0
| 0.119512
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
abba8252e2e03f0e7b2717493d6b63c946d04104
| 89
|
py
|
Python
|
06/00/copy.py
|
pylangstudy/201708
|
126b1af96a1d1f57522d5a1d435b58597bea2e57
|
[
"CC0-1.0"
] | null | null | null |
06/00/copy.py
|
pylangstudy/201708
|
126b1af96a1d1f57522d5a1d435b58597bea2e57
|
[
"CC0-1.0"
] | 39
|
2017-07-31T22:54:01.000Z
|
2017-08-31T00:19:03.000Z
|
06/00/copy.py
|
pylangstudy/201708
|
126b1af96a1d1f57522d5a1d435b58597bea2e57
|
[
"CC0-1.0"
] | null | null | null |
d = {'k1':'v1', 'k2':'v2'}; print(d)
e = d.copy(); print(e)
d['k1'] = 'v111'
print(d, e)
| 17.8
| 36
| 0.460674
| 18
| 89
| 2.277778
| 0.5
| 0.146341
| 0.341463
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106667
| 0.157303
| 89
| 4
| 37
| 22.25
| 0.44
| 0
| 0
| 0
| 0
| 0
| 0.157303
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.75
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
2803c393d2ff42532fe3d0e98f5509dcae7fcf79
| 172
|
py
|
Python
|
Models/__init__.py
|
pythonhubpy/ProDCube-Backend
|
7a8313027149be7e443f9be999ca4f6bd8c737ee
|
[
"BSD-3-Clause"
] | null | null | null |
Models/__init__.py
|
pythonhubpy/ProDCube-Backend
|
7a8313027149be7e443f9be999ca4f6bd8c737ee
|
[
"BSD-3-Clause"
] | 6
|
2022-02-21T15:36:18.000Z
|
2022-03-05T18:17:00.000Z
|
Models/__init__.py
|
pythonhubpy/ProDCube-Backend
|
7a8313027149be7e443f9be999ca4f6bd8c737ee
|
[
"BSD-3-Clause"
] | null | null | null |
from .BlogModel import BlogModel
from .ContactUsModel import ContactUsModel
from .InternalUserModel import InternalUserModel
from .SubscribersModel import SubscribersModel
| 34.4
| 48
| 0.883721
| 16
| 172
| 9.5
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 172
| 4
| 49
| 43
| 0.974359
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e6060255c4758284cfc01858b45c5dc21fc80bbe
| 1,829
|
py
|
Python
|
elib_config/__init__.py
|
etcher-be/elib_config
|
ec7e06cbf53de608b4071a91de108d15a09c8cc4
|
[
"MIT"
] | null | null | null |
elib_config/__init__.py
|
etcher-be/elib_config
|
ec7e06cbf53de608b4071a91de108d15a09c8cc4
|
[
"MIT"
] | 47
|
2018-10-15T12:18:50.000Z
|
2019-11-12T12:17:06.000Z
|
elib_config/__init__.py
|
etcher-be/elib_config
|
ec7e06cbf53de608b4071a91de108d15a09c8cc4
|
[
"MIT"
] | null | null | null |
# coding=utf-8
"""
This package manages configuration for other packages.
It is intended for my personal use only
"""
from pkg_resources import DistributionNotFound, get_distribution
# noinspection PyProtectedMember
from elib_config._file._exc import (
ConfigFileNotFoundError, EmptyValueError, IncompleteSetupError,
)
# noinspection PyProtectedMember
from elib_config._types import Types
# noinspection PyProtectedMember
from elib_config._value._exc import (
ConfigMissingValueError, ConfigValueError, ConfigValueTypeError, DuplicateConfigValueError, MissingTableKeyError,
MissingValueError, NotAFileError, NotAFolderError, OutOfBoundError, PathMustExistError, TableKeyTypeError,
)
# noinspection PyProtectedMember
from ._file._config_example import write_example_config
# noinspection PyProtectedMember
from ._logging import LOGGER
from ._setup import ELIBConfig
from ._validate import validate_config
# noinspection PyProtectedMember
from ._value._config_value import SENTINEL
# noinspection PyProtectedMember
from ._value._config_value_bool import ConfigValueBool
# noinspection PyProtectedMember
from ._value._config_value_float import ConfigValueFloat
# noinspection PyProtectedMember
from ._value._config_value_integer import ConfigValueInteger
# noinspection PyProtectedMember
from ._value._config_value_list import ConfigValueList
# noinspection PyProtectedMember
from ._value._config_value_path import ConfigValuePath
# noinspection PyProtectedMember
from ._value._config_value_string import ConfigValueString
# noinspection PyProtectedMember
from ._value._config_value_table import ConfigValueTableArray, ConfigValueTableKey
try:
__version__ = get_distribution('elib_config').version
except DistributionNotFound: # pragma: no cover
# package is not installed
__version__ = 'not installed'
| 37.326531
| 117
| 0.851832
| 179
| 1,829
| 8.368715
| 0.430168
| 0.251669
| 0.286382
| 0.202937
| 0.347797
| 0.261682
| 0
| 0
| 0
| 0
| 0
| 0.000609
| 0.102788
| 1,829
| 48
| 118
| 38.104167
| 0.912249
| 0.302898
| 0
| 0
| 0
| 0
| 0.019169
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.64
| 0
| 0.64
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e65911bc6c6f3da287a2a712585294ac0a63760a
| 96
|
py
|
Python
|
app/web/__init__.py
|
JordanDekker/ContainR
|
e88587dd1ff69a7fc3c1477298e1f37607a4afcb
|
[
"MIT"
] | null | null | null |
app/web/__init__.py
|
JordanDekker/ContainR
|
e88587dd1ff69a7fc3c1477298e1f37607a4afcb
|
[
"MIT"
] | null | null | null |
app/web/__init__.py
|
JordanDekker/ContainR
|
e88587dd1ff69a7fc3c1477298e1f37607a4afcb
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
bp = Blueprint('web', __name__)
from app.web import routes, forms
| 16
| 33
| 0.760417
| 14
| 96
| 4.928571
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15625
| 96
| 5
| 34
| 19.2
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0.03125
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 5
|
e66358352e9e259bafc626bbb3b6b531fcfe3ce1
| 2,686
|
py
|
Python
|
src/use_facenet_compare_pics.py
|
zhangbo2008/facenet
|
4dfabcb5cf14f99622dbe5f9f12f0539821c169c
|
[
"MIT"
] | null | null | null |
src/use_facenet_compare_pics.py
|
zhangbo2008/facenet
|
4dfabcb5cf14f99622dbe5f9f12f0539821c169c
|
[
"MIT"
] | 7
|
2019-12-16T22:10:01.000Z
|
2022-02-10T00:27:35.000Z
|
src/use_facenet_compare_pics.py
|
zhangbo2008/facenet
|
4dfabcb5cf14f99622dbe5f9f12f0539821c169c
|
[
"MIT"
] | null | null | null |
'''
这个文件调用compare.py
来实现,输入图片,调用别人配置号的参数来计算几个图片之间的距离.如果距离越小说明越接近.
'''
import compare
'''
#下面3行是该路径的写法
base_img_path=os.path.abspath(os.path.join(os.getcwd(), ".."))
base_img_path=os.path.abspath(os.path.join(base_img_path, ".."))
base_img_path=os.path.abspath(os.path.join(base_img_path, "data"))
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model', type=str,
help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('image_files', type=str, nargs='+', help='Images to compare')
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
return parser.parse_args(argv)
'''
import argparse
import os
import sys
a=os.path.abspath(os.path.join(os.getcwd(), '..','data','images','Anthony_Hopkins_0001.jpg'))
b=os.path.abspath(os.path.join(os.getcwd(), '..','data','images','Anthony_Hopkins_0002.jpg'))
c=os.path.abspath(os.path.join(os.getcwd(), '..','data','images','Anthony_Hopkins_0002.jpg'))
print(a)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
#the given name for arguments must be fronted by --or-
parser.add_argument('-model', type=str,
help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('-image_files', type=str, nargs='+', help='Images to compare')
parser.add_argument('-image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('-margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('-gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
return parser.parse_args(argv)
print(a)
print(b)
#把use_facenet_compare_pics.py作为一个函数封装起来.要运行就main()即可.
def main():
return compare.main(
(
parse_arguments(#arguments below
['-model','models','-image_files',a,b,'-image_size','160']
)
)
)
#a=main()
#print()
#print(a)
#print(type(a))
#print(type(main()))
print(main()) #返回得到的是所有的距离矩阵.
| 36.794521
| 113
| 0.667163
| 377
| 2,686
| 4.625995
| 0.278515
| 0.041284
| 0.097477
| 0.051606
| 0.784977
| 0.784977
| 0.784977
| 0.728784
| 0.724197
| 0.704702
| 0
| 0.013643
| 0.181311
| 2,686
| 72
| 114
| 37.305556
| 0.779445
| 0.095309
| 0
| 0.064516
| 0
| 0
| 0.350494
| 0.050776
| 0
| 0
| 0
| 0
| 0
| 1
| 0.064516
| false
| 0
| 0.129032
| 0.032258
| 0.258065
| 0.129032
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e6655e4ddbd0dbdd7c1a7beabea2027c23f43db0
| 4,411
|
py
|
Python
|
undine/server/driver/base_driver.py
|
Sungup/Undine
|
8b130b86bab8ae2a1662191d3352ea11987429da
|
[
"MIT"
] | 1
|
2018-01-01T07:50:04.000Z
|
2018-01-01T07:50:04.000Z
|
undine/server/driver/base_driver.py
|
Sungup/Undine
|
8b130b86bab8ae2a1662191d3352ea11987429da
|
[
"MIT"
] | null | null | null |
undine/server/driver/base_driver.py
|
Sungup/Undine
|
8b130b86bab8ae2a1662191d3352ea11987429da
|
[
"MIT"
] | null | null | null |
from undine.database.rabbitmq import RabbitMQConnector
from undine.utils.exception import UndineException, VirtualMethodException
from undine.utils.system import print_console_header
from undine.utils.system import System
from undine.utils import logging
import json
class BaseDriver:
_DRIVER_LOGGER_NAME = 'undine-driver'
_DRIVER_LOGGER_PATH = '/tmp/{}.log'.format(_DRIVER_LOGGER_NAME)
_DRIVER_LOGGER_LEVEL = 'ERROR'
_DEFAULT_CONFIG_EXT = '.json'
_ERROR_LOG_START = print_console_header('Error Message Start', '=')
_ERROR_LOG_END = print_console_header('Error Message End', '=')
def __init__(self, config, config_dir):
self._config_dir = config_dir
self._config_ext = config.setdefault('config_ext',
self._DEFAULT_CONFIG_EXT)
# Create logger instance
log_path = config.setdefault('log_file', self._DRIVER_LOGGER_PATH)
log_level = config.setdefault('log_level', self._DRIVER_LOGGER_LEVEL)
self._logger = logging.get_logger(self._DRIVER_LOGGER_NAME,
log_path, log_level)
def _error_logging(self, title, body):
self._logger.error('{0}\n{2}\n{1}\n{3}\n'.format(title, body,
self._ERROR_LOG_START,
self._ERROR_LOG_END))
def fetch(self):
raise VirtualMethodException(self.__class__, 'fetch')
def config(self, _cid):
raise VirtualMethodException(self.__class__, 'config')
def worker(self, _wid):
raise VirtualMethodException(self.__class__, 'worker')
def inputs(self, _iid):
raise VirtualMethodException(self.__class__, 'input')
def preempt(self, _tid):
raise VirtualMethodException(self.__class__, 'preempt')
def done(self, _tid, _contents, _report):
raise VirtualMethodException(self.__class__, 'done')
def cancel(self, _tid):
raise VirtualMethodException(self.__class__, 'cancel')
def fail(self, _tid, _message):
raise VirtualMethodException(self.__class__, 'fail')
def is_ready(self):
raise VirtualMethodException(self.__class__, '_wait_others')
class BaseNetworkDriver(BaseDriver):
#
# Constructor & Destructor
#
def __init__(self, task_queue, config, config_dir):
BaseDriver.__init__(self, config, config_dir)
if task_queue is None:
raise UndineException('Missing RabbitMQ option field (task_queue)')
self._queue = RabbitMQConnector(task_queue)
self._host = System.host_info()
self._logged_in()
def __del__(self):
self._logged_out()
#
# Private method
#
def __params(self, tid, **kwargs):
return dict(tid=tid, host=self._host.name, ip=self._host.ipv4, **kwargs)
@property
def host(self):
return self._host
@property
def _ip(self):
return self._host.ipv4
@property
def _hostname(self):
return self._host.name
#
# Protected inherited interface
#
def _task(self, _tid):
raise VirtualMethodException(self.__class__, '_task')
def _preempt(self, _tid, _host, _ip):
raise VirtualMethodException(self.__class__, '_preempt')
def _done(self, _tid, _host, _ip, _content, _report):
raise VirtualMethodException(self.__class__, '_done')
def _cancel(self, _tid, _host, _ip):
raise VirtualMethodException(self.__class__, '_cancel')
def _fail(self, _tid, _host, _ip, _message):
raise VirtualMethodException(self.__class__, '_fail')
def _logged_in(self):
raise VirtualMethodException(self.__class__, '_logged_in')
def _logged_out(self):
raise VirtualMethodException(self.__class__, '_logged_out')
#
# Public interface
#
def fetch(self):
return self._task(json.loads(self._queue.consume())['tid'])
def preempt(self, tid):
return self._preempt(**self.__params(tid))
def done(self, tid, content, report):
return self._done(**self.__params(tid, content=content, report=report))
def cancel(self, tid):
return self._cancel(**self.__params(tid))
def fail(self, tid, message):
return self._fail(**self.__params(tid, message=message))
def is_ready(self):
return True
| 30.42069
| 80
| 0.652913
| 488
| 4,411
| 5.436475
| 0.209016
| 0.162835
| 0.186958
| 0.217113
| 0.328685
| 0.230305
| 0.176781
| 0.139088
| 0.131926
| 0.04674
| 0
| 0.001796
| 0.242802
| 4,411
| 144
| 81
| 30.631944
| 0.792515
| 0.024711
| 0
| 0.077778
| 0
| 0
| 0.062966
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.066667
| 0.111111
| 0.6
| 0.033333
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
05589757ba5b2520e3edc7b7213af70f2409c0c3
| 952
|
py
|
Python
|
test/test_edit_contact.py
|
l0myy/python_testing
|
9c51afb007b67fbaa3ed7fc3b326c01d3747f8eb
|
[
"Apache-2.0"
] | null | null | null |
test/test_edit_contact.py
|
l0myy/python_testing
|
9c51afb007b67fbaa3ed7fc3b326c01d3747f8eb
|
[
"Apache-2.0"
] | null | null | null |
test/test_edit_contact.py
|
l0myy/python_testing
|
9c51afb007b67fbaa3ed7fc3b326c01d3747f8eb
|
[
"Apache-2.0"
] | null | null | null |
from model.contacts import Contact
import random
def test_edit_contact_firstname(app, db):
if app.contact.count() == 0:
app.contact.create(Contact(firstname="test"))
old_contacts = db.get_contact_list()
contact = random.choice(old_contacts)
new_contact = Contact(firstname="my firstname")
app.contact.edit_contact_by_id(contact.id, new_contact)
new_contacts = db.get_contact_list()
assert len(old_contacts) == len(new_contacts)
def test_edit_contact_full(app, db):
if app.contact.count() == 0:
app.contact.create(Contact(firstname="test"))
old_contacts = db.get_contact_list()
contact = random.choice(old_contacts)
new_contact = Contact(firstname="new firstname", middlename="new middlename", lastname="new lastname", nickname="l0my")
app.contact.edit_contact_by_id(contact.id, new_contact)
new_contacts = db.get_contact_list()
assert len(old_contacts) == len(new_contacts)
| 35.259259
| 123
| 0.731092
| 131
| 952
| 5.053435
| 0.236641
| 0.090634
| 0.07855
| 0.120846
| 0.746224
| 0.746224
| 0.746224
| 0.746224
| 0.746224
| 0.746224
| 0
| 0.003708
| 0.15021
| 952
| 26
| 124
| 36.615385
| 0.814586
| 0
| 0
| 0.7
| 0
| 0
| 0.066316
| 0
| 0
| 0
| 0
| 0
| 0.1
| 1
| 0.1
| false
| 0
| 0.1
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
05877919ca7ea9b7ea52a116b7d7ef9716ce7aa6
| 209
|
py
|
Python
|
TableStorageInput/__init__.py
|
dongheeHwang/functions-docs-python
|
9a645bceaefb9a96f722ac7778d6c4ca750ef28a
|
[
"MIT"
] | 3
|
2021-08-12T07:58:38.000Z
|
2022-02-12T21:28:06.000Z
|
TableStorageInput/__init__.py
|
dongheeHwang/functions-docs-python
|
9a645bceaefb9a96f722ac7778d6c4ca750ef28a
|
[
"MIT"
] | null | null | null |
TableStorageInput/__init__.py
|
dongheeHwang/functions-docs-python
|
9a645bceaefb9a96f722ac7778d6c4ca750ef28a
|
[
"MIT"
] | 9
|
2020-02-18T18:07:50.000Z
|
2022-02-10T10:51:01.000Z
|
import json
import azure.functions as func
def main(req: func.HttpRequest, messageJSON) -> func.HttpResponse:
message = json.loads(messageJSON)
return func.HttpResponse(f"Table row: {messageJSON}")
| 23.222222
| 66
| 0.751196
| 26
| 209
| 6.038462
| 0.692308
| 0.203822
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.143541
| 209
| 8
| 67
| 26.125
| 0.877095
| 0
| 0
| 0
| 0
| 0
| 0.114833
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
5536655aeb148df186032610d0f6f408f62f9e01
| 30
|
py
|
Python
|
aliyun-python-sdk-iot/aliyunsdkiot/__init__.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
aliyun-python-sdk-iot/aliyunsdkiot/__init__.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
aliyun-python-sdk-iot/aliyunsdkiot/__init__.py
|
ankitdobhal/aliyun-openapi-python-sdk
|
991b1c2d91adc468480defc23ba790d4369cce7b
|
[
"Apache-2.0"
] | null | null | null |
__version__ = '1.0.0-20210309'
| 30
| 30
| 0.733333
| 5
| 30
| 3.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.392857
| 0.066667
| 30
| 1
| 30
| 30
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0.451613
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
5573fbea42edb5f64f294533867dc645211277b0
| 61
|
py
|
Python
|
server/storage.py
|
WGreenlee04/cautious-sniffle
|
cc999b3bfcf762cfdeae984211648c2644383284
|
[
"MIT"
] | null | null | null |
server/storage.py
|
WGreenlee04/cautious-sniffle
|
cc999b3bfcf762cfdeae984211648c2644383284
|
[
"MIT"
] | null | null | null |
server/storage.py
|
WGreenlee04/cautious-sniffle
|
cc999b3bfcf762cfdeae984211648c2644383284
|
[
"MIT"
] | null | null | null |
class Player(object):
pass
class Game(object):
pass
| 10.166667
| 21
| 0.655738
| 8
| 61
| 5
| 0.625
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.245902
| 61
| 6
| 22
| 10.166667
| 0.869565
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
558fc0324e46c4d82f332a09588267aec22ec980
| 64
|
py
|
Python
|
src/data/__init__.py
|
jggomez/Python-Reference-Microservice
|
13723c5f5a205bf1f874c44dddbd4cab64839da7
|
[
"MIT"
] | 14
|
2020-07-09T22:31:09.000Z
|
2022-01-21T23:03:29.000Z
|
src/data/__init__.py
|
jggomez/Python-Reference-Microservice
|
13723c5f5a205bf1f874c44dddbd4cab64839da7
|
[
"MIT"
] | 1
|
2021-02-03T23:51:35.000Z
|
2021-02-03T23:51:35.000Z
|
src/data/__init__.py
|
jggomez/Python-Reference-Microservice
|
13723c5f5a205bf1f874c44dddbd4cab64839da7
|
[
"MIT"
] | 6
|
2020-07-10T04:07:11.000Z
|
2020-10-04T00:04:30.000Z
|
from .get_type_games_repository import GetAllTypeGamesRepository
| 64
| 64
| 0.9375
| 7
| 64
| 8.142857
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.046875
| 64
| 1
| 64
| 64
| 0.934426
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
55b09fc15752f7f3eee70610ba15bc60e1d86dde
| 104
|
py
|
Python
|
tplink_smarthome/__init__.py
|
rrauenza/python-tplink-smarthome
|
ddc33dcd25ebb57bf944ead015a5c7c6c0818b21
|
[
"MIT"
] | 7
|
2019-05-24T01:04:56.000Z
|
2021-09-11T15:07:49.000Z
|
tplink_smarthome/__init__.py
|
rrauenza/python-tplink-smarthome
|
ddc33dcd25ebb57bf944ead015a5c7c6c0818b21
|
[
"MIT"
] | 3
|
2019-07-10T00:55:02.000Z
|
2020-04-29T19:14:43.000Z
|
tplink_smarthome/__init__.py
|
rrauenza/python-tplink-smarthome
|
ddc33dcd25ebb57bf944ead015a5c7c6c0818b21
|
[
"MIT"
] | 2
|
2019-04-26T15:52:41.000Z
|
2020-04-29T16:59:39.000Z
|
# pylint: disable=unused-import
from .plug import TPLinkSmartPlug
from .device import TPLinkSmartDevice
| 26
| 37
| 0.836538
| 12
| 104
| 7.25
| 0.75
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| 0
| 0
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| 0.105769
| 104
| 3
| 38
| 34.666667
| 0.935484
| 0.278846
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| 1
| 0
| 1
| 0
|
0
| 5
|
e9a1ecf4fac9f1b2cf39e584c353e5c7cb9a9f73
| 88
|
py
|
Python
|
fountains/__init__.py
|
reity/fountains
|
c9decefb7c19d2bedd0f2d577400d9840ddcc85a
|
[
"MIT"
] | null | null | null |
fountains/__init__.py
|
reity/fountains
|
c9decefb7c19d2bedd0f2d577400d9840ddcc85a
|
[
"MIT"
] | null | null | null |
fountains/__init__.py
|
reity/fountains
|
c9decefb7c19d2bedd0f2d577400d9840ddcc85a
|
[
"MIT"
] | null | null | null |
"""Gives users direct access to the class."""
from fountains.fountains import fountains
| 29.333333
| 45
| 0.784091
| 12
| 88
| 5.75
| 0.833333
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| 0
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| 0.125
| 88
| 2
| 46
| 44
| 0.896104
| 0.443182
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 5
|
75671bf9a027c394a47c8751f70b2b1d12c197cd
| 3,043
|
py
|
Python
|
format.py
|
henrieger/wikipedia-bot
|
20d7e87a148e893c4688956708dedaffa1725583
|
[
"MIT"
] | null | null | null |
format.py
|
henrieger/wikipedia-bot
|
20d7e87a148e893c4688956708dedaffa1725583
|
[
"MIT"
] | null | null | null |
format.py
|
henrieger/wikipedia-bot
|
20d7e87a148e893c4688956708dedaffa1725583
|
[
"MIT"
] | null | null | null |
import re
# Format first paragraph to suitable HTML
def to_html(title: str, content: str):
# Remove unusable tags
new_content = re.sub(r'\<\/?p\>', '', content)
new_content = re.sub(r'\<\/?dfn.*?\>', '', new_content)
new_content = re.sub(r'\<\/?img.*?\>', '', new_content)
new_content = re.sub(r'\<\/?span.*?\>', '', new_content)
new_content = re.sub(r'\<\/?small.*?\>', '', new_content)
new_content = re.sub(r'\<\/?abbr.*?\>', '', new_content)
new_content = new_content.replace('<br/>', '')
# Remove <sub></sub> and <sup></sup>tags
new_content = re.sub(r'\<sup.*?\>.*?\<\/sup\>', '', new_content)
new_content = re.sub(r'\<sub.*?\>.*?\<\/sub\>', '', new_content)
return f"<b>{title}</b>\n\n{new_content}"
# Format first paragraph to plain text
def to_text(title: str, content: str):
new_content = re.sub(r'\<sup .*?\>.*?\<\/sup\>', '', content)
new_content = re.sub(r'\<sub .*?\>.*?\<\/sub\>', '', content)
new_content = re.sub(r'\<\/?.*?\>', '', new_content)
return f"{title}\n\n{new_content}"
# Format first pargraph to Markdown (no links)
def to_simple_md(title: str, content: str):
title = '**' + title + '**'
# Remove <p></p>, <dfn></dfn>, <a></a> and <span></span> tags
new_content = re.sub(r'\<\/?p\>', '', content)
new_content = re.sub(r'\<\/?dfn.*?\>', '', new_content)
new_content = re.sub(r'\<\/?small.*?\>', '', new_content)
new_content = re.sub(r'\<\/?span.*?\>', '', new_content)
new_content = re.sub(r'\<a .*?\>', '', new_content)
new_content = new_content.replace('</a>', '')
# Remove <sub></sub> and <sup></sup>tags
new_content = re.sub(r'\<sup.*?\>.*?\<\/sup\>', '', new_content)
new_content = re.sub(r'\<sub.*?\>.*?\<\/sub\>', '', new_content)
# Escape '.' chars
new_content = new_content.replace('.', '\\.')
# Change bold and italic to Markdown format
new_content = re.sub(r'\<\/?i.*?\>', '*', new_content)
new_content = re.sub(r'\<\/?b.*?\>', '**', new_content)
return f"{title}\n\n{new_content}"
# Format first paragraph to Markdown
def to_markdown(title: str, content: str):
title = '**' + title + '**'
# Remove <p></p>, <dfn></dfn> and <span></span> tags
new_content = re.sub(r'\<\/?p\>', '', content)
new_content = re.sub(r'\<\/?dfn.*?\>', '', new_content)
new_content = re.sub(r'\<\/?span.*?\>', '', new_content)
# Escape '.' chars
new_content = new_content.replace('.', '\\.')
# Change bold and italic to Markdown format
new_content = re.sub(r'\<\/?i.*?\>', '*', new_content)
new_content = re.sub(r'\<\/?b.*?\>', '**', new_content)
# Change links to Markdown format
for a in re.findall(r'\<a .*?\>.*?\<\/a\>', new_content):
link = re.findall(r'href\=\".+?\"', a)[0].split('"')[1]
new_link = re.sub(r'\<a .*?\>', '[', a)
new_link = new_link.replace('</a>', ']')
new_link = f"{new_link}({link})"
new_content = new_content.replace(a, new_link)
return f"{title}\n\n{new_content}"
| 40.573333
| 68
| 0.546829
| 417
| 3,043
| 3.820144
| 0.131894
| 0.376648
| 0.097928
| 0.235405
| 0.781544
| 0.781544
| 0.748901
| 0.673572
| 0.627119
| 0.627119
| 0
| 0.0008
| 0.178114
| 3,043
| 75
| 69
| 40.573333
| 0.636146
| 0.16957
| 0
| 0.565217
| 0
| 0
| 0.222443
| 0.076005
| 0
| 0
| 0
| 0
| 0
| 1
| 0.086957
| false
| 0
| 0.021739
| 0
| 0.195652
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
756f3fa5bc5785cc44e96058dff406b747cf00dd
| 223
|
py
|
Python
|
libraries/python/template/{{cookiecutter.package_name}}/tests/test_sample.py
|
tvlfyi/ghuntley
|
8bf49dd414b93185a5eeb9981c52051b2d7b33db
|
[
"BlueOak-1.0.0",
"Apache-2.0"
] | 48
|
2018-10-07T05:01:50.000Z
|
2022-03-08T07:58:51.000Z
|
libraries/python/template/{{cookiecutter.package_name}}/tests/test_sample.py
|
BenjaminAbt/ghuntley
|
d614f88a2c900101aa2d6097328f3f8bc8e5fc44
|
[
"BlueOak-1.0.0",
"Apache-2.0"
] | 61
|
2018-07-15T03:15:25.000Z
|
2022-02-27T11:36:20.000Z
|
libraries/python/template/{{cookiecutter.package_name}}/tests/test_sample.py
|
BenjaminAbt/ghuntley
|
d614f88a2c900101aa2d6097328f3f8bc8e5fc44
|
[
"BlueOak-1.0.0",
"Apache-2.0"
] | 15
|
2017-07-07T05:14:19.000Z
|
2022-03-25T08:35:21.000Z
|
# Copyright (c) 2020 Geoffrey Huntley <ghuntley@ghuntley.com>. All rights reserved.
# SPDX-License-Identifier: Proprietary
# Sample Test passing with nose and pytest
def test_pass():
assert True, "dummy sample test"
| 24.777778
| 83
| 0.753363
| 30
| 223
| 5.566667
| 0.866667
| 0.11976
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021277
| 0.156951
| 223
| 8
| 84
| 27.875
| 0.867021
| 0.713004
| 0
| 0
| 0
| 0
| 0.283333
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
7592c39f1988649c09b526b9c03e60437ec34398
| 182
|
py
|
Python
|
canary/tasks/base/__init__.py
|
rackerlabs/canary
|
22ea0380d2c69ab587f21099320ae62fa3281349
|
[
"Apache-2.0"
] | 5
|
2016-07-14T02:11:46.000Z
|
2017-09-15T21:50:57.000Z
|
canary/tasks/base/__init__.py
|
rackerlabs/canary
|
22ea0380d2c69ab587f21099320ae62fa3281349
|
[
"Apache-2.0"
] | 5
|
2015-05-29T18:15:24.000Z
|
2016-02-16T15:31:04.000Z
|
canary/tasks/base/__init__.py
|
rackerlabs/canary
|
22ea0380d2c69ab587f21099320ae62fa3281349
|
[
"Apache-2.0"
] | 10
|
2015-05-28T13:56:01.000Z
|
2020-10-25T02:17:14.000Z
|
from canary.tasks.base import driver
from canary.tasks.base import services
Driver = driver.DistributedTaskDriverBase
DistributedTaskServices = services.DistributedTaskServicesBase
| 30.333333
| 62
| 0.873626
| 18
| 182
| 8.833333
| 0.555556
| 0.125786
| 0.188679
| 0.238994
| 0.314465
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.082418
| 182
| 6
| 62
| 30.333333
| 0.952096
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
| 0
| false
| 0
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| null | 0
| 1
| 1
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
75b902832033cfc99196cc80f6eb138872c6addf
| 724
|
py
|
Python
|
datapipelines/__init__.py
|
meraki-analytics/datapipelines-python
|
dc38d7976a012039a15d67cd8b07ae77eb1e4a4c
|
[
"MIT"
] | 6
|
2018-07-27T16:16:55.000Z
|
2022-03-07T17:12:15.000Z
|
datapipelines/__init__.py
|
meraki-analytics/datapipelines
|
dc38d7976a012039a15d67cd8b07ae77eb1e4a4c
|
[
"MIT"
] | null | null | null |
datapipelines/__init__.py
|
meraki-analytics/datapipelines
|
dc38d7976a012039a15d67cd8b07ae77eb1e4a4c
|
[
"MIT"
] | 1
|
2016-10-20T11:54:20.000Z
|
2016-10-20T11:54:20.000Z
|
from .common import PipelineContext, UnsupportedError, NotFoundError, TYPE_WILDCARD
from .pipelines import DataPipeline, NoConversionError
from .queries import Query, QueryValidationError, QueryValidatorStructureError, validate_query
from .sinks import DataSink, CompositeDataSink
from .sources import DataSource, CompositeDataSource
from .transformers import DataTransformer, CompositeDataTransformer
__all__ = ["DataTransformer", "CompositeDataTransformer", "DataPipeline", "NoConversionError", "Query", "QueryValidationError", "QueryValidatorStructureError", "validate_query", "DataSource", "CompositeDataSource", "DataSink", "CompositeDataSink", "PipelineContext", "UnsupportedError", "NotFoundError", "TYPE_WILDCARD"]
| 80.444444
| 320
| 0.835635
| 55
| 724
| 10.854545
| 0.472727
| 0.103853
| 0.147404
| 0.160804
| 0.40871
| 0
| 0
| 0
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| 0
| 0.073204
| 724
| 8
| 321
| 90.5
| 0.889717
| 0
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| 0.339779
| 0.071823
| 0
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| 0
| false
| 0
| 0.857143
| 0
| 0.857143
| 0
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| 1
| null | 0
| 0
| 1
| 0
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| 0
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| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
f93499f51ece5849f56c8b9f5bd479fa76c7df10
| 6,328
|
py
|
Python
|
create_input.py
|
dyth/Carassius
|
1fad8c89ee7cf64815b84b7fc102309224c5c302
|
[
"MIT"
] | 1
|
2020-04-03T12:40:30.000Z
|
2020-04-03T12:40:30.000Z
|
create_input.py
|
dyth/Carassius
|
1fad8c89ee7cf64815b84b7fc102309224c5c302
|
[
"MIT"
] | null | null | null |
create_input.py
|
dyth/Carassius
|
1fad8c89ee7cf64815b84b7fc102309224c5c302
|
[
"MIT"
] | 1
|
2018-11-21T21:18:48.000Z
|
2018-11-21T21:18:48.000Z
|
#!/usr/bin/env python
"""
determine whether bitboard, piece-list coordinate or mailbox is best
bitboard (12, 8, 8): third order tensor
bitboard_vector (768): flattened third order tensor
small_bitboard_vector (384): with -1.0 values for black
piece_list (384): normalised rank, file, 8-rank and file for pieces in list
small_piece_list (192): normalised rank and file for pieces in list
mailbox (8, 8): normalised type of piece in its position in an 8 * 8 board
"""
import chess
import numpy as np
def fen_to_3D_bitboard(fen):
'convert fen to a 12 by 8 * 8 bitboard'
pos = chess.Board(fen)
bitboard = np.zeros((12, 8, 8))
# over all squares, get piece type and number
for r in range(8):
for f in range(8):
square = 8*r + f
index = pos.piece_type_at(square)
# if piece exists in square, if white, increment plane by 6
if index is not None:
piece = pos.piece_at(square).symbol()
offset = 0 if piece.istitle() else 6
bitboard[index + offset - 1, r, f] = 1.0
return bitboard
def fen_to_bitboard_vector(fen):
'convert fen to a 12 * 8 * 8 = 768 bitboard'
pos = chess.Board(fen)
bitboard = np.zeros((12, 8, 8))
# over all squares, get piece type and number
for r in range(8):
for f in range(8):
square = 8*r + f
index = pos.piece_type_at(square)
# if piece exists in square, if white, increment plane by 6
if index is not None:
piece = pos.piece_at(square).symbol()
offset = 0 if piece.istitle() else 6
bitboard[index + offset - 1, r, f] = 1.0
return bitboard.flatten()
def fen_to_small_bitboard_vector(fen):
'convert fen to a 6 * 8 * 8 = 384 bitboard'
pos = chess.Board(fen)
bitboard = np.zeros((6, 8, 8))
# over all squares, get piece type and number
for r in range(8):
for f in range(8):
square = 8*r + f
index = pos.piece_type_at(square)
# if piece exists in square, if white, increment else decrement
if index is not None:
piece = pos.piece_at(square).symbol()
player += 1.0 if piece.istitle() else -1.0
bitboard[index-1, r, f] = player
return bitboard.flatten()
def fen_to_piece_list(fen):
'convert fen to 384 piece list of coordinates'
pos = chess.Board(fen)
pieceList = np.zeros(384)
# {white, black} 8*Pawn, 10*Knight, 10*Bishop, 10*Rook, 9*Queen, 1*King
pieces = ['p', 'n', 'b', 'r', 'q', 'k', 'P', 'N', 'B', 'R', 'Q', 'K']
count = [8, 10, 10, 10, 9, 1, 8, 10, 10, 10, 9, 1]
# create {piece -> vectorposition} dictionary
vectorPosition = [4 * sum(count[:i]) for i in range(len(count))]
pieceIndex = dict(zip(pieces, vectorPosition))
# create pieceList and type
for f in range(8):
for r in range(8):
square = pos.piece_at(8*f + r)
# if piece exists in square, find first available index by adding 4
if square is not None:
index = pieceIndex[square.symbol()]
while pieceList[index] != 0.0:
index += 4
# set pieceList index to coordinate
pieceList[index] = (r+1.0) / 8.0
pieceList[index+1] = (8.0-r) / 8.0
pieceList[index+2] = (f+1.0) / 8.0
pieceList[index+3] = (8.0-f) / 8.0
return pieceList
def fen_to_small_piece_list(fen):
'convert fen to 192 piece list of coordinates'
pos = chess.Board(fen)
pieceList = np.zeros(192)
# {white, black} 8*Pawn, 10*Knight, 10*Bishop, 10*Rook, 9*Queen, 1*King
pieces = ['p', 'n', 'b', 'r', 'q', 'k', 'P', 'N', 'B', 'R', 'Q', 'K']
count = [8, 10, 10, 10, 9, 1, 8, 10, 10, 10, 9, 1]
# create {piece -> vectorposition} dictionary
vectorPosition = [2 * sum(count[:i]) for i in range(len(count))]
pieceIndex = dict(zip(pieces, vectorPosition))
# create pieceList and type
for f in range(8):
for r in range(8):
square = pos.piece_at(8*f + r)
# if piece exists in square, find first available index by adding 4
if square is not None:
index = pieceIndex[square.symbol()]
while pieceList[index] != 0.0:
index += 2
# set pieceList index to coordinate
pieceList[index] = (r+1.0) / 8.0
pieceList[index+1] = (f+1.0) / 8.0
return pieceList
def fen_to_mailbox(fen):
'convert fen to 8 * 8 matrix'
pos = chess.Board(fen)
bitboard = np.zeros((2, 8, 8))
# over all squares, get piece type and number
for r in range(8):
for f in range(8):
square = 8*r + f
index = pos.piece_type_at(square)
# if piece exists in square, if white, increment plane by 6
if index is not None:
piece = pos.piece_at(square).symbol()
offset = 0.0 if piece.istitle() else 6.0
encoding = (offset + index) / 12.0
bitboard[0, r, f] = encoding
bitboard[1, r, f] = 1.0 - encoding
return bitboard
def fen_to_mailbox_flat(fen):
'convert fen to 8 * 8 matrix'
pos = chess.Board(fen)
bitboard = np.zeros(2 * 8 * 8)
# over all squares, get piece type and number
for r in range(8):
for f in range(8):
square = 8*r + f
index = pos.piece_type_at(square)
# if piece exists in square, if white, increment plane by 6
if index is not None:
piece = pos.piece_at(square).symbol()
offset = 0.0 if piece.istitle() else 6.0
encoding = (offset + index) / 12.0
bitboard[2*square] = encoding
bitboard[2*square+1] = 1.0 - encoding
return bitboard
if __name__ == "__main__":
print(fen_to_bitboard_vector(chess.Board().fen()))
# print(fen_to_small_bitboard_vector(chess.Board().fen()))
# print(fen_to_piece_list(chess.Board().fen()))
# print(fen_to_small_piece_list(chess.Board().fen()))
# print(fen_to_mailbox(chess.Board().fen()))
# print(fen_to_mailbox_flat(chess.Board().fen()))
| 33.839572
| 79
| 0.567794
| 936
| 6,328
| 3.761752
| 0.132479
| 0.028401
| 0.031809
| 0.029821
| 0.84493
| 0.816529
| 0.779608
| 0.72025
| 0.66998
| 0.66998
| 0
| 0.052716
| 0.313527
| 6,328
| 186
| 80
| 34.021505
| 0.757827
| 0.311157
| 0
| 0.684685
| 0
| 0
| 0.063983
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.063063
| false
| 0
| 0.018018
| 0
| 0.144144
| 0.009009
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f969d6964aeb06ca1722be38e5e5aa130773911a
| 1,551
|
py
|
Python
|
action/lead_actions/case_one.py
|
obezludko/Subscription
|
beecd902459d47392d8113a67fa2108c85f66581
|
[
"MIT"
] | null | null | null |
action/lead_actions/case_one.py
|
obezludko/Subscription
|
beecd902459d47392d8113a67fa2108c85f66581
|
[
"MIT"
] | null | null | null |
action/lead_actions/case_one.py
|
obezludko/Subscription
|
beecd902459d47392d8113a67fa2108c85f66581
|
[
"MIT"
] | null | null | null |
import requests
from connection.connection_variables import link_for_lead
from parameters.lead.case_1.lead_params import *
def case_one_first_lead():
return requests.get(link_for_lead,case_1_first_lead_params)
def case_one_second_lead():
return requests.get(link_for_lead,case_1_second_lead_params)
def case_one_third_lead():
return requests.get(link_for_lead,case_1_third_lead_params)
def case_one_fourth_lead():
return requests.get(link_for_lead,case_1_fourth_lead_params)
def case_one_fifth_lead():
return requests.get(link_for_lead,case_1_fifth_lead_params)
def case_one_sixth_lead():
return requests.get(link_for_lead,case_1_sixth_lead_params)
def case_one_seventh_lead():
return requests.get(link_for_lead,case_1_seventh_lead_params)
def case_one_eighth_lead():
return requests.get(link_for_lead,case_1_eighth_lead_params)
def case_one_ninth_lead():
return requests.get(link_for_lead,case_1_ninth_lead_params)
def case_one_tenth_lead():
return requests.get(link_for_lead,case_1_tenth_lead_params)
def case_one_eleventh_lead():
return requests.get(link_for_lead,case_1_eleventh_lead_params)
def case_one_twelfth_lead():
return requests.get(link_for_lead,case_1_twelfth_lead_params)
def case_one_thirteenth_lead():
return requests.get(link_for_lead,case_1_thirteenth_lead_params)
def case_one_fourteenth_lead():
return requests.get(link_for_lead,case_1_fourteenth_lead_params)
def case_one_fifteenth_lead():
return requests.get(link_for_lead,case_1_fifteenth_lead_params)
| 31.653061
| 68
| 0.83559
| 258
| 1,551
| 4.48062
| 0.120155
| 0.096886
| 0.152249
| 0.272491
| 0.722318
| 0.480104
| 0.480104
| 0.480104
| 0.480104
| 0
| 0
| 0.01138
| 0.093488
| 1,551
| 49
| 69
| 31.653061
| 0.810811
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.454545
| true
| 0
| 0.090909
| 0.454545
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
f988ddcedcea9e24f75f6b6d5d3dc3e81eb9a472
| 265
|
py
|
Python
|
calliope_app/api/exceptions.py
|
jmorrisnrel/engage
|
250ecc24c0225d48363afe4e40335a89dd9b6a44
|
[
"BSD-3-Clause"
] | 3
|
2021-01-25T18:13:00.000Z
|
2021-04-30T12:17:42.000Z
|
calliope_app/api/exceptions.py
|
jmorrisnrel/engage
|
250ecc24c0225d48363afe4e40335a89dd9b6a44
|
[
"BSD-3-Clause"
] | 8
|
2020-12-11T22:28:17.000Z
|
2022-03-05T02:08:27.000Z
|
calliope_app/api/exceptions.py
|
jmorrisnrel/engage
|
250ecc24c0225d48363afe4e40335a89dd9b6a44
|
[
"BSD-3-Clause"
] | 1
|
2021-09-15T22:15:12.000Z
|
2021-09-15T22:15:12.000Z
|
class ModelAccessException(Exception):
"""Raise when user has no access to the model"""
class ModelNotExistException(Exception):
"""Raise when model is None"""
class AuthenticationFailedException(Exception):
"""Raise when authentication failed."""
| 22.083333
| 52
| 0.739623
| 27
| 265
| 7.259259
| 0.666667
| 0.214286
| 0.27551
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.154717
| 265
| 11
| 53
| 24.090909
| 0.875
| 0.381132
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
f9bc66ad49afc4d83c7b50567fece373c1732819
| 334
|
py
|
Python
|
django_sqlbuilder/signals.py
|
emacsway/sqlbuilder
|
72f32bbbfc1116550343c471dc43ef6284492a5a
|
[
"BSD-3-Clause"
] | 33
|
2017-07-26T02:33:48.000Z
|
2022-03-18T06:38:12.000Z
|
django_sqlbuilder/signals.py
|
emacsway/sqlbuilder
|
72f32bbbfc1116550343c471dc43ef6284492a5a
|
[
"BSD-3-Clause"
] | 1
|
2019-03-03T15:09:46.000Z
|
2019-03-03T15:09:46.000Z
|
django_sqlbuilder/signals.py
|
emacsway/sqlbuilder
|
72f32bbbfc1116550343c471dc43ef6284492a5a
|
[
"BSD-3-Clause"
] | 3
|
2017-09-25T03:00:11.000Z
|
2020-10-21T09:59:09.000Z
|
from __future__ import absolute_import, unicode_literals
import django.dispatch
field_conversion = django.dispatch.Signal(providing_args=["result", "field", "model"]) # Deprecated
field_mangling = django.dispatch.Signal(providing_args=["field", "model"])
column_mangling = django.dispatch.Signal(providing_args=["column", "model"])
| 47.714286
| 100
| 0.793413
| 39
| 334
| 6.487179
| 0.461538
| 0.221344
| 0.237154
| 0.343874
| 0.454545
| 0.324111
| 0
| 0
| 0
| 0
| 0
| 0
| 0.071856
| 334
| 6
| 101
| 55.666667
| 0.816129
| 0.02994
| 0
| 0
| 0
| 0
| 0.114907
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
f9c9c3609d8fb76685621814eca5ad400f311d78
| 211
|
py
|
Python
|
fusion/models.py
|
chr0nu5/core
|
aa80ccb3ae30dd12a5c848079d1184a830fcb83b
|
[
"MIT"
] | null | null | null |
fusion/models.py
|
chr0nu5/core
|
aa80ccb3ae30dd12a5c848079d1184a830fcb83b
|
[
"MIT"
] | null | null | null |
fusion/models.py
|
chr0nu5/core
|
aa80ccb3ae30dd12a5c848079d1184a830fcb83b
|
[
"MIT"
] | null | null | null |
from django.db import models
class Template(models.Model):
name = models.CharField(max_length=255)
json = models.TextField(blank=True, null=True)
javascript = models.TextField(blank=True, null=True)
| 35.166667
| 56
| 0.748815
| 29
| 211
| 5.413793
| 0.655172
| 0.191083
| 0.254777
| 0.305732
| 0.407643
| 0.407643
| 0
| 0
| 0
| 0
| 0
| 0.016484
| 0.137441
| 211
| 6
| 56
| 35.166667
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
f9de0a8f721fd89ee8adf336e273b7994e938dce
| 374
|
py
|
Python
|
Server/utils/Hash/SHA.py
|
TiagoJoseMagalhaes/AndroidERPSinf
|
f26cc193723159a5501c8a2068c80dfe0b7a713c
|
[
"MIT"
] | null | null | null |
Server/utils/Hash/SHA.py
|
TiagoJoseMagalhaes/AndroidERPSinf
|
f26cc193723159a5501c8a2068c80dfe0b7a713c
|
[
"MIT"
] | null | null | null |
Server/utils/Hash/SHA.py
|
TiagoJoseMagalhaes/AndroidERPSinf
|
f26cc193723159a5501c8a2068c80dfe0b7a713c
|
[
"MIT"
] | null | null | null |
import hashlib
class SHAHash:
__hash = None
def __init__(self, msg:str):
self.__hash = hashlib.sha512()
self.__hash.update(msg.encode("UTF-8"))
def getHash(self):
return self.__hash.hexdigest()
def getDigestSize(self):
return self.__hash.digest_size
def getBlockSize(self):
return self.__hash.block_size
| 19.684211
| 47
| 0.639037
| 45
| 374
| 4.911111
| 0.533333
| 0.180995
| 0.190045
| 0.244344
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014388
| 0.256684
| 374
| 18
| 48
| 20.777778
| 0.780576
| 0
| 0
| 0
| 0
| 0
| 0.013369
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.083333
| 0.25
| 0.833333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
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