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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1461194865efdfe1fca45cc14e784db4dd403dfc
| 653
|
py
|
Python
|
tools/leetcode.190.Reverse Bits/leetcode.190.Reverse Bits.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | 4
|
2015-10-10T00:30:55.000Z
|
2020-07-27T19:45:54.000Z
|
tools/leetcode.190.Reverse Bits/leetcode.190.Reverse Bits.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | null | null | null |
tools/leetcode.190.Reverse Bits/leetcode.190.Reverse Bits.submission4.py
|
tedye/leetcode
|
975d7e3b8cb9b6be9e80e07febf4bcf6414acd46
|
[
"MIT"
] | null | null | null |
class Solution:
# @param n, an integer
# @return an integer
def __init__(self):
self.mapping = [0,8,4,12,2,10,6,14,1,9,5,13,3,11,7,15]
def reverseBits(self, n):
temp = 0
temp += self.mapping[(n&0xf0000000)>>28]
temp += self.mapping[(n&0x0f000000)>>24] << 4
temp += self.mapping[(n&0x00f00000)>>20] << 8
temp += self.mapping[(n&0x000f0000)>>16] << 12
temp += self.mapping[(n&0x0000f000)>>12] << 16
temp += self.mapping[(n&0x00000f00)>>8 ] << 20
temp += self.mapping[(n&0x000000f0)>>4 ] << 24
temp += self.mapping[(n&0x0000000f) ] << 28
return temp
| 653
| 653
| 0.546708
| 92
| 653
| 3.836957
| 0.445652
| 0.280453
| 0.339943
| 0.362606
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.23029
| 0.261868
| 653
| 1
| 653
| 653
| 0.502075
| 0.059724
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130719
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0
| 0
| 0.285714
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
148cb3336d780a29eca7fb5893abe40499487ca8
| 129
|
py
|
Python
|
{{cookiecutter.project_slug}}/templates/python/{{cookiecutter.project_slug}}/tests/test_version.py
|
davehadley/monorepo-python
|
0df9e6aa3bea449c69ecdf9c980bca56a63e66a9
|
[
"MIT"
] | 1
|
2021-08-25T14:38:35.000Z
|
2021-08-25T14:38:35.000Z
|
{{cookiecutter.project_slug}}/templates/python/{{cookiecutter.project_slug}}/tests/test_version.py
|
davehadley/monorepo-python
|
0df9e6aa3bea449c69ecdf9c980bca56a63e66a9
|
[
"MIT"
] | 4
|
2021-09-15T19:29:47.000Z
|
2022-02-21T18:25:23.000Z
|
{{cookiecutter.project_slug}}/templates/python/{{cookiecutter.project_slug}}/tests/test_version.py
|
davehadley/monorepo-python
|
0df9e6aa3bea449c69ecdf9c980bca56a63e66a9
|
[
"MIT"
] | null | null | null |
def test_version():
import {{cookiecutter.project_slug}}
assert {{cookiecutter.project_slug}}.__version__ == "0.1.0"
| 32.25
| 63
| 0.689922
| 15
| 129
| 5.466667
| 0.666667
| 0.463415
| 0.560976
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027523
| 0.155039
| 129
| 4
| 63
| 32.25
| 0.724771
| 0
| 0
| 0
| 0
| 0
| 0.038462
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 0
| null | null | 0
| 0.333333
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
14a828cc0b69fb0a5b64936b158797f864753db5
| 3,571
|
py
|
Python
|
alpha_vantage/cryptocurrencies.py
|
cclauss/alpha_vantage
|
401d262431dc7d544a01c870e03a6fd1b68c0f87
|
[
"MIT"
] | 2
|
2020-03-25T23:06:06.000Z
|
2020-03-26T19:08:15.000Z
|
alpha_vantage/cryptocurrencies.py
|
beavis28/alpha_vantage
|
91a93e6c988ee716e1f20621078dd000f9808fd7
|
[
"MIT"
] | null | null | null |
alpha_vantage/cryptocurrencies.py
|
beavis28/alpha_vantage
|
91a93e6c988ee716e1f20621078dd000f9808fd7
|
[
"MIT"
] | 1
|
2020-07-11T21:37:10.000Z
|
2020-07-11T21:37:10.000Z
|
from .alphavantage import AlphaVantage as av
class CryptoCurrencies(av):
"""This class implements all the crypto currencies api calls
"""
@av._output_format
@av._call_api_on_func
def get_digital_currency_daily(self, symbol, market):
""" Returns the daily historical time series for a digital currency
(e.g., BTC) traded on a specific market (e.g., CNY/Chinese Yuan),
refreshed daily at midnight (UTC). Prices and volumes are quoted in
both the market-specific currency and USD..
Keyword Arguments:
symbol: The digital/crypto currency of your choice. It can be any
of the currencies in the digital currency list. For example:
symbol=BTC.
market: The exchange market of your choice. It can be any of the
market in the market list. For example: market=CNY.
"""
_FUNCTION_KEY = 'DIGITAL_CURRENCY_DAILY'
return _FUNCTION_KEY, 'Time Series (Digital Currency Daily)', 'Meta Data'
@av._output_format
@av._call_api_on_func
def get_digital_currency_weekly(self, symbol, market):
""" Returns the weekly historical time series for a digital currency
(e.g., BTC) traded on a specific market (e.g., CNY/Chinese Yuan),
refreshed daily at midnight (UTC). Prices and volumes are quoted in
both the market-specific currency and USD..
Keyword Arguments:
symbol: The digital/crypto currency of your choice. It can be any
of the currencies in the digital currency list. For example:
symbol=BTC.
market: The exchange market of your choice. It can be any of the
market in the market list. For example: market=CNY.
"""
_FUNCTION_KEY = 'DIGITAL_CURRENCY_WEEKLY'
return _FUNCTION_KEY, 'Time Series (Digital Currency Weekly)', 'Meta Data'
@av._output_format
@av._call_api_on_func
def get_digital_currency_monthly(self, symbol, market):
""" Returns the monthly historical time series for a digital currency
(e.g., BTC) traded on a specific market (e.g., CNY/Chinese Yuan),
refreshed daily at midnight (UTC). Prices and volumes are quoted in
both the market-specific currency and USD..
Keyword Arguments:
symbol: The digital/crypto currency of your choice. It can be any
of the currencies in the digital currency list. For example:
symbol=BTC.
market: The exchange market of your choice. It can be any of the
market in the market list. For example: market=CNY.
"""
_FUNCTION_KEY = 'DIGITAL_CURRENCY_MONTHLY'
return _FUNCTION_KEY, 'Time Series (Digital Currency Monthly)', 'Meta Data'
@av._output_format
@av._call_api_on_func
def get_digital_currency_exchange_rate(self, symbol, market):
""" Returns the current exchange rate for a digital currency
(e.g., BTC) traded on a specific market (e.g., CNY/Chinese Yuan),
and when it was last updated.
Keyword Arguments:
symbol: The digital/crypto currency of your choice. It can be any
of the currencies in the digital currency list. For example:
symbol=BTC.
market: The exchange market of your choice. It can be any of the
market in the market list. For example: market=CNY.
"""
_FUNCTION_KEY = 'CURRENCY_EXCHANGE_RATE'
return _FUNCTION_KEY, 'Dictonary (Digital Currency Exchange Rate)', 'Meta Data'
| 46.376623
| 87
| 0.661999
| 486
| 3,571
| 4.738683
| 0.164609
| 0.123752
| 0.041685
| 0.048632
| 0.862353
| 0.817195
| 0.817195
| 0.762484
| 0.762484
| 0.762484
| 0
| 0
| 0.271633
| 3,571
| 76
| 88
| 46.986842
| 0.885429
| 0.596752
| 0
| 0.363636
| 0
| 0
| 0.255942
| 0.083181
| 0
| 0
| 0
| 0
| 0
| 1
| 0.181818
| false
| 0
| 0.045455
| 0
| 0.454545
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
214f2e575845e853035739a6085f500a957313b0
| 215
|
py
|
Python
|
moto/iotdata/__init__.py
|
gvlproject/moto
|
b1c51faaf5dbf79a76eca29724b7d22b87e27502
|
[
"Apache-2.0"
] | 2
|
2018-01-29T14:50:38.000Z
|
2018-05-12T10:45:31.000Z
|
moto/iotdata/__init__.py
|
gvlproject/moto
|
b1c51faaf5dbf79a76eca29724b7d22b87e27502
|
[
"Apache-2.0"
] | 5
|
2018-04-25T21:04:20.000Z
|
2018-11-02T19:59:27.000Z
|
moto/iotdata/__init__.py
|
gvlproject/moto
|
b1c51faaf5dbf79a76eca29724b7d22b87e27502
|
[
"Apache-2.0"
] | 12
|
2017-09-06T22:11:15.000Z
|
2021-05-28T17:22:31.000Z
|
from __future__ import unicode_literals
from .models import iotdata_backends
from ..core.models import base_decorator
iotdata_backend = iotdata_backends['us-east-1']
mock_iotdata = base_decorator(iotdata_backends)
| 30.714286
| 47
| 0.846512
| 29
| 215
| 5.862069
| 0.551724
| 0.264706
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005102
| 0.088372
| 215
| 6
| 48
| 35.833333
| 0.862245
| 0
| 0
| 0
| 0
| 0
| 0.04186
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.6
| 0
| 0.6
| 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
| 1
| 0
|
0
| 6
|
dccc6e1a256c5c581749dda54fe284227b291297
| 12,246
|
py
|
Python
|
src/AuShadha/visit/visit_procedures/dijit_fields_constants.py
|
GosthMan/AuShadha
|
3ab48825a0dba19bf880b6ac6141ab7a6adf1f3e
|
[
"PostgreSQL"
] | 46
|
2015-03-04T14:19:47.000Z
|
2021-12-09T02:58:46.000Z
|
src/AuShadha/visit/visit_procedures/dijit_fields_constants.py
|
aytida23/AuShadha
|
3ab48825a0dba19bf880b6ac6141ab7a6adf1f3e
|
[
"PostgreSQL"
] | 2
|
2015-06-05T10:29:04.000Z
|
2015-12-06T16:54:10.000Z
|
src/AuShadha/visit/visit_procedures/dijit_fields_constants.py
|
aytida23/AuShadha
|
3ab48825a0dba19bf880b6ac6141ab7a6adf1f3e
|
[
"PostgreSQL"
] | 24
|
2015-03-23T01:38:11.000Z
|
2022-01-24T16:23:42.000Z
|
VISIT_DETAIL_FORM_CONSTANTS = {
'visit_date':{
'max_length': 100,
"data-dojo-type": "dijit.form.DateTextBox",
"data-dojo-props": r"'required' :true"
},
'op_surgeon':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' : true"
},
'referring_doctor':{
'max_length': 100,
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' :false"
},
'consult_nature':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'status':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'remarks':{
'max_length': 150,
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' :false"
}
}
VISIT_COMPLAINTS_FORM_CONSTANTS = {
#{"field" : 'visit_detail',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.Select",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
#{"field" : 'parent_clinic',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.Select",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
#{"field" : 'base_model',
#'max_length' : '100' ,
#"data-dojo-type" : "dijit.form.ValidationTextBox",
# "data-dojo-props": r"'required' : false ,'readOnly':true,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
#'style':r"display:none;"
#},
'complaint':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
},
'duration':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-props": r"'required' : false ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'",
}
}
VISIT_HPI_FORM_CONSTANTS = {
'hpi':{
'max_length': '1000',
"data-dojo-type": "dijit/form/SimpleTextarea",
"data-dojo-id": "visit_hpi",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'",
"style" : r"width: 70%;min-width:50%;"
}
}
VISIT_PAST_HISTORY_FORM_CONSTANTS = {
'past_history':{
'max_length': '1000',
"data-dojo-type": "dijit.form.SimpleTextarea",
"data-dojo-id": "visit_past_history",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-_:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_IMAGING_FORM_CONSTANTS = {
'modality':{
'max_length': '100',
"data-dojo-type": "dijit.form.Select",
"data-dojo-id": "visit_imaging_imaging",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'finding':{
'max_length': '1000',
"data-dojo-type": "dijit.form.SimpleTextarea",
"data-dojo-id": "visit_imaging_finding",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_INVESTIGATION_FORM_CONSTANTS = {
'investigation':{
'max_length': '100',
"data-dojo-type": "dijit.form.Select",
"data-dojo-id": "visit_investigation_investigation",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'value':{
'max_length': '100',
"data-dojo-type": "dijit.form.ValidationTextBox",
"data-dojo-id": "visit_investigation_value",
"data-dojo-props": r"'required' : 'true' ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_ROS_FORM_CONSTANTS = {
'const_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'eye_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'ent_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'cvs_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'resp_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'gi_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'gu_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'ms_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'integ_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'neuro_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'psych_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'endocr_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'immuno_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
'hemat_symp':{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_FOLLOW_UP_FORM_CONSTANTS = {
'visit_date':{
'max_length': 100,
"data-dojo-type": "dijit.form.DateTextBox",
"data-dojo-props": r"'required' :true"
},
'op_surgeon':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' : true"
},
'consult_nature':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
'status':{
'max_length': 100,
"data-dojo-type": "dijit.form.Select",
"data-dojo-props": r"'required' :true"
},
"subjective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"objective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"assessment":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"plan":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
VISIT_SOAP_FORM_CONSTANTS = {
"subjective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"objective":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"assessment":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
},
"plan":{
'max_length': '500',
"data-dojo-type": "dijit.form.Textarea",
"data-dojo-props": r"'required' : true ,'regExp':'[a-zA-Z /-:0-9#]+','invalidMessage' : 'Invalid Character'"
}
}
| 42.818182
| 140
| 0.435571
| 1,111
| 12,246
| 4.70207
| 0.081008
| 0.140888
| 0.098775
| 0.139931
| 0.918645
| 0.909648
| 0.908691
| 0.908691
| 0.908691
| 0.908691
| 0
| 0.026684
| 0.381839
| 12,246
| 286
| 141
| 42.818182
| 0.663408
| 0.065001
| 0
| 0.586667
| 0
| 0.133333
| 0.507787
| 0.1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
dcd4bac9b76758e8b5743be1359631412171f6ff
| 97,143
|
py
|
Python
|
Bioinformatics I/Week II/Skew.py
|
egeulgen/Bioinformatics_Specialization
|
38581b471a54c41d780d9eeb26a7033eb57f3a01
|
[
"MIT"
] | 3
|
2021-04-03T23:46:42.000Z
|
2021-08-08T01:19:32.000Z
|
Bioinformatics I/Week II/Skew.py
|
egeulgen/Bioinformatics_Specialization
|
38581b471a54c41d780d9eeb26a7033eb57f3a01
|
[
"MIT"
] | null | null | null |
Bioinformatics I/Week II/Skew.py
|
egeulgen/Bioinformatics_Specialization
|
38581b471a54c41d780d9eeb26a7033eb57f3a01
|
[
"MIT"
] | null | null | null |
def Skew(Genome):
res = [0]
for nuc in Genome:
to_app = res[-1]
if nuc == 'C':
to_app -= 1
elif nuc == 'G':
to_app += 1
res.append(to_app)
return res
def MinimumSkew(Genome):
min_val = 0
current_val = 0
min_pos = [0]
for i, nuc in enumerate(Genome):
if nuc == 'C':
current_val -= 1
elif nuc == 'G':
current_val += 1
if current_val < min_val:
min_pos = [i + 1]
min_val = current_val
elif current_val == min_val:
min_pos.append(i + 1)
return min_pos
Genome = 'GATGCAAAGGGCGCGGCCTGAATCATCTTTAAAACAAAACAATTCGGGTTTTTCTGGGGCAGCCGTCCTGACAACTCAATATCATTATTATTACGCGACCGCTAGGCCCTTTGCGATTGTAGGCGTTTCCAAGCTACAGGAGTACAACAAGTATGATCGGTTTAGATTATCTCGATGGACTGCGTCTACGTCTCTTGACGTCACCCTCCTTTGGGGCGTGCTCCTGCTCCGGCGTTTTGGCGTATGCAGCGGTTGACCGACCACCACACAGATATGCCGATATTTGAACCGAGCTGCACAAAATCGACTTATGTTGGAGTGGTCTACTACGCTATGCCACCGTGATCGTTATTCCTAAAAGTCTCAGACTTTCCTCTCGAGGGTTACAGGGGAAACACTTATCAGTCTCCCAGGGGCCCAACATTAGCTTCATCTATAGGTTTAAGCGGCGATTTGGCTTTGGCTAGGGACACTGTCAAAGTTGCGGTATGATGATCTCAGATTGTACCATCTAGGTAAGAGTGCGTAGAAGAATTGGAAAGTACGGAACGTGTTCATGAGGAACTTTCATTGCACGGTGGTTACCTCCACGCTGCCACAAGAGGACCGCTGTATCGTAGGGCGGATCGCGACACCGTCTTCTAGCTCCATATAGCAGAAGGTCCTTCGGGAGGTCTATCTACACTCAAAAAAAAGATTTCTCGACATCTAAGGGCCCATGAAACTTCGAAGGTAACGCACTCCCTTGCGCAGACACCTACAGCAAATCGCTTAAGTTGCGGAGATTGCTAATCTAGCTGTGCTTAGGGGGCTATACAGAGGGGATAATGCGGCCAACGTTCACACTTTGAAAAGTACACGATGCGGCCGGAAGGAACTTAGTTTAGGATATTACGTTTTTGACGTAACTAAAACTGATTTTGGCGAACCGAATCCTGCGTATTACTGCAGTTTCGTAACAGCCCCTTAGTCTCGGAGCGATATGCGTGTACAATGCAAGGCTAGAATTGTGACTAATGTCCAAGTATGTCACATGATCGGTGTGTCAAAGGGCGGGCAACACGTACGGACTTTACAAACAGGGTATTGAGCGTTGATCCCTCAAGCTTGACCGACGGCACGTTTACGCGCATTAGCTAGACACATAATAGCGTCAACGACCCCCACACTCGAGTCATCAACGCCGAGAGAACAAGCAGTGTACAGCACGCGGACGGATGCGGCTGTCGGGGAAGGATCGGACTTCAAGTAGTGTCGTCTGATTTACGGATTTAGCTGCTCCCCGAAATTCGCTAGTGAGAAACAGCGAGAAATCGAGCTGATTAGTGAGGGGGCACGACGGGGTCCGTTGAGTGCACACAAAGGGTCCCCGTGCGTCGGTTTGCACGCGATACGTCTTAGACCCATAGTACAGGTGACCCTACGCAAGGCGTGCACTTTGTCGTCTTACCCCCCAGTTAGAAGGCCTCTACAGAAGACAGCTTCAAGTTGCACAACACGATGTTTCGACGCGTAATAAAACGAGCTATATTGGAATACAGACTCTGCACGTGCTACGTATACCAGGACAGGGGACCTAGATCCGTGTCAATTCCCGGCTTCTCTTTTCAGTGACGACCAGTTCTCCAAAGGGCGAGGGTTGGCCAACGAACACTTAGGTTAGTATGGGACACGCTTTCTGTTCTTCTCCGTTATGCCTCTCAGCGGTAGTCTCGATGGACTCGCCACAGACGCATCTGACTTTTCGGATAAATCACATGAACAGTGGAGAAATAGAATAAGCCGGGTTTCTGATAGTGGAATTAGAGTCTCCAACGAGCTAAAGTCTGTTGCGTTCCATTGGCCTCCCTTTGTGGTACCCACGGGCTGGTGTTCCCACGATAGCTCAGCAAGCCGATGGTGACTAAGCTTGGCATACCAATGACAAGGCCGTCATGAAAATCATCCCTGGGCGCCGAGGGAAGGAGCGCTCTCCATGAAATGAGAGCGTTGGGAAGACATTATGCGACGCTCTGCGCAAAATACGCATGAGGGGATACCCAACTGGTTCGGCCGGGCTAACTAAAGTCTTATAGTACGCTCCCGAAGTTAGCCTCCAGCTTGTTCGTCGCTTGCAGGAGCATTATTTCCAAGCTCCAATTTCTTAAGCGCGTCAAGAGCACTGATACGGTGCAGCGTAGTTGTTGACATCCCGAGCCCTTCATCAACAGGTAACTCCAGTTGAGTCGTACCGAGGGCATGTAATCCAAACATCAACCGAAATAAATAAACTTCCCCCAGTCGCTAGTGTCCATATGTAGCATCCACAAGTCCTGCGCGCCAATAAGAGCATAATGGCATGTCTACTAATCGGGATGAAGTGCAGAGGAACACTAAGATGATAAATTAACCCCGTCGAATACGATTTCGCTGGAACATCTTAGTTGAGGTCTAAATCTCTATAGGGTCCGGGAAGAGTACGAAAAAGGGGGCTGATCGGAGCACCTGAGAAAACCGTAAGCGGTTTCGGTGAAATGAGCGATGTTTGTGACTGTTGCTCCGTCGAATGCTCCGAGTAGAATACAACGCCCGTTTAGACACGCGTTGGGGAAAGTATAAAGGGCGATGTATCACCTGTCCGCTCGCAAAGTTGGACGACACTGTTGAATGAAAACTTGCACAGGCACGACTGACCGGGTCTATTACCGCGAGCGAAATACGCCGTGTCCTGCACTGCATTATGAAACGCACGGACAGACATCAGTTGACATACTGAACCACTGAGTAAGCAATTTGCATCCCATCTGGGATAAATCAACACCCAGTACGCTACTTACCAAGAGGCGTCAGACCGACAATAAGCCTCACGGGGCCTTAAGTATCGCTCTCTTAAAATTCACTACACCGCCAGCTAGGGTAGGACACTCATCCCCAGGCATGGGGGTAATCGCTCTGTCCAGGGGTGAACTCGAGTCACGATTCGATTGTACAGAATCGCGAGATACTTCCTATCAACTCCAAAGACCTAATCGGTTCAGTCAAAGCCATGTCTAACTGACACACAGGCAGTATGTGAGAGGGCCCGCGTCTGGACGCCGATCTTGGTGAGGCGGCTCATGCATTGGGCCACATGAAAGGACAGTTCAACCGGATAAAGAACCCGACCGTTCCTTAAACCGCTAACTCAACAGGGCGAGTCCAAACGTAGACAAGACCAAACCGCATCACCAGGTGAAAATTGGAAGCGTCTACTCGTATGTTAGGAACAAATGAGCTTCGTATGAACTCCGATCGATCAAATAACCTGCATCGTACTGGGGGTGCTGTAGGTTTGCCTAGAGGGTCAGACGAAAACCAAATGGCGATGCTCTGGTTCCGTAGCGGGCCCCTCTTGCGGATGACATTGCAGGGAGCGGATCCTGCGCGTTTATATAACAGCGATCCTTGCAGGGCATTGACAGAGCAAATCAATCAACTGAACCGAACGCGTGCCCACTCATTGAGTCGACTTCGTATGGTCCTTCGGTCACAGTACTTGACGCGGGAATGATAAGGCCCGTGTGCACTCCCCTAGGTCAACATCTGTATCTACCTAGGAAGTAGGACGATTGCAAATTGGCGCATCAAAATCCGATGTAACTCTTAGATCTCGAGGATCGAGCTCGACCGTCGCGAGTGGAAAATACAAATGCCTCAGTGGCCCTTGTTCGGCTCAGTATGAGCTCCTTGTTTCCGCGACCGCATCTAATGTGATCCAACATCTTACCTCCTCTGGAGACGGAGGCTACAGATAGATGAGTCTAATTTTCTTACAACTAATAAATCGAAAATACGGCTCATGGCGTTGAGCAAGCAAAGTACGACACGTCCGTAAAAGTCGGATTCCTCAGCAAGTGTCTTGACCATCACAGAGAATATTTCAGCAAGCTGGCCCATGTAAAAACTACTGACACTTTCGACCTGTCACACCAGAACTTACAGAAAAAACTCTCCTGAGGGAGCCCCTAACTCCGGAAGATCTTAATGATCGATCGTGCAACCTGGATGCCGTGTGAGTGTGGCTACCTCATTCCTACGGGGCTGTCCGCAGGTTCGGTCGCGTAGGTCTGGCTGGCCAGTGTACTTGGATGGAAAAAGATTTACCAGCTCAGGACGCGCCCCTTAGAGACCTGCAAATCCGTATCAAATTCACTCGTCGAAAAACTCGCCTTGTAATTTTTGTGATCACATTATATTTACGATGCTGTCCAAGGTAATAGGGTATATCCCAGTATAACTGGTACACGCTCGCACACGTGCCGAGTACCAACTCCCTCGCTTGGTTGATCTATCAGGGGTAGGGAAGCTTCGAGGGCCAGGCGTAGCTCCAAATCTCAAGAACGGTTCTAATCCTCAACCCGTAGGATCTCTCGTTCATACGTCGCCCTACGACTCTCCGAAAGTATGGGCACACTCGCCATAAATATCTAAAAACGATCTTCGCGCACGTGCAACGGTCTGATGGCCTTATGCTGTAATTCGGTTTCTGTGGGAATTCGGTAACGGATAGTCACTGAGCTAGTGCCGGTCGCTGCTGGTTTAGATGAATGCAGGAGATCCGCATCAGCGCCGAGCGTTGCGGGCGTCTCGGCTGTAAGTTGTGGCCAATGCGGGCGTACTGTTATACATACAGGGGAATTGGCGTCAAACCGTTTATGCGAAATGCTGTCTATGCCAGGTGAGCATTACACACGTGAGATCGAATCGAACGTAGGAAAGGTGGTCGTATGGGAAACGTCATGTTGATGAGACTTCCATTAACGGGCCGAAACTAACGTCTTAGCGTCGTTGGTAAAGTTCAGCTGGGATACTTTTGAGGTCGCTCATCTCCCCGCGTCACTTAGCATCTACTCTAGCCTTTCTAGAGGGGTTAGTACTCCTGGGAGCCGCTCTGCAGATATTCAATGAAAGCCCAATTGAGGCGGCATGTGGATTGTAATTCACCCTCCGCCTACAGTAGACATAGAGCTGTGCTCAAATAGGTTGAGAGGGGCGCCATTAACCATCGTGGTACGGACCTATTTAGAGCGAATATCTAACCAGCAATTTCTCTCAGAGGATACATCCTACTCAAGATGCTGTACAGGCATTGCCAAATTACCGTGATTCCAAACTTTTGGGAGGCTTTCCCTTTTCTAGGTGGATAGTCACGTCCATACTATCAAGCTAAGAGCCACGCTGCTTTCTACTCCTAAATTTCTTAAAGAACTCTCTGTCCTAAGTTTGCCACAACGACTGCTACGCGCCAAAAAAACCGACCAGCACACACGGTTATCGACCTTTGATAACTGGCCGCCGGACAGATCAAATGGACCCCAGTCAAGCCCGGTAACTTATGCGCAAGTTTGCACAGGGGAACGACACGGACTCTGCCAATTATATACTACATGTAACTCGCGCAAATTGGCAGCATACGTACGTCTTCGTATGTCCTGCACCCGCATGCCGTAACGGACCTCAATTCGGCACCTTGAAGTTGCGTCTTTCTTCCCTATCGCCATTCTGTAGACAATCAGACTTGGTATTAGGTCCCGCTATTCAAACCCCCAACCGATCGCGCAGAGTAACGATTCCTGCAATTATGCGGAATGGTCGCCCACGGGCGGATTGGGTTCGTTGCAGACATTCAGTAGTTCTTCTGCATGACGTCGGGCTAATAGCACGACTTCCTTCAATACCGCAATCATCGGTCACCCCTCAGACCACTCTTTCCCAATAGCAGGACAGAACCCCATCTGCATTGGACTCAGGTTACTCTAACCGAAAGTTCCTTTCTTTAGTGACAGCGAGATGGGTATGCGGAGGGCGTGCAATCACTGTGACAAATTCCTCGTTTTTACCTGAAACTTTCCTTTGTAGGCGTCTCGGAAGTAGATTTCACCAACGTGATACCAACAGAATGGGTCAGACGTGGTGTATTCCAGGGTGAGGAAACCTCCCCTGAACCGAGGCGACGAACCTTAGATTACGTGTTGAGATTTCAAATCGTTTCTAGTGTGCGCACAATTACCTTGGCAGACCTAACTCTCACGCATACATAGGACACCTCACCATAGCCATTTGCGATGTTCCCCCGTACTGCCCTAGACAAGCTATACGACACCCCCTACGACATGACTCGTAACCCGTTTCCATGTAGGCCAGTTTATCATGATACATGAGTAAACACAATGTATAGTGAGCTTGTTAGTACTATCTCAGTGGGTGACGACTGCTAAAGCTCTCATAAAAAGTCGGGCGAAAACGACACTCGCATCCGATGGTTTCCGGTCTTTCGGGCTGCCCATGAAGGGGCCCCTATAACCGACAGTTAGTGAAGCGAATAGTGACTGTGCGCCGCAAAACGCGCGTACAAAGCGTAACCCATAACCTGGTGGTCCTCACATGATTCCAGAAGTGACAAACCAACAAGAATAGTTTATTCTGCTTTGCCCCGGGCGGTCTGTTCTCACTTTGTCCATATGCCGGCCGCGTTGGTCTCAGAACAGTGAAGATCTTTTTGAAAGTAAACCCATTCCGGATTTCGTGTCTATCCCGATTCATACCTCCGTCGGGCACTTGTTAAGGGGGACGATCCGCATATTGTACTGTAAGCTTAGATCACTTCGCGAGAAATGATAGATCGCAGTACACTTCCGTCTATACCAATACTTTGACTGCTGCAACCATCTAACGGCCCTGCGGGCCATAGCCGAAAATTATCAATGGTTGGTCCTGCGTGGGCACCCAGTACAAGATTTCTTCTAACCCCTGAGCAGTAACTATTGACCCTAGGTACAAAAACATACAGGACCTTGATCGATGATCAGAGAAGGACACAGATCCGGATTATATGCCTCTATAGTAACTCCGGAATAAGCGAAGGAACCTGGCTGACCCCTGAACAATCAGAGCGGAGGGCGATCTGTAAACTATAGAAAGCGTTCAATTCAATCGAAGACAGGCCGTATCCCATTTTGTACAAGGGTTGCCGGGACAGAAAATAGCCGGTCGAAGTTCCGGCATTCCAAAAGCAGTTTAGCAACGATACAATCAAGCGAGAGAGACTATATAGACAGGTAGTCCGGTCGTGGTTATCCGATTTCGTAGACACGGTACGGCCAAGCTGGGAAACGAACGGTGTGTTTGGGCATAAGTCTGTTGAGTCAGAAGGTCGCGGTTTCTAGATCGGTGAGGTCTGTGAAAAGGAGGTGTGACATGCTTAGGCATATTAAGAGTCTTCGAACACAATTGTGTTGACACGTTGCATACCGCTCTCTACTGAACGCACAGGCCGTGAGCGTTATATATACTGAACCAGCGCATAGGCCGTACCTTCCCCCAATAGTTCTTTGGCGAGGGAGAACCTATGAACAGGCCGAGTTTCTCTTTAATTAGTCTCGCGTCTAAACTCGGCCAGTTCTAGGCATTTATATTTACCAGAATCGGCAAAAGGGAAAATTAACATAGCAAAAACACATGGGTTCTATCGTTAGTTATAGGCGTATTTAATCGAGCCGTGCACGGTTCTCATTGCTATAGGAGGGTACTGCTATGCGACAATTACCGACAACAGCTATAACTGCTTATTATCATAACTGAGCAGTCAGCTTCACGTACGTAAGAACAGAAAGCCTCTTACGGACATACCATCCCTTTCGGAAAGACGTGCATAAGCATAGTGCGCTGGGCCTGGTCCACTTAGCGTCTAGTCCTCTAATCATCACCATACACACTATAATACGCCTCCCGAGCAAGATGGAATACACTGGTGAGGCTAATGCGCCAGCGGACGAGAGGGATGGTTCGCCATCGTATTATGACTATGCCGGTAGATAGGCCCTGGCCGACTAGGATAAGCCGCAAAAAGCTACGATGCGAAACTCCGAGACGAGCCGAAAGCGTATAACAGACGACGTCCAGGAACTCATGTACCCCCTCACGGGTACGTGCATGATGCTCACCGCGATCATCCCTCCCTTTAGGGCTGCCATTTATTGTTTGTGAATTCAAATCACAGTACACCTTATTTTACAGAAAGCCAGGCACCTCAGATGTGGCTGTTGCGTCGACTAGTGCAGGTCTAGAATGTGGTGTTACGCTAGGAAGACCCCGTCGGTAACAACCACAATGTGGGGTAGGTACATTGGGGCGTACCGTAGTGAAGCTGCATGCGAACTCCTTTGTCGCGTGAATTCAAACCCTGCGGACCGTTTTTTGGGGTGTATCCATGTCGAGAAGCTGCTAGGCTTGGTCGTATAAGGATAGAGTCACCTCCAATCTCTGGTCTAGTTTGGAGCTTACCGCATTGTTCGAGTGGTCATGCTCCGGTCGTTCGTACGAATATATACAGGAAAGCTTCTCATCGCGTATCTACTTGCCTCGTCGTCATTGCGTGATTCCAATCGTGCTTTACGCGAAAAGCAAGCAGCCCTCTCGAAGGCTTAAGAGGGGGTACTGATACCCTGGGATCCAAGATTGTGCGCGTTCGGTTTCAGAAATCATCTTTGCCTGCTTGGGTAAATGGCAAACCTAAAGCGAATATAATCTGCGCGACACTTTCTCCGACACTTCGTATTGCACGACCAAAGGATCCTCGGTCCTTTATTCTTTTGACTGAGGAATGCCACTCGAGCTTGTTTCTACAGCGACACCTTGAAGAGTTCTGCAATATCACGGAGTGTTGTTATAGGATGCCACCCTGTCTCTAATCCACACCCCACCGCTTGTTTCTATCTCAATCATCTGTGGGTTGCTGCTGGAGCAGCTACTTCTGCATGGGCCGCGAACTCCACAGTCTTGGGTAGCCCAGGAACTGCAACGGAATAACGCACGCGTCGGTTCTCCAGCAGTGATTTTATAGTTATGACTGGAGGTTTTAAAGAGGACCGGGCTGAGCGGGCTATCGTTTGACTGCTATGCAAGGGCTTAATCGAATCTAGCTAGAGGTCACCTGGAAAAACTGCTCTTTGTAGACAAAATCCGCGTCGGCGTAAATTGATATGGGAGCCAATTTGGGTCAACTTCGACAAACTACAATCGGGACCTAACAGACGCAGCGTATCTGTGCACATAGCTTTAGACACGTTGTCTTGAGCATTAGATGCGCAACTAATTATTCAACCACTGAATTCCTAAATCGCCGAGAAGACTCCTGTGAGCAGTTACAAATGAGAGGCATCGCCCTGCTGAACCGCCGTGCAAAGTATTAGCAGACGATTCGCGACTCAACATCGGGGCGGCGCGGGAAAAGCTCCGCCAATGCTACCGGAATAGATTAACGCAGAGGCTGCAGTGACTATAGTTTCCGTGGAAGGTTTACGCGATTCGATGGCTGGCCACAGGACTGCCGACAAAACGGAGCCGCATTCCGCATGCTACCAGGCATAATAGTGAAACGTCGATTTTTCTACATCCGGACCTGCTTTCGTTGAACCTCAGCCTTCGCAGGGCAGAAGTTCTGAGAAAAGCTAAAATGAGGTGTTTGACTAGCCTTTTGTGTAAGTGCGTGAGAGTTTTTATCCGTAACTGAGAGCGATACACGGCCATACCTAAACTAGAGGAGCGCCTTACCGTTTTACGTGCAGAGCGACAAACCAATTAGATCGGTGGATATCCAATTCATTGACTGCGTCGTCCAGTACAGTCAAGAGACGTGAATACCGTTCGAATTTCGTGCATAACTCCTGGGAGAAGAACTTACACGATAGCCATTCGGGTGGCCACTATAAGTTTGATAGCTGAGTTCAGTCCTGCCACACTAGGGGGTCGCTAACGAGCGACCTGCTACTGCGCTTGTCCGGCTGAACTTGTGGTCTGCGATCGCTCTTGCTCGTACCCCTACGGTTCGGTAGGAGCTTTGGGCTAGGTAGTACAGATGACTTATGCGAGCCCTCAAGCACATAGAGAAGGATGTTCGTAATCCACATAACTACTATACCTTCTAAGGGATCCCATCTTATCTCCCGGCGTAATGTTATCGTCTTGGCAAAAGTTCGACCGTGACTAAAAGAGACTCGATCGGTTTCAGCGTTTTGAGACATCCTCTTAGTGAGATGGTGACTCCACGAGGTAACGAAGACCCAGGGTTCTATCGTCCGTAGCGCTGGTACCGTTATTCACTTCAGTCTTCTATCGTGATTCTTATGTAAGCTAGTTAACCTGTTCAATTGGTAGAGCGCGCGCGAATCCTACGTATAAACGTTAACGTGGCGACATGTCAGGGCTGATTGCCATCGGCTTCCGCGGTTGCTACTCGGCCCACACGTCCTTTCGCGACGGATTATACAAAGGACGATCTCGCGCACGTGTATTCCGGAGGTAGTGCACCCAAGGAGGTATAAGTTTTATCAATGAGTGGGAATGCGGACTGGTGCACTAGGTTGCGGGTGGTGGTACACAAGAGTATGGTCCCTGTGGCTAGTACATTTACAGAGACAGCCTCAGGCGGGGGTCCCTCTCGCCTGTATGGAGCAGACTAACTCGGATTATCCTGCCAAGAGGCCCAGACGTGTGCGTTTTCAAACACTTAATATCCGGAGAACCGGCCAGTGCTAGTACCGGAACAGACACTGCACACATTGGAGGGGAGTACCAATAGAAACAATTCGCGTAAAGTGCCAAGTCAGGTCCACTCGAGCGCTCCAGAGACCAAGGTCATGCTTCGATTGGCTATTCATTGCCTTCTCCCACTCGTTACCTTGACTAGATCACGCTGTAACGGACGATTATATCGCGTTGTGTATCGAGTGCAACGGTTCAGTCCACCCGGAATACCATTTTACTCGGCGCCATGGGACATCCGGGGGCGAGCTGAGACGACTAAATTTCTAACCGGCCGCGCTCCAGAAAAAGGTCCGATCAGAAGACGGTTCTCCTCTCGACTGCCTTGATCTGGCCAAATCCGTTCACCTCTCAGCATTATAGGGGCACAAAGGACCTGTCTCTCAAACTACATCCGCCATGTTTGATGGCCAACTACCAACGTAAAAAGAGGTTCTCTCGAGTCGGCTCATACTTTGGTCGAACACCCACACCTTTGTTAGAAACTTGCATGCCACTACCTTCTGTCATCTAGCAAGAAACTACTGTCCAGCGAGCGCAGGCCCAATGCGAAGACCAGGATCTTTACCGCTATGGCCTGTCGCGGCGTCGTCTCAAGCAAAGCGTTAGTCTGCACTGGCTACGTAACTAAAGTGCGTGGAACGCGCCACCTCGGACGGGTTTTAGTGCTGGCCACGGAAGCATCTGGCATTGGTCGTGCCCATGCAATACGCGTAATTGAATCCATAAAACACGAACTTTTAAGCAGCGTACGGAACCTTTTTTCAGTCTCCCCCAATCCCGATTCTGTGGTACGGATGCCCGGGGGGGCGGTGTACCGTGCAGGCAGCAGGATAATGGGCAAATAAGAGAGCTACCATGCCGAGAGACCTAGCCTCCGACGTTATTTCGGAAGAATCGGATCATTGGACTGAGTAGAGCCCGTTGAATGGCTTGGCGTACGATCTGCCAATTGCGTAATCCTGCATTTCATGATTGAAAGCATCCCCTAAAACTTTGCTATCGGAGACTGCTAGACTACGTTATGGAGAGGAAGCTGACGGCGGTGCTAGGTCCTACGTCATAATATTGCCTGGATTACTGTTTAAGGATGGTCACTCCTATTGTAGCTCGTTCCTTTCGCACAGACCACGATCGGAGTATAGTATGCGGGAATGAAGATATATAAGTGCCTCTCTTGCTCTAATGGGACCGTTCATGCTACGTGAGTGAGTTTTATTAAGCCATACGAAATGCCGTGTGATCGTGCATAAGCTAGGAGTCTCGTGGAGCGCCTCCATGTCCGTAGGTGGGTACGATCTTTTCCTGTTTACTTAATCTATAACCACACCTGGTGCCACTCCTGAATAGATAACGTTTGTGTACCAGACGGCGCGCCCTTTGGCATGTCTTTCGGCCCCGAAAGAGCGGCTGGTTCGCTCTTGACTGGTGAAGTGGGAAATTGTGTTAAGAAGTGCCACGAGCTCCGTCTTTTCAATGCCCGTACGTGCTGTTGACGCGTTCAGACCCGGACGCTTTGTTGTTATACAGTGCTTCGTCCTAGGCCTCACTGTCCCGCGAGATAATCTTTACTGTGTGACTCAACCTTCGACTCGCCCTCTCATCCTTCCACATACTTGCACCTCAAACATCCTGATGTTACAGGCTAAAGAGAACTTGCTTCGCGTAACTCCGCACGCAACTGGTATCTACTTCGTGGGCTCGGCGGTGTCGCTTTTGAACCCCCCCCTCGCATGGCCCAGAAACCAGCCACCGCCCTTTACCCCACTAACCGATTTCTGGTAACTTCACCTGAAAAATACTCATACTATTCACCCAACACCTACCGGGTAATAACCTCGGGAGTAATCGCGTATCTGTACCTTGCAGGCAAATCAAGAACCTGCAAGAGACATGAGTACCAGCAGTTTCGCGCTGCAGGTCGGCCCAATTACGATCACATTGTCAAACTCCCCCGGTTTAATTACTCGGAGTGCTTATTAACCCTAATGTCGACCGCTGGCGTAGCTCTGCTTGACTAGAGCAGAAAGGCACCTACTACGCCAGATGAACCCGATCTCAATGCTTTCACCAAATCCTCCGTGATCATGTCCACAGGAAGGGCAAGGCCTTAGTCCAAGGCTATGAAGGGGCTATCAAAAAACCCTTACACGACAGCTCCGGCAAACATGCGGCCCAGGACGTAAGCCTGTACTAACAAGTCCCGTGAGCGGTCTCGTTGGTTGGTTGTATGTGTGTCGCCACGCGTACTGCGTGGAGCGGTCGATATGACATAAGCAGCAGGGACCGTTAAAATAAGCCTCGAGTTGGGGTTCCTCCCATTCCTGATTTTGACCTCGTAGTTTATCAGGTTATCATTACTTAGCTTATACGCCCACGAGTACGGAGTAAGTCGCCATGGCTATCCTTATCGTTTACCTACCCGTCCTCCGGTTCACACTACGGGATGGGACGCCAGGAAACACATATGGTGCAGCTGTGCTCTAGTAGGGTTCTGAACGGATTTCATGGGAGTTAGACATTGGGGATCACATGTCCTGTACAGACATGATAACGCTCCAAGCTGTGTGTAACAGGTGCGACTTAAAGCTATGGATCTTAGAGTTCGGTTGCCATGCTATTAGGGGTTCACATGTCTTAAATCTAGGGGATCCTAATTTCAAAACAGGACGTAAATGCTGTAAAGTGGTATAATCCTTCTCATACAAGATGTGGGAAAAAGGCCGATCCAGTTCCACCCTCGGATCTGCGTGAATTTAGAATTATGTATGTAGCAAATCAAGCTCCAGGCCACGCCCATGTCGACAGCTGAGGACGATGCCCGGCTAACCATTTAGGTGATCTCCGTTGAAGCGATCCGAAAAATAGGTGGGTCGGTTCCGGGCGAGACGAGTGATTCAATTCCCGTTAAAAAGTTCCTCGCGGTACGAGCCGGTCGCTCAGGCCCATGAAAGCTAAGACGGCACGTCGCGGTAAACTCGAATGGCTGTCTCGGAAAATACTAGAACCATTCCCAATGCTGCCTAGTGTTATACATGCTCACCTGGTCCTTAGATACAAGGTACCACAGCCGCATGGTGCAAGAGGAGCAGCCTCGTGGTGCTCATCACCGCAATCTTCGTATCGTCATATTCCCGTCGTGAAGGAAAGCGACTCGACTACCTAACCCTGTGCTCCGAGCAGCAACTTTTTTGCATTGCGTCAGGCATCTCTCCGACGAGAAGCCGGAGCGTAAGGGAAGCCATTACGGGCATGTGAGGCAGTTATTTCTGACTACACGATAGCGTCCATAGGCAAGTAGACACTGGCTTATCTCGATATGGGGTATAGCGAAGCTACCATCCATGGAGTCCTCAGTTAGGTCCCGCTACACTCGACTACCGACAGGAGCTAGCTCGTGTCTATACCCTGAACGGGAGCCGTCTTATGCCCAAGAATATATCCGTTATAACCGGCAGCCACGTGAATTACAGGACCAATGAGACTTATTCGTAGAACAACCGTCTGGTAGTACCCCTATGTATTGGCAGTGACAGGTCAACATATCTAGACATCTTGAGGAAGGGATCAAGAGGAGAGCAAAGTGCGTTCGATCTACTGTCCAATACATTCCACGTCACGAAGTGGACAATGGGTACGGACACCTTGGGACTCAAGCGGGCAACTATTCCATTCTATACATGATCGCAACTATTGTCAGTCTCGCACTCGAGGGTACGAACTCGGCGGGGAAGGGCTGTCAGTGCCGCATTTGCACTCACTTATCCCCGCCACCGTTAACAGGGTAACGTTTGCAAAATTGGTGAGTAAATCGGTCGATTGCATATCAGACGTGCGTGGCAGAATATAAGCCGATAGCTTCGACTATTGATTCCTCATCGTCCAGGGTTGTTGACTGGGGAGAGCTCATATCATGGGGCACCTACGTTGCTGGAACTGTTGGATACGCACTATAAACCTCATAGAGCTCTGCCCACGTCATAAGCCTTAAGCATGAAGCTCGCGTTTTGTGGCAGCCCAGGGTAACAAGCACATATGGTTGCCCGCGATACCTGAGATAGATGTTATCGCTATAACTATAGCTCCTATCAAGTCATTAATATCTATGCTACAGAAGAAAAGGTAAAGCATACTACCTCACTTAAACTACCTAGTCTTTGAAATGCCACTCCTGTCAAGGGGAGGTGTTACTCCACGCAGTATAGACTCCAGCATGATGGACGCCGGGAAACGCCTCGTCGTCTCTTAACGATAACGTTATTAGAAAGGGGACAGACGTTACGTTCGTGACGTGTTAGATAATGCGTGAGAAATTGTGTTCATCGTCAAAGTTAGTATATCGTATATCCCTGTTACCATTGCGTTTTTTAGTGGGAGGCTTAGCCCAGCCGTGTGACCACCGGCAGGTACATAACAAGTGTGACTCGGCGTGTGCATACACTGGCATCAGCTGGCTGACTTGCTCCATGACGAGCCACCCCTGAAACCTAGCAACTGACCTCATGCGGAATTTACGGGGTATTGAGTTCTTCACCCGAAGCTGCTGGTTAGTGTGTCCGATATGCGAGCTGCGAGGTCATTCTTGCTGAAACTAATATGCTTCGAACTTAATCGCTTTGAGTAGCTGGATGATTTGCTCATGCCGAGATGCGCGTTGATCGTTGTCGGCTCAGGTTTTCCCTTTACAACATCTTCTGGGATGAATCTGGAACACTCGCACGGTTCATACTAGAAATTCCTCGCAAGGAGAGAGGGCCTCCGTTCGGAAGAGTCCGATCGTTAGACTATGTTGTTCAGGTCGTTCCTGCATTGAAGAAAACCTACGAACCGGAAGGGCTACCCTCCTTCGTCTCATAGGTGCCTAAACCGTCCGGATTGCACTAGGCACGATACTGCGCATTTTACTTCTGCCTGGGTGCTATAGGAAGTGCCCGGTAGAGATCACGCCGAAGATTGATGTAATCAATAAGGCTGCTGAAATCTGTACAGACGGCCGTCAGGACATCAGGGGAAAACTCTCGTGAGTGAGTACAAAATTAAATGGCCCTGGGGAGACTGAACATTTAGACTAACGAAGTCACGGTCGTCGCGTCATAAGAAATTCGAACAGTGGCCTGGGCGACTAGGATCTCGGTAGCGATTAACACGGTATCGAATCGAACGTAACTATTGATCGGTAAATCCCCCGGGCTCTAGGGGTCGGGGGTATACTTTCCTTTTTAATAGGCCCGACCCCCGAAACTGGCTCATCGCATATCCCGTTTGCAATGGAAGCGGTAGTTTTGTATAATTACGATTCGTTATAAAGTACATGCCACTTCGAGCACCGTCATGTGATCACGCCAGAGCACTCCGTTTGGCTGTGGGCCGTGGCACCACCGTAGTTCATATTCATTGCGAGCTCTTAGAGTTCATGTCCTCACCGTAGACCGGAGCAGAACCGCTCACCACGAACGCTAGAAGGCGGCTGTGCTCGGCCAACTGAACCACCAGCAGGGCAGACGATGGTTAGTATGGACCTGCTGGTTATCGGTTTTCAACGGGCTGTCGCTAATATTGTGGCTACCTCGATTAAGCGCCGCCGCAGCATTCGCGGGAGGAAGACCAATAGTCCGTACCTATTGACGCTCCCCGTGTATCCGAAGACGCCAGGGACCCTCTTCTGGTCGCCGACCGAAAGAAAGAAATTACTGGGATTGGTAGCGTCCCGTGGTTGAGGCATCGGCAGGCGGGATCTTCTAGTTAGAACTAGAATTCGGTACCTGCGCCCTCATCATCTTTACCATTCCCGATGACCCCTTAGGCCGAGTCGAGGACGTTCCCTAAGTCAGATTAGCTCTAGCCTGAGAAGCTCTTTACGGGACCAACTACTTGAGTGGATCGTGCCTTTCTATATCCTTGTCGCTTATGAGTTTCGGGACCTCGAGTTTTTCTGCAACCACTTGGCTACGTTAGGACTGTTAAATTGGCCACTTAAGTCCCAGAGCGATGTAGACATAATAATTATCGCCTCTTTTCGAGATATAAAAAGTATTCTGTTCTAACATCGTAACATAATTACGTTGCGTCTGCTCCAGGACGGAGATGGATTACGCGGCTTGCCTTGGCGAATAAACCTGTCCGAAAGCTCCGCGCGTCGAATAGTAGCAGTCATAGTGCACCGTGGGTGATCTGGTACCGCAAACATAGGGGTTCCCTACTCGACTAGACATAGAGGAAAGTACCGACATACAGAAATTACTGGGCCCGGGATGGCTTCCTGATCTACAACGTTAGAAGGATCCTGTGCCCCCGCGGGGAATCCTGTTACGGACTCACGCGCTCAGACACTTGCGATTAAGCTGGAAACCTTCGCTTCGCCCCAATGCGAGTCCTGACTGGGCGGTACCACGACCGAAATGCGTTACGCGTGTGCGTAGGACGTGTCGCTTCCAAAATCGGAGGCATTTCCCAAGGTTCCTGAGGGGGGGTGCTGAGGGAAGCTAAGCAGTAGACGACGCGTGTTACGCGAGCGGATTACTTCCTGGAGTTCTTCGATTCTTCACAGATTCGATAATAGTCCAGCAGCCGATATGCTTAACCTGGAGTTGAGCATCTCGCTACAGCGATGTCTCTAGCCCATTTTCTCGTGTGCTTGAGTCTTCTCACACGCAAACACAGTTTCCTTCTACAGATAGTTGGCTATTTTCGGACCGGTGTGAGTGTCATGAGCCAAGGGCTCGCGCCCAGACCATGAAGTCGGAGAAGTCTGGTCAGGTCCCGCCCGATACGTGCTATGTTCAAGCAATTTGGCTAGCTCTTAGCGCCCCGAGTGAGTATACATTCTTTGACGCTGGGCCTCTGGCGACGCCAACGAGGGTCCAATATGGCTACCGTAAACTGCACCACGGGGTGGAGGTCAAAGATATCGGCCGCCTATAATGTTGCATCACCAACTCAAGATATAACTGCTACGAGCTACTGTGTCCCCCAGTAAATAAGCGACGGCTCGGCACAATTTGTGGGCAACCGAAGTGGCATTCTCTCACGGATTTACGATAATAAGAGCGACAGGCGAGTGCCTACTTAGGCTATCAATTTTAGACAAAGATACGTGGGTGGAGTAGTCTCTCGAAACCGGGAACCTATCGTATAAAGCTATTGACTAATTGACCTACTGCGAAGGTGACAGCAGCCGCCATCTATAAGTCCGCCCAGCAGTGATGTTTGCGCACTATTGACGGACGCACTCTTTCAATCTGCGAGAGACCAGCCCCCATGCATGGCGAGTGACCGACACGAGGATGGATTAACTCCTCGGCTGTATCTTTTGCCTGATGCGCATTCGACACGAAAGAGAGCTTTACTAGCAAAGGATCACCGTCTCAGTGTGTGAAGCCAAGTTCTTGCCTATGGAAAGATCGCGGTAGACAGCTTCTTGGCTATTACGAGGTGTTGCCCCGGCACGAGCCTACAGTCCTAGCCAGAGGCCATCCCTCAGGGTCACAAATGTGCGTCTCGCCGGGAGGGTTCATTAGAGTCCTGTGCCAACGTAAGAGTAAGACTTATCCGGCGAACTTTGTGTGAGTCTTATGGAAACACCATCGACGTTTATTATGTATATGCACGTGGCCTGACCTAGGAGCGTAGCGACATAATGGGCGGTGAGTTACCGCTCACGGTACTATTGCTGACTACAATAGTCATCGTAGAGTGATCAGTCCGAGGCGTATCACGTAAGTTAGATAGGTAAACGACATTAGGGCTACGATCCGGCCGAGGAGATAACTATGACTAAAGTCCTCCGCATAATTCAAGTCTCAACTTAATGATCGGACAAATACGTGGATTTTACTTCCGTTCGCGCGAAACCGTCGGGAACTCATCGTTACCCTGCGAGTTCCCGCCCCTGCGATACATGGAAATCCTGGAACCGTTCTACCACAGTAGAGTATGAAGTAACCGCATCGGTCCCGTGATCAGTCGCCAAACGAATTAGGAGTGGGTTTTTAACGATGCCACCTACGGCACTGCGAGGTGAAGGGGCCGCCCAACTATAGAGAAGAGGAAAGGTTTGACTGAAGGGCGTCCTGATAGCCGGCATATTGTTTAGGGGCAGTCGAGGGACCAGTCCAGCGTTCCAGCGGCACAGAAGGCTTGTAACATGTTAATTCCTGGCGAAAACTGTTACACGACAGCGGTGTAGTATCTAGTGTGGATGATATTGGTGTATATTCGAGTTTGGAGATTATCTATATTACAAGCAACCACAGGGTATCCATGGCAGGACACAACTTGCCTTCTAGTGCTATGTTATATGATTCGCCAGAGGTGCGGTCCTCCTTCAATGCATATGGGGCGATATTGTTTAGTTTAGGCTCGGTAGTAGTATTGTGTGATCTGAGGGCACACTGAGACTTTCATGCTGAACGGTCGTGACTGGCGAGATGAAAGTTAGTGTTTGAGCGGGGACCCGGCGAGATCTAAGATGCTAATGACACTGTGGTCCCGCGCCCGTAAAGAGTACCCCTATTGACCTACGGCCATAAGGATTGCCGTTGAGGTAAGGGTCCATAGGTCTCGAGCGGCACTTACCCTGGCTAGGCTGATGCTTTATCCTATATCCTCTGTGGATGTCACATGACGTTCGTTACTACTTATGACTTGCTTCCTTGAGGGTAGTGGAGTTGATTCGGGCACGTGATGCGCGCGCAGGCGGGCCACAGCAACCACGTCACCTCATCACAATGCCTCTAGACCGTTTTCCCATCATAGTTCAATATACACAATTGGCATGCAAGTCCGCACCTCTGTAGTACGATCGCAAGGGGGGTCCCATTTAGACGCCGCATTATACTAAGCTCATACTTCAGTGATTGATTCGTGGTCCCCACTCGGGACAACGTTTCTTCCCCACGCGAAAGTCTAAGCGGCTTTTTGTTTTCTCATCTAAAGATAGCTCCCATTATCAACAGAGACTGATCCTGCTGTTTGTGTTGTGTTAGACGGGGAAATGCAGTAACGTAGCGATCCTTATGGAATTATAACAGCAGTTAGTCCACCAATGAGCATTAGACCCTACGTAGGTCAACCTGAGATGGGTGATGGTTACCTATTCCTGCACAACAATTCTGTTCTGCACCGCATTCGCATTCTATGACGATTCTAAGGTGTGACCTCTAAAATGTTCTCCCGGGGAGGCTTATCACCGGAATAGTTAGCCTGACCCCCCACAATAGATGAATGGTGAGGATTAACTTTACTGCGATTAGAGTCGCATTGTTTTCCAGGCAATAGCGGCGGACCCCTTACTCCACTCAAGCAGGAGCTCGAGCCATGCGCTTCTCTGCATTCCACGCTAACCGCTGTCGATCTCTGAGAATGGTGCAGTATGCGACTCCAGTCCCTAGATAGTCGCTAAACCAGTGATGAACTCAGCTTAGATGGGTTTTGACTTCATGCAATAAACAAATGCATCAAACGGTGTGACTCGGTCATGATTGCCAGAGAATCGTGGAGGCAAATAATTACAGGAGCCGCACCCGAATTTCGGCAATGCTTCTGTCACAAGAATGGAAACATGACCTGTTTACAGTTATTCGCCTTACTGCCTCGTTCGATTGTACCTGCTTCTGAATGATACACGAGTTCGCTAACCGCGTTTGTTTTCAAGTAGAACGAACGATTGGTCTGTAAGCACAACCACTTCGCTTTGCACATGGCTCACGATGTCTCGTCCGCGTCGGTACGCGCGGTTGCCAGGCCAGTGCCAATCTATTACCCGTTGCCATTTTGGTGTCACTCACGTGTATAATTGCCATGGGATTCACGCCTGCGATACTAGACATCCTAAATTTTGAACTCAACTCATGAGGAGCATTTGCGTAACAGCGTTTCAGTACGCCCTTTTGTGCTTTTAATAGGCGCGTCGCCCATATCACGAGGTATCGGCTTGGGTATCGTCATGGCTCGAGGTGAGGCATCCTATCTGGCCAAGAAACTCAACGAAGACGGAGGCTGGTTTTATCTGTTTCTCTCTAACAGGCCCAGCGCGCTAGCCAAGGCCTCCGTCCCCAGCCATAATCCAAAGGCCTATGTTTATACTGTTGGTCTCTCTATAATTCTCGCTATTCGTATCTTGAGTAATTATTACCGGAGTATTGCTCGCTCAGAAGCCTATTATCTATCCCGTGTTGACAGTGCCACTTCTGTCCTCACTCTGGAGTGGCTTACCATGGAGAGATTGAAACAAATCTGGCTACCCTAGCAGATTATTACTAGTAGCTTAGCGAAGCATCGCGGTGAGGACGAGAGCATTCCGTGTCGCTACCAGGCCTCCCCGTCAGCACGACCTTGGAACGATAAAGCAATTAAATAATGAAGGTGCAAACGTGGATAGAATGAGAGGCCCACGCACTCATGAAAAGGGTCCACTTCGCTTGTCATATGATTTTCGCCCCCTATTTGAGTACACACCACTACTAAGGCCTAAAACCAACCGTACCGCTGGGACGTTTCACGGGACTCATACGGTTGTTGTTAAGAGGCCGTCATAAAAGGATGGCCGGCGATAATAGCTGCACTTGTGTCAACATGTATGTTAACAGTCACGCCGCTGGATAGTTAAAGTTGCCAGTTTAGATTTCGCCCCGTGATCCCATCTCTAACTCCCATCGGAGTTGCCGGATAATCCGGGTAGTCCAAAGCTATGTACATGCTGGATATGAATTAACCGCGATTTCACCTAGAGCTCGAGAGCTGTGGAGATCTGTATCGGAGCGCTGAAACCATTCTTACAATCATTGGAGACAGACATAGGGCATCCAGTCATAACAACACGTTTAATCACCATGACACTTTATCACTTGTCGGCGGGTGCAGCCCACCACGCGCCAAAAGGAGTCATCACAGTGTACTGTGGGTGTGGCGTAAGGTTAACCCCAGTTCAAAACGTGAAGAGACCAGGGGTCTTGCACGTGTAAGGATTTTCCGGTTTCTCATAGGTACTGTTTCCCATATAGGTTTAAACCGCTGACCGACGAAGGCTCGGGGCCCAACGTTACTCCATTGAAATACATTCGAGAGAATGAGATCGACGTACCCCTCTAAACTGGCTAATCTTATGCCCCAATGAAAGATCCCCCGGAATGACTATATTGATAGGTGGCCCGGCGGTAGTGTCAAGTGCCAAAGAGACCACTTAGGGCACAGACGAGATCCTGAAGTGGGTCAACCTCGACACCTTAGCAGAGGACGACGAGCGAGTGCGCCAGCCCTGAGCGCGCCTTCATCTTTGACTATTGATGGCGGCGACGTGTTTATCGGAGCATGCATTACATATGGGAGAGGTCCCGAGACGACGCAGTAACCAATTTAAGAGGCAAAACAAGATGACCGCCTAAGGATCTTACTTGGAGCACATCATCCCATGGTTAGGTGGCCAGAGAATAGGAGGTTTAACCCTGGACCGAGACGATCATATACAGCGCTTTAACTACCACGTTTCACGCGACTTAATAACCATTCTACGGGACCTATTAGGAGGCTATAGCGAGACAAACATGTTTTTTTGTCCTCTCCATCTGACTTGTTAGAGGGTAGACTTAGCGAGCTTACGAAGAAAAGCCGTCACCCTCCAACTGAAGCCGAATGTCCCTAGCCGTGAAGAAGAAGTAGAAACTTATGCGTTCTCCCTTAAGTAGCTGATCATTCGATTTGATCGGGTTGGACGATGTTCTAGCGCAAAAAGCAAGACTAGGGTTAAAATATTGCCGCTCCGGAATGCCGTTGATGCCTCTGCCGCACGATGCCCCTTGAGGCCCTACCAGCATTAGCACACTTGCTTTCCCTTTTGCGAGAACACGTAAAGCTAGACAGAATACTATCGACTGGGCGAATAGGCAGGGGAAATTTCGACGAGTCGGTAAAGAACTAGGGGGACTGAAATTTGTATATATCCCGTGCTTCTAAACCGGTTACTCGGTGAATGTAGTCGGTCTCCTAGGCAGTATTATAACACAGCGGTATATCTGGTCCATTAACCGAAGACTGTAGTAGGCGCCCGTGTTTAGGTACAGAGTCGGTGTGGGTAATCAGATACCTATACTGGGGTGGATTCCATCCCTTGCAGAAGTATTTAGGGTAAATGTAGAGGTGCTGTGTAGTCCTTAATCTTATGACCCTGCCTATGAAATGCTGTCAAATCCTCCGATACGTCTCTAAGGACTCGTACGCAATCCGGCATCTTGAAGCAACGCCTTCCGTATCGGCTTCAACGTAGCCATATGAGGCCTGTCTATAACGTATTCATGCCCCACGATTGGTTCAAGCGGGCCTTTTGATGCCGGTAGATAGTATCAACCCGTACATCTGACAAAAAAATTGTCACACCGGCCCAAAGCGACTGTAATGGACCCTGCTTATCGTGGTTTCAGTGCGGCAGAGATTTCGATTTGTCGTTGCTTTATACCTACAAACCACTGGAGCGTCCTACTACGGTCATCTTTAGTCGGTAGACTACTAAGTTAGATGACTTAAGTTAGGAATAGAGTGTCTCAGCATACATTGTGTGGACTACGTAAGCGCAAGAATAACTTAACAATAAAATGAACGAGCTCGTATCAGTGCGAGCGAACTGGGAGCACTGGCACCGTTCTGATATGGTCCAGTATTTGTACGAGCGTTGTCTAACAGCGATAGGGCTGGAGTACGAGACTGAACCGATCTTCGGTATCGTGCTTCTCACATCCACAGCGAAAACTGAGAGTTGCATCTTGGTGAATGCAGTCACCGGGAGTGACATCCTGCAGAAAATTACCGCTGTACCATCGCTTAACGCCGAGCCCTATACTTATTTACGTTGATAACGTGAGTGGAGAAGCAATCTGAAACGTAACTCCCAAAAACCCCTCATTGAGAATTCAGGCGACCCAAGACCGCCAACTTTCACCCATCTTATCTGATGTCACTTTAAACGAGCGACCTTGACCGAATGAGGGGCTTGCCACCACCTCCAACTCCAAGTCGGAGTTCGCTCGCACAGTGTTCTTCAAGTCGTGATGTTGATCTGTCTGGGGCCAATTATGCTAAATACTGTCGGTCGCCGTTGCTTTAATAAGCCGAAACACCAGCTTAAAAAAAAGCGACGGGCCTAGCAGCGATCAAGCGCGTACCTCGATAGGCTTGTGCATGCCTTCCAATGTTGAGGATCGAGGTCGGCAAATTACAGGCTTTATGGATCATTGTAATTCAAATCAGGTTGGTGAAAACCGTTACCTCTAGGCTCGTCAATGCGATCGGGGTCCAATGGCACGGGACATATAAGGGGGCTATGGGTGTGATTACGCACAAATATTGATTGAAATGGACAGTTTGGGTCATGCGCGCTCACCATGTGCACTGTGGGTAGACGCAGTAAGTCGAATTCCTTAGTTTCCGGCCGGTCCTGTGTCTAGAACACAGATGCAGCGTCTCAAACCGCTACACTAGCAGTGTTCGTAGGTAGACGTTCGACGACTTCACCAGTGCTCACGATAGCCACCGGGGTACACACCTTCACCTCAGGTGTTTAATTGGCAGCCAACCTCGGCTCTGTTCCCTGCTGGTCATGCTGAACTAGCCTAGCACTCCTGGACGAGAAGAGACTTGCGGCCGTCTCCTACACATGTCTACTTAGCAGGGTAACTGCCCATAATGGCCTCCTATTCCTGGAGGTCCATGGGTATACGCTTGGAACCACGCCTTTGTGAATTATGCGAAAGGGCACTACGCAATATCAGTGGGAACAAGTACGGCAATTCCGAATTATGCGATATTCAAGGCCCTCATCAGTTGCTAAACTGGGTACCGGTACGCGCACTCATCGGGAGACTCAGGTAGCGGGTCGCAGCTACCCACCGGTCCGTGAGTCCCAATACACAAACTTCCATCGGGCTCCCCGAAATAGTTGGCCGTCGCACTTTACTACCCCGCTGCGCATCTGAGCAGGATTGTTAGTTTGTTGGCATCAGGTAGTCGTTAGCTCCGCAGTAAACCATCATACCGTACAGAGTGAACCTGTTTATGTGGATTGTTTTACCTGCGATTAACGTTACTAAGAGAGGCGTGGTGTGCCAACTCAGCGAAACTTTAGGGCTGCGGAGTCAAGTTTTCGTGATTTACAGCCACTTCACGCGATGGTCTACTGTCACACTGAAATTACGCTCCAGAGAATGCAGTTAATGTAGGAGGAGGTCTTGCTTTACACTAGCCGACTAACAGGACTCTCGTATATCGATGTGCCATGTGCCACCTTGCCCCCCGTTACCTGTATGCATGGCATCTATGGATGCTGCTCTAGATATTGTGGTAATCTGCGGGCCCAGCACGCCGCACGACCTCCGCCCATTGATAGTGCATCAGGTGGTGCCTTTAAACCTCGATGTACCCGGGGTCGTCTACCTCAGGGCTTCACTAATGTGAGTACACATACAATCAAGCCCCACCACCGAATTTGTTGATATGCTCGACGAAGTTGCTTCAGATGACCGTGAGCAGTCGAATCTGCCCCGTACTCGGAATTGTGACAACTTAGTTGATACTTCGCTCGTGATGGTAGATGGTACGTACGCAATTTAGTAATTGTGAATCCTGGAGTTAGGACAAGTCTGCAAATCACTGCCCGCGAAGAAGTGTCGCAGCGCGAGTGTGTTCTCCGCGGGATAAGCATTGTATCCAGGAGCTTTGACGTTTAAACGCAATGACCCCAGTGTCGTCAGCTCGGCGTGCTTTCACGTATCAGCAAGAGGCTTCAATCTCCCTGCGGCTTAGGCCCAAGAAACCCGAGCACTATTGGGATTGTGCAGCGGCCCGCTCGGGATTTCACTCTTCATGTCCTGTTGTGTGTGTGGTATAGCGGCGAGTTATCAATATATATCGCTCCGAAGAACGGCAAACGATGAATAACGTACAACCGAATAGTGTTCGCTTTATCCCTCAGCTTAACTTGCAGGTGAACAGAGTTCCTCTCAGTCACAAGGTCTACGATACTTACAGTGGGGAATAGCGCGTTGATACCATATGTAAGTTCAGCCCCAGCCGGGGCCGCCGGACCCTCCGATTCGAGTCTGTATTCAGTGAGGGCGTAACGAAGTTCTGGCCGAGAAATCAAGCGGGTGAACGAGCGCTTGCGCGCGTCGAATTGTCAAGGAGAGAGCAAAAACGCTGTATTTAGCCCAATCCCGCAGCTCCTGCAGCGGGAATCTGGAGAGAGTTTGGCAGGAGGCCTTTCGAGTGCAAAGCTTTCACGGCAACGGTGATGATGCTCAGGCGGCCCTAGTCTGCTACAGGCACTCAAATGCACGGCTTACAGAGTTGTCGGAGACTCGCGTCATATACTTCTACCTGCCGTTTTGTGGGGTGAAGATGGGCGGATGACCCTTATCGACTATATCTTGAGGGGCAGTGGACGGCCGTCAGGGGAATTAATGATGAAAAATATTTAAAACGGGGGTGCTCCAGATGAAGCCGGGATTTACAACAGCAGGCCAACCCACCGCAAACTTACAGGAAGAATTTCACCCTTCTCGGGTAGAGTGACGAGTAAGCCGACCGAGACGCTCCTTCCATGGATTAGTACGAGTTCGGAGAAACAGGTATTCTAGAATTGCCACCAAAACGCGTCTCCACCTCCCAAAGATTAACACACTCGCATTGTGAGCCTTTTCAAGGGGCGCCTCACAAGGTCACCCACGTGGAATCGGTGATGACAAAGACGTATCAAAGAATCCTGACTGCTTCGGAGTGCTCTAAAAGAGTTATGATCAAGCAATCCTGGACGAATAGACCGCCGGGGCAGGGGGGACCGGCTCGGGGTAGCACGCACTAGCACTCGTCGTATTGGTCGCTCACAAGAGTCATTTGGCCATTGCACGGAGCGATCAGATGGGGATCGGCGCTTAGGGGATAATATTACTCTCACCAAGGAAAGCAACCTTCGAAAGGAGACGTTTGGAAAGCCATCGACGACGCCTCGGACTATAGAGCTTGAGGGAATACGCCAGCTATCAGCGCGGACCCCGAGCTTTTCGGCACGGTGTGAAGGGTAACAATCGATTACTCATGCGGTACAGACACAGTAGGTCAATCAGCACTTTTAAAGTGTTTCACAGAGCCCATAAAGTCTCTTCAGAGTCTAACGAAAGGAAGGTGTAACTGTGTTAGACTCCGGCCCGACCCCCAAAGCGGCCTCGATCGATCAGTTATCCTGTGATAAATGAGTATGGAATACCATACTTAATAGTAGCCTCAACTTGACGGCACCACCATACTCGACCGATCTCAAACAGGGCTAGGGACGCGCCGGGATACCTGGCCCGTTGTTCTACGGAAGCTTAAAGTGATATGCTGCATTAGGATTTTTGTCTTTTATGGGTACAACCTTTACCTGCGCAGTGTCTAGGTACCTGCGGTCGCCCTCAACAAGGTTGAGCATTTCACGGAAGACCTATCTCTATGACGCTCTGCTTAGTCGCCAAAAGTCGTTATTCTAACATCATACACCGAAAAAGTTGCTACCGGGGCGGCTTTGATCGACATATCGTTTAGGACTAAGAGCATTCATCTTTAGGTGCTGTGTAGAGTCACGTTCGCACCACTCAAACGCAGTCCGCCATAAAACAGCTCGGCAAGATCAGGATCCCCTTTTCGAGCCCCCCAACACTATATAATGCCTCAGGTTGGCATTAGTGTGGGATCATGAGTAGAGAGGAGTGAAGGCTTCGTCAAAGGAGGAACACAGGATTTAGGCGCAGATGTGTCATAAGTTCATTGCATCTGTGATGCCTCGGGGGTCGGGGTGGCTGCCTATGCAAAACACTATAAGAACTTTGTCCTTTTTGTGATTCGACCGCGCGGAGGCCAAACTCATAATCATCTCGAGCATCACGCCAGAATTGATCATCTCTCAAAGCTGGTAGATTGGCTCATCTGCCTACTGTACCGGCTCTATTAGACGGGTGATGACATCATAGGTATGAGAAGCAGTCAGTACAATCGATACTGTCCGATCCTCTTCAGCGAGTCGGCTAGCGCCAAAATTCGCTCAGCTGACCGTAGTGAGACTAATTCCGGTGAGAGCTAGCGCCGATATGTTGGAAACCGTTTTCAGCGGATTCTTAAAAGAACTCCCATGGCTATAGTAGTCACGCCTAACGGGCTGGGCAGAATTTTTCCAACCCGAACGAGATTTCCAGAATTGGCATGGCATGAGGCTATAAACCCGAGTTCTTTTGTGGGGTCTAACGACGAGGTTATCACACGACGACGGGCACACATAGGGATGGTGGATTTGTATGTATCTGCTTCTCAATTTTGGTAGCTGTAATACCATCCGGCAAGTGATCCGATACATCTGGTAGCGAATTGAACGCCCCTGGCTATCACACCATCTGAGCGCTAGTCCGTATGAGTAGTACCATTGATATTGTTGCGGGCGCCACACGGTTTACGCACTAAGCCTATGTTTTACAAGGTAGGAGCCCGCCGTAGAGTAAGATAGCCGCGTCTCCCCCCCACTCCAGTACGCCGCCGGAACGGTTCGTTATGCTGGAAACGACTTCCCGCCACTGGCAATAACTGCTAAGCGTAGTTTCCAGCTCAGCGACGCAGGGCACACGAGAGTGTTTCTGACTGACCGGATCAACGCTAGTTCACACGGCCCTGTACATGACGAGGCGCCCGTTCCTAACACTTTCAGGAAGGCAAGTGGGCGGTTCCAAAGGTTGTTGAGCTCCCCCGGTATGGCGTCTGCGAGCCGCGCCCGCCGCCACGCGCTTGCCAAACGATTACCGCGAGGGAGTGTAACCCGGTTTAGGATGATTTTATTTGATGGATTCAGTGAGTTAAGCTGTACTCAAGCCCCGTATCACTTACGCATATCATAGGGTTCAGTGGAACTAGATATGCGCCGTTTGGCGATGATGGAGATTACTGGGATCCAGGTCTGATTGTCATATCCCTGTGTCGTCAACTAGAGGGTTGAAAAGACCAATTCTCCTCACGTCGGTCCGATCGCCAGGACGAAACTAATGGATCCCTTATTTTTACCCGTAACTACATACGTTTAATACCAGAACCGCCAATCTACTGGCTCTGAGTTAGCGGCAGCCCGTTACTAAAATGACTGTACTTGTAGTACGCTTTCGACACTTTACGGAGAGCCTTTCTTCAGCCTTCCTGAACGAGTTTGCGATTCACGATGTTTGTCGCGTCATATTGGAGCTCTGGAAGAGAAGGGGAAAACATGCTTGGCCATAGAACGGAAACAGCTGGTTGGGCTGTCGTCTTATCGTTGGGAGACGCCATACCCACGCGCAACGCAGTCGGTCCTCATTGGAGTAATAATACCCGAGCGCAACGAGAGGACTTATAAACCATTGGAAAAATACAGAATAAAAAGCCACGAAGCCTGGAACAGCTTACCCCTTATAATCAGTCGCCTCCTCTTATCAACATAGATCAGGCCGTGCATTGATTACGTGAGCGAGGAGGCTAATCGATAATTTCGTTGTACGAGACGAACGGACGTAGCAGTACCTGGGGTCAATTACGCCTATATTGGTCAATAATTCAGGTCACCACGTGATTTGGGGTAGACTACCGGAGGTCCATTCACGGTAAGTGTCGGTTGAATCGCGATTCCGATGTCATCGTTACCGTTCTGCGTTTTTGAAATCGGACCTGCATTGGAGCGTTAACTATCCACTTGTACTGGAACCTTATGAGATCCAGCAGATCCTATCACCCGGGCTGAATCGTTGACAGCTTCTATTAGTGGCGGGGTTCTAGCAACCATTGAGTATTGAGAGGAAATGCAGACACTGATGCCGAAGTCACAGGAGATTCTTACTGCCATTCTCAGCACCAACTGAGGCTCCAGAAAGTTGGGCCAGGCCTAATTTAGCTAAGGTATGGAATACACGGTTCCGTTTAATAGGGGTAAATCTTTCGGAGGAGCTCGAAGTTAAACCTCTGTCGCTCAAATTCCCGGCGTTTCTCCCATTGTTCGACTGTTGATCGGTAGTGCCGTCCCGGGAAGCCCCAGGCATTTCAGCACATGTAACTGCCAATGGTAATTTTGTCTAGGCGCATCACGCAAAACTGTAGGCTCGAGGCACCAAGTTCCTAGTATCAGCAGTTTGTGGAAATGGGACGGTGACCTGAAGGTTACTACCATATAGCATTCAAACATTCTAGATGAAGTTCGTTTCGACCGCTTTTGACAACTGTCTTGGAGAACTAGCAAATTATGAAAGCAGGGTCCCCGCCAACCTCTGTAATCATTATGTACTCTTGGGTTCGAAAATGAAAACCCTGATACCTCAATAATAGCGGTTGGATGAGAGCACCTCCAGCAGTAAATCTATACGGTTCTAGGACGCTTAACACACCAAAATATACCGCTCCTTGCGTTGAAAAATCGACGAAAGCGTCCTCGGCCGGGCTAAGTACCTGTACAATATGTCGATGAAGCGGGACCGGTTTCTATTGACTTAATAAGGACCCCCGCACCTTATGCGGTCAGGCTCCTGACTCAAGCATTACCTAATCTGGTACGTGACGTTGTTTGTTGTGTTATAGAGAGTATTTAGAACACGAGACTGGCTTAACCTCCGGCTCAGCCAGCCAACCTGATTACATCGCCGGTGACATCCGAGCCCGAGAAATTTCCCCATCGGGTGCTTTGTATGGAAATGACTGTTTTAGCGGTTGACGGATTGGCTCAGTGAGCATTTCGCGCTTAGCGGGCAAGGCCGGGTCATTATTTACGCCGGGCTACCGAAGCGGATTTGATTGGGGAAATTTTTATGTAAGGGAATCCTAACGGCTGCCGTTGTGGAGCTTGCCGGATAGTGCGGACAAGCTCCAGCTAGCAGCACTTCCCAGCCTATATGCTACCAGTTGAGCATACCAGAATAGCTTTGCGGTGCACGAGTAGAGAGAAGGGCAGGATTCAGACAATACCTAAAAAGCCGTAAAGCAGCCTAGACGTCTATTGGTTTTCTCCGGTCTGGACGTCTTCCTTGTAGCGAAGTTCTACAAGTTCTAACACGATGATAGGGAGAACAAGAACCACTTTTCGATGTCATGGGAGTGTACTCCAATAAACTCTAGACCGGTTAATGAAGCATCACTGTCGGTTCTGCAGACACCCACCTAATGCGTTCGCTCTGCCAAACCTTCATCTCAGCGACTGTACACAATTCGCTGTTAAGAAACGTTTCATTTCGAAATCACGACGCTTACACCGACCTGAAGTGGAATTACTAGGTGAACAATAGAGTAGGCCAGAACGCGTACGACTAGGGAAGGTTTTCAGGCCATGAGGGCCTAGTTTATTTCACGAAATCTGGAAGTTCCACCATGAGTCTGACGGCTCGTGTCCGGCGTGCTGTAGGTCAATGCCGCTTAGCTGCGCCGAACTGGCGATTTATCCAAGGCAATGTGTAAAGGAAGTGTCTGTGCGGTGACGATGTTGGGGAATTACCCAGAGTTCAATTGGCCGTTAGATCCCCCCGCCGTCCTGACTGAGGACCAGCACTGCAGTTCTGTTGCCTCCCAAAGAGCCCTCCCCGGGTGACTGAGTAGCAGGGGCTGCAAGACAAGGCCCGACTAGCCGTTCTAGTGGCCGAGAACCCGGAGTTTTGTTGAGCCCGGGTCGCGAAGGTTCTTTATAACTGCCTTGGCCATCAATTGGTATAGCGTCCCCGTCATGTCGGTTTTAAGCTCGTCGGACCCTCATAAAGTCGATTCGTAAACCATCATTATATGCGCCACTGGGTCGTACGACTGCAGAGAGCCGGGCGGAACATAATTCCGAGCGTCGATTATCCCAGCACAGCACGCGTCGGGTTTTAAGCTACAGCTAGAGCCGTCACTAGATATTTAATCGGCATCGTATTAGGATGTCATCCATGCCAGTAAACGCCGCTCAGCTCTAATACGATCCTTGTGGTGAAGAGGACGAAGGCCGTTCTGATCTGTGTGGTCTATCTTGTCGAGATGGACATGATGCTCAACCTGAACCTTAGTTTCGTATAACGCTCGGAGCCTTGGGACAGCGAAGGTCAGAGCGTATTCTGTTATTTAGCTGGCAACGTTGCGCTTACCTAGGTGCGGCCTTAATGAAAGAATTTGAGTTCTCGTCGTTACAACTTTTGCACTCCCCAATTAGATACTTAGTGAACCCACTCCTACCAGCATACACACTATCACGATCTGGATTCAGGCCTTCGTTGATGTCTCATCGTTACGATCTTACCAAAACGGGATGGGGCAAACAGAAACTCACCAAACGGGCCCCCCTCCCGAGACCTACATCGGCACAATCATTAGTCCACAAAAGACAGATATGCAGCCAGCTCTTGAAGTTGGGATTTCGTAGTAATTCGTATACCTTCCCGGTATGCCGTACATGTGTGGTTAGCAGATCGGGATAAGAAATCGTCACCGGCAAGAAGGCTGATGGATTTAGGGTCGCGAGGGAAACGGGTAAGTGCTAGTCGTCTACGGCGAACTGTTTCCTAAAGGAGGCTATCGTAATTCCGAATCGGCCACCAAGTTCTATCGTGCTCCGTACGCTGGGTTAACACCAATGATTCGTTGTGTCCCGTCATTCGTTATAGGCCGATCAGCGTCGTTACTGAGTTATAATCGGGCCCGGACTTTCGAACATTCTCAGGGAGATGTGTGTGTAGAGAAGCTATTAGTGCACCAACAGTTCAAGAACTTGAATATCCCAAAGGGCCATAAGTTATGCGTATAAAGACGAGAGTTAATGTGAATGTTAGAGGGTGCTCTCTAGTATGAGGGCATCCTCCTGGCATTAAGTACTGCTGATAATCACCTGATCTAGGGACAACAGTAGCGCTTGGCAGTGGGGTTACACCTACATCGACGGTGCACAATGGAATTGGTAGGCCCTCGAACGTGTACCATATAGTCCTGCACGGACATTTTAATTATCAATTGCTACCTTACGGGACAACTATCCGGTATCAGTGAAGAGACATCCAGTGCATATCAGTAACACCTTCCGGAGGACGCTCACACTATCCCCCCGTTGCAACCTGAATTATCTTAGGAGGCTTACACGACCTGATATGGGGGAGCCCCTTTGCTGTAGGCACTTATTGCACTGTGATTGTAATCATAGACGTCCGAGTCTTCCCCGGCTGCTGCCATAACTCACGTGAGTTTAATGGAGTTGAGCAACTAGTAAGGGCTGGCGACGAGTAAAATGGTTCCTGTTACAGAAGGAGGGGTCTTGTAATAGGGTTGGTCGGCACATGTGGCAGGAACGTCTGGGGGGTTCCTCCAGTATCAAACCAGAATGACGCCGAATCTACACCTAACGTTTTGCGTGTCTACCTCTAATCATCTCAACTGACGCAAGACGACAAGCGCTGGCGTTGTACCTATCTAGACCCCTGCCTCCCAACAGACCGAATGTAAACTAGTATGATCAAAAGCTCGGTCTCATTTACACTTCAGGAACCGTGCACTCATAGCCGGCCTTAGGTCTATTGCGTAAGACCACGGGATAGATCCCTGATCTCCAAGCAGCTAAATGTTTTGCGATACTTGAGACTAGAATTTATGCTAACAATATAGATAGTGGGAAGGTAAAATGACGGCCCGGTAAATCCGACAGGTTTGCATCGGCGTCATTTAACGGAACTGGTCATTGTGGAGGGGTTGTGGTATAGATACGTCCGTTCATTGTTTAATTAGTTGTTTATAGGTTGACATTTCACGGGGACCCGTGAGTCCTAAATTTGGCACACTGTGCGGGCCGACGTCCTTTAGTGTCAAGGTCTAGTTAATTCCACACTTGAGCATCCTTTCACAGAACGAGCGACTGGCGAAGACAGTCTGTCCGTGCTTACTGGTGAAGGTATCCTGGATCTATGTGATGACTGAAGAAGCCTCCAACAGGTAGGACAGTTACAATTCATCGGCCAAACCGTACATGGACGGATTGTCCCCTGGAGTCTGATAGATAATAAAGGTCCCGTCAGCGTCCGAACGGGTTGCTCGCTCAGAGAGACAGATGACACAAGTATCCGGGTTGGCGTCCACTTCTGAGATCCAATGGGCTTCGCCAGCGGAGACATTCGGTTTAATAGGAGCGGCACATCGTTTTTATCCAAACTGACTCCCCCGGATTATAGCACGGCATCTGTTACTTCGTCTTCGTAATTAGCCGGTTATGAAGGTTGGCAGCAACGCCCCATGCAAGAATCAAGCCATCGATAAGCCATGCCTGATAGCCTTTTCTACCGGTGATGAGTTGAAGACTCCCAATCTCCATCCTTCGAGCGCGCCGTCGCTTACGGTGCATGCCGAGGGCTACGGCCTCCTGTACGCGTCCCTAGCTGTCGCGGAGTGTTATTTGTCATGTGGTACTACATTTCGCTGAAACCTACAGCGGGGGAGGCCTGACGTTAACTGGGAAAATCCTTCATCCCCTACGTCTTCTCTTGTGTACGAGTACTCCCCATTTAAGACTGGTTCTAACCCTACTATAGCCTGAGGCAGGACCAAGAGACGCAGCCGACACCAAAACCTACCAAGAGCGGCAGATTCGGTTACCCGCGGAAGCCTGAATTGTGGAAATAACTCAAGCACACACTCAGGGATACTCTTACCCGCCGGTGCGTTGCACGTGCGCCGACACTGTCCATCCCCCCACGGGAGTCGTCCATAATCCTCAAAGATATAATCCCTCGCAGGAGTGAGCCCGCGATATCGGCGTAACAGCGAATTACGTCGCGGGTTCGGGTAGAGACTAGACATGCAGGCGCGAAGACCTCGGTATTTCCCCGCGATGAATAGACGTCGTAGCGGAAATTGAACGATGGACCACGGACTTATACTAAATTTTCCCATATGCGAAAGTATTAGGAATGTCGACAACGCGCCATAGACACATCCCATAGTTTACTGACCTATCGACCCTAATAGATCCGCAGTATCTGATGTTTCTCTTACAAGAGACGCGCAGGTAACACTTGAGCCCTTCGCTTAGCGTGCATCTGAGCTAGGCAACGGCGCAAGCGTCTTTTTTGCTCTCGGGTGGGAAACCAGTTGGAGATCTCCATGACTAGAGACAGGGGTGATGACCTGGTCGGGAGTCCTCACGGGTGGCTGCAAGGACAAGCTTAATTAGGTTCACAACTATGAAACATAGGATTTATGAAGCAGGTGCTATCAGTTTTCATGGCTGGCATTTACTCTAATTCCACCTACTTGTGTGACCTAGGGGAACGTAACGTAGTTCAAAATGGCTTTCGCCCACTCTCAAGGGCAGCGAATCCTCAAATAATACCACGCCCTAACCGTATTGCTGTTTTGTGTAAGCTCTATGTGGCATGCAGGAGGCCTATCCAGTGATGAAATAGATCTGGAAATACTCTGACCATTGGAGGACCTGTCGTTAAAGAATACGGCTACAACCCAAGATACGTACCCGGCTTCGCGAATATGTTATAGATTGCCTGTGCAGCATTAAGTGATCTTCATACGAAATGAGGAGGACCTAGGAACGGATGTCGATAATGACCGCCGTAATTTGACTCCCCGGCGAAGTCACTGTGAGAACATCGCTTGCCCGCGCATCGCGTAGGGATAATATATGCAGTGCCGACTATGTCGCAGATAGTCCTATTTTAGTTGACCACACTACCGAGATAACTCGACAAGCGATGGGGGGGACGGAATGAGTCAGCCATCGTTCAAGATATGATAGAAGGGTATTTGGTCGCTCGCTACATACCGGTCAAATCCTGTCTCAATTCGTCGTCACGCTTCACAACGATGTATGATTAGAATTGATTCGGCACCGACTCAGACTAATAATAATCTGTTTAAGGGGGGGCTAATCTGAAGTGCGATCTAGGTGAGCTTGAGTCGCGAACGCGAGTTAGTGTTTATCGAAGTCTTAACACCGCTTGGTCATAGCAAAATTAGTTCGCTCACCCCTTCCTCCCGTATTCGCCGCACACCCCTTCCGGTCCGCAAGTGATCTCGAGAGGGAGTCGCGTTCAAGTGAGCACGCCGATCTAGGTCGACTAGCTGTCCCTTGGAAGACGTACAGCCAGGGTATTCGTAAGGCACAAAGTCGTGTGCTACTACTCAACCTGCCGAGGGAATGCACGAAATTACCGATCAAGAGCCAATCCTAGACGATTCGCAAATCTTGTAGACCACCCATCGGCCATGGGACAGGCGTGATATGTCTCCACAGATTCGGGGTCTAAGTTGTTGGCACAACATTAAAGTCCTTTCTTTCTTTTTTCCAGCGGGGAACTATTGCTTCTTGCCCCCTGTGATATTGGTTTAGTGAGCTATAGCGTCGATTATTCGTACAAGTCCGACGCTGCGGAGAGTTCGAAAAATAGGATCAATAGAACAGGACTAAGTCCCCTTCAACGCCCCATATGAACCTACGGATTTGTTATACACGGTCCGTCCGCTAAGGCAAACGTCGATAATAACGCATTAGTCGGATACAACCGCGTGGTCGGTGCTCAGATTGCCAGACGTTACAAGGCGGCACCAACAACAAATGCATCCATGATATATTTGAGCCGAATGACGAGCGCCAATAAGTGGCTTGGGTCGTGATGCCGCCCCCTACGGTCCGTTGAAGACTGAGTCTTGGAATTCGAGGGGGCCTGTGAACAGGTGTGTTGCCGATGTTTCACTGTGCACTCTGAGTCTGCTACACGAGCAAACAACAATATGCAACCTTCTTAGCGGCCCAAATCGCTTGGTTAAAGGCAGTTTGAAGTCACCATTGGTGAAGTCACGCCTGTGTTTAGGTGTGGTCCAGGGTATGGATATACCCCTCGTGAGACGCCAAACAAAGGATGTCTACTTTATGGCCCTATTTTCTAATTGAACATCGGCACTATCACGTCCTTCAGTTTGAAGGCAGGAGGGCTTAGTTATTAGGGGCTTCGCCGTTCTCCCGAAGACCTTTAGTGGGATCTATACATGGCGTCGTCTGTTGTTGGTGCTATCCTTTGAAAGTTCGAGTGGCGCCGAAACTTGAGCGGTCTGGAAGATGGAAACGCGACGTAACCAGGGATTTTAATGTATTTGCCGATATTAACAGCCGTAAGTCGGGATGACCTGGCCGAATGTTCGGGGAAATTATGGGGCCCGCCTAATGAAGTGCGCTTATCAGCAAAACCAGGCTTACACGATCCCGGTCGACCGCCCTGGTTATGAGGTGAACGCGGACCTGGCCTTCTTGACGTGTCTCTGCCCCAGGTAGGTTTGCAAAGGCTGATGTTGCGACATAGACTGTGGTGAGTAGTCCACGCGCCGACGTACCGGGCGAGCTATCGTACCCATCAGCTTGAGATAATGTGTCGCCCCTAAGCATAAAGATCTAACGAACAAGGTGGTTCGCACTACCATCTGCGGAGCTGAAGCACCTACCTACTGGCGCAACGGTATCAATGTTGTGGCAACTCCGCACGTATGAAGGAAGGGAGTCCTACCAACGGATTCGGTCATCTGTATACACGCTCATAACTAACCTACTCACCAACGTTAAATAAACGGATTTCGACAATGGACGCACTAAGGCATTGAAGTGACTACCTGGTAGGAGTAATTCGGTTCATTTACGAATATGTAATATTCGTTTATGGCTTATTTACTGTTAGGATGTGGTCGAACTCGCACTAGATATCATAGTAAAGGGCGGACTTTACTGTATCAGTGAGTTGACCTGGCAATTCAACAAAGCGTATGTCTCTAAAGAAATTAGCAACCGAGCCTCAAAACCTTATCGAGATCCTGGCATCCAAGGAGAGTGTCCTCATTGCTCCACAGCGAGACTTGCGACCCTGGACAGCATGCGGTTTAATGTGTTCGGGTACTGTTTATACGGGTGAGTTTACGGTTGATCACTTACAACTTCTATTGTCCCATACAGTTAAGCCCCGACGGTCTCTAAAGCCAGTTCGGAGCCATCTTTCGCTTGTGGGAGGCGCCAAGGGCCTACACACCGGATCCGCGCTCTGCCAAGGTCGCGGTACTGGAGAGTCGTGTTGTCGTAACACATGCTACCAATACCGATCTAAAAAACGCTAATAATTGTGTGCCGCAGGACCTTCTCTTAAGTCGACAGTCCTTGACTCCGTCTATGATCGCGCCGGCGTTGTACCAACGATTCTCCGCCGGGGAATGATCGATCAACATTGTCTGGCGAGCTAGCCAAATCAAGACCTTCAGCATAGACAAGTCGGCATAGTGTGGTATAGATAGCTCACCTCTACGCTTCCCTTCCTAGGCGTTCGGTCATCTCGTCGAGGGAGTGTGTACGTGTGTCTATGAGGCCAACATAGCCCTCTCTATTACTCAAGATGTCTGATACATTTACAATCCCACTATCCCCTCCGAGACGACGGGTACACTAAGTTGCTCGTCCCCGCGAAGACGCATACATTACCTGCGGTTATCCGGGGTTGCAACAGCCTGCAGGCGCTGGCTCATTACCTTTCCAAATGCAGCAATGTCCCACGTGATATCAACATGTGTGGGACAATGGCCTAAAGGTGGTTCCATACTAGCTTATACAGCGTCCAATAGCACCAGTTGCTTCTCAAGGAACGGTCTAGGGTTAGTAGCCCATTTGCCATCTGGACCCACCGTACCCGTTCCGGCAAAGGTCCAAAAAGCACATCGTGTTGAGCACTGTAAAGCCAAAGTGCAGCTGAAGACGAGATGTCCGGTGGACTGACAGAGTAGTCGTAGCAGTCTAGGCGACCGCTGAGACATCAAACAACCCCACGCGACTTTTTTCGCAAGCGCAAGGTTACACCTGTCTCCACATGCCACGCTAGGTCGTGCGAAACTGGTTTGAAGGCCGGGATAGATTACTCAATAACATACACCGCGTGTATCCAACTAGACACGCCGCTATTCCGGACTCAGTTGGGATTCCATAATCTCAGTCTGAATATTACAAGCCACACACCCGGCACCATGGAATTTTTCGGGAGGCAATCGCTATTTTAGCCGTCAACTCTTAGGATAGCAACGTCATGAGAAAGGTCTATCCTGGCGCTGAGCACAGAGTGGGCAACAAGTCGTTGCGGAACTTACCGCTGTCGGGTGCCGCTAGGCTTAGATTAAGTGACCAAGACATTGACCTCTCAACAATGGTCTGCACAGATTCCTGGGCCTCAAAACCAACTGTCGGTTCCTATCCTACATTGGGGGCGGTTCATCCAGTCGAACCGGTGCTTTAGTATCCGCGGTGACAGCGTTTGTTATGCACTATGTAGCGCCACGACTCGCCCGGAACTGGTCCGAAGTGGTTACAAACGAGTATCATTAATGCAATCCTTGTTGACCTAGTCATAAGTCCTAATTCAAGTTATTAAAGATACAGAGGGGATTTGCCTTTTATTTCAGCTTCCCGCCACGGATTTTCATGGGCCCGTTTTACCTAGATGCTGCCGTGTGCCCGTACTGTAATAGGCGGGGTGGAAAACAATTTCCTTTGTATTAAACGTTTATGCTGCTGGAGGCCCAATGGGACACACGACTCCCGTCATGAGAAGTTAGATTAGCGGATCTCGCGATGAACTTTGGGTCAGCCGGCCAAAACGCCCAAGAAGTCGGATTGAGTCAGAATGTCCCTGCACGCCGGGTCTGGCTGCGAAACGATACTGGGTGAGGAGAAACTGGCAAGCCAAACTCGGCTGTGTTCGTTCTAGTCACATATCCCTACTTGAGCTCACCCTTATGTCTATCATTGTCGATCTGTACTACGGCTAATTTATTAGATGAGTTGTTACAAGGCTTCATCATTCACACAGGACAAAGTAAGATGGGTCTTAAGCACCGGCAATGGATGCACATAAGAATAGCGACAGAACAGCAACAGCGCAGGTATGCGATAAGCACGAACCCTGTTATGCAGCATACCAACCCTCATATATGAGGGATCCTCTAGTTGCCGGGTGGCAACGTATGGGAGGTCCATCGCCTAGCGCGTGGTACGGTGAGTAGATCACACGCTTGTCCTGCCGCCATGCGGTAAAACAGAGAGTACTTGCTATTCAAGATGGTCGGTGGTTGTTTGTGTTAGGATGAGTGGATAGCCAAAGACTCGCTATGGGGATTAGTCCAAGCTGGATAGATAATCCGGATAAAGGGAATTTGACGTGGTCCCTTGCATGGCTCGCGGACCGCTAGTGATCTCCTAAATGCCGCCAAGTTCATAGAAGGTAGAGCGATAGACTGGTGGTAGCGGCTACTCAGACAGTAATATCCTCGAGGAAAGAGTAGGTAAGCATGGATACCCACCATTACGCCGTTTTTCTCGTCGGTCTATGGGATATTGTGGGTAGTATGACGTGGGGAGCCAGCGATCTACAACTCATAATTTACCATTAAACGCCTAGTTCGGTGTGGGTTTTCGAAAGGTAGGAATGTGCGATGTAGTAGGAGTGGGTTTAATACGGACAGTGCACACTACTGAGGCCACATACTCAGTATGAGTGGATGTCAGACTGAACTCGGCCTCGTAATCGATGCCAAGTCTGCCAAATTGTTCCTGTTCGCGTACCCGTATCATAGCCCCTTATGCTAGGAGAGTCTCGAGGATCCGTCTCACCAACTTTCGCAGACACTGGTTCTTAACGTAACGAATGAAAAGTTCGGGACATCGTACAATGTTGCGCAACATCCCGGATAAGAGGCACTCAAGTGGTCCAACGTGCGCCTCAAGTACCAGAAATTCTGCTTCGCCTAGCAGAGAACTCACACACACGCCCCTCCCCTGCACACGAGGCAACGGTCTAGGTTAGTTCGCCTGAGCAAAAGAATATCGCGTGGGGATGGTGCTTAGAGCCATGTCTGTTCTCTCATCAACTGTCTCTGAGCCTGCTATGCGGCTAATGAAAGTATCACCAAGCTGTCATATGAAACCCAAAAGTGTATCTGGTACGCAATTTAATCCTCCAAAAATTCCGTCCGTAGGGCTTCCGCTGCCACCGGGCCTTACAATAAGGACCTGGGAACCGAACCCATTGTACACGTCTTACCGGTTGTACAGTAATTGATACTTGGGTGCGTCGGGCACAGAGAGGGCCAGTCTTGGGCCGAATATACGTCTGAGTCCGTCCTTTCCGAGATCGGCGTATTAGATCCTTATATACGCCCCCGGCTGCAATGGAGGCCCGAGGAGGGCCTAAGCTTCCGCATATCAGGAGGAAAGATATTAAACATCATGAGCGCATGACGCCTGACCGCGATTGAAAAGGAATTTCTTCCAGCTAGAATACTACATGCGGCCATGAATACGCCGTTCACTCGCTCACCCGGTCGTATGTGACGCAATACTGCAATTCATCGGTCTGGCTATAGCGTGCGGGCGGGAAGGCCATAAGATCAACTCAAGTTCCAACAGCAAGAGGATTGGTCAGTCGGGTAGGTATGAGGCCAATTATAAGTACAGACGGGAGTCTCCTAAACTCACTCAGCGTATATATCAAGTGTTCGCGTTTTTCCATATCAAGCGAGACTCGGACCACCTCTGTTAATGCTACCATGAGTAAGTATGAGATCGAACCGTTGTAATTGAATAAAGTCGTTTATTCTAAAGCGATTGTGCGCTACACCGGGCGTAAAGAGCCACGTCTAATAACGAGCCGGTGGTTGATGCTCGTGTGGCTGCTATTAGTAATCCCTATCGAAAGGTTCGGCAATAGGTAACGCGTCGAGAGTGGGCCTCCACGATCTCTACTGACTGTACGCTTGATTGTTCGGTGCATATTATTACTAACCGGTTTGACAACCAACGTCATAGGTTGACAGCTAAGAGAATGTGATTCATTGGGCGAGTATGATCTAGGTCAAAGGGTTTAACGTATTTCGCGTAATACCCTTCACGAGTCGTGCTTATAGTCAGTAGGGGTGTGTATACTCCAGCCGCCGCTGAACCAGAGCGTTAACCCGTGGCCTATTAACACCGCCCTACTGTCCGCACCACTCATTTACCGGCGGAACCTAGACGCAGCTATATTAATGAACAGTCTCACGACGACCTTATAACTTGGCAAAATCGTTCTAACAAGATACGCCCTGGTTTATACTATTAGAGGGCGGAATATTCGTATACAACAAATAGGACAAGTCGGGACCTGCTATATTGGTTCATTCGTCACGTCCTATCACCGCTTGATCGATCTGACCATGTGCCGCTAGAACTTTCCGACATGGGCCCGCTTCTCGTACCAGTCTTCGAGTTCCTGTTCGTCCTTCTGACCTCCCCCACTATAGGGCGTCAACATCGGTATGAGGCAGACGTGAAATTTACTCGCGCTTTAGAGCTGGTGCTTGATTATTCGCCCCGTCCGACCTTAGCCAAGGATACTTCACGATTTTTCGTCCGGGCCCTAGGGACACCCCACATAACATCAACTGGAGTTTAGTGCTAACAGTGCGAAGAAAAAGTTGTGCCGACCACCAAGACGGGTCGACGAGATGATAATAGCGCGACACAGTGATAGCCATTAGGATCAATGGCGTTTAGACCGTAATAGCATTGCGGCCAAGCTCGACATTGTTACGTAGCACTCATGGTCGCTGTTGTTGCGGTTACGGACCCTCGACTGGTCATGGTATCGTGCTTACCCCCTGTCCGGGTGAATAACAAGACCGTGGTCAAGATAGCCCGTACGTGGTTGTCTGCTTTTCGGAATTGACCGCGGCTTTAAGAGACAGAGCGATTTGCGCTCTTAACAATGAAAGAGCGCCATAGACAATTGGTTTAGAGCCTGAATATCTTCTCTCGACGTGGGTTTATTCATGTGCGCCGCGCACTGTGCCCGCGGGTCCGCTTTTGGCGGGCTGTCCGGCAATTGGGGAGGGCCTAACTCGAAGGGAGTTCTCTTGAATCCTAGCTGATGATTCAGAACCTGGTTCACCACGTTGAACATTTAACATGGAGCGACGGCAGGAATGGGGCCTCCCAGCAGTGTATTATTGGCGGCCTGGGTACTAAAAAGAGACGCCTACGCGTGTTTGGAAGTCGGGATGAGACTTTCTTCGTTTCGGCGACATGGGCAAACCTCTCCACTTAAAGAATAAATCCAGCTGTTGCGTGAGCATCGTGTCGAACCGCTAGACTCCATTGCCGCTAGCAATTGGCCTGAGCGGAAAGACCCCACTGAAGTAGCAGACCAGTGGCGCGCCATGTATAAGGAGGACACCACTCCCAGTTTAAAGCCTCTTTGCAGTCATGCGTATTCCCTCTGAAGGTTTGCTGAAGCGACTGACGACCAGGTTTTTGGAATTGTATATCCTGTCCACATCAATGGTTCAGTTGACTTGTTTCTAAATTGACACCCCTTGATTTGAGGCTGACAGTATAACAGGTTGTAGCGCACACACAACAAGGAACTGAGTCTATATGGAAGTAGCCTATTTGAACTAGTCTTTGCTGATCTATGTAGCGGCGTACCGACGAATCTGTTGGAATACTATTGCGTCAACTACGGATCGAGCATGCCCATTAGAACCCAGACGAGATTCCCGAAAAGTTATTCAAGTTTGATCCGGTTTCTAGCGTGCTGATTTTATCTGGTGATGGATTTGGGGGTAGCCCACTGGTTTGATATCAGAAGGACAAACTTCTGACCGGTGACAACATTTGTCAAATGCAATCCTGAGATCTGAAGGAAAACTTTAGTTTGTCCCGGGCGTGTTAACCCTGGACCGAGCAGCAGAGATATGTTGCTCCAGGCTCGCGGCTGACATATGTTGGTGAGTAATCGGGCACTATTTATAATTTGCGCATTCATCTGCGCTACCTGGACACCGGCGCATGGTTTGGTGCTCCTAAAGCCTACCTTCCCGACCCCGAAGAAAAACATACCATTTGTGTTCTGGCACAGTTTAACTGACCTATGCGCACAGTCGAACAGAGGATATACCCATTTAACTCAAGGTTCACTATTCTTTATTATCCAGCTCGAGATGTTCTCCGTGTAAGACGCAGACTCGAATTCAAGTCGGGCCGAACTCCAAGGCAGCGGTGTGCACTTCGGCGTTCACCGAAACCGCGGTGACTTCAGGTCATGACGCTGAACCGATGGCTTTTTCCGGACCCCAGATCTAGCTATCCACAAGGGTTTTATTGCAATGGTGCGCATCGCCGCGGCTTGGAAGTTCATCGTAATTCGCGTCAAGGCTTAACTCTCTGCGTCGAACCCTACATTTGAGCAACCTGCGTAGTGTACTTGTCTTGCTCATGGTGCTTCTCCATGTATAATTGTTATACTCCTGTGTCATATGCATAATGCAAGCGATCCAGAGGAGTCGCGGTCGAGTTAAGCAACCATTGGTACCTGTGCGAGCCTGAATTAGTTTTGACAAGACCGGTCTAGTGGTCGGGCGTTTGACTGGAAACAGATACATACTTATCGACAATGTTATAACTGCAGTACGTTACTCGCGTGGACATCTTGTGTGCACTCTTGACGTAGCCAACATGCAGCATTCGTTAGTACGTGAATTCAACTGCATCACGCACATCGACAATGGTCATGAACTGTCCGTTGTCACCGTATCACCTTAGGAGTAGCGAGGATTTTGGGTGCCACTACAGACCCACCTTCTACTTTTAAAAACGCGAAACCCCTCGGTAAGAAGCCACTAAGAAACGACGTTGGTCTGACGAATGACATCTCCTCACATTAAAGTGAGAGTTAACATTCATCTGTATGCCCCTGGTGCCCACATTGCTAGTAACTGAGCCGGCTGTCACGGTTCTGACCTACCGCTTTGCGAGACAGAGTCATTTGGCGCCGAAGGACGTTGCGCCGCCTCCTCTCGATAGTGAGCCACCATCAGAGAGTCCCTTAGCGTCGCGGCGTGGCCGTTCGTACTGTCGCCACACAAAATACTACATTCCCCACTCGAACGGAGTAGGATTAGCACCCTCTATGCACTCTACCACTCATCCTAGGCCCCTCGACTGGACGTGCATCCCGGCAGCTGTTTTGGCGGCCTGCAATTATGCGAGTGGTAAGCTCCATCCAATGACCTCTTATGCAAAATCATAATATAGGCGAAAAGTTCTCTAGCCAGAGTTGGAGACATTGCTGACGGAGACCCCCCGACTGTTGTAACTGCAACTACAACCAGGAACCGGGCAAATTAACTAACGGGGGCAGTCGCGCGTAGGTCTCAGTAGGCGAGACGCTGTAAGGCCTAGCAATATCATAAGCGAGTCCACTCAACTTACACTAGCAAAATGGAGGGTTGATTACTACACCCTGCTTGATACTACGATCTCCTGGCATTTGTCATCGGTAGCTAATCTATTGCATAGAGCCCGACAGCGACCCAACCCGCAAGTAGATCACGGAAAAGCTGAGCCGGCCAAAGTATCCGCGGCTTTGGTTTCAGAGACACCTGCTACCAAGGTAGTGCCGAGTCGAGTCCATCATTACACTATTCCCTTGGCGCGTCATGAGTTCACTGATGGGATTGTCCTGCACGCCTATTGTCCTAACATTCGTTCTTGGGTCACGCAGTTCTACGCAGGTTAGACTAGGCGTTTGACAGCCCGGCCGTCGTCCAGTACACGGTTAAATATACGCTCAATACTAAACACGGGAAGTGGTGGAGCTGGCTCAAGTCACGGTTATTTAGGCCCATAAACACATTACATCAGAAGAGTAAGCACACTGGCCAGAGAGGTTTGGACCAACTCAAACGCCACAGCCGTGCCGTTCTTTTCCCTGGCGACCATATCTTATTTGTCCGGGTAGGGATCTTTGGGCAGCGTCCACCCTTTCCACTCACTCAGTGCCGATCTCGGACGGAGGCGGCTTGACGTTAACAAGCCTATACTAGGCAGAGTGGGGCCAGGTTACCGGTGCGAGACTACCTGAGCAACCCCTGGCTCGTCGTTCGCATCTATCCTGCCGCAAAGTCATAATCTTCATCAGGGGGCGAAGGCCCATCACGGGGTGCTGTTACTATAAGACCTAACGTAGCCTCGTAAGTTTTGGTCGCAGTTGAGAGTGTCACCTTTAGCAAAATATTTTGCCTACTCCTAGCCTGGTTAATGGGAGACCACGCTATTTGAGTGGTAACTGGAGAGACGTCCCAGTCCATACCCCGTCGGCACAATCCCCCGGCGCTCAGGAAACGGAGGCTTTTCAGAGCTCGCTGTATTGCAGCAGCGAGTCACTGCAATAAATGTTCCAACCCGGACCTCATCTGCAAACAGCTTGCAAACCTGCGTTTGCTTACTTCCCAATATTGAGTCCTATTAATACCGCAAGGACCACTGTAGAACAGCGTAAATGGGTACCTCGAAATTACACTTCCAGCCAGTACTTACCCCCCTTGTCCTCCTGACTTTCAGTCCAATATGGACAGATCGCGATTAGTTGCTGCGTCAACCGCGTTGACTCCTCTCAAGATGGATACTCTGGAAGAAACCTTACCGCGCGATAACAGTCCCGTATAGCTAAATTATACGCGCCGGCACATGGCTTTTTGGCTACTTGCCCACGTGGGACTAGTGGCAGCTGATTACTGAGACGATGTACTGGTAATACCTAGGGGATGCCTAGCACCACCTACGGATATCGGGAGTCTCGGCATGCCATCTGTATCGATGAATTAACTCACGTCCTCGCGTCTGACCATGATGCAGGCCAGGCTCTCGATATACAATAAGCATTGGTGTTACATATCAGTACAAAAATTGAGCTAAGGGTAAATTATTGAACCGGGAAAAGCTTCCAAAGTCGGCTCTAAGTAGTGGGCTTGAGGGGGATCTGATGATGCGGCTCTGAATAGTTCAGATCCTGGCCATAACGTGTCCTACGAACCATGCCGTACTGCAGGGCCATTCGCCACGGGGCCAACTGTAGAAGCCCCGGTCCCGCTGTGATATATTAGGGTAACCTAAGGGAGCCTCCGCTAAGCGTCCACCTCTTTGGGGTGAAGTCCGTCATCGGCCAGCGGCAATGGCCTCGAAACTTGTTGCTATTGAGCGGGATTGGCATCGGTTAGCGCGCTTAGTCTGGGATGCAATAGTGTTCAAGCAGGTAAAGAGCTCAAATTATGCAGGATCATGTGGTGCATTGAGACATTTTGAAAATGGGTATCTATTCTAGGGACACTCTATCTTTGTTCCATTATATAGCAGTAGGTTGTTACCACGAGTGGTGTTCTCCCTCGTGAGAAATACCAAGAGGGAGAACATCTTAGTATTTTTATTATACGACTCAATTTCCGCAACTGCCAGGATGAGCGCAGATTCAACCACGTCAAGATTCGGGGCATTACCGGTGGTCGTTAAGCAGCAGAACTGTGCCCGGCGCATTGTCGACCACCCATGCTCGGTAACTCTCTGCGGTCGACTGAAGAGCGTGGGAGGACGGATTGCGCGTAATCGTACGGTTTGCTGGACGTAGTAATTAAGACGCATCGACAGAACAATTACGCGGCATAGCACAGTGGACTCCCTAAAACGTCTATTTTCCTCGGATGGGCTTACATAGACTGTCCCCAGAATACGCACACGATGGGAGGTAGGTGACTCCAGACTAAGTCCCACAGGTTCACGTCCGTTGGGCACGCGAGTACCAGTTCCTCCATGGCCGCATGAACATTTTCCATGCTCGCGTCATCCAGAGCCATTCGAGGTACCGTGCACGATAGCGCTCACGTCCTCGTTGATACTCGGAGAGCTGTCGTGCCTAACGGGTGTGCTGACTTGCAAGAATTGTTGCCCGGGCAAGAAGGCAAATAGGTTCCCGCCGCAGCCGCGAGCACCTAAACCCGGTATCTTATGAAGTGGGTAAGGAAATGCTTTTAGACCACGAATAGTTATAGATCATCCTCCGAACTGATAACGAAAAGCCTACTATGCACGTACAGTCAATACCCAAATCCCCCTAACCGGCAGAAGATTTGTAGCGCATTCAGCAGGTGCACCGGAAGTGTGGGGCTTAGGGTTTGCTCATCAACGAATCTGTGAGATCGTTCCACTATGAAAAAGTAACCCCACAGATTCCGGGAATTCTGTTTATCGACGGAGCGGCATAATAACCATCGCGTGCCAACTCTTGAAAATCGGCTTAACCGACCTGCAAGAAGCTGGAAACCGACTTTGTTTTAAACGCACCCATAATTCCTGGCCGCACGAGGAAGGAGCAGACCCAAAAGGATTGAACGCACAAGCGCGCGACAGCTGACAAGGTAACCTTCGCGGTACGGACTAACACGGTGATCCTATTGGTGTCATGCAGCGCTTTGTAGTGTCAAAGGAGCCGTCGAGGACTTCGGCGTTGCAGAGGGGCATCAGAGGGCTGGAAAACAGGTTCCCTGGACTTGTAGGAATGTGGGTTGATACCAGCATGCTAAGCTAGGCACGTATCAGGTAAAAACCGTACCTTCACGTTAATGGTGCATCGAGCAGGACCGCAACGCTTTTAGTTAAGTTTTGAGGCCGCACGACTGTTGTGGCGTCTATTGATATTGAATTCAGACCGCTACTTAAAATCACAGCGAGGGCACACCGAATAGAGGCGCCGGAATCTGGATCTTACATACTGAAGAAAAAATTGTCATTCCCACTTTCAAACGCTGTTGATAGTAGTTGTGTTTATAGAGGTAACGAGGACGTCGACCGTAAGCCAAGAATGGTTTATCTTGGGATTCAAACCATTAAATCGCTGCTTCTGGACAATGATTCTAATAGCCGCCCCCGTTTAACCGAAAGGTGGTTGTCACGATACGCCTATCGAGGGGACGGATCCTCTCACTTCTTGCGCACCACTCGCGCAGGTATCCGAGCGTGGCCGCTTGTAACCATGAAATTTTCAAATTCATCACCCCTTCGAGATTATACAATGTTCTACTACCCTCCTATCGACAAATAACTCGTGCCATGATCGCACGTTTGTGCACCATGGTGGTCGAGCGGAAGTATCGCGTTCCGGACTTGCTAGGCTCGATTGTGTTGAACTAGGTCGGTGGTAAAGCATTATACCAGCGTCAGAGTTCTGCACAAAAATTTCGGTTCGAGGTCGCACGATAGAGGGGCCAGGAAACCGTAACCCCTATGGAGAACCTTGCCGGTGTCTACCAATGCGTAAGCTCAGACTCAGGCGACGTACCTTCACTCCCCATACTGAAACTCGTCGGGTACGTCGGTTATCAAGAGCCACCCGTGTGCACCGACATTTCAGGGTTCCTATGTTATCACTTTTAACTTCGTTGGAAAACGACAGTGTACCGGTCCTGTAGACAGCTATGACAGATTACGGTTACAACAGAGCATCAATGCTGCAGAGCTTCTAGCTTTCGGTATTCGATTGTGGACCGTTGGGGGCATACGGCCGTTGCATGGGTTTTGCTTCCACGATATGTTGGTGACGCCCACGCTCCCCATAGCGCAGAAATCATATACCCTGTCGCTGGTTCCGCAGTTTGGACACCCTACTCCCTGATCAGATCACTCGACTCGACCTAATGATAACAGCCCTGCCTCCATGAGACCATGCCGACATCGGCTAGTGTACAACAGACCCTCTTAGGCGATTTCATTCGGGTGTATGGGACGCCCCATCTGGAGTGGATCACGTGCCAATCAAAAGTGGTCGCAATGGGCGCCCTAAATACTCTGGCTCACCTCCCCCCAGGCGGGCTATGGCGATGATGACTCCGAGCGTCTGGCTATTTTGGGTCCACTAGCCAAGGTAATTGCCGATATTAAGGGGTCCATACGATATGTTACAAACACCGAGACGTTCGAACCAACAGATCACATTTTTACACGAAGGAGTAGAGTCAACAAGTTCCGGGGCACCATATAACTTTGTATTCAGCCCCAAGCTATGATGCAACTGAAACGTGTAAAAGAGGAAAAGGACCAACTGGAGCTGGGATCTTTTTTACGCTGCGGCTAGGAGCTATAGATCGCAAGGGTACCAAAATATAAAAGACAGGCTGGGGAATGCTGTGCAGTGCGTCCTGAGGTACTGGAACGCGCGGGCGGCAGTGTAGAACTGGAGGGGGAGCCAACAGGGGACCCGTAATCATGTGCTAAAGCACACTCGCGAGCCGTCCGAATCCTTCTCACATGTCTCAAGTGGTCCGGCGCGGCTATGGACACGACTTCTTTTTTGATGTAGGTATCAAACTCATGTGAGGTATGCAGGCCACTCGGCCACTAACATCCCACCCAGCTTAAAGAAAGCACAAAATGTCGCGGAGCTTCCCCCCCTACGATCCAGGTTGTCCTATGTATGATCAAGGCCCATGGGACGATTCACTGAGTCGAGTTTCAACACAGGATGCACAATAAGGTGACCTAACTCGAGAGTGCTCCAGTTTGGATAACCACCGTGCCCCCCCTTACAATGCGAACTATAAACACTTGCGACAAATCCCTCTGAAATGATCGGCGGTATCGCCGCTCCCATGCAGGCTGGGCGAGGACTCCAGCAATGTACACTACAAAGTTGACTTAGACTCCGGTGCGTCCGATCAATCTGGTTGGTCCCTGTGTACCCTAAGGGTGTATTTCCGATTACTATGACACCGTGACAATACTGGAGTGGACGAGATACTACAATAGCGTAACACTTGTGCGTTATAATCAACCGTGGGCAACATTTGTACCAGGCGACGCTGCTGTCCTTCCCCATTCTTTTCATCTTCCGCTAAGGTCCTAATTGTGGTACGTTAAGCGATGCATCGGCGCTCTATACGGATCCTAATCCTAGGTAAGCGAGCAACCTTTTTCTCACTTCTCCAGTTGTCCAAGCCAACCTTAAATGATATATGTAATATATAGTAGACCTGTCTCATTCCGGCAAATTCGGCAGGCTTGCCCTGGAAGTAAACCCAAATTGGTAAACGGGTAGCTGTGGTAATTGTTGGGGGTGGTATTACATGCCAACCCCAAGGTTTTAGTTGTGTCAGTAGAGCCAAACAAGGAGTTAGAATTCGTTGAGAAGTTACGGTCAGGAGAGCGTAGCACCCACGAACAGTTTCCGTCGCCGTACGTCGAGTGCCGTGCTGGCTTCGAGTGGCAAAGGACCATAGAGCTATGGAGACACGCGTGTAAGTGGTACTAGGTGTAGGTCCTGCCCTGTTGGGCCATCTATGGTCTAAGGCAGCATATTGGTGGTTGGTACTCGAGTAATGCCAAGAGAATGACACGCGAATTAATGTAAGAGATCCGTGAGAAGCTCGCGTGGGTTTGTAAGACTTTTCCAGAGCGGTTTCTCTATTCCTGGGAAAACGACTTGTCTAAAGACGAAATAATTCCCTAGTTGCATCGCCAAAATCGCCATGAGCAGTATCGTTAAAACTCCACTACGCGGTTAAGATAGGTGGGTCTCAGAATCCAGATTATTTCATTGGAGTCTGTTAACATCTCCTGGTATTACTCTAATGTATTTAGTTGTCACGATTAGCCACCCGATTCGGTCGATAATCAAATCACTCTAATCCTAAGGTTCAGCTTCACACGAATCCTATAATCAACGTGAATCAAAATGTAGGCTGTTCACAGAACACTGTAGTCGACAATTCAAGAACATCGACATACGGCACGACGGTTCCCATTGCAGATATATGGTGGAGAGACTTCTGAGGAGTACCCAGAAACATAAGATTAGTAGGGTCTGCTCCGAGTACGGAGGATTGCCAACAAGACAGGAATTGTATAGAACGAGGCTTTATGTATACCCTCTTGGCGCTCTATCGAGTGATCCCTGGGACACTAGGTTCAGTTCAACTAGACAAATGTACCGAGGAGGTCGCCGACCAGCTGTATAAGCTCCCGATTATAACGTCATGGTATGTCAAGACATAGTTCCGGTGGGTTGGTGTGTGACATAGCCCAACCGGACCCGGCCTGTGGACGAGCTTTGGTTTCATAGTTCGGTGGAGACACATCCAACTGAGGCGTTTTGGAATCTAAGTAAGGAAGCTATGTTCCCACTCATGTTCCGTGGTGTTGTTCCCCCTTCTAATCTGGTGGTACCAGCCACCGCCGGAGATTTGATGCGCTTGGCCCAGGACGGTAGACCCGACGACTAGCGAGTTCCAAGCTATTCTGCACGACACTAAGCTATGGCTGGATAGCTAATACACCACATATGAAGCGCACGGCCGTGCCGGATTCAGATGTGCGCACCGCGGCTTGGTGGCTGCGACTCAAGCATCCACTCCTACGCCCCTGGGCTACTAGAAGCGGCGCGCAATATATAGCAAGTGCGAACCATCGGATATATACCTGGGTAGGTCAGCTCATGTGAAAAGGCCTCTGGGGACTTACCCATATACTAGTGGTATGGTCTTCCTAGATCATCTGATAATGGGTTGTGGTGTTCAATATAAAATAGGACCGTACACAATCGAGTATCGCATTCCTATTCCTCGAGATCACCACCCCTAGCATCTGCGATTGATGGTACCTATCTGGATAGGAATAACCACCTCCGTTTGGGGTTTCCTTGAAAGCGATCGGCTATCCGCCAGAGCGTAAGTTCGGAAATGTCGTAATGGCGGTCCGTGCTTTGAAGGAAAACCCGACGTACTACATCTTCACTAGGCGGGATTGCGCCGATGCGAGGGTGAAGTATATGCCCGCGACGACGGTGCTAGCCGACTGAAGAGGTTAAGATCACACCGTCGTATAACAGGTTCCTTTTTCTTCTCAACCGACACCGCTGTCCTACCTTACATGCAATAAAGTATTGGCTCCTTCCCCGGTGAGACTCAGCTAGGGCGTAGCCTCCTTGGCTATGACATCAGAAAACGCAGCGTGCATTTGGAGTTATTTGCGCTGTTAGCCTCATATGTATTGTAATCTGCTGTCTTGTCTCACAAGAGCCCACACCTCGTTTAAAGGAGGCAAATCTTGAACTCTCGGCGATCAGATTATGGCTTATGTCTATCAGCCGTTTCGCAGTACTTCCGACCTTTATAGAGAGTACTATAGAACGATGCGGTGAGGATGAGTTTTAACAGATGTGATTTGTTATGCGTGCGGGCAAACCCAGATTATATACAACATTCAGGACAGAGCCTTTATGATTTAATCCAGTTTCTAGGTAGTAGGCTGTTTAATCCTCTCAATAAGTACCGGCTCCTTAATTTAGATAAAGGCAGAGTGCCAGCGTTTGCACAAACACTGGCAATCAAAGACGGCGCTGCATGGTCGCACGGTCGCTCGCTTGTTTTTTTTATTGCTAAGAACCACGTGTTGCTCTGAGAACGCCCCGTGCAAGCCACACAAGCAATTCGGCCGGTCTCTCGCTGATCACGGTCGTCACGTGAAATACGTGCATTTAGTACAGAAGTGTCGCTACCGTGGACACGGGCAATTTTACCGAGTTCCCATACACTATGTGCTGCCGTTACCTTTAGAGAGTAGAGCTAACAGGATCACAAATGTGTCTCACGCCCCAGGCGTTTCCGAGCGGCCTGTGTGCGGACTAGACCCGCCTCGCCTATGGGGCTTAACTTACAACCTCCCCACCGGCCCCTGCATCATATTTACCCCGCACAGCAGTTCTTACTAGAACTCCATTCATGAAGAGATACCTGAACGGTGCCCGGGAGAACCTTTTCAAACGGCCGGGTCGTTGAAAGTAGCCAAATTACACCTGACCTCGGTTGATTTCTGTGTTAGGCGACGAAGTTTTCCCACCTACGACAACAACGTCTACCACGGTGAAGGCTCGCCGGGAGTAGTGCCGTAACTAATGGAGCTGTGTAACTGCACCGTGCCTCATCACGTTACCACCGTGACCCAGACGATGACGATTGAAATTATCCATATGTGCTTAAGCGCCGTATTATGCGTGGAACCGTGTCCCGAAAACGATTGGCAGCTGTACCCACCTGCTAGCTCATACAGCAGCCAATGGTGAGTTAGCTTGGTATTCGACAAATTTGCATTGGAGAGTCACCAACGCCAGTCACCTTCTATCTGGACTCAGAGAGCAGCGTGGAGCTCGCCCTATAGACAGACTCGATCTTCTCTTACCTATGGAGAATCGCCCCGCCGCGAGGCGGTGCAAGATTGCATTACAGACCGGAGGCATGGAGACCTGCTCGAGAAAGGAACCGGGATTGGAAACCGAGTCACTGGCACAGATTATCTAGGACCCTTAGCCGCAGTGCCGCTCCACAAGGTCGTCGTTAATGGAATGTTGTAGCCCGCGTATTGATGAGATTAGCGAGGCTCCTGTGTTCATCACAGGCCTCCCGAATTAATTAAAGATGATGTACGGAGTGATAATATCATCTTCAGTATACAACCAGAAGCCGGAACTCCAACTCATAACCATCCAGAGACTGCCGCCCAGGTTGCTGTTCAGCTCCCCGCTTAAAATGTGGGCATAGGTGTTGACTCGAACCTCCAATGTCAACAAATATTGCTTGCCCCCGTGTTGTATGCGAGCGTGTTCAGTTCTCTCAGGATGAGATTTCGCGGCGATGGTCTTAATGTATTGGGTCATGCACTTTAGAGTGGTTCTCAGCATCTCCTGTTGAGCTTGGATGGCGTGTGACCCCTATCTGTGCAAGTTCTCAATTAGAGTAGGGTTATGAGATAGGTGCGCCCTATTACTTTCAGTCTCAGCAGGTCAGGGGTTGACCGGTGAGAGACCCCACTGACATAGTGCCGCCATAACCGGACAGCGCTGCGAGCAGAAAAAGTACTTCGGGGGCTATTGAAGTTGACGGTACGAGCTTCCAGTATATTGGAATGGGCCCCATTCGAGTCATTCTATTCATGAGGATTTAATTTCGAACCTTATACAGACTTACGCTCCGCTAGTGGCTCAGCTAAGGTTCATCGGAGCCACGTACTTACGATTATCGCAAGATACTCCCCCATTTGATGCCATACTTGCCCACCGCACGTTGCTACTAATGATAACAGCCTCGCCGTTATGGCGCCTAGGGAAGACATCAACCGCTGCAATGGGCAACAGATTTAGACTGTGCGGCACCAATTTCCCTGGATATCCGTTACAACATGCCGGCTCTCCGACAAGATTTGACATCCGCATCTCCTGCTGCTTCGACTGTCTAATACAGAGTAAGGGTCAGGTCATGTGTGACGGTTAATACGAGACGAAGGACAGGGTACGGCTCGGTGTGCTGCTCCTGCCTCTGAGCCCCACCGACTGGCGCAGCTGTCAGCCTTTTCCCATTTAGTCTGATGCTAAGGACGAACTCAGCAATCTACCCATGCATAGTAACCCCAAGATATAGATCCGGGAGACGTCTACTACACCTGATAGAGTGTGATCGTCTGGTTCATGCTAATCAGTGTCCCTCGCAACTGGGGCTATTCTGGTCGTACCAGGCCTAAGTCAGTGAGCTGTGCCTTGGAGCCACCAGGTAAGAGTAGTGTGCTTACACATGTTACGTCCTAGGGGGGACGCATGTCTACTCCTGCTCGCCTAATGAGAAGACCCAGAGACCGGAGTCTAGCGCGACCAAAAGTTGGGTATGCCAGCCCCAAGGGCCTCCTGGAATCTACATGCAAGATCGTTACCTATGAATACCTTCGCAATAATACTCAGTACTCGGTTCCAAACGTCAGACAGTAAAATATGAGACCCACTGGTGTCACTGACGTGGTTGTCTAGAGTAGGCCTCGACCCGCAATGCACGGCCTTCGTGACGTTGGCTGAAGCATATGGACTGTTTCTCCGCAAGTTGGGTGTGATTCCCCTTATAGGGTTAATGTAGGAGAGAAGAGTTGTGCCTTTTGGGTGGGTTTCACGCAAAGTTAGCCTATTATGTAAGGGCCACCCACAAGTCAGATAGTTTGAAAGACCCGAACCTAAATCGCACTCGGCTCACGTTCAAATGCTACCGGCCACCGGGATATTATAAGCGTACAATGCTCCAGACCCCTACGGTAAATAGGGGTTGTTGCACTGCCAGCGCAACTTCGTGATGGGGCTTGGATTTCACATATCTATCGAAGTCTTTGTTGGAATTGTAGTGGGGTCGCGCACCTTCGTCCATCCTCTTTGCCGGAGGAATGTGCTGATGAACGTGGGTGTAGCACATCAGCTGTATGTTCGGCATTACTGGAAAAGTCCGCCTTATAGCGTTGCGCTAGCGCAGAGAGAACGACTCATTAGTGTGGAACCCGGCGAGGAAAGCGGGAGGATAATAGCCACATGTCCGCGCTCCCGAGGGTGTAACTATTCGGGTCGTTGATAACAACTCGATTGAAGACGCAGCAGGAAGGGCGCATCCGTGGGACTGTAAACGCCCGGGGAATAAGCTTTCAGCTGTGAGTGCCTTATCACTTAACGCGTGAAGGTCCGTTTTGTGTAAATACCCGTTCTAAAGAGTGGATGCCTGGACGAGTCTGTCACAGATGCCCAATCACCTTCTCGTAAGTCGCGAATTTGAACTTGGTGAACTGTATCTAAGATGCGCCCTACCACGGTGGGCGCCGGGACTCCGCTAGTTTGACAGCATGTAGATGAGGGGAGACTCTTGACTGACTAAAGCGCTCCACTTCCGGTGGGCCCCTCGCGCCTCGACATATCAGGATAACAAGCCTCACACTGATCATCGGCTGTTAAGAAGCGTCGGTGGCATTAGTGATCCGACATTTGTCCGAGTTTACCTTTGACCTCGTAGGTTACTGAATAAATACAAAATTATGTTAGGTCGATGCAACGCAAGCAGCAAAGAATTCGATACCCAACTGGACGGCGCCATTAACGCTTCATTAGAAAATTTACAGTAGCCGACTTCTATCTCAATGAACGCCCCGGCCGTCACTTCAAGGAGAAACAACCGCTAATCTACAGTGCTTTTACTATCGCACCGGACCCTTCCACGCCGAAACGAAGTCATCTGATGTAAACGTTGGTCCGAGTTTAGCGGTATTGTCTTAAGCGTTCTAGCGGACTTGTAATTTAGGCAAAGCTGGCCGGTAAGGCCTTTCTGTTCACGACCTGTTACCTTCAGTCGCGGCACCCGAACGACGTGGACGTCTCGGAACGGGTATCAGATATAAGCTTATACCTGTAGCAGTCGCGAGTTGACCCTACTTCAAGATCGCATTCCGTTGCCATCCAATACGAAGCTCTCTCAGTCCATATCTCGCACCGTACTATGGGGAATCTACTACTCGGATGTAGTCCATTCAGCTTGTAGGGGGTCATGGTATATTTAATTCTCTACCTCAATCGTACGTTGAACTTACCGGTTGTTTCGTCATCATCGGTTGTACGTGGTAGGTATTTGAGGGCTGTCTGAGTCGCGACCTAAGTTAAGGTTCCGGGCTTTATATTTTACCATGGCTTTTGGAATTTCCGCACCACTAATCGGCTCTCAGGCGTTCGAAAATTCAGTAACGACAACCTAGCAGCGCAGTCTCTACGCTTACCAAGGGGCTGGGACTGCCGCAATTATAGTCGGGTTTTTACATCTTGAAGTGGCGTCATATAGTGTGCCTTGATGATGCTGCTTGCTTTCGCTTATGGTGGAATTATCTCGGGATAGGGCAACAGAATACGTGAGCCCCGTGGGGAAGGCCTCCATGAGAGCATGCGCCTTAAGCCTTCATGGGAATTTGGACTCAAAAAAATATCGCGTAGTTGAGAGTTCCTTTTCCACGTTACAGTGCCGGGGCCTTTCAGACCGGTGCTTCGGAGTGGTAGACGGCTCGAGGCTGACTGACCACGGTCGACCCATATCACAGGTAAATCTGGGGGTATTGAGTGACCTCACGGTTCCTACTAAACGTGGACGCTCTTATAACTGGACGGTGGTCTCAGCTAGAGGGAGTTCGGGGGAGTACGCTTAGGAGTTTTGCACGACAGTTTGTGTTAACTGTGAACGCGGTCGCATGACGGTGCACGCAGCAAGCTCCTATGGGCGATCTCTCTGTGTTTTACTAATTTAAAGCAGAGCTTGACCAACACTAGAGTCATCCCTCGATTCTCACTGTCATAATACGAACTCCATTGACATCTACGTCATCGGTTCCTCGATACATATCTAAACGAGGGACTTTCCAAGAGTCCCAGCCGTAGTCGGCTACTAAGGACCCGCGCTTTCCCGACATGCCAACACATCGACGACGGACTAGGTCCGAGCTTCATGTGTGCCACCAAAGTCGGCTAGACGGTGACCTTAAAAATCCCAACGTCAGTCCGCGGGCCAAAGCCGGCAGTTCAGGGGTGCAACCAGGGCTTGGTGGGATAGCCCCACTTAAGTTATTTTGGATAGCGTCTGAGGCGTAAGTCAACTAGACGTGAGTTCACCCAGGGGTGCTCCTATAACCCAAAACGAGTATGGTGCAACGTCGTGCCTTAAGACATAACTGCTCCGGAACGTCCATAAACTTTGCGTCTGCCCAGTCGTACCGTGGCGTGATCCTCGCAGAACCGAGATCGAAAACTCGTACTGACCGGCAAAACCACTGTGCGTTGTCTATGATATTCGCGACCTCGGACAACCCAGAACCAGCAATAAGGGGGTCCGTGTTGAGAGAGCCCGCATAAGAGCCGCGAATTATGCAGGAGTTTACTCGGGTTACATCGTGCTGTGACGTAAGCTGAGATAGGAGAACGGGCAATATGTTAACCTCCCACGTGCGTTCCATTATAGGGTCTCTCTTCCCGCGCGCGTAATCCAGAGGGGTCCGTGCGCATGGGTAGCACAGGACATTAGGTCGGGAACGATGTTTCAACCACCTATTAAGGCGCAGGAGCGCCGAGACAGCTTTCGAAGCATATCTAATGCTTTACGGGCCCGGGCAGCTACTACTTTACCCCCGTCTAAAAGTCTTCCCTGCCCGGTTCATTCTAACCAATCCCGTACACGAAATGAATGGGAATTAACCTTAGGGAGAATTTAAATTTGAAGGACGCACTAGAGGAGACATCGGGGGGAGGCGATCCGGTGCAAGTGAGTTCGTCCACGCCACTTGATAAGATAGGTTCATTCCATGACTTCCGTATCCGAAGAATGCTTCTTCGAGTCTTTACCGTATTAAAATTATATCTTCCGGGGATCTTCGGACGTGCTATACGTTCTGGGCCCTAAATAGCATCCGTACGGATAGATTTCTCCTTGCTCGACGAGCTAACTTCCTTTAACCCCACCGTAGTCAATGGCAACCAGTTCCTGTGATAGGTAAGCAGTCACCTGTATGGCCCTCTGGTGTCAACGATACATACTTATATCGTTGCAACAAATAAGAAAATGCGTTCCATCCCCTACTAATTCGCCCTAGAACCTTCAGTACGGGACCTCTAGACGGGTGTCAAAGGCTCGGCAAAGTTTTTGGATCCTTATATCAAACAGAGATAGTCAACATGTCGGGGAAGGATTAAGGCTCCATGCTCGAGCGTTTAACTGTCGTTGCGTGATAGTGCAGTCAATGATACATTGCGGTACCCAGTGTCAGGTTGGGATTCCGTTTAACGACTTAGTTCGTTTCCGTGCAGAGCCCACAGTCGATTCGCTAGTGTCAATTCAGATGCGGTGTCGCCTAGCCCCACGGCCCCTATCCGGAATGAACCGAGACTCTTACATGAGGTAGGTACGTTAACCGAATTAGCTCGCTCTCCACGAACACTGTCTTCGCACTTCACCTCGGGTATAAAGACTACTGCGCGACAGTAGCTGAGTCAGGCGTTACTAGTGTGTAGCGATCTTCGAAGTCTATGGTGACTCAGTCACGCTTGTAGCTATGAAATAGGAGAACGGGGTGGATTATATTGAGCCAGCCAGGTCCAATCTGAGGCTTTTCTCATACGACAACGCTGAGTACCACGCCCTAATCCATATCCCCCCCCCATTACTTTGACTGTATCCCTCTTTATGGTTGCATTCGTCAAGTATTAACCGAGTAGCCATACGTCTCCCTGAGAGAAGCGCTTATGCATTTGATATATAAAAAGCGTCCTGGCGCGACAGACCGATACAGGCCCCGTCGCAAGCTGGCGGTGAGAATTGACGGTTCGTCCAGGCATGCCACAAGATAGTGTCGGTCGTATACGGAGCATTAGCGATCACCGAAAATGTATGGTCCTCGCATACCGCGAGACCTAGAACGCACTAGTTCGCAGGCATCTGACAGAAAGGGCTCTTTGTGGGCTTGGCTAACGGTAACTCGGCCCAGCGGTCCGATTCTGACAGCACATACTTGATCCGGGCTATACATCGTCGATCCAGCAATGGTAACCAACACAGGGTTTATCGATATGAATCCCCAAGTACAGTGCCGCAAACTGCGTTATCCACGGAACATGATGGTTAGTAGGTGGCGACCAAGCAACAATGACTGTGAGCAACACATAGGTTAACACGCTAAATTCGAGCAATTCGAGAGCGTGTTCCAAATTTTGTCTTAAGTATATCTCGGAAGTAAATTAAATTTCACGTGATCCCACTATCATATATCTTCACACCCCATCTCAGCTGCCGTGATACCTAATACGCTTGATGGCTAGCGATGTTCTATGACAACCTAACGTGAGAGTTGAGAGTATTCTTGAAGGTGGGAAAGTTGAGGCCCTATCACGATCTTTACCGGGGGCTTATCCAAAAAATGCCTATGAGTATATTGTTGCGAAGGTCTAGGTGTTGAGATTTGTTACAGGCCTCAGCGATATGGTTAGATTAGAGTCCTGTTCATTCTCCCGCCTTGGTGTCCAAAGAAGCGCCTGGAACCGACAAGGAGGGGCACAAGTCTGCGAGGCCGAATCAACCTTCAGGAACATATAGTGTTTATACGTCACCCTGGTACGACATGCAGCCGTCCTGCTGAGCGAAAGGCAGGACCCGCAGCCGGTAACTCAGCGCGTTATGGGAGCGTTAGCTAGTTATAGTACAATTTATTTGATCAGACTACGCCTCCTACGTAGCTCATCTAATGTCCACTTTACATACCCACAACAAGTACATAGTGGGTCTCGCTCAGGTTAATCAAAGTGAGTACGAACGCGTGTACAATGTCAGAGGAAAAGACAATCAGGCAAGCCTGTACCCCCATATGCTTACGGCCAAGGCGGTAGTTTACATCCCTGTGGATAAGGCAGGGGGCCCGCGTAACAGCAAGGTGCCCTCAACGGCAGATCTCTCGGCCTATTGGTCTTGCTAGTTGTGCTGGGTAGTGGTGCCAATTGACTCGCGGCTACAGAGAGAGTGGCGAGTGCAGATCAATCTCGGATCGCGGAGTAACCGTAGCTAAAGGCTCGTCGGTGACGAACCCGTGACCATTGTATACGGGAGTTCCGATAAACTTGTCAAGAAGTTATATGTTACGATAGGCGGGACGCGTATGACTTGCCGATGACCGTCATTCCACCATAGTTGACCGGTTTAGGGCAATGGAGCACACCCCGCGCGGTTGCAAGCTGCCGGCAGCGCACGCGGTAATGGCTCAGGGAAGAATATGGGTGCACTACGCGTAGGTGGATCGTTAAGGTGAAGGCCTACTACAGCCCTAGCTCAGACTAAGAGGCTACGGCGCGGCGCCGCGTCCACTATATCCGCAGGGGTCGTCGTTCCAAACCGGGGGATGACGGACTCAGGAGGGAGCCTGCCCAGACTGGAGGAAGACGGGTTATCAAGGGGGTACCAGAATTCTAATGCGTAGTTTGTAAGAAATCAAGCGAACCCCCACTCAAACTGCCAGGGTGCCGAAGGTTGAAGCATCAGCATATCCCGCATTTTAGTGATAGCTTGGACCTACACCTCCCGAATATCCTCGTCTAGATGAGGGCGAGCTACGCGGCAGTTCAATCGACGCCTCAGCAGTATCGCAAAGCGACTACAAACGTGCTCTAGACAACAGAATGTCGCCTTAAATATAATCGAATGCCGTCTGACCGGAGCTAACGTCGTCCTCTCAAGCTCGCCACACCCCTGACGCGACCCCTAAGTGCTGTCGGGAGGTCGACGCTATGTTAACGTCCGTAACGGCGAAGTTGCATACGAATATTATTTTCGTGCCTTTGTAATCCGGAGTACTTGGAGAACCGGGAAACTTATCGCGCACAGGTCATTTCCCTTATGGGTAACTACGAGCGATGAGCAAGCAGCGTGCTGCCTTGAGTACGTTATCTCCTACATTACTTGAGACTGTCCTTTGCGGGGTCCATCATTGTTATCTCTCTCCAGCTGTGTGTTTCAAAGCAGTTAAAACAGTCTATATCGGGCCTGAGATGTTTTTTCTCGTAACGCAATGGTATTAGGGCTTGACTCTTGACTCGTTCCAAAGAAATTTCACGTGCATGTCGTATGCACAGGGGTATCTGAATTGCGAGCAGGAGAAATCTCGCTATCGGTTGGGAAGGGCACAATATCATCTGCGCCTAATCCGTATGGGATAGTATGACGATCTGGCGCACATTCTTAGCGTGGGATAGGATGTGTCGAATCCAGTTCGAAGACTATACCTGTTCCATTGCCAGTATACTACAAGTAGTCGGCCAACGTTACCTGAACAGGATCGCTCATTCTCTGGTATGTTTTCCCCGATGGCTCGCCCCACTCGGATAAGAAAACCAGAGCGATTTTTGCTTCGAAACGCGGCTCAGCAATTCGCGGTGTTGTACGCATATGGTGAGTCGGTCCAAACATGAGAATTCGTGTGAGGACAGAGATATCCCGTCGGAGGTACGGTTACTTGTATGGACATCCAAAGTAGGCTACCAAGTTCGCATTACGCGTAACGTAGCCACCGCCGCATTTGCGTACTGCATGGCCCAGTTCGAAAGGTTGATTATCCCCATAAACTTAAGTGATCAACTCTCAGGTGTTATTTAACGCTGTTATGGCGACCGACCCCGGCATTGGGTGCCCAAATGTACACGGTAGCCCATGCCTGAGGCGAACATTAAGGGCATCACTGTCAGTTTAGGCTGGGGATACCCCGATTGGGCCAGAAAATTCTTCGCTAAAACCATTCGATGCCTGCTTGGATATTACTAATGCGCAGTTTACCCTTCCACGAGATCGCCGGGCTCAACCGTAGCTGCACGGAGTGGGTGGCTGTTTGGTTTCCGAGGACCCGGCGGTTACCCGCGCGGGCGCTGTGGGAAGCTACGGCGAGCCCCACAGGCGATTAAAATCTTTCTGCTAACGATGCTGGTAGTACTAAATTGCTGAACAGTTGAGTCGGTCGCTACTGCCCTATCCGCATGGGAATTCTGTATAAGTGATTTCGGACGTTTTGTGGCCCGCTGTAGAGCCGAAGGATATGGAGTTAGGTGTCCTGGACTATTTGTATCCAAGCTCGCGTTGACTTCTATTACACACGACCTCTCTGTGTTGATCAACGGGCGTTGACAACGGGTTTCTGTACCCCCTGCTCGGCGGAATCGGGCCCTAGTCCACTTACAGCGAGTGTGGGCTTGGCTCTAGTTCATGATCCGACCTAAGAATGTGTCCATGCCGCCCTCAACTACAATAATGGTCAGTCCGATACACTAGCCTGTGCAGTTAGTCGGTCTAAGGTCTGTTCGCTCAAAACTACCAGTTACGTTAGGGCACCTAGATCCCCGCACGCCGTGGTCGTCCACCCCGCGGTCGGACGAGATCCATATAACGAAAGCATAAAGCATTTAAAGTATGTCTTGCAGGGGTCCGTCGTTGTCGCCTCAACGTTGTTAATTTACATTCAGATGTTAAGGTTAGCGGTATAGCCAGGATAGGAAGGAGATTGTCGGCTTTTGCGTCCACAGCAGTTTACGAGTAGCATGAAGGATTAGCACAATTAAGTCCCAGTGCCATCGAGTAATAGCAAAGGTGCCCAGAATGCATGGTCACTAGTAGTGTTAAGAGGCGCCTATTTAGGTCCGAATTTAAATGACGTTGTGTGAGTGCATGCCTTAATGATTTGTGGTGTTAGGCGATATCCTCCGTAGCCAGCTTGAGGCTCCTGATCAGTGTGTGTCCCCATGTGGCGGGCTATCCACACCGGATCTCTCCTGGCAGGTTTAAATGCTCCGGTTCGTATTTGGTCGCCAGCAGTACAGATATAAGTGTTCTAACATCACGGTCCGCCGATTAAACCCGCTACTCAGTAAATATCCACGTAGCTGCCAGAGACTTCCTAGCGCCAATCTGGTAAGGACCCTAAGCGATCCTACGTGCTAATGGCTAAATCTCAACAACACTCAATGTCTCTTGAGTTGATCACTAGACCCCGGATGCTCCAGGATTGCTAGTTGATGATTCCGCATCCTAGACCGGTTCAAATACATCCCTAAGTACCGGGGTACGGCGCAGATAGCCGAGAATCGATGACCAATAGTGGTCATAATACAGCGAGGAGGGGCAACATGCTTCACTTATAAGTAAACAATGCCGGGGTTCCATATAAACATGCTTTTTTGGTTGGCGCAGTTAAATTTCGTCCGTAGTAGAAACGCTCGTTAGTTATGTTACGCCAGTTCGAGGGTTATGCTCGGTACATGTCCTGGCTCAGTCTCGCTCTTCTATTAAGTGGCAGGGTTCGAACAGGTGCCCTGACAGTGTTGCACATGCCTCGACGCTTCCCTTTATAGCACTACTACTAGGCTGTATAGCATCGGAACCAAGGTGTTCCTCGCCTAATTTGGAGCTCGAGAAGGGCGGCGAAACACCCAATTTGAAATACATCGTGTGAACGTTGTCGAGAGTTCGTAGTGCGAGAAACCGATCAAGATATTGTGTACAACGCCCAATAGCCTCCTTCGCGATTATTCACCATGCCTACTACGCGCCGCTCATAACTTGCAAAGGCGTAATTCTTATGATAGATTGCGCACCGTGACCGGTTCAATCTTCTTGGAGACACATGACTAATAGCTTGTATCATACCACTTTGACTTTCTTGCTTCAGTTTTGTTCCAATTAGGCCTTGTGAAGACGCCTGCAGTATAAATAGTTGCTATCATCCATTTGTGATATATGCCGACGCGACCCAGCTGCAATATTGGCGTGTCGATATCGTAAAGGACTAAGACCATCACGCAAGACCGGTTTATTAAATGAGTACATTGCCTTTCAGTCCCCGTCAGTTCGCGTGATTACTGCCCATTCTTCCATCCTGATCCGCGTCATGTACTCAAGTACCAGAATGTGAACGATATTCCGGTAATCTTAGTGGGGGAGTATAACATGCCGTCACAGCTCGCGTACGGTACAGGCTAAGTACACGTAATGCTAGTGCAAGGGGGCATTATGTCGAGTATCGTTTCGTGGGATTATAAGTCCCTTGTTTCCCTAAATTCTCCGCGGCGTTTCTGTATCATTAACTTCAGAAGAATTCCGCTCGTCCAGGTGAGTGGTCTGGTATAGAGCTCCATTACAGGAGTCTTCATTTAGGTTCTGGATGTCAAGAACGGACCAGGATACTTACTGTAGCATGGTTCAGCACACCCAGAAGAGCCCTAGGTCCCTGCGACGCCTGGTGCAACTAATATAGAGGAACCTAGATTATTTGCGTCCATATGTTGTGAAAAAGATGCTATGAGAAGCTATGGTGCTTGGGGGCGTCGCTAGTCGTGCATAACACTGACGAGATAAGAGCCCGGCGCGAGAGGATCAACAATAATCAACACTCGCGTGCCCCTTGGGACTCAGCAACGACGGGTGGCTATTCTACATCCCGCTGCTAACGCTCCACCGCACCCTCCCGGCCTTCTGGTTCAAGGGTGGCTTGAGCCATTAAAGACTCAACCCCATCTAAAATAAATATCTGCGTAGACACTCTAGCGTAACTGCTCGAGGAGGACTCGTACGGAAGAATACAGATTCGTCTTCTGTCACCCTATTTAAGAAGGATATGGTAAGTGCTAGATTAAGCGTAATAATTCGCCTAAGATCATACGTTAGCCGACCTTCATCAAGGGAACGGGGTCTTGATTGCGTAGTGATTCTCGATTTCACAGTCGACATATAGTCGTGGAGCCCATGTAAAGTCGTTGTCGGCGGTTTTCTCAGCCTCGATCTACTTATCTGGCCCATCTTTCGTAATTACCTGAGCGGCAAGATATATTACCCCTCCCACCCAAACTTTCGTTAACACTAAGGTCCGCCACGTAAGTTAAAGTTATGGGGGCAATCTACGCAGGCGCAGCCACCCAATCGTGACGGGTCTGTCGCCCACGGCCTGCAAAAGGCGTGGGTTAGCTCAGTATTTACCTACATATTGGACGTAGACAGCCCGCCGTGATGCACTCAGGAATTGGGTAGTAAGGTCGATAGCTAGACCGTTACACTGGAAACTGAGATTAGTTTGTGCCAGTGTGTAGTCGACACACACACGTAGATCTACTTCGTCTAGCCGCGCCATTAACAAGCTTCCAAATGTGTCACTGCCCATTGGGCCACGGCATCCTAGTTGCTTTTAAACCGCCACCCAATTTCTCTGTCCACATGCTTAACATGAGAGTACGTAAGTCTTGCCGCCTCACACGCGTGTCCACTGGAATAGAGCTTTTCATCGTTTAAACTATAAAAACATCTGCAGTAATCATCGATGCACCAACCTGCGCAGAGTCAACACGGAGCCTGTTTCGGGCCACGTCACGTATAGGACGCTGACACCTGAGCAGATCCAACTCCTGGCAACCCGCAATATAAATGGCCCCGCCGTGTGAGGTCGTGTCCGGGATAGTAGAGCCGTTTATAGCTATAAATATCGGTACCAATGCAGAAGTCAATCGTCGTCAAAGTATACAATGTTGTGAGTATCACAAAAGCTTCCCTACTAACAGGCACAGCGCCGCGCGCGGCAATAGGGCGTGCACTCGAACGAACCGCATCGCAAGGCACATGCTTTGCGCTCTACCCGTAGCTAACCATTCACACACCGAACTTGTATATCTCCATAACAAATCTGTTGGCAACTACAGCGGTCTCGAGGACTATTAGCCACTACGGGGGAATTCAACGCTTGGAGTCAGGATATACTACCCAAAATGACTACTTTACTCCTCTTTAATAGGCTACATAGAGGGCTTTCGAGTGTCTTAGCTGAACTCGATTTGAGTAACGTTAGGGACTAACCGTCTCAAGCCGGCTCTTGTTTCAAGACTCCCGCGGCAATACACGTCTCACCTTCCTCTGACTCGTGATCAATCAGCTAAGGGCCTCGGGCACAAGAAAAAAACCTATTCGTCTATCCCTCGCGGGGGAGTTGCGTCGCTGCCAGAGTAATGTTTATTGTCTTCCTAGGATAGCGGGTACGAATCTCGCCTATTAAGAACCGTCTAATGATTAGAAACCGTTTTACCTCTATTCGAGCATAGTTATGCAAATCTCGCCCCAATGTCCGGAACGTGCTCAGAAGATCTATGGCGAGAAGGGGAGTTTAATACTGCTGACTATGCGTCGGAATTAATTCTACTCAAACTGGAGTTGCCAGTACTCACTAGACATCTACGCCTCTGGCAGCGGATTAGCTTCTATCAACGCGCACCGCGACCTTGTTACCCTGTAACTGAGTACTTTCGAGCCCGGTGTTTCGCCTGAGGTTAAGTTGACACCAGACAGGAGGTGTTAAGACAAAGTGAATATCCTCAAACCACCTCAATTGCGACGGACAGGGTGTGTGACCAAGAGGGGGGACAAAATAGGAAGCTCTTAGTTCGAGAAAATGCGCTATTCCACACAGGCCTCTAGCGGTGTAGGAGTCTATACCACGCGTGTTCTCAGTATCAGACGAGTTGCATAGAAACGTGATCGGTCAGATAACGAGAAGAACTGAACTCTGGTTAACATGAGTTGGTCGGTCCCACTCTAGGACGGGTAGCACATGCACTGAGACCCGCGGGCGTCTTTACGGTTGTATAAAACAAAAAGGTCTAACGAAGAGCTTCAAGAGATCAAGGCATGTGAACGTAATATGAATATGTGACGAGGCAAAGGTGCCCTAATTGGACAGTTGATCAAAGCATGGACTGAGAGAGGGGCATATAAAACTGGGTAATCGACGCCCTGCAGTAAGCCCTCGGCCCGTTGTAACTGGCCTCGCCGATCAGTTTACCATCTCTCTGCATACTAAAGGCTGGTTCGATTACCTAGCGTTACCCGACCACCTATGTTGCCAATCCTAACCGATTGATGTAAGACAATGGTTAATTAGCCTCAGACGCACAGCTCCGCAAAGAGGCTTCAGAACTGTCATGAGTTCAACGCATTCCGGCCAGGCTCTGAGGAACATCCCTCATGAAGTCCTGGCAGTCTTCGCGACTTTACTGCCATCTCCGCTAACTGGTGCTCGGTACTTCAGTTAGACATGATAGTGGTGTTTTAGAAAAAGTGTTCCGCACCGATATCCGGCAGAACGCGTAAGCTGTACACGACATATTCACAAGATGCGGACGCTGTGGCAATAGTCGCAGAAAGTTTTGAACATGTACAACGACGATCATTAGCGCAGAATGGTGAGCGTCGGACTTAGCTCCCAAGCGATTTGCTAGAAACAAGATGGTCCGATCGACGACACGCACCCCCACAGGGCTGTTCTTTCTGGCGATGCAGCCGTATCGAAGCATATCATATAATTTATCATAGGCACGGGCCTCTCACCAAGGGTCTAACAGATCGACCTTGGCCGACCACGAGTCAATTGGTCTCAAAACGGTCACTGTAAAATAAGGCACTAACAACACGGGGACCGGTAATACGGATGCCTGCGTATACTCGCTATACGATCTCCGGACGCACTGGGATGACACCGTCGTACACTCTTCATCAGGGGCGGTCACCGCAGTGCTACGGTGTGTCTTTTTTCACGATACGTAATGTAGTACCCATTACACGGACTCTTCAAAGGAGAAAATGGAGCGACCGCTTACATTCTGTTAAAATAGTTAGATACTCCCTACCGGTATTATAGCTCACCTACGGCTCTACCACACCGGACTCCCGTTGTTCGGGATGTAAAGAGGGGGGTTGAAGTATTGCTCAATAGTACTTCGGAAACTTAGAATAAAATGAGGTCTTCTCTGATCTGCGCCTAGATCCAATTGATGAGCGGTTCCTGTGAAACGCGGTACAATTCGACAGGTGACCAAGAATGAACGTGGCAACCACTGCTCTGCACCACGAATGCGAAAACATACGGAGTCTCCGACACTCTAGGATGACAACCCATAACTTCAAATACTAATTCACGACACGGTCGGGGGGCGCGTACCGACGGAGAACTTTCTACAGAGAAGCCCTGGCAGACGTTGTGGGGGCCTACTCTGCACGTTGGCGTTAGACAAGGCGGGACCAGACTTAGATCAACTTGTCTCCACTTATCGGTACGATACGGCCGCGTCGCACCAATTAAAATCCTGTTAGCCATCCTCTGGATGGGTGAAAGGGACAATGGCGACTATATGAGGGGCGGCTAGGCGCCGCTTATGTCGGGAGACCGGGGCTTACTCAGAGGATGACTGAACTACAAAATGCTGAGATAGCTTGGCGGTTGCATTTTGAGGTAGTCGTTGGGGAAACCTCTCTTCGTCTTACGGGATGAGTTTTGGGCCCAACCCTATATTGGGGCTCGGTGGTTGCTATTACTATGTCCCTTGTAAAAGCCTTTAGAATCTGACAACCAAACAGCCTGTAGACTCGGTGTGAGCCAACAAGTAGGGCGGCAGTGATCCTAGGTCAGGCTATTGTATGTCAATGCCCAGGAGTTGGGTCCCCCATTTGGTAGACGTTATTTCGACTAGCTCCCGTCGTACTTTGGTAGACGGACGGACGTCTAGTGCGGGTGCAACCGGTCAAAGATTTTCAAACTCCTCTTAAATGGGAGAAGGGGTAACAATCCGCTTTGAAGGCATAATTCGGTACCCGACTTATACGCCACCATTAGCGCGAACTACACATCTAGCTAGCGGGAGCAGCCCTAAGTTAGAGAATCCATCCGCGATTACCCTATCGATCCCCATCATCCATTCTTAGTATGTCCCATCCGCAGACCTTTAACACGGAGAAAGGGCTCTGTCCAACGCATCGCGTACTCAACTCATGCGCGGTCACAGTTTACAGATGACTAACACGAAGTAGGCGTCGCGCCCTTCACGGACATGGGCTGTCAATCAAGAGGTTTTGAGTGCGCATCAAGCGGGTATCACCTTCGCATTTAGCGTAGAGGATTTCTTCGATTATATATCGTCATGCAGTTATCGCCAATGGTGCATGGTGGCCTTTTTTCGTTCAGGTTGATGCTCGCTCACCTTCGTGCATACTTGCTTGACGTTGTGGAATCGAACCGAGTAGGAATAATTTATATTACCTCGTGATACTACCGATTGGACGTCACCGAGCCAACGTGGTGTACGATGTAGCCCCCTTGCGCTGACCTTCCGTTTGACTAGGGTAGATCAAAACGTATGCTGGTTTTGCCTGGCCGTGAACCTACGGATACACACACATTTACGTTGCTTATTAGGGGATTCCAGGTCCACAGCTTGCTCGGAAGGGGCGAGTGAGCGTTCCTTACCCGGTCCCAGGGGGGGCCTACACCTGCGGTACTTCCTACCTCGTCGAGTAGGGCATTTGGCCCGCGAGATTCTGAGAGTGATGAGTTCGCTTCGAGACTAGAACATCCCAAAGGGGGTAAGCCCGAAGTGATCCGCTACCTACACCGCATCATCCGTCGACAACGGGGCGGTTCAGTGGATGGACTTCTGCTCGGATCGTTTTTATGGTGCTACCTTACGTCCTGCAAAACTTCCAAAAGCCGAGCCTCATATGTGCTCAGGACCAGCCGTGACCGACATGTAAGCTGCCCCTGTCCTAGATCTGGGATCCTACCAGACATAGAACATGTTTTAGGTAGCATTGAGGTCGTTACTAATACAAGGTTCGTCCGGGCTTGGCCTTGGTACTTAAAGTGATGTTAGTTTGGTCTAATCCACGTATTGACGATTGTGTGGCTTGACTTAGATCGGCGGTGTTTGCCAGGAAGTTAGATCAGTAGATGTGCAGCTAAACAATGGAGGTGTTCGTGGCCTCTCGCCACTCTAATGTTGCCCACCTAGGGTCACAGGGGTTGAAGCGGGCTTGGATATACGTTGGCACTATACGTCCGGAGGACAGTAATGCCTCTAGATACAACGGTGGGGGGTCCTTGTGATGTATGGTTGATTTTGGCCCACTCACTTCCCTGCACCGGATATAACTGTGGCGATGTCCCAAATTACTGGATTCGTACTGGGATAGCGCGCAGGTTACATAACGCTTGGACGAGTGTGTGTACCCTGAAAGCGCATACAATACTGCAGGCAATTGCTATCAATACCCGTGAAGGTAAACGTTTGCCGAAGGGTGATTCTTCTACCTCGTCTTAGGCGTTTTAGCCGGATCGCGAAGACAAGAAATATAATCGCTTTTTCGAAACGTAGTAGATATGAATGGGGACTTGTCAGTTCAAATTAGACCTACATAGGCGTTATTCACGCAACTGTAAACACTTCCGGCAAACTATACACCCCAACGTTTCAAGTCGTCGAGGTTACACCATCATTCAGCGACTACGGACTCTTGTATCAGCCTAGAGAGATAAGTGCGAACCAGTCTATAAAACGCCCAGCCCAGGAGCTTAGGGCTCTAGGATTCTTCGTTGACACCCGCGGTCACTAAATCTCTGTTTGGCGAATTAGGGTCAATCGCTTCTGCTCGAGTGTCCCGGTGATGTTGCGGCTGCCAACGAGGGGCCCGCCCCGCTCAGGTGAGGTGGGAACTGGGCCGTCCACTTTAGCGCGATACACATCTCTACGATTAGGATCGGTTTTTCATCATCAGATCGCTCGATAAACTATATACGGGGAACCACCTCTCCTGACCATATCCAATAGAACATGTTGCCTAGGATGGCGAAGATTGCATGTTAAATTTTTAGAATACACCCTGATCGGTCTATAGGTATGACTCCCTTGAGACGGAAAGGAGGTGTCCTCGTTGTGCCTCTATCACGCACTAGCAAATAAGACCCCCCGCGTTTCGGGACACTTGGCGGCTTGCGACTCGTAGAAGCTTTGTTTCCGGTTTATCTGCTCTTATTGGAAAGTCTTTGCTGAATCCTGCCGGGTACCATGAATCGCTATTCATAACCACTGGTTACGACACATCAGAGTTACATTGGTCTTGGCATGATATGGTGCGCTCTCGCTGTCGGGATCACTAGCGGCAGAAGCGTGGAGCCACAGCTTAGAAAAAACCACAAGCAGAAGGGCGCTAGCCACGTCGTATCCCTGGTTACACTTCCCTACGTCCCACAGTAAAGTGTAGCCCTATCGCCAAGGCTGGTTTTCTAATTATTAGACGAAGTGCAGGCCTTCGAGGATCCGGAGGCACCTTCTTCTGGTCGTATCCCAGTAAACGTGTCCGGGACTCAGGCATATACTGCCCCTATTAGGTTCTCGGAGGTCTGAAGCTAAGCAAAATATGACCTTGGTTACGTACCGGGCCCGGATTAATGGACATGACTGGTCATGTGATCCCTATGTGACTTCATCTATTTGCGACGGCCAGAAACGGTGGGTCGATAACGAGCATCTGTAAGACGAGCCGACACTCCGGCAATCGATTGTACAAATGGACTATTTTTGCAATGCTATAACCTGTGCGGACAAATGAGTTTGAAACATCAAGATAAGAGTTCATGGTAACTCACAGTTCAACAAGTATCAAGGTCCGTCTGCGCTCGATGGACAATGATTGAGCACGGTCATTTGATTGCCGCTGAAGAACTAGAACTACGTAACGTCATTAAACCACAACCGACACCCTCATTACTGAGGACTTAACTGGTCGTCGAAAATCATCTTATTAAGAGAGAGAGCGTGTGCGGTAGGCCCATCTTTCACAATATTACCTGCCCAAATCTAGTGGTCTATAATCTCACGGAGTAATGTGCGACCGAGCGTCCTTTGCCCTCCTGGTGGTGCCGACGCGTCCACCGGAGCCAACTCCAGCTGTAGAGCACATCAAATTTATGGTAACACAACAGACCACCTCGACAACGGAGGTATAAAGCTAACGTTAGTAATGGACAGATAAGTGTATCAAAGAGACGAAAGTTATACCCTCGGCCATGACTCAACGCTCTCACATGGTAAAGAACTTACTTACATTTGTAGCCCTAAAAGAAATCCACGCCGAGAGGTTAGTGGTAATTTCGAAGTCTGGCTGAATCACTACTCGACATGCCAACGTAGCGAATCACGGTCCGTTCGATGAAGTGGCGCACGGAAAAGTCTTGCCCGGCTTGCGCAAGACCCGCGCTCGACGGAACCGGCTTTCACACATGAACTTAGTTTACGGAAATGCGCAGCAGAGTCCGAATAAGATACAGACCCCGAAGCTCACCAATGTCGAGCTATAGTGCTAAGACAGACCAATCAATCGGTCTCTCTCATGACGATAGGAGGCTGTGCGCACGAAGGGACATCATCGACATGTTGAGAAGAGGTCTTTTTGTGCAATACGACTTCATACGTCAATAACTAGTGTCGGCGTCGAATAGCTTTTACATCTAAGATGTCTGTAGTTCGAATGTATAATCGTACGGCGCTCCGGGCGGAGACTAATAGCAATACTAGCGTCTCCATGTTCTGAATCGTTGCCGCTTCTGTCTAATTAAAATGGAAATTTTATTCGAGGCCCTAAGATGAGCAGTAGTAGCTCATATGGTATCGATAGTCGGTACGAGAATTAATAGTCGTCTATAAAACTACTCGATCGCGTCGGTAGAGCCCTGGGACCTCCGTGACGTGTAGCTGGTTTAGCAAGGCGCAGTATCTGAAACTCCAGTCAAACGTCAGCTTCGTAAGCGACTAACCAGGCAGATGTGAACATCTCAAGATGCTAGAGATAGAATCATGAAATCACAACTATCGCGATTTGTAACAGTAAAGTATAGTGGTAATAGTGTGCTATCCTAACCCAATGTCAGTGTGGGTCTGTCTAAATAACACCATGAAAGGTGTTGGAACCCCCCCTAATATTCTTGCCGGACTGGAATTAGTGTAGTTGTCTCTGAAGAATTCCCCCTAGACTGATAAATCATCGTCGTACTCTAGAATATCTATTAGATTGAAACGCGAGAGCAACGAACAGAGGCTTTATACCGGTAACAGACGCCTCCAATATTGCTCACGACTCGCTAACACCTACCACCCCTCTGTAGATGTGTTCGGCTAGGAGAGTCCGTTTTTCGTCTGGCTGAACGCCTTCAGACACGGATTCCCGAATCGCTAGCAACGCCTTCGCCGAGTCTCTGTGGAATAAAACTACTGTGCCAGTCATAGCACACGCACACATTTAGCATAAGCTTCGAAACCCTAGAGTAGCAAAGGAACCTTTAATTCGCCCAAGGACACGGTGCTTAATGACGAGAGTGAGTGCTCATGAGCCGACGGTTAATGAACCATAAAGCGCCACTCGATCTATTTGATGAGAGCGGGTATGCAGGTGAATGGCCGTGGATAACCAACGCAGAGGGAACTGCATCTGGTTCGTTTTTTCGCGGCCCATTCGACCGATGATAACAGCAAGAGACGGCTGGGACTCCTATACAAGTTCGGCTACACGATGCTTCCTTGGGTTGACTGAGCTCTGCACTATATCAGCGTGACAGCGGCCTCCCGATGAGCAGTTTTAGCTTAATGGATAATGACACTGCTTAATGTGGCACTAACCCATCGCGTCGTACTAATAACCGCTAACTCCGTGTGTTTGCTCCTACGGGATATCCGCAATTAGTTTGAGAAACTCCGGGCGGACGGTCCGGGCAACTAAATACTGGACCGGTTATATCAGTGTGCGGAATGGTAGTGCGATTTCTTCTCAGACACCGAGGTCACCCAATCGTTTACCCTTTCTCGTAACGAAAAAGCAGGTGAGTGGGTCAGTGTCCTTGCCGTAAAAAGACTCGTCTTACATGCAAAAGAGGACACTAACAATGTTCAAGGCGGTCCTGACCCCAACTTTCTGGATTCGTCGGTACTATCGTGCTCATATCCCGGGCCAATTTGGTAGAACGCCTGCGTCCAAGTTTACTCCTGCTTACCCTCCACTAAGGACTGCACGGCGTCTTGCGCTGAGAACGAAATCTTATCAGCCTCTCCTCCCAGCTTAGTGTGGTAGTGCTCGAACAATTGCGTCTGCCAGTATCATTTTGCAAAACGGCATAACACTCAGCCTCGGATAGGATAGGGGTGAATCTGCTGAGAGTGTAGCTTAAATAGGTTGGTTCATGTGGTAATAACCTATTGAGTATATGACGACGTAGCCCAGTGCCAAACAAGTTATTAGGGCATCAACATCGGCCAAGGAAGGTGCGTCCGTCTCCCCTAGGATATGGACGGCGTATATTAGTGCACCTTCTGAGTATACAATCAGTCGGGAGTCCTCGAGAGAATCGGCAAATCAAGAGAGGAGGTCGCAGATGGAATTGCCCCGTAACTCACAGCGTGCCTTCTAAGTGACGCATGTACGTGCGAGTCCGAGAAAATTCGAAAGCATGGAGCAAAGGAGACATGGAGAGTCCTTTTCACAATCTATGACCACGTCCAGATCCAATTGAACTTGCGAGTTGTGTAGTTTCCGGTAAGGTTCCCCCCAATACTAGTTCGTTCATCCAAAGGGCTTCATCTGTGCTGAGAGGGATTACCATGACCGTGAACCAGCAACATGTTACCGCGTGCATAGGAGGTCGAACGATTGACGCCATCTTAATCATCCGGATGTTTGACGCCTTCAGAAAAGAACCCTATGGCGACGTACCATTATCACGGGGGTAACAATCAGGATAGTGGGGTTGGCATAAACCATGCTGCAGTACGCCAGAAAACGCTGGGACTTTGCCGCGGACCTACGTGCATTACGCAAGGAAAAATCGGAGATAGCTATTACCCGCCCTCCAACTAAAAACTTAACCATCTAATATACCTGATCAGATTGAAGGCCAGAAACTAAGTCCCCGATACCGCATGTAACGCAAGGTTAGGTGGCTTAGATAAACAACTAAGTCTTAATGATTCTTCGAGCCCGTTCTTGTAATGACTCCAGTTGCTAGGGGCGATCGGATGACGCCCATCATCGTCCTCGCCCCAGGCACTAATCCTTCCCAACTAAGGGCCGAAACGTATGTCCGGCCTTGACGCTTTGAAAGAGCGCCGTAAGCAAATCATGTTGGCGGGTCTCGATGATATGTTCGTTAACAAGTGGAAGACCGTCTCAATCCGGTAGCATCCCGTTTAGGAAATGGGGAAAGTTCGTGCCGTTGAGCCCTGCGTCCCGGATCTGTATTTCAAACCCTAAAACGACTCCTTTTCTTCGCGCACCTATTGCCGCGATTATATACTCTTTGCGGTTGAACCATAGCAGTAGGTTCACCAGGGCGGTCCGGGCTAGTACCGCATTGCATTGTTTTGGAACGTTGAGTGTGGCCACGTGATGCTCCGGGCATGTCGCCGGCTTGAGGACCTAACCATAAAGTTACGGGCATAACCCCAGTCCTATTGAGTTCGACCGGTTTGTTAATATCAGTAATGACGATCAGTACCAGCTTAACAACCCGACTCGACATCACCCTTTTTTTATATACGGTAACCCATTGCCAGTAATCTACCTCGCACTGTGGCGTGTAGGGGTCCCCTCAGTCAGGTCGATCAACTTTGATCGTGCATCCTTTGCTACCAGCACCGGTGGGGGTCCGCTTAGCACGCATCTCCAGATTTTCCCCATTCTGGTTTGCTTCGTACAGGCCATGGATGGGGTGGATTCTGTCGCACGGACACTCATTAGCCCGAAAGATCCAGGAGACAGGTACGACGTGACATATGATGGGGGCTCCTGCGACACTTGAAACGACAATCGCCCCCCAGATGTTGAAAGTGCAAGTTTGACTGACCCTTCAGGGGAAAATTTGGCTCCGGATGAACAGGAGCCTTCTCCGTTTATTGAAGATACGGTGTTGATTATGAGAAGAGTCCGTCTTCGAACGGTTTGTGAGCAGCGCGGTGAACCGGACTCGGGACGAGGGCCGTCAAGTAGGCTTTCTAAAATTTTACCACGTGATCCTCAGATGGACGTCTTGTATCCTAAAAACAACGCTGCAGAGGCACGCACCGCAAAAGTCCTATGGCGACAGGCGTGAACAATTCCGCTAGCCTTGAACTTGGCGAAGGGTTGGGGTCCTTTCTCTATAGAGAGGGCGCAGGAATGCATCACACAATCCCGCTAGATACCAAGCATCACCGAACCGTTCACCGTGTCTCAACTTTGAAACCCGCCCGAGTGTTCTACGCGTGTACTATTTGTACTACTCTGCTAGAAGCGACCCGAACCTCCCACCGGATGTACATGAACTGGCACCAGCGACTAACGAAAGATACCTATAATCTTGAGATGAAGGGGTCATAGCCACTCAGAGTCTCCCTGCGGATATCTTGCGGTAAGTCGACGCCGGCCTTTAAATGCAGTGCGTGATACCAGCTAAGTGGCACCCGGTTTCAGGAAGGGAGCTTCCGGTCTGGATGGGTGGGGAAGTCTTAAGACTTTGAAGTACTATGTTTTAACGATAATCGACCGGATCAAACCACCGCTCACTGCTCGGACATTCAACAGTAACGTACGCTATCTTCTACGGCGCTGGCTACATAGCCTTATTGCCTGACAAGTTAGGATACTTTTTGCAAAGGCTGAAAAGTTACGAGACCCGCATTTCTAAACTTCTAGAGATATGCTACTGGTAGCATTGCGTGGAACGAAAGATCAAAGTGTGGCATGGAGTCGCCATGCCACCCATTCAACTAAGTATAGCGGGGACCGTTAAATGGTGAAGTCCCTGAAGGCCACCCAATTTGCCCATGTTGATAACTACTCGAGCACCGCTCAGCCTTTCTCCTTGCGACTTGGAAGCACAGTGTGTCCGGTCCCTAGCGAAGGCGGAGCGCGGAGAATGCGAAGAGGCCTACCTGCCAGCGATTTAGAGCCTCGCGGGAGCGGCATTAGCGAGATCGTACCTGTTCAGGTTGCTACTTAGAGCATCTGGAGACAGATGGACGAATTAAACCTTTAATTTCGATGCGTCGTCCCTCGGTATGCCTCAGCCCGGCGTGGGATGGACCCTGCGTTTATCACAAAATGGCAATACCTATGCGGCTCAGGTTATTTTCAAGATAGCCCCCGTTCGTACCCGTACAAACCGCCCAAGTAGTACCGCTCTGGTGAAACGCGCTTCCTCTATGTAATGCTGTACCTTAATCGTCTGCCGGTTAATTATTGGATGTCTCTGAGTAGTCATACCCTGCGTTCAATCCTCGCGTTTTTTCCCGGAGCTTCATCAACAGCAAAAGGAAAAGTACATAGTACTGTACTGAGGGACAGAAATGCTGATACAACCGTGACAAACTGTGGGTTATAAAGAGCCACCACGAAACAGGATTACTAGCCCCAGTGGCGTTTGTACGCTCTCGATATTAGGGATAGAATGTATACCAGTTTGTCTCGTATAGACCAGCATATAATTCGCAATCCATTACATGTGTCAATCCCGTACTTTGCACACTCTGTAGTTTGTAGCAAAGGCTAACCTAGCGTCGCCAAGAGGCACTTAACCGCGATTTGGCGGCATTGACATCTAGAGTCTATCCTCACAATTCTACGCGCTGGCTACGGCTGCTGAGTCAGCAACGCCTTACGTCGTCCAGCGAACCACGGGTTACCCATTCAGATTGCCCGGCTGACTGGAAAACGTGGTAATTGCCGTACTCCACTCGGCATTAAGAAAATGGCGACGAGAGAACTTATGTAACCGTTTCCTTCAACCCTTAGATAAAGTTAGCATCCATCCATATGTTGAAAGGGCCTATAAAGACGCACAATGAGGCCGTGAGTCTTGCGGTATAGCTGACTTGACGGCAAAGTTACGCAATTGAACACGTCTACTCCTAGCTCCTTGTCAGTTAGTGAAGGACGACTCCTGTATTGACACCGGGCAGCTATCTAAACAGAGTAATGCGATCTTGTGTACACATTGGGCAAAATTGATGCGTCTTATGGTCTTCGTCACCTGCCCGTTTGGCTATGTTTAACATGGGCTGCTCCAGTATAGGGCGCCAGTGACAGCAGAAATTAACCAGTCAAACTTGCCTATACAGGTCTGATCGATCAGAGCTGCGTTGAAGTAAGATCCACATTAGCAGTGCAAAGTGCGGTGCTGCGCGGGCAGGTCTGGATTCTTCGACCTTCCACTCTTGACATCAAACAAAGGGCTGCGACAGGGAATCTTAATGGAGTGATCTTTGTACGAGTTTACCAGTATATCCGACCTCCTGATCCTCCCCGATGTGAGTCGCTACTAGACGGTAGGGCAATCGGTCGTATTAGAACCGGACTGGGTTTAGTTTAACTGGCTGACAGCCAGCGGGCCACTGGTACGCTAGAATAGCCGACAAGGTGTCGTAGATTCACTTAACCGGGAGAAAATACGGAGTCATCTTATGCAACATAGATGCACGTCAAGTCCTACACAGAGCCATTCAAATGAATTCCACTACGTGCCAACATAGTCGTTTTGGAACGAGCGTAGCGACGTAAATGGGCTCTCAGATTATAAAGGAATGCGGCCGGACGTACTTCCCCATCCGAACTGCTAACAATCAAGGGAGTCGGCGTCGCTCAAAGGGATCTGACCTCCTACTATAACATCGGGAAGCGGAGCAGTTCTTTGCATTATATGGACTCCCTATGTAGGCACGAGCTAGGAGTACATAACAACGATCTTTCGATGTTCCTGCCCATAAATGGCAAGACCGGGGAGGATATACCGATCACGTCATGGTTTTAGGTAAGATTTACCTGAGCAGCGCTGTACGAGTTGGATGCGCAAATTGTTCACGTCATAAATTGTGCCTGCTTCCAGTGCGGTAGCAATGTCTAACTAAATTTGGCTTGTCTGCCACTGAGCCACACCATCAGTCGATAAAATTTGCAACGAGCATCTTGTACAACAATCTGGTAAGTTACCTCTTCAACCAAGGCGCTACGAGTTACTAACAGCATATGTAGGATATAACCTAAATGTGGTTAAGAGTTCGAAGGTTGCGAGGTTGTGCAATTTATAACTACAGCGTTCGCTACTGAACCTTGTTTTTACTTACCCAGTCGCCTAACCCGGCAGTTGCGACTTGGATATAGATTAACTGCCCACCTACCGATACGGCCGCAGTTCCGGGTAAAATCACCCGTATGGGTATTAAAAAAATGAATAACACGTGACGTGTCCTGGCTTCAAGGTCGATGGGATGGATCCTAGCATTATCCTTATGGCGGTGATTCATATTCACAGCGTGGCCGCATAGTCTCTAACCCGGACGGCAGGCGATTGTCCCAGCCCCTAACCTTGAAATCGTGGGTGGGGGCGGGAATATTCCATCCTTCGCTCCCCGTCGGGGCGGGAGATCGGGCCATATCTGCCGTAAGCGCCACCCGGCACATAATACTTAATGATAGTCTCTAATTACACGCCAGCAGAGCGCTTGAAGCTCTAAGTCTAGAAGTTTGGTCGCGATCTCCTTAGCCGCAATTGGTCGCGCACAGTCTTTCCGGGGCAAACGAATTCTATTTCCACGATATAGAGTGCCAGGGATACAGAGTCACATCACTCGGGCCTTCGATTCGGCTTATAACACGTTAAGACTTGATAACCCCTTCCCCGTTCACCGTGGCCGCACGGTGACCAGTACAGTGACAGCCAACACATATCATCTTACTGTTACTACGCCGACCTTCGTGTGCCCGCTCGATTAAAAATGACTTGAGTGGTATAGTAACGGCGAAAGTAATGTCTGGATTTCTGGTGAGTAGTAGCGGGTGGGTTTCTACCAGCGGAGACACGCGCGGATTGCGGACGGAATGTGTTCTTGATGCAAATGGAAAGTTGCGCGGAAAGAGCTATCCGCTCCAGCTACACGTGGACTGAGTGATGATCATATCAGCATAGCCACAAGCTCTACAACTGTCATAACGACAGGTGGGGTCCGGGTACACTCAACAATACTGGAGGCCTAGGAGGGTCCGCAAGGCCAATTATTGGCCCTCCAGCACAAGCGTCATAAAGCGGTTGGTGACGTTCACATACGAGACGATTGTCCTATCAGACTATTATGGCCCACCTAACTTAAGACACGCATCCTTGGGGGCTAAGTTGGAGTTCGAGCGTCAGACATCGCACACCGAAGCAGTCAGCTCTAAATTTCGAGTAGGGTATCATAATTTCTACGTACCCTATGGTCACGCGCTTTCGAAATGTCTAGTGCAGACCGCTGTATCAGCGGCCTCAAGAGCCGCTGTAACGGTGGCGAGAGTATGCTAGTTTCGCCGGCTGGTCAATTCCAATTAACTAAGTGGGCGTCGAGCAGCGCTGCCACTATAGGCCACATGATTCATACCACATACCATATTCAAAACCTAGCTCGCCCGCGAATGACGAGCCATAACTGGCATACGCAGCGTCGTGTGACGGGGGGCTACATGGTAGACTGCATTGACCGATTCGTAGCGACGCGTGTTCTAGGGACACGGCAGCTAACGGTCTGTCACTCGACTTTGTCAAGAAGCGGTGCGGATCGAGGTATTCTGCTCGATCATAAGCCCCACGCACGAGAGTCTGCGAAAGAGGCGGTACCCTGCACCATACCCGTCCGCACGGGGCGTCCTAAAACTTAATCAATAAACTTGCGCTAGGCTAGTGTGTAAATAGAGAATTGCCGCGGCTCCAAGCTAGAGGACGTTTCAAAGCTTCGTGTTTATGCTTCCGCGCCACAACACGTCGCACGACTAGGTGCGACGGAATTGTACCACCGTCATGAGAGGTGAGGTGTCCTCTAATATTTGAACGTCTATGAAGATTTGCGCCGGTCATCATCGGGAGTGTGTCGCCTCAGGATATGACGTAGTGAGAGATTAGCAGGTCAGTGTGATTAATACCCGGCCGTTGGTGGGCTGTTGAACACAGAAGATGGGATAATATACCACGTAAGGCTGTTAGTCTCTGAGGTGGTCAGGGGAACGTGCCTACCCGTGGACTTCTCATACATCACTACGCGCGGCGAATGCGTTTCTTTACCTTAGATTTAGTCATACAACAAACAAGCATGAAGTGGGGGTGAGTTCTGATTCCGTAAAACCACTTGCGAACGAATGTATGTAGATCCCGATGATGCAATGACTTGTCGTAAACGTTATCATTTTAGCCGGCATCGTTATCTGCTCTACCGCCTGCTGTAATAGCAGTTAACCGCGTGTTACAGAGAATCAAGATGCTGATATGTAGTAACAATGTGACGTGGGGTAGCCGCTCCTTTAAACGGATAAAGTCCCATAGGAGCCCTCGCATTATCGATTTAATAGGGCTACCGAAGCTACAATAGTCTAAGACGGCTTAGGACTGGCATACGGAAAACCCCGAGATCGTTACAACAGGGAATAATATCACTAGCCGTGGTGCTCGGCAAGCGGAACATATTTTCTACCTTTTAGTGAGGTCGACAGCTGCAGCCGCTCAGCAGCATTTGGATTGTCCCCAGCAGTTCAGCGATCGTCATTGTCATCTCCAAATCTGAACTGAAATGTAGACGCTTCTGTGTCGTGACGCCCTGATCCCCCTGATACATCGCCTGGGGTGACGCAGATCGATGTTAAAGAATGAACCAAACAGTGAAACTAGGACCATGCCGTAGGTAGCCTATCGCGCTTTATATAGTAACGGTGTGCCTTCCAATCTATGGGACGTGTACATGGGCTCGTCAGGTTTCTGGTCATGCTGGAAAAGTCCGCGTAGCAAGGTCGCCTGCCGCATGCTGCCGAGTTTTTTGATCCAGACCCTTGTACATGCTAAGGCCTTCCTAGTTCTTCAGATATTTGTAAAGAATTGCTGTGGCAATGAACCCCATGATCCAGTTATCTCCATAAGCACCGTCCCCCACACCTGGTTATTCACAAGAATGCTCAACCCACAAGGACGTCTATAGTAATCGCCGTGGCCGAGGGTCCGTGATGGACTGTTGTACTCAGCACGGTTGGCTGTATTGTCGAGGCCACCTATTCTATCTTTCGAGACTTCTTACCCTCTCATAGTACACACAGGTTGGTCAATTGGGCACTTTCTTTCGCCTGAAGGTCGACAGTTTTTTAGAGCCTTCTAAAAGCCACTAGATTATTGGGCGGACGCTAGCGTCGAACTAGCTCACACTGCATCAGCAGGGATTTTAGAATGATGGATAAAGCCTACCGCACGACTCTCCTCGGGCTTGCCACCGAAGTGAATCGTATAATCACAGCGCATTCCCGAGGCTGCTTGGGGACTGATGGTGATGATTGAATTTGGTAGGGCTCGGCCATCGCCCGCAATCCTGTAATTACGAGTTGGCTAGACATCACAGCTGGGACTAATAGCAACGCGATTTTAGCCCCGCTAGGTTCAAGTATTTGTTGGTCGCACTGGCAATTCTATGCACCGACACAGCGTTGTGCACTAGAGACACTAATTCCCTTAGAGACCATTTCCCGTACTAGGAGGCGCTGCGGTATGATACCACCAGGAGACATTCACTGATGAAAGCCAGACTTTTGAGATTCCATGACTCAGCGAGGACACTACCTAACACCGCTTTTGGGGTCCAGAATACCTTATACTCCTCCTTCGCTCCGGGTCTTGCCGCCCCCCCTCCCTTTGAGTGATTTACGAAAAGGTAACAAGGGAAGCAAGACTTGGACCTAGATTCATCCCTGACCATATATCCAGCCAGCGTTTTATTAATAGTCTATTAGCAACCCTCTTCGCATTTCAGACAATAGCCCCACGGTTGCCACAGGTATAGTGTGCAGTTAATCCCTCCGCACGACTGTACCAAGGCTTGTCATAACTAACCGTCCTTCATACTAGGTGCCCGTAACACGGTGCAGTCATTTCCCATACTTTACTTTGCTGCTGACCAAATAGGTTCGCACATATATAGCTGGGATTGAGGCTTGTGATTGATGATATCTCGTGATCCCTTGGATAAATATGTGTAAGATGAGCTAGGACGCGCAAAAGTTTGAAACTGAAGATCGCCTCTATGCGGGGACAGAGAATCTTCTTAGACAAGGTATAGCTAACTGCTAATGGCTCTGTAGGGTTACAAGATCACCTACGTGGCAGACAGAGTAGTCCTTCGAGGGCAATATTTAGGCCGTTTCTGCCCGAGCTAGGGTACTACCGTGACCTTGTAGCAGATTTATTCTCTGGGTGCTTTTGCACCTGCAGCCGTGTCCCATAACGGCCGTTTGGTAATATAACCCTGTTCTGTCTCTGCCTTGAGTCGGACTGGCATTCTATCCTCACTACCCTTATAGCCAGGTGAGCTATCCCCTAATGCCGACAGCGTTTAGACTGCTTTTGAGAATATGCCGGCCCTTGGGGATTGAATAAGTTTATACTGCGACCCAACGGTGCATCCCAGCATTCCTATTCCTTTGGTATCAGGTGGCCTCCAGATAATCAATGTACAGCTTACCTTCTACGAAATTAGGTGACGGCCAGATCCCGGTTGCAGGATATTCGATGCTCCAGGGCTTGTACAATATTCCGAGAAGCGAGGTCGGACACGGTATACACTTTACCTTCTGTTAGAAACTGTACACATGGGCCTGGAGAAAGGCACAACCGACGTGGGTGCTGTTACGGGTCATAGGAGCACTGACGAGAGTTTGAAAAATCCCATGTAAGGTTCTACTGAGCCTGTCCACCAATACCGCACGGCAATGCGACGGTGTAGCTGCCCCTGATGACCAAGGAGAAGTGACTGTAACATACGGAATACCTTGTCGAAAAGTCTCTTACGTGCCGTAACCATACGTATCATACTAAAAGTAAGCGTATCTATTCTTTATTGACACCAGTACTGAGACGGAAGGGACGTTCGTCCAGGAACTCAGGTCTACCTCAACCGGACTTTGCTAGCTGCAACCTACCGTCTTTGCTATCCACTTTCTGCCGTGGGTGTTCGTAACTCTCACCACCTCACATATGGGGCCATGAGGCCACACCTCCCGCCCCCCGGCGCTTACCCGGAGTGGACATAATCTAGGAATACTGACCCACGGGTGCTTCTTTTGATTTCGAGGACTCTTGTTCTTAGAATAAGTCTAGAAGTCCTTATACCAAAAGCGTCGGATCTGCCAACCGTATACGTAAACTACATCCAGACGCCAGGAAGTTCCTTGCACCAAAATTCAAGATTCCATAATATGTAACGCCACCCAGACTGGGGACAAGTCACCTACTATGTCCACCGACGGGAGGGCCGAGAGGGCCGGTTCGTTAGGAGGTATTCCTTGTCACCCCCGGCGTAAGCTTTTTAGCGCCTTCTACTTTTCGGCAGATCAGCCACGGGTGAGAAGGGGCGTAACGCATTTACCCATATCTGAGAGATAACACATAAAATACTTTTGAACCTTAATATATCGCACATAGTACAACCAGACACCGCTGAATAATCTTACCCACGACGAACGGAATTCGGTTGTGGGATTACCTTGGTTACTGGCCGTAAGCCCCCGCCAAAGACGCTTACACGATCCAAGGAAGTTGGGTTCCCGCGAAACTACAGCTGATCTCATCTTACACGAGCAGGGTGCCTCCAGTTGGTAGGTTATAGGACTAAGCCGCGCCATTGTCGCTGATTCCTGACGAGCGCCCTACCCTCAAGCAAACACACTAAAAGGCATGGATCGTTCTCATGAAAGGAGTTCGAGCGAAGATCGATGTGTATGCACATAGAGGTTCTGTCACACACCTGTAATAAACTTGCATCACGAGTACCCGCATGATAAACTGTCGTAAACGTTCACATTGCCTTCGCAGCCCTGAGCTTCCCTGACTTACATTCCGTACCAGGTTGATAGCAGAAAACCGAGTCGGAGGCTCGGAAATGGGTTAACCCTTACAAAAAGTGTAAATTACGGATTCTTTGCTCGCCTTGGACCTAGACGAGTGGATTCGCCTCGAGACACTAGAGTCAGGACACCAAGCTCAAGAGTGTTTTTCAGTCCCGGGATTAGGGTGGCTCAAGGCTTCCAGCGGGAACTAAGCGTCTGCCTACCTGGTATTCCTTGCACATCGGGATGCTGACCACTCCGATCCGTACCAAGACATCGTGACCGTTTGGTCCTCGTCAGGGTGCCTTCGCGTACCCTCATGAATCCGGACCGCACTGCAACTTTATGTACCGGTATGCTGGTCCCGACGATGCACTTATGAAGATCGTGAACAGGGCGGCGCGCCAACTAAAGTTCCTCACTTGTCCATCTCAAAACTTCTATCCTCGCACAACGTCAGGTGATGCCTATCCGTCGATTTCTGGAACTTATGGACTAAGGCCCGATGCGTCCTAGTAGGCACGCATTTATTAGTGTTAACGAAATTACATCATTTGACAGCTCCATTCACTCAAACCTCCAGGCGACCCCTCTTACCAGCTCTTGTTATGCTAGAGCATCTTAAAAGGACATCTCTTATCCCCACACAAGGGTAAGGCATCTAGCGAGGGAACGGTAATCTGAATTTGATACGGACCTCGTAGACTCTGTTAACAAAAGACTAGGTCCGCCTCGTCCCACCCGTGCTCTACGTGCGTCCCAGTCAATTAATTGTGGGCACGGAGTACGAGCTTAGTAGACCGCAGAACATCCTCGGCGCGGGGCTTGGACAGACCTTACGCTGTGGTTACTAGTGGTCAACACCTGGAAGTACTAACCTCTCACATTGTCCCGAACATAGCTTTCGGACGTGGGCGAGCACGGGGTGGCTGCTTAAATGGACCAGGGTAACGCCCAGAAACACGGTATCATATTATCATCGGCAAGCGCCCTCCACAATATGTAAGATGACGGTCTTTACCGTCACGCGCCCAGTCTTCCCGTCGTGGGTCGTATTTATGCCACAATTCCCGAGTGGTTCTCCATCTCGTCCACGTCGCCGCGCAGGTCTCAACCTAAGACACCCCTCCCTTGCCCCAAGATAAAATTATAGTCATCCGCGCTAATGTCTAGGTATATGCTTGGCGGTAAGCTACTAGCGAACAAAACACTTTTTGCTCACTTCAACCTGGTTACGCGTCGAACAACCTCTCTGCGCGATGCTCGGTAACCGCTTGTAAAACTCCCGGCACACGAATCTGATGCTATTCAGTTGAGATTGAGACAATTCCATAAACACACCTCCTCGCCAGTGCAGATCCGGGTACACGTCATTGTAACTACGTCGGACGCCCCGTATGGATCGACAGACTACCCCTCCAGAGCGTTTCACTTATATCACATGTACCAGAGTGTATATGGTAGCCGACGTCTTAGGAAGGATCTAACCCTCAGTGAGCCCGGTAGCTTCGGGTAACCAAACCGGTCGTCGCGGGGAATCACCAGGCATAATTATAAAGAGGGTATCCCAGTTAAGGTTCCAATGTTGCTATCTGCCGCGCTAGATTTAACGCTGGCAATGTTTAGATTCGACAGTTCGGGTTTTTTCACTGCTTAATGCGGGCTCCTTCTCTAGTCCTCTTCGCGACAGAGCAATATAAATGTTAACCCGTTCACGACTGGGCAGGGGAACGCCGGCCAGTGAGCTTCGCCATGCAGATCCAGGCAGACTGGCATGAGTTAGGGGAGCGTACGTGGAAACGAGTAGCACGGCTTAACCAGTAGTTCCATATAACCAACGGGTTTATGGGTCAGAAGCGCTTTGACCCCGGTGCGGACGAGTGGCTCCCCCGTGACAGGTTCTGAGAGGCGGATCACCTCATCTCCAGAGTGCATATTAGGATTTGGGCCGGGGCGTTAACCGTGTCAGGACTTCCTAGACTTGGAAAACCGAACATGGAAACATCATCCCTCCTCAGTCAAGCTCCTTCCAAACGATTTCGGTACACCATTCAGCTCCATTACCGGTTCCTTTCTCAAATAATTCTTTACAGTGGTCAGTAAAAACACAATATCTAATCGCTCAGAGGGCTCGCCTTCACCTTGCACAAAAACCCGAGTGAGAGAGTGAGGCTGTCGGTGCTTACCTGGAGGGCTTGGTTCTGAGTTCTCACCGGATACAGAGTCTTTAGTCCTGGGGCTCTATCGAGCAGGGAAACGCTCGCACCAACTGGACGCCCTTTTAACCCATTAGGAAGTCATACGTGGGAAGCCGTGAACTGTGGGTAGGACGTGAACGTGAAATACCACAAATGATAATTACGTCGGGATTCGGATGGATGAAGAAAAACGGCCGCCCATCAGAATGGGCGAACGGTAGATTGATTCCAGCGAATGGAACCTCCATGCTAGGAGCGTACGCTTGTGTTGACTATTGATGCTAAGCGATGTTGGAGGCCATCATCCTGACCATCTGAAGATACTAATATGTTACGGGGGAGGCGCTCACGTAGTAACATAGCGCACACGCCCTGGTCCAAGTCGCGGGTCTTACTTTTAGGACCATGATTCGCGAACAAAACGATGTAGATCCTACTGGGGAGTGTAAAGCACCTTAGGTTGCAGCATGAGACCCCGAAAGCGTGAACGGTTCTAAAATAGTGTGGCCCAAGTCATGTGGAGCGCAAGTTATAAGTGTGGAGCGAAGATACGCACGCTGTAATGCGCGAATATAGGCGGCTACAGCTCAGGACCTGCTTACCGTTCGTTCAGGCACGAGCCTCAGCTATGGTCGTACTGAGCAGGGGGCTGTGTCGCAGATATTTGGGAGCAATATTTGCAAATAGTCCTACATAGTAACATTGGCGTCGAAACGGCTAGGAGAGCGGGCCGGATTTGCCATTCAAGCTGGGTTAGCGCAAACGACAATAAGCAGTCCCACGAGCAGAATGCGGGTGGGTACCCTCTCGACTAACATTTGCCCGTCTGTTGCCTACAGAACAACCCCTATTTGCGCAATTTACCTGCGTGTCAGACCGATGAATTTTCAGGTGTATGCTCTATGACGCAGCGAACACCTTAAAATCCCAGAGTAGCAAGCTTCCCCCGCATTATGGTGAGCAAACCTTGATCGCTCACCCGCGATCGCTTCTCCTTAATTAGCGAAACGGTTGCCTTCTCACTCTCTCAAGTCTAAATCCCTCCCCCGCCAAGCAGTCGGCGCTAGGATCTTGCAAAAACGGATTGGATCACTATGGTGAGGACTACTTGATTACAGCTGATTTCTAGCCGATGCCGGGAGATTCCGAAAGTGTTCGCGGGTTATAAGCTTCGAGACTGGTTTCTTCAACCCTAAGACAGTCGTTGCATCGCACAGGGAACCGCTTGCAGCGCCCAGCCTCTCATTGGGCTCATAGCCCGCAGTGAGACCACAGTCGATAACAGAATGGTTATCTGTTTACCGAGTACGGTACTCCGGACGTGATGCCAACTGGCTCCTAATTCGTATCCCTGATCTATGCTGGATCCATTGCGGAGGGTTCCGCCACCAAGCAGCGAGCAGTAGTCGGGATTTGTGTTCTAGACTACCCATTTACGCCAGGGTGTTTCATTTCAATCCACTATCGCTCAAATCCGCGTCAGCTAGTCCCGTCAGCGTGCTCCCACACCGACCGCCGATTCCCTGATATATTGCCAGCTCCGGATTCCATTGCTTGCTCGTCCCCCTATGCGCGGCATTACCGCGCATATTGTGGACCTGAGTCGTCTGCAATCCCGGGCCACTTGGCAATTACATTTTAAAGCGACAGGGTAGTGCAAGAGAGATCCGACAGGATCCTAAATCGTGAGTCTCATGTAGAGGCCCAGTCTTACAGACTATGATGTTCACGCCCGTAAATGACACACGGGAAAGATAGAGACTCAAGAGATGACTGTAACGATAGATGGCTTAGGAGCACGGGCATGGAGTCTACCGGCCGGACAGTGCAGCTGATGGGTAAATCTGTCGTGATATCGAGCCGATCTTGCACAAAGGCCGCACGCGACACCCCGGTCTTGCGCATACGCTCCTCGCCTGATACAGGTCGTGACCTGGTGTAATGCGGGGGTATAGCTTGACTGCGCCTTGTATCAGATCAAACCCAGCGAGTACGGTGAAGAAGTTGTTAAGTACGGATTTCCCGACGAAGCCTTTGTAGTACGTACCGCTAGAGCCAGGCGTTGGAGGAGATCGCTGGCGTTTCGGTCGATCAACTAGCTACCAAACCGGCCAATTAGGGGGAAGCTAATAGTGGCCAAGGGGATCGGAGCGTTGGCTAGGGCCAGCCGAAGGAGCAATCCCACGCGCCGTCCTTTTCTAGTTTGTCCCGCTTTTTAACTTGAACGCGCGGAGTGTCGAAGCTAGTCAGGTTCATAACAGAGGTCTGGGAGAATCCTATCGACACGCACATGCCATGCGAAAACTACAAGATACTTTGTCGCGCTACGAAGCAGAGAAGATGCTTATGTGAGATTTTTAAAGACTCTGTTTCGAATTCGTCTCTTAACACCTGGCGACCGGATTTATGGCGCTGTAAGGGACTGCAGGTGATCTATCAACTATACGTCATAGGGGCCAACGCAGTTTTCAGCTACGCTCGCCAAATACGGGCGTCATGCCGAACAGGCACTTATAAGAGGGCGATAACGTTCATTCCCGACTCCGCGACCAGCTATTAGCGATTTGATGCTGCTATAAGACAACTTATGAGACGGGTACTAGCGGTTGGCCTTTGGTTGAATAAATGCCCGCCACGATGGACTGGCTCAGATCAGCGGAGGCGCCCCTTGCACACGGCCCTATCGTTTGCACGCTTCGGTGATTCCGCGCATCGAACAACGCTTAGCGCGTCAATGTCAAAAGATCTACCCGTAGCCCCAAAGTATATCGTCACATGACAACGACGATGGTATACGCGTTTAAGCATGGTGATCGTTGTAGTACGGGTCCGACGCGCATACTTTGAGTTCCGCCGCATATTGTATTCCGTACGCGTTCTACCCCGCAATTATCTGGAGTTAGTGGGCCCAGTAAGGATATAAGTGGGAATCTCCACATACATCTTCAAATAAGGGGCCATGCCGCTCAGCCGAACTTTGGATCGATGGGATAGGTGAACCGAGGGCAAGTTGCCTACCACGTAGCACTCCGCAGGGCAAGCCTCGGATCGATGCCGACTCCCCAACATGTTTCTCTTAAAGTCTCTATAAACGGCCGTCTCCAGCTTCAGTGTTAAGATCTCATCTGAAATACTCCAAGGTAGATTGATCAAGGGAATGACGCACCGGCATCAATAATCAGACTCCACGCGTATGCAGCTACTAATTACCTGTGGCCTAAGCACGCGAGTCGGAAAGCGCAGCTGTGTCCAACACTGCACCCAGCAGTTATCGGGCCAAAGTCCAGGCCGGAATGGATTAGTGTGGATTTTGCCATTAATGAAATACCGGTTACAAATATCGACTCTCATAGGAAGGTGTATACAATTAGTCCGTCTACCGCCTTAGGTCATCTTTCATTACAAGCACAGCCTTTGCATGTCCGCCACCACCCCAAGATATTTGGTATCTGGAAAAAATTTTCATTATGTCGACTTATTGGGCCATCTTACGACGTACACCTTGTTAGTGCTTAGAATCTCGGAACATAGAGGAAATCTCCGACCTTTAAGAATATGTGTTCACCTTAAAGAGGCTAAAAGCGTGTGTTAGCCAGGTCGTAGTAGAGCGAATCTAAGGCAGTGTACTCGGAATCGATCGTTGACTGTAGGATACGCACGGGCTAACTTAGGAGCGACGATGCGTACTTGTGCACGCTAAAGCCGCGTCCGGCGTCAGAAACACTACGAAGTTGTGTTGCTCCTTTACACCCAGTCCTAGACCCCACTTTACGCACCTGGCCACGTGGGCCGAGCGTAACCGGCTTGCACCGCGAAGCTAGGCACCGCTTTGTCCTTAACCGTACAGCACTCCACGGTTCGACTTATCTTGTTGATGGATCTAAGAGTCATATGTAGGGTGGCGTCAGAATGACCTCCACATGAATTCCGCAGCCTTTAACGGCACTCATTTTGAGCGCAACTAAACGCCCCTGAGGTAATATCGAGCGTTGTGATGACAGCGCATCTTGTGGTAACTCAACCCAAACATATAGCGCGCTATCTTCGTTTGTCTCAGCCGTCATTCTCGGCAATAGCACTCACTCTGGGCGAAAGGGTATCCAGGTAGACTGGCACAGCCTCTACTTGTTGGGTGTTACCCCTGCGCCGGAGTACACAGTAGCACATACTAATCCGGGCCTCAGTGAACCTAGAAGGAGTATGTGTATACACACGAGTGAAACGTGCCAAGAGACTACCCAAGCGCGAATCCGCGTACACATAGGTCAGGGCATCCCCCACACTGTTATCCTAACGGCTCACGGTCATCAAATTACTGTGATGTTGGTCTATGGGTCTTGCTGGTGAGCCGGCACATTCAAGGTAGGACTGACTTAATCCTGCATGAATGCCCTAGCGGCATGCAAACCTAACTCCAAGTGGCGCTGGGGAAACTCATGAACTCGAAAACACTCATGCGAAGCCTACAGGAGCCGATGAGAATCGAAAGTAACCGCAAGCAGACGAGGTACAGCACTCTCAGATAATTTCTCGTTACGTCAAGAAATGACAAATTCCACTAAAGTTGAGTTAGAATCAACGTCCGCGACACCCAATAAGTGTCAAGGGCTCCCCGGAGACTGGGCGATGCTTTCTTGCTTGGGACCACGTTACATCCATGTGCGTTCAGAACCTCTATTAGGTGGCAGTGCCCGTCTGCCAGGAGTCGATAAGATCAGGTTTGATTACGATTTAGAATATTAATTAGAACCTCGGGCCGCTATTGAATCCACTGAATACTTACATCGCTTAACTGCGGCACGCCGCCTGTACGCTCATCCATTACGTCGGAGCTACACGTTTTTGTGACGACATGTTACATACCTGTATACGAGGGCTCAGGATTTCATCCGCCAGAGACATCTATGGGATAGTGTTCACGGGTGGGTACTCTGTAGGGGAGGGTGATGTATGTTGTCTCATCATCGTCAGAATAAATGAAGTTGTTCTCGGTGCTGACGATGCGTCATCTATTACACGCACCAGTAGCAATCTCGTCAGCTCCATTCTGTGCGTGGCAGGTTCCTTTCGCTGGACCAATGCAATCCTTTGATTACGGTGTACACGCATCTACAACGGCTCACTTTGTTGTGACATGCCTCGACCTGGTCTGAGGCTTTTGTACGTCCTATTCATGAGGGTAAGGTGCAATTCGAGGAGGCCGGCCACGGGCGGAACGTAACTCAACTTCGCACAATTGATCCCGTGCGCGGCCTCCAACTTATAGTTAATCCAGCAAGTCCCCTTAGAGTAGAGAAACAAATAGTATTTGATTTTCCCCTCTTCCTCATTTTAACGGCTACATCCCACTGCGGTCGTAATGACCGGACCGCGCGGGTCGTTTCATGACGCCCGCTCCATCCTCAAGGAGGGTGGCTTACTCTCCAGCAAGCGGTCAAGGGATTTCCGAGACGATACTCACTGTCTTTCGTGCTCTTTGGTTAGTGCTTGGCATACCGGCGTTAGAGCTCATCACAGCCGGCTACCCCATGACGAGTGCGATCCAGTCGTTGCGTTCGCTGTGTCGTATTGCTCTCACCCACTTTAGGACAGGCCACAAACCGCCAGGGTCCAACCGCGGCAAACAGAGTTGATTAGTCTAAATATGGTCTATAAGTACCAATCAATCTCAGCTGTCCGGAAATACCCGTGCATTCATTGTCCGGCAAACTTCGGAAGCTTTTCCAAGCGGCGGTTCTTAGGATGTTTTCGCAATAATTAGCGAATAGTGGTGGTTCCACCCCTTGGAGTTTAAACGGGACGCTGCCTTTAAACTCCTTGTCCTCGAGCTTAGGTATTAAACCTAGAAGGTCTCAAGACAAAATATTAGGTAAGTAACCTAACGATAAGGGCAATGATTCGGATTTATCACTCGGTCCATATATCGCTCATCTCTCTAAGCATTATCTGCCGGAGACGAACTAAGAGCTGGCGGGTCATACCACAAGCCGCGCAAATTAGATGCAACAATCTAGTCAACACTTGAAACATTTCACTCCTTACTCACTGCTCTGGCACTATGCGTACCACACATGGCGTAGCGACCGTTTGCCTGTGTCAGCAGGTGGAGTTGGTCTGTCATTCACAGCGCTGCAAGTAGGCTTAACTATAAGAATGCAATTAGTGTCAAGGGAACAGTCAAAGCACCCGTTCATGATAGTAAGTACGTTTCCAACCGCAGATCTTAAAGCCAGGCACCGTAGTATCTTGTCTGGGATCTTCAACTCACCAACTACATCACTCGGTTACTCTCCTTTGCCAGGTATCTAATGAGTGCGTATAGAATACATCAACAACAGGCAGTGTTGTGCACGAGTCCGGGCGCCCCATTGGGATCGTTACGTGCGCGCTGTGAGTGCGTCGGTTACTTTCGATGCACTACTTAACGGAGCCTCTCAAGCCAAACCCGTATGTGGTCCTTACAGTAAGCCATGTTGATATGAATCAAAACCGGCTCGGTTCATTAGCTCTGTATCGCCCTGGTCATCCCCGGTTATATTTCTAGCATTATGCAAGGCGAAATTGGAACTAGTGCCAGGGTTCACTTAGGCAGTATCATTCGTATCGCCAGTCTTCACAACTCTTACTGAAACTAGGCTACGTTAAGACGGAAGAAGCATTTTTTATTATAGGCCACAGTCGAGCCAGGCGCACAGAGGGCCTCCGCAATAGTGGGACAACTACGTGATGCGCCCGTCGGATATGACCCATAAACTGGTACTGTACCGGAGACCCTCGTCGTCCATCGGCGGATCTACGGCTCTCTAGGTCTGGGGTTTTACCATGCAGATGTCAGATTATTCTCTATCCTATGGTCCCCAAGGACTTTTAATTCCTTCGGTTTCAGCGAAACAAGTGTAACTGGCGCATGTCCAGAGCCCTGCTAACCTGGACGTCGTCCTCCCGATCTACTAAGCCACGCACGACTCCCGTCTAACGGGTGGTCTTACGAAGTTTTATAGGTATGTAGTGGCCTTGACTACCGGCGCCCACTCGGTCGAGTTCAAACACTTTCCACCTAGTGCATATTTAGTAGTTATTCAAGCTATCTGGCCCGAACCTGCAATACGCAAGCGTTTGGTTGCGAACTTATTTATGAAAGTCTTCGCGTACGCGCGTAAGTGACAAGATCTCGGGTACATCTAGTACAGAGTCAGCGGTTAGACTCGGTTTTCCATCTGCATAAGTCGCGCATTGTCTGGAATGTCCTTAACGTGCTTCGAACGAGTCCCTATGGTCCGACGGTTCGATCGTATAAATGACATTAGCCGCCAGCTGTCCTCCCGGAGCGACGCCTTAAGTTGCATGCTAATCGTCTATTGGGCCCCGCGCACGTGCCCTGTACGGGAGCACGTTTTCGTACCTGAGCGGTTCGAGACTCCCAAAAGAGACGCTTAGCGTCGTCTTTGTGAAGACCTATGCAGTTGCACGATAGAACATGCTTTACCCCACGCATTCGCGATAGAACGTTTTCCAACCTTTGGAAGATTAAAGCAGTGCACCAGAAAACCGGCCTGCATGGTTCGCTGTCGAGCGGGCTTTTGTTGATAAGAGGCTCAAACGTAACCGGCGGAGTAATAGCGGTGCTTATGGGTCAACCTTGGAGTTCATGGTCCAACCCGCACGACAACCAGTAGACTGGTCGACACAGTCACCCGATCTGTCGAGAAGCTCATAGGGTTCTATATCTAATAGCCCTAAGGTGGCGTACGCAATAGATATTGACTTCATTCTGTGGGACCTTGACTAGGGAGCGATTCGACTCATAGTCGGAATTTAAACCAGTTGCAGGCCTATTCGCCACCGTCGGACGACGGGAGATAGTCTTCTGACTCCTGACAGCAGGAGCCGCCCCTAGCCTATTGCCGTCAAGCATAGACTGGGTTTTGGAATGGTTACGGTGAGCGTCGCTTAAGCAGAGTGCAATGTACATACCAATGGTTGCCCTTACACCTGTGAGGCCATAGAACCAACCTAAATAGCCAGGATTGGACGCCATCGTTTATTCACCTTCTAATTAACACTTGACCACAAGGTAGGTGCTATGAGCGATGACTCGCTTATGAAAAACTGGATGCAGGCAGCAAGCGGTAATATAGGGGCAACACGTAAGGACCCTAGTTTTCTGAGAACGACGCACTGAAGTGGTAACACGCCCATAAGTTATATGGCACCGAAGACTGTCTTAACAAGCTGCATCTTCCTACCTTTTTCTATTAGCCTAGAATTGGCAGATGGAATCTTGATATGCTTGACGCTAGGAAGACGGTATTCTTCATACAAACGAATACAGTCTCGATCTCGCCGAACCATCAGTAGCTATTGACGGCTATCAATCAGGCCGGATTATTGTGGGCCTTTCATACTCCTCCCAGGCAATTTTCATCCTAGAAAAAACCAGTCATTTCAACTCATCTTCTTCGGTGGCCAGCGAGATGGGAATGCTACTATTCTCACCTGTGGTCACAAACAGCTTACGAATGGCTCTTACGCCGCTGTTATCTAGCTCTCTAATTCGCGCTCTTTCTCTACACGGGACAGTTAGAGATTTCTCTAGATGCTTCTCTGAAAATCCTGGCTTCATATGATTAGATCAAGATAGGTGCGTTCCAGGTCACATGAGCATGACACGTGGAGACAACAGATGCTGAGGGCTTGCTCTTGCTTCTACACGGCAAGGCGAGACCAAACAAGAGGGACCGACCGTTCGGATTTGTCCTCGGCGGAGCTCGATTTAGCTGGTACCCTATGATCCGGTCTTTCACCAGTCGCGAGCCGACTGGCGTGCGAGTTTTCAGAGACGAGCGCCCCGAAAGGGCACC'
result = MinimumSkew(Genome)
' '.join(map(str, result))
| 2,943.727273
| 96,588
| 0.997725
| 96
| 97,143
| 1,009.416667
| 0.302083
| 0.000619
| 0.000124
| 0.000186
| 0.000454
| 0.000454
| 0
| 0
| 0
| 0
| 0
| 0.000113
| 0.001431
| 97,143
| 32
| 96,589
| 3,035.71875
| 0.998856
| 0
| 0
| 0.142857
| 0
| 0
| 0.994245
| 0.994194
| 0
| 1
| 0
| 0
| 0
| 1
| 0.071429
| false
| 0
| 0
| 0
| 0.142857
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dcee8527c5bcba5ef9cbc5efa865f2f38bf2cd09
| 29
|
py
|
Python
|
networkx_astar_path/__init__.py
|
escaped/networkx-astar-path
|
b53ea312b2a5bcea79488f9ccf2e024c2d0bf91d
|
[
"BSD-3-Clause"
] | 1
|
2021-09-26T15:11:59.000Z
|
2021-09-26T15:11:59.000Z
|
networkx_astar_path/__init__.py
|
escaped/networkx-astar-path
|
b53ea312b2a5bcea79488f9ccf2e024c2d0bf91d
|
[
"BSD-3-Clause"
] | 4
|
2020-12-06T21:37:27.000Z
|
2021-01-12T01:25:40.000Z
|
networkx_astar_path/__init__.py
|
escaped/networkx-astar-path
|
b53ea312b2a5bcea79488f9ccf2e024c2d0bf91d
|
[
"BSD-3-Clause"
] | null | null | null |
from .astar import * # noqa
| 14.5
| 28
| 0.655172
| 4
| 29
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.241379
| 29
| 1
| 29
| 29
| 0.863636
| 0.137931
| 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
| 1
| 0
|
0
| 6
|
dcf521a0e21f37b8ad15d488ce49ab444c600e54
| 221
|
py
|
Python
|
prnu/__init__.py
|
ocrim1996/prnu-python
|
e7131a563d8da7d290c8ee972340464672041434
|
[
"MIT"
] | null | null | null |
prnu/__init__.py
|
ocrim1996/prnu-python
|
e7131a563d8da7d290c8ee972340464672041434
|
[
"MIT"
] | null | null | null |
prnu/__init__.py
|
ocrim1996/prnu-python
|
e7131a563d8da7d290c8ee972340464672041434
|
[
"MIT"
] | 1
|
2021-11-03T13:49:04.000Z
|
2021-11-03T13:49:04.000Z
|
# -*- coding: UTF-8 -*-
"""
@author: Mirco Ceccarelli (mirco.ceccarelli@stud.unifi.it)
@author: Francesco Argentieri (francesco.argentieri@stud.unifi.it)
Università degli Studi di Firenze 2021
"""
from .functions import *
| 31.571429
| 66
| 0.742081
| 28
| 221
| 5.857143
| 0.714286
| 0.182927
| 0.134146
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025381
| 0.108597
| 221
| 7
| 67
| 31.571429
| 0.807107
| 0.846154
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0d31bac10150346d6403ba7fae1fe5f50b22cb97
| 181
|
py
|
Python
|
huskar_api/service/organization/exc.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 59
|
2019-10-31T10:50:10.000Z
|
2021-11-26T04:32:25.000Z
|
huskar_api/service/organization/exc.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 5
|
2019-10-31T10:37:30.000Z
|
2020-03-02T06:45:46.000Z
|
huskar_api/service/organization/exc.py
|
mowangdk/huskar
|
7692fbc5672a5ae6e2a33616c493466a7137f8cd
|
[
"MIT"
] | 9
|
2019-10-31T10:35:00.000Z
|
2019-12-01T14:13:58.000Z
|
from huskar_api.service.exc import HuskarApiException
class ApplicationNotExistedError(HuskarApiException):
pass
class ApplicationExistedError(HuskarApiException):
pass
| 18.1
| 53
| 0.834254
| 15
| 181
| 10
| 0.733333
| 0.293333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121547
| 181
| 9
| 54
| 20.111111
| 0.943396
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.4
| 0.2
| 0
| 0.6
| 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
| 1
| 0
|
0
| 6
|
b4ccd86f0b7de4f0dc2c89684094474cc3cf43c8
| 1,559
|
py
|
Python
|
behavioral/iterator/main.py
|
plocinskipiotr/Design_Patterns
|
8e9433dccef7bb49d9fbe4f248cb93da465bd880
|
[
"MIT"
] | null | null | null |
behavioral/iterator/main.py
|
plocinskipiotr/Design_Patterns
|
8e9433dccef7bb49d9fbe4f248cb93da465bd880
|
[
"MIT"
] | null | null | null |
behavioral/iterator/main.py
|
plocinskipiotr/Design_Patterns
|
8e9433dccef7bb49d9fbe4f248cb93da465bd880
|
[
"MIT"
] | null | null | null |
"""
Main loop for iterator example
"""
from custom_list import CustomList
if __name__ == '__main__':
lst = CustomList([6,5,1,5,3,7,8,9])
print("basic iterator")
results_1 = list()
a = lst.create_iterator()
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1.append(a.get_next())
print(a)
print(a.has_more())
results_1 = [x for x in results_1 if x is not None]
print(results_1)
print("filtering iterator")
results_2 = list()
b = lst.create_filtering_iterator(3)
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2.append(b.get_next())
print(b)
print(b.has_more())
results_2 = [x for x in results_2 if x is not None]
print(results_2)
| 22.926471
| 55
| 0.621552
| 257
| 1,559
| 3.521401
| 0.14786
| 0.119337
| 0.247514
| 0.119337
| 0.777901
| 0.746961
| 0.746961
| 0.693923
| 0.693923
| 0.693923
| 0
| 0.02541
| 0.217447
| 1,559
| 67
| 56
| 23.268657
| 0.716393
| 0.019243
| 0
| 0.779661
| 0
| 0
| 0.026298
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016949
| 0
| 0.016949
| 0.610169
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 0
| 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
| 1
|
0
| 6
|
b4d4052afdcd73cb4abb0cfbb55e6f9f93ede3f1
| 52
|
py
|
Python
|
src/spotipy_random/__init__.py
|
michimalek/spotipy-random
|
ff14bdffe2f70b94ae7a092635d8af376661764b
|
[
"MIT"
] | null | null | null |
src/spotipy_random/__init__.py
|
michimalek/spotipy-random
|
ff14bdffe2f70b94ae7a092635d8af376661764b
|
[
"MIT"
] | null | null | null |
src/spotipy_random/__init__.py
|
michimalek/spotipy-random
|
ff14bdffe2f70b94ae7a092635d8af376661764b
|
[
"MIT"
] | null | null | null |
from .spotipy_random import get_random as get_random
| 52
| 52
| 0.884615
| 9
| 52
| 4.777778
| 0.666667
| 0.418605
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.096154
| 52
| 1
| 52
| 52
| 0.914894
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
b4fef3690cfbadfaa65f9c8eb398830bbbcef8bd
| 6,521
|
py
|
Python
|
polyaxon_lib/layers/recurrent.py
|
polyaxon/polyaxon-lib
|
d357b7fee03b2f47cfad8bd7e028d3e265a10575
|
[
"MIT"
] | 7
|
2018-03-05T08:01:45.000Z
|
2021-03-12T09:00:11.000Z
|
polyaxon_lib/layers/recurrent.py
|
polyaxon/polyaxon-api
|
d357b7fee03b2f47cfad8bd7e028d3e265a10575
|
[
"MIT"
] | 23
|
2017-07-10T16:52:25.000Z
|
2018-01-01T15:17:32.000Z
|
polyaxon_lib/layers/recurrent.py
|
polyaxon/polyaxon-api
|
d357b7fee03b2f47cfad8bd7e028d3e265a10575
|
[
"MIT"
] | 4
|
2017-07-11T10:16:14.000Z
|
2017-12-11T12:49:10.000Z
|
# .CONFIG.IDENTIFIER*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
try:
from tensorflow.python.keras._impl.keras.layers import recurrent
except ImportError:
from tensorflow.contrib.keras.python.keras.layers import recurrent
from polyaxon_schemas.layers.recurrent import (
RecurrentConfig,
SimpleRNNConfig,
GRUConfig,
LSTMConfig,
)
from polyaxon_lib.libs import getters
from polyaxon_lib.libs.base_object import BaseObject
class Recurrent(BaseObject, recurrent.Recurrent):
CONFIG = RecurrentConfig
__doc__ = RecurrentConfig.__doc__
class SimpleRNN(BaseObject, recurrent.SimpleRNN):
CONFIG = SimpleRNNConfig
__doc__ = SimpleRNNConfig.__doc__
def __init__(self,
units,
activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(SimpleRNN, self).__init__(
units=units,
activation=getters.get_activation(activation),
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
recurrent_initializer=getters.get_initializer(recurrent_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
recurrent_regularizer=getters.get_regularizer(recurrent_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
recurrent_constraint=getters.get_constraint(recurrent_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
dropout=dropout,
recurrent_dropout=recurrent_dropout,
**kwargs)
class GRU(BaseObject, recurrent.GRU):
CONFIG = GRUConfig
__doc__ = GRUConfig.__doc__
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(GRU, self).__init__(
units=units,
activation=getters.get_activation(activation),
recurrent_activation=getters.get_activation(recurrent_activation),
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
recurrent_initializer=getters.get_initializer(recurrent_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
recurrent_regularizer=getters.get_regularizer(recurrent_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
recurrent_constraint=getters.get_constraint(recurrent_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
dropout=dropout,
recurrent_dropout=recurrent_dropout,
**kwargs)
class LSTM(BaseObject, recurrent.LSTM):
CONFIG = LSTMConfig
__doc__ = LSTMConfig.__doc__
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(LSTM, self).__init__(
units=units,
activation=getters.get_activation(activation),
recurrent_activation=getters.get_activation(recurrent_activation),
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
recurrent_initializer=getters.get_initializer(recurrent_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
unit_forget_bias=unit_forget_bias,
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
recurrent_regularizer=getters.get_regularizer(recurrent_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
recurrent_constraint=getters.get_constraint(recurrent_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
dropout=dropout,
recurrent_dropout=recurrent_dropout,
**kwargs)
RECURRENT_LAYERS = OrderedDict([
(Recurrent.CONFIG.IDENTIFIER, Recurrent),
(SimpleRNN.CONFIG.IDENTIFIER, SimpleRNN),
(GRU.CONFIG.IDENTIFIER, GRU),
(LSTM.CONFIG.IDENTIFIER, LSTM),
])
| 41.012579
| 81
| 0.651127
| 567
| 6,521
| 7.08642
| 0.121693
| 0.087108
| 0.062718
| 0.09557
| 0.780239
| 0.780239
| 0.780239
| 0.772026
| 0.772026
| 0.76008
| 0
| 0.001486
| 0.277411
| 6,521
| 158
| 82
| 41.272152
| 0.851231
| 0.005827
| 0
| 0.735714
| 0
| 0
| 0.018979
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.021429
| false
| 0
| 0.057143
| 0
| 0.164286
| 0.007143
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 6
|
2c100c3d698bffbfdab1367c7f4608913011f2ca
| 136
|
py
|
Python
|
fei_webhook/app_webhook/views.py
|
colingong/fei_webhook
|
2acb8b8e36d05f87f06dcc1bf056beb92a41b157
|
[
"Apache-2.0"
] | null | null | null |
fei_webhook/app_webhook/views.py
|
colingong/fei_webhook
|
2acb8b8e36d05f87f06dcc1bf056beb92a41b157
|
[
"Apache-2.0"
] | 6
|
2021-03-19T09:41:23.000Z
|
2022-02-10T11:13:35.000Z
|
fei_webhook/app_webhook/views.py
|
colingong/fei_webhook
|
2acb8b8e36d05f87f06dcc1bf056beb92a41b157
|
[
"Apache-2.0"
] | null | null | null |
from django.shortcuts import render, HttpResponse
# Create your views here.
def alive(request):
return HttpResponse('alive!')
| 19.428571
| 49
| 0.735294
| 16
| 136
| 6.25
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 136
| 7
| 50
| 19.428571
| 0.892857
| 0.169118
| 0
| 0
| 0
| 0
| 0.053571
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
2c1699f7f5adc22f225f1f43c7e19fa5874b5a8d
| 32
|
py
|
Python
|
tests/__init__.py
|
SamuelLarkin/BLEU
|
e301fd54fab306c94d4adacd84e260f0bc3d8711
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
SamuelLarkin/BLEU
|
e301fd54fab306c94d4adacd84e260f0bc3d8711
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
SamuelLarkin/BLEU
|
e301fd54fab306c94d4adacd84e260f0bc3d8711
|
[
"MIT"
] | null | null | null |
from .test_bleu import TestBleu
| 16
| 31
| 0.84375
| 5
| 32
| 5.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 1
| 32
| 32
| 0.928571
| 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
| 1
| 0
|
0
| 6
|
257b762b3411e822a67f9366b1629053a403398c
| 87
|
py
|
Python
|
elf/types/header/ident/ei_pad.py
|
Valmarelox/elftoolsng
|
99c3f4913a7e477007b1d81df83274d7657bf693
|
[
"MIT"
] | null | null | null |
elf/types/header/ident/ei_pad.py
|
Valmarelox/elftoolsng
|
99c3f4913a7e477007b1d81df83274d7657bf693
|
[
"MIT"
] | null | null | null |
elf/types/header/ident/ei_pad.py
|
Valmarelox/elftoolsng
|
99c3f4913a7e477007b1d81df83274d7657bf693
|
[
"MIT"
] | null | null | null |
from elf.types.base.bytes import ElfTypeBytes
class EIPad(ElfTypeBytes(7)):
pass
| 14.5
| 45
| 0.758621
| 12
| 87
| 5.5
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.013514
| 0.149425
| 87
| 5
| 46
| 17.4
| 0.878378
| 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
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
25937dcef42c94eda48b621960fb8be7004f7b82
| 75
|
py
|
Python
|
locations/song.py
|
cjpeterson/LADXR
|
2c983bfdb5f4d5b32896976a575704e8fc753410
|
[
"MIT"
] | 13
|
2020-09-13T16:50:28.000Z
|
2022-03-22T20:49:54.000Z
|
locations/song.py
|
derekalan18/LADXR
|
c14291d9090c81a1f61f05e0a345b15b011d88ff
|
[
"MIT"
] | 10
|
2020-06-27T12:34:38.000Z
|
2022-01-03T12:15:42.000Z
|
locations/song.py
|
derekalan18/LADXR
|
c14291d9090c81a1f61f05e0a345b15b011d88ff
|
[
"MIT"
] | 18
|
2020-05-29T17:48:04.000Z
|
2022-02-08T03:36:08.000Z
|
from .droppedKey import DroppedKey
class Song(DroppedKey):
pass
| 12.5
| 35
| 0.706667
| 8
| 75
| 6.625
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.24
| 75
| 5
| 36
| 15
| 0.929825
| 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
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
259d7399a844a8ce7269afdb735c46f9604d9349
| 16
|
py
|
Python
|
pyhcl/tester/executer.py
|
raybdzhou/PyChip-py-hcl
|
08edc6ad4d2978eb417482f6f92678f8f9a1e3c7
|
[
"MIT"
] | null | null | null |
pyhcl/tester/executer.py
|
raybdzhou/PyChip-py-hcl
|
08edc6ad4d2978eb417482f6f92678f8f9a1e3c7
|
[
"MIT"
] | null | null | null |
pyhcl/tester/executer.py
|
raybdzhou/PyChip-py-hcl
|
08edc6ad4d2978eb417482f6f92678f8f9a1e3c7
|
[
"MIT"
] | null | null | null |
# TODO: executer
| 16
| 16
| 0.75
| 2
| 16
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 16
| 1
| 16
| 16
| 0.857143
| 0.875
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
d344df850b1e9879224b8f4bfe38c59f483943ee
| 3,509
|
py
|
Python
|
tests/test_geocoder_autocomplete_api.py
|
DimmyJing/HerePy
|
503eab1fdcf1ae78f73f8543a6b9fd2c432a61a1
|
[
"MIT"
] | null | null | null |
tests/test_geocoder_autocomplete_api.py
|
DimmyJing/HerePy
|
503eab1fdcf1ae78f73f8543a6b9fd2c432a61a1
|
[
"MIT"
] | null | null | null |
tests/test_geocoder_autocomplete_api.py
|
DimmyJing/HerePy
|
503eab1fdcf1ae78f73f8543a6b9fd2c432a61a1
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import os
import time
import unittest
import json
import responses
import herepy
class GeocoderAutoCompleteApiTest(unittest.TestCase):
def setUp(self):
api = herepy.GeocoderAutoCompleteApi('api_key')
self._api = api
def test_initiation(self):
self.assertIsInstance(self._api, herepy.GeocoderAutoCompleteApi)
self.assertEqual(self._api._api_key, 'api_key')
self.assertEqual(self._api._base_url, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json')
@responses.activate
def test_addresssuggestion_whensucceed(self):
with open('testdata/models/geocoder_autocomplete.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
response = self._api.address_suggestion('High', [51.5035, -0.1616], 100)
self.assertTrue(response)
self.assertIsInstance(response, herepy.GeocoderAutoCompleteResponse)
@responses.activate
def test_addresssuggestion_whenerroroccured(self):
with open('testdata/models/geocoder_autocomplete_error.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
with self.assertRaises(herepy.HEREError):
self._api.address_suggestion('', [51.5035, -0.1616], 100)
@responses.activate
def test_limitresultsbyaddress_whensucceed(self):
with open('testdata/models/geocoder_autocomplete.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
response = self._api.limit_results_byaddress('Nis', 'USA')
self.assertTrue(response)
self.assertIsInstance(response, herepy.GeocoderAutoCompleteResponse)
@responses.activate
def test_limitresultsbyaddress_whenerroroccured(self):
with open('testdata/models/geocoder_autocomplete_error.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
with self.assertRaises(herepy.HEREError):
self._api.limit_results_byaddress('', '')
@responses.activate
def test_highlightingmatches_whensucceed(self):
with open('testdata/models/geocoder_autocomplete.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
response = self._api.highlighting_matches('Wacker Chic', '**', '**')
self.assertTrue(response)
self.assertIsInstance(response, herepy.GeocoderAutoCompleteResponse)
@responses.activate
def test_highlightingmatches_whenerroroccured(self):
with open('testdata/models/geocoder_autocomplete_error.json', 'r') as f:
expectedResponse = f.read()
responses.add(responses.GET, 'https://autocomplete.geocoder.ls.hereapi.com/6.2/suggest.json',
expectedResponse, status=200)
with self.assertRaises(herepy.HEREError):
self._api.highlighting_matches('', '**', '**')
| 45.571429
| 110
| 0.68709
| 373
| 3,509
| 6.340483
| 0.206434
| 0.032558
| 0.073996
| 0.079915
| 0.820719
| 0.712474
| 0.712474
| 0.712474
| 0.712474
| 0.712474
| 0
| 0.021179
| 0.192647
| 3,509
| 76
| 111
| 46.171053
| 0.813625
| 0.0057
| 0
| 0.6
| 0
| 0
| 0.213876
| 0.077408
| 0
| 0
| 0
| 0
| 0.184615
| 1
| 0.123077
| false
| 0
| 0.092308
| 0
| 0.230769
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
d3726210f2d0e3b1b3eaa52f1b99c3c7d2d13dcb
| 24
|
py
|
Python
|
dryxPython/htmlframework/urls/__init__.py
|
thespacedoctor/dryxPython
|
8f34f997192eebef9403bd40e4b7c1b1d216f53c
|
[
"BSD-3-Clause"
] | 2
|
2015-08-01T16:00:44.000Z
|
2017-02-24T21:06:50.000Z
|
dryxPython/htmlframework/urls/__init__.py
|
thespacedoctor/dryxPython
|
8f34f997192eebef9403bd40e4b7c1b1d216f53c
|
[
"BSD-3-Clause"
] | null | null | null |
dryxPython/htmlframework/urls/__init__.py
|
thespacedoctor/dryxPython
|
8f34f997192eebef9403bd40e4b7c1b1d216f53c
|
[
"BSD-3-Clause"
] | null | null | null |
import default_fields
| 6
| 21
| 0.833333
| 3
| 24
| 6.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 3
| 22
| 8
| 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
| 1
| 0
|
0
| 6
|
6c9ed5fcff0ef855f44a7c425dd640d9144553c1
| 8,795
|
py
|
Python
|
analysis.py
|
921kiyo/symbolic-rl
|
14db62adb32beca506ff94a55809d423ac375d86
|
[
"MIT"
] | 52
|
2019-02-23T19:17:47.000Z
|
2022-03-23T09:43:31.000Z
|
analysis.py
|
921kiyo/Symbolic-RL
|
27168c044306cf5e5846ab5679862921adb15382
|
[
"MIT"
] | 5
|
2018-11-03T11:01:55.000Z
|
2018-11-03T11:02:14.000Z
|
analysis.py
|
921kiyo/Symbolic_RL
|
27168c044306cf5e5846ab5679862921adb15382
|
[
"MIT"
] | 12
|
2019-03-02T08:57:19.000Z
|
2021-10-10T01:00:47.000Z
|
from lib import plotting
import gym
import gym_vgdl
import os.path
base_dir = os.path.dirname(os.path.abspath(__file__))
def experiment_learning(exp_no):
pkl_dir = os.path.join(base_dir, "result_pkl/experiment{}_x".format(str(exp_no)))
pkl_dir_q = os.path.join(base_dir, "result_pkl/experiment{}_q".format(str(exp_no)))
# pkl_dir = os.path.join(base_dir, "result_pkl/experiment1_delete")
# pkl_dir_q = os.path.join(base_dir, "result_pkl/experiment1_q_del")
# make average score for training
plotting.average_score(base_dir, pkl_dir, "exp{}_v".format(str(exp_no)), 100, 30)
# plotting.average_score(base_dir, pkl_dir_q, "exp{}_v".format(str(exp_no)), 100, 30)
# make average score for test
plotting.average_score(base_dir, pkl_dir, "exp{}_test_v".format(str(exp_no)), 100, 30)
# plotting.average_score(base_dir, pkl_dir_q, "exp{}_test_v".format(str(exp_no)), 100, 30)
runtime, total = plotting.average_ILASP(base_dir, pkl_dir, "exp{}_ilasp_v".format(str(exp_no)), 100, 250, 30)
# Load the pkl files
stats = plotting.load_stats(pkl_dir, "exp{}_v_average".format(str(exp_no)))
stats_q = plotting.load_stats(pkl_dir_q, "exp{}_v_average".format(str(exp_no)))
stats_test = plotting.load_stats(pkl_dir, "exp{}_test_v_average".format(str(exp_no)))
stats_q_test = plotting.load_stats(pkl_dir_q, "exp{}_test_v_average".format(str(exp_no)))
stats_ilasp = plotting.load_stats(pkl_dir, "exp{}_ilasp_v_average".format(str(exp_no)))
# plotting.plot_episode_stats_learning(stats, stats_q)
plotting.plot_episode_stats_learning(stats_test, stats_q_test)
plotting.plot_episode_stats_runtime(stats, stats_q)
plotting.plot_ILASP_progress(stats_ilasp)
# plotting.plot_episode_stats_runtime(stats, stats_q)
# def ilasp_runtime(exp_no):
# pkl_dir = os.path.join(base_dir, "result_pkl/experiment{}".format(str(exp_no)))
# pkl_dir_q = os.path.join(base_dir, "result_pkl/experiment{}_q".format(str(exp_no)))
# # pkl_dir = os.path.join(base_dir, "result_pkl/experiment1_delete")
# # pkl_dir_q = os.path.join(base_dir, "result_pkl/experiment1_q_del")
# # make average score for training
# plotting.average_ILASP(base_dir, pkl_dir, "exp{}_v".format(str(exp_no)), 100, 30)
# plotting.average_ILASP(base_dir, pkl_dir_q, "exp{}_v".format(str(exp_no)), 100, 30)
# plotting.plot_ILASP_progress
def experiment_transfer():
pkl_dir = os.path.join(base_dir, "result_pkl/experiment3_after_noTL_noGoal")
pkl_dir2 = os.path.join(base_dir, "result_pkl/experiment3_after_noTL_goal")
pkl_dir3 = os.path.join(base_dir, "result_pkl/experiment3_after_TL")
pkl_dir_q = os.path.join(base_dir, "result_pkl/experiment3_q")
# make average score
plotting.average_score(base_dir, pkl_dir, "exp3_test_v", 100, 30)
plotting.average_score(base_dir, pkl_dir2, "exp4_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir3, "exp3_v", 100, 30)
plotting.average_score(base_dir, pkl_dir_q, "exp3_v", 100, 30)
# Load the pkl files
stats = plotting.load_stats(pkl_dir, "exp3_v_average")
stats2 = plotting.load_stats(pkl_dir2, "exp4_v_average")
stats3 = plotting.load_stats(pkl_dir3, "exp4_after_TL_v_average")
stats_q = plotting.load_stats(pkl_dir_q, "exp3_v_average")
# import ipdb; ipdb.set_trace()
plotting.plot_episode_stats_transfer(stats, stats2, stats3, stats_q)
def experiment3_test():
pkl_dir = os.path.join(base_dir, "result_pkl/experiment3_after_noTL_noGoal")
pkl_dir2 = os.path.join(base_dir, "result_pkl/experiment3_after_noTL_goal")
pkl_dir3 = os.path.join(base_dir, "result_pkl/experiment3_after_TL")
# pkl_dir3 = os.path.join(base_dir, "result_pkl/experiment3_q")
# make average score
# plotting.average_score(base_dir, pkl_dir, "exp3_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir2, "exp4_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir3, "exp3_test_TL_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir_q, "exp3_v", 100, 30)
# Load the pkl files
stats = plotting.load_stats(pkl_dir, "exp3_test_v_average")
stats2 = plotting.load_stats(pkl_dir2, "exp4_test_v_average")
# stats3 = plotting.load_stats(pkl_dir3, "exp4_test_after_TL_v_average")
stats3 = plotting.load_stats(pkl_dir3, "exp3_test_TL_v_average")
plotting.plot_episode_stats_transfer(stats, stats2, stats3)
# experiment3_test()
def experiment4_test():
pkl_dir = os.path.join(base_dir, "result_pkl/experiment4_after_noTL_noGoal")
pkl_dir2 = os.path.join(base_dir, "result_pkl/experiment4_after_noTL_goal")
pkl_dir_afterTL = os.path.join(base_dir, "result_pkl/experiment4_after_TL")
# make average score
# plotting.average_score(base_dir, pkl_dir, "exp4_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir2, "exp4_test_v", 100, 9)
plotting.average_score(base_dir, pkl_dir_afterTL, "exp4_test_TL_v", 100, 1)
# plotting.average_score(base_dir, pkl_dir_q, "exp4_test_v", 100, 30)
# Load the pkl files
stats = plotting.load_stats(pkl_dir, "exp4_test_v_average")
stats2 = plotting.load_stats(pkl_dir2, "exp4_test_v_average")
stats3 = plotting.load_stats(pkl_dir_afterTL, "exp4_test_TL_v_average")
# XXX = os.path.join(base_dir, "result_pkl/experiment_keep_first/experiment4_after_noTL")
# stats2 = plotting.load_stats(XXX, "exp4_test_after_noTL_v_average")
# stats3 = plotting.load_stats(pkl_dir_afterTL, "exp4_test_TL_v0")
# stats_q = plotting.load_stats(pkl_dir_q, "exp4_test_v_average")
# import ipdb; ipdb.set_trace()
plotting.plot_episode_stats_transfer(stats, stats2, stats3)
# plotting.plot_episode_stats_simple(stats2)
# experiment4_test()
def experiment5_test():
pkl_noTL = os.path.join(base_dir, "result_pkl/experiment_keep_first/experiment4_after_noTL")
pkl_noTL_goal = os.path.join(base_dir, "result_pkl/experiment4_after_noTL_goal")
pkl_TL = os.path.join(base_dir, "result_pkl/experiment_keep_first/experiment4_after_TL")
# pkl_dir2 = os.path.join(base_dir, "result_pkl/experiment4_after_noTL_goal")
# pkl_dir_afterTL = os.path.join(base_dir, "result_pkl/experiment4_after_TL")
# pkl_dir2 = os.path.join(base_dir, "result_pkl/experiment4_after_noTL_goal")
# make average score
# plotting.average_score(base_dir, pkl_noTL, "exp4_test_after_noTL_v", 100, 30)
# plotting.average_score(base_dir, pkl_TL, "exp4_test_after_TL_v", 100, 30)
# plotting.average_score(base_dir, pkl_noTL_noGoal, "exp4_test_v", 100, 9)
# plotting.average_score(base_dir, pkl_dir_q, "exp4_test_v", 100, 30)
# Load the pkl files
stats = plotting.load_stats(pkl_noTL, "exp4_test_after_noTL_v_average")
stats2 = plotting.load_stats(pkl_noTL_goal, "exp4_test_v_average")
stats3 = plotting.load_stats(pkl_TL, "exp4_test_after_TL_v_average")
# stats3 = plotting.load_stats(pkl_dir_afterTL, "exp4_test_TL_v0")
# stats_q = plotting.load_stats(pkl_dir_q, "exp4_test_v_average")
# import ipdb; ipdb.set_trace()
plotting.plot_episode_stats_transfer(stats, stats2, stats3)
experiment5_test()
# pkl_dir = os.path.join(base_dir, "result_pkl/experiment4_after_TL")
# stats = plotting.load_stats(pkl_dir, "exp4_test_TL_v0")
# stats = plotting.load_stats(pkl_dir, "exp4_TL_v0")
# import ipdb; ipdb.set_trace()
# plotting.plot_episode_stats_simple(stats)
# experiment3_test()
# experiment_learning(1)
# experiment_transfer()
# plotting.average_score(base_dir, pkl_dir, "exp4_after_TL_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir, "exp1_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir, "exp3_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir_q, "exp3_test_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir, "exp5_test_TL_v", 100, 30)
# plotting.average_score(base_dir, pkl_dir_q, "exp5_test_noTL_v", 100, 30)
# plotting.average_ILASP(base_dir, pkl_dir, "exp2_v", 2, 250, 30)
# stats = plotting.load_stats(pkl_dir, "exp1_v_average")
# stats_q = plotting.load_stats(pkl_dir_q, "temp_test_v_average")
# stats = plotting.load_stats(pkl_dir, "exp1_test_v_average")
# plotting.plot_ILASP_progress(stats)
# stats = plotting.load_stats(pkl_dir, "exp2_v_average")
# import ipdb; ipdb.set_trace()
# stats_q = plotting.load_stats(pkl_dir_q, "exp5_test_noTL_v_average")
# stats_q = plotting.load_stats(pkl_dir_q, "exp3_test_v_average")
# for i in range(12):
# print("-----------------------")
# print("No.", i)
# stats = plotting.load_stats(pkl_dir, "exp3_test_v{}".format(str(i)))
# print(stats)
# plotting.plot_episode_stats_simple(stats, smoothing_window=1)
# plotting.plot_episode_stats_multiple(stats, stats_q)
# plotting.plot_ILASP_progress(stats)
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| 114
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0
| 6
|
9f137934f987a9dbb92e4e840adbfa3957d97fe1
| 159
|
py
|
Python
|
grapevine/managers.py
|
craiglabenz/django-grapevine
|
d71d510814ba965fe836b3e6a522945e74c01120
|
[
"MIT"
] | 7
|
2015-04-02T20:47:55.000Z
|
2022-01-20T13:49:31.000Z
|
grapevine/managers.py
|
craiglabenz/django-grapevine
|
d71d510814ba965fe836b3e6a522945e74c01120
|
[
"MIT"
] | 3
|
2020-02-12T00:31:44.000Z
|
2021-06-10T20:07:23.000Z
|
grapevine/managers.py
|
craiglabenz/django-grapevine
|
d71d510814ba965fe836b3e6a522945e74c01120
|
[
"MIT"
] | 2
|
2015-05-21T16:23:52.000Z
|
2020-09-04T21:31:39.000Z
|
from __future__ import unicode_literals
# 3rd Party
from model_utils.managers import PassThroughManager
class SendableManager(PassThroughManager):
pass
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| 51
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0
| 6
|
9f15863e58aaf57c63447072ee1186a4706e7e36
| 11,028
|
py
|
Python
|
app/examples/example_tweet.py
|
davimiku/squawker
|
968de5b82ddc1d610af248a70840533c9d7b5cb9
|
[
"MIT"
] | null | null | null |
app/examples/example_tweet.py
|
davimiku/squawker
|
968de5b82ddc1d610af248a70840533c9d7b5cb9
|
[
"MIT"
] | null | null | null |
app/examples/example_tweet.py
|
davimiku/squawker
|
968de5b82ddc1d610af248a70840533c9d7b5cb9
|
[
"MIT"
] | null | null | null |
Status(_api=<tweepy.api.API object at 0x00000189262E6F98>,
_json={
'created_at': 'Tue Jul 24 19:33:01 +0000 2018',
'id': 1021840695659843584,
'id_str': '1021840695659843584',
'text': '"95% of people think they\'re self-aware, but the real number is closer to 10 to 15%. On a good day, 80% of us are l▒ https://t.co/PgOk28Ef0w',
'truncated': True,
'entities': {
'hashtags': [],
'symbols': [],
'user_mentions': [],
'urls': [
{
'url': 'https://t.co/PgOk28Ef0w',
'expanded_url': 'https://twitter.com/i/web/status/1021840695659843584',
'display_url': 'twitter.com/i/web/status/1▒',
'indices': [117, 140]
}
]
},
'source': '<a href="https://sproutsocial.com" rel="nofollow">Sprout Social</a>',
'in_reply_to_status_id': None,
'in_reply_to_status_id_str': None,
'in_reply_to_user_id': None,
'in_reply_to_user_id_str': None,
'in_reply_to_screen_name': None,
'user': {
'id': 15492359,
'id_str': '15492359',
'name': 'TED Talks',
'screen_name': 'TEDTalks',
'location': 'New York, NY',
'description': 'Official tweets by https://t.co/XyGsGk67aO. Ideas worth spreading.',
'url': 'http://t.co/7qVI5vqFrr',
'entities': {
'url': {
'urls': [
{
'url': 'http://t.co/7qVI5vqFrr',
'expanded_url': 'http://www.ted.com',
'display_url': 'ted.com',
'indices': [0, 22]
}
]
},
'description': {
'urls': [
{
'url': 'https://t.co/XyGsGk67aO',
'expanded_url': 'http://TED.com',
'display_url': 'TED.com', 'indices': [19, 42]
}
]
}
},
'protected': False,
'followers_count': 10995081,
'friends_count': 576,
'listed_count': 55721,
'created_at': 'Sat Jul 19 13:22:50 +0000 2008',
'favourites_count': 4724,
'utc_offset': None,
'time_zone': None,
'geo_enabled': False,
'verified': True,
'statuses_count': 28387,
'lang': 'en',
'contributors_enabled': False,
'is_translator': False,
'is_translation_enabled': False,
'profile_background_color': '000000',
'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_tile': False,
'profile_image_url': 'http://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_image_url_https': 'https://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_banner_url': 'https://pbs.twimg.com/profile_banners/15492359/1519826443',
'profile_link_color': 'FF2B06',
'profile_sidebar_border_color': 'FFFFFF',
'profile_sidebar_fill_color': 'E0E3DE',
'profile_text_color': '333333',
'profile_use_background_image': True,
'has_extended_profile': False,
'default_profile': False,
'default_profile_image': False,
'following': True,
'follow_request_sent': False,
'notifications': False,
'translator_type': 'none'
},
'geo': None,
'coordinates': None,
'place': None,
'contributors': None,
'is_quote_status': False,
'retweet_count': 193,
'favorite_count': 501,
'favorited': False,
'retweeted': False,
'possibly_sensitive': False,
'possibly_sensitive_appealable': False,
'lang': 'en'
},
created_at=datetime.datetime(2018, 7, 24, 19, 33, 1),
author=User(_api=<tweepy.api.API object at 0x00000189262E6F98>,
_json={
'id': 15492359,
'id_str': '15492359',
'name': 'TED Talks',
'screen_name': 'TEDTalks',
'location': 'New York, NY',
'description': 'Official tweets by https://t.co/XyGsGk67aO. Ideas worth spreading.',
'url': 'http://t.co/7qVI5vqFrr',
'entities': {
'url': {
'urls': [
{
'url': 'http://t.co/7qVI5vqFrr',
'expanded_url': 'http://www.ted.com',
'display_url': 'ted.com',
'indices': [0, 22]
}
]
},
'description': {
'urls': [
{
'url': 'https://t.co/XyGsGk67aO',
'expanded_url': 'http://TED.com',
'display_url': 'TED.com',
'indices': [19, 42]
}
]
}
},
'protected': False,
'followers_count': 10995081,
'friends_count': 576,
'listed_count': 55721,
'created_at': 'Sat Jul 19 13:22:50 +0000 2008',
'favourites_count': 4724,
'utc_offset': None,
'time_zone': None,
'geo_enabled': False,
'verified': True,
'statuses_count': 28387,
'lang': 'en',
'contributors_enabled': False,
'is_translator': False,
'is_translation_enabled': False,
'profile_background_color': '000000',
'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_tile': False,
'profile_image_url': 'http://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_image_url_https': 'https://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_banner_url': 'https://pbs.twimg.com/profile_banners/15492359/1519826443',
'profile_link_color': 'FF2B06',
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'profile_text_color': '333333',
'profile_use_background_image': True,
'has_extended_profile': False,
'default_profile': False,
'default_profile_image': False,
'following': True,
'follow_request_sent': False,
'notifications': False,
'translator_type': 'none'
},
user=User(_api=<tweepy.api.API object at 0x00000189262E6F98>,
_json={
'id': 15492359,
'id_str': '15492359',
'name': 'TED Talks',
'screen_name': 'TEDTalks',
'location': 'New York, NY',
'description': 'Official tweets by https://t.co/XyGsGk67aO. Ideas worth spreading.',
'url': 'http://t.co/7qVI5vqFrr',
'entities': {
'url': {
'urls': [
{
'url': 'http://t.co/7qVI5vqFrr',
'expanded_url': 'http://www.ted.com',
'display_url': 'ted.com',
'indices': [0, 22]
}
]
},
'description': {
'urls': [
{
'url': 'https://t.co/XyGsGk67aO',
'expanded_url': 'http://TED.com',
'display_url': 'TED.com',
'indices': [19, 42]
}
]
}
},
'protected': False,
'followers_count': 10995081,
'friends_count': 576,
'listed_count': 55721,
'created_at': 'Sat Jul 19 13:22:50 +0000 2008',
'favourites_count': 4724,
'utc_offset': None,
'time_zone': None,
'geo_enabled': False,
'verified': True,
'statuses_count': 28387,
'lang': 'en',
'contributors_enabled': False,
'is_translator': False,
'is_translation_enabled': False,
'profile_background_color': '000000',
'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',
'profile_background_tile': False,
'profile_image_url': 'http://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_image_url_https': 'https://pbs.twimg.com/profile_images/877631054525472768/Xp5FAPD5_normal.jpg',
'profile_banner_url': 'https://pbs.twimg.com/profile_banners/15492359/1519826443',
'profile_link_color': 'FF2B06',
'profile_sidebar_border_color': 'FFFFFF',
'profile_sidebar_fill_color': 'E0E3DE',
'profile_text_color': '333333',
'profile_use_background_image': True,
'has_extended_profile': False,
'default_profile': False,
'default_profile_image': False,
'following': True,
'follow_request_sent': False,
'notifications': False,
'translator_type': 'none'
},
{
'hashtags': [
{
'text': 'Birdland',
'indices': [78, 87]
}
],
'symbols': [],
'user_mentions': [
{
'screen_name': 'Dylan_Bundy',
'name': 'Dylan Bundy',
'id': 331661369,
'id_str': '331661369',
'indices': [52, 64]
},
{
'screen_name': 'masnOrioles',
'name': 'Orioles on MASN',
'id': 102061698,
'id_str': '102061698',
'indices': [120, 132]
},
{
'screen_name': 'espn',
'name': 'ESPN',
'id': 2557521,
'id_str': '2557521',
'indices': [134, 139]
},
{
'screen_name': '1057TheFan',
'name': '105.7 The Fan',
'id': 22644782,
'id_str': '22644782',
'indices': [143, 154]
}
],
'urls': [],
'media': [
{
'id': 1022257358460923909,
'id_str': '1022257358460923909',
'indices': [174, 197],
'media_url': 'http://pbs.twimg.com/media/Di_JpsAXsAUpLxd.jpg',
'media_url_https': 'https://pbs.twimg.com/media/Di_JpsAXsAUpLxd.jpg',
'url': 'https://t.co/00E5NYzQIY',
'display_url': 'pic.twitter.com/00E5NYzQIY',
'expanded_url': 'https://twitter.com/Orioles/status/1022257363833839617/photo/1',
'type': 'photo',
'sizes': {
'small': {
'w': 680,
'h': 453,
'resize': 'fit'
},
'thumb': {
'w': 150,
'h': 150,
'resize':
'crop'
},
'medium': {
'w': 1200,
'h': 799,
'resize': 'fit'
},
'large': {
'w': 2048,
'h': 1364,
'resize': 'fit'
}
}
}
]
}
| 35.233227
| 157
| 0.512332
| 1,064
| 11,028
| 5.078947
| 0.222744
| 0.024611
| 0.022391
| 0.029978
| 0.779608
| 0.753701
| 0.740378
| 0.728164
| 0.719837
| 0.719837
| 0
| 0.109188
| 0.334784
| 11,028
| 313
| 158
| 35.233227
| 0.627181
| 0
| 0
| 0.551282
| 0
| 0
| 0.471122
| 0.087587
| 0
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| 0.004896
| 0
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| 0
| null | null | 0
| 0
| null | null | 0
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| null | 0
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| 0
| 0
| 0
|
0
| 6
|
9fb710c5bf0ac05da5ef1e28edaec6abde7a3b7b
| 85
|
py
|
Python
|
funcs/__init__.py
|
ridhwan-aziz/zipra_bot
|
de328e518aa6876fa91e96e007c198aae8ff2fd4
|
[
"MIT"
] | 4
|
2021-11-11T03:44:21.000Z
|
2022-03-26T14:32:20.000Z
|
funcs/__init__.py
|
ridhwan-aziz/zipra_bot
|
de328e518aa6876fa91e96e007c198aae8ff2fd4
|
[
"MIT"
] | 2
|
2021-11-23T07:02:35.000Z
|
2022-02-11T13:49:07.000Z
|
funcs/__init__.py
|
ridhwan-aziz/zipra_bot
|
de328e518aa6876fa91e96e007c198aae8ff2fd4
|
[
"MIT"
] | 1
|
2021-11-25T13:30:24.000Z
|
2021-11-25T13:30:24.000Z
|
from funcs import dbg, start, ping, help, lang, bans
from funcs import other, for_fun
| 42.5
| 52
| 0.776471
| 15
| 85
| 4.333333
| 0.8
| 0.276923
| 0.461538
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| 85
| 2
| 53
| 42.5
| 0.902778
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| 1
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| 0
|
0
| 6
|
9fbbb4e6817b333bd0ca9f2a07897f578520d1b0
| 36
|
py
|
Python
|
plottify/__init__.py
|
belskikh/plottify
|
3146ebe139c0b84b228d90785cabbbc6b898346d
|
[
"MIT"
] | 6
|
2018-10-24T09:10:59.000Z
|
2020-11-22T13:18:29.000Z
|
plottify/__init__.py
|
belskikh/plottify
|
3146ebe139c0b84b228d90785cabbbc6b898346d
|
[
"MIT"
] | null | null | null |
plottify/__init__.py
|
belskikh/plottify
|
3146ebe139c0b84b228d90785cabbbc6b898346d
|
[
"MIT"
] | 2
|
2018-11-06T07:30:40.000Z
|
2020-09-03T00:30:37.000Z
|
from . import plot, poly, rle, utils
| 36
| 36
| 0.722222
| 6
| 36
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
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| 0.166667
| 36
| 1
| 36
| 36
| 0.866667
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| 1
| 0
|
0
| 6
|
4cae8c6077f9e836605e83b27255a27d7cf12ecd
| 21
|
py
|
Python
|
saxonius/__init__.py
|
iwanb/saxonius
|
c6ac6497306bcbf518a2d63947e11f9927767474
|
[
"Apache-2.0"
] | 2
|
2019-09-08T09:31:01.000Z
|
2019-09-08T15:20:23.000Z
|
saxonius/__init__.py
|
iwanb/saxonius
|
c6ac6497306bcbf518a2d63947e11f9927767474
|
[
"Apache-2.0"
] | null | null | null |
saxonius/__init__.py
|
iwanb/saxonius
|
c6ac6497306bcbf518a2d63947e11f9927767474
|
[
"Apache-2.0"
] | null | null | null |
from .saxon import *
| 10.5
| 20
| 0.714286
| 3
| 21
| 5
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| 1
| 0
|
0
| 6
|
4cc1921a95054446b8949045b9379225ef783b34
| 34
|
py
|
Python
|
rosbuild_ws/src/controller_manager/src/controller_manager/srv/__init__.py
|
Boberito25/ButlerBot
|
959f961bbc8c43be0ccb533dd2e2af5c55b0cc2a
|
[
"BSD-3-Clause"
] | null | null | null |
rosbuild_ws/src/controller_manager/src/controller_manager/srv/__init__.py
|
Boberito25/ButlerBot
|
959f961bbc8c43be0ccb533dd2e2af5c55b0cc2a
|
[
"BSD-3-Clause"
] | 1
|
2015-06-08T19:55:40.000Z
|
2015-06-08T19:55:40.000Z
|
rosbuild_ws/src/controller_manager/src/controller_manager/srv/__init__.py
|
Boberito25/ButlerBot
|
959f961bbc8c43be0ccb533dd2e2af5c55b0cc2a
|
[
"BSD-3-Clause"
] | null | null | null |
from ._RequestController import *
| 17
| 33
| 0.823529
| 3
| 34
| 9
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|
0
| 6
|
e23dc5f35f215853ae7c41cdd0ac1444b4034c84
| 138
|
py
|
Python
|
example.py
|
DennisThomas-25/bq-code
|
23313538304b86f15313b5969dbbf2db1632c7b1
|
[
"Apache-2.0"
] | null | null | null |
example.py
|
DennisThomas-25/bq-code
|
23313538304b86f15313b5969dbbf2db1632c7b1
|
[
"Apache-2.0"
] | null | null | null |
example.py
|
DennisThomas-25/bq-code
|
23313538304b86f15313b5969dbbf2db1632c7b1
|
[
"Apache-2.0"
] | null | null | null |
def add(a,b):
return a+b
def substract(a,b):
return a * b
## Imagine I made a valid change
def absolut(a,b):
return np.abs(a,b)
| 11.5
| 32
| 0.630435
| 29
| 138
| 3
| 0.482759
| 0.137931
| 0.275862
| 0.206897
| 0.229885
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.224638
| 138
| 11
| 33
| 12.545455
| 0.813084
| 0.210145
| 0
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| 0.5
| false
| 0
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| null | 0
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| 1
| 0
|
0
| 6
|
e24633162df779a38c44b32ecd43fd1690eca694
| 29
|
py
|
Python
|
src/amuse/community/flash/__init__.py
|
sibonyves/amuse
|
5557bf88d14df1aa02133a199b6d60c0c57dcab7
|
[
"Apache-2.0"
] | null | null | null |
src/amuse/community/flash/__init__.py
|
sibonyves/amuse
|
5557bf88d14df1aa02133a199b6d60c0c57dcab7
|
[
"Apache-2.0"
] | 12
|
2021-11-15T09:13:03.000Z
|
2022-02-02T14:53:04.000Z
|
src/amuse/community/flash/__init__.py
|
sibonyves/amuse
|
5557bf88d14df1aa02133a199b6d60c0c57dcab7
|
[
"Apache-2.0"
] | null | null | null |
from .interface import Flash
| 14.5
| 28
| 0.827586
| 4
| 29
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.96
| 0
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| true
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| 1
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| null | 0
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| 0
| 1
| 0
|
0
| 6
|
e246e504d0f5509065f779300d57520724184cca
| 156
|
py
|
Python
|
examples/pwn/ex_plt.py
|
devonnuri/nurlib
|
96e52499fa1fef1c5b35c10d07279f00df9e204f
|
[
"MIT"
] | null | null | null |
examples/pwn/ex_plt.py
|
devonnuri/nurlib
|
96e52499fa1fef1c5b35c10d07279f00df9e204f
|
[
"MIT"
] | null | null | null |
examples/pwn/ex_plt.py
|
devonnuri/nurlib
|
96e52499fa1fef1c5b35c10d07279f00df9e204f
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
from ptrlib import *
elf = ELF("/bin/ls")
print(hex(elf.got("calloc")))
print(hex(elf.plt("calloc")))
print(hex(elf.plt("calloc")))
| 17.333333
| 29
| 0.653846
| 26
| 156
| 3.923077
| 0.538462
| 0.235294
| 0.323529
| 0.333333
| 0.45098
| 0.45098
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089744
| 156
| 8
| 30
| 19.5
| 0.71831
| 0.128205
| 0
| 0.4
| 0
| 0
| 0.185185
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0.6
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
e2511d3ce58e976c7cef5f590a3dd8ff466fb854
| 134
|
py
|
Python
|
sample/mobile/steps/page_steps/pages/navigation_menu.py
|
Softeq/PyCats
|
c71d54ca7fa958c22ca8c78ba9889c6a32b827be
|
[
"Apache-2.0"
] | 7
|
2020-06-12T15:16:10.000Z
|
2020-06-20T18:42:07.000Z
|
sample/mobile/steps/page_steps/pages/navigation_menu.py
|
Softeq/PyCats
|
c71d54ca7fa958c22ca8c78ba9889c6a32b827be
|
[
"Apache-2.0"
] | 4
|
2020-06-15T20:08:32.000Z
|
2020-06-29T16:51:57.000Z
|
sample/mobile/steps/page_steps/pages/navigation_menu.py
|
Softeq/SCAF
|
c71d54ca7fa958c22ca8c78ba9889c6a32b827be
|
[
"Apache-2.0"
] | 3
|
2020-07-27T10:45:36.000Z
|
2021-01-13T12:10:46.000Z
|
from sample.mobile.src.pages.navigation_menu_page import NavigationMenuPage
class NavigationMenuSteps(NavigationMenuPage):
pass
| 22.333333
| 75
| 0.850746
| 14
| 134
| 8
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097015
| 134
| 5
| 76
| 26.8
| 0.92562
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
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| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
e27ac5aceaa0567ccbed2dd8710053b57ed5b9e0
| 64,510
|
py
|
Python
|
pylurk/performance_tests/tests.py
|
mglt/pylurk
|
022ede1af666aeeb62c0c73a9e67aac9e43b5064
|
[
"BSD-3-Clause"
] | 1
|
2018-06-18T02:33:55.000Z
|
2018-06-18T02:33:55.000Z
|
pylurk/performance_tests/tests.py
|
mglt/pylurk
|
022ede1af666aeeb62c0c73a9e67aac9e43b5064
|
[
"BSD-3-Clause"
] | null | null | null |
pylurk/performance_tests/tests.py
|
mglt/pylurk
|
022ede1af666aeeb62c0c73a9e67aac9e43b5064
|
[
"BSD-3-Clause"
] | null | null | null |
from os.path import join
import pkg_resources
from pylurk.performance_tests.performance_utils import latency_test, cpu_overhead_test, get_RTT
from copy import deepcopy
def authentication_methods_test (sheet_name, excel_file, graph_path, thread, request_nb, set_nb):
'''
This method performs some latency tests on authentication methods (RSA, RSA_extended and ECDHE) by varying the prf (256 (reference), 384, 512),
ECDHE: 'sig_and_hash': ('sha256', 'rsa'),'sig_and_hash': ('sha512', 'rsa').
It saves the results in the specified excel file and plot them in 2 box graphs (one for latency values and another for ratios)
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:return:
'''
payload_params = {
'udpLocal':[
{
'type': 'rsa_master',
'column_name': 'rsa_master_ref_prf_sha256_pfs_sha256', # name of column as it appears in excel file, if it contains 'ref', this means that it will be used as ref values
'ref': 'rsa_master_ref_prf_sha256_pfs_sha256', # ref column name when calculating ref values
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_master',
'column_name': 'rsa_master_prf_sha384_pfs_sha256',
'ref': 'rsa_master_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha384',
'freshness_funct': 'sha256'
},
{
'type': "rsa_master",
'column_name': 'rsa_master_prf_sha512_pfs_sha256',
'ref': 'rsa_master_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha512',
'freshness_funct': 'sha256'
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_ref_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256'
},
{
'type': "rsa_extended_master",
'column_name': 'rsa_extended_master_prf_sha384_pfs_sha256',
'ref': 'rsa_extended_master_ref_prf_sha256_pfs_sha256',
'prf_hash': "sha384",
'freshness_funct': "sha256"
},
{
'type': "rsa_extended_master",
'column_name': 'rsa_extended_master_prf_sha512_pfs_sha256',
'ref': 'rsa_extended_master_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha512',
'freshness_funct': 'sha256'
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_ref_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_sig_sha512rsa_pfs_sha256',
'ref': 'ecdhe_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha512', 'rsa'),
'freshness_funct': 'sha256'
},
]
}
data_dir = pkg_resources.resource_filename(__name__, '../data/')
connectivity_conf= {
'udpLocal':{
'type': "udp", # "udp", "local",
'ip_address': "127.0.0.1",
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt')
}
}
graph_params = {'title': '',
'xlabel': 'Authentication Methods',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'RSA', # label on xaxis depicting all the data in data
'color': ['white','white'],#['blue', 'green'],
# color of the box of each data in data, set 'White if no color is desired
'hatch': ['*', 'o'],
# pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['rsa_master_prf_sha384_pfs_sha256', 'rsa_master_prf_sha512_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': ['prf = sha384', 'prf = sha512']
# legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'RSA_Extended',
'color':['white','white'],# ['blue', 'green'], # same color and hatch as previous group to have same legend
'hatch': ['*', 'o'],
'data': ['rsa_extended_master_prf_sha384_pfs_sha256',
'rsa_extended_master_prf_sha512_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
{'tick_label': 'ECDHE',
'color': ['white'],#['green'],
'hatch': ['o'],
'data': ['ecdhe_sig_sha512rsa_pfs_sha256'],
'legends': [],
},
]}
latency_test (payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file = excel_file, thread=thread, request_nb_list = [request_nb], set_nb =set_nb)
def mechanism_overhead_pfs_test (sheet_name, excel_file, graph_path, thread, request_nb, set_nb):
'''
This method performs some latency tests on authentication methods (RSA, RSA_extended and ECDHE) by varying the pfs (null (reference), 256),
ECDHE: 'sig_and_hash': ('sha256', 'rsa').
It saves the results in the specified excel file and plot them in 2 box graphs (one for latency values and another for ratios)
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:return:
'''
payload_params = {
'udpLocal': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_ref_prf_sha256_pfs_null',
'ref': 'rsa_master_ref_prf_sha256_pfs_null',
'prf_hash': 'sha256',
'freshness_funct': 'null',
},
{
'type': 'rsa_master',
'column_name': 'rsa_master_prf_sha256_pfs_sha256',
'ref': 'rsa_master_ref_prf_sha256_pfs_null',
'prf_hash': 'sha256',
'freshness_funct': 'sha256'
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_ref_prf_sha256_pfs_null',
'ref': 'rsa_extended_master_ref_prf_sha256_pfs_null',
'prf_hash': 'sha256',
'freshness_funct': 'null'
},
{
'type': "rsa_extended_master",
'column_name': 'rsa_extended_master_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_ref_prf_sha256_pfs_null',
'prf_hash': "sha256",
'freshness_funct': "sha256"
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_ref_sig_sha256rsa_pfs_null',
'ref': 'ecdhe_ref_sig_sha256rsa_pfs_null',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'null'
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_ref_sig_sha256rsa_pfs_null',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
]
}
data_dir = pkg_resources.resource_filename(__name__, '../data/')
connectivity_conf = {
'udpLocal': {
'type': "udp", # "udp", "local",
'ip_address': "127.0.0.1",
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt')
}
}
graph_params = {'title': '',
'xlabel': 'Authentication Methods',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'RSA', # label on xaxis depicting all the data in data
'color': ['white'],
# color of the box of each data in data, set 'White if no color is desired
'hatch': ['*'],
# pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['rsa_master_prf_sha256_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': ['pfs = sha256']
# legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'RSA_Extended',
'color': ['white'], # same color and hatch as previous group to have same legend
'hatch': ['*'],
'data': ['rsa_extended_master_prf_sha256_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
{'tick_label': 'ECDHE',
'color': ['white'],
'hatch': ['*'],
'data': ['ecdhe_sig_sha256rsa_pfs_sha256'],
'legends': [],
},
]}
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=[request_nb], set_nb=set_nb)
def mechanism_overhead_poh_test (sheet_name, excel_file, graph_path, thread, request_nb, set_nb):
'''
This method performs some latency tests to check the overhead of the proof of handshake on authentication methods (RSA, RSA_extended)
It saves the results in the specified excel file and plot them in 2 box graphs (one for latency values and another for ratios)
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:return:
'''
payload_params = {
'udpLocal': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_prf_sha256_pfs_sha256',
'ref': 'rsa_master_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256'
},
{
'type': 'rsa_master_with_poh',
'column_name': 'rsa_master_with_poh_prf_sha256_pfs_sha256',
'ref': 'rsa_master_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256'
},
{
'type': "rsa_extended_master",
'column_name': 'rsa_extended_master_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_prf_sha256_pfs_sha256',
'prf_hash': "sha256",
'freshness_funct': "sha256"
},
{
'type': "rsa_extended_master_with_poh",
'column_name': 'rsa_extended_master_with_poh_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_prf_sha256_pfs_sha256',
'prf_hash': "sha256",
'freshness_funct': "sha256"
},
]
}
data_dir = pkg_resources.resource_filename(__name__, '../data/')
connectivity_conf = {
'udpLocal': {
'type': "udp", # "udp", "local",
'ip_address': "127.0.0.1",
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt')
}
}
graph_params = {'title': '',
'xlabel': 'Authentication Methods',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'RSA', # label on xaxis depicting all the data in data
'color': ['white'], #['blue', 'green'],
# color of the box of each data in data, set 'White if no color is desired
'hatch': ['*'],
# pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['rsa_master_with_poh_prf_sha256_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': [ 'With PoH']
# legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'RSA_Extended',
'color': ['white'],#['blue' 'green'], # same color and hatch as previous group to have same legend
'hatch': ['*'],
'data': ['rsa_extended_master_with_poh_prf_sha256_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
]}
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=[request_nb], set_nb=set_nb)
def mechanism_overhead_poo_test (sheet_name, excel_file, graph_path, thread, request_nb, set_nb):
'''
This method performs some latency tests to check the overhead of the proof of ownership on ECDHE by varying the poo_prf (null (reference), 128, 256),
It saves the results in the specified excel file and plot them in 2 box graphs (one for latency values and another for ratios)
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:return:
'''
payload_params = {
'udpLocal': [
{
'type': 'ecdhe',
'column_name': 'ecdhe_ref_poo_null_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_ref_poo_null_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256',
'poo_prf': ["null"],
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_poo_sha256_128_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_ref_poo_null_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256',
'poo_prf': [ "sha256_128"],
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_poo_sha256_256_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_ref_poo_null_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256',
'poo_prf': [ "sha256_256"],
},
]
}
data_dir = pkg_resources.resource_filename(__name__, '../data/')
connectivity_conf = {
'udpLocal': {
'type': "udp", # "udp", "local",
'ip_address': "127.0.0.1",
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt')
}
}
graph_params = {'title': '',
'xlabel': 'PoO',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'sha256_128', # label on xaxis depicting all the data in data
'color': ['white'],#['blue'], # color of the box of each data in data, set 'White if no color is desired
'hatch': ['*'], # pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['ecdhe_poo_sha256_128_sig_sha256rsa_pfs_sha256'], # colummn name of the data to plot as defined in excel sheet
'legends': ['With PoO'] # legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'sha256_256',
'color': ['white'],#['blue' ],
'hatch': ['*'],
'data': ['ecdhe_poo_sha256_256_sig_sha256rsa_pfs_sha256'],
'legends': None,
},
]}
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=[request_nb], set_nb=set_nb)
def transport_protocol_test( sheet_name, excel_file, graph_path, thread, request_nb, set_nb, server_ip,remote_user, server_password):
'''
This performs the transport protocol tests between a client and a server
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:param server_ip: Ip of the server to which we want to connect remotly
:param remote_user: username of remote server
:param server_password: password of remote server
:return:
'''
payload_params = {
'udpLocal':[
{
'type': 'rsa_master',
'column_name': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'udp': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_udp_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_udp_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_udp_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'tcp': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_tcp_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_tcp_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_tcp_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'http': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_http_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_http_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_http_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
}
#define connectivity conf fo client and server
data_dir = pkg_resources.resource_filename(__name__, '../data/')
conf = { 'type': "tcp",
'ip_address': server_ip,
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
'remote_user':remote_user,
'password': server_password,
'path_to_erilurk':"Desktop/HyameServer/projects/erilurk"
}
connectivity_conf = {
# ensure local connection for udlLocal (do not set remote_user
'udpLocal': {
'ip_address': '127.0.0.1',
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
}
}
for type in [ 'udp','tcp', 'http']:
connectivity_conf[type] = deepcopy(conf)
connectivity_conf[type]['type'] = type
graph_params = {'title': '',
'xlabel': 'Athentication Methods',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'RSA', # label on xaxis depicting all the data in data
'color': ['white','white','white'],#['blue', 'green', 'orange'],
# color of the box of each data in data, set 'White if no color is desired
'hatch': [ '*','/', 'o'], # pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': [ 'rsa_master_udp_prf_sha256_pfs_sha256', 'rsa_master_tcp_prf_sha256_pfs_sha256', 'rsa_master_http_prf_sha256_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': ['UDP', 'TCP', 'HTTP'] # legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'RSA_Extended',
'color': ['white','white','white'],#['blue', 'green', 'orange'], # same color and hatch as previous group to have same legend
'hatch': ['*','/', 'o'],
'data': ['rsa_extended_master_udp_prf_sha256_pfs_sha256','rsa_extended_master_tcp_prf_sha256_pfs_sha256','rsa_extended_master_http_prf_sha256_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
{'tick_label': 'ECDHE',
'color': ['white','white','white'],#['blue', 'green', 'orange'],
'hatch': ['*','/', 'o'],
'data': ['ecdhe_udp_sig_sha256rsa_pfs_sha256', 'ecdhe_tcp_sig_sha256rsa_pfs_sha256', 'ecdhe_http_sig_sha256rsa_pfs_sha256'],
'legends': [],
},
]}
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=[request_nb], set_nb=set_nb, remote_connection=True)
def security_overhead_test( sheet_name, excel_file, graph_path, thread, request_nb, set_nb, server_ip,remote_user, server_password):
'''
This performs the security overhead latency tests (tcp, tcp+tls, http, https) between a client and a server
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb: requests number to test per set.
:param set_nb: number of sets to test
:param server_ip: Ip of the server to which we want to connect remotly
:param remote_user: username of remote server
:param server_password: password of remote server
:return:
'''
payload_params = {
'tcp+tls': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_tcptls_prf_sha256_pfs_sha256',
'ref': 'rsa_master_tcp_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
],
'tcp': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_tcp_prf_sha256_pfs_sha256',
'ref': 'rsa_master_tcp_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
],
'http': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_http_prf_sha256_pfs_sha256',
'ref': 'rsa_master_http_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
],
'https': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_https_prf_sha256_pfs_sha256',
'ref': 'rsa_master_http_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
],
}
#define connectivity conf fo client and server
data_dir = pkg_resources.resource_filename(__name__, '../data/')
conf = { 'type': "tcp",
'ip_address': server_ip,
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
'remote_user':remote_user,
'password': server_password,
'path_to_erilurk':"Desktop/HyameServer/projects/erilurk"
}
connectivity_conf = {}
for type in ['tcp', 'tcp+tls', 'http', 'https']:
connectivity_conf[type] = deepcopy(conf)
connectivity_conf[type]['type'] = type
graph_params = {'title': '',
'xlabel': 'Transport Protocol',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'lower right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'TCP+TLS', # label on xaxis depicting all the data in data
'color': ['white'],#['blue'], # color of the box of each data in data, set 'White if no color is desired
'hatch': ['*'], # pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['rsa_master_tcptls_prf_sha256_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': [] # legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'HTTPS',
'color': ['white'],#['blue'], # same color and hatch as previous group to have same legend
'hatch': ['*'],
'data': ['rsa_master_https_prf_sha256_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
]}
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=[request_nb], set_nb=set_nb, remote_connection = True)
def multithreading_test( sheet_name, excel_file, graph_path, request_nb_list, set_nb, server_ip,remote_user, server_password, thread=True):
'''
This method performs a multithreading tests using different transport protocol (udplocal(reference), udp, tcp, http) between a client and a server
:param sheet_name: the excel sheet name to store the results
:param excel_file: path to the excel file that will contain the results. The file is created if it does not exists
:param graph_path: path to the graphs depicting the results (e.g. results/ (do not start the path with "/")
:param thread: True or False depicting if we want to
:param request_nb_list: list of requests number to test per set.
:param set_nb: number of sets to test
:param server_ip: Ip of the server to which we want to connect remotly
:param remote_user: username of remote server
:param server_password: password of remote server
:return:
'''
# define connectivity conf fo client and server
data_dir = pkg_resources.resource_filename(__name__, '../data/')
conf = { 'type': "tcp",
'ip_address': server_ip,
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
'remote_user':remote_user,
'password': server_password,
'path_to_erilurk':"Desktop/HyameServer/projects/erilurk"
}
connectivity_conf = {
#ensure local connection for udlLocal (do not set remote_user
'udpLocal':{
'ip_address': '127.0.0.1',
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
}
}
for type in [ 'udp','tcp', 'http']:
connectivity_conf[type] = deepcopy(conf)
connectivity_conf[type]['type'] = type
graph_params = {'title': '',
'xlabel': 'Number of requests',
'ylabel': 'Latency (sec)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'upper right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
]
}
payload_params = {
'udpLocal': [],
'udp': [],
'tcp': [],
'http': [],
}
count=0
for request_nb in request_nb_list:
# start by setting payload parameters
udplocal_param = {
'type': 'rsa_master',
'column_name': 'udpLocal_ref_' + str(request_nb) + '_request',
'ref': 'udpLocal_ref_' + str(request_nb_list[0]) + '_request',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
}
udp_param = {
'type': 'rsa_master',
'column_name': 'udp_ref_' + str(request_nb) + '_request',
'ref': 'udp_ref_' + str(request_nb_list[0]) + '_request',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
}
tcp_param = {
'type': 'rsa_master',
'column_name': 'tcp_ref_' + str(request_nb) + '_request',
'ref': 'tcp_ref_' + str(request_nb_list[0]) + '_request',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
}
http_param = {
'type': 'rsa_master',
'column_name': 'http_ref_' + str(request_nb) + '_request',
'ref': 'http_ref_' + str(request_nb_list[0]) + '_request',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
}
payload_params['udpLocal'].append(udplocal_param)
payload_params['udp'].append(udp_param)
payload_params['tcp'].append(tcp_param)
payload_params['http'].append(http_param)
# skip reference (do not plot reference)
if request_nb == request_nb_list[0]:
continue
elif count ==1:#add the legends once
group = {'tick_label': request_nb,
'color': ['white','white','white','white' ],#['blue', 'green', 'orange', 'cyan'],
'hatch': ['*','/', 'o', 'x'],
'data': ['udpLocal_ref_' + str(request_nb) + '_request', 'udp_ref_' + str(request_nb) + '_request',
'tcp_ref_' + str(request_nb) + '_request', 'http_ref_' + str(request_nb) + '_request'],
'legends': ['Local', 'UDP', 'TCP', 'HTTP']
}
else:
group = {'tick_label': request_nb,
'color': ['white', 'white', 'white', 'white'], # ['blue', 'green', 'orange', 'cyan'],
'hatch': ['*', '/', 'o', 'x'],
'data': ['udpLocal_ref_' + str(request_nb) + '_request', 'udp_ref_' + str(request_nb) + '_request',
'tcp_ref_' + str(request_nb) + '_request', 'http_ref_' + str(request_nb) + '_request'],
'legends': []
}
# add groups to display in the graph
graph_params['groups'].append(group)
count+=1
latency_test(payload_params, connectivity_conf, graph_params, sheet_name, graph_path, excel_file=excel_file,
thread=thread, request_nb_list=request_nb_list, set_nb=set_nb, remote_connection=True)
def cpu_overhead_protocols_test( file_path, total_requests_persec, requests_per_client, iterations, wait_time, server_ip,remote_user, server_password, cpuNb, thread=False):
'''
This method will check the cpu overhead on the client and server side with TOp command for all transport protocols and authentication methods.
The top results are put in a file based on the pauload_params[column_name] _server or _client based on the client or server test results
the results should be averaged over total_requests_persec on iterations (as nb of sets). First 2 iterations should be disgarded
:param file_path: path to place te file with the top results
:param total_requests_persec:total requests to be sent per sec by all the clients
:param requests_per_client: number of requests that a client should send per second. This includes the resolve time+waiting time to reach 1 sec
:param total_time: total time of each test in payload_params. After this time the client and server processes are killed
:param iterations: number of iterations that the top command should performs
:param wait_time: time to wait between top command iterations
:param server_ip: Ip of the server to which we want to connect remotley
:param remote_user: username of remote server
:param server_password: password of remote server
:param cpuNb: nb of cpu to average over for one iteration of the top
:param thread: true if multi threading should be used
:return:
'''
# define connectivity conf fo client and server
data_dir = pkg_resources.resource_filename(__name__, '../data/')
conf = { 'type': "tcp",
'ip_address': server_ip,
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
'remote_user':remote_user,
'password': server_password,
'path_to_erilurk':"Desktop/HyameServer/projects/erilurk"
}
connectivity_conf = { #ensure local connection for udlLocal (do not set remote_user
'udpLocal':{
'ip_address': '127.0.0.1',
'port': 6789,
'key': join(data_dir, 'key_tls12_rsa_server.key'),
'cert': join(data_dir, 'cert_tls12_rsa_server.crt'),
'key_peer': join(data_dir, 'key_tls12_rsa_client.key'),
'cert_peer': join(data_dir, 'cert_tls12_rsa_client.crt'),
}
}
for type in [ 'udplocal','udp_freshnull','udp_fresh256','tcp', 'http', 'https', 'tcp+tls']:
connectivity_conf[type] = deepcopy(conf)
if type in ['udp_freshnull','udp_fresh256','udpLocal']:
connectivity_conf[type]['type'] = 'udp'
else:
connectivity_conf[type]['type'] = type
payload_params = {
'udpLocal':[
{
'type': 'rsa_master',
'column_name': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'udp_fresh256': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_udp_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_udp_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_udp_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'udp_freshnull': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_udp_prf_sha256_pfs_null',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'null',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_udp_prf_sha256_pfs_null',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'null',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_udp_sig_sha256rsa_pfs_null',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'null'
},
],
'tcp': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_tcp_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_tcp_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_tcp_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'http': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_http_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_http_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_http_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'https': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_https_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_https_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_https_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
'tcp+tls': [
{
'type': 'rsa_master',
'column_name': 'rsa_master_tcptls_prf_sha256_pfs_sha256',
'ref': 'rsa_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'rsa_extended_master',
'column_name': 'rsa_extended_master_tcptls_prf_sha256_pfs_sha256',
'ref': 'rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256',
'prf_hash': 'sha256',
'freshness_funct': 'sha256',
},
{
'type': 'ecdhe',
'column_name': 'ecdhe_tcptls_sig_sha256rsa_pfs_sha256',
'ref': 'ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256',
'sig_and_hash': ('sha256', 'rsa'),
'freshness_funct': 'sha256'
},
],
}
graph_params = {'title': '',
'xlabel': 'Athentication Methods',
'ylabel': 'Cpu Overhead (%)',
'box_width': 0.5, # width of each box in the graph
'start_position': 1, # the position of the first box to draw
'show_grid': True, # show grid in the graph
'legend': {
'location': 'upper right',
# location of the legend. Can take one of the following values:'best','upper right','upper left','lower left','lower right','right','center left','center right','lower center','upper center','center'
'font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
}
},
'font_properties': { # font properties of title, ylabel and xlabel
# 'fontname':'Calibri',
'size': '14',
'weight': 'bold',
},
'ticks_font_properties': {
# 'fontname':'Calibri',
'size': '12',
# 'weight': 'bold',
},
# data to plot grouped into multiple group. if no group is desired, a dictionary for each data to plot should be added
'groups': [
{'tick_label': 'RSA', # label on xaxis depicting all the data in data
'color': ['white', 'white', 'white','white', 'white', 'white'], # ['blue', 'green', 'orange'],
# color of the box of each data in data, set 'White if no color is desired
'hatch': ['*', '/', 'o', '-','x','/'],
# pattern of each box in data. Set '' if no hatch is desired. It can take one of the following patterns = ('-', '+', 'x', '\\', '*', 'o', 'O', '.', '/')
'data': ['rsa_master_udpLocal_ref_prf_sha256_pfs_sha256','rsa_master_udp_prf_sha256_pfs_sha256','rsa_master_tcp_prf_sha256_pfs_sha256','rsa_master_tcptls_prf_sha256_pfs_sha256',
'rsa_master_http_prf_sha256_pfs_sha256', 'rsa_master_https_prf_sha256_pfs_sha256'],
# colummn name of the data to plot as defined in excel sheet
'legends': ['UDP_Local','UDP', 'TCP','TCP+TLS', 'HTTP', 'HTTPS']
# legend corresponding to each data, set None if no legend to be added to a specified data or provide an empty list
},
{'tick_label': 'RSA_Extended',
'color': ['white', 'white', 'white','white', 'white', 'white'],
# ['blue', 'green', 'orange'], # same color and hatch as previous group to have same legend
'hatch': ['*', '/', 'o', '-','x','/'],
'data': ['rsa_extended_master_udpLocal_ref_prf_sha256_pfs_sha256','rsa_extended_master_udp_prf_sha256_pfs_sha256','rsa_extended_master_tcp_prf_sha256_pfs_sha256','rsa_extended_master_tcptls_prf_sha256_pfs_sha256',
'rsa_extended_master_http_prf_sha256_pfs_sha256', 'rsa_extended_master_https_prf_sha256_pfs_sha256'],
'legends': [], # empty list to have one legend per color as specified in previous group
},
{'tick_label': 'ECDHE',
'color': ['white', 'white', 'white','white', 'white', 'white'], # ['blue', 'green', 'orange'],
'hatch': ['*', '/', 'o', '-','x','/'],
'data': ['ecdhe_udpLocal_ref_sig_sha256rsa_pfs_sha256','ecdhe_udp_sig_sha256rsa_pfs_sha256','ecdhe_tcp_sig_sha256rsa_pfs_sha256','ecdhe_tcptls_sig_sha256rsa_pfs_sha256','ecdhe_http_sig_sha256rsa_pfs_sha256','ecdhe_https_sig_sha256rsa_pfs_sha256'],
'legends': [],
},
]}
cpu_overhead_test(payload_params, connectivity_conf, graph_params, file_path, total_requests_persec, requests_per_client,
iterations, wait_time, cpuNb, thread=thread, remote_connection=True)
if __name__=="__main__":
thread = False
request_nb =1
set_nb = 50
results_dir = 'results/'
graph_dir = results_dir+'graphs/'
server_ip ='192.168.0.108'#.108
remote_user='xubuntu_server'
password = 'xubuntu6789'
print("--------------------Starting Security Overhead Test----------------------------")
security_overhead_test('security', results_dir + 'security_overhead.xlsx', graph_dir, thread, request_nb, set_nb, server_ip, remote_user, password)
print("--------------------Starting Transport Protocol Test----------------------------")
transport_protocol_test('transport', results_dir+'transport_protocol.xlsx', graph_dir, thread, request_nb, set_nb,server_ip, remote_user, password)
thread = True
request_nb_list = [1, 100, 200, 400, 600, 800, 1000]
print("--------------------Starting Multithreading Test----------------------------")
multithreading_test('multithread', results_dir+'multithreading.xlsx', graph_dir, request_nb_list, set_nb, server_ip, remote_user, password, thread=thread)
thread =False
print("--------------------Starting Authentication Methods Test----------------------------")
authentication_methods_test('authentication', results_dir + 'authentication_methods.xlsx', graph_dir, thread, request_nb, set_nb)
print("--------------------Starting Mechanism Overhead pfs Test----------------------------")
mechanism_overhead_pfs_test('pfs', results_dir + 'mechanism_overhead_pfs.xlsx', graph_dir, thread, request_nb, set_nb)
print("--------------------Starting Mechanism Overhead poh Test----------------------------")
mechanism_overhead_poh_test('poh', results_dir + 'mechanism_overhead_poh.xlsx', graph_dir, thread, request_nb, set_nb)
print("--------------------Starting Mechanism Overhead poo Test----------------------------")
mechanism_overhead_poo_test('poo', results_dir + 'mechanism_overhead_poo.xlsx', graph_dir, thread, request_nb, set_nb)
total_requests_persec = 100
requests_per_client = 1
iterations = 50
wait_time = 5#wait 5 sec after each iteration
cpuNb=8
thread = True
cpu_overhead_protocols_test(results_dir, total_requests_persec, requests_per_client, iterations, wait_time,
server_ip, remote_user, password, cpuNb, thread=thread)
| 49.623077
| 272
| 0.519749
| 6,813
| 64,510
| 4.611038
| 0.046235
| 0.041827
| 0.038198
| 0.052714
| 0.89626
| 0.889543
| 0.87455
| 0.864332
| 0.851154
| 0.844183
| 0
| 0.040452
| 0.363107
| 64,510
| 1,299
| 273
| 49.661278
| 0.724164
| 0.24452
| 0
| 0.645418
| 0
| 0
| 0.347651
| 0.174949
| 0
| 0
| 0
| 0
| 0
| 1
| 0.007968
| false
| 0.012948
| 0.003984
| 0
| 0.011952
| 0.006972
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
e2ae946206f59079b6907d7e19ec82b03dae4887
| 49
|
py
|
Python
|
pypureclient/flashblade/__init__.py
|
bcai-ps/py-pure-client
|
d23de5cde4f4db17b85b1ba137235ae368a59c8c
|
[
"BSD-2-Clause"
] | null | null | null |
pypureclient/flashblade/__init__.py
|
bcai-ps/py-pure-client
|
d23de5cde4f4db17b85b1ba137235ae368a59c8c
|
[
"BSD-2-Clause"
] | null | null | null |
pypureclient/flashblade/__init__.py
|
bcai-ps/py-pure-client
|
d23de5cde4f4db17b85b1ba137235ae368a59c8c
|
[
"BSD-2-Clause"
] | null | null | null |
from .FB_2_0 import *
from .client import Client
| 16.333333
| 26
| 0.77551
| 9
| 49
| 4
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04878
| 0.163265
| 49
| 2
| 27
| 24.5
| 0.829268
| 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
| 1
| 0
|
0
| 6
|
2c4b3f14664306c9b3b6cf64782c589c29277366
| 79
|
py
|
Python
|
indra/assemblers/english/__init__.py
|
zebulon2/indra
|
7727ddcab52ad8012eb6592635bfa114e904bd48
|
[
"BSD-2-Clause"
] | 136
|
2016-02-11T22:06:37.000Z
|
2022-03-31T17:26:20.000Z
|
indra/assemblers/english/__init__.py
|
zebulon2/indra
|
7727ddcab52ad8012eb6592635bfa114e904bd48
|
[
"BSD-2-Clause"
] | 748
|
2016-02-03T16:27:56.000Z
|
2022-03-09T14:27:54.000Z
|
indra/assemblers/english/__init__.py
|
zebulon2/indra
|
7727ddcab52ad8012eb6592635bfa114e904bd48
|
[
"BSD-2-Clause"
] | 56
|
2015-08-28T14:03:44.000Z
|
2022-02-04T06:15:55.000Z
|
from .assembler import EnglishAssembler, AgentWithCoordinates, SentenceBuilder
| 39.5
| 78
| 0.886076
| 6
| 79
| 11.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075949
| 79
| 1
| 79
| 79
| 0.958904
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| 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
| 6
|
2c7b23b7628299f1113d367a80f7877aa0071952
| 589
|
py
|
Python
|
csgame/views.py
|
someshdube/Crowdsourcing
|
497fd46415b4c0fc2be69d42e0661d7fe423b278
|
[
"Apache-2.0"
] | null | null | null |
csgame/views.py
|
someshdube/Crowdsourcing
|
497fd46415b4c0fc2be69d42e0661d7fe423b278
|
[
"Apache-2.0"
] | null | null | null |
csgame/views.py
|
someshdube/Crowdsourcing
|
497fd46415b4c0fc2be69d42e0661d7fe423b278
|
[
"Apache-2.0"
] | null | null | null |
from django.http import *
from django.shortcuts import render_to_response,redirect,render
def profile(request):
return render(request, 'profile.html')
def over(request):
return render(request, 'over.html')
def about(request):
return render(request, 'about.html')
def handler404(request, *args, **argv):
return render(request, '404.html')
def handler500(request, *args, **argv):
return render(request, '500.html')
def phase01b(request):
return render(request, 'phase01b.html')
def stop(request):
return render(request, 'stop.html')
| 24.541667
| 64
| 0.689304
| 73
| 589
| 5.534247
| 0.328767
| 0.207921
| 0.329208
| 0.321782
| 0.168317
| 0.168317
| 0
| 0
| 0
| 0
| 0
| 0.033058
| 0.178268
| 589
| 23
| 65
| 25.608696
| 0.801653
| 0
| 0
| 0
| 0
| 0
| 0.121908
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4375
| false
| 0
| 0.125
| 0.4375
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
e2df672f0f055e562f71e906961839795338a245
| 33
|
py
|
Python
|
delivery/__main__.py
|
rafael-torraca/delivery
|
298db3c5d74938dc34687e7b65ee72a847e4deeb
|
[
"MIT"
] | null | null | null |
delivery/__main__.py
|
rafael-torraca/delivery
|
298db3c5d74938dc34687e7b65ee72a847e4deeb
|
[
"MIT"
] | null | null | null |
delivery/__main__.py
|
rafael-torraca/delivery
|
298db3c5d74938dc34687e7b65ee72a847e4deeb
|
[
"MIT"
] | null | null | null |
print("hello this is delivery!")
| 16.5
| 32
| 0.727273
| 5
| 33
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.827586
| 0
| 0
| 0
| 0
| 0
| 0.69697
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
e2e7afbd25e5cfa22e6611b125f12af3de1be88c
| 8,422
|
py
|
Python
|
tests/test_registration.py
|
berpress/shop_tests
|
c07329b93902a84f30043a38ec68f4e9d1576d94
|
[
"Apache-2.0"
] | null | null | null |
tests/test_registration.py
|
berpress/shop_tests
|
c07329b93902a84f30043a38ec68f4e9d1576d94
|
[
"Apache-2.0"
] | 44
|
2021-02-03T18:19:31.000Z
|
2021-02-10T15:20:54.000Z
|
tests/test_registration.py
|
berpress/shop_tests
|
c07329b93902a84f30043a38ec68f4e9d1576d94
|
[
"Apache-2.0"
] | null | null | null |
from models.fake_data import PersonalInformationData, Address
from common.constants import Users, Registration as reg, RandomData as rand
import allure
import pytest
from models.regdata import RegData
class TestRegistration:
@allure.story("Регистрация")
@allure.severity("critical")
@pytest.mark.skip(
reason="Не доделан переход на regdata"
)
def test_registration(self, app):
"""
Позитивный тест
1. Открыть главную страницу
2. Нажать на кнопку Sign in в хедере
3. Ввести e-mail
4. Нажать create an account
5. Заполнить все поля на форме
6. Нажать кнопку Register
"""
app.login.logout_button_click()
user = RegData.random()
email = user.login
addr = Address.random()
app.open_main_page()
app.registration.go_to_registration_form(email)
app.registration.fill_personal_information(
user.passwd, user.firstname, user.lastname, user.years
)
app.registration.fill_address(
user.firstname,
user.lastname,
addr.address,
addr.city,
addr.country,
addr.phone,
)
assert app.registration.account_header() == "MY ACCOUNT"
# @pytest.mark.parametrize(
# "email, expected_result",
# [
# pytest.param(Users.INVALID_EMAIL_2, reg.EMAIL_ERROR,
# id='Invalid email address'),
# pytest.param(Users.EMPTY_EMAIL, reg.EMAIL_ERROR,
# id='Empty email address'),
# pytest.param(Users.EMAIL, reg.EMAIL_EXISTS,
# id='Existing email'),
# ],
# )
# @allure.story("Регистрация")
# @allure.severity("minor")
# def test_registration_wrong_email(self, app, email, expected_result):
# """
# Негативные тесты для первого шага регистрации,
# где требуется только ввод email
# 1. Открыть главную страницу
# 2. Нажать на кнопку Sign In в правом верхнем углу
# 3. Ввести некорректный e-mail
# 4. Ожидается возникновение ошибок
# """
# app.open_main_page()
# app.registration.go_to_registration_form(email)
# error_message = str(app.registration.wrong_email_alert(expected_result))
#
# assert expected_result in error_message,
# f"Текст ошибки не соответствует ожидаемому.
# Текст ошибки:\n {error_message}\n, ожидаемый результат:\n {expected_result}"
#
# @pytest.mark.parametrize(
# "firstname, lastname, address1, city, expected_result",
# [
# pytest.param(rand.user.first_name, '', rand.addr.address,
# rand.addr.city,
# reg.LASTNAME_REQUIRED, id='Empty lastname'),
# pytest.param('', rand.user.last_name, rand.addr.address,
# rand.addr.city,
# reg.FIRSTNAME_REQUIRED, id='Empty firstname'),
# pytest.param(rand.user.first_name, rand.user.last_name, '',
# rand.addr.city,
# reg.ADDRESS_REQUIRED, id='Empty address'),
# pytest.param(rand.user.first_name, rand.user.last_name,
# rand.addr.address,
# '', reg.CITY_REQUIRED, id='Empty city')
# app.registration.go_to_registration_form(rand.email)
# app.registration.fill_personal_information(
# rand.user.password,
# rand.user.first_name,
# rand.user.last_name,
# rand.date.year,
# )
# app.registration.fill_address(
# rand.user.first_name,
# rand.user.last_name,
# rand.addr.address,
# rand.addr.city,
# rand.addr.country,
# rand.addr.phone,
# )
# assert app.registration.account_header() == "MY ACCOUNT"
# app.login.logout_button_click()
# @pytest.mark.parametrize(
# "email, expected_result",
# [
# pytest.param(
# Users.INVALID_EMAIL_2, reg.EMAIL_ERROR, id="Invalid email address"
# ),
# pytest.param(Users.EMPTY_EMAIL, reg.EMAIL_ERROR, id="Empty email address"),
# pytest.param(Users.EMAIL, reg.EMAIL_EXISTS, id="Existing email"),
# ],
# )
# @allure.story("Регистрация")
# @allure.severity("minor")
# def test_registration_wrong_email(self, app, email, expected_result):
# """
# Негативные тесты для первого шага регистрации,
# где требуется только ввод email
# 1. Открыть главную страницу
# 2. Нажать на кнопку Sign In в правом верхнем углу
# 3. Ввести некорректный e-mail
# 4. Ожидается возникновение ошибок
# """
# user = RegData.random()
# app.open_main_page()
# app.registration.go_to_registration_form(email)
# app.registration.fill_personal_information(user)
# error_message = str(app.registration.wrong_email_alert(expected_result))
# assert expected_result in error_message, (
# f"Текст ошибки не соответствует ожидаемому. Текст ошибки:\n "
# f"{error_message}\n, ожидаемый результат:\n {expected_result}"
# )
# @pytest.mark.parametrize(
# "firstname, lastname, address1, city, expected_result",
# [
# pytest.param(
# rand.user.first_name,
# "",
# rand.addr.address,
# rand.addr.city,
# reg.LASTNAME_REQUIRED,
# id="Empty lastname",
# ),
# pytest.param(
# "",
# rand.user.last_name,
# rand.addr.address,
# rand.addr.city,
# reg.FIRSTNAME_REQUIRED,
# id="Empty firstname",
# ),
# pytest.param(
# rand.user.first_name,
# rand.user.last_name,
# "",
# rand.addr.city,
# reg.ADDRESS_REQUIRED,
# id="Empty address",
# ),
# pytest.param(
# rand.user.first_name,
# rand.user.last_name,
# rand.addr.address,
# "",
# reg.CITY_REQUIRED,
# id="Empty city",
# ),
# ],
# )
# @allure.story("Регистрация")
# @allure.severity("minor")
# def test_registration_empty_fields(self, app, firstname,
# lastname, address1, city, expected_result):
# def test_registration_empty_fields(
# self, app, firstname, lastname, address1, city, expected_result
# ):
# """
# Негативный тест на возникновение ошибки при незаполненных обязательных полях.
# 1. Открыть главную страницу
# 2. Перейти на форму регистрации
# 3. Заполнить секцию личной информации, игнорируя обязательные поля
# 4. Заполнить секцию адреса, игнорируя обязательные поля
# 5. Ожидается возникновение ошибки
# """
# app.open_main_page()
# app.registration.go_to_registration_form(rand.addr.email)
# app.registration.fill_personal_information(
# rand.user.password, rand.user.first_name, rand.user.last_name,
# rand.date.year
# )
# app.registration.fill_address(
# rand.user.first_name,
# rand.user.last_name,
# rand.addr.address,
# rand.addr.city,
# rand.addr.country,
# rand.addr.phone,
# )
# assert app.registration.account_header() == MyAccount.MY_ACCOUNT,
# "Тест упал. Текст ошибки не совпадает с ожидаемым"
# app.login.logout_button_click()
# user = RegData.random()
# app.open_main_page()
# app.registration.go_to_registration_form(rand.addr.email)
# app.registration.fill_personal_information(
# user.password, user.firstname, user.lastname, user.years
# )
# app.registration.fill_address(
# user.firstname,
# user.lastname,
# addr.address,
# addr.city,
# addr.country,
# addr.phone,
# )
# assert (
# app.registration.account_header() == MyAccount.MY_ACCOUNT
# ), "Тест упал. Текст ошибки не совпадает с ожидаемым"
| 37.101322
| 89
| 0.56958
| 859
| 8,422
| 5.430733
| 0.182771
| 0.037728
| 0.027867
| 0.036442
| 0.863666
| 0.845445
| 0.845445
| 0.836227
| 0.821865
| 0.809218
| 0
| 0.004383
| 0.322726
| 8,422
| 226
| 90
| 37.265487
| 0.813464
| 0.76407
| 0
| 0
| 0
| 0
| 0.033372
| 0
| 0
| 0
| 0
| 0
| 0.033333
| 1
| 0.033333
| false
| 0.033333
| 0.166667
| 0
| 0.233333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
e2f5e9c8a09680181641113cfaa9943a094941ba
| 2,609
|
py
|
Python
|
test/generate_tones.py
|
merlinran/acorn-precision-farming-rover
|
228bbeb537550df79ae57985c427975ffa828bcd
|
[
"Apache-2.0"
] | 143
|
2021-02-23T16:17:32.000Z
|
2022-03-30T09:42:27.000Z
|
test/generate_tones.py
|
Twisted-Fields/acorn-precision-farming-rover
|
228bbeb537550df79ae57985c427975ffa828bcd
|
[
"Apache-2.0"
] | 19
|
2021-05-13T19:03:21.000Z
|
2022-03-25T08:46:44.000Z
|
test/generate_tones.py
|
merlinran/acorn-precision-farming-rover
|
228bbeb537550df79ae57985c427975ffa828bcd
|
[
"Apache-2.0"
] | 17
|
2021-02-23T22:02:24.000Z
|
2022-03-20T15:12:20.000Z
|
from tones import SINE_WAVE, SAWTOOTH_WAVE
from tones.mixer import Mixer
# Create mixer, set sample rate and amplitude
mixer = Mixer(44100, 0.5)
# Create two monophonic tracks that will play simultaneously, and set
# initial values for note attack, decay and vibrato frequency (these can
# be changed again at any time, see documentation for tones.Mixer
mixer.create_track(0, SAWTOOTH_WAVE, vibrato_frequency=20.0,
vibrato_variance=30.0, attack=0.01, decay=0.1)
#mixer.create_track(1, SINE_WAVE, attack=0.01, decay=0.1)
# Add a 1-second tone on track 0, slide pitch from c# to f#)
mixer.add_note(0, note='c#', octave=5, duration=1.0, endnote='f#')
# Add a 1-second tone on track 1, slide pitch from f# to g#)
# mixer.add_note(0, note='f#', octave=5, duration=1.0, endnote='g#')
# Mix all tracks into a single list of samples and write to .wav file
mixer.write_wav('complete.wav')
# Create mixer, set sample rate and amplitude
mixer = Mixer(44100, 0.5)
# Create two monophonic tracks that will play simultaneously, and set
# initial values for note attack, decay and vibrato frequency (these can
# be changed again at any time, see documentation for tones.Mixer
#mixer.create_track(0, SAWTOOTH_WAVE, vibrato_frequency=7.0, vibrato_variance=30.0, attack=0.01, decay=0.1)
mixer.create_track(0, SINE_WAVE, attack=0.01, decay=0.1)
# Add a 1-second tone on track 0, slide pitch from c# to f#)
mixer.add_note(0, note='a', octave=5, duration=0.25,
endnote='a', vibrato_frequency=7.0)
# Add a 1-second tone on track 1, slide pitch from f# to g#)
# mixer.add_note(0, note='c', octave=5, duration=1.0, endnote='a')
# Mix all tracks into a single list of samples and write to .wav file
mixer.write_wav('wait.wav')
# Create mixer, set sample rate and amplitude
mixer = Mixer(44100, 0.5)
# Create two monophonic tracks that will play simultaneously, and set
# initial values for note attack, decay and vibrato frequency (these can
# be changed again at any time, see documentation for tones.Mixer
mixer.create_track(0, SAWTOOTH_WAVE, vibrato_frequency=7.0,
vibrato_variance=30.0, attack=0.01, decay=0.1)
#mixer.create_track(0, SINE_WAVE, attack=0.01, decay=0.1)
# Add a 1-second tone on track 0, slide pitch from c# to f#)
mixer.add_note(0, note='f', octave=5, duration=4.0,
endnote='g', vibrato_frequency=2.0)
# Add a 1-second tone on track 1, slide pitch from f# to g#)
# mixer.add_note(0, note='c', octave=5, duration=1.0, endnote='a')
# Mix all tracks into a single list of samples and write to .wav file
mixer.write_wav('error.wav')
| 41.412698
| 107
| 0.721732
| 467
| 2,609
| 3.965739
| 0.167024
| 0.069114
| 0.051836
| 0.045356
| 0.915767
| 0.915767
| 0.910907
| 0.910907
| 0.910907
| 0.893629
| 0
| 0.052294
| 0.164431
| 2,609
| 62
| 108
| 42.080645
| 0.797248
| 0.651207
| 0
| 0.277778
| 0
| 0
| 0.042627
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.111111
| 0
| 0.111111
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
1a300feeb1489d80c5e1aa38555ac20a50c24d8e
| 34
|
py
|
Python
|
cdot/hgvs/dataproviders/__init__.py
|
SACGF/seedot
|
1f525b163b3e2f15fc8437c4f71acc15b804cccb
|
[
"MIT"
] | 6
|
2022-02-03T06:38:11.000Z
|
2022-02-22T08:46:56.000Z
|
cdot/hgvs/dataproviders/__init__.py
|
SACGF/seedot
|
1f525b163b3e2f15fc8437c4f71acc15b804cccb
|
[
"MIT"
] | 8
|
2022-01-19T23:06:47.000Z
|
2022-02-02T06:43:09.000Z
|
cdot/hgvs/dataproviders/__init__.py
|
SACGF/cdot
|
1f525b163b3e2f15fc8437c4f71acc15b804cccb
|
[
"MIT"
] | null | null | null |
from .json_data_provider import *
| 17
| 33
| 0.823529
| 5
| 34
| 5.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 34
| 1
| 34
| 34
| 0.866667
| 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
| 1
| 0
|
0
| 6
|
1a45f0bccc4a78e28a2f11339d96818bfcf3d924
| 145
|
py
|
Python
|
gpsearch/__init__.py
|
Fluid-Dynamics-Group/gpsearch
|
8c5758c9fb2b623ef79952c3e9c113cb157d79bc
|
[
"MIT"
] | 6
|
2020-07-13T00:02:17.000Z
|
2022-03-11T08:49:27.000Z
|
gpsearch/__init__.py
|
Fluid-Dynamics-Group/gpsearch
|
8c5758c9fb2b623ef79952c3e9c113cb157d79bc
|
[
"MIT"
] | null | null | null |
gpsearch/__init__.py
|
Fluid-Dynamics-Group/gpsearch
|
8c5758c9fb2b623ef79952c3e9c113cb157d79bc
|
[
"MIT"
] | 9
|
2020-07-18T13:29:46.000Z
|
2022-03-22T15:14:14.000Z
|
from .core import *
from .plotting import *
from .benchmarks import *
from .examples import *
import warnings
warnings.filterwarnings("ignore")
| 18.125
| 33
| 0.772414
| 17
| 145
| 6.588235
| 0.529412
| 0.267857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 145
| 7
| 34
| 20.714286
| 0.896
| 0
| 0
| 0
| 0
| 0
| 0.041379
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.833333
| 0
| 0.833333
| 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
| 1
| 0
|
0
| 6
|
1a5924e6067593ef52dc6f3579f5b229420b417a
| 26
|
py
|
Python
|
custom_components/sshhio_door_sensor/__init__.py
|
sshh12/home-assistant-sshhio
|
4f93b732b33f490a2364ae05b609fab8e28a84ae
|
[
"MIT"
] | null | null | null |
custom_components/sshhio_door_sensor/__init__.py
|
sshh12/home-assistant-sshhio
|
4f93b732b33f490a2364ae05b609fab8e28a84ae
|
[
"MIT"
] | null | null | null |
custom_components/sshhio_door_sensor/__init__.py
|
sshh12/home-assistant-sshhio
|
4f93b732b33f490a2364ae05b609fab8e28a84ae
|
[
"MIT"
] | null | null | null |
"Camera based door sensor"
| 26
| 26
| 0.807692
| 4
| 26
| 5.25
| 1
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0
| 6
|
1a76a13680d04999a4929b73ac9f1c1bd038881b
| 67,219
|
py
|
Python
|
Vide/plevels.py
|
FrankBuss/bloxorz
|
573d5112572b38907b0a9a82c92dcb1adc5a0d45
|
[
"MIT"
] | 10
|
2018-08-28T17:50:57.000Z
|
2022-01-21T06:27:34.000Z
|
Vide/plevels.py
|
FrankBuss/bloxorz
|
573d5112572b38907b0a9a82c92dcb1adc5a0d45
|
[
"MIT"
] | null | null | null |
Vide/plevels.py
|
FrankBuss/bloxorz
|
573d5112572b38907b0a9a82c92dcb1adc5a0d45
|
[
"MIT"
] | 2
|
2019-05-30T06:26:58.000Z
|
2020-11-03T01:25:44.000Z
|
levels = [
#{
# 'geometry': [ ' bbb ',
# ' bbb ',
# ' bbb ',
# ' bbb ',
# ' b ',
# ' b ',
# ' bbb ',
# ' bbbbb ',
# ' bbb ',
# ' b ',
# ' ',
# ' b e ',
# ' b b ',
# ' b b ',
# ' v b '],
# 'start': {'x': 4, 'y': 11},
# 'swatches': [ { 'fields': [ { 'action': 'split1',
# 'position': {'x': 6, 'y': 12}},
# { 'action': 'split2',
# 'position': {'x': 6, 'y': 14}}],
# 'position': {'x': 4, 'y': 14},
# 'type': 'v'}]},
{ 'geometry': [ ' ',
' ',
' bbb ',
' bbbb ',
' bbbb ',
' bbb ',
' bbb ',
' bbbb ',
' bbbb ',
' bebb ',
' bbbb ',
' bb ',
' ',
' ',
' '],
'start': {'x': 6, 'y': 3},
'swatches': []},
{ 'geometry': [ ' bbbbb ',
' bbbbb ',
' bbbsb ',
' bbbbb ',
' l ',
' r ',
' bbbbbb ',
' bbbbbb ',
' bbbbhb ',
' bbbbbb ',
' l ',
' r ',
' bbbbb ',
' bbbeb ',
' bbbbb '],
'start': {'x': 4, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 4, 'y': 10}},
{ 'action': 'onoff',
'position': {'x': 4, 'y': 11}}],
'position': {'x': 7, 'y': 8},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 4, 'y': 4}},
{ 'action': 'onoff',
'position': {'x': 4, 'y': 5}}],
'position': {'x': 6, 'y': 2},
'type': 's'}]},
{ 'geometry': [ ' bbbb ',
' bbbb ',
' bbbb ',
' bbbb ',
' b ',
' b ',
' bbb ',
' bbb ',
' bbb ',
' b ',
' b ',
' bbbbb ',
' bbbbbb ',
' bbeb ',
' bbbb '],
'start': {'x': 4, 'y': 1},
'swatches': []},
{ 'geometry': [ ' bbbbb ',
' bbbbb ',
' bbbbb ',
' bff ',
' ff ',
'bbbb ff ',
'bebb ff ',
'bbbb ff ',
' bb ff ',
' ff bff ',
'ffffbbb ',
'ffffbbb ',
'fbff ',
'ffff ',
' '],
'start': {'x': 3, 'y': 1},
'swatches': []},
{ 'geometry': [ 'bbb ',
'beb bbbb ',
'bbb bbbb ',
'bb bbsbb ',
' b bbbbb ',
' k b k ',
' q s q ',
' b b b ',
' b k s ',
' b q b ',
' bbbb b ',
' bbbb bb',
' bbbb bbb',
' bb bbb',
' bs bbb'],
'start': {'x': 8, 'y': 13},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 8, 'y': 6}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 5}}],
'position': {'x': 8, 'y': 8},
'type': 's'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 1, 'y': 5}},
{ 'action': 'on',
'position': {'x': 1, 'y': 6}}],
'position': {'x': 6, 'y': 3},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 1, 'y': 5}},
{ 'action': 'off',
'position': {'x': 1, 'y': 6}}],
'position': {'x': 4, 'y': 6},
'type': 's'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 1, 'y': 5}},
{ 'action': 'onoff',
'position': {'x': 1, 'y': 6}}],
'position': {'x': 3, 'y': 14},
'type': 's'}]},
{ 'geometry': [ ' b ',
' b ',
' b ',
' b ',
' bbb ',
' bbbbbb',
' bbbbb b',
'bbb b',
'bbb bbb',
'bbbb bbb',
' bbb bbb',
' bbb ',
' bbbb ',
' beb ',
' bbb '],
'start': {'x': 6, 'y': 0},
'swatches': []},
{ 'geometry': [ ' bbbb ',
' bbbbb ',
' bbbbbb ',
' bl b ',
' b b ',
' b b ',
' b b ',
' bbbbb ',
' bbbbbb ',
' bh bb ',
' bb ',
' bbb ',
' bbbb ',
' bbeb ',
' bbbb '],
'start': {'x': 5, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 2, 'y': 3}}],
'position': {'x': 4, 'y': 9},
'type': 'h'}]},
{ 'geometry': [ ' bbb ',
' bbb ',
' bbb ',
' bbb ',
' bvb ',
' bbb ',
' ',
' ',
' ',
' bbbbbbbbb',
' bbbbbbbbb',
' bbbbbbbbb',
' bbb ',
' beb ',
' bbb '],
'start': {'x': 5, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'split1',
'position': {'x': 8, 'y': 10}},
{ 'action': 'split2',
'position': {'x': 2, 'y': 10}}],
'position': {'x': 5, 'y': 4},
'type': 'v'}]},
{ 'geometry': [ ' bbb ',
' bbb ',
' bbb ',
' bbb ',
' b ',
' b ',
' bbb ',
' bebbb ',
' bbb ',
' b ',
' b ',
' bbb ',
' bbb ',
' bvb ',
' bbb '],
'start': {'x': 5, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'split1',
'position': {'x': 5, 'y': 12}},
{ 'action': 'split2',
'position': {'x': 5, 'y': 2}}],
'position': {'x': 5, 'y': 13},
'type': 'v'}]},
{ 'geometry': [ ' ',
' bbb',
' beb',
' bbb',
' l ',
'bb r ',
'sb b ',
' b l ',
' b r ',
'bb bbb',
'b bbbb',
'b bbbb',
'hbb bbbbb',
'bbbbbbllvb',
' bb'],
'start': {'x': 8, 'y': 10},
'swatches': [ { 'fields': [ { 'action': 'split1',
'position': {'x': 8, 'y': 13}},
{ 'action': 'split2',
'position': {'x': 8, 'y': 10}}],
'position': {'x': 8, 'y': 13},
'type': 'v'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 8, 'y': 4}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 5}}],
'position': {'x': 0, 'y': 6},
'type': 's'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 8, 'y': 7}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 8}},
{ 'action': 'onoff',
'position': {'x': 7, 'y': 13}},
{ 'action': 'onoff',
'position': {'x': 6, 'y': 13}}],
'position': {'x': 0, 'y': 12},
'type': 'h'}]},
{ 'geometry': [ ' ',
' ',
' b ',
' bbbbbb',
' b beb',
' b bbb',
' b kk',
' bbbbbb ',
' bbsbbb ',
' bb b ',
'bbb b ',
'bb bbb ',
'bb bbb ',
' bbbb ',
' '],
'start': {'x': 4, 'y': 2},
'swatches': [ { 'fields': [ { 'action': 'off',
'position': {'x': 9, 'y': 6}},
{ 'action': 'off',
'position': {'x': 8, 'y': 6}}],
'position': {'x': 3, 'y': 8},
'type': 's'}]},
{ 'geometry': [ ' ',
' bb ',
' bbb ',
' bbb ',
' bbbbb ',
' beb ',
'bb bbbbb ',
'bbb lbhb ',
'bbb bbb ',
' b b ',
' bbb b ',
' bbbbbbb ',
' bbbbbb ',
' bb lbh',
' '],
'start': {'x': 3, 'y': 3},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 5, 'y': 7}}],
'position': {'x': 9, 'y': 13},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 7, 'y': 13}}],
'position': {'x': 7, 'y': 7},
'type': 'h'}]},
{ 'geometry': [ ' ',
' bbbbbb',
' bbbbbb',
' bbbbb b',
'bbb f f',
'bbb f b',
'bfffff b',
' fffbbb b',
' fbfbeb b',
'bfffbbb f',
'bfff b',
' ffb bb',
' bbbbbbb',
' bbbbb',
' bbb '],
'start': {'x': 6, 'y': 13},
'swatches': []},
{ 'geometry': [ ' bbbbbb ',
' bb ll ',
'bbb rr ',
'beb bbb ',
'bbb bbb ',
' bbb ',
' b ',
' b ',
'bbbb bbb',
'bbbb bbb',
'bbbb bbb',
'b b b ',
'b bbbhb ',
'h bbbbb ',
' '],
'start': {'x': 7, 'y': 4},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 7, 'y': 1}},
{ 'action': 'onoff',
'position': {'x': 7, 'y': 2}}],
'position': {'x': 6, 'y': 12},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 6, 'y': 1}},
{ 'action': 'onoff',
'position': {'x': 6, 'y': 2}}],
'position': {'x': 0, 'y': 13},
'type': 'h'}]},
{ 'geometry': [ 'bbb bbb ',
'bbbbbbbb ',
'bbb bl ',
' b br ',
' b bbb ',
' b k ',
'bbb q ',
'bbbbv bbb',
'bbb sbbb',
' k bbb',
' q l ',
'sbs r ',
'beb bhb',
'bbb bbb',
' bbb'],
'start': {'x': 1, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'onoff',
'position': {'x': 7, 'y': 2}},
{ 'action': 'onoff',
'position': {'x': 7, 'y': 3}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 5}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 6}}],
'position': {'x': 8, 'y': 12},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 8, 'y': 5}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 6}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 10}},
{ 'action': 'onoff',
'position': {'x': 8, 'y': 11}}],
'position': {'x': 6, 'y': 8},
'type': 's'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 8, 'y': 13}},
{ 'action': 'split2',
'position': {'x': 1, 'y': 1}}],
'position': {'x': 4, 'y': 7},
'type': 'v'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 1, 'y': 9}},
{ 'action': 'off',
'position': {'x': 1, 'y': 10}}],
'position': {'x': 2, 'y': 11},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 1, 'y': 9}},
{ 'action': 'off',
'position': {'x': 1, 'y': 10}}],
'position': {'x': 0, 'y': 11},
'type': 's'}]},
{ 'geometry': [ ' v ',
' vbv ',
'bbb v ',
'bbb l ',
'bbb r ',
' b h ',
' b h ',
' b b ',
'bbb l ',
'bvb r ',
'bbb bbb ',
' beb ',
' bbb ',
' ',
' '],
'start': {'x': 1, 'y': 3},
'swatches': [ { 'fields': [ { 'action': 'split1',
'position': {'x': 6, 'y': 7}},
{ 'action': 'split2',
'position': {'x': 6, 'y': 5}}],
'position': {'x': 7, 'y': 1},
'type': 'v'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 6, 'y': 2}},
{ 'action': 'split2',
'position': {'x': 7, 'y': 1}}],
'position': {'x': 6, 'y': 0},
'type': 'v'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 6, 'y': 0}},
{ 'action': 'split2',
'position': {'x': 6, 'y': 2}}],
'position': {'x': 6, 'y': 2},
'type': 'v'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 6, 'y': 3}},
{ 'action': 'on',
'position': {'x': 6, 'y': 4}}],
'position': {'x': 6, 'y': 5},
'type': 'h'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 6, 'y': 8}},
{ 'action': 'on',
'position': {'x': 6, 'y': 9}}],
'position': {'x': 6, 'y': 6},
'type': 'h'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 5, 'y': 1}},
{ 'action': 'split2',
'position': {'x': 6, 'y': 0}}],
'position': {'x': 5, 'y': 1},
'type': 'v'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 7, 'y': 1}},
{ 'action': 'split2',
'position': {'x': 6, 'y': 0}}],
'position': {'x': 1, 'y': 9},
'type': 'v'}]},
{ 'geometry': [ 'bbbbbbbbbb',
'bsbbbbbbbb',
'bbbbbbbbbb',
' b b ',
' b b ',
' b b ',
' br b ',
' bb rb ',
' lb bb ',
' b bl ',
' b b ',
'bbbb b ',
'hbbh hbb ',
' heb ',
' bbb '],
'start': {'x': 8, 'y': 1},
'swatches': [ { 'fields': [ { 'action': 'off',
'position': {'x': 3, 'y': 6}}],
'position': {'x': 6, 'y': 12},
'type': 'h'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 3, 'y': 6}}],
'position': {'x': 6, 'y': 13},
'type': 'h'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 7, 'y': 7}}],
'position': {'x': 3, 'y': 12},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
'position': {'x': 2, 'y': 8}}],
'position': {'x': 1, 'y': 1},
'type': 's'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 8, 'y': 9}},
{ 'action': 'off',
'position': {'x': 2, 'y': 8}}],
'position': {'x': 0, 'y': 12},
'type': 'h'}]},
{ 'geometry': [ 'bbbbbbbb ',
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'r sbbbs ',
'h bbbbb ',
' bbb ',
' lb ',
' b ',
' bbbs ',
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' r ',
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'bbb b ',
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'bbb r ',
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'start': {'x': 5, 'y': 2},
'swatches': [ { 'fields': [ { 'action': 'on',
'position': {'x': 5, 'y': 8}},
{ 'action': 'on',
'position': {'x': 5, 'y': 9}}],
'position': {'x': 8, 'y': 7},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 5, 'y': 12}},
{ 'action': 'off',
'position': {'x': 5, 'y': 13}},
{ 'action': 'off',
'position': {'x': 0, 'y': 1}},
{ 'action': 'off',
'position': {'x': 0, 'y': 2}}],
'position': {'x': 7, 'y': 2},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 5, 'y': 8}},
{ 'action': 'off',
'position': {'x': 5, 'y': 9}}],
'position': {'x': 5, 'y': 1},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 5, 'y': 12}},
{ 'action': 'off',
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{ 'action': 'off',
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{ 'action': 'off',
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'position': {'x': 3, 'y': 2},
'type': 's'},
{ 'fields': [ { 'action': 'on',
'position': {'x': 5, 'y': 12}},
{ 'action': 'on',
'position': {'x': 5, 'y': 13}},
{ 'action': 'on',
'position': {'x': 0, 'y': 1}},
{ 'action': 'on',
'position': {'x': 0, 'y': 2}}],
'position': {'x': 2, 'y': 8},
'type': 's'},
{ 'fields': [ { 'action': 'onoff',
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{ 'action': 'onoff',
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'type': 's'},
{ 'fields': [ { 'action': 'off',
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{ 'action': 'off',
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'position': {'x': 4, 'y': 10},
'type': 's'},
{ 'fields': [ { 'action': 'on',
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{ 'action': 'on',
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'position': {'x': 0, 'y': 10},
'type': 's'}]},
{ 'geometry': [ ' bb ',
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' q ',
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' bbsbbb ',
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'bebb bbb',
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'start': {'x': 7, 'y': 8},
'swatches': [ { 'fields': [ { 'action': 'off',
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{ 'action': 'off',
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'position': {'x': 7, 'y': 7},
'type': 's'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 8, 'y': 5}},
{ 'action': 'off',
'position': {'x': 8, 'y': 6}}],
'position': {'x': 5, 'y': 3},
'type': 's'},
{ 'fields': [ { 'action': 'split1',
'position': {'x': 8, 'y': 13}},
{ 'action': 'split2',
'position': {'x': 2, 'y': 13}}],
'position': {'x': 5, 'y': 7},
'type': 'v'},
{ 'fields': [ { 'action': 'off',
'position': {'x': 8, 'y': 5}},
{ 'action': 'off',
'position': {'x': 8, 'y': 6}}],
'position': {'x': 5, 'y': 9},
'type': 's'},
{ 'fields': [ { 'action': 'onoff',
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'position': {'x': 4, 'y': 8},
'type': 'h'},
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{ 'action': 'off',
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'type': 's'},
{ 'fields': [ { 'action': 'onoff',
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'position': {'x': 0, 'y': 3},
'type': 'h'},
{ 'fields': [ { 'action': 'onoff',
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0
| 6
|
1a97568cb802b0e58539125dddab7a776c367a98
| 2,345
|
py
|
Python
|
authors/apps/articles/renderers.py
|
andela/ah-backend-odin
|
0e9ef1a10c8a3f6736999a5111736f7bd7236689
|
[
"BSD-3-Clause"
] | null | null | null |
authors/apps/articles/renderers.py
|
andela/ah-backend-odin
|
0e9ef1a10c8a3f6736999a5111736f7bd7236689
|
[
"BSD-3-Clause"
] | 43
|
2018-10-25T10:14:52.000Z
|
2022-03-11T23:33:46.000Z
|
authors/apps/articles/renderers.py
|
andela/ah-backend-odin
|
0e9ef1a10c8a3f6736999a5111736f7bd7236689
|
[
"BSD-3-Clause"
] | 4
|
2018-10-29T07:04:58.000Z
|
2020-04-02T14:15:10.000Z
|
import json
from rest_framework.renderers import JSONRenderer
from rest_framework.utils.serializer_helpers import ReturnDict, ReturnList
class ArticleJSONRenderer(JSONRenderer):
charset = 'utf-8'
db_object_label = 'object'
def render(self, data, media_type=None, renderer_context=None):
if type(data) != ReturnList:
errors = data.get('errors', None)
if errors is not None:
return super(ArticleJSONRenderer, self).render(data)
if type(data) == ReturnDict:
return json.dumps({
self.db_object_label: data
})
return json.dumps({
'article': data
})
class CommentJsonRenderer(JSONRenderer):
charset = 'utf-8'
db_object_label = 'Comment'
def render(self, data, media_type=None, renderer_context=None):
if type(data) != ReturnList:
errors = data.get('errors', None)
if errors is not None:
return super(CommentJsonRenderer, self).render(data)
if type(data) == ReturnDict:
return json.dumps({
self.db_object_label: data
})
return json.dumps({
'comment': data
})
class ThreadJsonRenderer(JSONRenderer):
charset = 'utf-8'
db_object_label = 'Comment'
def render(self, data, media_type=None, renderer_context=None):
if type(data) != ReturnList:
errors = data.get('errors', None)
if errors is not None:
return super(ThreadJsonRenderer, self).render(data)
if type(data) == ReturnDict:
return json.dumps({
self.db_object_label: data
})
return json.dumps({
'comment': data
})
class BookMarkJSONRenderer(JSONRenderer):
charset = 'utf-8'
db_object_label = 'object'
def render(self, data, media_type=None, renderer_context=None):
if type(data) != ReturnList:
errors = data.get('errors', None)
if errors is not None:
return super(BookMarkJSONRenderer, self).render(data)
if type(data) == ReturnDict:
return json.dumps({
self.db_object_label: data
})
return json.dumps({
'Bookmark': data
})
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| 74
| 0.576972
| 245
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| 0
| 0
| 0
|
0
| 6
|
1aa9f34dd65c09470bc551c9807c2676882e252d
| 110
|
py
|
Python
|
model/__init__.py
|
zaky-fetoh/MNIST-BayesianConvAutoEncoder
|
3c483122f93165d664415f156bac5211adb40e22
|
[
"MIT"
] | null | null | null |
model/__init__.py
|
zaky-fetoh/MNIST-BayesianConvAutoEncoder
|
3c483122f93165d664415f156bac5211adb40e22
|
[
"MIT"
] | null | null | null |
model/__init__.py
|
zaky-fetoh/MNIST-BayesianConvAutoEncoder
|
3c483122f93165d664415f156bac5211adb40e22
|
[
"MIT"
] | null | null | null |
from .BaysianLayer import *
from .Baysian_klLoss import *
from .autoencoder import *
from .SSIM_loss import *
| 22
| 29
| 0.781818
| 14
| 110
| 6
| 0.571429
| 0.357143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145455
| 110
| 4
| 30
| 27.5
| 0.893617
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
1ab6b096defc0c59723c7efad322fab83e7c78f4
| 171
|
py
|
Python
|
task05/task5.py
|
SimeonHristov99/CodeEveryDay
|
ffeed48d7d55910be87b40b4113e8073f29a265d
|
[
"MIT"
] | null | null | null |
task05/task5.py
|
SimeonHristov99/CodeEveryDay
|
ffeed48d7d55910be87b40b4113e8073f29a265d
|
[
"MIT"
] | null | null | null |
task05/task5.py
|
SimeonHristov99/CodeEveryDay
|
ffeed48d7d55910be87b40b4113e8073f29a265d
|
[
"MIT"
] | null | null | null |
def multiplication_table(size):
return [[ x * y for y in range(1, size + 1)] for x in range(1, size + 1)]
print(multiplication_table(3)) # [[1,2,3],[2,4,6],[3,6,9]]
| 34.2
| 77
| 0.608187
| 34
| 171
| 3
| 0.5
| 0.372549
| 0.156863
| 0.235294
| 0.254902
| 0
| 0
| 0
| 0
| 0
| 0
| 0.099291
| 0.175439
| 171
| 5
| 78
| 34.2
| 0.624113
| 0.146199
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0.333333
| 0
| 0
| 0
| null | 1
| 0
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
46d61564ce18aabb3fff08bcba0068e9bb316463
| 1,341
|
py
|
Python
|
pylinex/loglikelihood/__init__.py
|
CU-NESS/pylinex
|
b6f342595b6a154e129eb303782e5268088f34d5
|
[
"Apache-2.0"
] | null | null | null |
pylinex/loglikelihood/__init__.py
|
CU-NESS/pylinex
|
b6f342595b6a154e129eb303782e5268088f34d5
|
[
"Apache-2.0"
] | null | null | null |
pylinex/loglikelihood/__init__.py
|
CU-NESS/pylinex
|
b6f342595b6a154e129eb303782e5268088f34d5
|
[
"Apache-2.0"
] | null | null | null |
"""
File: pylinex/loglikelihood/__init__.py
Author: Keith Tauscher
Date: 4 Mar 2019
Description: File containing imports for the loglikelihood module. The classes
in this module concern the saving, loading, storing, and
evaluation of various likelihood functions.
"""
from pylinex.loglikelihood.Loglikelihood import Loglikelihood
from pylinex.loglikelihood.RosenbrockLoglikelihood import\
RosenbrockLoglikelihood
from pylinex.loglikelihood.LoglikelihoodWithData import LoglikelihoodWithData
from pylinex.loglikelihood.LoglikelihoodWithModel import LoglikelihoodWithModel
from pylinex.loglikelihood.GaussianLoglikelihood import GaussianLoglikelihood
from pylinex.loglikelihood.PoissonLoglikelihood import PoissonLoglikelihood
from pylinex.loglikelihood.GammaLoglikelihood import GammaLoglikelihood
from pylinex.loglikelihood.LinearTruncationLoglikelihood import\
LinearTruncationLoglikelihood
from pylinex.loglikelihood.NonlinearTruncationLoglikelihood import\
NonlinearTruncationLoglikelihood
from pylinex.loglikelihood.ConditionalFitGaussianLoglikelihood import\
ConditionalFitGaussianLoglikelihood
from pylinex.loglikelihood.LoadLoglikelihood import\
load_loglikelihood_from_hdf5_group
from pylinex.loglikelihood.LikelihoodDistributionHarmonizer import\
LikelihoodDistributionHarmonizer
| 46.241379
| 79
| 0.863535
| 112
| 1,341
| 10.267857
| 0.419643
| 0.226087
| 0.250435
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004975
| 0.100671
| 1,341
| 28
| 80
| 47.892857
| 0.94859
| 0.213274
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 0
| 0
| 1
| 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
| 1
| 0
| 1
| 0
|
0
| 6
|
201b7409a6479ae84fa5d3175bc874bc4f5e3a9c
| 37
|
py
|
Python
|
Contents/Libraries/Shared/subzero/modification/dictionaries/__init__.py
|
jippo015/Sub-Zero.bundle
|
734e0f7128c05c0f639e11e7dfc77daa1014064b
|
[
"MIT"
] | 1,553
|
2015-11-09T02:17:06.000Z
|
2022-03-31T20:24:52.000Z
|
Contents/Libraries/Shared/subzero/modification/dictionaries/__init__.py
|
saiterlz/Sub-Zero.bundle
|
1a0bb9c3e4be84be35d46672907783363fe5a87b
|
[
"MIT"
] | 691
|
2015-11-05T21:32:26.000Z
|
2022-03-17T10:52:45.000Z
|
Contents/Libraries/Shared/subzero/modification/dictionaries/__init__.py
|
saiterlz/Sub-Zero.bundle
|
1a0bb9c3e4be84be35d46672907783363fe5a87b
|
[
"MIT"
] | 162
|
2015-11-06T19:38:55.000Z
|
2022-03-16T02:42:41.000Z
|
# coding=utf-8
from data import data
| 12.333333
| 21
| 0.756757
| 7
| 37
| 4
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.032258
| 0.162162
| 37
| 3
| 21
| 12.333333
| 0.870968
| 0.324324
| 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
| 1
| 0
|
0
| 6
|
2047b44fc7021050e4a1797cc7cfcc5fc6237f32
| 39
|
py
|
Python
|
labdrivers/keithley/__init__.py
|
pbnjeff89/labdrivers
|
1091b9f746a5a011d94cd63abf5010fc8cde1556
|
[
"MIT"
] | 12
|
2016-10-14T09:50:32.000Z
|
2022-03-28T00:36:31.000Z
|
labdrivers/keithley/__init__.py
|
pbnjeff89/labdrivers
|
1091b9f746a5a011d94cd63abf5010fc8cde1556
|
[
"MIT"
] | 21
|
2016-04-13T20:03:36.000Z
|
2019-09-25T13:00:52.000Z
|
labdrivers/keithley/__init__.py
|
pbnjeff89/labdrivers
|
1091b9f746a5a011d94cd63abf5010fc8cde1556
|
[
"MIT"
] | 3
|
2017-08-30T02:01:27.000Z
|
2020-03-04T01:50:52.000Z
|
from .keithley2400 import Keithley2400
| 19.5
| 38
| 0.871795
| 4
| 39
| 8.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.228571
| 0.102564
| 39
| 1
| 39
| 39
| 0.742857
| 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
| 1
| 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
| 6
|
205105fa49c68059e5fc5da2731450a1ebc39802
| 12,203
|
py
|
Python
|
tests/test_resource_discipline.py
|
andraune/Run4IT_BackEnd
|
a481427a0d1189a1f08c42e7ac1b452af6bbfc8d
|
[
"MIT"
] | 1
|
2022-03-29T06:11:20.000Z
|
2022-03-29T06:11:20.000Z
|
tests/test_resource_discipline.py
|
andraune/run4it_backend
|
a481427a0d1189a1f08c42e7ac1b452af6bbfc8d
|
[
"MIT"
] | null | null | null |
tests/test_resource_discipline.py
|
andraune/run4it_backend
|
a481427a0d1189a1f08c42e7ac1b452af6bbfc8d
|
[
"MIT"
] | null | null | null |
import pytest
from run4it.api.discipline import DisciplineModel, DisciplineResource, DisciplineListResource
from .helpers import get_response_json, register_and_login_confirmed_user, get_authorization_header
@pytest.mark.usefixtures('db')
class TestDisciplineResource:
def _create_disciplines(self, num, db):
i = 0
max = 100
while i < num and i < max:
disc = DisciplineModel("disc{}".format(i + 1), max - i)
disc.save(False)
i += 1
db.session.commit()
def test_disciplinelist_content_type_is_json(self, api, client):
url = api.url_for(DisciplineListResource)
response = client.get(url)
assert(response.headers["Content-Type"] == 'application/json')
def test_get_disciplinelist_no_data(self, api, client):
url = api.url_for(DisciplineListResource)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 0)
def test_get_disciplinelist_with_data(self, api, client):
disc_1 = DisciplineModel("disc1", 1000, "user1")
disc_1.save()
url = api.url_for(DisciplineListResource)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 1)
assert(response_json[0]["id"] == 1)
assert(response_json[0]["length"] == 1000)
assert(response_json[0]["username"] == "user1")
def test_create_discipines_helper(self, api, client, db):
self._create_disciplines(10, db)
assert(DisciplineModel.query.count() == 10)
disc_first = DisciplineModel.get_by_id(1)
disc_last = DisciplineModel.get_by_id(10)
assert(disc_first.name == "disc1")
assert(disc_first.length == 100)
assert(disc_last.name == "disc10")
assert(disc_last.length == 91)
def test_disciplinelist_ordered_by_length_ascending(self, api, client, db):
self._create_disciplines(10, db)
url = api.url_for(DisciplineListResource)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 10)
assert(response_json[0]["id"] == 10)
assert(response_json[0]["length"] == 91)
assert(response_json[1]["id"] == 9)
assert(response_json[1]["length"] == 92)
assert(response_json[9]["id"] == 1)
assert(response_json[9]["length"] == 100)
def test_disciplinelist_default_limit(self, api, client, db):
self._create_disciplines(21, db)
url = api.url_for(DisciplineListResource)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 20) # default limit 20
def test_disciplinelist_limit_param_large(self, api, client, db):
self._create_disciplines(30, db)
url = api.url_for(DisciplineListResource, limit=23)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 23) # default is 20
def test_disciplinelist_limit_param_small(self, api, client, db):
self._create_disciplines(10, db)
url = api.url_for(DisciplineListResource, limit=3)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(len(response_json) == 3)
def test_disciplinelist_pagination(self, api, client, db):
self._create_disciplines(10, db)
url = api.url_for(DisciplineListResource, limit=4, offset=0)
response_json1 = get_response_json(client.get(url).data)
url = api.url_for(DisciplineListResource, limit=4, offset=4)
response_json2 = get_response_json(client.get(url).data)
url = api.url_for(DisciplineListResource, limit=4, offset=8)
response_json3 = get_response_json(client.get(url).data)
assert(len(response_json1) == 4)
assert(len(response_json2) == 4)
assert(len(response_json3) == 2)
assert(response_json1[0]["id"] == 10)
assert(response_json1[3]["id"] == 7)
assert(response_json2[0]["id"] == 6)
assert(response_json2[3]["id"] == 3)
assert(response_json3[0]["id"] == 2)
assert(response_json3[1]["id"] == 1)
def test_post_discipline_not_logged_in(self, api, client):
url = api.url_for(DisciplineListResource)
response = client.post(url)
response_json = get_response_json(response.data)
assert(response.status_code == 401)
assert(response_json["errors"]["auth"] is not None)
def test_post_disciplinelist_new_discipline(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
response = client.post(url, data={ "name":"new_disc", "length":1234, "isRoute":True }, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(response_json["id"] == 1)
assert(response_json["name"] == "new_disc")
assert(response_json["length"] == 1234)
assert(response_json["username"] == "run4it")
assert(response_json["isRoute"] == True)
def test_post_discipline_list_new_discipline_location_header(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
response = client.post(url, data={ "name":"new_disc", "length":1234, "isRoute":False }, headers=get_authorization_header(token))
assert(response.headers["Location"] == api.url_for(DisciplineResource, disc_id=1, _external=True))
def test_post_disciplinelist_new_discipline_duplicate_name(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
client.post(url, data={ "name":"new_disc", "length":1234, "isRoute":True }, headers=get_authorization_header(token))
response = client.post(url, data={ "name":"new_disc", "length":12345, "isRoute":False }, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 409)
assert(response_json["errors"]["discipline"] is not None)
def test_post_disciplinelist_new_discipline_invalid_name(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
response = client.post(url, data={ "name":"d", "length":1234 }, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 422)
assert(response_json["errors"]["name"] is not None)
def test_post_disciplinelist_new_discipline_invalid_length(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
response = client.post(url, data={ "name":"disc", "length":0 }, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 422)
assert(response_json["errors"]["length"] is not None)
def test_post_disciplinelist_new_discipline_missing_params(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineListResource)
response = client.post(url, data={}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 422)
assert(response_json["errors"]["name"] is not None)
assert(response_json["errors"]["length"] is not None)
def test_put_disciplinelist_not_supported(self, api, client):
url = api.url_for(DisciplineListResource)
response = client.put(url)
assert(response.status_code == 405) # not allowed
def test_delete_disciplinelist_not_supported(self, api, client):
url = api.url_for(DisciplineListResource)
response = client.delete(url)
assert(response.status_code == 405) # not allowed
def test_get_discipline_doesnt_exist(self, api, client):
url = api.url_for(DisciplineResource, disc_id=1)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 404)
assert(response_json["errors"]["discipline"] is not None)
def test_get_discipline_invalid_id(self, api, client):
url = api.url_for(DisciplineResource, disc_id=-1)
response = client.get(url)
get_response_json(response.data)
assert(response.status_code == 404)
def test_get_discipline_by_id(self, api, client, db):
self._create_disciplines(3, db)
url = api.url_for(DisciplineResource, disc_id=2)
response = client.get(url)
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(response_json["id"] == 2)
assert(response_json["name"] == "disc2")
def test_update_discipline_not_logged_in(self, api, client):
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url)
response_json = get_response_json(response.data)
assert(response.status_code == 401)
assert(response_json["errors"]["auth"] is not None)
def test_update_discipline(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
disc = DisciplineModel("disc1", 1000, "run4it", False)
disc.save()
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url, data={'name':'new_name','length':999, 'isRoute':True}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 200)
assert(response_json["name"] == "new_name")
assert(response_json["length"] == 999)
assert(response_json["username"] == "run4it")
assert(response_json["isRoute"] == True)
def test_update_discipline_other_user(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
disc = DisciplineModel("disc1", 1000, "other")
disc.save()
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url, data={'name':'new_name','length':999,'isRoute':False}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 403)
assert(response_json["errors"]["discipline"] is not None)
def test_update_discipline_not_found(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url, data={'name':'new_name','length':999,'isRoute':False}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 404)
assert(response_json["errors"]["discipline"] is not None)
def test_update_discipline_missing_params(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
disc = DisciplineModel("disc1", 1000, "run4it")
disc.save()
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url, data={}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 422)
assert(response_json["errors"]["name"] is not None)
assert(response_json["errors"]["length"] is not None)
def test_update_discipline_invalid_params(self, api, client):
token,_ = register_and_login_confirmed_user(api, client, "run4it", "run4@it.com", "passwd")
disc = DisciplineModel("disc1", 1000, "run4it")
disc.save()
url = api.url_for(DisciplineResource, disc_id=1)
response = client.put(url, data={"name":"","length":99999999}, headers=get_authorization_header(token))
response_json = get_response_json(response.data)
assert(response.status_code == 422)
assert(response_json["errors"]["name"] is not None)
assert(response_json["errors"]["length"] is not None)
def test_post_discipline_not_supported(self, api, client):
url = api.url_for(DisciplineResource, disc_id=1)
response = client.post(url)
assert(response.status_code == 405) # not allowed
def test_delete_discipline_not_supported(self, api, client):
url = api.url_for(DisciplineResource, disc_id=1)
response = client.delete(url)
assert(response.status_code == 405) # not allowed
| 45.533582
| 131
| 0.749406
| 1,671
| 12,203
| 5.210652
| 0.086176
| 0.117147
| 0.070288
| 0.041346
| 0.815666
| 0.788905
| 0.776961
| 0.741243
| 0.740439
| 0.727116
| 0
| 0.027673
| 0.111612
| 12,203
| 267
| 132
| 45.70412
| 0.775482
| 0.006392
| 0
| 0.523404
| 0
| 0
| 0.073527
| 0
| 0
| 0
| 0
| 0
| 0.344681
| 1
| 0.12766
| false
| 0.046809
| 0.012766
| 0
| 0.144681
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
64c72ddef7df6b68f8fb92a37000ff563876bfc6
| 85
|
py
|
Python
|
src/test.py
|
gideongrinberg/py2llvm-mirror
|
f6b450813f8695adbaa384e24004bfd06f93d988
|
[
"Apache-2.0"
] | null | null | null |
src/test.py
|
gideongrinberg/py2llvm-mirror
|
f6b450813f8695adbaa384e24004bfd06f93d988
|
[
"Apache-2.0"
] | null | null | null |
src/test.py
|
gideongrinberg/py2llvm-mirror
|
f6b450813f8695adbaa384e24004bfd06f93d988
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
from MUDA import *
def test(a = vec):
return a + a
| 9.444444
| 21
| 0.564706
| 14
| 85
| 3.428571
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.305882
| 85
| 8
| 22
| 10.625
| 0.813559
| 0.235294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 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
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
37729e2f2c24192dab4dbb8598a5f94719032c61
| 47
|
py
|
Python
|
Calculator/square_root.py
|
mcp48/StatsCalculator
|
6defb5b55e75ce2528ba58bdf5efc4f016f645d7
|
[
"MIT"
] | null | null | null |
Calculator/square_root.py
|
mcp48/StatsCalculator
|
6defb5b55e75ce2528ba58bdf5efc4f016f645d7
|
[
"MIT"
] | 1
|
2021-07-08T19:41:11.000Z
|
2021-07-08T19:41:11.000Z
|
Calculator/square_root.py
|
mcp48/StatsCalculator
|
6defb5b55e75ce2528ba58bdf5efc4f016f645d7
|
[
"MIT"
] | null | null | null |
def square_root(a):
return float(a) ** 0.5
| 15.666667
| 26
| 0.617021
| 9
| 47
| 3.111111
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.054054
| 0.212766
| 47
| 2
| 27
| 23.5
| 0.702703
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
379c7b3c61add9bbb149d019be8ab0540ec82c8f
| 21,065
|
py
|
Python
|
openprocurement/bridge/templatesregistry/tests/test_handlers.py
|
quintagroup/openprocurement.bridge.templatesregistry
|
c058dff9fcd9bd880d916b9005caf41eb93c3861
|
[
"Apache-2.0"
] | null | null | null |
openprocurement/bridge/templatesregistry/tests/test_handlers.py
|
quintagroup/openprocurement.bridge.templatesregistry
|
c058dff9fcd9bd880d916b9005caf41eb93c3861
|
[
"Apache-2.0"
] | null | null | null |
openprocurement/bridge/templatesregistry/tests/test_handlers.py
|
quintagroup/openprocurement.bridge.templatesregistry
|
c058dff9fcd9bd880d916b9005caf41eb93c3861
|
[
"Apache-2.0"
] | 1
|
2021-01-19T14:29:32.000Z
|
2021-01-19T14:29:32.000Z
|
# -*- coding: utf-8 -*-
import os
import unittest
from copy import deepcopy
from mock import patch, MagicMock, call
from munch import munchify
from openprocurement.bridge.templatesregistry.handlers import (
TemplateUploaderHandler
)
tender = {
"procurementMethod": "limited",
"status": "active.tendering",
"id": "1" * 32,
"documents": [
{
"hash": "md5:04951bc3f8e3fe51a37912dda2665f76",
"format": "application/pkcs7-signature",
"url": "https://public.docs.openprocurement.org/get/940de573f6d44e639c1b592672dec3c0?KeyID=52462340&Signature=Lvq2amp3b64NBAOGLEkax64WoprseL1cwPqRQ5g9KS2LNHb8XgqWkHDB0BA8UQyRSGCvsXNqJ9D7dNVi7Ne%2FCw%253D%253D",
"title": "sign.p7s",
"documentOf": "tender",
"datePublished": "2020-05-08T12:29:57.154151+03:00",
"dateModified": "2020-05-08T12:29:57.154185+03:00",
"relatedItem": "b7bf55c6a01a4c8d9eb3b24053f7118b",
"id": "42e3c31bfb0e4bdf80d42d57f75df019"
},
{
"hash": "md5:00001bc3f8e3fe51a37912dda2665076",
"format": "application/msword",
"title": "paper0000001.docx",
"documentOf": "tender",
"documentType": "contractProforma",
"templateId": "paper0000001",
"datePublished": "2020-05-08T12:29:57.154151+03:00",
"dateModified": "2020-05-08T12:29:57.154185+03:00",
"relatedItem": "b7bf55c6a01a4c8d9eb3b24053f7118b",
"id": "0003c31bfb0e4bdf80d42d57f75df000"
}
]
}
date_older_contract_proforma = '2020-04-08T12:29:57.154185+03:00'
date_newer_contract_proforma = '2020-06-08T12:29:57.154185+03:00'
template_docs = [
{
"hash": "md5:00001bc3f8e3fe51a37912dda2665076",
"format": "application/msword",
"documentOf": "document",
"documentType": "contractTemplate",
"datePublished": "2020-05-08T12:29:57.154151+03:00",
"dateModified": date_older_contract_proforma,
"relatedItem": "0003c31bfb0e4bdf80d42d57f75df000",
"id": "1" * 32
},
{
"hash": "md5:00001bc3f8e3fe51a37912dda2665076",
"format": "application/msword",
"documentOf": "document",
"documentType": "contractSchema",
"datePublished": "2020-05-08T12:29:57.154151+03:00",
"dateModified": date_older_contract_proforma,
"relatedItem": "0003c31bfb0e4bdf80d42d57f75df000",
"id": "2" * 32
},
{
"hash": "md5:00001bc3f8e3fe51a37912dda2665076",
"format": "application/msword",
"documentOf": "document",
"documentType": "contractForm",
"datePublished": "2020-05-08T12:29:57.154151+03:00",
"dateModified": date_older_contract_proforma,
"relatedItem": "0003c31bfb0e4bdf80d42d57f75df000",
"id": "3" * 32
},
]
def prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls):
template_downloader = MagicMock()
factory = MagicMock()
factory.get_template_downloader.return_value = template_downloader
mocked_downloader_factory_cls.return_value = factory
mocked_client = MagicMock()
mocked_client_cls.return_value = mocked_client
return mocked_client, template_downloader, factory
def prepare_template_downloader_result(mocked_td):
template_info = {
'template': 'template',
'scheme': 'scheme',
'form': 'form',
}
mocked_td.get_template_by_id.side_effect = [
deepcopy(template_info),
]
return template_info
dir_path = os.path.dirname(__file__)
registry_path = os.path.join(dir_path, 'registry')
class TestTemplateUploaderHandler(unittest.TestCase):
config = {
'worker_config':
{
'handler_templateUploader': {
'resources_api_token': 'resources_api_token',
'resources_api_version': 'resources_api_version',
'resources_api_server': 'resources_api_server',
'resource': 'resource',
'output_resource': 'output_resource',
'DS': {
'host_url': 'host_url',
'auth_ds': 'auth_ds',
},
'template_downloader': {
'type': 'registry_file',
'registry_path': registry_path,
'filename': 'registry.yaml'
}
},
},
'public_resources_api_server': 'http://localhost:6543',
'resources_api_token': 'resources_api_token',
'resources_api_version': 'resources_api_version',
'resources_api_server': 'resources_api_server',
'resource': 'resource',
}
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_init(self, mocked_downloader_factory_cls, mocked_client_cls, _):
mocked_client, _, factory = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
handler = TemplateUploaderHandler(self.config, 'cache_db')
self.assertEquals(handler.cache_db, 'cache_db')
self.assertEquals(handler.handler_config, self.config['worker_config']['handler_templateUploader'])
self.assertEquals(handler.main_config, self.config)
self.assertEqual(mocked_client_cls.call_count, 1)
mocked_client_cls.assert_called_with(
key=handler.handler_config.get('resources_api_token'),
resource=handler.handler_config['resource'],
host_url=handler.handler_config['resources_api_server'],
api_version=handler.handler_config['resources_api_version'],
ds_config=handler.handler_config.get('DS', {}),
)
self.assertEqual(mocked_downloader_factory_cls.call_count, 1)
self.assertEqual(factory.get_template_downloader.call_count, 1)
factory.get_template_downloader.assert_called_with(handler.handler_config['template_downloader'])
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_api_upload_document(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
mocked_client.get_resource_item_subitem.return_value = munchify({'data': tender['documents'][1]})
template_info = prepare_template_downloader_result(template_downloader)
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler = TemplateUploaderHandler(self.config, 'cache_db')
handler.process_resource(tender)
self.assertEqual(template_downloader.get_template_by_id.call_count, 1)
cp_doc = handler.get_contract_proforma_documents(tender)[0]
template_downloader.get_template_by_id.assert_called_with(cp_doc['templateId'])
calls = [
call(template_info['template']),
call(template_info['scheme']),
call(template_info['form']),
]
mocked_bytesio.assert_has_calls(calls, any_order=True)
self.assertEqual(mocked_client.upload_document.call_count, 3)
doc = tender['documents'][1]
additional_data = {
'documentOf': 'document',
'relatedItem': doc['id'],
}
calls = [
call(wrapped_file, tender['id'], doc_type='contractTemplate', additional_doc_data=additional_data),
call(wrapped_file, tender['id'], doc_type='contractSchema', additional_doc_data=additional_data),
call(wrapped_file, tender['id'], doc_type='contractForm',additional_doc_data=additional_data),
]
mocked_client.upload_document.assert_has_calls(calls, any_order=True)
self.assertEqual(mocked_client.update_document.call_count, 0)
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_api_update_all_documents(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
updated_document = deepcopy(tender['documents'][1])
updated_document['previousVersions'] = [deepcopy(tender['documents'][1])]
updated_document['templateId'] = 'paper0000002'
mocked_client.get_resource_item_subitem.return_value = munchify({'data': updated_document})
template_info = prepare_template_downloader_result(template_downloader)
custom_tender = deepcopy(tender)
custom_tender['documents'] += template_docs
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler = TemplateUploaderHandler(self.config, 'cache_db')
handler.process_resource(custom_tender)
self.assertEqual(template_downloader.get_template_by_id.call_count, 1)
cp_doc = handler.get_contract_proforma_documents(tender)[0]
template_downloader.get_template_by_id.assert_called_with(cp_doc['templateId'])
calls = [
call(template_info['template']),
call(template_info['scheme']),
call(template_info['form']),
]
mocked_bytesio.assert_has_calls(calls, any_order=True)
self.assertEqual(mocked_client.update_document.call_count, 3)
doc = tender['documents'][1]
additional_data = {
'documentOf': 'document',
'relatedItem': doc['id'],
}
calls = [
call(wrapped_file, tender['id'], template_docs[0]['id'], doc_type='contractTemplate', additional_doc_data=additional_data),
call(wrapped_file, tender['id'], template_docs[1]['id'], doc_type='contractSchema', additional_doc_data=additional_data),
call(wrapped_file, tender['id'], template_docs[2]['id'], doc_type='contractForm',additional_doc_data=additional_data),
]
mocked_client.update_document.assert_has_calls(calls, any_order=True)
self.assertEqual(mocked_client.upload_document.call_count, 0)
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_api_update_one_old_template(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
updated_document = deepcopy(tender['documents'][1])
updated_document['previousVersions'] = [deepcopy(tender['documents'][1])]
mocked_client.get_resource_item_subitem.return_value = munchify({'data': updated_document})
template_info = prepare_template_downloader_result(template_downloader)
custom_tender = deepcopy(tender)
custom_template_docs = deepcopy(template_docs)
custom_template_docs[1]['dateModified'] = date_newer_contract_proforma
custom_tender['documents'] += custom_template_docs
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler = TemplateUploaderHandler(self.config, 'cache_db')
handler.process_resource(custom_tender)
self.assertEqual(template_downloader.get_template_by_id.call_count, 1)
cp_doc = handler.get_contract_proforma_documents(tender)[0]
template_downloader.get_template_by_id.assert_called_with(cp_doc['templateId'])
self.assertEqual(mocked_bytesio.call_count, 0)
self.assertEqual(mocked_client.update_document.call_count, 0)
self.assertEqual(mocked_client.upload_document.call_count, 0)
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_api_update_missed_file(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
mocked_client.get_resource_item_subitem.return_value = munchify({'data': tender['documents'][1]})
template_info = prepare_template_downloader_result(template_downloader)
custom_tender = deepcopy(tender)
custom_template_docs = deepcopy(template_docs)
custom_template_docs.pop(1)
custom_template_docs[0]['dateModified'] = date_newer_contract_proforma
custom_template_docs[1]['dateModified'] = date_newer_contract_proforma
custom_tender['documents'] += custom_template_docs
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler = TemplateUploaderHandler(self.config, 'cache_db')
handler.process_resource(custom_tender)
self.assertEqual(template_downloader.get_template_by_id.call_count, 1)
cp_doc = handler.get_contract_proforma_documents(tender)[0]
template_downloader.get_template_by_id.assert_called_with(cp_doc['templateId'])
mocked_bytesio.assert_called_once_with(template_info['scheme'])
self.assertEqual(mocked_client.upload_document.call_count, 1)
doc = tender['documents'][1]
additional_data = {
'documentOf': 'document',
'relatedItem': doc['id'],
}
mocked_client.upload_document.assert_called_once_with(
wrapped_file,
tender['id'],
doc_type='contractSchema',
additional_doc_data=additional_data
)
self.assertEqual(mocked_client.update_document.call_count, 0)
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_api_update_for_old_contract_proforma(self, mocked_downloader_factory_cls, mocked_client_cls, _):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
updated_document = deepcopy(tender['documents'][1])
updated_document['previousVersions'] = [deepcopy(tender['documents'][1])]
updated_document['templateId'] = 'paper0000001'
mocked_client.get_resource_item_subitem.return_value = munchify({'data': updated_document})
prepare_template_downloader_result(template_downloader)
custom_tender = deepcopy(tender)
custom_template_docs = deepcopy(template_docs)
custom_template_docs[0]['dateModified'] = date_newer_contract_proforma
custom_template_docs[1]['dateModified'] = date_newer_contract_proforma
custom_template_docs[2]['dateModified'] = date_newer_contract_proforma
custom_tender['documents'] += custom_template_docs
handler = TemplateUploaderHandler(self.config, 'cache_db')
handler.process_resource(custom_tender)
self.assertEqual(template_downloader.get_template_by_id.call_count, 1)
self.assertEqual(mocked_client.update_document.call_count, 0)
self.assertEqual(mocked_client.upload_document.call_count, 0)
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_get_contract_proforma_document(self, mocked_downloader_factory_cls, mocked_client_cls, _):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls, mocked_downloader_factory_cls)
handler = TemplateUploaderHandler(self.config, 'cache_db')
test_data = {
'documents': [
{
'field1': 'field1',
'documentType': 'notContractProforma'
},
{
'field2': 'field2',
'documentType': 'contractProforma'
},
{
'field3': 'field3',
'documentType': 'contractProforma'
}
]
}
docs = handler.get_contract_proforma_documents(test_data)
self.assertEqual(len(docs), 2)
self.assertEqual(docs[0], test_data['documents'][1])
self.assertEqual(docs[1], test_data['documents'][2])
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_upload_document_to_api(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls,
mocked_downloader_factory_cls)
handler = TemplateUploaderHandler(self.config, 'cache_db')
doc = {
'data': {
'id': 'someid',
'some': 'field'
}
}
doc = munchify(doc)
mocked_client.upload_document.return_value = deepcopy(doc)
test_resource = {
'id': 'resource_id'
}
test_doc = {
'id': 'doc_id'
}
test_file = 'test_file'
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler._upload_document_to_api(test_resource, test_doc, test_file, 'test_doc_type')
mocked_bytesio.assert_called_with(test_file)
additional_data = {
'documentOf': 'document',
'relatedItem': test_doc['id'],
}
mocked_client.upload_document.assert_called_once_with(
wrapped_file,
test_resource['id'],
doc_type='test_doc_type',
additional_doc_data=additional_data
)
@patch('openprocurement.bridge.templatesregistry.handlers.BytesIO')
@patch('openprocurement.bridge.basic.handlers.APIClient')
@patch('openprocurement.bridge.templatesregistry.handlers.APIResourceClient')
@patch('openprocurement.bridge.templatesregistry.handlers.TemplateDownloaderFactory')
def test_update_document_to_api(self, mocked_downloader_factory_cls, mocked_client_cls, _, mocked_bytesio):
mocked_client, template_downloader, _ = prepare_mocks_handler_mocks(mocked_client_cls,
mocked_downloader_factory_cls)
handler = TemplateUploaderHandler(self.config, 'cache_db')
doc = {
'data': {
'id': 'someid',
'some': 'field'
}
}
doc = munchify(doc)
mocked_client.update_document.return_value = deepcopy(doc)
test_resource = {
'id': 'resource_id'
}
test_template_doc = {
'id': 'template doc id'
}
test_cp_doc = {
'id': 'contract proforma doc id'
}
test_file = 'test_file'
wrapped_file = 'wrappedfile'
mocked_bytesio.return_value = wrapped_file
handler._update_document_in_api(test_resource, test_template_doc, test_cp_doc, test_file, 'test_doc_type')
mocked_bytesio.assert_called_with(test_file)
additional_data = {
'documentOf': 'document',
'relatedItem': test_cp_doc['id'],
}
mocked_client.update_document.assert_called_once_with(
wrapped_file,
test_resource['id'],
test_template_doc['id'],
doc_type='test_doc_type',
additional_doc_data=additional_data
)
def suite():
suite = unittest.TestSuite()
return suite
if __name__ == "__main__":
unittest.main(defaultTest='suite')
| 43.343621
| 222
| 0.679421
| 2,077
| 21,065
| 6.523832
| 0.094848
| 0.049594
| 0.063321
| 0.084871
| 0.80369
| 0.782362
| 0.772915
| 0.771292
| 0.766716
| 0.759926
| 0
| 0.037347
| 0.216995
| 21,065
| 485
| 223
| 43.43299
| 0.784164
| 0.000997
| 0
| 0.533666
| 0
| 0.002494
| 0.25777
| 0.139816
| 0
| 0
| 0
| 0
| 0.102244
| 1
| 0.029925
| false
| 0
| 0.014963
| 0
| 0.057357
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
805ac5fdd164311a1a9ce27f1e3802c32c11a37d
| 134
|
py
|
Python
|
flopt/solvers/sequential_update_searches/__init__.py
|
nariaki3551/flopt
|
fdf18deb63827463d90658c107c520aec4f0f707
|
[
"MIT"
] | 4
|
2020-06-14T10:10:33.000Z
|
2022-03-11T18:34:02.000Z
|
flopt/solvers/sequential_update_searches/__init__.py
|
flab-coder/flopt
|
681d2f98824a52bb95de73676823d7ae59c6a013
|
[
"MIT"
] | 36
|
2020-04-30T12:07:15.000Z
|
2021-11-02T05:30:04.000Z
|
flopt/solvers/sequential_update_searches/__init__.py
|
flab-coder/flopt
|
681d2f98824a52bb95de73676823d7ae59c6a013
|
[
"MIT"
] | 4
|
2020-04-30T11:32:15.000Z
|
2021-07-15T09:09:42.000Z
|
from .base_sequential_update import SequentialUpdateSearch
from .sampling_search import RandomSearch
from .local_search import TwoOpt
| 33.5
| 58
| 0.88806
| 16
| 134
| 7.1875
| 0.6875
| 0.208696
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089552
| 134
| 3
| 59
| 44.666667
| 0.942623
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
8088ae63b94ff7b7347119435a1e5d70d63ea1b7
| 6,590
|
py
|
Python
|
integration_tests/interflux_tests/test_interop_endpoints.py
|
TjadenFroyda/pyStratis
|
9cc7620d7506637f8a2b84003d931eceb36ac5f2
|
[
"MIT"
] | 8
|
2021-06-30T20:44:22.000Z
|
2021-12-07T14:42:22.000Z
|
integration_tests/interflux_tests/test_interop_endpoints.py
|
TjadenFroyda/pyStratis
|
9cc7620d7506637f8a2b84003d931eceb36ac5f2
|
[
"MIT"
] | 2
|
2021-07-01T11:50:18.000Z
|
2022-01-25T18:39:49.000Z
|
integration_tests/interflux_tests/test_interop_endpoints.py
|
TjadenFroyda/pyStratis
|
9cc7620d7506637f8a2b84003d931eceb36ac5f2
|
[
"MIT"
] | 4
|
2021-07-01T04:36:42.000Z
|
2021-09-17T10:54:19.000Z
|
import pytest
from pystratis.api.interop.responsemodels import *
from pystratis.core import PubKey, DestinationChain
from pystratis.core.types import uint256, hexstr, Money
from pystratis.core.networks import CirrusMain, StraxMain
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_status_burns(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.status_burns()
assert isinstance(response, list)
for item in response:
assert isinstance(item, ConversionRequestModel)
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_status_mints(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.status_mints()
assert isinstance(response, list)
for item in response:
assert isinstance(item, ConversionRequestModel)
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_status_votes(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.status_votes()
assert isinstance(response, dict)
for item in response:
assert isinstance(item, Pubkey)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_owners(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.owners(destination_chain=DestinationChain.ETH)
assert isinstance(response, list)
for item in response:
assert isinstance(item, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_addowner(interflux_cirrusminer_node, generate_p2pkh_address):
response = interflux_cirrusminer_node.interop.add_owner(
destination_chain=DestinationChain.ETH,
new_owner_address=generate_p2pkh_address(network=CirrusMain()),
gas_price=100
)
assert isinstance(response, uint256)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_removeowner(interflux_cirrusminer_node, generate_p2pkh_address):
response = interflux_cirrusminer_node.interop.remove_owner(
destination_chain=DestinationChain.ETH,
existing_owner_address=generate_p2pkh_address(network=CirrusMain()),
gas_price=100
)
assert isinstance(response, uint256)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_confirmtransaction(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.confirm_transaction(
destination_chain=DestinationChain.ETH,
transaction_id=1,
gas_price=100
)
assert isinstance(response, uint256)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_changerequirement(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.change_requirement(
destination_chain=DestinationChain.ETH,
requirement=1,
gas_price=100
)
assert isinstance(response, uint256)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_multisigtransaction(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.multisig_transaction(
destination_chain=DestinationChain.ETH,
transaction_id=1,
raw=False
)
assert isinstance(response, TransactionResponseModel)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_multisigtransaction(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.multisig_transaction(
destination_chain=DestinationChain.ETH,
transaction_id=1,
raw=True
)
assert isinstance(response, hexstr)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_multisigconfirmations(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.multisig_confirmations(
destination_chain=DestinationChain.ETH,
transaction_id=1
)
assert isinstance(response, list)
for item in response:
assert isinstance(item, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_balance(interflux_cirrusminer_node, generate_p2pkh_address):
response = interflux_cirrusminer_node.interop.balance(
destination_chain=DestinationChain.ETH,
account=generate_p2pkh_address(network=StraxMain())
)
assert isinstance(response, Money)
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_requests_delete(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.requests_delete()
assert isinstance(response, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_requests_setoriginator(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.requests_setoriginator(
request_id=1
)
assert isinstance(response, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_requests_setnotoriginator(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.requests_setnotoriginator(
request_id=1
)
assert isinstance(response, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_requests_reprocess_burn(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.requests_reprocess_burn(
request_id=1,
height=1
)
assert isinstance(response, str)
@pytest.mark.skip(reason='Unable to test in regtest environment.')
@pytest.mark.integration_test
@pytest.mark.interflux_integration_test
def test_requests_pushvote(interflux_cirrusminer_node):
response = interflux_cirrusminer_node.interop.requests_pushvote(
request_id=1,
vote_id=1
)
assert isinstance(response, str)
| 35.053191
| 96
| 0.793475
| 763
| 6,590
| 6.592398
| 0.117955
| 0.093439
| 0.162227
| 0.084493
| 0.833996
| 0.818091
| 0.812127
| 0.79503
| 0.745726
| 0.619881
| 0
| 0.007666
| 0.128983
| 6,590
| 187
| 97
| 35.240642
| 0.868641
| 0
| 0
| 0.601307
| 0
| 0
| 0.074962
| 0
| 0
| 0
| 0
| 0
| 0.143791
| 1
| 0.111111
| false
| 0
| 0.03268
| 0
| 0.143791
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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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
| 6
|
80a41d57c208190a4d0e04737d60a6377b7c5a73
| 103
|
py
|
Python
|
pybpodgui_plugin_session_history/models/subject/__init__.py
|
pybpod/pybpod-gui-plugin-session-history
|
6767c5a6590001a8f0420cfdb5327f924b98dc5a
|
[
"MIT"
] | null | null | null |
pybpodgui_plugin_session_history/models/subject/__init__.py
|
pybpod/pybpod-gui-plugin-session-history
|
6767c5a6590001a8f0420cfdb5327f924b98dc5a
|
[
"MIT"
] | null | null | null |
pybpodgui_plugin_session_history/models/subject/__init__.py
|
pybpod/pybpod-gui-plugin-session-history
|
6767c5a6590001a8f0420cfdb5327f924b98dc5a
|
[
"MIT"
] | null | null | null |
from pybpodgui_plugin_session_history.models.subject.subject_treenode import SubjectTreeNode as Subject
| 103
| 103
| 0.92233
| 13
| 103
| 7
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.048544
| 103
| 1
| 103
| 103
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
80dd9e0f465078175e5f2608d23246198f4757f2
| 111
|
py
|
Python
|
Asap-3.8.4/Python/asap3/Filters/FixCoordinates.py
|
auag92/n2dm
|
03403ef8da303b79478580ae76466e374ec9da60
|
[
"MIT"
] | 1
|
2021-10-19T11:35:34.000Z
|
2021-10-19T11:35:34.000Z
|
Asap-3.8.4/Python/asap3/Filters/FixCoordinates.py
|
auag92/n2dm
|
03403ef8da303b79478580ae76466e374ec9da60
|
[
"MIT"
] | null | null | null |
Asap-3.8.4/Python/asap3/Filters/FixCoordinates.py
|
auag92/n2dm
|
03403ef8da303b79478580ae76466e374ec9da60
|
[
"MIT"
] | 3
|
2016-07-18T19:22:48.000Z
|
2021-07-06T03:06:42.000Z
|
from ASE.Filters.FixCoordinates import FixCoordinates
import Asap.fixepydoc
Asap.fixepydoc.fix(FixCoordinates)
| 27.75
| 53
| 0.873874
| 13
| 111
| 7.461538
| 0.615385
| 0.412371
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063063
| 111
| 3
| 54
| 37
| 0.932692
| 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 | 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
| 1
| 0
|
0
| 6
|
03caea26f9ea0c1b8ea69d797cf25f25de5d1c49
| 23
|
py
|
Python
|
tools/__init__.py
|
SunYanCN/tendecomlib
|
3473d05855c87e39c162c3fea6fe59f94344735b
|
[
"MIT"
] | null | null | null |
tools/__init__.py
|
SunYanCN/tendecomlib
|
3473d05855c87e39c162c3fea6fe59f94344735b
|
[
"MIT"
] | null | null | null |
tools/__init__.py
|
SunYanCN/tendecomlib
|
3473d05855c87e39c162c3fea6fe59f94344735b
|
[
"MIT"
] | null | null | null |
from .tools import prod
| 23
| 23
| 0.826087
| 4
| 23
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 23
| 1
| 23
| 23
| 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
| 1
| 0
|
0
| 6
|
206bcaa2f82738f098178f8ab64983f8add15b59
| 87
|
py
|
Python
|
src/style_transfer/content_fusion/__init__.py
|
trbay/style_transfer_via_texture_synthesis
|
4824fa1b74573e48d3e340fb691dea8d502cfd50
|
[
"MIT"
] | null | null | null |
src/style_transfer/content_fusion/__init__.py
|
trbay/style_transfer_via_texture_synthesis
|
4824fa1b74573e48d3e340fb691dea8d502cfd50
|
[
"MIT"
] | null | null | null |
src/style_transfer/content_fusion/__init__.py
|
trbay/style_transfer_via_texture_synthesis
|
4824fa1b74573e48d3e340fb691dea8d502cfd50
|
[
"MIT"
] | null | null | null |
from .edge_weight import *
from .fuse_content import *
from .unique_weight import *
| 21.75
| 29
| 0.758621
| 12
| 87
| 5.25
| 0.583333
| 0.380952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172414
| 87
| 3
| 30
| 29
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
20c8e4efc81a890d1fa473dedc89e8480ffe833f
| 32
|
py
|
Python
|
keckcode/esiredux/__init__.py
|
cdfassnacht/keck_code
|
a952b3806b3e64eef70deec2b2d1352e6ef6dfa0
|
[
"MIT"
] | null | null | null |
keckcode/esiredux/__init__.py
|
cdfassnacht/keck_code
|
a952b3806b3e64eef70deec2b2d1352e6ef6dfa0
|
[
"MIT"
] | null | null | null |
keckcode/esiredux/__init__.py
|
cdfassnacht/keck_code
|
a952b3806b3e64eef70deec2b2d1352e6ef6dfa0
|
[
"MIT"
] | 1
|
2020-07-15T23:16:36.000Z
|
2020-07-15T23:16:36.000Z
|
from . import calibration,bgsub
| 16
| 31
| 0.8125
| 4
| 32
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 32
| 1
| 32
| 32
| 0.928571
| 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
| 1
| 0
|
0
| 6
|
4567aff5727428b575beaa2538401a6e2e60194e
| 39
|
py
|
Python
|
plugins/claims/__init__.py
|
StarryPy/StarryPy-Historic
|
b9dbd552b8c4631a5a8e9dda98b7ba447eca59da
|
[
"WTFPL"
] | 38
|
2015-02-12T11:57:59.000Z
|
2018-11-15T16:03:45.000Z
|
plugins/claims/__init__.py
|
StarryPy/StarryPy-Historic
|
b9dbd552b8c4631a5a8e9dda98b7ba447eca59da
|
[
"WTFPL"
] | 68
|
2015-02-05T23:29:47.000Z
|
2017-12-27T08:26:25.000Z
|
plugins/claims/__init__.py
|
StarryPy/StarryPy-Historic
|
b9dbd552b8c4631a5a8e9dda98b7ba447eca59da
|
[
"WTFPL"
] | 21
|
2015-02-06T18:58:21.000Z
|
2017-12-24T20:08:59.000Z
|
from claims_plugin import ClaimsPlugin
| 19.5
| 38
| 0.897436
| 5
| 39
| 6.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 39
| 1
| 39
| 39
| 0.971429
| 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
| 1
| 0
|
0
| 6
|
457c8972831525cd4e2f8a2054736fb757e81027
| 7,974
|
py
|
Python
|
api/tests/test_organization_members.py
|
amcquistan/project-time-tracker-api-django
|
da8a4129964fa4e330939178f12f24097527e77d
|
[
"MIT"
] | null | null | null |
api/tests/test_organization_members.py
|
amcquistan/project-time-tracker-api-django
|
da8a4129964fa4e330939178f12f24097527e77d
|
[
"MIT"
] | null | null | null |
api/tests/test_organization_members.py
|
amcquistan/project-time-tracker-api-django
|
da8a4129964fa4e330939178f12f24097527e77d
|
[
"MIT"
] | 1
|
2021-01-01T14:58:11.000Z
|
2021-01-01T14:58:11.000Z
|
from copy import deepcopy
from django.core.exceptions import ObjectDoesNotExist
from django.shortcuts import reverse
from django.test import TestCase
from rest_framework import status
from rest_framework.test import APIClient
from api.tests.testing_utils import (
create_user,
authenticate_jwt,
admin_creds,
johndoe_creds,
janedoe_creds,
batman_creds,
create_organization,
)
from core.models import Organization, Project, ProjectContributor
class TestOrganizationMembersAPI(TestCase):
create_list_view_name = 'organization-member-list-create'
delete_view_name = 'organization-member-delete'
@classmethod
def setUpTestData(cls):
cls.admin_user = admin_creds.create_user(
is_active=True,
is_staff=True
)
cls.johndoe_user = johndoe_creds.create_user(is_active=True)
cls.janedoe_user = janedoe_creds.create_user(is_active=True)
cls.batman_user = batman_creds.create_user(is_active=True)
def test_create_with_admin_succeeds(self):
'''Tests that an admin can add a member to an organization'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
admin_client = APIClient()
authenticate_jwt(admin_creds, admin_client)
url = reverse(self.create_list_view_name, kwargs={'org_slug': johndoe_org.slug})
payload = {'user_id': self.batman_user.id}
response = admin_client.post(url, payload, format='json')
self.assertEqual(status.HTTP_201_CREATED, response.status_code)
data = response.data
self.assertIn('members', data)
# john doe and batman are the expected members
self.assertEqual(2, len(data['members']))
def test_create_with_org_contact_succeeds(self):
'''Tests that an org-contact can add a member to their org'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
johndoe_client = APIClient()
authenticate_jwt(johndoe_creds, johndoe_client)
url = reverse(self.create_list_view_name, kwargs={'org_slug': johndoe_org.slug})
payload = {'user_id': self.batman_user.id}
response = johndoe_client.post(url, payload, format='json')
self.assertEqual(status.HTTP_201_CREATED, response.status_code)
data = response.data
self.assertIn('members', data)
# john doe and batman are the expected members
self.assertEqual(2, len(data['members']))
def test_create_with_non_admin_non_org_contact_fails(self):
'''Tests that a non-admin / non-org-contact cannot add member to organization'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
janedoe_client = APIClient()
authenticate_jwt(janedoe_creds, janedoe_client)
url = reverse(self.create_list_view_name, kwargs={'org_slug': johndoe_org.slug})
payload = {'user_id': self.batman_user.id}
response = janedoe_client.post(url, payload, format='json')
self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code)
data = response.data
self.assertNotIn('members', data)
def test_delete_by_admin_succeeds(self):
'''Tests that an admin can delete / remove a member from a organization'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
johndoe_org.members.add(self.batman_user)
project1 = Project.objects.create(
name='Org 1 Project 1',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
project2 = Project.objects.create(
name='Org 1 Project 2',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project1
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project2
)
admin_client = APIClient()
authenticate_jwt(admin_creds, admin_client)
url = reverse(self.delete_view_name, kwargs={'org_slug': johndoe_org.slug, 'pk': self.batman_user.id})
response = admin_client.delete(url, format='json')
self.assertEqual(status.HTTP_204_NO_CONTENT, response.status_code)
johndoe_org.refresh_from_db()
with self.assertRaises(ObjectDoesNotExist):
johndoe_org.members.get(id=self.batman_user.id)
with self.assertRaises(ObjectDoesNotExist):
ProjectContributor.objects.get(user=self.batman_user, project=project1)
with self.assertRaises(ObjectDoesNotExist):
ProjectContributor.objects.get(user=self.batman_user, project=project2)
def test_delete_by_org_contact_succeeds(self):
'''Tests that an org contact can delete a member from their org'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
johndoe_org.members.add(self.batman_user)
project1 = Project.objects.create(
name='Org 1 Project 1',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
project2 = Project.objects.create(
name='Org 1 Project 2',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project1
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project2
)
johndoe_client = APIClient()
authenticate_jwt(johndoe_creds, johndoe_client)
url = reverse(self.delete_view_name, kwargs={'org_slug': johndoe_org.slug, 'pk': self.batman_user.id})
response = johndoe_client.delete(url, format='json')
self.assertEqual(status.HTTP_204_NO_CONTENT, response.status_code)
johndoe_org.refresh_from_db()
with self.assertRaises(ObjectDoesNotExist):
johndoe_org.members.get(id=self.batman_user.id)
with self.assertRaises(ObjectDoesNotExist):
ProjectContributor.objects.get(user=self.batman_user, project=project1)
with self.assertRaises(ObjectDoesNotExist):
ProjectContributor.objects.get(user=self.batman_user, project=project2)
def test_delete_by_non_admin_non_org_contact_fails(self):
'''Tests that a non-admin / non-org-contact cannot delete an org's member'''
johndoe_org = create_organization('Org 1', self.johndoe_user)
johndoe_org.members.add(self.batman_user)
project1 = Project.objects.create(
name='Org 1 Project 1',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
project2 = Project.objects.create(
name='Org 1 Project 2',
description='ABC ...',
creator=self.johndoe_user,
organization=johndoe_org
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project1
)
ProjectContributor.objects.create(
user=self.batman_user,
project=project2
)
janedoe_client = APIClient()
authenticate_jwt(janedoe_creds, janedoe_client)
url = reverse(self.delete_view_name, kwargs={'org_slug': johndoe_org.slug, 'pk': self.batman_user.id})
response = janedoe_client.delete(url, format='json')
self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code)
self.assertIsNotNone(johndoe_org.members.get(id=self.batman_user.id))
self.assertIsNotNone(ProjectContributor.objects.get(project=project1, user=self.batman_user))
self.assertIsNotNone(ProjectContributor.objects.get(project=project2, user=self.batman_user))
| 35.598214
| 110
| 0.669551
| 921
| 7,974
| 5.566775
| 0.120521
| 0.050712
| 0.065535
| 0.04213
| 0.843378
| 0.838697
| 0.8071
| 0.784084
| 0.770041
| 0.759704
| 0
| 0.009206
| 0.237146
| 7,974
| 223
| 111
| 35.757848
| 0.833635
| 0.059945
| 0
| 0.652174
| 0
| 0
| 0.047294
| 0.007637
| 0
| 0
| 0
| 0
| 0.124224
| 1
| 0.043478
| false
| 0
| 0.049689
| 0
| 0.111801
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
45e329d26af802bd699ccbf5f9545ba39795d7f1
| 115
|
py
|
Python
|
capitulo6/exe1.py
|
Maciel-Ifac/Curso-Python
|
d29f0752eb84bdb64c0b75b53c5674d4551839a6
|
[
"Apache-2.0"
] | null | null | null |
capitulo6/exe1.py
|
Maciel-Ifac/Curso-Python
|
d29f0752eb84bdb64c0b75b53c5674d4551839a6
|
[
"Apache-2.0"
] | null | null | null |
capitulo6/exe1.py
|
Maciel-Ifac/Curso-Python
|
d29f0752eb84bdb64c0b75b53c5674d4551839a6
|
[
"Apache-2.0"
] | null | null | null |
import numpy as np
def polar_to_comp(r,phi):
return r*(np.exp(complex(0,1)*phi))
print(polar_to_comp(10,10))
| 19.166667
| 39
| 0.704348
| 24
| 115
| 3.208333
| 0.708333
| 0.181818
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.06
| 0.130435
| 115
| 6
| 40
| 19.166667
| 0.71
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.25
| 0.25
| 0.75
| 0.25
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
afeb82e374ba64dc7021a78b0d7dd784370a67ed
| 44
|
py
|
Python
|
supreme/noise/__init__.py
|
KirillDZR/supreme
|
c296722599363bd0cbcce6877bd9de9b066cb74b
|
[
"BSD-3-Clause"
] | 95
|
2015-01-17T09:48:20.000Z
|
2021-11-07T16:02:38.000Z
|
supreme/noise/__init__.py
|
KirillDZR/supreme
|
c296722599363bd0cbcce6877bd9de9b066cb74b
|
[
"BSD-3-Clause"
] | 4
|
2015-10-23T15:13:34.000Z
|
2019-09-23T22:47:10.000Z
|
supreme/noise/__init__.py
|
KirillDZR/supreme
|
c296722599363bd0cbcce6877bd9de9b066cb74b
|
[
"BSD-3-Clause"
] | 34
|
2015-02-22T20:54:40.000Z
|
2022-02-27T13:39:32.000Z
|
from var_est import *
from wavelet import *
| 14.666667
| 21
| 0.772727
| 7
| 44
| 4.714286
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 44
| 2
| 22
| 22
| 0.916667
| 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
| 1
| 0
|
0
| 6
|
afff3275dc30d50f6339f0d01bf6c2a87fabf425
| 223
|
py
|
Python
|
blasy/tests/data/foo_with_init.py
|
bristi/blasy
|
40f775a27093abb1a061c206ecf41c96fc4d5d69
|
[
"MIT"
] | 1
|
2018-04-02T15:45:06.000Z
|
2018-04-02T15:45:06.000Z
|
blasy/tests/data/foo_with_init.py
|
bristi/blasy
|
40f775a27093abb1a061c206ecf41c96fc4d5d69
|
[
"MIT"
] | 1
|
2017-08-15T19:30:34.000Z
|
2017-08-15T19:30:34.000Z
|
blasy/tests/data/foo_with_init.py
|
bristi/blasy
|
40f775a27093abb1a061c206ecf41c96fc4d5d69
|
[
"MIT"
] | 1
|
2021-03-05T04:22:29.000Z
|
2021-03-05T04:22:29.000Z
|
from blasy.blasy import IPlugin
class Foo(IPlugin):
def __init__(self, favourite_colour="Yellow"):
self.i_live = 1
self.favourite_colour = favourite_colour
def say_hi(self):
return "Hi!"
| 18.583333
| 50
| 0.659193
| 29
| 223
| 4.758621
| 0.62069
| 0.326087
| 0.275362
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005952
| 0.246637
| 223
| 12
| 51
| 18.583333
| 0.815476
| 0
| 0
| 0
| 0
| 0
| 0.040179
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.142857
| 0.142857
| 0.714286
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
b3154a4e7e7d23ba1e27714af75098fe8450a2b0
| 50
|
py
|
Python
|
p2016_05_28_python_path_find/turbo.py
|
zhyq0826/blog-code
|
4369d653dea4a7a054dc796d14faea727973258f
|
[
"MIT"
] | 1
|
2018-07-07T14:35:55.000Z
|
2018-07-07T14:35:55.000Z
|
p2016_05_28_python_path_find/turbo.py
|
zhyq0826/blog-code
|
4369d653dea4a7a054dc796d14faea727973258f
|
[
"MIT"
] | null | null | null |
p2016_05_28_python_path_find/turbo.py
|
zhyq0826/blog-code
|
4369d653dea4a7a054dc796d14faea727973258f
|
[
"MIT"
] | null | null | null |
print 'I am turbo in p2016_05_28_python_path_find'
| 50
| 50
| 0.86
| 11
| 50
| 3.454545
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.177778
| 0.1
| 50
| 1
| 50
| 50
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0.823529
| 0.54902
| 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
| 1
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
b31a9fd5b4302202b74a11f1f5b721ad7890d6ec
| 100
|
py
|
Python
|
ilf/fuzzers/ga/__init__.py
|
RainOfPhone/ilf
|
83c34c2147f05a26fde81f94acb58fe5719b05a2
|
[
"Apache-2.0"
] | 2
|
2022-02-24T10:05:54.000Z
|
2022-03-21T06:29:56.000Z
|
ilf/fuzzers/ga/__init__.py
|
RainOfPhone/ilf
|
83c34c2147f05a26fde81f94acb58fe5719b05a2
|
[
"Apache-2.0"
] | null | null | null |
ilf/fuzzers/ga/__init__.py
|
RainOfPhone/ilf
|
83c34c2147f05a26fde81f94acb58fe5719b05a2
|
[
"Apache-2.0"
] | null | null | null |
from .obs_ga import ObsGA
from .environment_ga import EnvironmentGA
from .policy_ga import PolicyGA
| 25
| 41
| 0.85
| 15
| 100
| 5.466667
| 0.6
| 0.292683
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 100
| 3
| 42
| 33.333333
| 0.931818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
b326c2db0d56457949c6d402ab1eb0ee023e56dd
| 26,953
|
py
|
Python
|
recommender.py
|
gmenchetti/Hybrid-Recommender
|
da0ec9c515252f9853ac14b55dfc7685d2cd3a86
|
[
"MIT"
] | 2
|
2018-02-18T18:10:55.000Z
|
2018-03-17T18:55:49.000Z
|
recommender.py
|
gmenchetti/Hybrid-Recommender
|
da0ec9c515252f9853ac14b55dfc7685d2cd3a86
|
[
"MIT"
] | null | null | null |
recommender.py
|
gmenchetti/Hybrid-Recommender
|
da0ec9c515252f9853ac14b55dfc7685d2cd3a86
|
[
"MIT"
] | null | null | null |
from utils import Utilities as utils
from scipy.sparse.linalg import svds
from abc import ABC, abstractmethod
from scipy.sparse import linalg
from scipy import sparse
import numpy as np
from tqdm import *
##
## @brief Class for recommender.
##
class Recommender(ABC):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
##
@abstractmethod
def __init__(self, user_rating_matrix):
self.urm = user_rating_matrix
##
## @brief Abstract method providing an interface for the computation of the similarity matrix
##
## @param self The object
## @param kwargs Eventual optional arguments
##
## @return The similarity matrix.
##
@abstractmethod
def _compute_similarity_matrix(self, **kwargs):
pass
##
## @brief Abstract method providing the interface for fitting the model
##
## @param self The object
## @param kwargs Eventual optional arguments
##
## @return None
##
@abstractmethod
def fit(self, **kwargs):
pass
##
## @brief Abstract method providing the interface for the prediction
##
## @param self The object
## @param kwargs Eventual optional arguments
##
## @return The preficted ratings
##
@abstractmethod
def predict(self, target):
pass
##
## @brief Class for basic content based filtering.
##
class BasicContentBasedFiltering(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
##
def __init__(self, user_rating_matrix):
super().__init__(user_rating_matrix)
self.sm = None
##
## @brief Fits the model computing the similarity between items according to their features
##
## @param self The object
## @param item_content_matrix The item content matrix
## @param k_nearest_neighbours The k nearest neighbours
##
## @return None
##
def fit(self, item_content_matrix, k_nearest_neighbours):
self.sm = self._compute_similarity_matrix(item_content_matrix, k_nearest_neighbours)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param icm The icm
## @param knn The knn
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, icm, knn):
s_tmp = []
n_items = icm.shape[0]
m = icm.tocsr()
m_t = m.T.tocsr()
for i in tqdm(range(n_items)):
mat = m[i, :].dot(m_t)
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target The user vector of interactions
## @param remove_known Whether to remove known interactions
##
## @return The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for basic collaborative filtering.
##
class BasicCollaborativeFiltering(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
##
def __init__(self, user_rating_matrix):
super().__init__(user_rating_matrix)
self.sm = None
##
## @brief Fits the model computing the similarity between items according to their interactions
##
## @param self The object
## @param k_nearest_neighbours The number of nearest neighbours
##
## @return None
##
def fit(self, k_nearest_neighbours):
self.sm = self._compute_similarity_matrix(k_nearest_neighbours)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param knn The number of nearest neighbours
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, knn):
ucm = self.urm.T
s_tmp = []
n_items = ucm.shape[0]
m = ucm.tocsr()
m_t = m.T.tocsr()
for i in tqdm(range(n_items)):
mat = m[i, :].dot(m_t)
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for svd matrix factorization.
##
class SVDMatrixFactorization(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param n_factors (Integer) The number of factor to use in the decomposition
##
def __init__(self, user_rating_matrix, n_factors):
super().__init__(user_rating_matrix)
self.n_factors = n_factors
self.sm = None
##
## @brief Computes the SVD decomposition of the user rating matrix
##
## @param self The object
##
## @return (numpy array) The item factors of the decomposed matrix
##
def SVD(self):
_, _, v_t = svds(urm.tocsc(), self.n_factors, return_singular_vectors='vh')
return v_t
##
## @brief Fits the model computing the similarity between items computed with the item latent factors of the SVD factorization
##
## @param self The object
## @param k_nearest_neighbours (Integer) The number of nearest neighbours
## @param precomputed_similarity (numpy array) The precomputed similarity matrix computed by the dot product of the item factors
##
## @return None
##
def fit(self, k_nearest_neighbours, precomputed_similarity=None):
if precomputed_similarity is None:
self.sm = self._compute_similarity_matrix(k_nearest_neighbours, n_factors, lam, n_iterations)
else:
self.sm = precomputed_similarity
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param knn (Integer) The number of nearest neighbours
## @param n_factors (Integer) The number of factor to use in the decomposition
##
## @return (numpy array) The similarity matrix.
##
def _compute_similarity_matrix(self, knn, n_factors):
s_tmp = []
item_factors = self.ALS(self.urm, n_factors, lam, n_iterations)
n_items = item_factors.shape[0]
item_factors = sparse.csr_matrix(item_factors)
item_factors_T = item_factors.T
for i in tqdm(range(n_items)):
mat = item_factors[i, :].dot(item_factors_T)
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for als matrix factorization.
##
class ALSMatrixFactorization(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param n_factors (Integer) The number of factor to use in the decomposition
## @param regularization (Float) The regularization factor for the
## @param n_iterations (Integer) The number of iterations of the ALS algorithm
##
def __init__(self, user_rating_matrix, n_factors, regularization, n_iterations):
super().__init__(user_rating_matrix)
self.n_factors = n_factors
self.lam = regularization
self.n_iterations = n_iterations
self.sm = None
##
## @brief Computes the ALS decomposition of the user rating matrix
##
## @param self The object
##
## @return (numpy array) The item factors of the decomposed matrix
##
def ALS(self):
m, n = self.urm.shape
Y = np.mat(np.random.rand(self.n_factors, n))
for i in range(self.n_iterations):
X = np.mat(linalg.spsolve((Y * Y.T) + self.lam * sparse.eye(self.n_factors), (Y * self.urm.T)).T)
Y = np.mat(linalg.spsolve((X.T * X) + self.lam * sparse.eye(self.n_factors), (X.T * self.urm)))
return np.array(X), np.array(Y.T)
##
## @brief Fits the model computing the similarity between items computed with the item latent factors of the ALS factorization
##
## @param self The object
## @param k_nearest_neighbours (Integer) The number of nearest neighbours
## @param precomputed_similarity (numpy array) The precomputed similarity matrix computed by the dot product of the item factors
##
## @return None
##
def fit(self, k_nearest_neighbours, precomputed_similarity=None):
if precomputed_similarity is None:
self.sm = self._compute_similarity_matrix(k_nearest_neighbours)
else:
self.sm = precomputed_similarity
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param knn The knn
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, knn):
s_tmp = []
user_factors, item_factors = self.ALS()
n_items = item_factors.shape[0]
item_factors = sparse.csr_matrix(item_factors)
item_factors_T = item_factors.T
for i in tqdm(range(n_items)):
mat = item_factors[i, :].dot(item_factors_T)
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for slimbpr.
##
class SLIMBPR(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param learning_rate (Float) The learning rate
## @param epochs (Integer) The number of epochs of training
## @param pir (Float) The positive item regularization
## @param nir (Float) The negative item regularization
##
def __init__(self, user_rating_matrix, learning_rate, epochs, pir, nir):
super().__init__(user_rating_matrix)
self.epochs = epochs
self.n_users = self.urm.shape[0]
self.n_items = self.urm.shape[1]
self.learning_rate = learning_rate
self.positive_item_regularization = pir
self.negative_item_regularization = nir
self.sm = np.zeros((self.n_items, self.n_items))
##
## @brief Samples a random triplet from the user rating matrix
##
## @param self The object
##
## @return The index of the sampled user, the index of a positive interaction, the index of a negative interaction
##
def sample(self):
user_index = np.random.choice(self.n_users)
interactions = self.urm[user_index].indices
interaction_index = np.random.choice(interactions)
selected = False
while not selected:
negative_interaction_index = np.random.randint(0, self.n_items)
if negative_interaction_index not in interactions: selected = True
return user_index, interaction_index, negative_interaction_index
##
## @brief updates the similarity matrix once for each positive interaction
##
## @param self The object
##
## @return None
##
def iteration(self):
num_positive_iteractions = int(self.urm.nnz)
for _ in tqdm(range(num_positive_iteractions)):
user_index, positive_item_id, negative_item_id = self.sample()
user_interactions = self.urm[user_index, :].indices
x_i = self.sm[positive_item_id, user_interactions].sum()
x_j = self.sm[negative_item_id, user_interactions].sum()
z = 1. / (1. + np.exp(x_i - x_j))
for v in user_interactions:
d = z - self.positive_item_regularization * x_i
self.sm[positive_item_id, v] += self.learning_rate * d
d = z - self.negative_item_regularization * x_j
self.sm[negative_item_id, v] -= self.learning_rate * d
self.sm[positive_item_id, positive_item_id] = 0
self.sm[negative_item_id, negative_item_id] = 0
##
## @brief Fits the model computing the similarity between items that maximises
##
## @param self The object
## @param k_nearest_neighbours (Integer) The number of nearest neighbours
##
## @return None
##
def fit(self, k_nearest_neighbours):
self._compute_similarity_matrix(k_nearest_neighbours)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param knn The knn
##
## @return None
##
def _compute_similarity_matrix(self, knn):
for e in range(self.epochs):
self.iteration()
s_tmp = []
for i in tqdm(range(self.n_items)):
mat = self.sm[i, :]
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
self.sm = s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for cbfcb hybrid.
##
class CBF_CB_Hybrid(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param cbf_weight (Float) The cbf weight
## @param cf_weight (Float) The cf weight
##
def __init__(self, user_rating_matrix, cbf_weight, cf_weight):
super().__init__(user_rating_matrix)
self.cbf_weight = cbf_weight
self.cf_weight = cf_weight
self.sm = None
##
## @brief Fits the model computing the similarity between items according the weighted average of
## the similarities computed according to the interactions and the similarities computed according to their features
##
## @param self The object
## @param k_nearest_neighbours The number of nearest neighbours
##
## @return None
##
def fit(self, item_content_matrix, k_nearest_neighbours):
self.sm = self._compute_similarity_matrix(item_content_matrix, k_nearest_neighbours)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param icm The icm
## @param knn The knn
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, icm, knn):
s_tmp = []
ucm = self.urm.T
n_items = icm.shape[0]
m1 = icm.tocsr()
m1_t = m1.T.tocsr()
m2 = ucm.tocsr()
m2_t = m2.T.tocsr()
for i in tqdm(range(n_items)):
cfb = m1[i, :].dot(m1_t)
cf = m2[i, :].dot(m2_t)
mat = self.cbf_weight*cfb + self.cf_weight*cf
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
ratings = (target * self.sm).toarray().flatten()
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for CBF CF SLIM BPR hybrid.
##
class CBF_CF_SLIMBPR_Hybrid(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param slim_lr (Float) Slim learning rate
## @param slim_epochs (Integer) Number of slim epochs
## @param slim_pir (Float) Slim positive item regularization
## @param slim_nir (Float) Slim negative item regularization
## @param slim_knn (Integer) Number of nearest neighbours of the slim similarity
## @param cbf_weight (Float) The cbf weight
## @param cf_weight (Float) The cf weight
## @param slim_weight (Float) The slim weight
##
def __init__(self, user_rating_matrix, slim_lr, slim_epochs, slim_pir, slim_nir,
slim_knn, cbf_weight, cf_weight, slim_weight):
super().__init__(user_rating_matrix)
self.cbf_weight = cbf_weight
self.cf_weight = cf_weight
self.slim_weight = slim_weight
self.slim_knn = slim_knn
self.slim_bpr = SLIMBPR(self.urm, slim_lr, slim_epochs, slim_pir, slim_nir)
self.sm = None
##
## @brief Fits the model computing the similarity between items according the weighted average of
## the similarities computed according to the interactions and the similarities computed
## according to their features also fits the slim bpr model
##
## @param self The object
## @param item_content_matrix (numpy array) The item content matrix
## @param k_nearest_neighbours (Integer) The number of nearest neighbours
##
## @return { description_of_the_return_value }
##
def fit(self, item_content_matrix, k_nearest_neighbours):
self.sm = self._compute_similarity_matrix(item_content_matrix, k_nearest_neighbours)
self.slim_bpr.fit(self.slim_knn)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param icm The icm
## @param knn The knn
## @param als The als
## @param svd The svd
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, icm, knn):
s_tmp = []
ucm = self.urm.T
n_items = icm.shape[0]
m1 = icm.tocsr()
m1_t = m1.T.tocsr()
m2 = ucm.tocsr()
m2_t = m2.T.tocsr()
for i in tqdm(range(n_items)):
cfb_i = m1[i, :].dot(m1_t)
cf_i = m2[i, :].dot(m2_t)
mat = self.cbf_weight*cfb_i + self.cf_weight*cf_i
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target combining the slim ratings and the similairty hybrid ratings
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
slim_ratings = self.slim_bpr.predict(target, False)
hybrid_ratings = (target * self.sm).toarray().flatten()
ratings = self.slim_weight * slim_ratings + (1. - self.slim_weight) * hybrid_ratings
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
##
## @brief Class for full hybrid.
##
class MixedHybrid(Recommender):
##
## @brief Constructs the object.
##
## @param self The object
## @param user_rating_matrix (numpy array) The user rating matrix
## @param slim_lr (Float) Slim learning rate
## @param slim_epochs (Integer) Number of slim epochs
## @param slim_pir (Float) Slim positive item regularization
## @param slim_nir (Float) Slim negative item regularization
## @param slim_knn (Integer) Number of nearest neighbours of the slim similarity
## @param cbf_weight (Float) The cbf weight
## @param cf_weight (Float) The cf weight
## @param als_weight (Float) The als weight
## @param svd_weight (Float) The svd weight
## @param slim_weight (Float) The slim weight
##
def __init__(self, user_rating_matrix, slim_lr, slim_epochs, slim_pir, slim_nir,
slim_knn, cbf_weight, cf_weight, als_weight, svd_weight, slim_weight):
super().__init__(user_rating_matrix)
self.cbf_weight = cbf_weight
self.cf_weight = cf_weight
self.als_weight = als_weight
self.svd_weight = svd_weight
self.slim_weight = slim_weight
self.slim_knn = slim_knn
self.slim_bpr = SLIMBPR(self.urm, slim_lr, slim_epochs, slim_pir, slim_nir)
self.sm = None
##
## @brief Fits the model computing the similarity between items according the weighted average of
## the similarities computed according to the interactions, the similarities computed according to their features
## and the similarities computed with factorization, also fits the slim bpr model
##
## @param self The object
## @param item_content_matrix (numpy array) The item content matrix
## @param k_nearest_neighbours (Integer) The number of nearest neighbours
## @param computed_als (numpy array) The precomputed als similarity matrix
## @param computed_svd (numpy array) The precomputed svd similarity matrix
##
## @return { description_of_the_return_value }
##
def fit(self, item_content_matrix, k_nearest_neighbours, computed_als, computed_svd):
self.sm = self._compute_similarity_matrix(item_content_matrix, k_nearest_neighbours, computed_als, computed_svd)
self.slim_bpr.fit(self.slim_knn)
##
## @brief Calculates the similarity matrix.
##
## @param self The object
## @param icm The icm
## @param knn The knn
## @param als The als
## @param svd The svd
##
## @return The similarity matrix.
##
def _compute_similarity_matrix(self, icm, knn, als, svd):
s_tmp = []
ucm = self.urm.T
n_items = icm.shape[0]
m1 = icm.tocsr()
m1_t = m1.T.tocsr()
m2 = ucm.tocsr()
m2_t = m2.T.tocsr()
for i in tqdm(range(n_items)):
cfb_i = m1[i, :].dot(m1_t)
cf_i = m2[i, :].dot(m2_t)
als_i = als[i, :]
svd_i = svd[i, :]
mat = self.cbf_weight*cfb_i + self.cf_weight*cf_i + self.als_weight*als_i + self.svd_weight*svd_i
s_tmp.append(utils.knn(mat, knn))
s = sparse.vstack(s_tmp, format='csr')
s.setdiag(0)
return s
##
## @brief Predicts the rating for the given target combining the slim ratings and the similairty hybrid ratings
##
## @param self The object
## @param target (numpy array) The user vector of interactions
## @param remove_known (Boolean) Whether to remove known interactions
##
## @return (numpy array) The predicted ratings
##
def predict(self, target, remove_known=True):
slim_ratings = self.slim_bpr.predict(target, False)
hybrid_ratings = (target * self.sm).toarray().flatten()
ratings = self.slim_weight * slim_ratings + (1. - self.slim_weight) * hybrid_ratings
if remove_known:
known_items = np.nonzero(target)[1]
ratings[known_items] = 0
return ratings
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| 0
| 0
| 0
| 0
| 0
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0
| 6
|
b366173f4ef181ab44788e0bf6d4d6a6157645db
| 38,896
|
py
|
Python
|
analysis/utils/analysis_graphs.py
|
cogtoolslab/projection_block_construction
|
faae126fbb7da45f2c5f4df05a2ff606a2b86d59
|
[
"MIT"
] | null | null | null |
analysis/utils/analysis_graphs.py
|
cogtoolslab/projection_block_construction
|
faae126fbb7da45f2c5f4df05a2ff606a2b86d59
|
[
"MIT"
] | 1
|
2022-02-19T00:04:14.000Z
|
2022-02-19T00:04:14.000Z
|
analysis/utils/analysis_graphs.py
|
cogtoolslab/tools_block_construction
|
e573b28b2a53d27268414dab17b9be4dda257230
|
[
"MIT"
] | null | null | null |
"""This file contains code for graphs. These expect to be passed a dataframe output of experiment_runner (not the run dataframe, but the dataframe containing rows with agents and so) with a preselection already made."""
from operator import contains
from textwrap import wrap
from matplotlib.pyplot import legend
from analysis.utils.analysis_helper import *
import textwrap
import analysis.utils.trajectory as trajectory
#Color constants for easy theming
TOOL_COLOR = 'coral'
NO_TOOL_COLOR = 'purple'
ALL_COLOR = 'blue' #not currently used
WIN_COLOR = 'green'
FAIL_COLOR = 'orange'
PADDING = 20 #How long should runs be padded to ensure no missing value for early termination?
#per agent
def mean_win_per_agent(df):
df = final_rows(df)
agents = df['agent_attributes'].unique()
scores = [mean_win(df[df['agent_attributes']==a]) for a in agents]
plt.bar(np.arange(len(scores)),scores,align='center',label=agent_labels(agents,df))
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylim(0,1)
plt.ylabel("Proportion of runs with perfect reconstruction")
plt.title("Perfect reconstruction")
plt.show()
def mean_failure_reason_per_agent(df,fast_fail=False):
agents = df['agent_attributes'].unique()
df = final_rows(df)
#Full
scores = [mean_failure_reason(df[(df['agent_attributes']==a) & (df['world_status'].isin(['Fail','Ongoing']))],"Full") for a in agents]
plt.bar(np.arange(len(scores))+0,scores,align='center',label="Full",width=0.15)
#Unstable
scores = [mean_failure_reason(df[(df['agent_attributes']==a) & (df['world_status'].isin(['Fail','Ongoing']))],"Unstable") for a in agents]
plt.bar(np.arange(len(scores))+.15,scores,align='center',label="Unstable",color=WIN_COLOR,width=0.15)
#Ongoing
scores = [mean_failure_reason(df[(df['agent_attributes']==a) & (df['world_status'].isin(['Fail','Ongoing']))],"None") for a in agents]
plt.bar(np.arange(len(scores))+.3,scores,align='center',label="Did not finish",color='yellow',width=0.15)
if fast_fail:
#Outside
scores = [mean_failure_reason(df[df['agent_attributes']==a],"Outside") for a in agents]
plt.bar(np.arange(len(scores))+.45,scores,align='center',label="Outside",color=FAIL_COLOR,width=0.15)
#Holes
scores = [mean_failure_reason(df[df['agent_attributes']==a],"Holes") for a in agents]
plt.bar(np.arange(len(scores))+.6,scores,align='center',label="Holes",color='red',width=0.15)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Proportion of failed runs with failure mode")
plt.ylim(0)
plt.title("Reasons for failure in runs without perfect reconstruction")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def avg_steps_to_end_per_agent(df):
agents = df['agent_attributes'].unique()
#all
results = [avg_steps_to_end(df[df['agent_attributes']==a]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [avg_steps_to_end(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [avg_steps_to_end(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Average number of steps")
plt.ylim(0)
plt.title("Average number of steps to end of run")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_score_per_agent(df,scoring_function=bw.F1score):
agents = df['agent_attributes'].unique()
#all
results = [mean_score(df[df['agent_attributes']==a],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [mean_peak_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [mean_peak_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Mean "+scoring_function.__name__+" at end")
plt.ylim(0)
plt.title("Mean end score: "+scoring_function.__name__)
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_peak_score_per_agent(df,scoring_function=bw.F1score):
agents = df['agent_attributes'].unique()
#all
results = [mean_peak_score(df[df['agent_attributes']==a],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [mean_peak_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [mean_peak_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Mean "+scoring_function.__name__+" at peak")
plt.ylim(0)
plt.title("Mean peak score: "+scoring_function.__name__)
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_num_subgoals_per_agent(df):
agents = df['agent_attributes'].unique()
#all
scores = [sum(~df[df['agent_attributes']==a]['decomposed_silhouette'].isna()) / len(df[df['agent_attributes']==a]['run_ID'].unique()) for a in agents]
plt.bar(np.arange(len(scores))+0,scores,align='center',label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
scores = [sum(~df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['decomposed_silhouette'].isna()) / len(df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['run_ID'].unique()) if len(df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['run_ID'].unique()) != 0 else 0 for a in agents]
plt.bar(np.arange(len(scores))+.2,scores,align='center',label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
scores = [sum(~df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['decomposed_silhouette'].isna()) / len(df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['run_ID'].unique()) if len(df[(df['agent_attributes']==a) & (df['run_ID'].isin(run_IDs))]['run_ID'].unique()) != 0 else 0 for a in agents]
plt.bar(np.arange(len(scores))+.4,scores,align='center',label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Mean number of actual subgoals used")
plt.ylim(0)
plt.title("Mean number of subgoals per run")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_avg_area_under_curve_to_peakF1_per_agent(df):
agents = df['agent_attributes'].unique()
scoring_function = bw.F1score
#all
results = [mean_avg_area_under_curve_to_peakF1(df[df['agent_attributes']==a],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [mean_avg_area_under_curve_to_peakF1(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [mean_avg_area_under_curve_to_peakF1(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Mean average F1 score")
plt.ylim(0)
plt.title("Mean average F1 score during run")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def graph_mean_F1_over_time_per_agent(df):
#plot mean F1,std over time for chosen world and over agents in one plot (with continuation)
agents = df['agent_attributes'].unique()
agent_names = agent_labels(agents,df)
for a,agent in enumerate(agents): #plot per agent
a_runs = get_runs(df[df['agent_attributes'] == agent])
#hacky color coding for tool/no tool
color = TOOL_COLOR if '|' in agent_names[a] else NO_TOOL_COLOR
run_scores = []
for i,row in enumerate(a_runs): #for each run of the agent
blockmaps = row['blockmap'] #get sequence of blockmaps
#calculate the score for each blockmap
scores = []
for bm in blockmaps:
#make a State to score
state = State(row['_world'].tail(1).item(),bm)
score = bw.F1score(state)
scores.append(score)
#append (pad) score with last value to xlim as a way of handling the early termination of trials
scores = [scores[i] if i < len(scores) else scores[-1] for i in range(PADDING+1)]
run_scores.append(scores)
#avg,std
avgs = np.mean(run_scores,axis=0)
stds = np.std(run_scores,axis=0)
#plot
# plt.plot(range(len(avgs)),avgs)
plt.errorbar(range(len(avgs)),avgs,stds,label=agent_names[a],color=color)
plt.xlim(0,PADDING)
plt.ylim(0,1)
plt.title('Mean F1 score over steps')
plt.ylabel("Mean F1 score")
plt.xlabel("Step")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def graph_avg_blocksize_over_time_per_agent(df):
agents = df['agent_attributes'].unique()
agent_names = agent_labels(agents,df)
for a,agent in enumerate(agents): #plot per agent
a_runs = get_runs(df[df['agent_attributes'] == agent])
run_scores = []
for run in a_runs: #for each run of the agent
scores = list(run['action_block_width']*run['action_block_height'])
#append (pad) score with last value to xlim as a way of handling the early termination of trials
scores = [scores[i] if i < len(scores) else np.nan for i in range(PADDING+1)]
run_scores.append(scores)
#avg,std
run_scores = np.array(run_scores)
avgs = np.nanmean(run_scores,axis=0)
stds = np.nanstd(run_scores,axis=0)
#plot
# plt.plot(range(len(avgs)),avgs)
plt.errorbar(range(len(avgs)),avgs,stds,label=agent_names[a])
plt.xlim(0,PADDING)
# plt.ylim(0,1)
plt.title('Average block size over steps')
plt.ylabel("Block size")
plt.xlabel("Step")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_touching_last_block_per_agent(df):
agents = df['agent_attributes'].unique()
#all
results = [touching_last_block_score(df[df['agent_attributes']==a]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [touching_last_block_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [touching_last_block_score(df[(df['run_ID'].isin(run_IDs)) & (df['agent_attributes']==a)]) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Proportion of block placements touching last placed block")
plt.ylim(0,1)
plt.title("Proportion of local placements")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_pairwise_raw_euclidean_distance_between_runs(df):
agents = df['agent_attributes'].unique()
worlds =df['world'].unique()
#all
results = [[pairwise_raw_euclidean_distance_between_blocks_across_all_runs(df[(df['agent_attributes']==a) & (df['world'] == w)]) for w in worlds] for a in agents]
scores = [statistics.mean([l for aw in a for l in aw]) for a in results]
stds = [statistics.stdev([l for aw in a for l in aw]) for a in results]
plt.bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
results = [[pairwise_raw_euclidean_distance_between_blocks_across_all_runs(df[(df['run_ID'].isin(run_IDs)) & (df['world'] == w) & (df['agent_attributes'] == a)]) for w in worlds] for a in agents] #if statement is to prevent empty lists
scores = [statistics.mean([l for aw in a for l in aw]) if np.nansum([len(r) for r in a]) else 0 for a in results]
stds = [statistics.stdev([l for aw in a for l in aw]) if np.nansum([len(r) for r in a]) > 1 else 0 for a in results]
plt.bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
results = [[pairwise_raw_euclidean_distance_between_blocks_across_all_runs(df[(df['run_ID'].isin(run_IDs)) & (df['world'] == w) & (df['agent_attributes'] == a)]) for w in worlds] for a in agents]
scores = [statistics.mean([l for aw in a for l in aw]) for a in results]
stds = [statistics.stdev([l for aw in a for l in aw]) for a in results]
plt.bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylabel("Mean Euclidean distance")
plt.title("Average pairwise Euclidean distance between runs on same silhouette per agent")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def total_avg_states_evaluated_per_agent(df):
agents = df['agent_attributes'].unique()
#all
scores = [[np.nansum(run['states_evaluated']) for run in get_runs(df[ df['agent_attributes'] == a ])] for a in agents]
means = [statistics.mean(r) for r in scores]
stds = [statistics.stdev(r) for r in scores]
plt.bar(np.arange(len(scores))+0,means,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = df[df['world_status'] == 'Win']['run_ID'].unique()
scores = [[np.nansum(run['states_evaluated']) for run in get_runs(df[ (df['agent_attributes'] == a ) & (df['run_ID'].isin(run_IDs))])] for a in agents]
means = [statistics.mean(r) if r != [] else 0 for r in scores]
stds = [statistics.stdev(r) if r != [] else 0 for r in scores]
plt.bar(np.arange(len(scores))+.2,means,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = df[df['world_status'] == 'Fail']['run_ID'].unique()
scores = [[np.nansum(run['states_evaluated']) for run in get_runs(df[ (df['agent_attributes'] == a ) & (df['run_ID'].isin(run_IDs))])] for a in agents]
means = [statistics.mean(r) if r != [] else 0 for r in scores]
stds = [statistics.stdev(r) if r != [] else 0 for r in scores]
plt.bar(np.arange(len(scores))+.4,means,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
plt.yscale('log')
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.ylim(bottom=1)
plt.ylabel("Average planning cost (log)")
plt.title("Average planning cost (number of states evaluated) per run")
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
#on worlds
def illustrate_worlds(df):
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
plt.figure(figsize=(20,20))
for i,(name,world_obj) in enumerate(list(unique_world_obj.items())):
plt.subplot(round(math.sqrt(len(unique_world_obj))),round(math.sqrt(len(unique_world_obj)))+1,i+1)
plt.imshow(world_obj.silhouette)
plt.title(name)
plt.xticks([])
plt.yticks([])
plt.show()
def mean_win_per_agent_over_worlds(df):
df = final_rows(df)
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
plt.rcParams.update({'font.size': 22})
fig, axes = plt.subplots(len(unique_world_names),2,figsize=(10,20))
fig.suptitle("Perfect reconstruction per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
#from mean_win_per_agent: plot
scores = [mean_win(_df[_df['agent_attributes']==a]) for a in agents]
axes[i,1].bar(np.arange(len(scores)),scores,align='center',label=agent_labels(agents,df))
# axes[i,1].set_xticks(np.arange(len(scores)),agent_labels(agents,df))
axes[i,1].set_xticks([])
axes[i,1].set_ylim(0,1)
# axes[i,1].set_ylabel("Proportion of runs with perfect reconstruction")
# axes[i,1].set_title("Perfect reconstruction on "+world_name)
#only show agent labels at the bottom
axes[len(unique_world_obj)-1,1].set_xticks(np.arange(len(scores)))
axes[len(unique_world_obj)-1,1].set_xticklabels(agent_labels(agents,df))
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.show()
def mean_peak_F1_per_agent_over_worlds(df):
scoring_function = bw.F1score
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
plt.rcParams.update({'font.size': 22})
fig, axes = plt.subplots(len(unique_world_names),2,figsize=(10,20))
fig.suptitle("Perfect reconstruction per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
#from mean_peak_score_per_agent: plot
#all
results = [mean_peak_score(_df[_df['agent_attributes']==a],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
axes[i,1].bar(np.arange(len(scores))+0,scores,align='center',yerr=stds,label="All",width=0.2)
#win
run_IDs = _df[_df['world_status'] == 'Win']['run_ID'].unique()
results = [mean_peak_score(_df[(_df['run_ID'].isin(run_IDs)) & (_df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
axes[i,1].bar(np.arange(len(scores))+.2,scores,align='center',yerr=stds,label="Win",color=WIN_COLOR,width=0.2)
#fail
run_IDs = _df[_df['world_status'] == 'Fail']['run_ID'].unique()
results = [mean_peak_score(_df[(_df['run_ID'].isin(run_IDs)) & (_df['agent_attributes']==a)],scoring_function) for a in agents]
scores = [score for score,std in results]
stds = [std for score,std in results]
axes[i,1].bar(np.arange(len(scores))+.4,scores,align='center',yerr=stds,label="Fail",color=FAIL_COLOR,width=0.2)
axes[i,1].set_xticks([])
axes[i,1].set_ylim(0,1)
#only show agent labels at the bottom
axes[len(unique_world_obj)-1,1].set_xticks(np.arange(len(scores)))
axes[len(unique_world_obj)-1,1].set_xticklabels(agent_labels(agents,df))
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def mean_failure_reason_per_agent_over_worlds(df,fast_fail=False):
df = final_rows(df)
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
plt.rcParams.update({'font.size': 22})
fig, axes = plt.subplots(len(unique_world_names),2,figsize=(20,20))
fig.suptitle("Perfect reconstruction per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
#from mean_peak_score_per_agent: plot
#full
scores = [mean_failure_reason(_df[(_df['agent_attributes']==a) & (_df['world_status'].isin(['Fail','Ongoing']))],"Full") for a in agents]
axes[i,1].bar(np.arange(len(scores))+0,scores,align='center',label="Full",width=0.15)
#Unstable
scores = [mean_failure_reason(_df[(_df['agent_attributes']==a) & (_df['world_status'].isin(['Fail','Ongoing']))],"Unstable") for a in agents]
axes[i,1].bar(np.arange(len(scores))+.15,scores,align='center',label="Unstable",color=WIN_COLOR,width=0.15)
#Ongoing
scores = [mean_failure_reason(_df[(_df['agent_attributes']==a) & (_df['world_status'].isin(['Fail','Ongoing']))],"None") for a in agents]
axes[i,1].bar(np.arange(len(scores))+.3,scores,align='center',label="Did not finish",color='yellow',width=0.15)
if fast_fail:
#Outside
scores = [mean_failure_reason(_df[_df['agent_attributes']==a],"Outside") for a in agents]
axes[i,1].bar(np.arange(len(scores))+.45,scores,align='center',label="Outside",color=FAIL_COLOR,width=0.15)
#Holes
scores = [mean_failure_reason(_df[_df['agent_attributes']==a],"Holes") for a in agents]
axes[i,1].bar(np.arange(len(scores))+.6,scores,align='center',label="Holes",color='red',width=0.15)
axes[i,1].set_ylim(0)
axes[i,1].set_xticks([])
#only show agent labels at the bottom
axes[len(unique_world_obj)-1,1].set_xticks(np.arange(len(scores)))
axes[len(unique_world_obj)-1,1].set_xticklabels(agent_labels(agents,df))
plt.xticks(np.arange(len(scores)),agent_labels(agents,df),rotation=45,ha='right')
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.show()
def heatmaps_at_peak_per_agent_over_world(df):
df = peak_F1_rows(df)
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
fig, axes = plt.subplots(len(unique_world_names),len(agents)+1,figsize=(2+2*len(agents),4+2*len(unique_world_names)))
fig.suptitle("Heatmap at peak F1 per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
axes[i,0].set_title(textwrap.fill(world_name,width=20), fontsize=10,wrap=True)
#generate heatmaps
for j,agent in enumerate(agents):
bms = df[(df['agent_attributes'] == agent) & (df['world'] == world_name)]['blockmap'] #get the correct bms
if len(bms) == 0:
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
continue
shape = bms.head(1).item().shape
bms = bms.apply(lambda x: (x > np.zeros(shape))*1.) #make bitmap
heatmap = np.sum(bms)
axes[i,j+1].imshow(heatmap,cmap='viridis')
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
tit = agent_labels(agents,df)[j]
axes[i,j+1].set_title(textwrap.fill(tit,width=20), fontsize=10,wrap=True)
plt.show()
def heatmaps_per_agent_over_world(df):
df = final_rows(df)
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
fig, axes = plt.subplots(len(unique_world_names),len(agents)+1,figsize=(2+2*len(agents),4+2*len(unique_world_names)))
fig.suptitle("Heatmap at end per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
axes[i,0].set_title(textwrap.fill(world_name,width=20), fontsize=10,wrap=True)
#generate heatmaps
for j,agent in enumerate(agents):
bms = df[(df['agent_attributes'] == agent) & (df['world'] == world_name)]['blockmap'] #get the correct bms
if len(bms) == 0:
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
continue
shape = bms.head(1).item().shape
bms = bms.apply(lambda x: (x > np.zeros(shape))*1.) #make bitmap
heatmap = np.sum(bms)
axes[i,j+1].imshow(heatmap,cmap='viridis')
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
tit = agent_labels(agents,df)[j]
axes[i,j+1].set_title(textwrap.fill(tit,width=20), fontsize=10,wrap=True)
plt.show()
def trajectory_per_agent_over_world(df):
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
unique_world_names = df['world'].unique()
#create plot
fig, axes = plt.subplots(len(unique_world_names),len(agents)+1,figsize=(2+4*len(agents),2+3*len(unique_world_names)))
fig.suptitle("Trajectory graph per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
dfic = trajectory.agentdf_to_dfic(_df)
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
axes[i,0].set_title(textwrap.fill(world_name,width=20), fontsize=10,wrap=True)
#generate heatmaps
for j,agent in enumerate(dfic['agent'].unique()):
img = trajectory.plot_trajectory_graph(data=dfic,
target_name = world_name,
agent = agent,
show = False,
save = False,
x_upper_bound = world_obj.silhouette.shape[0])
axes[i,j+1].imshow(img)
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
tit = agent_labels(agents,df)[j]
axes[i,j+1].set_title(textwrap.fill(tit,width=20), fontsize=10,wrap=True)
plt.show()
# scatter plots
def scatter_success_cost(df):
"""Assumes a preprocessed df. Computes cost per step."""
costs = df.query('final_row == True').groupby('agent_label')['avg_cost_per_step_for_run'].mean()
perfects = df.query('final_row == True').groupby('agent_label')['perfect'].mean()
agents = perfects.keys()
for i in range(len(costs)):
# #figure out the color
# if "Construction_Paper_Agent" in df.loc[df.agent_label == agents[i]].head(1)['agent_attributes_string'].item():
# color = TOOL_COLOR
# kind = "with tool"
# else:
# color = NO_TOOL_COLOR
# kind = "without tool"
# plt.scatter(costs[i],perfects[i],color=color,label=kind,marker = get_marker(agents[i]))
plt.scatter(costs[i],perfects[i],label=agents[i],marker = get_marker(agents[i]))
axes = plt.gca()
axes.set_xscale('log')
plt.annotate(
agents[i],
(costs[i],perfects[i]),
xytext=(5, -5),
textcoords='offset points',
ha='left',
va='top',
fontsize=12,
wrap=True
)
#remove duplicate labels
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(),bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.title("Success and computational cost per step")
plt.xlabel("States evaluated")
plt.ylabel("Proportion of perfect reconstructions")
plt.show()
def scatter_success_pairs(df,agent_mappings=None):
"""Agent mapping expects tuples of (no tool, tool, label) 'agent_parameters_string'. These can be drawn out of the perfects series using debug inspection. """
df = final_rows(df)
if agent_mappings is None: agent_mappings = generate_pairs(df)
perfects = df.query('final_row == True').groupby('agent_label')['perfect'].mean()
plt.plot([[0,0],[1,1]],color='grey',alpha=.4) #advantage line
for no_tool_agent, tool_agent, label in agent_mappings:
plt.scatter(perfects[no_tool_agent],perfects[tool_agent],label=label,marker=get_marker(no_tool_agent))
plt.annotate(
tool_agent,
(perfects[no_tool_agent],perfects[tool_agent]),
xytext=(5, -5),
textcoords='offset points',
ha='left',
va='top',
fontsize=12,
wrap=True
)
plt.xlim((0,1))
plt.ylim((0,1))
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.xlabel("Perfect reconstruction without tool")
plt.ylabel("Perfect reconstruction with tool")
plt.title("Rate of perfect reconstruction for agent with and without tool")
plt.show()
def scatter_cost_pairs(df,agent_mappings=None):
"""Agent mapping expects tuples of (no tool, tool, label) 'agent_parameters_string'. These can be drawn out of the perfects series using debug inspection. """
df = final_rows(df)
if agent_mappings is None: agent_mappings = generate_pairs(df)
scores = df.query('final_row == True').groupby('agent_label')['avg_cost_per_step_for_run'].mean()
top = max(scores)
#advantage line
plt.plot([0,top*1.1],[0,top*1.1],color='grey',alpha=.4)
for no_tool_agent, tool_agent, label in agent_mappings:
plt.scatter(scores[no_tool_agent],scores[tool_agent],label=label,marker=get_marker(no_tool_agent))
plt.annotate(
tool_agent,
(scores[no_tool_agent],scores[tool_agent]),
xytext=(5, -5),
textcoords='offset points',
ha='left',
va='top',
fontsize=12,
wrap=True
)
#symmetric axes
axes = plt.gca()
axes.set_xscale('log')
axes.set_yscale('log')
plt.xlim((1,top*1.1))
plt.ylim((1,top*1.1))
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.xlabel("States evaluated without tool")
plt.ylabel("States evaluated with tool")
plt.title("Computational cost for agent with and without tool")
plt.show()
def scatter_cost_per_step_pairs(df,agent_mappings=None):
"""Agent mapping expects tuples of (no tool, tool, label) 'agent_parameters_string'. These can be drawn out of the perfects series using debug inspection. """
df = final_rows(df)
if agent_mappings is None: agent_mappings = generate_pairs(df)
scores = df.query('final_row == True').groupby('agent_label')['cost_per_step'].mean()
top = max(scores)
bottom = min(scores)
#advantage line
plt.plot([0,top*1.1],[0,top*1.1],color='grey',alpha=.4)
for no_tool_agent, tool_agent, label in agent_mappings:
plt.scatter(scores[no_tool_agent],scores[tool_agent],label=label,marker=get_marker(no_tool_agent))
plt.annotate(
tool_agent,
(scores[no_tool_agent],scores[tool_agent]),
xytext=(5, -5),
textcoords='offset points',
ha='left',
va='top',
fontsize=12,
wrap=True
)
#symmetric axes
axes = plt.gca()
axes.set_xscale('log')
axes.set_yscale('log')
plt.xlim((bottom,top*1.1))
plt.ylim((bottom,top*1.1))
plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
plt.xlabel("States evaluated per step without tool")
plt.ylabel("States evaluated per step with tool")
plt.title("Computational cost for agent for one step with and without tool")
plt.show()
# order heatmap
def heatmaps_block_index_per_agent_over_world(df):
def zero_to_nan(x):
"""Helper function"""
x = x.astype(float)
x[x==0] = np.nan
return x
df = final_rows(df)
agents = df['agent_attributes'].unique()
unique_world_names = df['world'].unique()
unique_world_obj = {w:df[df['world'] == w].head(1)['_world'].item() for w in unique_world_names}
unique_world_obj = {key: value for key, value in sorted(unique_world_obj.items(), key=lambda item: item[0])}
#create plot
fig, axes = plt.subplots(len(unique_world_names),len(agents)+1,figsize=(2+2*len(agents),4+2*len(unique_world_names)))
fig.suptitle("Heatmap of mean block index per agent over silhouettes")
for i, (world_name,world_obj) in enumerate(list(unique_world_obj.items())):
_df = df[df['world'] == world_name]
# illustrate world
axes[i,0].imshow(world_obj.silhouette)
# axes[i,0].set_title(world_name)
axes[i,0].set_xticks([])
axes[i,0].set_yticks([])
axes[i,0].set_title(textwrap.fill(world_name,width=20), fontsize=10,wrap=True)
#generate heatmaps
for j,agent in enumerate(agents):
bms = df[(df['agent_attributes'] == agent) & (df['world'] == world_name)]['blockmap'] #get the correct bms
if len(bms) == 0:
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
continue
# bms = bms.apply(zero_to_nan) #replace 0 with nan
heatmap = np.mean(bms)
axes[i,j+1].imshow(heatmap,cmap='viridis')
axes[i,j+1].set_yticks([])
axes[i,j+1].set_xticks([])
tit = agent_labels(agents,df)[j]
axes[i,j+1].set_title(textwrap.fill(tit,width=20), fontsize=10,wrap=True)
plt.show()
| 53.136612
| 322
| 0.648961
| 5,934
| 38,896
| 4.090327
| 0.062858
| 0.015326
| 0.047627
| 0.039222
| 0.867749
| 0.851022
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| 0.817691
| 0.806114
| 0.801582
| 0
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| 0.187371
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| 731
| 323
| 53.209302
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| 0.003174
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| 1
| 0.042808
| false
| 0
| 0.010274
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| 0
| 0
|
0
| 6
|
2fd7d02e960dd9a7138bd399562d988985122b1f
| 94
|
py
|
Python
|
Chosnale/apps/chosnale/__init__.py
|
senaps/chosnale
|
3470abe047b954549f3009e3899aa322729f3ab2
|
[
"MIT"
] | 1
|
2019-04-22T06:40:18.000Z
|
2019-04-22T06:40:18.000Z
|
Chosnale/apps/chosnale/__init__.py
|
senaps/chosnale
|
3470abe047b954549f3009e3899aa322729f3ab2
|
[
"MIT"
] | null | null | null |
Chosnale/apps/chosnale/__init__.py
|
senaps/chosnale
|
3470abe047b954549f3009e3899aa322729f3ab2
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
chosnale = Blueprint('chosnale', __name__)
from .views import *
| 15.666667
| 42
| 0.765957
| 11
| 94
| 6.181818
| 0.636364
| 0.5
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| 0.148936
| 94
| 5
| 43
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| 1
| 1
|
0
| 6
|
2fe8cedb13b8499ca747554a9bf71eefc0b26131
| 45
|
py
|
Python
|
catkin_ws/devel/lib/python2.7/dist-packages/my_pkg2/msg/__init__.py
|
min-chuir-Park/ROS_Tutorials
|
4c19e7673ec7098019c747833c45f0d32b85dab4
|
[
"MIT"
] | 1
|
2019-07-04T04:49:05.000Z
|
2019-07-04T04:49:05.000Z
|
catkin_ws/devel/lib/python2.7/dist-packages/my_pkg2/msg/__init__.py
|
min-chuir-Park/ROS_Tutorials
|
4c19e7673ec7098019c747833c45f0d32b85dab4
|
[
"MIT"
] | null | null | null |
catkin_ws/devel/lib/python2.7/dist-packages/my_pkg2/msg/__init__.py
|
min-chuir-Park/ROS_Tutorials
|
4c19e7673ec7098019c747833c45f0d32b85dab4
|
[
"MIT"
] | null | null | null |
from ._MABX import *
from ._Message import *
| 15
| 23
| 0.733333
| 6
| 45
| 5.166667
| 0.666667
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| 45
| 2
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| 22.5
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|
0
| 6
|
6441d6740fa6947297f43969d01cc8ab93fa25f7
| 58
|
py
|
Python
|
cloud_computing/test.py
|
yanzhenxing123/algorithms
|
cffc78b4998fc607dafe196f68970f6902bdd55f
|
[
"Apache-2.0"
] | 2
|
2021-08-07T04:49:54.000Z
|
2021-09-22T19:10:55.000Z
|
cloud_computing/test.py
|
yanzhenxing123/algorithms
|
cffc78b4998fc607dafe196f68970f6902bdd55f
|
[
"Apache-2.0"
] | 1
|
2021-08-07T05:15:57.000Z
|
2021-08-07T05:15:57.000Z
|
cloud_computing/test.py
|
yanzhenxing123/algorithms
|
cffc78b4998fc607dafe196f68970f6902bdd55f
|
[
"Apache-2.0"
] | 1
|
2021-08-07T04:49:55.000Z
|
2021-08-07T04:49:55.000Z
|
"""
@Author: yanzx
@Date: 2021-09-16 08:43:36
@Desc:
"""
| 9.666667
| 26
| 0.568966
| 10
| 58
| 3.3
| 1
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| 0
| 0.285714
| 0.155172
| 58
| 5
| 27
| 11.6
| 0.387755
| 0.827586
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ff67aa03f82ad40da897252ac4f2ecf6ee55fb34
| 4,024
|
py
|
Python
|
tests/unit/test_boards_endpoint.py
|
amrout/lichess_python_SDK
|
1e2a545b65111bfc58bb963c44ad56be9b4d0835
|
[
"Apache-2.0"
] | 8
|
2020-03-14T23:01:59.000Z
|
2021-04-02T16:02:32.000Z
|
tests/unit/test_boards_endpoint.py
|
amrout/lichess_python_SDK
|
1e2a545b65111bfc58bb963c44ad56be9b4d0835
|
[
"Apache-2.0"
] | 13
|
2020-03-08T23:38:53.000Z
|
2020-03-14T20:51:16.000Z
|
tests/unit/test_boards_endpoint.py
|
amrout/lichess_python_SDK
|
1e2a545b65111bfc58bb963c44ad56be9b4d0835
|
[
"Apache-2.0"
] | 7
|
2020-04-11T16:54:43.000Z
|
2021-07-18T21:24:15.000Z
|
import unittest
from lichess_client import APIClient
from lichess_client.helpers import Response
from lichess_client.utils.enums import StatusTypes
from tests.utils import get_token_from_config, async_test
# TODO: write some functional test with pytest (fire in Github workflow)
class TestBoardsEndpoint(unittest.TestCase):
client = None
token = get_token_from_config('amasend')
game_id = 'IxJ26EAH'
@classmethod
def setUp(cls) -> None:
cls.client = APIClient(token=cls.token)
@unittest.SkipTest
@async_test
async def test_01__stream_incoming_events__fetching_information_about_incoming_game__response_object_returned_with_success(self):
async for response in self.client.boards.stream_incoming_events():
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_02__create_a_seek__seeking_the_game__response_object_returned_with_success(self):
response = await self.client.boards.create_a_seek(time=15, increment=15, rated=True)
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_03__stream_game_state__fetching_current_game_state__response_object_returned_with_success(self):
async for response in self.client.boards.stream_game_state(game_id=self.game_id):
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_04__make_move__send_a_move__response_object_returned_with_success(self):
response = await self.client.boards.make_move(game_id=self.game_id, move='g8f6')
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_05__abort_game__aborting_a_game__response_object_returned_with_success(self):
response = await self.client.boards.abort_game(game_id=self.game_id)
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_06__resign_game__resigning_a_game__response_object_returned_with_success(self):
response = await self.client.boards.resign_game(game_id=self.game_id)
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
@unittest.SkipTest
@async_test
async def test_07__write_in_chat__posting_user_message__response_object_returned_with_success(self):
response = await self.client.boards.write_in_chat(game_id=self.game_id, message="Hello!")
print(response)
from lichess_client.utils.enums import RoomTypes
response = await self.client.boards.write_in_chat(game_id=self.game_id, message="Hi all!",
room=RoomTypes.SPECTATOR)
print(response)
self.assertIsInstance(response, Response, msg="Response in not of type \"Response\"")
self.assertEqual(response.entity.status, StatusTypes.SUCCESS, msg="Request was unsuccessful.")
if __name__ == '__main__':
unittest.main()
| 45.213483
| 133
| 0.738569
| 500
| 4,024
| 5.63
| 0.21
| 0.05968
| 0.045471
| 0.062167
| 0.749911
| 0.744227
| 0.744227
| 0.700533
| 0.700533
| 0.700533
| 0
| 0.006619
| 0.173956
| 4,024
| 88
| 134
| 45.727273
| 0.840253
| 0.017396
| 0
| 0.537313
| 0
| 0
| 0.098684
| 0
| 0
| 0
| 0
| 0.011364
| 0.208955
| 1
| 0.014925
| false
| 0
| 0.089552
| 0
| 0.164179
| 0.119403
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
ff9250c415fd64c8afe8f39d6c3bc8373552ecfe
| 103
|
py
|
Python
|
srcs/parser/tokens/name_token.py
|
pomponchik/computor_v2
|
742b3f3b47c8d46806b2f733b4ec07ae63a23f00
|
[
"MIT"
] | null | null | null |
srcs/parser/tokens/name_token.py
|
pomponchik/computor_v2
|
742b3f3b47c8d46806b2f733b4ec07ae63a23f00
|
[
"MIT"
] | null | null | null |
srcs/parser/tokens/name_token.py
|
pomponchik/computor_v2
|
742b3f3b47c8d46806b2f733b4ec07ae63a23f00
|
[
"MIT"
] | null | null | null |
from srcs.parser.tokens.abstract_token import AbstractToken
class NameToken(AbstractToken):
pass
| 17.166667
| 59
| 0.815534
| 12
| 103
| 6.916667
| 0.916667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.126214
| 103
| 5
| 60
| 20.6
| 0.922222
| 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
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
4409856b64fa9065b4ea61edef62d248453ac7e5
| 74
|
py
|
Python
|
I.P.E. Pratico/Aula 06/exercicio_06.py
|
GuilhermeSbizero0804/I.P.E
|
53288fb70e1c8bbaf516bea0912f65f703f91497
|
[
"MIT"
] | null | null | null |
I.P.E. Pratico/Aula 06/exercicio_06.py
|
GuilhermeSbizero0804/I.P.E
|
53288fb70e1c8bbaf516bea0912f65f703f91497
|
[
"MIT"
] | null | null | null |
I.P.E. Pratico/Aula 06/exercicio_06.py
|
GuilhermeSbizero0804/I.P.E
|
53288fb70e1c8bbaf516bea0912f65f703f91497
|
[
"MIT"
] | null | null | null |
x = 0
while x <= 10:
print(f" 6 x {x} = {6*x}")
x = x + 1
| 14.8
| 31
| 0.324324
| 15
| 74
| 1.6
| 0.533333
| 0.25
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 0.459459
| 74
| 5
| 32
| 14.8
| 0.45
| 0
| 0
| 0
| 0
| 0
| 0.225352
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0
| 1
| null | 1
| 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
| 0
|
0
| 6
|
441c476ad911b0f73ffee834ce6d1255485ec0cf
| 153
|
py
|
Python
|
src/zsl/db/model/__init__.py
|
AtteqCom/zsl
|
0d418ef957c9780263b1031dbc59482cd974bc04
|
[
"MIT"
] | 2
|
2017-05-17T08:08:52.000Z
|
2019-03-25T00:24:51.000Z
|
src/zsl/db/model/__init__.py
|
AtteqCom/zsl
|
0d418ef957c9780263b1031dbc59482cd974bc04
|
[
"MIT"
] | 100
|
2017-01-11T13:43:11.000Z
|
2022-02-10T09:27:18.000Z
|
src/zsl/db/model/__init__.py
|
AtteqCom/zsl
|
0d418ef957c9780263b1031dbc59482cd974bc04
|
[
"MIT"
] | 1
|
2017-05-10T10:27:01.000Z
|
2017-05-10T10:27:01.000Z
|
from __future__ import unicode_literals
from zsl.db.model.app_model import AppModel
from zsl.db.model.app_model_json_encoder import AppModelJSONEncoder
| 30.6
| 67
| 0.875817
| 23
| 153
| 5.434783
| 0.565217
| 0.112
| 0.144
| 0.224
| 0.352
| 0.352
| 0
| 0
| 0
| 0
| 0
| 0
| 0.084967
| 153
| 4
| 68
| 38.25
| 0.892857
| 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
| 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
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4430ed5f93c5437dbe0fa66a9cf9deecb12b293e
| 1,328
|
py
|
Python
|
tests/test_bifid.py
|
onlykood/pycipher
|
8f1d7cf3cba4e12171e27d9ce723ad890194de19
|
[
"MIT"
] | 196
|
2015-01-16T19:09:19.000Z
|
2022-03-13T16:19:21.000Z
|
tests/test_bifid.py
|
rafaelmessias/pycipher
|
787eb947a173138869ddd388b5331559e5cd3a5a
|
[
"MIT"
] | 9
|
2015-10-09T18:07:32.000Z
|
2021-12-22T12:04:00.000Z
|
tests/test_bifid.py
|
rafaelmessias/pycipher
|
787eb947a173138869ddd388b5331559e5cd3a5a
|
[
"MIT"
] | 76
|
2015-02-08T23:17:43.000Z
|
2021-12-27T04:15:30.000Z
|
from pycipher import Bifid
import unittest
class TestBifid(unittest.TestCase):
def test_encipher(self):
keys = (('tgcmpfyxuiewdhbzrvalknqso',5),
('ezrxdkuatgvncmiwhsqpyfblo',6))
plaintext = ('abcdefghiiklmnopqrstuvwxyzabcdefghiiklmnopqrstuvwxyz',
'abcdefghiiklmnopqrstuvwxyzabcdefghiiklmnopqrstuvwxyz')
ciphertext = ('vchqefwfuospksiplpwzuwuwwaeeldwcfglizoprksoqugvfvxuf',
'gvdciztcgfoxclwhoshawmkxygvzcidtczfogclxhowhasmkwyxz')
for i,key in enumerate(keys):
enc = Bifid(*key).encipher(plaintext[i])
self.assertEqual(enc.upper(), ciphertext[i].upper())
def test_decipher(self):
keys = (('tgcmpfyxuiewdhbzrvalknqso',5),
('ezrxdkuatgvncmiwhsqpyfblo',6))
plaintext= ('abcdefghiiklmnopqrstuvwxyzabcdefghiiklmnopqrstuvwxyz',
'abcdefghiiklmnopqrstuvwxyzabcdefghiiklmnopqrstuvwxyz')
ciphertext = ('vchqefwfuospksiplpwzuwuwwaeeldwcfglizoprksoqugvfvxuf',
'gvdciztcgfoxclwhoshawmkxygvzcidtczfogclxhowhasmkwyxz')
for i,key in enumerate(keys):
dec = Bifid(*key).decipher(ciphertext[i])
self.assertEqual(dec.upper(), plaintext[i].upper())
if __name__ == '__main__':
unittest.main()
| 44.266667
| 77
| 0.66491
| 83
| 1,328
| 10.518072
| 0.421687
| 0.016037
| 0.075601
| 0.077892
| 0.707904
| 0.707904
| 0.707904
| 0.707904
| 0.707904
| 0.707904
| 0
| 0.003956
| 0.238705
| 1,328
| 29
| 78
| 45.793103
| 0.859545
| 0
| 0
| 0.56
| 0
| 0
| 0.394578
| 0.388554
| 0
| 0
| 0
| 0
| 0.08
| 1
| 0.08
| false
| 0
| 0.08
| 0
| 0.2
| 0
| 0
| 0
| 1
| 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
| 6
|
443964ed92aad20ed86616bd2040a00f6c3438f6
| 1,602
|
py
|
Python
|
users-backend/users/admin_api/tests/query/test_me_query.py
|
pythonitalia/pycon
|
14e03b2158916f9437fdbde70e48e5bf5266997e
|
[
"MIT"
] | 56
|
2018-01-20T17:18:40.000Z
|
2022-03-28T22:42:04.000Z
|
users-backend/users/admin_api/tests/query/test_me_query.py
|
pythonitalia/pycon
|
14e03b2158916f9437fdbde70e48e5bf5266997e
|
[
"MIT"
] | 2,029
|
2018-01-20T11:37:24.000Z
|
2022-03-31T04:10:51.000Z
|
users-backend/users/admin_api/tests/query/test_me_query.py
|
pythonitalia/pycon
|
14e03b2158916f9437fdbde70e48e5bf5266997e
|
[
"MIT"
] | 17
|
2018-03-17T09:44:28.000Z
|
2021-12-27T19:57:35.000Z
|
from ward import test
from users.tests.api import admin_graphql_client
from users.tests.factories import user_factory
from users.tests.session import db
@test("unlogged cannot fetch me")
async def _(
admin_graphql_client=admin_graphql_client, db=db, user_factory=user_factory
):
user = await user_factory(email="user@email.it", is_staff=False)
admin_graphql_client.force_login(user)
query = """query {
me {
id
email
}
}"""
response = await admin_graphql_client.query(query)
assert response.errors[0]["message"] == "Unauthorized"
@test("fetch me")
async def _(
admin_graphql_client=admin_graphql_client, db=db, user_factory=user_factory
):
logged_user = await user_factory(email="user@email.it", is_staff=True)
admin_graphql_client.force_login(logged_user)
query = """query {
me {
id
email
}
}"""
response = await admin_graphql_client.query(query)
assert not response.errors
assert response.data["me"] == {
"id": str(logged_user.id),
"email": logged_user.email,
}
@test("only staff accounts can fetch me")
async def _(
admin_graphql_client=admin_graphql_client, db=db, user_factory=user_factory
):
logged_user = await user_factory(email="user@email.it", is_staff=False)
admin_graphql_client.force_login(logged_user)
query = """query {
me {
id
email
}
}"""
response = await admin_graphql_client.query(query)
assert response.errors[0]["message"] == "Unauthorized"
| 25.03125
| 79
| 0.661049
| 202
| 1,602
| 4.990099
| 0.222772
| 0.154762
| 0.232143
| 0.044643
| 0.75
| 0.75
| 0.75
| 0.75
| 0.75
| 0.75
| 0
| 0.001623
| 0.230961
| 1,602
| 63
| 80
| 25.428571
| 0.816558
| 0
| 0
| 0.62
| 0
| 0
| 0.222846
| 0
| 0
| 0
| 0
| 0
| 0.08
| 1
| 0
| false
| 0
| 0.08
| 0
| 0.08
| 0
| 0
| 0
| 0
| null | 0
| 1
| 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
| 6
|
9263f753926c7f4f78c74d8b99b9a6e9dfba914d
| 32
|
py
|
Python
|
src/gcs/utils.py
|
wangyeee/MiniGCS
|
70458734591a56bd3918b347a729c3c201320142
|
[
"BSD-3-Clause"
] | 6
|
2019-03-24T08:30:07.000Z
|
2022-03-29T08:31:08.000Z
|
src/gcs/utils.py
|
wangyeee/MiniGCS
|
70458734591a56bd3918b347a729c3c201320142
|
[
"BSD-3-Clause"
] | 2
|
2019-05-15T05:18:58.000Z
|
2019-05-30T10:48:15.000Z
|
src/gcs/utils.py
|
wangyeee/MiniGCS
|
70458734591a56bd3918b347a729c3c201320142
|
[
"BSD-3-Clause"
] | 6
|
2020-07-08T12:10:21.000Z
|
2022-01-05T20:06:54.000Z
|
def unused(*args):
pass
| 8
| 19
| 0.53125
| 4
| 32
| 4.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.34375
| 32
| 3
| 20
| 10.666667
| 0.809524
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 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
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
9282a9b01ad8b7a255482394015c60d61c0235ec
| 34
|
py
|
Python
|
HappyRandomBirthday/__init__.py
|
PauCaBu/HappyRandomBirthday
|
5ef4820ec539362fde5aaec2066b80d91f055dbc
|
[
"MIT"
] | null | null | null |
HappyRandomBirthday/__init__.py
|
PauCaBu/HappyRandomBirthday
|
5ef4820ec539362fde5aaec2066b80d91f055dbc
|
[
"MIT"
] | null | null | null |
HappyRandomBirthday/__init__.py
|
PauCaBu/HappyRandomBirthday
|
5ef4820ec539362fde5aaec2066b80d91f055dbc
|
[
"MIT"
] | null | null | null |
from .HappyRandomBirthday import *
| 34
| 34
| 0.852941
| 3
| 34
| 9.666667
| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088235
| 34
| 1
| 34
| 34
| 0.935484
| 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
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| 1
| 0
|
0
| 6
|
92a846e79169fe8b3b03a92c15c591b3ad568423
| 49,264
|
py
|
Python
|
gui_ML.py
|
ReninPrince/GUI_ML
|
b7c55bd7a9079ec28368f81f44cbe0533c43f390
|
[
"Apache-2.0"
] | null | null | null |
gui_ML.py
|
ReninPrince/GUI_ML
|
b7c55bd7a9079ec28368f81f44cbe0533c43f390
|
[
"Apache-2.0"
] | null | null | null |
gui_ML.py
|
ReninPrince/GUI_ML
|
b7c55bd7a9079ec28368f81f44cbe0533c43f390
|
[
"Apache-2.0"
] | null | null | null |
import pymongo
from pymongo import MongoClient
from tkinter import *
import time;
import datetime
import random
from tkinter import messagebox
import numpy as np
import pandas as pd
from tkinter import simpledialog
#GLOBAL VALUES
d_c = []
x = pd.DataFrame()
y = pd.DataFrame()
X_train = pd.DataFrame()
X_test = pd.DataFrame()
y_train = pd.DataFrame()
y_test = pd.DataFrame()
X_poly = pd.DataFrame()
y_pred = pd.DataFrame()
alldata = pd.DataFrame()
radio = []
radio1 = []
Values2 = []
Values1 = []
Values = []
ScaleV = 0
SplitV = 0
size = 0
algs = str(0)
answer = str(0)
def fourth():
root4 = Tk()
root4.overrideredirect(True)
root4.geometry("{0}x{1}+0+0".format(root4.winfo_screenwidth(), root4.winfo_screenheight()))
root4.title("Store Name")
#-------------------------------------------------------------------------------------------------------------------------------------------
global y_pred,x,y,X_train, X_test, y_train, y_test,X_poly,ScaleV,SplitV,Yscale,algs,answer,size
predictor = StringVar()
predicted = StringVar()
k = []
tp = []
try:
col = list(y.columns)
col1 = list(y)
for i in range(0,10):
for j in col1:
k.append(y[j][i])
t = y_pred[i][0]
tp.append(round(t,2))
except:
print("went wrong")
pass
#-------------------------------------------------------------------------------------------------------------------------------------------
Titlecard = Frame(root4, width = 1280, height = 100, bd = 7, bg = 'dodgerblue', relief = GROOVE)
Titlecard.pack(side = 'top', anchor = CENTER, fill = X)
rt = time.strftime("%d/%m/%y")
body = Frame(root4, width = 1280, height = 600, bd = 9, bg = 'dodgerblue3', relief = FLAT)
body.pack(side = 'top',expand = 1 ,fill = BOTH)
login = Frame(body, width = 600, height = 400, bd = 7, bg = 'Steelblue2', relief = RAISED)
login.pack(side = TOP, anchor = CENTER ,expand = 1, fill = BOTH, ipady = 20,ipadx = 10)
loginbtns = Frame(body, width = 700, height = 30, bd = 7, bg = 'Steelblue2', relief = RAISED)
loginbtns.pack(side = BOTTOM,anchor = CENTER, fill = X)
#-------------------------------------------------------------------------------------------------------------------------------------------
def predictor1():
global y_pred,x,y,X_train, X_test, y_train, y_test,X_poly,ScaleV,SplitV,Yscale,algs,answer,size
pro = round(float(predictor.get()),2)
pru = str(str(pro) + ',')
lsp = pru.split(',')
prel = lsp[:-1]
pre = pd.DataFrame(prel)
if len(x) != 0 and len(y) != 0:
if SplitV == 1 and ScaleV == 1 :
size1 = size
yscale = Yscale
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = size1, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
if yscale > 0:
y_train = sc_X.fit_transform(y_train)
y_test = sc_X.transform(y_test)
if str(algs) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Multiple Linear Regression":
pass
elif str(algs) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train,y_train)
from sklearn.preprocessing import PolynomialFeatures
## answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(X_train)
reg2 = LinearRegression()
reg2.fit(X_poly, np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Support Vector Regression":
## answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Decision Tree Regression":
## answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Random Forest Regression":
## answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(X_train,np.ravel( y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
predicted.set(predicted1)
elif SplitV == 1 and ScaleV == 0:
size1 = size
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = size1, random_state = 0)
if str(algs) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Multiple Linear Regression":
pass
elif str(algs) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train,np.ravel(y_train))
from sklearn.preprocessing import PolynomialFeatures
## answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(X_train)
reg2 = LinearRegression()
reg2.fit(X_poly,np.ravel( y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Support Vector Regression":
## answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Decision Tree Regression":
## answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(X_train,np.ravel(y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Random Forest Regression":
## answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(X_train,np.ravel( y_train))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
predicted.set(predicted1)
elif SplitV == 0 and ScaleV == 1:
yscale1 = Yscale
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
x = sc_X.fit_transform(x)
if yscale1 > 0:
y = sc_X.fit_transform(y)
if str(algs) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Multiple Linear Regression":
pass
elif str(algs) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x,np.ravel(y))
from sklearn.preprocessing import PolynomialFeatures
## answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(x)
reg2 = LinearRegression()
reg2.fit(X_poly, np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Support Vector Regression":
## answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Decision Tree Regression":
## answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Random Forest Regression":
## answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(x, np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
predicted.set(predicted1)
else:
if str(algs) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Multiple Linear Regression":
pass
elif str(algs) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x,np.ravel(y))
from sklearn.preprocessing import PolynomialFeatures
## answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(x)
reg2 = LinearRegression()
reg2.fit(X_poly, np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Support Vector Regression":
## answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Decision Tree Regression":
## answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(x,np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
elif str(algs) == "Random Forest Regression":
## answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(x, np.ravel(y))
y_pred1 = regressor.predict(pre)
predicted1 = str(y_pred1)
predicted.set(predicted1)
def backk():
global y_pred,x,y,X_train, X_test, y_train, y_test,X_poly,ScaleV,SplitV,Yscale,algs,answer,size,tp,k
y_pred = pd.DataFrame()
X_train = pd.DataFrame()
X_test = pd.DataFrame()
y_train = pd.DataFrame()
y_test = pd.DataFrame()
ScaleV = 0
SplitV = 0
size = 0
algs = str(0)
answer = str(0)
tp = []
k = []
root4.destroy()
third()
def exiit():
qexit = messagebox.askyesno("GUI","DO YOU WISH TO EXIT")
if qexit > 0:
root4.destroy()
#-------------------------------------------------------------------------------------------------------------------------------------------
date1 = Label(Titlecard, text = "DATE:" + rt,relief = GROOVE, width = 17, bd = 7,bg = 'white', fg = 'black',font = ('arial', 15, 'italic'))
date1.pack(side = RIGHT, anchor = NW, pady = 15)
Title = Label(Titlecard, text = "SHOP NAME", relief = GROOVE, width = 15 , bd = 7, bg = 'dodgerblue4',
fg = 'lightSkyblue2', font = ('arial', 20, 'italic'))
Title.pack(side = LEFT,pady = 15, ipadx = 35, padx =45)
logintitle = Label(login, text = "Predicted values :", relief = FLAT, width = 10 , bd = 6, bg = 'black',
fg = 'Steelblue', font = ('arial', 20, 'italic'))
logintitle.grid(row = 0, column = 0, columnspan = 3)
#-------------------------------------------------------------------------------------------------------------------------------------------
Label(login, text = "Predicted values :", relief = FLAT, width = 15 , bd = 6, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 0, column = 1)
Label(login, text = "Dependent values :", relief = FLAT, width = 15 , bd = 6, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 0, column = 2)
Label(login, text = "Enter the value \nto predict :", relief = FLAT, width = 15 , bd = 6, bg = 'Steelblue2',
fg = 'Steelblue2', font = ('arial', 20, 'italic')).grid(row = 0, column = 3)
Label(login, text = "Enter the value \nto predict :", relief = FLAT, width = 15 , bd = 6, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 0, column = 4)
Entry(login, relief=SUNKEN,font = ('arial', 15, 'italic'), textvariable = predictor,
bd = 9, insertwidth = 3).grid(row=1,column=4,pady = 20)
Label(login, text = "Predicted value :", relief = FLAT, width = 15 , bd = 6, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 2, column = 4)
Label(login, textvariable = predicted, relief=FLAT,font = ('arial', 15, 'italic'),width = 15 , bd = 6, bg = 'white',
fg = 'black').grid(row=3,column=4,pady = 20)
btn1 = Button(login, text = "PREDICT",command=predictor1, relief = GROOVE, width = 10 , bd = 5, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).grid(row = 1, column = 5)
btn1 = Button(loginbtns, text = "BACK" ,command = backk, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X,ipady = 6)
btn2 = Button(loginbtns, text = "EXIT",command = exiit, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X,ipady = 6)
try:
r = 1
for i in range(6):
Label(login, text = str(tp[i]), relief = GROOVE, width = 15 , bd = 4, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = r, column = 1)
r = r + 1
r = 1
for i in range(6):
Label(login, text = str(round(k[i],2)), relief = GROOVE, width = 15 , bd = 4, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = r, column = 2)
r = r + 1
except:
print("something here went wrong")
Label(login, text = "Couldn't\n import \ndata", relief = GROOVE, width = 15 , bd = 4, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 1, column = 1)
Label(login, text = "Couldn't\n import \ndata", relief = GROOVE, width = 15 , bd = 4, bg = 'Steelblue2',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 1, column = 2)
pass
root4.mainloop()
#-------------------------------------------------------------------------------------------------------------------------------------------
def third():
root2 = Tk()
root2.overrideredirect(True)
root2.geometry("{0}x{1}+0+0".format(root2.winfo_screenwidth(), root2.winfo_screenheight()))
root2.title("GUI for ML algorithims")
#-------------------------------------------------------------------------------------------------------------------------------------------
Titlecard = Frame(root2, width = 1280, height = 100, bd = 7, bg = 'blue', relief = GROOVE)
Titlecard.pack(side = 'top', anchor = CENTER, fill = X)
rt = time.strftime("%d/%m/%y")
body = Frame(root2, width = 1280, height = 600, bd = 9, bg = 'dodgerblue3', relief = FLAT)
body.pack(side = 'top',expand=1,fill = BOTH)
login = Frame(body, width = 1000, height = 600, bd = 7, bg = 'dodgerblue3', relief = RAISED)
login.pack(side = TOP,expand=1, anchor = CENTER, fill = BOTH, ipady = 40,ipadx = 10)
loginbtns = Frame(body, width = 700, height = 50, bd = 7, bg = 'Steelblue2', relief = RAISED)
loginbtns.pack(side = BOTTOM,anchor = CENTER, fill = X)
#-------------------------------------------------------------------------------------------------------------------------------------------
Scale = IntVar()
Split = IntVar()
Spsize = StringVar()
tkvar = StringVar()
#-------------------------------------------------------------------------------------------------------------------------------------------
def back():
global d_c,alldata,x,y,radio,radio1,Values,Values1,Values2,X_train, X_test, y_train, y_test,y_pred
radio = []
radio1 = []
Values2 = []
Values1 = []
Values = []
X_train = pd.DataFrame()
X_test = pd.DataFrame()
y_train = pd.DataFrame()
y_test = pd.DataFrame()
y_pred = pd.DataFrame()
root2.destroy()
second()
def okay():
global x,y,X_train, X_test, y_train, y_test,X_poly,ScaleV,SplitV,Yscale,algs,answer,size,y_pred
if len(x) != 0 and len(y) != 0:
ScaleV = Scale.get()
SplitV = Split.get()
algs = str(tkvar.get())
if Split.get() == 1 and Scale.get() == 1 :
size = float(Spsize.get())
yscale = messagebox.askyesno("GUI","Do you want to scale dependent variable?")
Yscale = yscale
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = size, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)
if yscale > 0:
y_train = sc_X.fit_transform(y_train)
y_test = sc_X.transform(y_test)
algs = str(tkvar().get)
if str(tkvar.get()) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Multiple Linear Regression":
pass
elif str(tkvar.get()) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train,np.ravel(y_train))
from sklearn.preprocessing import PolynomialFeatures
answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(X_train)
reg2 = LinearRegression()
reg2.fit(X_poly, np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Support Vector Regression":
answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Decision Tree Regression":
answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Random Forest Regression":
answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(X_train, np.ravel(y_train))
y_pred = regressor.predict(X_test)
root2.destroy()
fourth()
elif Split.get() == 1 and Scale.get() == 0:
size = float(Spsize.get())
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size = size, random_state = 0)
if str(tkvar.get()) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Multiple Linear Regression":
pass
elif str(tkvar.get()) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train,y_train)
from sklearn.preprocessing import PolynomialFeatures
answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(X_train)
reg2 = LinearRegression()
reg2.fit(X_poly, np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Support Vector Regression":
answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Decision Tree Regression":
answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(X_train,np.ravel(y_train))
y_pred = regressor.predict(X_test)
elif str(tkvar.get()) == "Random Forest Regression":
answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(X_train, np.ravel(y_train))
y_pred = regressor.predict(X_test)
root2.destroy()
fourth()
elif Split.get() == 0 and Scale.get() == 1:
yscale1 = messagebox.askyesno("GUI","Do you want to scale dependent variable?")
Yscale = yscale1
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
x = sc_X.fit_transform(x)
if yscale1 > 0:
y = sc_X.fit_transform(y)
if str(tkvar.get()) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Multiple Linear Regression":
pass
elif str(tkvar.get()) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x,y)
from sklearn.preprocessing import PolynomialFeatures
answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(x)
reg2 = LinearRegression()
reg2.fit(X_poly, y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Support Vector Regression":
answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Decision Tree Regression":
answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Random Forest Regression":
answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(x, y)
y_pred = regressor.predict(x)
root2.destroy()
fourth()
else:
if str(tkvar.get()) == "Simple Linear Regression":
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Multiple Linear Regression":
pass
elif str(tkvar.get()) == "Polynomial Regression":
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(x,y)
from sklearn.preprocessing import PolynomialFeatures
answer = simpledialog.askstring("GUI", ["Degree:"])
poly_reg = PolynomialFeatures(degree = int(answer))
X_poly = poly_reg.fit_transform(x)
reg2 = LinearRegression()
reg2.fit(X_poly, y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Support Vector Regression":
answer = simpledialog.askstring("GUI", ["Kernel:"])
from sklearn.svm import SVR
regressor = SVR(kernel = answer)
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Decision Tree Regression":
answer = simpledialog.askstring("GUI", ["Random state:"])
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor(random_state = int(answer))
regressor.fit(x,y)
y_pred = regressor.predict(x)
elif str(tkvar.get()) == "Random Forest Regression":
answer = simpledialog.askstring("GUI", ["n_estimators:"])
from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators = int(answer), random_state = 0)
regressor.fit(x, y)
y_pred = regressor.predict(x)
root2.destroy()
fourth()
#-------------------------------------------------------------------------------------------------------------------------------------------
date1 = Label(Titlecard, text = "DATE:" + rt,relief = GROOVE, width = 17, bd = 7,bg = 'white', fg = 'black',font = ('arial', 15, 'italic'))
date1.pack(side = RIGHT, anchor = NW, pady = 15)
Title = Label(Titlecard, text = "GUI for ML algorithims", relief = GROOVE, width = 15 , bd = 7, bg = 'dodgerblue4',
fg = 'lightSkyblue2', font = ('arial', 20, 'italic'))
Title.pack(side = LEFT,pady = 15, ipadx = 35, padx =45)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=0,padx = 25, pady = 15,ipady = 2)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=1,column=0,padx = 25, pady = 15,ipady = 2)
#heading
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=25, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=1,column=0,padx = 25, pady = 15,ipady = 2)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=1,column=2,padx = 25, pady = 15,ipady = 2)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=3,column=2,padx = 25, pady = 15,ipady = 2)
Radiobutton(login, text = "YES",value = 1,variable=Scale,indicatoron=0
,bg = 'steelblue',font = ('arial', 15, 'bold')).grid(row = 2,column = 0,padx =5, ipadx =15)
Radiobutton(login, text = "NO",value = 2,variable=Scale,indicatoron=0
,bg = 'steelblue',font = ('arial', 15, 'bold')).grid(row = 4,column = 0,padx =5, ipadx =15)
#heading
Label(login, text="Do you wish to split the data? ", relief=FLAT,width=25, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=1,column=2,padx = 25, pady = 15,ipady = 2)
Radiobutton(login, text = "YES",value = 1,variable=Split,indicatoron=0
,bg = 'steelblue',font = ('arial', 15, 'bold')).grid(row = 2,column = 2,padx =5, ipadx =15)
Radiobutton(login, text = "NO",value = 2,variable=Split,indicatoron=0
,bg = 'steelblue',font = ('arial', 15, 'bold')).grid(row = 4,column = 2,padx =5, ipadx =15)
#heading
Label(login, text="Enter split size : ", relief=FLAT,width=25, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=1,column=4,padx = 25, pady = 15,ipady = 2)
Entry(login,relief=SUNKEN,font = ('arial', 15, 'italic'), textvariable = Spsize,
bd = 9, insertwidth = 3).grid(row=2,column=4)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=5,column=0,padx = 25, pady = 7,ipady = 2)
#dummy
Label(login, text="Do you wish to scale the datas? ", relief=FLAT,width=20, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=7,column=0,padx = 25, pady = 7,ipady = 2)
#heading
Label(login, text="Select your algorithim : ", relief=FLAT,width=30, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=6,column=0,padx = 25, pady = 7,ipady = 2)
#heading_under construction
Label(login, text="Select your error correction : ", relief=FLAT,width=30, bd = 4, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=8,column=0,padx = 25, pady = 7,ipady = 2)
choices = { 'Simple Linear Regression','Multiple Linear Regression','Polynomial Regression',
'Support Vector Regression','Decision Tree Regression','Random Forest Regression'}
tkvar.set('Simple Linear Regression') # set the default option
popupMenu = OptionMenu(login, tkvar, *choices)
popupMenu.config(fg = 'black',bg = 'dodgerblue3', relief=GROOVE, bd = 7)
popupMenu["menu"].config(fg = 'black',bg = 'dodgerblue3', relief=FLAT ,bd = 10)
popupMenu.grid(row=6,column=3,columnspan=4,padx = 30, pady = 7,ipadx = 25)
btn1 = Button(loginbtns, text = "OKAY",command=okay, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X)
btn3 = Button(loginbtns, text = "BACK",command=back, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X)
root2.mainloop()
def second():
root1 = Tk()
root1.overrideredirect(True)
root1.geometry("{0}x{1}+0+0".format(root1.winfo_screenwidth(), root1.winfo_screenheight()))
root1.title("GUI for ML algorithims")
#-------------------------------------------------------------------------------------------------------------------------------------------
Titlecard = Frame(root1, width = 1280, height = 100, bd = 7, bg = 'blue', relief = GROOVE)
Titlecard.pack(side = 'top', anchor = CENTER, fill = X)
rt = time.strftime("%d/%m/%y")
body = Frame(root1, width = 1280, height = 600, bd = 9, bg = 'dodgerblue3', relief = FLAT)
body.pack(side = 'top',expand = 1,fill = BOTH)
login = Frame(body, width = 1000, height = 600, bd = 7, bg = 'dodgerblue3', relief = RAISED)
login.pack(side = TOP, anchor = CENTER,expand=1, fill = X, ipady = 40,ipadx = 10)
#-------------------------------------------------------------------------------------------------------------------------------------------
var = IntVar()
var1 = IntVar()
global d_c,radio,radio1,Values,Values1,Values2
for i in range(len(d_c)):
text = str(d_c[i])
Values.append(text)
length = len(alldata[text])
Values1.append(length)
if length != len(alldata):
g = len(alldata) - length
Values2.append(g)
else:
Values2.append('NULL')
text1 = str(str(text) + "1")
text1 = IntVar()
radio.append(text1)
text2 = str(str(text) + "2")
text2 = IntVar()
radio1.append(text2)
rn = len(d_c)
#-------------------------------------------------------------------------------------------------------------------------------------------
def back():
global d_c,alldata,x,y,radio,radio1,Values,Values1,Values2
root1.destroy()
d_c = []
x = pd.DataFrame()
y = pd.DataFrame()
alldata = pd.DataFrame()
radio = []
radio1 = []
Values2 = []
Values1 = []
Values = []
main()
def clear():
for y in Values:
y.set("")
def exiit():
qexit = messagebox.askyesno("GUI","DO YOU WISH TO EXIT")
if qexit > 0:
root.destroy()
def assign():
global x,y,radio,radio1,Values
for i in range(len(radio)):
if radio[i].get() == 1:
x[str(Values[i])] = alldata[str(Values[i])]
for j in range(len(radio1)):
if radio1[j].get() == 1:
y[str(Values[j])] = alldata[str(Values[j])]
root1.destroy()
third()
#-------------------------------------------------------------------------------------------------------------------------------------------
date1 = Label(Titlecard, text = "DATE:" + rt,relief = GROOVE, width = 17, bd = 7,bg = 'white', fg = 'black',font = ('arial', 15, 'italic'))
date1.pack(side = RIGHT, anchor = NW, pady = 15)
Title = Label(Titlecard, text = "GUI for ML algorithims", relief = GROOVE, width = 15 , bd = 7, bg = 'dodgerblue4',
fg = 'lightSkyblue2', font = ('arial', 20, 'italic'))
Title.pack(side = LEFT,pady = 15, ipadx = 35, padx =45)
Label(login, text="Column name: ", relief=FLAT,width=20, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=0,padx = 15, pady = 15,ipady = 2)
Label(login, text="Number of datas :", relief=FLAT,width=15, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=1,padx = 25, pady = 15,ipady = 2)
Label(login, text="Number of \n missing values : ", relief=FLAT,width=15, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=2,padx = 15, pady = 15,ipady = 2)
Label(login, text="Select \n independent values : ", relief=FLAT,width=16, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=3,padx = 15, pady = 15,ipady = 2)
Label(login, text="Select \n dependent values : ", relief=FLAT,width=16, bd = 4, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=4,padx = 15, pady = 15,ipady = 2)
r = 1
for t in Values:
Label(login, text=t, relief=FLAT,width=20, bd = 6, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=r,column=0,padx = 15, pady = 15,ipady = 2)
r = r + 1
r = 1
for t in Values1:
Label(login, text=t, relief=FLAT,width=20, bd = 6, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=r,column=1,padx = 40, pady = 15,ipady = 2)
r = r + 1
r = 1
for t in Values2:
Label(login, text=t, relief=FLAT,width=15, bd = 6, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=r,column=2,padx = 25, pady = 15,ipady = 2)
r = r + 1
r = 1
for t in radio:
Checkbutton(login,variable=t, fg = 'black',bg = 'dodgerblue3'
).grid(row=r,column=3,padx = 25, pady = 15,ipady = 2)
r = r + 1
r = 1
for t in radio1:
Checkbutton(login, variable=t, fg = 'black',bg = 'dodgerblue3'
).grid(row=r,column=4,padx = 25, pady = 15,ipady = 2)
r = r + 1
btn1 = Button(body, text = "OKAY" ,command = assign, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X,ipady = 10)
btn2 = Button(body, text = "CLEAR", relief = FLAT, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'Steelblue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X,ipady = 10)
btn3 = Button(body, text = "BACK",command = back, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).pack(side =LEFT, anchor = CENTER,expand = 2, fill = X,ipady = 10)
#-------------------------------------------------------------------------------------------------------------------------------------------
root1.mainloop()
def main():
root = Tk()
root.overrideredirect(True)
root.geometry("{0}x{1}+0+0".format(root.winfo_screenwidth(), root.winfo_screenheight()))
root.title("GUI for ML algorithims")
#-------------------------------------------------------------------------------------------------------------------------------------------
Titlecard = Frame(root, width = 1280, height = 100, bd = 7, bg = 'blue', relief = GROOVE)
Titlecard.pack(side = 'top', anchor = CENTER, fill = X)
rt = time.strftime("%d/%m/%y")
body = Frame(root, width = 1280, height = 600, bd = 9, bg = 'dodgerblue3', relief = FLAT)
body.pack(side = 'top',fill = BOTH)
login = Frame(body, width = 600, height = 600, bd = 7, bg = 'dodgerblue3', relief = RAISED)
login.pack(side = TOP, anchor = CENTER, fill = Y, ipady = 100,ipadx = 10)
#-------------------------------------------------------------------------------------------------------------------------------------------
Username = StringVar()
Password = StringVar()
Values1 = ['File name :']
Values = [Username]
#-------------------------------------------------------------------------------------------------------------------------------------------
def clear():
for y in Values:
y.set("")
def exiit():
qexit = messagebox.askyesno("GUI","DO YOU WISH TO EXIT")
if qexit > 0:
root.destroy()
def logn():
global d_c,alldata
Username2 = str(str(Username.get()) + ".csv")
try:
dataset = pd.read_csv(Username2)
for i in range(len(dataset.columns)):
d_c.append(dataset.columns[i])
if len(dataset) > 0:
alldata = alldata.append(dataset, ignore_index = True)
root.destroy()
second()
else:
print('nw')
except:
print("no file")
messagebox.showerror("GUI", "Incorrect file name")
#-------------------------------------------------------------------------------------------------------------------------------------------
date1 = Label(Titlecard, text = "DATE:" + rt,relief = GROOVE, width = 17, bd = 7,bg = 'white', fg = 'black',font = ('arial', 15, 'italic'))
date1.pack(side = RIGHT, anchor = NW, pady = 15)
Title = Label(Titlecard, text = "GUI for ML algorithims", relief = GROOVE, width = 15 , bd = 7, bg = 'dodgerblue4',
fg = 'lightSkyblue2', font = ('arial', 20, 'italic'))
Title.pack(side = LEFT,pady = 15, ipadx = 35, padx =45)
Label(login, text='File name', relief=FLAT,width=10,padx = 10, pady = 10, bd = 6, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=0,column=0)
logintitle = Label(login, text = "Enter the excel file", relief = FLAT, width = 20 , bd = 6, bg = 'dodgerblue3',
fg = 'black', font = ('arial', 20, 'italic'))
logintitle.grid(row = 1, column = 0, columnspan = 3)
## Label(login, text='File name', relief=FLAT,width=10,padx = 10, pady = 10, bd = 6, fg = 'dodgerblue3',bg = 'dodgerblue3',
## font = ('arial', 15, 'bold')).grid(row=2,column=0)
Label(login, text='File name', relief=FLAT,width=10,padx = 10, pady = 10, bd = 6, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=3,column=0)
Label(login, text='File name', relief=FLAT,width=10,padx = 10, pady = 10, bd = 6, fg = 'black',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=4,column=0)
Label(login, text='File name', relief=FLAT,width=10,padx = 10, pady = 10, bd = 6, fg = 'dodgerblue3',bg = 'dodgerblue3',
font = ('arial', 15, 'bold')).grid(row=5,column=0)
Entry(login, relief=SUNKEN,font = ('arial', 15, 'italic'), textvariable = Username,
bd = 9, insertwidth = 3).grid(row=4,column=1,pady = (20,20))
#-------------------------------------------------------------------------------------------------------------------------------------------
btn1 = Button(login, text = "OK",command = logn, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).grid(row = 6, column = 0,columnspan = 3,pady = (4,20))
btn2 = Button(login, text = "CLEAR",command = clear, relief = RAISED, width = 10 , bd = 6, bg = 'Steelblue2',
fg = 'blue2', font = ('arial', 20, 'italic')).grid(row = 7, column = 0, columnspan = 3,pady = (4,20))
btn4 = Button(login, text = "EXIT",command = exiit, relief = RAISED, width = 10 , bd = 6, bg = 'red',
fg = 'black', font = ('arial', 20, 'italic')).grid(row = 8, column = 0, columnspan = 3,pady = (4,20))
#-------------------------------------------------------------------------------------------------------------------------------------------
root.mainloop()
main()
| 49.461847
| 145
| 0.490784
| 5,123
| 49,264
| 4.646691
| 0.061878
| 0.02344
| 0.016635
| 0.040328
| 0.873976
| 0.855325
| 0.843856
| 0.82218
| 0.802647
| 0.789624
| 0
| 0.033347
| 0.330424
| 49,264
| 995
| 146
| 49.511558
| 0.688322
| 0.088259
| 0
| 0.687084
| 0
| 0
| 0.103717
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.021305
| false
| 0.014647
| 0.090546
| 0
| 0.111851
| 0.005326
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2ba797ebdf086ca95db840a1f681e43e5c667c1d
| 23
|
py
|
Python
|
glow/__init__.py
|
vvvm23/glow
|
10f3de164a301d42eee7178be278d6f80ea98bad
|
[
"MIT"
] | 1
|
2021-11-28T01:35:02.000Z
|
2021-11-28T01:35:02.000Z
|
glow/__init__.py
|
vvvm23/glow
|
10f3de164a301d42eee7178be278d6f80ea98bad
|
[
"MIT"
] | null | null | null |
glow/__init__.py
|
vvvm23/glow
|
10f3de164a301d42eee7178be278d6f80ea98bad
|
[
"MIT"
] | null | null | null |
from .glow import Glow
| 11.5
| 22
| 0.782609
| 4
| 23
| 4.5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 23
| 1
| 23
| 23
| 0.947368
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
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| 1
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| 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
| 1
| 0
|
0
| 6
|
2bc09103f6d81c3f356cb1b10c3a4de96a7714cb
| 182
|
py
|
Python
|
src/dizest/__init__.py
|
season-framework/dizest
|
2b36300e84dccb437535d76b294b2053e5ac00e6
|
[
"MIT"
] | 2
|
2022-01-18T02:38:19.000Z
|
2022-01-18T04:26:03.000Z
|
src/dizest/__init__.py
|
season-framework/dizest
|
2b36300e84dccb437535d76b294b2053e5ac00e6
|
[
"MIT"
] | null | null | null |
src/dizest/__init__.py
|
season-framework/dizest
|
2b36300e84dccb437535d76b294b2053e5ac00e6
|
[
"MIT"
] | null | null | null |
from dizest import util, core
Workflow = core.Workflow
from .version import VERSION_STRING, VERSIONS
version = VERSION = __version__ = __VERSION__= VERSION_STRING
versions = VERSIONS
| 36.4
| 61
| 0.818681
| 22
| 182
| 6.318182
| 0.409091
| 0.402878
| 0.453237
| 0.402878
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.126374
| 182
| 5
| 62
| 36.4
| 0.874214
| 0
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| 0
| 0
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| 0
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| 0
| 1
| 0
| false
| 0
| 0.4
| 0
| 0.4
| 0
| 1
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| 0
| null | 1
| 1
| 1
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| 0
| 0
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| 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
| 6
|
2be36e7d7b988e43e352fb19b82b7cede208757b
| 46
|
py
|
Python
|
modules/__init__.py
|
KyleKing/PiSlideshow
|
b2ef2f49b7ae99c8fdbccdc87841e289ecadad74
|
[
"MIT"
] | 4
|
2016-11-28T02:27:37.000Z
|
2020-10-16T16:00:46.000Z
|
modules/__init__.py
|
KyleKing/PiSlideshow
|
b2ef2f49b7ae99c8fdbccdc87841e289ecadad74
|
[
"MIT"
] | null | null | null |
modules/__init__.py
|
KyleKing/PiSlideshow
|
b2ef2f49b7ae99c8fdbccdc87841e289ecadad74
|
[
"MIT"
] | null | null | null |
import config
import dbox_sync
import display
| 11.5
| 16
| 0.869565
| 7
| 46
| 5.571429
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 46
| 3
| 17
| 15.333333
| 0.975
| 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
| 1
| 0
|
0
| 6
|
2bf09dbdda1ff1ff1717c76fefc8995f84842137
| 1,460
|
py
|
Python
|
tests/legacy/responses/repos/traffic/pageviews_fixtrue.py
|
timmo001/aiogithubapi
|
9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac
|
[
"MIT"
] | 8
|
2019-07-24T18:14:25.000Z
|
2022-03-01T18:33:53.000Z
|
tests/legacy/responses/repos/traffic/pageviews_fixtrue.py
|
timmo001/aiogithubapi
|
9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac
|
[
"MIT"
] | 33
|
2019-12-18T22:15:06.000Z
|
2022-03-30T06:08:38.000Z
|
tests/legacy/responses/repos/traffic/pageviews_fixtrue.py
|
timmo001/aiogithubapi
|
9d33bad77e49f8ee720bcd81c2cbab8a4cf8ebac
|
[
"MIT"
] | 14
|
2019-09-02T17:50:16.000Z
|
2022-03-14T10:30:37.000Z
|
"""
Generated by generate/generate.py - 2020-08-02 12:25:07.157407
"""
import pytest
@pytest.fixture()
def pageviews_fixtrue_response():
return {
"count": 14850,
"uniques": 3782,
"views": [
{"timestamp": "2016-10-10T00:00:00Z", "count": 440, "uniques": 143},
{"timestamp": "2016-10-11T00:00:00Z", "count": 1308, "uniques": 414},
{"timestamp": "2016-10-12T00:00:00Z", "count": 1486, "uniques": 452},
{"timestamp": "2016-10-13T00:00:00Z", "count": 1170, "uniques": 401},
{"timestamp": "2016-10-14T00:00:00Z", "count": 868, "uniques": 266},
{"timestamp": "2016-10-15T00:00:00Z", "count": 495, "uniques": 157},
{"timestamp": "2016-10-16T00:00:00Z", "count": 524, "uniques": 175},
{"timestamp": "2016-10-17T00:00:00Z", "count": 1263, "uniques": 412},
{"timestamp": "2016-10-18T00:00:00Z", "count": 1402, "uniques": 417},
{"timestamp": "2016-10-19T00:00:00Z", "count": 1394, "uniques": 424},
{"timestamp": "2016-10-20T00:00:00Z", "count": 1492, "uniques": 448},
{"timestamp": "2016-10-21T00:00:00Z", "count": 1153, "uniques": 332},
{"timestamp": "2016-10-22T00:00:00Z", "count": 566, "uniques": 168},
{"timestamp": "2016-10-23T00:00:00Z", "count": 675, "uniques": 184},
{"timestamp": "2016-10-24T00:00:00Z", "count": 614, "uniques": 237},
],
}
| 48.666667
| 81
| 0.539726
| 176
| 1,460
| 4.465909
| 0.420455
| 0.248092
| 0.28626
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.299556
| 0.229452
| 1,460
| 29
| 82
| 50.344828
| 0.399111
| 0.042466
| 0
| 0
| 1
| 0
| 0.454676
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.041667
| true
| 0
| 0.041667
| 0.041667
| 0.125
| 0
| 0
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2bf35da5974fc4ff2c71c34ff52b17c663224738
| 127
|
py
|
Python
|
python/taichi/linalg/__init__.py
|
qinmengzhu/taichi
|
434607f73c04208362f9ad8de9b811f2add4beb7
|
[
"MIT"
] | null | null | null |
python/taichi/linalg/__init__.py
|
qinmengzhu/taichi
|
434607f73c04208362f9ad8de9b811f2add4beb7
|
[
"MIT"
] | null | null | null |
python/taichi/linalg/__init__.py
|
qinmengzhu/taichi
|
434607f73c04208362f9ad8de9b811f2add4beb7
|
[
"MIT"
] | null | null | null |
from taichi.linalg.sparse_matrix import SparseMatrix, SparseMatrixBuilder
from taichi.linalg.sparse_solver import SparseSolver
| 42.333333
| 73
| 0.889764
| 15
| 127
| 7.4
| 0.666667
| 0.18018
| 0.288288
| 0.396396
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070866
| 127
| 2
| 74
| 63.5
| 0.940678
| 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
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
a6093d7a2a94cb5336a77813fcc748988e5c38d0
| 216
|
py
|
Python
|
my_pytest/fibonacci/tests/test_perfomance.py
|
Anych/pytest_project
|
6ed90b3688212d0b5f035b0d9761f2cf5f99a82f
|
[
"MIT"
] | null | null | null |
my_pytest/fibonacci/tests/test_perfomance.py
|
Anych/pytest_project
|
6ed90b3688212d0b5f035b0d9761f2cf5f99a82f
|
[
"MIT"
] | null | null | null |
my_pytest/fibonacci/tests/test_perfomance.py
|
Anych/pytest_project
|
6ed90b3688212d0b5f035b0d9761f2cf5f99a82f
|
[
"MIT"
] | null | null | null |
import pytest
from fibonacci.dynamic import fibonacci_dynamic_v2
from fibonacci.conftest import track_performance
@pytest.mark.performance
@track_performance
def test_performance():
fibonacci_dynamic_v2(1000)
| 19.636364
| 50
| 0.847222
| 27
| 216
| 6.518519
| 0.481481
| 0.272727
| 0.204545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030928
| 0.101852
| 216
| 10
| 51
| 21.6
| 0.876289
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0
| 0.428571
| 0
| 0.571429
| 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
| 1
| 0
| 1
| 0
|
0
| 6
|
a61781f9e2bbd77d6baa87a5a8a89b8f735f5eab
| 111
|
py
|
Python
|
sandbox/image/__init__.py
|
Mandrenkov/SVBRDF-Texture-Synthesis
|
7e7282698befd53383cbd6566039340babb0a289
|
[
"MIT"
] | 2
|
2021-04-26T14:41:11.000Z
|
2021-08-20T09:13:03.000Z
|
sandbox/image/__init__.py
|
Mandrenkov/SVBRDF-Texture-Synthesis
|
7e7282698befd53383cbd6566039340babb0a289
|
[
"MIT"
] | null | null | null |
sandbox/image/__init__.py
|
Mandrenkov/SVBRDF-Texture-Synthesis
|
7e7282698befd53383cbd6566039340babb0a289
|
[
"MIT"
] | null | null | null |
from .point import Point # noqa
from .image import Image # noqa
from .pyramid import Pyramid # noqa
| 27.75
| 36
| 0.684685
| 15
| 111
| 5.066667
| 0.4
| 0.210526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.261261
| 111
| 3
| 37
| 37
| 0.926829
| 0.126126
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
a6a3e9c484190386ee85fcb776cbb4e2a8c15068
| 2,376
|
py
|
Python
|
migrations/versions/eb2980d5acfe_update_employment_constraint.py
|
MTES-MCT/mobilic-api
|
b3754de2282262fd60a27dc90e40777df9c1e230
|
[
"MIT"
] | null | null | null |
migrations/versions/eb2980d5acfe_update_employment_constraint.py
|
MTES-MCT/mobilic-api
|
b3754de2282262fd60a27dc90e40777df9c1e230
|
[
"MIT"
] | 8
|
2021-04-19T17:47:55.000Z
|
2022-02-16T17:40:18.000Z
|
migrations/versions/eb2980d5acfe_update_employment_constraint.py
|
MTES-MCT/mobilic-api
|
b3754de2282262fd60a27dc90e40777df9c1e230
|
[
"MIT"
] | null | null | null |
"""Update employment constraint
Revision ID: eb2980d5acfe
Revises: 1fe02d4c1330
Create Date: 2020-09-29 12:04:31.643877
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = "eb2980d5acfe"
down_revision = "1fe02d4c1330"
branch_labels = None
depends_on = None
def upgrade():
op.drop_constraint(
"only_one_current_primary_employment_per_user", "employment"
)
op.execute(
"""
ALTER TABLE employment ADD CONSTRAINT only_one_current_primary_employment_per_user
EXCLUDE USING GIST (
user_id WITH =,
daterange(start_date, CASE WHEN end_date is not null THEN end_date ELSE '2100-01-01' END, '[]') WITH &&
)
WHERE (is_primary AND validation_status != 'rejected' AND dismissed_at IS NULL)
"""
)
op.drop_constraint(
"no_simultaneous_employments_for_the_same_company", "employment"
)
op.execute(
"""
ALTER TABLE employment ADD CONSTRAINT no_simultaneous_employments_for_the_same_company
EXCLUDE USING GIST (
user_id WITH =,
company_id WITH =,
daterange(start_date, CASE WHEN end_date is not null THEN end_date ELSE '2100-01-01' END, '[]') WITH &&
)
WHERE (validation_status != 'rejected' AND dismissed_at IS NULL)
"""
)
def downgrade():
op.drop_constraint(
"only_one_current_primary_employment_per_user", "employment"
)
op.execute(
"""
ALTER TABLE employment ADD CONSTRAINT only_one_current_primary_employment_per_user
EXCLUDE USING GIST (
user_id WITH =,
daterange(start_date, CASE WHEN end_date is not null THEN end_date ELSE '2100-01-01' END, '[]') WITH &&
)
WHERE (is_primary AND validation_status != 'rejected')
"""
)
op.drop_constraint(
"no_simultaneous_employments_for_the_same_company", "employment"
)
op.execute(
"""
ALTER TABLE employment ADD CONSTRAINT no_simultaneous_employments_for_the_same_company
EXCLUDE USING GIST (
user_id WITH =,
company_id WITH =,
daterange(start_date, CASE WHEN end_date is not null THEN end_date ELSE '2100-01-01' END, '[]') WITH &&
)
WHERE (validation_status != 'rejected')
"""
)
| 30.075949
| 115
| 0.645623
| 283
| 2,376
| 5.134276
| 0.272085
| 0.038541
| 0.044047
| 0.06607
| 0.818995
| 0.818995
| 0.818995
| 0.818995
| 0.791466
| 0.791466
| 0
| 0.045009
| 0.270623
| 2,376
| 78
| 116
| 30.461538
| 0.793422
| 0.066077
| 0
| 0.428571
| 0
| 0
| 0.347826
| 0.258065
| 0
| 0
| 0
| 0
| 0
| 1
| 0.071429
| false
| 0
| 0.071429
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 6
|
a6c9a8e8af2f3be95be5aee447a11048d52e7244
| 77
|
py
|
Python
|
PIPS/utils/__init__.py
|
SterlingYM/astroPIPS
|
cf9ceda0670ded1289c8616a4cff753af4383012
|
[
"MIT"
] | 4
|
2021-05-09T03:07:17.000Z
|
2022-01-10T08:55:18.000Z
|
PIPS/utils/__init__.py
|
SterlingYM/PIPS
|
d8168c105013e35fe2c027ff8725188f298fd0ae
|
[
"MIT"
] | 23
|
2020-10-28T06:08:42.000Z
|
2021-03-02T06:54:38.000Z
|
PIPS/utils/__init__.py
|
SterlingYM/astroPIPS
|
cf9ceda0670ded1289c8616a4cff753af4383012
|
[
"MIT"
] | null | null | null |
from .connect_LPP import *
from .get_Temp import *
from .read_filter import *
| 25.666667
| 26
| 0.779221
| 12
| 77
| 4.75
| 0.666667
| 0.350877
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 77
| 3
| 27
| 25.666667
| 0.863636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
5b3879be5854ec9ec652e49dff3ddecc419467ab
| 105
|
py
|
Python
|
prt/screen.py
|
tsmanner/python-ray-tracer
|
664279abe37e11ec1fef847635f56ed5bcdead04
|
[
"MIT"
] | null | null | null |
prt/screen.py
|
tsmanner/python-ray-tracer
|
664279abe37e11ec1fef847635f56ed5bcdead04
|
[
"MIT"
] | null | null | null |
prt/screen.py
|
tsmanner/python-ray-tracer
|
664279abe37e11ec1fef847635f56ed5bcdead04
|
[
"MIT"
] | null | null | null |
class Screen:
def __init__(self, background_color):
self.background_color = background_color
| 26.25
| 48
| 0.742857
| 12
| 105
| 5.916667
| 0.583333
| 0.633803
| 0.535211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 105
| 3
| 49
| 35
| 0.835294
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
5ba5c6217539ae77a72e80fa3a30df1bd7e42569
| 43
|
py
|
Python
|
src/__init__.py
|
jawaxa/tabayyun-bot
|
d67ec8d6bf38afa722e0ab4bae6a7b951bc90c36
|
[
"MIT"
] | null | null | null |
src/__init__.py
|
jawaxa/tabayyun-bot
|
d67ec8d6bf38afa722e0ab4bae6a7b951bc90c36
|
[
"MIT"
] | 1
|
2022-03-24T16:16:41.000Z
|
2022-03-24T16:16:41.000Z
|
src/__init__.py
|
jawaxa/tabayyun-bot
|
d67ec8d6bf38afa722e0ab4bae6a7b951bc90c36
|
[
"MIT"
] | null | null | null |
from .view import *
from .service import *
| 14.333333
| 22
| 0.72093
| 6
| 43
| 5.166667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.186047
| 43
| 2
| 23
| 21.5
| 0.885714
| 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
| 1
| 0
|
0
| 6
|
5bc0fc06f4d1f4e64d10f75785901596f5c4c4b2
| 140
|
py
|
Python
|
micro_grids/test_cases/__init__.py
|
Matrixeigs/EnergyManagementSourceCodes
|
1ea824941fe87528622ec7aa8148024752a3947c
|
[
"MIT"
] | 3
|
2021-10-21T07:28:38.000Z
|
2022-02-17T11:30:52.000Z
|
micro_grids/test_cases/__init__.py
|
Matrixeigs/EnergyManagementSourceCodes
|
1ea824941fe87528622ec7aa8148024752a3947c
|
[
"MIT"
] | null | null | null |
micro_grids/test_cases/__init__.py
|
Matrixeigs/EnergyManagementSourceCodes
|
1ea824941fe87528622ec7aa8148024752a3947c
|
[
"MIT"
] | null | null | null |
"""
Test cases for micro-grid systems
The following systems are considered
1. AC micro-grid
2. DC micro-grid
3. Hybrid AC/DC micro-grid
"""
| 17.5
| 36
| 0.742857
| 25
| 140
| 4.16
| 0.64
| 0.346154
| 0.211538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025424
| 0.157143
| 140
| 7
| 37
| 20
| 0.855932
| 0.935714
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 1
| 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
| 6
|
5bfbc218769d399c0f40ce7ac89432f96f940f08
| 33
|
py
|
Python
|
database/__init__.py
|
Untesler/fra641-backend
|
f7fbb10273d6259fd59c0d8e99b6557af91d405b
|
[
"MIT"
] | 1
|
2021-11-26T07:29:45.000Z
|
2021-11-26T07:29:45.000Z
|
DCAE-Backend/database/__init__.py
|
Untesler/DCAEImageCompression
|
908d21dfefcab43575520a7cf4d5dd9d83a23b5c
|
[
"MIT"
] | null | null | null |
DCAE-Backend/database/__init__.py
|
Untesler/DCAEImageCompression
|
908d21dfefcab43575520a7cf4d5dd9d83a23b5c
|
[
"MIT"
] | null | null | null |
from database.database import db
| 16.5
| 32
| 0.848485
| 5
| 33
| 5.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.965517
| 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
| 1
| 0
|
0
| 6
|
7546a72e3d29405de68043d351910f36413ab977
| 108
|
py
|
Python
|
soap_as_rest_server/starter.py
|
frankmendonca/soap-proxy
|
4ee19b0f4d039a3a7ab1dc904009afc3121a1c5a
|
[
"MIT"
] | 3
|
2019-11-22T09:13:19.000Z
|
2020-02-24T10:11:55.000Z
|
soap_as_rest_server/starter.py
|
frankmendonca/soap-proxy
|
4ee19b0f4d039a3a7ab1dc904009afc3121a1c5a
|
[
"MIT"
] | null | null | null |
soap_as_rest_server/starter.py
|
frankmendonca/soap-proxy
|
4ee19b0f4d039a3a7ab1dc904009afc3121a1c5a
|
[
"MIT"
] | null | null | null |
def start():
from .infos import show_infos
from . import server
show_infos()
server.init()
| 15.428571
| 33
| 0.638889
| 14
| 108
| 4.785714
| 0.571429
| 0.268657
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.268519
| 108
| 6
| 34
| 18
| 0.848101
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0
| 0.6
| 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
| 1
| 0
|
0
| 6
|
f3b7253eb1e75b5bdde37fe87d6819de0b8f22ed
| 157
|
py
|
Python
|
crystal_functions/__init__.py
|
Filo3dg/crystal_functions
|
2184e1359cac703891b42d2328150e0848537174
|
[
"MIT"
] | null | null | null |
crystal_functions/__init__.py
|
Filo3dg/crystal_functions
|
2184e1359cac703891b42d2328150e0848537174
|
[
"MIT"
] | null | null | null |
crystal_functions/__init__.py
|
Filo3dg/crystal_functions
|
2184e1359cac703891b42d2328150e0848537174
|
[
"MIT"
] | 1
|
2022-03-30T09:37:53.000Z
|
2022-03-30T09:37:53.000Z
|
from . import adsorb
from . import calculate
from . import convert
from . import execute
from . import file_readwrite
from . import plot
from . import utils
| 19.625
| 28
| 0.77707
| 22
| 157
| 5.5
| 0.454545
| 0.578512
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178344
| 157
| 7
| 29
| 22.428571
| 0.937985
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
|
0
| 6
|
45f8c6f79e06f57787b2d6157e4dd758bbd68973
| 31
|
py
|
Python
|
Chapter13/todo_stage/reports/__init__.py
|
PacktPublishing/Odoo-11-Development-Essentials-Third-Edition
|
3cfeae0c2ce5a81d69f62a5be1ed28d74a7a78f5
|
[
"MIT"
] | 31
|
2018-05-29T00:16:45.000Z
|
2021-07-20T00:45:13.000Z
|
Chapter11/todo_stage/reports/__init__.py
|
PacktPublishing/Odoo-11-Development-Essentials-Third-Edition
|
3cfeae0c2ce5a81d69f62a5be1ed28d74a7a78f5
|
[
"MIT"
] | 1
|
2018-04-27T08:47:12.000Z
|
2018-06-27T06:56:44.000Z
|
Chapter11/todo_stage/reports/__init__.py
|
PacktPublishing/Odoo-11-Development-Essentials-Third-Edition
|
3cfeae0c2ce5a81d69f62a5be1ed28d74a7a78f5
|
[
"MIT"
] | 31
|
2018-03-30T08:43:10.000Z
|
2020-08-31T13:36:33.000Z
|
from . import todo_task_report
| 15.5
| 30
| 0.83871
| 5
| 31
| 4.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.888889
| 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
| 1
| 0
|
0
| 6
|
caed420aaa31df630e752f38a92bde21d16fb26d
| 44
|
py
|
Python
|
python/testData/refactoring/move/functionToUsage/before/src/b.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/refactoring/move/functionToUsage/before/src/b.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/refactoring/move/functionToUsage/before/src/b.py
|
truthiswill/intellij-community
|
fff88cfb0dc168eea18ecb745d3e5b93f57b0b95
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
import lib1
from a import f
import lib2
f()
| 8.8
| 15
| 0.75
| 9
| 44
| 3.666667
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057143
| 0.204545
| 44
| 5
| 16
| 8.8
| 0.885714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.75
| 0
| 0.75
| 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
| 1
| 0
|
0
| 6
|
1b39c51b148f576f95725918f695d8841b975b30
| 106
|
py
|
Python
|
Python/easy/e363.py
|
tlgs/dailyprogrammer
|
6e7d3352616fa54a8e9caf8564a9cfb951eb0af9
|
[
"Unlicense"
] | 4
|
2017-10-18T02:17:02.000Z
|
2022-02-02T01:19:02.000Z
|
Python/easy/e363.py
|
tlseabra/dailyprogrammer
|
6e7d3352616fa54a8e9caf8564a9cfb951eb0af9
|
[
"Unlicense"
] | 4
|
2016-01-24T20:30:02.000Z
|
2017-01-18T16:01:23.000Z
|
Python/easy/e363.py
|
tlgs/dailyprogrammer
|
6e7d3352616fa54a8e9caf8564a9cfb951eb0af9
|
[
"Unlicense"
] | null | null | null |
# 12/06/2018
def check(word):
return word.count('ei') == word.count('cei') and word.count("cie") == 0
| 26.5
| 75
| 0.613208
| 18
| 106
| 3.611111
| 0.722222
| 0.415385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 0.150943
| 106
| 4
| 75
| 26.5
| 0.622222
| 0.09434
| 0
| 0
| 0
| 0
| 0.084211
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
1b4c7800f9bb5b764362fb959691ead860800ad8
| 81
|
py
|
Python
|
opol_processing/__init__.py
|
vlouf/opol_processing
|
111fb498c4d9f6deeba71d9d43f692427eeebe9e
|
[
"MIT"
] | 2
|
2020-06-26T08:53:32.000Z
|
2020-07-24T15:09:57.000Z
|
opol_processing/__init__.py
|
vlouf/opol_processing
|
111fb498c4d9f6deeba71d9d43f692427eeebe9e
|
[
"MIT"
] | null | null | null |
opol_processing/__init__.py
|
vlouf/opol_processing
|
111fb498c4d9f6deeba71d9d43f692427eeebe9e
|
[
"MIT"
] | null | null | null |
from .production import production_line
from .production import process_and_save
| 27
| 40
| 0.876543
| 11
| 81
| 6.181818
| 0.636364
| 0.411765
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098765
| 81
| 2
| 41
| 40.5
| 0.931507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
1b567cd9be524446b817195ed72994fd5c86e45b
| 70
|
py
|
Python
|
common/eval/__init__.py
|
sukrutrao/Adversarial-Patch-Training
|
b7322e6f4d94029ceb0dcb946d2b6852c795990f
|
[
"Unlicense"
] | 21
|
2020-08-04T12:47:03.000Z
|
2022-03-22T09:34:29.000Z
|
common/eval/__init__.py
|
sukrutrao/Adversarial-Patch-Training
|
b7322e6f4d94029ceb0dcb946d2b6852c795990f
|
[
"Unlicense"
] | 3
|
2021-06-08T22:13:00.000Z
|
2022-03-12T00:45:16.000Z
|
common/eval/__init__.py
|
sukrutrao/Adversarial-Patch-Training
|
b7322e6f4d94029ceb0dcb946d2b6852c795990f
|
[
"Unlicense"
] | 6
|
2020-08-04T12:47:05.000Z
|
2022-02-13T00:58:03.000Z
|
from .clean_evaluation import *
from .adversarial_evaluation import *
| 23.333333
| 37
| 0.828571
| 8
| 70
| 7
| 0.625
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 70
| 2
| 38
| 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 | 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
| 1
| 0
|
0
| 6
|
1bb65d6662ac26f1a6a7a38b507c972dbd26ec7d
| 163
|
py
|
Python
|
tracker.py
|
sumeshpremraj/1RM-tracker-web
|
6596c2182143abcd294967f6c65a99c499c446cf
|
[
"MIT"
] | null | null | null |
tracker.py
|
sumeshpremraj/1RM-tracker-web
|
6596c2182143abcd294967f6c65a99c499c446cf
|
[
"MIT"
] | null | null | null |
tracker.py
|
sumeshpremraj/1RM-tracker-web
|
6596c2182143abcd294967f6c65a99c499c446cf
|
[
"MIT"
] | null | null | null |
from app import app, db
from app.models import User, Lift
@app.shell_context_processor
def make_shell_context():
return {'db': db, 'User': User, 'Lift': Lift}
| 27.166667
| 49
| 0.723926
| 26
| 163
| 4.384615
| 0.5
| 0.122807
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147239
| 163
| 6
| 49
| 27.166667
| 0.820144
| 0
| 0
| 0
| 0
| 0
| 0.060976
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0.2
| 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
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
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