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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
69f409b13ad0b46a22392d114e37fc306b39bf56
| 43
|
py
|
Python
|
dangerfile.py
|
elpassion/danger-flake8
|
87703148ee6fef44fc13799dba79c7ef3130fd0c
|
[
"MIT"
] | 1
|
2020-02-17T10:19:45.000Z
|
2020-02-17T10:19:45.000Z
|
dangerfile.py
|
elpassion/danger-flake8
|
87703148ee6fef44fc13799dba79c7ef3130fd0c
|
[
"MIT"
] | null | null | null |
dangerfile.py
|
elpassion/danger-flake8
|
87703148ee6fef44fc13799dba79c7ef3130fd0c
|
[
"MIT"
] | null | null | null |
import danger_flake8
danger_flake8.lint()
| 10.75
| 20
| 0.837209
| 6
| 43
| 5.666667
| 0.666667
| 0.705882
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051282
| 0.093023
| 43
| 3
| 21
| 14.333333
| 0.820513
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
0e0c9ebf10bf29904582876b920722df911a8630
| 40
|
py
|
Python
|
app/api/comment/__init__.py
|
codingjerk/ztd.blunders-web
|
38d4c1049dc3d0bd0b4294ffa419d25cbfbf2b83
|
[
"MIT"
] | null | null | null |
app/api/comment/__init__.py
|
codingjerk/ztd.blunders-web
|
38d4c1049dc3d0bd0b4294ffa419d25cbfbf2b83
|
[
"MIT"
] | null | null | null |
app/api/comment/__init__.py
|
codingjerk/ztd.blunders-web
|
38d4c1049dc3d0bd0b4294ffa419d25cbfbf2b83
|
[
"MIT"
] | null | null | null |
from app.api.comment import send, vote
| 13.333333
| 38
| 0.775
| 7
| 40
| 4.428571
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15
| 40
| 2
| 39
| 20
| 0.911765
| 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
|
38a5c225f1d09b1b089f6dd63b17dd6f15759da1
| 23,815
|
py
|
Python
|
test/units/pattern/test_resub.py
|
larsborn/refinery
|
c8b19156b17e5fa5de5c72bc668a14d646584560
|
[
"BSD-3-Clause"
] | null | null | null |
test/units/pattern/test_resub.py
|
larsborn/refinery
|
c8b19156b17e5fa5de5c72bc668a14d646584560
|
[
"BSD-3-Clause"
] | null | null | null |
test/units/pattern/test_resub.py
|
larsborn/refinery
|
c8b19156b17e5fa5de5c72bc668a14d646584560
|
[
"BSD-3-Clause"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from .. import TestUnitBase
class TestRegexSubstitution(TestUnitBase):
def test_real_world_obfuscated_code(self):
autoit_obfuscated = '''
Func lwmmqmcfqg($vdata, $vcryptkey)
Local $__g_acryptinternaldata[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("Z3HQHwba"), 8)]
Local $tbuff
Local $ttempstruct
Local $iplaintextsize
Local $vreturn
Local $e = Execute
Local $b = $e(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("TBcwikuolltnBKaXRCvPyrmryuExoTwTdFoJhmYZYSXetpmsANtrqziMqtItanxlgpHOPP"), 5))
$vdata = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("n0xORjC42ZklD69yUXZ6EceMm61aNxH72uLfv79ijBJ54jpqI6FMYFD53iare74Brqw72niBl69LgbR6EbsWi67ytvY28CTBh24hmlx76ndBJ44XGiE61nhOH74SYYI61KgyQ29Mep"), 3)))
Local $aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 5)))
$__g_acryptinternaldata[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("k2QjJkAc"), 8)] = $aret[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("K1VWn"), 5)]
$aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 6)))
$hcrypthash = $aret[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("y5YWkY"), 6)]
$tbuff = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 7)))
$e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("F0HHYxecc4Oso4SJA6ZiJCHNo6kAWClxj5qlb3twW7LLO4lvh7Kov2EDz7UgQ5fvx6Kyl3BCX7JiV4ROO5WBu3xSg6mMN5yBw7wTg4Rwr4ptk4xdJ6ikj1Nqc7AMT4bhh6OeW1JJT2Xkl8JoE2MqS4udB7xdy4hLP4vXz2PkY7LdV5ryc6KVg6sra6WXZ6zFB2IKfCErE2TJg0MHn4ATh5knY7rJU8ipK6lue5OxL6rQW3Ayg7lSH5pqY7OvY4YTs6jsL5WBI2Ugz8XvS2LzF2RVu3urz1Vay2sqC2tvU2bln9FME2oAvCrnf2wHo0qPE2ATX4bdD7rsO6KMD4LGa3jFV7nEG2VaC7dyD9tty7bKb0cxT7IXA4TKN4nQEBhSG6bFN5kTD7SxF9eux2yAY9Cp"), 4)))
$aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 4)))
$aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 6)))
$vreturn = $aret[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("y5YWkY"), 6)]
$e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("s0dxS4X4Y6jCq6tCc4t3o6z1f6rCj6lCc2q8T2f2g4v1R6d4n7D6R6X1u7g0f6f9X3z3S3q2z2xEw6I4Z6MCE6fCn2c2o2YCe2d0Z2r2u6r2W6XFm6oFJ6VCs2t2Z2vCO2J0C2f2Z4s3l7F2t7k9g7U0R7l4p4L4O6T5S7q3h7n4L7F2d6VFE7L9o4b8p6F1N7i3R6e8A2J2y2lCS2U0Y2K2k6C8G6a1y6YEg6z4b6ICO6A5Y2X2b2ECo2A0q2r4u6b8H4i3U7L2k7F9U7G0y7N4p4a8U6b1P7D3m6Q8u2Q9"), 2)))
$vcryptkey = $vreturn
$tbuff = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("e0xUgqD44HXur6CdPGJ6CVGXn53nFbV74jkaX72cPAl75nwAe63xOCd74exHz43Jjgj72lgkr65wcZT61kuiT74GaJg65FdlD28JlKs22JicG62dlfD79RLar74toXo65xUdP5BHXuV22gUqx20cAGb26Zint20QPWe42XCrF69YNih6EwfPf61QlWO72fznN79lAnL4CaSjG65oyVB6EOSPP28sXrx24dBMn76AlUD44COUc61WTCJ74reqO61qTHb29Qrea20EVWn2BQjCZ20mLMG22YEPU31geWM30WXJC30NHiw30Txzv22cZkf20ZMaU26swtf20fQYB22cuyU5DzEfu22ZAsh29VGm"), 3)))
$e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("O0DPxNYQGVK4Tk4crmoWs6tfCUdeXvg6gECGeAfyN5Rj3QDuELd7jo4oBMhIw7oL2PMTkIt7GG5lorFfl6pN3DGAPtU7QP4QgtfLv5jZ3Pjpamb6wg5oZwPmJ7DE4yFLQHR4Vz4ErvmRu6Qa1sQeCpa7Bo4TFjVsC6HB1PMdQUr2xT8QiojeP2UK4WtJsKo7xa4WndMgg4bz2uCKiig7BA5LzsSnK6Er6zwkIMq6ao6DxOdMm2JlCoKgNZB2pL0iysoAN4Mf5MHjKBn7dT8vfbiUn6yX5gZqzLS6Yq3zDHrpo7pl5izLFtK7aZ4ndgvlR6ug5vTAOYU2Um8TbJkDd2gl2pLpnXd3wT1rxYOTF2IC2AUIvrn2ob9yafiWv2KpCuvwfGJ2rg0gGWwpt2Of4gcTJCU7CL6nywDIX4vO4IYNJrW6Rc1BkCHZM7eS4ApMTPN6aa1lStkJy2jo9IHkVJ"), 5)))
$aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 6)))
$iplaintextsize = $aret[nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("q6yMySvX"), 8)]
$ttempstruct = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 5)))
$vreturn = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 7)))
$aret = $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 9)))
$e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 9)))
$e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("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"), 9)))
Return $e($b(nvbjtycmyxlfrdbypxqk(sjwakhwbtxwwb("b0IHQDGxPjeFg4tVyRq2tnbEj6BmRRf9zMtSr6dEDjPEoyYUX6vUcFl1VnixX7wCGVP2qfKlt7wjNLc9KrjOS2zyEGP8WRFor2bqwjP4IHnDJ7chEjN6KXzSU5HdQnY2qcqqY6CJSgD5NhxOV7gJAAk4pWRbr7EgLoO5njyTM7Rqpmz2WFBrF6WKEUkEgXfnC2oNyzR9nFEV"), 6)))
EndFunc
'''.encode('ASCII')
autoit_cleaned_up = '''
Func lwmmqmcfqg($vdata, $vcryptkey)
Local $__g_acryptinternaldata["3"]
Local $tbuff
Local $ttempstruct
Local $iplaintextsize
Local $vreturn
Local $e = Execute
Local $b = $e("BinaryToString")
$vdata = BinaryToString($vData)
Local $aret = DllCall("Advapi32.dll", "bool", "CryptAcquireContext", "handle*", "0", "ptr", "0", "ptr", "0", "dword", "24", "dword", "0xF0000000")
$__g_acryptinternaldata["2"] = $aret["1"]
$aret = DllCall("Advapi32.dll", "bool", "CryptCreateHash", "handle", $__g_aCryptInternalData["2"], "uint", "0x00008003", "ptr", "0", "dword", "0", "handle*", "0")
$hcrypthash = $aret["5"]
$tbuff = DllStructCreate("byte[" & BinaryLen($vCryptKey) & "]")
DllStructSetData($tBuff, Execute("1"), $vCryptKey)
$aret = DllCall("Advapi32.dll", "bool", "CryptHashData", "handle", $hCryptHash, "struct*", $tBuff, "dword", DllStructGetSize($tBuff), "dword", "1")
$aret = DllCall("Advapi32.dll", "bool", "CryptDeriveKey", "handle",$__g_aCryptInternalData["2"], "uint", "0x00006610", "handle", $hCryptHash, "dword", "0x00000001", "handle*", "0")
$vreturn = $aret["5"]
DllCall("Advapi32.dll", "bool", "CryptDestroyHash", "handle", $hCryptHash)
$vcryptkey = $vreturn
$tbuff = DllStructCreate("byte[" & BinaryLen($vData) + "1000" & "]")
DllStructSetData($tBuff, Execute("1"), $vData)
$aret = DllCall("Advapi32.dll", "bool", "CryptDecrypt", "handle", $vCryptKey, "handle", "0", "bool", Execute("1"), "dword", "0", "struct*", $tBuff, "dword*", BinaryLen($vData))
$iplaintextsize = $aret["6"]
$ttempstruct = DllStructCreate("byte[" & $iPlainTextSize + "1" & "]", DllStructGetPtr($tBuff))
$vreturn = BinaryMid(DllStructGetData($tTempStruct, Execute("1")), "1", $iPlainTextSize)
$aret = DllCall("Advapi32.dll", "bool", "CryptDestroyKey", "handle", $vCryptKey)
DllCall("Advapi32.dll", "bool", "CryptDestroyKey", "handle", $vCryptKey)
DllCall("Advapi32.dll", "bool", "CryptReleaseContext", "handle", $__g_aCryptInternalData["2"], "dword", "0")
Return Binary($vReturn)
EndFunc
'''.encode('ASCII')
layer1 = self.load(R'sjwakhwbtxwwb\("(.*?)"\)', R'"$(1 | resub (.)(.) $$2$$1)"')
layer2 = self.load(R'nvbjtycmyxlfrdbypxqk\("([^"]+)",\s*(\d+)\)', R'"$(1 | snip ::$2)"')
layer3 = self.load(R'\$e\(\$b\("([^"]+)"\)\)', R'$(1 | base)')
autoit_refined = autoit_obfuscated
autoit_refined = layer1(autoit_refined)
autoit_refined = layer2(autoit_refined)
autoit_refined = layer3(autoit_refined)
self.assertEqual(autoit_refined, autoit_cleaned_up)
def test_resub_powershell_variables(self):
resub = self.load(R'\$\{(\w+)\}', '$$$1')
self.assertEqual(resub(B'(^& ${R} ${dAtA} (${iV}+${K}))'), B'(^& $R $dAtA ($iV+$K))')
def test_dollar_escapes_01(self):
resub = self.load(R'FOO(.)', '$1$$1')
self.assertEqual(resub(B'FOOP FOOZ'), B'P$1 Z$1')
def test_dollar_escapes_02(self):
resub = self.load(R'FOO(.)', '$$$$x$1$(1|hex -R)$$1')
self.assertEqual(resub(B'FOO3 FOO2'), B'$$$x333$1 $$$x232$1')
def test_dollar_escapes_03(self):
resub = self.load(R'FOO(.)', '$$$$$$$$x$1$$$')
self.assertEqual(resub(B'FOOP'), B'$$$$$$$xP$$')
def test_binary_replacement(self):
resub = self.load(R'yara:(FEED)(BAAD)(F00D)', R'$1\xBE\xEF')
data = bytes.fromhex('AAAAAAFEEDBAADF00DAAAAAA')
self.assertEqual(resub(data).hex().upper(), 'AAAAAAFEEDBEEFAAAAAA')
def test_substitution_count_limit(self):
resub = self.load('E(.)', 'AH$1', count=2)
data = B'BINERY REFINERY'
self.assertEqual(resub(data), B'BINAHRY RAHFINERY')
| 210.752212
| 2,146
| 0.890699
| 614
| 23,815
| 34.456026
| 0.265472
| 0.037436
| 0.017678
| 0.028124
| 0.186377
| 0.163122
| 0.149603
| 0.144687
| 0.142513
| 0.142513
| 0
| 0.127324
| 0.058115
| 23,815
| 112
| 2,147
| 212.633929
| 0.815835
| 0.001806
| 0
| 0.185567
| 0
| 0.082474
| 0.942533
| 0.822802
| 0
| 1
| 0.001683
| 0
| 0.072165
| 1
| 0.072165
| false
| 0
| 0.010309
| 0
| 0.092784
| 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
|
38c6d17d77ad5f717c8828619b159902bb405af2
| 43,822
|
py
|
Python
|
openapi_client/api/inpy_versions_api.py
|
nens/threedi-api-client
|
43b0eb1bd47310b1783f87f6ad8bfbfe0fb4d90a
|
[
"BSD-3-Clause"
] | null | null | null |
openapi_client/api/inpy_versions_api.py
|
nens/threedi-api-client
|
43b0eb1bd47310b1783f87f6ad8bfbfe0fb4d90a
|
[
"BSD-3-Clause"
] | 16
|
2021-05-31T09:52:04.000Z
|
2022-03-14T16:07:19.000Z
|
openapi_client/api/inpy_versions_api.py
|
nens/threedi-api-client
|
43b0eb1bd47310b1783f87f6ad8bfbfe0fb4d90a
|
[
"BSD-3-Clause"
] | null | null | null |
# coding: utf-8
"""
3Di API
3Di simulation API (latest version: 3.0) Framework release: 1.0.16 3Di core release: 2.0.11 deployed on: 07:33AM (UTC) on September 04, 2020 # noqa: E501
The version of the OpenAPI document: 3.0
Contact: info@nelen-schuurmans.nl
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from openapi_client.api_client import ApiClient
from openapi_client.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class InpyVersionsApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def inpy_versions_create(self, data, **kwargs): # noqa: E501
"""inpy_versions_create # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_create(data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param InpyVersion data: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InpyVersion
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_create_with_http_info(data, **kwargs) # noqa: E501
def inpy_versions_create_with_http_info(self, data, **kwargs): # noqa: E501
"""inpy_versions_create # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_create_with_http_info(data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param InpyVersion data: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InpyVersion, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'data'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_create" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'data' is set
if self.api_client.client_side_validation and ('data' not in local_var_params or # noqa: E501
local_var_params['data'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `data` when calling `inpy_versions_create`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'data' in local_var_params:
body_params = local_var_params['data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InpyVersion', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def inpy_versions_delete(self, id, **kwargs): # noqa: E501
"""inpy_versions_delete # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_delete(id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_delete_with_http_info(id, **kwargs) # noqa: E501
def inpy_versions_delete_with_http_info(self, id, **kwargs): # noqa: E501
"""inpy_versions_delete # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_delete_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: None
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_delete" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `inpy_versions_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/{id}/', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def inpy_versions_list(self, **kwargs): # noqa: E501
"""inpy_versions_list # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_list(async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str threedi_version:
:param str threedi_version__contains:
:param str threedi_version__icontains:
:param str threedi_version__in: Multiple values may be separated by commas.
:param str threedi_version__startswith:
:param str threedi_version__istartswith:
:param str threedi_version__endswith:
:param str threedi_version__regex:
:param str threedicore_version:
:param str threedicore_version__contains:
:param str threedicore_version__icontains:
:param str threedicore_version__in: Multiple values may be separated by commas.
:param str threedicore_version__startswith:
:param str threedicore_version__istartswith:
:param str threedicore_version__endswith:
:param str threedicore_version__regex:
:param str slug:
:param str slug__contains:
:param str slug__icontains:
:param str slug__in: Multiple values may be separated by commas.
:param str slug__startswith:
:param str slug__istartswith:
:param str slug__endswith:
:param str slug__regex:
:param int limit: Number of results to return per page.
:param int offset: The initial index from which to return the results.
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InlineResponse2003
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_list_with_http_info(**kwargs) # noqa: E501
def inpy_versions_list_with_http_info(self, **kwargs): # noqa: E501
"""inpy_versions_list # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_list_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param str threedi_version:
:param str threedi_version__contains:
:param str threedi_version__icontains:
:param str threedi_version__in: Multiple values may be separated by commas.
:param str threedi_version__startswith:
:param str threedi_version__istartswith:
:param str threedi_version__endswith:
:param str threedi_version__regex:
:param str threedicore_version:
:param str threedicore_version__contains:
:param str threedicore_version__icontains:
:param str threedicore_version__in: Multiple values may be separated by commas.
:param str threedicore_version__startswith:
:param str threedicore_version__istartswith:
:param str threedicore_version__endswith:
:param str threedicore_version__regex:
:param str slug:
:param str slug__contains:
:param str slug__icontains:
:param str slug__in: Multiple values may be separated by commas.
:param str slug__startswith:
:param str slug__istartswith:
:param str slug__endswith:
:param str slug__regex:
:param int limit: Number of results to return per page.
:param int offset: The initial index from which to return the results.
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InlineResponse2003, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'threedi_version',
'threedi_version__contains',
'threedi_version__icontains',
'threedi_version__in',
'threedi_version__startswith',
'threedi_version__istartswith',
'threedi_version__endswith',
'threedi_version__regex',
'threedicore_version',
'threedicore_version__contains',
'threedicore_version__icontains',
'threedicore_version__in',
'threedicore_version__startswith',
'threedicore_version__istartswith',
'threedicore_version__endswith',
'threedicore_version__regex',
'slug',
'slug__contains',
'slug__icontains',
'slug__in',
'slug__startswith',
'slug__istartswith',
'slug__endswith',
'slug__regex',
'limit',
'offset'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_list" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'threedi_version' in local_var_params and local_var_params['threedi_version'] is not None: # noqa: E501
query_params.append(('threedi_version', local_var_params['threedi_version'])) # noqa: E501
if 'threedi_version__contains' in local_var_params and local_var_params['threedi_version__contains'] is not None: # noqa: E501
query_params.append(('threedi_version__contains', local_var_params['threedi_version__contains'])) # noqa: E501
if 'threedi_version__icontains' in local_var_params and local_var_params['threedi_version__icontains'] is not None: # noqa: E501
query_params.append(('threedi_version__icontains', local_var_params['threedi_version__icontains'])) # noqa: E501
if 'threedi_version__in' in local_var_params and local_var_params['threedi_version__in'] is not None: # noqa: E501
query_params.append(('threedi_version__in', local_var_params['threedi_version__in'])) # noqa: E501
if 'threedi_version__startswith' in local_var_params and local_var_params['threedi_version__startswith'] is not None: # noqa: E501
query_params.append(('threedi_version__startswith', local_var_params['threedi_version__startswith'])) # noqa: E501
if 'threedi_version__istartswith' in local_var_params and local_var_params['threedi_version__istartswith'] is not None: # noqa: E501
query_params.append(('threedi_version__istartswith', local_var_params['threedi_version__istartswith'])) # noqa: E501
if 'threedi_version__endswith' in local_var_params and local_var_params['threedi_version__endswith'] is not None: # noqa: E501
query_params.append(('threedi_version__endswith', local_var_params['threedi_version__endswith'])) # noqa: E501
if 'threedi_version__regex' in local_var_params and local_var_params['threedi_version__regex'] is not None: # noqa: E501
query_params.append(('threedi_version__regex', local_var_params['threedi_version__regex'])) # noqa: E501
if 'threedicore_version' in local_var_params and local_var_params['threedicore_version'] is not None: # noqa: E501
query_params.append(('threedicore_version', local_var_params['threedicore_version'])) # noqa: E501
if 'threedicore_version__contains' in local_var_params and local_var_params['threedicore_version__contains'] is not None: # noqa: E501
query_params.append(('threedicore_version__contains', local_var_params['threedicore_version__contains'])) # noqa: E501
if 'threedicore_version__icontains' in local_var_params and local_var_params['threedicore_version__icontains'] is not None: # noqa: E501
query_params.append(('threedicore_version__icontains', local_var_params['threedicore_version__icontains'])) # noqa: E501
if 'threedicore_version__in' in local_var_params and local_var_params['threedicore_version__in'] is not None: # noqa: E501
query_params.append(('threedicore_version__in', local_var_params['threedicore_version__in'])) # noqa: E501
if 'threedicore_version__startswith' in local_var_params and local_var_params['threedicore_version__startswith'] is not None: # noqa: E501
query_params.append(('threedicore_version__startswith', local_var_params['threedicore_version__startswith'])) # noqa: E501
if 'threedicore_version__istartswith' in local_var_params and local_var_params['threedicore_version__istartswith'] is not None: # noqa: E501
query_params.append(('threedicore_version__istartswith', local_var_params['threedicore_version__istartswith'])) # noqa: E501
if 'threedicore_version__endswith' in local_var_params and local_var_params['threedicore_version__endswith'] is not None: # noqa: E501
query_params.append(('threedicore_version__endswith', local_var_params['threedicore_version__endswith'])) # noqa: E501
if 'threedicore_version__regex' in local_var_params and local_var_params['threedicore_version__regex'] is not None: # noqa: E501
query_params.append(('threedicore_version__regex', local_var_params['threedicore_version__regex'])) # noqa: E501
if 'slug' in local_var_params and local_var_params['slug'] is not None: # noqa: E501
query_params.append(('slug', local_var_params['slug'])) # noqa: E501
if 'slug__contains' in local_var_params and local_var_params['slug__contains'] is not None: # noqa: E501
query_params.append(('slug__contains', local_var_params['slug__contains'])) # noqa: E501
if 'slug__icontains' in local_var_params and local_var_params['slug__icontains'] is not None: # noqa: E501
query_params.append(('slug__icontains', local_var_params['slug__icontains'])) # noqa: E501
if 'slug__in' in local_var_params and local_var_params['slug__in'] is not None: # noqa: E501
query_params.append(('slug__in', local_var_params['slug__in'])) # noqa: E501
if 'slug__startswith' in local_var_params and local_var_params['slug__startswith'] is not None: # noqa: E501
query_params.append(('slug__startswith', local_var_params['slug__startswith'])) # noqa: E501
if 'slug__istartswith' in local_var_params and local_var_params['slug__istartswith'] is not None: # noqa: E501
query_params.append(('slug__istartswith', local_var_params['slug__istartswith'])) # noqa: E501
if 'slug__endswith' in local_var_params and local_var_params['slug__endswith'] is not None: # noqa: E501
query_params.append(('slug__endswith', local_var_params['slug__endswith'])) # noqa: E501
if 'slug__regex' in local_var_params and local_var_params['slug__regex'] is not None: # noqa: E501
query_params.append(('slug__regex', local_var_params['slug__regex'])) # noqa: E501
if 'limit' in local_var_params and local_var_params['limit'] is not None: # noqa: E501
query_params.append(('limit', local_var_params['limit'])) # noqa: E501
if 'offset' in local_var_params and local_var_params['offset'] is not None: # noqa: E501
query_params.append(('offset', local_var_params['offset'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InlineResponse2003', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def inpy_versions_partial_update(self, id, data, **kwargs): # noqa: E501
"""inpy_versions_partial_update # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_partial_update(id, data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param InpyVersion data: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InpyVersion
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_partial_update_with_http_info(id, data, **kwargs) # noqa: E501
def inpy_versions_partial_update_with_http_info(self, id, data, **kwargs): # noqa: E501
"""inpy_versions_partial_update # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_partial_update_with_http_info(id, data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param InpyVersion data: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InpyVersion, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'id',
'data'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_partial_update" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `inpy_versions_partial_update`") # noqa: E501
# verify the required parameter 'data' is set
if self.api_client.client_side_validation and ('data' not in local_var_params or # noqa: E501
local_var_params['data'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `data` when calling `inpy_versions_partial_update`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'data' in local_var_params:
body_params = local_var_params['data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/{id}/', 'PATCH',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InpyVersion', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def inpy_versions_read(self, id, **kwargs): # noqa: E501
"""inpy_versions_read # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_read(id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InpyVersion
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_read_with_http_info(id, **kwargs) # noqa: E501
def inpy_versions_read_with_http_info(self, id, **kwargs): # noqa: E501
"""inpy_versions_read # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_read_with_http_info(id, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InpyVersion, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'id'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_read" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `inpy_versions_read`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/{id}/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InpyVersion', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
def inpy_versions_update(self, id, data, **kwargs): # noqa: E501
"""inpy_versions_update # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_update(id, data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param InpyVersion data: (required)
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: InpyVersion
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
return self.inpy_versions_update_with_http_info(id, data, **kwargs) # noqa: E501
def inpy_versions_update_with_http_info(self, id, data, **kwargs): # noqa: E501
"""inpy_versions_update # noqa: E501
Inpy is the service for preparing models to become usable by the Threedi calculation core. Updates in Inpy often result in updates in the calculation core. This resource keeps track of updates to the Inpy service. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.inpy_versions_update_with_http_info(id, data, async_req=True)
>>> result = thread.get()
:param async_req bool: execute request asynchronously
:param int id: A unique integer value identifying this inpy version. (required)
:param InpyVersion data: (required)
:param _return_http_data_only: response data without head status code
and headers
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: tuple(InpyVersion, status_code(int), headers(HTTPHeaderDict))
If the method is called asynchronously,
returns the request thread.
"""
local_var_params = locals()
all_params = [
'id',
'data'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method inpy_versions_update" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'id' is set
if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501
local_var_params['id'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `id` when calling `inpy_versions_update`") # noqa: E501
# verify the required parameter 'data' is set
if self.api_client.client_side_validation and ('data' not in local_var_params or # noqa: E501
local_var_params['data'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `data` when calling `inpy_versions_update`") # noqa: E501
collection_formats = {}
path_params = {}
if 'id' in local_var_params:
path_params['id'] = local_var_params['id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'data' in local_var_params:
body_params = local_var_params['data']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['Bearer'] # noqa: E501
return self.api_client.call_api(
'/inpy-versions/{id}/', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='InpyVersion', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats)
| 50.370115
| 236
| 0.624412
| 5,063
| 43,822
| 5.117124
| 0.046218
| 0.051258
| 0.083218
| 0.026556
| 0.942836
| 0.918172
| 0.889108
| 0.88602
| 0.880307
| 0.851783
| 0
| 0.016557
| 0.30398
| 43,822
| 869
| 237
| 50.428078
| 0.832858
| 0.444571
| 0
| 0.623529
| 0
| 0
| 0.229856
| 0.107113
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030588
| false
| 0
| 0.011765
| 0
| 0.072941
| 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
|
c7f773dff5885a94aa3558ed2fda8940dbab0ef0
| 36
|
py
|
Python
|
sftp/data_reader/batch_sampler/__init__.py
|
hiaoxui/span-finder
|
c5f9886eae12921796b33bdb84ffcb6bfa905cb4
|
[
"Apache-2.0"
] | 3
|
2021-05-08T15:35:21.000Z
|
2022-01-24T02:52:55.000Z
|
sftp/data_reader/batch_sampler/__init__.py
|
hiaoxui/span-finder
|
c5f9886eae12921796b33bdb84ffcb6bfa905cb4
|
[
"Apache-2.0"
] | null | null | null |
sftp/data_reader/batch_sampler/__init__.py
|
hiaoxui/span-finder
|
c5f9886eae12921796b33bdb84ffcb6bfa905cb4
|
[
"Apache-2.0"
] | 1
|
2021-09-07T22:31:40.000Z
|
2021-09-07T22:31:40.000Z
|
from .mix_sampler import MixSampler
| 18
| 35
| 0.861111
| 5
| 36
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 36
| 1
| 36
| 36
| 0.9375
| 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
|
2a02444410b64e55a6d66f9e55ad48385cf2dec7
| 54
|
py
|
Python
|
advertise/helpers/__init__.py
|
sadagatasgarov1/sahibinden
|
76b16c64551d37b055f80b82808736f107afb92c
|
[
"MIT"
] | 2
|
2020-08-13T19:50:43.000Z
|
2021-01-16T17:15:43.000Z
|
advertise/helpers/__init__.py
|
sadagatasgarov1/sahibinden
|
76b16c64551d37b055f80b82808736f107afb92c
|
[
"MIT"
] | 5
|
2021-04-08T21:50:04.000Z
|
2022-02-10T12:34:46.000Z
|
advertise/helpers/__init__.py
|
ozanteoman/sahibinden
|
76b16c64551d37b055f80b82808736f107afb92c
|
[
"MIT"
] | 3
|
2020-09-26T13:17:06.000Z
|
2022-01-26T20:02:56.000Z
|
from advertise.helpers.upload_to import upload_to_user
| 54
| 54
| 0.907407
| 9
| 54
| 5.111111
| 0.777778
| 0.347826
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.055556
| 54
| 1
| 54
| 54
| 0.901961
| 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
|
2a3da89e02104dc953104dee75248994b1d11a3e
| 34
|
py
|
Python
|
cometblue_lite/__init__.py
|
fleXible/python-cometblue_lite
|
804f8665bad5a9c926a5779a1a1ff9b0f37d1cb3
|
[
"MIT"
] | 4
|
2019-12-29T22:06:13.000Z
|
2020-11-24T14:29:28.000Z
|
cometblue_lite/__init__.py
|
fleXible/python-cometblue_lite
|
804f8665bad5a9c926a5779a1a1ff9b0f37d1cb3
|
[
"MIT"
] | 3
|
2020-03-12T19:51:19.000Z
|
2021-05-30T19:28:43.000Z
|
cometblue_lite/__init__.py
|
fleXible/python-cometblue_lite
|
804f8665bad5a9c926a5779a1a1ff9b0f37d1cb3
|
[
"MIT"
] | 2
|
2020-02-09T19:09:23.000Z
|
2021-12-09T07:53:13.000Z
|
from .cometblue import CometBlue
| 11.333333
| 32
| 0.823529
| 4
| 34
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147059
| 34
| 2
| 33
| 17
| 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
|
aa551fb8c21b13a054c44c5d84a7465938408a6c
| 18
|
py
|
Python
|
nus_tools/content/__init__.py
|
arcticdiv/nus_tools
|
24bd7945e720376f24d54fc5dbdcaf126b7294f1
|
[
"Apache-2.0"
] | 1
|
2020-01-28T17:10:51.000Z
|
2020-01-28T17:10:51.000Z
|
nus_tools/content/__init__.py
|
arcticdiv/nus_tools
|
24bd7945e720376f24d54fc5dbdcaf126b7294f1
|
[
"Apache-2.0"
] | 20
|
2019-11-15T14:53:57.000Z
|
2022-03-02T06:20:14.000Z
|
radio/__init__.py
|
MrJacob12/Radio
|
fecf4734da429152f88cd186e4661237a72155ec
|
[
"MIT"
] | null | null | null |
from . import app
| 9
| 17
| 0.722222
| 3
| 18
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 18
| 1
| 18
| 18
| 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
|
aaa698973c4ab0bf689d2878cfd0b1392a850c84
| 28
|
py
|
Python
|
script.py
|
fluthi/casimir-programming
|
b3f0bc2bc0f84aa757399ff7cf8957576e32e5c8
|
[
"MIT"
] | null | null | null |
script.py
|
fluthi/casimir-programming
|
b3f0bc2bc0f84aa757399ff7cf8957576e32e5c8
|
[
"MIT"
] | null | null | null |
script.py
|
fluthi/casimir-programming
|
b3f0bc2bc0f84aa757399ff7cf8957576e32e5c8
|
[
"MIT"
] | null | null | null |
print('this is so awesome!')
| 28
| 28
| 0.714286
| 5
| 28
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0.655172
| 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
|
aabfd8cc20759c0cb30a7b22ee19e4208913726b
| 8,593
|
py
|
Python
|
py_entitymatching/tests/test_blockercombiner.py
|
kvpradap/py_entitymatching
|
4ff803df1a03cf4d77ef935357355e6de5dd9438
|
[
"BSD-3-Clause"
] | 165
|
2016-08-28T14:30:01.000Z
|
2022-03-29T17:24:03.000Z
|
py_entitymatching/tests/test_blockercombiner.py
|
mvahit/py_entitymatching
|
6724081d7d95c547e5a51625b4a8207c6c1737f8
|
[
"MIT",
"BSD-2-Clause",
"BSD-3-Clause"
] | 70
|
2016-11-22T00:35:22.000Z
|
2022-03-11T22:26:26.000Z
|
py_entitymatching/tests/test_blockercombiner.py
|
mvahit/py_entitymatching
|
6724081d7d95c547e5a51625b4a8207c6c1737f8
|
[
"MIT",
"BSD-2-Clause",
"BSD-3-Clause"
] | 53
|
2016-09-22T02:07:34.000Z
|
2022-03-19T18:57:06.000Z
|
# coding=utf-8
import os
from nose.tools import *
import unittest
import pandas as pd
import six
from py_entitymatching.utils.generic_helper import get_install_path
import py_entitymatching.catalog.catalog_manager as cm
from py_entitymatching.io.parsers import read_csv_metadata, to_csv_metadata
from py_entitymatching.blockercombiner.blockercombiner import combine_blocker_outputs_via_union
datasets_path = os.sep.join([get_install_path(), 'tests', 'test_datasets'])
bc_datasets_path = os.sep.join([get_install_path(), 'tests', 'test_datasets',
'blockercombiner'])
path_a = os.sep.join([datasets_path, 'A.csv'])
path_b = os.sep.join([datasets_path, 'B.csv'])
path_c = os.sep.join([datasets_path, 'C.csv'])
path_c1 = os.sep.join([bc_datasets_path, 'C1.csv'])
path_c2 = os.sep.join([bc_datasets_path, 'C2.csv'])
path_c3 = os.sep.join([bc_datasets_path, 'C3.csv'])
class BlockerCombinerTestCases(unittest.TestCase):
def setUp(self):
cm.del_catalog()
def tearDown(self):
cm.del_catalog()
def test_blocker_combiner_valid_1(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(path_c1, ltable=A, rtable=B)
C2 = read_csv_metadata(path_c2, ltable=A, rtable=B)
C3 = read_csv_metadata(path_c3, ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C2, C3])
C_exp = read_csv_metadata(path_c, ltable=A, rtable=B)
# try:
# C_exp.sort_values(['ltable_ID', 'rtable_ID'], inplace=True)
# except AttributeError:
# C_exp.sort(['ltable_ID', 'rtable_ID'], inplace=True)
# to_csv_metadata(C_exp, path_c)
C_exp.reset_index(inplace=True, drop=True)
C_exp['_id'] = six.moves.range(0, len(C_exp))
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_2(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C1_ex_1.csv']), ltable=A, rtable=B)
C2 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C2_ex_1.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C2])
C_exp = read_csv_metadata(os.sep.join([bc_datasets_path, 'C_ex_1.csv']), ltable=A, rtable=B)
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_3(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C1_ex_1.csv']), ltable=A, rtable=B)
C2 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C3_ex_2.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C2])
C_exp = read_csv_metadata(os.sep.join([bc_datasets_path, 'C_ex_2.csv']), ltable=A, rtable=B)
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_4(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C1_ex_1.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C1])
# try:
# C1.sort_values(['ltable_ID', 'rtable_ID'], inplace=True)
# except AttributeError:
# C1.sort(['ltable_ID', 'rtable_ID'], inplace=True)
# to_csv_metadata(C1, os.sep.join([bc_datasets_path, 'C1_ex_1.csv']))
C1.reset_index(inplace=True, drop=True)
C1['_id'] = six.moves.range(0, len(C1))
if os.name != 'nt':
self.assertEqual(C.equals(C1), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C1)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_5(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C3_ex_2.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C1])
self.assertEqual(len(C), 0)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C1)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_6(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_1.csv']), ltable=A, rtable=B)
C2 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_2.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C2], 'l_', 'r_')
C_exp = read_csv_metadata(os.sep.join([bc_datasets_path, 'C_ex_4.csv']), ltable=A, rtable=B)
# try:
# C_exp.sort_values(['l_ID', 'r_ID'], inplace=True)
# except AttributeError:
# C_exp.sort(['l_ID', 'r_ID'], inplace=True)
C_exp.reset_index(inplace=True, drop=True)
C_exp['_id'] = six.moves.range(0, len(C_exp))
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_7(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_1.csv']), ltable=A, rtable=B)
C1.rename(columns={'r_address':'address'}, inplace=True)
C2 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_2.csv']), ltable=A, rtable=B)
C = combine_blocker_outputs_via_union([C1, C2], 'l_', 'r_')
C_exp = read_csv_metadata(os.sep.join([bc_datasets_path, 'C_ex_4.csv']), ltable=A, rtable=B)
# C_exp.sort_values(['l_ID', 'r_ID'], inplace=True)
# C_exp.reset_index(inplace=True, drop=True)
# C_exp['_id'] = six.moves.range(0, len(C_exp))
C_exp.drop('r_address', axis=1, inplace=True)
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
def test_blocker_combiner_valid_8(self):
A = read_csv_metadata(path_a)
B = read_csv_metadata(path_b, key='ID')
C1 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_1.csv']), ltable=A, rtable=B)
C1.rename(columns={'l_ID':'ltable_ID'}, inplace=True)
C1.rename(columns={'r_ID':'rtable_ID'}, inplace=True)
cm.set_fk_ltable(C1, 'ltable_ID')
cm.set_fk_rtable(C1, 'rtable_ID')
C2 = read_csv_metadata(os.sep.join([bc_datasets_path, 'C4_ex_2.csv']), ltable=A, rtable=B)
C2.rename(columns={'l_ID':'ltable_ID'}, inplace=True)
C2.rename(columns={'r_ID':'rtable_ID'}, inplace=True)
cm.set_fk_ltable(C2, 'ltable_ID')
cm.set_fk_rtable(C2, 'rtable_ID')
C = combine_blocker_outputs_via_union([C1, C2], 'l_', 'r_')
C_exp = read_csv_metadata(os.sep.join([bc_datasets_path, 'C_ex_4.csv']), ltable=A, rtable=B)
C_exp.rename(columns={'l_ID':'ltable_ID'}, inplace=True)
C_exp.rename(columns={'r_ID':'rtable_ID'}, inplace=True)
cm.set_fk_ltable(C_exp, 'ltable_ID')
cm.set_fk_rtable(C_exp, 'rtable_ID')
# C_exp.sort_values(['l_ID', 'r_ID'], inplace=True)
# C_exp.reset_index(inplace=True, drop=True)
# C_exp['_id'] = six.moves.range(0, len(C_exp))
# C_exp.drop('r_address', axis=1, inplace=True)
if os.name != 'nt':
self.assertEqual(C.equals(C_exp), True)
p1 = cm.get_all_properties(C)
p2 = cm.get_all_properties(C_exp)
self.assertEqual(p1, p2)
@raises(AssertionError)
def test_blocker_combiner_invalid_df(self):
combine_blocker_outputs_via_union([10, 10])
@raises(AssertionError)
def test_blocker_combiner_invalid_lprefix(self):
combine_blocker_outputs_via_union([pd.DataFrame()], None, 'rtable_')
@raises(AssertionError)
def test_blocker_combiner_invalid_rprefix(self):
combine_blocker_outputs_via_union([pd.DataFrame()], 'ltable_', None)
| 43.39899
| 100
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2ad11b961d22ec96a71e67ff3edcd58067150fc4
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|
py
|
Python
|
app/main/model/persistence/__init__.py
|
justTill/whereToLunch
|
555ffd9c987cbbb17bd4b69ab48fe23b58691bbd
|
[
"Unlicense"
] | 1
|
2020-05-21T15:12:19.000Z
|
2020-05-21T15:12:19.000Z
|
app/main/model/persistence/__init__.py
|
justTill/whereToLunch
|
555ffd9c987cbbb17bd4b69ab48fe23b58691bbd
|
[
"Unlicense"
] | 10
|
2020-04-08T14:44:17.000Z
|
2021-06-10T20:00:10.000Z
|
app/main/model/persistence/__init__.py
|
justTill/whereToLunch
|
555ffd9c987cbbb17bd4b69ab48fe23b58691bbd
|
[
"Unlicense"
] | null | null | null |
from .absenceDAO import *
from .customizeDAO import *
from .restaurantDAO import *
from .restaurantStatisticsDAO import *
from .userDAO import *
from .voteDAO import *
| 27.833333
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2ad3588657becb39912282a5e389df4cec9140ce
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py
|
Python
|
tests/test_load_link16.py
|
debrief/pepys-import
|
12d29c0e0f69e1119400334983947893e7679b6b
|
[
"Apache-2.0"
] | 4
|
2021-05-14T08:22:47.000Z
|
2022-02-04T19:48:25.000Z
|
tests/test_load_link16.py
|
debrief/pepys-import
|
12d29c0e0f69e1119400334983947893e7679b6b
|
[
"Apache-2.0"
] | 1,083
|
2019-11-06T17:01:07.000Z
|
2022-03-25T10:26:51.000Z
|
tests/test_load_link16.py
|
debrief/pepys-import
|
12d29c0e0f69e1119400334983947893e7679b6b
|
[
"Apache-2.0"
] | 4
|
2019-11-06T12:00:45.000Z
|
2021-06-09T04:18:28.000Z
|
import os
import unittest
from datetime import datetime
from pint import UnitRegistry
from sqlalchemy import func
from importers.link_16_importer import Link16Importer
from pepys_import.core.store.data_store import DataStore
from pepys_import.file.file_processor import FileProcessor
from tests.utils import check_errors_for_file_contents
FILE_PATH = os.path.dirname(__file__)
DATA_PATH_V1 = os.path.join(
FILE_PATH, "sample_data/track_files/Link16/V1_GEV_09-05-2021T03-54-05.raw-PPLI_201.csv"
)
DATA_PATH_V2 = os.path.join(
FILE_PATH, "sample_data/track_files/Link16/V2_GEV_16-05-2021T00-00-00.raw-SLOTS_JMSG.csv"
)
DATA_PATH_TIGHT_ROLLOVER = os.path.join(
FILE_PATH,
"sample_data/track_files/Link16/V1_Tight_Rollover_GEV_09-05-2021T09-58-16.raw-PPLI_201.csv",
)
DATA_PATH_INVALID_TIME = os.path.join(
FILE_PATH, "sample_data/track_files/Link16/INVALID_10-10-2020T56-24-12.test.csv"
)
DATA_PATH_MIDDLE_DATE = os.path.join(
FILE_PATH, "sample_data/track_files/Link16/GEV_01-02-2019T02-03-04_ii-ii.raw-PPLI_201.csv"
)
DATA_PATH_HOURS_IN_DATA = os.path.join(
FILE_PATH,
"sample_data/track_files/Link16/GEV_hours_in_data_16-05-2021T05-02-15.raw-PPLI_201.csv",
)
DATA_PATH_NO_TIMESTAMP = os.path.join(
FILE_PATH,
"sample_data/track_files/Link16/GEV_no_timestamp.raw-PPLI_201.csv",
)
DATA_PATH_BINARY_IN_HEADER = os.path.join(
FILE_PATH,
"sample_data/track_files/Link16/V2_GEV_binary_in_header_16-05-2021T00-00-00.raw-SLOTS_JMSG.csv",
)
class TestLoadLink16(unittest.TestCase):
def setUp(self):
self.store = DataStore("", "", "", 0, ":memory:", db_type="sqlite")
self.store.initialise()
def tearDown(self):
pass
def test_extract_timestamp_relative_v1(self):
filename = "GEV_09-05-2021T16-10-00.raw-PPLI_201.csv"
assert Link16Importer.extract_timestamp(filename) == "09-05-2021T16-10-00"
def test_extract_timestamp_relative_v2(self):
filename = "GEV_12-09-2021T09-25-00.raw-SLOTS_JMSG.csv"
assert Link16Importer.extract_timestamp(filename) == "12-09-2021T09-25-00"
def test_extract_timestamp_middle_of_filename(self):
filename = "GEV_01-02-2019T02-03-04_ii-ii.raw-PPLI_201.csv"
result = Link16Importer.extract_timestamp(filename)
assert result == "01-02-2019T02-03-04"
assert Link16Importer.timestamp_to_datetime(result) == datetime(2019, 2, 1, 2, 3, 4)
def test_convert_timestamp_ambiguous_day_month(self):
timestamp = "09-05-2021T16-10-00"
assert Link16Importer.timestamp_to_datetime(timestamp) == datetime(2021, 5, 9, 16, 10, 0)
def test_convert_timestamp_missing(self):
timestamp = ""
assert Link16Importer.timestamp_to_datetime(timestamp) is False
def test_convert_timestamp_invalid(self):
timestamp = "123456-7-8-9"
assert Link16Importer.timestamp_to_datetime(timestamp) is False
def test_process_link16_v1_data(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_V1, self.store, False)
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 8
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 7
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 1
results = (
self.store.session.query(self.store.db_classes.State)
# Elevation == 4222 ft
.filter(func.round(self.store.db_classes.State.elevation, 1) == 1286.9)
.order_by(self.store.db_classes.State.time)
.all()
)
# Correct elevations
assert len(results) == 3
# Timestamp checks. Note first measurement has minutes lower than
# minutes in filename. So, it is assumed we're in the next hour,
# so hours have been incremented.
# The altitude filter means the first row in the results set is actually
# the third row in the data.
assert results[0].time == datetime(2021, 5, 9, 5, 46, 38, 100000)
assert results[1].time == datetime(2021, 5, 9, 6, 40, 47, 400000)
assert results[2].time == datetime(2021, 5, 9, 7, 16, 35, 800000)
ureg = UnitRegistry()
# Location
assert round(results[0].location.latitude, 6) == 0.534946
assert round(results[0].location.longitude, 6) == 0.739102
# Heading
assert results[0].heading.to(ureg.degree).magnitude == 63
# Speed
assert results[0].speed.to(ureg.foot_per_second).magnitude == 262
# Platform uses STN
assert results[0].platform.name == "172"
assert results[1].platform.name == "385"
assert results[2].platform.name == "865"
def test_process_link16_v2_data(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_V2, self.store, False)
assert len(processor.importers[0].errors) == 0
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 8
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 8
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 1
results = (
self.store.session.query(self.store.db_classes.State)
# Elevation == 77344 ft
.filter(func.round(self.store.db_classes.State.elevation, 1) == 23574.5).all()
)
assert len(results) == 1
assert results[0].time == datetime(2021, 5, 16, 2, 8, 40, 500000)
ureg = UnitRegistry()
# Location
assert round(results[0].location.latitude, 6) == 0.004833
assert round(results[0].location.longitude, 6) == 0.659078
# Heading
assert results[0].heading.to(ureg.degree).magnitude == 215
# Speed
assert results[0].speed.to(ureg.foot_per_second).magnitude == 0.020808275
# Platform uses STN
assert results[0].platform.name == "892"
def test_invalid_timestamp(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# parse the data
processor.process(DATA_PATH_INVALID_TIME, self.store, False)
errors = processor.importers[0].errors
assert len(errors) == 1
joined_errors = "\n".join(errors[0].values())
assert "Error reading file" in joined_errors
assert "Unable to read date from" in joined_errors
def test_filename_without_timestamp(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# parse the data
processor.process(DATA_PATH_NO_TIMESTAMP, self.store, False)
errors = processor.importers[0].errors
assert len(errors) == 1
joined_errors = "\n".join(errors[0].values())
assert "Error reading file" in joined_errors
assert "Unable to read date from" in joined_errors
def test_file_with_datetime_in_middle_of_filename(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_MIDDLE_DATE, self.store, False)
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 3
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 3
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 1
results = (
self.store.session.query(self.store.db_classes.State)
.filter(func.round(self.store.db_classes.State.elevation, 1) == 1286.9)
.all()
)
assert len(results) == 1
# Timestamp checks
assert results[0].time == datetime(2019, 2, 1, 3, 46, 38, 100000)
ureg = UnitRegistry()
# Location
assert round(results[0].location.latitude, 6) == 0.534946
assert round(results[0].location.longitude, 6) == 0.739102
# Heading
assert results[0].heading.to(ureg.degree).magnitude == 63
# Speed
assert results[0].speed.to(ureg.foot_per_second).magnitude == 262
# Platform uses STN
assert results[0].platform.name == "172"
def test_file_with_hours(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_HOURS_IN_DATA, self.store, False)
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 3
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 3
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 1
results = (
self.store.session.query(self.store.db_classes.State)
.order_by(self.store.db_classes.State.time)
.all()
)
# Correct elevations
assert len(results) == 3
# Timestamp checks
assert results[0].time == datetime(2021, 5, 16, 1, 49, 23, 700000)
assert results[1].time == datetime(2021, 5, 16, 2, 8, 24, 600000)
assert results[2].time == datetime(2021, 5, 16, 2, 46, 38, 100000)
# Only focus on the last one to make sure there isn't anything odd
# about parsing when we have hour dates
ureg = UnitRegistry()
# Location
assert round(results[2].location.latitude, 6) == 0.534946
assert round(results[2].location.longitude, 6) == 0.739102
# Heading
assert results[2].heading.to(ureg.degree).magnitude == 63
# Speed
assert results[2].speed.to(ureg.foot_per_second).magnitude == 262
# Platform uses STN
assert results[2].platform.name == "172"
def test_invalid_file_contents_v1(self):
link16_importer = Link16Importer()
# Not enough tokens test
check_errors_for_file_contents(
"PPLI,TOD\nSomeStr,49:23.7",
"Not enough tokens",
link16_importer,
filename="link16_10-10-2020T01-02-03.test.csv",
)
def test_invalid_file_contents_v2(self):
link16_importer = Link16Importer()
# Not enough tokens test
check_errors_for_file_contents(
"Xmt/Rcv,SlotTime\nSomeStr,59:31.6",
"Not enough tokens",
link16_importer,
filename="link16_10-10-2020T01-02-03.test.csv",
)
def test_non_zero_timestamp(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_TIGHT_ROLLOVER, self.store, False)
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
self.assertEqual(len(states), 8)
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
self.assertEqual(len(platforms), 7)
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
self.assertEqual(len(datafiles), 1)
# Heading changed to control ordering
results = (
self.store.session.query(self.store.db_classes.State)
.order_by(self.store.db_classes.State.heading)
.all()
)
assert len(results) == 8
assert results[0].time == datetime(2021, 5, 9, 10, 3, 12, 230000)
assert results[1].time == datetime(2021, 5, 9, 10, 8, 24, 600000)
assert results[2].time == datetime(2021, 5, 9, 10, 46, 38, 100000)
assert results[3].time == datetime(2021, 5, 9, 11, 38, 18, 0)
def test_binary_in_header(self):
processor = FileProcessor(archive=False)
processor.register_importer(Link16Importer())
# check states empty
with self.store.session_scope():
# there must be no states at the beginning
states = self.store.session.query(self.store.db_classes.State).all()
assert len(states) == 0
# there must be no platforms at the beginning
platforms = self.store.session.query(self.store.db_classes.Platform).all()
assert len(platforms) == 0
# there must be no datafiles at the beginning
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
assert len(datafiles) == 0
# parse the data
processor.process(DATA_PATH_BINARY_IN_HEADER, self.store, False)
# check data got created
with self.store.session_scope():
# there must be states after the import
states = self.store.session.query(self.store.db_classes.State).all()
self.assertEqual(len(states), 2)
# there must be platforms after the import
platforms = self.store.session.query(self.store.db_classes.Platform).all()
self.assertEqual(len(platforms), 2)
# there must be one datafile afterwards
datafiles = self.store.session.query(self.store.db_classes.Datafile).all()
self.assertEqual(len(datafiles), 1)
# Heading changed to control ordering
results = (
self.store.session.query(self.store.db_classes.State)
.order_by(self.store.db_classes.State.heading)
.all()
)
assert len(results) == 2
assert results[0].time == datetime(2021, 5, 16, 0, 59, 31, 600000)
assert results[1].time == datetime(2021, 5, 16, 1, 18, 45, 200000)
if __name__ == "__main__":
unittest.main()
| 41.148472
| 100
| 0.624589
| 2,359
| 18,846
| 4.860534
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| 0.809437
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| 457
| 101
| 41.238512
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| 0
| 0
|
0
| 6
|
2af7c07c9ae2b14b7cf18224debd5711a814e231
| 71
|
py
|
Python
|
banana_octo_py/utils.py
|
charlesreid1/banana-octo-py
|
aab96d7550121dc876ebcac7d0343aebda9199c8
|
[
"MIT"
] | null | null | null |
banana_octo_py/utils.py
|
charlesreid1/banana-octo-py
|
aab96d7550121dc876ebcac7d0343aebda9199c8
|
[
"MIT"
] | null | null | null |
banana_octo_py/utils.py
|
charlesreid1/banana-octo-py
|
aab96d7550121dc876ebcac7d0343aebda9199c8
|
[
"MIT"
] | null | null | null |
def hello_utils():
return "Hello world! This is the utils.py file"
| 23.666667
| 51
| 0.704225
| 12
| 71
| 4.083333
| 0.833333
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| 71
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| 0
|
0
| 6
|
6309e059b8d95ee0bba161437252b990b25a2813
| 11,166
|
py
|
Python
|
src/PythonUnitTests/HistogramTests.py
|
AvenSun/numpy.net
|
c055f7eac174692fc801c923b6cfd08f634eca9c
|
[
"BSD-3-Clause"
] | 59
|
2019-01-20T19:43:05.000Z
|
2022-03-26T06:08:51.000Z
|
src/PythonUnitTests/HistogramTests.py
|
AvenSun/numpy.net
|
c055f7eac174692fc801c923b6cfd08f634eca9c
|
[
"BSD-3-Clause"
] | 21
|
2019-06-06T17:45:01.000Z
|
2022-03-30T10:37:24.000Z
|
src/PythonUnitTests/HistogramTests.py
|
AvenSun/numpy.net
|
c055f7eac174692fc801c923b6cfd08f634eca9c
|
[
"BSD-3-Clause"
] | 7
|
2019-05-12T21:06:18.000Z
|
2022-02-13T12:23:23.000Z
|
import unittest
import numpy as np
from nptest import nptest
class HistogramTests(unittest.TestCase):
#region bincount
def test_bincount_1(self):
x = np.arange(5)
a = np.bincount(x)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7])
a = np.bincount(x)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
a = np.bincount(x)
print(a)
print(a.size == np.amax(x)+1)
def test_bincount_2(self):
x = np.arange(5, dtype=np.int64)
a = np.bincount(x)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7], dtype=np.int16)
a = np.bincount(x)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7, 23], dtype=np.int8)
a = np.bincount(x)
print(a)
print(a.size == np.amax(x)+1)
def test_bincount_3(self):
w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
x = np.arange(6, dtype=np.int64)
a = np.bincount(x, weights=w)
print(a)
x = np.array([0, 1, 3, 2, 1, 7], dtype=np.int16)
a = np.bincount(x,weights=w)
print(a)
x = np.array([0, 1, 3, 2, 1, 7], dtype=np.int8)
a = np.bincount(x, weights=w)
print(a)
def test_bincount_4(self):
x = np.arange(5, dtype=np.int64)
a = np.bincount(x, minlength=8)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7], dtype=np.int16)
a = np.bincount(x, minlength=10)
print(a)
x = np.array([0, 1, 1, 3, 2, 1, 7, 23], dtype=np.int8)
a = np.bincount(x, minlength=32)
print(a)
print(a.size == np.amax(x)+1)
def test_bincount_slice(self):
w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6, .19, -0.8, 0.3, 0.5 ]) # weights
x = np.arange(10, dtype=np.int64)
a = np.bincount(x[::2], weights=w[::2])
print(a)
def test_bincount_uint64(self):
try :
x = np.arange(5, dtype=np.uint64)
a = np.bincount(x)
print(a)
self.fail("should have thrown exception")
except:
print("Exception occured")
def test_bincount_double(self):
try :
x = np.arange(5, dtype=np.float64)
a = np.bincount(x)
print(a)
self.fail("should have thrown exception")
except:
print("Exception occured")
def test_bincount_not1d(self):
try :
x = np.arange(100, dtype=np.int64).reshape(10,10);
a = np.bincount(x)
print(a)
self.fail("should have thrown exception")
except:
print("Exception occured")
#endregion
#region digitize
def test_digitize_1(self):
x = np.array([0.2, 6.4, 3.0, 1.6])
bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
inds = np.digitize(x, bins)
print(inds)
def test_digitize_2(self):
x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
bins = np.array([0, 5, 10, 15, 20])
inds = np.digitize(x, bins, right=True)
print(inds)
inds = np.digitize(x, bins, right=False)
print(inds)
def test_digitize_3(self):
x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
bins = np.array([20, 15, 10, 5, 0])
inds = np.digitize(x, bins, right=True)
print(inds)
inds = np.digitize(x, bins, right=False)
print(inds)
#endregion
#region Histogram
def test_histogram_1(self):
x = np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
print(x)
x= np.histogram(np.arange(4), bins=np.arange(5), density=True)
print(x)
x= np.histogram(np.arange(4), bins=np.arange(5), density=False)
print(x)
x = np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
print(x)
def test_histogram_1a(self):
x = np.histogram([1, 2.0, 1.0], bins=[0.0, 1.0, 2.0, 3.0])
print(x)
x= np.histogram(np.arange(4, dtype = np.float), bins=np.arange(5, dtype=np.float64), density=True)
print(x)
x= np.histogram(np.arange(4, dtype=np.int16), bins=np.arange(5, dtype=np.float64), density=False)
print(x)
x = np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
print(x)
def test_histogram_2(self):
x = np.histogram([1, 2, 1], bins=4)
print(x)
x= np.histogram(np.arange(4), bins=5, density=True)
print(x)
x= np.histogram(np.arange(4), bins=5, density=False)
print(x)
x = np.histogram([[1, 2, 1], [1, 0, 1]], bins=8)
print(x)
def test_histogram_3(self):
x = np.histogram([1, 2, 1], bins="auto")
print(x)
x= np.histogram(np.arange(4), bins="doane", density=True)
print(x)
x= np.histogram(np.arange(4), bins="fd", density=False)
print(x)
x = np.histogram([[1, 2, 1], [1, 0, 1]], bins="rice")
print(x)
x= np.histogram(np.arange(4), bins="scott", density=True)
print(x)
x= np.histogram(np.arange(4), bins="sqrt", density=False)
print(x)
x = np.histogram([[1, 2, 1], [1, 0, 1]], bins="sturges")
print(x)
def test_histogram_4(self):
x= np.histogram(np.arange(40), bins=np.arange(5), range=(15.0, 30.0), density=True)
print(x)
x= np.histogram(np.arange(40), bins=np.arange(4), range=(15.0, 30.0), density=False)
print(x)
x= np.histogram(np.arange(40), bins=6, range=(15.0, 30.0), density=True)
print(x)
x= np.histogram(np.arange(40), bins=4, range=(15.0, 30.0), density=False)
print(x)
def test_histogram_5(self):
weights = np.arange(40, dtype=np.float64);
weights.fill(0.5)
x= np.histogram(np.arange(40), bins=np.arange(5), range=(15.0, 30.0), weights=weights, density=True)
print(x)
x= np.histogram(np.arange(40), bins=np.arange(4), range=(15.0, 30.0), weights=weights, density=False)
print(x)
x= np.histogram(np.arange(40), bins=6, range=(15.0, 30.0), weights=weights, density=True)
print(x)
x= np.histogram(np.arange(40), bins=4, range=(15.0, 30.0), weights=weights, density=False)
print(x)
#endregion
#region histogram_bin_edges
def test_histogram_bin_edges_1(self):
arr = np.arange(40, dtype=np.float64);
x = np.histogram_bin_edges(arr, bins='auto', range=(10, 30))
print(x)
x = np.histogram_bin_edges(arr, bins=4, range=(10, 30))
print(x)
x = np.histogram_bin_edges(arr, bins=np.arange(5), range=(10, 30))
print(x)
#endregion
#region histogramdd
def test_histogramdd_1(self):
np.random.seed(8765);
r = np.random.randint(10, 30, 3000)
x = np.histogramdd(r.reshape(-1,4), bins=[2,2,2,2])
print(x)
x = nptest.histogramdd(r.reshape(-1,4), bins=[2.0,2.0,2.0,2.0])
print(x)
x= np.histogramdd(r.reshape(-1,2), bins=[3,3], density=True)
print(x)
x= np.histogramdd(r.reshape(-1,3), bins=[4,4,4], density=False)
print(x)
def test_histogramdd_2(self):
np.random.seed(8765);
r = np.random.randint(10, 30, 300000)
x = nptest.histogramdd(r.reshape(-1,4), bins=[2,2,2,2], range=[(15, 25), (15,25), (15,25), (15,25)])
print(x)
x = nptest.histogramdd(r.reshape(-1,4), bins=[2.0,2.0,2.0,2.0], range=[(20, 20), (20,20), (20,20), (20,20)])
print(x)
x= np.histogramdd(r.reshape(-1,2), bins=[3,3], density=True, range=[(15, 25), (15,25)])
print(x)
x= np.histogramdd(r.reshape(-1,3), bins=[4,4,4], density=False, range=[(15, 25), (15,25), (15,25)])
print(x)
def test_histogramdd_3(self):
np.random.seed(8765);
r = np.random.randint(10, 30, 300000)
weights = np.arange(300000/4, dtype=np.float64);
weights.fill(0.5)
x = nptest.histogramdd(r.reshape(-1,4), bins=[2,2,2,2], range=[(15, 25), (15,25), (15,25), (15,25)], weights=weights)
print(x)
def test_histogramdd_4(self):
np.random.seed(8765);
r = np.random.randint(10, 30, 300000)
x = nptest.histogramdd(r.reshape(-1,4), bins=3, range=[(15, 25), (15,25), (15,25), (15,25)])
print(x)
print("")
print("*******************")
print("")
x= nptest.histogramdd(r.reshape(-1,2), bins=2, normed=True, range=[(15, 25), (15,25)])
print(x)
def test_histogramdd_5(self):
np.random.seed(8765);
r = np.random.randint(10, 30, 300000)
x = nptest.histogramdd(r.reshape(-1,4), bins= np.array([2,2,2,2]), range=[(15, 25), (15,25), (15,25), (15,25)])
print(x)
x = nptest.histogramdd(r.reshape(-1,4), bins= np.array([2.0,2.0,2.0,2.0]), range=[(20, 20), (20,20), (20,20), (20,20)])
print(x)
x= np.histogramdd(r.reshape(-1,2), bins= np.array([3,3]), density=True, range=[(15, 25), (15,25)])
print(x)
x= np.histogramdd(r.reshape(-1,3), bins= np.array([4,4,4]), density=False, range=[(15, 25), (15,25), (15,25)])
print(x)
#endregion
#region histogram2d
def test_histogram2d_1(self):
np.random.seed(8765);
x = np.random.normal(2, 1, 100)
y = np.random.normal(1, 1, 100)
xedges = [0, 1, 3, 5]
yedges = [0, 2, 3, 4, 6]
weights = np.arange(300000/4, dtype=np.float64);
weights.fill(0.5)
H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
print(H)
print(xedges)
print(yedges)
def test_histogram2d_2(self):
np.random.seed(8765);
x = np.random.normal(2, 1, 100)
y = np.random.normal(1, 1, 100)
xedges = [0, 1, 3, 5]
yedges = [0, 2, 3, 4, 6]
weights = np.arange(300000/4, dtype=np.float64);
weights.fill(0.5)
H, xedges, yedges = np.histogram2d(x, y, bins=2)
print(H)
print(xedges)
print(yedges)
def test_histogram2d_3(self):
np.random.seed(8765);
x = np.random.normal(2, 1, 100)
y = np.random.normal(1, 1, 100)
xedges = np.array([0, 1, 3, 5])
yedges = np.array([0, 2, 3, 4, 6])
weights = np.arange(300000/4, dtype=np.float64);
weights.fill(0.5)
H, xedges, yedges = np.histogram2d(x, y, bins=(xedges, yedges))
print(H)
print(xedges)
print(yedges)
#endregion
def test_histogram_prep_1(self):
x = np.arange(1, 21, dtype=np.int64)
binsize = nptest._hist_bin_sqrt(x);
print(binsize);
binsize = nptest._hist_bin_sturges(x);
print(binsize);
binsize = nptest._hist_bin_rice(x);
print(binsize);
binsize = nptest._hist_bin_scott(x);
print(binsize);
binsize = nptest._hist_bin_doane(x);
print(binsize);
binsize = nptest._hist_bin_fd(x);
print(binsize);
binsize = nptest._hist_bin_auto(x);
print(binsize);
if __name__ == '__main__':
unittest.main()
| 25.377273
| 127
| 0.529554
| 1,738
| 11,166
| 3.348101
| 0.06214
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| 0.81526
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| 0.745489
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|
0
| 6
|
630b020e59687209bd27dcd1dd370c0b192745cd
| 83
|
py
|
Python
|
accounts/forms/__init__.py
|
tavoxr/django-crm
|
d6ce34b8e8e93c3ae9853df34641868d4c891125
|
[
"MIT"
] | null | null | null |
accounts/forms/__init__.py
|
tavoxr/django-crm
|
d6ce34b8e8e93c3ae9853df34641868d4c891125
|
[
"MIT"
] | null | null | null |
accounts/forms/__init__.py
|
tavoxr/django-crm
|
d6ce34b8e8e93c3ae9853df34641868d4c891125
|
[
"MIT"
] | null | null | null |
from .orderForm import *
from .CreateUserForm import *
from .customerForm import *
| 27.666667
| 30
| 0.783133
| 9
| 83
| 7.222222
| 0.555556
| 0.307692
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| 83
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| 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
|
2d55510e2c5fb5e22371bda18205dcfb1b5ebd80
| 438
|
py
|
Python
|
password_manager/tests.py
|
BillyGLW/__password_manager
|
7066a1380c8a95dc3e64ca64248305773784c406
|
[
"MIT"
] | null | null | null |
password_manager/tests.py
|
BillyGLW/__password_manager
|
7066a1380c8a95dc3e64ca64248305773784c406
|
[
"MIT"
] | 5
|
2020-08-14T11:04:47.000Z
|
2022-02-10T10:08:39.000Z
|
password_manager/tests.py
|
BillyGLW/__password_manager
|
7066a1380c8a95dc3e64ca64248305773784c406
|
[
"MIT"
] | 1
|
2020-08-13T20:43:57.000Z
|
2020-08-13T20:43:57.000Z
|
from django.test import TestCase
from django.test.client import Client
from django.urls import reverse
class Password_Manager_CryptoTestCase(TestCase):
def setUp(self):
self.c = Client()
def test_password_manager_redirect(self):
''' since pm is on TODO list it should always return 302 '''
response = self.c.get(reverse("rrol"))
# self.assertEqual(response.status_code, 404)
self.assertEqual(response.status_code, 302)
| 24.333333
| 62
| 0.762557
| 62
| 438
| 5.274194
| 0.564516
| 0.091743
| 0.085627
| 0.17737
| 0.201835
| 0
| 0
| 0
| 0
| 0
| 0
| 0.023873
| 0.139269
| 438
| 17
| 63
| 25.764706
| 0.843501
| 0.223744
| 0
| 0
| 0
| 0
| 0.012048
| 0
| 0
| 0
| 0
| 0.058824
| 0.111111
| 1
| 0.222222
| false
| 0.222222
| 0.333333
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
2d61191f7495e1652e9569f63929b5c9d23ce619
| 19,787
|
py
|
Python
|
CUB-experiments/model.py
|
ashleylqx/AIB
|
77e418cac52f0ca5f2a7c54927468a7bd75a8fc9
|
[
"MIT"
] | 5
|
2021-05-23T13:05:45.000Z
|
2022-02-13T21:40:59.000Z
|
CUB-experiments/model.py
|
ashleylqx/AIB
|
77e418cac52f0ca5f2a7c54927468a7bd75a8fc9
|
[
"MIT"
] | null | null | null |
CUB-experiments/model.py
|
ashleylqx/AIB
|
77e418cac52f0ca5f2a7c54927468a7bd75a8fc9
|
[
"MIT"
] | 3
|
2021-08-11T03:23:31.000Z
|
2021-11-17T01:48:52.000Z
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
from utils import cuda
import time
from numbers import Number
import pdb
from torchvision import models
from torchvision.models.resnet import Bottleneck, conv1x1, BasicBlock
from config import *
from torch.distributions import Normal, Independent, kl
def xavier_init(ms):
for m in ms:
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight,gain=nn.init.calculate_gain('relu'))
m.bias.data.zero_()
# conv part, MLP part, deconv part
class Flatten(torch.nn.Module):
def forward(self, x):
batch_size = x.shape[0]
return x.view(batch_size, -1)
class Reshape(nn.Module):
def __init__(self, shape):
super(Reshape, self).__init__()
self.shape = shape
def forward(self, x):
return x.view((x.size(0),) + self.shape)
class VaNet_vgg(nn.Module):
def __init__(self, K=K_dim, att_K=A_dim, return_att=False, backbone='vgg'):
super(VaNet_vgg, self).__init__()
print('VaNet_vgg')
self.K = K
self.att_K = att_K
self.return_att = return_att
assert backbone in ['vgg16', 'wrn_50_2'], 'backbone must be vgg16 or wrn_50_2'
self.backbone = backbone
''''''
'''------- vgg16 ------'''
blocks = models.vgg16(pretrained=False)
self.features = blocks.features[:-1] # f29 vgg16 (512, 6, 6)
self.channel = 512 * 3 * 3
self.encode = nn.Sequential(
nn.AdaptiveAvgPool2d((3, 3)), # the first model
Flatten(),
nn.Linear(self.channel, 2 * self.K)) # train0714_CF10
self.att_dim = 14 # wol for f29
self.att_module_mu = nn.Sequential(
# nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), # [bs, 512, 6, 6]
# nn.ReLU(inplace=True), # [bs, 512, 6, 6] mu_m1
# nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), # [bs, 512, 6, 6]
# nn.ReLU(inplace=True), # [bs, 512, 6, 6] mu_m2
# nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), # [bs, 512, 6, 6]
# nn.ReLU(inplace=True), # =================== # [bs, 512, 6, 6] _mu_m3
nn.Conv2d(512, 1, 3, stride=1, padding=1), # f11 original
nn.Sigmoid() # sigmu
)
'''------ common part ---------'''
self.att_module_std = nn.Sequential(
nn.Conv2d(1, 1, 3, stride=1, padding=1), #conv2
)
self.decode = nn.Sequential(
nn.Linear(self.K, self.K),
nn.ReLU(True),
nn.Linear(self.K, n_class))
# f4 f7 f11
def forward(self, x, num_sample=1, train=True):
f = self.features(x) # for vgg16
att_mu = self.att_module_mu(f)
#att_mu = att_mu.view(x.size(0), -1) # conv
att_std = self.att_module_std(att_mu) # conv, conv2
att_mu = att_mu.view(x.size(0), -1)
att_std = att_std.view(x.size(0), -1) # conv2
#att_std = self.att_module_std(f)
#att_std = att_std.view(x.size(0), -1) # conv3
att_std = F.softplus(att_std - 5, beta=1) # comment this for new5, new6; best choice?
# att_std = F.softplus(att_std, beta=1) # new3_2_sigmu
# att_std = F.softplus(att_std - 10, beta=1) # new3_2_sigmu
att_mask = self.reparametrize_n(att_mu, att_std, num_sample) # (num_sample, bs, self.att_K)
# in the new version, is (num_sample, bs, self.att_dim * self.att_dim)
# pdb.set_trace()
att_mask = att_mask.view(num_sample, x.size(0), self.att_dim * self.att_dim)
# pdb.set_trace()
att_mask_reshape = att_mask.view(-1, self.att_dim * self.att_dim)
att_mask_reshape = att_mask_reshape.view(-1, self.att_dim, self.att_dim).unsqueeze(1)
# pdb.set_trace()
# pdb.set_trace()
# f = self.encode_pre(f)
if num_sample > 1:
f_rpt = f.repeat(num_sample, 1, 1, 1)
f_att = torch.mul(f_rpt, att_mask_reshape) # (num_sample, bs, h, w)-->(num_sample*bs, h, w)
f_att = f_rpt + f_att
else:
f_att = torch.mul(f, att_mask_reshape) # (num_sample, bs, h, w)-->(num_sample*bs, h, w)
f_att = f + f_att
statistics = self.encode(f_att)
mu = statistics[:,:self.K]
std = F.softplus(statistics[:,self.K:]-5, beta=1)
encoding = self.reparametrize_n(mu,std,num_sample) # (num_sample, num_sample*bs, self.K)
encoding = encoding.view(num_sample, num_sample, x.size(0), self.K) # (num_sample*num_sample*bs, self.K)
logit = self.decode(encoding)
# pdb.set_trace()
# if num_sample == 1 : logit = logit.squeeze(0).squeeze(0)
# # elif num_sample > 1 : logit = F.softmax(logit, dim=2).mean(0)
# elif num_sample > 1 : logit = logit.mean(0).mean(0)
logit = logit.mean(0).mean(0)
if train:
# # normal_prior = Independent(Normal(torch.tensor([0.0]), torch.tensor([1.0])), 1)
# normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
# # ori_latent_prior = Independent(Normal(loc=mu_ori, scale=torch.exp(std_ori)).expand(normal_prior.batch_shape), 1)
# ori_latent_prior = Independent(Normal(loc=mu_ori, scale=torch.exp(std_ori)), 1)
# latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1) # Independent(base_distribution, reinterpreted_batch_ndims, validate_args=None)
#
# latent_loss1 = torch.mean(self.kl_divergence(ori_latent_prior, normal_prior))
# latent_loss2 = torch.mean(self.kl_divergence(latent_prior, ori_latent_prior))
# -----------------
normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1)
latent_loss = torch.mean(self.kl_divergence(latent_prior, normal_prior))
return logit, logit, latent_loss, latent_loss # repeat to fulfil the format
# ---------old-------------
# normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
# latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1)
# latent_loss = torch.mean(self.kl_divergence(latent_prior, normal_prior))
#
# return logit, logit_ori, latent_loss, latent_loss
# ---------old2-----------
# return logit, logit_ori, mu, std
# ---------dcal-----------
else:
return logit, logit, att_mask # repeat to fulfil the format
# pdb.set_trace()
# if self.return_att:
# # return logit, att_mask.mean(0)
# return logit, att_mask
#
# else:
# return (att_mu, att_std), (mu, std), logit
def reparametrize_n(self, mu, std, n=1):
# reference :
# http://pytorch.org/docs/0.3.1/_modules/torch/distributions.html#Distribution.sample_n
def expand(v):
if isinstance(v, Number):
return torch.Tensor([v]).expand(n, 1)
else:
return v.expand(n, *v.size())
if n != 1 :
mu = expand(mu)
std = expand(std)
eps = Variable(cuda(std.data.new(std.size()).normal_(), std.is_cuda))
return mu + eps * std
def weight_init(self):
for m in self._modules:
xavier_init(self._modules[m])
if pre_train:
print('Loading pretrained weights ...')
ckpt_file = base_path + 'DataSets/GazeFollow/checkpoints/vgg16.pth'
pretrained_dict = torch.load(ckpt_file)
model_dict = self.state_dict()
# pdb.set_trace()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
self.load_state_dict(model_dict)
def _make_layer(self, block, planes, blocks, stride = 1, dilate = False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def kl_divergence(self, latent_space1, latent_space2):
kl_div = kl.kl_divergence(latent_space1, latent_space2)
return kl_div
class VaNet_wrn(nn.Module):
def __init__(self, K=K_dim, att_K=A_dim, return_att=False, backbone='vgg'):
super(VaNet_wrn, self).__init__()
print('VaNet_wrn')
self.K = K
self.att_K = att_K
self.return_att = return_att
assert backbone in ['vgg16', 'wrn_50_2'], 'backbone must be vgg16 or wrn_50_2'
self.backbone = backbone
''''''
'''------- wrn_50_2 ------'''
blocks = models.wide_resnet50_2(pretrained=False)
self.conv1 = blocks.conv1
self.bn1 = blocks.bn1
self.relu = blocks.relu
self.maxpool = blocks.maxpool
self.layer1 = blocks.layer1
self.layer2 = blocks.layer2
self.layer3 = blocks.layer3
self.layer4 = blocks.layer4
block = Bottleneck
layers = [3,4,6,3]
width_per_group = 64 * 2
self._norm_layer = nn.BatchNorm2d
self.inplanes = 64
self.dilation = 1
self.groups = 1
self.base_width = width_per_group
self.tmp_layer = self._make_layer(block, 64, layers[0])
self.tmp_layer = self._make_layer(block, 128, layers[1], stride=2, dilate=False)
self.tmp_layer = self._make_layer(block, 256, layers[2])
self.att_layer4 = self._make_layer(block, 512, layers[3])
self.channel = 2048
self.encode = nn.Sequential(
# self.layer3,
self.layer4,
nn.AdaptiveAvgPool2d((1, 1)), # the first model
Flatten(),
nn.Linear(self.channel, 2 * self.K)) # train0714_CF10
self.att_dim = 14 # wol for layer2
self.att_module_mu = nn.Sequential(
self.att_layer4,
nn.Conv2d(self.channel, 1, 3, stride=1, padding=1),
nn.Sigmoid() # sigmu
)
'''------ common part ---------'''
self.att_module_std = nn.Sequential(
nn.Conv2d(1, 1, 3, stride=1, padding=1), #conv2
)
self.decode = nn.Sequential(
nn.Linear(self.K, self.K),
nn.ReLU(True),
nn.Linear(self.K, n_class))
# f4 f7 f11
def forward(self, x, num_sample=1, train=True):
f = self.maxpool(self.relu(self.bn1(self.conv1(x))))
f = self.layer1(f)
f = self.layer2(f)
f = self.layer3(f)
# pdb.set_trace()
att_mu = self.att_module_mu(f)
#att_mu = att_mu.view(x.size(0), -1) # conv
att_std = self.att_module_std(att_mu) # conv, conv2
att_mu = att_mu.view(x.size(0), -1)
att_std = att_std.view(x.size(0), -1) # conv2
#att_std = self.att_module_std(f)
#att_std = att_std.view(x.size(0), -1) # conv3
att_std = F.softplus(att_std - 5, beta=1) # comment this for new5, new6; best choice?
# att_std = F.softplus(att_std, beta=1) # new3_2_sigmu
# att_std = F.softplus(att_std - 10, beta=1) # new3_2_sigmu
att_mask = self.reparametrize_n(att_mu, att_std, num_sample) # (num_sample, bs, self.att_K)
# in the new version, is (num_sample, bs, self.att_dim * self.att_dim)
# pdb.set_trace()
att_mask = att_mask.view(num_sample, x.size(0), self.att_dim * self.att_dim)
# pdb.set_trace()
att_mask_reshape = att_mask.view(-1, self.att_dim * self.att_dim)
att_mask_reshape = att_mask_reshape.view(-1, self.att_dim, self.att_dim).unsqueeze(1)
# pdb.set_trace()
# pdb.set_trace()
# f = self.encode_pre(f)
if num_sample > 1:
f_rpt = f.repeat(num_sample, 1, 1, 1)
f_att = torch.mul(f_rpt, att_mask_reshape) # (num_sample, bs, h, w)-->(num_sample*bs, h, w)
f_att = f_rpt + f_att
else:
f_att = torch.mul(f, att_mask_reshape) # (num_sample, bs, h, w)-->(num_sample*bs, h, w)
f_att = f + f_att
statistics = self.encode(f_att)
mu = statistics[:,:self.K]
std = F.softplus(statistics[:,self.K:]-5, beta=1)
encoding = self.reparametrize_n(mu,std,num_sample) # (num_sample, num_sample*bs, self.K)
encoding = encoding.view(num_sample, num_sample, x.size(0), self.K) # (num_sample*num_sample*bs, self.K)
logit = self.decode(encoding)
# pdb.set_trace()
# if num_sample == 1 : logit = logit.squeeze(0).squeeze(0)
# # elif num_sample > 1 : logit = F.softmax(logit, dim=2).mean(0)
# elif num_sample > 1 : logit = logit.mean(0).mean(0)
logit = logit.mean(0).mean(0)
if train:
# # normal_prior = Independent(Normal(torch.tensor([0.0]), torch.tensor([1.0])), 1)
# normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
# # ori_latent_prior = Independent(Normal(loc=mu_ori, scale=torch.exp(std_ori)).expand(normal_prior.batch_shape), 1)
# ori_latent_prior = Independent(Normal(loc=mu_ori, scale=torch.exp(std_ori)), 1)
# latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1) # Independent(base_distribution, reinterpreted_batch_ndims, validate_args=None)
#
# latent_loss1 = torch.mean(self.kl_divergence(ori_latent_prior, normal_prior))
# latent_loss2 = torch.mean(self.kl_divergence(latent_prior, ori_latent_prior))
# -----------------
normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1)
latent_loss = torch.mean(self.kl_divergence(latent_prior, normal_prior))
return logit, logit, latent_loss, latent_loss # repeat to fulfil the format
# ---------old-------------
# normal_prior = Independent(Normal(torch.zeros_like(mu), torch.ones_like(std)), 1)
# latent_prior = Independent(Normal(loc=mu, scale=torch.exp(std)), 1)
# latent_loss = torch.mean(self.kl_divergence(latent_prior, normal_prior))
#
# return logit, logit_ori, latent_loss, latent_loss
# ---------old2-----------
# return logit, logit_ori, mu, std
# ---------dcal-----------
else:
return logit, logit, att_mask # repeat to fulfil the format
# pdb.set_trace()
# if self.return_att:
# # return logit, att_mask.mean(0)
# return logit, att_mask
#
# else:
# return (att_mu, att_std), (mu, std), logit
def reparametrize_n(self, mu, std, n=1):
# reference :
# http://pytorch.org/docs/0.3.1/_modules/torch/distributions.html#Distribution.sample_n
def expand(v):
if isinstance(v, Number):
return torch.Tensor([v]).expand(n, 1)
else:
return v.expand(n, *v.size())
if n != 1 :
mu = expand(mu)
std = expand(std)
eps = Variable(cuda(std.data.new(std.size()).normal_(), std.is_cuda))
return mu + eps * std
def weight_init(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
zero_init_residual = False
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
if pre_train:
print('Loading pretrained weights ...')
ckpt_file = base_path + 'DataSets/GazeFollow/checkpoints/wide_resnet50_2.pth'
pretrained_dict = torch.load(ckpt_file)
model_dict = self.state_dict()
# pdb.set_trace()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
self.load_state_dict(model_dict)
def _make_layer(self, block, planes, blocks, stride = 1, dilate = False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def kl_divergence(self, latent_space1, latent_space2):
kl_div = kl.kl_divergence(latent_space1, latent_space2)
return kl_div
| 39.182178
| 162
| 0.56709
| 2,588
| 19,787
| 4.137558
| 0.110124
| 0.035301
| 0.01681
| 0.01681
| 0.816586
| 0.814811
| 0.798468
| 0.790064
| 0.786048
| 0.786048
| 0
| 0.030766
| 0.30515
| 19,787
| 504
| 163
| 39.259921
| 0.748054
| 0.280588
| 0
| 0.676364
| 0
| 0
| 0.021229
| 0.006853
| 0
| 0
| 0
| 0
| 0.007273
| 1
| 0.065455
| false
| 0
| 0.047273
| 0.003636
| 0.185455
| 0.014545
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2dd32dac76fa0db63ea04c1690e20eb3251e8b36
| 260,287
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int18e/95.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int18e/95.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int18e/95.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 34521
passenger_arriving = (
(11, 11, 6, 9, 7, 3, 2, 1, 2, 1, 5, 2, 0, 9, 6, 8, 9, 8, 6, 5, 8, 3, 4, 2, 1, 0), # 0
(7, 7, 4, 10, 8, 7, 6, 2, 1, 2, 1, 1, 0, 11, 11, 8, 7, 2, 5, 6, 2, 3, 1, 1, 0, 0), # 1
(12, 6, 5, 6, 5, 2, 3, 3, 5, 1, 1, 0, 0, 12, 13, 10, 7, 11, 4, 4, 3, 0, 2, 2, 2, 0), # 2
(10, 5, 4, 12, 8, 2, 5, 4, 3, 2, 2, 1, 0, 14, 8, 8, 8, 7, 5, 5, 7, 2, 4, 0, 0, 0), # 3
(10, 11, 9, 11, 6, 4, 7, 3, 9, 3, 1, 1, 0, 13, 11, 10, 5, 9, 8, 2, 1, 3, 1, 2, 0, 0), # 4
(9, 12, 11, 7, 6, 4, 5, 3, 1, 3, 1, 0, 0, 16, 11, 5, 13, 15, 11, 3, 5, 5, 2, 1, 1, 0), # 5
(11, 21, 9, 9, 7, 4, 7, 8, 5, 4, 0, 1, 0, 13, 11, 13, 6, 11, 7, 8, 4, 2, 9, 3, 1, 0), # 6
(14, 11, 13, 14, 10, 10, 6, 6, 3, 0, 0, 2, 0, 16, 8, 10, 4, 12, 8, 9, 2, 5, 4, 2, 2, 0), # 7
(14, 13, 8, 12, 5, 8, 9, 4, 2, 8, 0, 0, 0, 17, 12, 14, 8, 4, 7, 9, 4, 2, 7, 0, 3, 0), # 8
(17, 13, 11, 11, 8, 5, 7, 6, 12, 3, 1, 2, 0, 12, 9, 13, 12, 8, 8, 5, 4, 3, 4, 9, 0, 0), # 9
(18, 16, 14, 19, 10, 6, 7, 4, 2, 1, 1, 1, 0, 15, 10, 7, 7, 12, 7, 5, 7, 3, 5, 4, 1, 0), # 10
(9, 13, 10, 15, 14, 4, 3, 5, 5, 4, 1, 0, 0, 30, 13, 9, 11, 15, 5, 3, 3, 3, 5, 4, 0, 0), # 11
(8, 14, 16, 21, 10, 9, 5, 8, 5, 4, 5, 0, 0, 10, 9, 15, 10, 16, 8, 9, 4, 5, 7, 0, 0, 0), # 12
(9, 19, 6, 21, 12, 8, 10, 8, 5, 0, 1, 1, 0, 4, 14, 13, 12, 16, 6, 7, 5, 5, 3, 0, 1, 0), # 13
(13, 14, 8, 16, 10, 3, 7, 2, 7, 1, 2, 1, 0, 23, 14, 11, 13, 12, 10, 16, 3, 6, 8, 5, 1, 0), # 14
(18, 18, 13, 15, 9, 8, 8, 5, 7, 5, 0, 1, 0, 14, 19, 10, 9, 12, 8, 9, 5, 2, 5, 2, 1, 0), # 15
(25, 19, 17, 11, 13, 11, 8, 2, 3, 1, 2, 0, 0, 15, 11, 14, 16, 12, 13, 6, 4, 10, 5, 3, 0, 0), # 16
(19, 16, 10, 21, 9, 6, 7, 10, 6, 6, 3, 1, 0, 12, 18, 10, 10, 12, 14, 6, 5, 4, 8, 2, 1, 0), # 17
(16, 15, 7, 12, 15, 9, 3, 4, 7, 3, 1, 3, 0, 22, 14, 3, 5, 11, 12, 5, 1, 7, 6, 5, 1, 0), # 18
(21, 19, 17, 16, 12, 4, 8, 4, 3, 1, 2, 0, 0, 20, 17, 15, 10, 17, 17, 9, 7, 4, 2, 0, 1, 0), # 19
(14, 20, 13, 16, 15, 3, 10, 3, 5, 5, 0, 0, 0, 19, 14, 17, 12, 14, 9, 6, 8, 8, 6, 3, 2, 0), # 20
(17, 18, 8, 21, 17, 3, 5, 7, 5, 5, 3, 4, 0, 18, 7, 19, 5, 11, 18, 6, 5, 8, 1, 4, 1, 0), # 21
(14, 16, 14, 10, 16, 6, 6, 11, 5, 4, 3, 1, 0, 21, 16, 13, 13, 22, 9, 7, 5, 3, 6, 0, 2, 0), # 22
(20, 12, 11, 19, 15, 9, 8, 10, 2, 5, 3, 0, 0, 20, 17, 15, 14, 10, 10, 7, 7, 4, 10, 2, 2, 0), # 23
(16, 18, 17, 14, 13, 7, 7, 5, 7, 5, 4, 0, 0, 14, 22, 20, 15, 13, 6, 9, 4, 9, 8, 3, 1, 0), # 24
(34, 18, 17, 20, 18, 12, 4, 11, 6, 3, 3, 0, 0, 18, 15, 12, 10, 16, 10, 7, 4, 7, 7, 0, 0, 0), # 25
(17, 15, 18, 15, 8, 4, 7, 6, 4, 0, 4, 1, 0, 17, 21, 10, 9, 6, 12, 10, 2, 3, 0, 2, 1, 0), # 26
(13, 19, 18, 21, 15, 4, 7, 4, 6, 4, 3, 1, 0, 32, 19, 18, 12, 9, 12, 9, 4, 9, 10, 2, 1, 0), # 27
(14, 20, 22, 23, 14, 10, 9, 7, 4, 7, 5, 1, 0, 20, 17, 12, 17, 9, 5, 6, 3, 10, 8, 5, 1, 0), # 28
(24, 13, 10, 24, 20, 7, 7, 5, 8, 2, 2, 1, 0, 19, 11, 15, 10, 19, 9, 12, 2, 10, 3, 3, 0, 0), # 29
(21, 17, 11, 17, 14, 9, 6, 10, 6, 0, 1, 0, 0, 14, 16, 12, 8, 13, 9, 12, 2, 9, 8, 2, 2, 0), # 30
(27, 13, 14, 15, 14, 6, 7, 6, 5, 3, 4, 1, 0, 20, 16, 18, 18, 12, 12, 6, 4, 6, 4, 0, 0, 0), # 31
(15, 19, 15, 18, 17, 7, 10, 5, 7, 2, 2, 3, 0, 19, 21, 19, 11, 14, 8, 12, 4, 5, 7, 3, 0, 0), # 32
(12, 24, 18, 27, 14, 6, 11, 7, 13, 1, 3, 2, 0, 14, 9, 12, 9, 14, 12, 7, 6, 6, 4, 4, 1, 0), # 33
(23, 17, 15, 20, 14, 7, 7, 7, 6, 4, 0, 1, 0, 23, 16, 9, 12, 14, 10, 7, 4, 10, 6, 3, 6, 0), # 34
(19, 22, 12, 18, 11, 6, 7, 7, 5, 4, 1, 2, 0, 23, 17, 13, 8, 11, 9, 7, 7, 5, 6, 3, 1, 0), # 35
(18, 12, 15, 18, 2, 13, 8, 7, 10, 2, 2, 2, 0, 25, 21, 12, 15, 15, 11, 9, 4, 8, 4, 4, 0, 0), # 36
(16, 18, 14, 18, 14, 5, 5, 8, 7, 4, 4, 0, 0, 13, 26, 15, 6, 9, 9, 5, 2, 11, 7, 5, 3, 0), # 37
(22, 16, 10, 17, 13, 8, 9, 4, 15, 3, 2, 1, 0, 16, 24, 9, 11, 22, 9, 11, 1, 7, 3, 1, 0, 0), # 38
(24, 16, 17, 16, 11, 5, 11, 8, 10, 3, 0, 3, 0, 19, 23, 11, 9, 17, 9, 12, 5, 11, 6, 6, 3, 0), # 39
(21, 15, 11, 21, 24, 8, 7, 5, 10, 1, 0, 0, 0, 20, 12, 8, 6, 10, 9, 9, 2, 6, 6, 3, 2, 0), # 40
(18, 13, 17, 11, 12, 7, 7, 6, 12, 3, 5, 1, 0, 18, 14, 21, 12, 17, 10, 5, 6, 5, 3, 1, 2, 0), # 41
(18, 18, 26, 19, 18, 7, 5, 9, 8, 3, 2, 1, 0, 15, 17, 9, 10, 15, 15, 4, 11, 8, 9, 2, 0, 0), # 42
(17, 11, 12, 9, 21, 6, 9, 5, 6, 1, 3, 1, 0, 26, 16, 14, 4, 13, 13, 13, 5, 5, 4, 5, 0, 0), # 43
(14, 13, 15, 23, 15, 7, 9, 4, 11, 3, 3, 3, 0, 21, 17, 11, 11, 14, 14, 6, 6, 11, 3, 3, 0, 0), # 44
(18, 17, 16, 12, 25, 5, 6, 7, 6, 8, 4, 1, 0, 16, 17, 11, 9, 15, 8, 6, 4, 9, 5, 3, 3, 0), # 45
(15, 30, 13, 19, 19, 3, 10, 4, 6, 5, 0, 3, 0, 14, 17, 15, 9, 15, 5, 5, 5, 8, 3, 3, 1, 0), # 46
(22, 19, 20, 16, 14, 6, 8, 8, 8, 2, 1, 2, 0, 15, 18, 12, 12, 13, 8, 10, 8, 4, 7, 4, 3, 0), # 47
(15, 19, 12, 21, 17, 9, 5, 5, 6, 4, 2, 0, 0, 24, 12, 11, 15, 17, 10, 6, 3, 7, 3, 3, 0, 0), # 48
(24, 21, 13, 18, 14, 9, 5, 8, 7, 0, 1, 1, 0, 11, 16, 14, 9, 12, 14, 7, 5, 7, 6, 2, 4, 0), # 49
(23, 18, 25, 22, 13, 1, 6, 8, 10, 6, 0, 3, 0, 24, 18, 12, 16, 9, 14, 7, 2, 6, 5, 4, 3, 0), # 50
(17, 18, 21, 22, 10, 10, 5, 3, 7, 1, 1, 0, 0, 22, 24, 18, 16, 12, 10, 4, 8, 9, 6, 1, 0, 0), # 51
(17, 17, 22, 9, 8, 11, 11, 6, 7, 3, 1, 1, 0, 12, 14, 10, 12, 14, 4, 4, 5, 8, 7, 3, 0, 0), # 52
(14, 16, 20, 13, 17, 5, 7, 9, 6, 2, 4, 0, 0, 19, 15, 9, 8, 11, 9, 6, 6, 7, 4, 3, 1, 0), # 53
(23, 18, 11, 18, 11, 8, 6, 4, 6, 2, 1, 0, 0, 20, 15, 10, 8, 12, 9, 9, 5, 5, 10, 7, 1, 0), # 54
(22, 14, 16, 18, 15, 4, 13, 9, 7, 5, 1, 0, 0, 18, 12, 9, 9, 15, 9, 9, 6, 7, 6, 1, 2, 0), # 55
(23, 13, 19, 18, 13, 7, 8, 9, 9, 11, 1, 2, 0, 13, 10, 8, 6, 13, 7, 5, 2, 5, 4, 4, 2, 0), # 56
(22, 14, 14, 13, 11, 8, 12, 3, 8, 2, 3, 5, 0, 14, 20, 12, 8, 26, 7, 12, 8, 6, 4, 2, 2, 0), # 57
(18, 10, 13, 22, 11, 6, 9, 5, 5, 3, 3, 2, 0, 13, 16, 10, 10, 8, 9, 8, 5, 4, 9, 2, 4, 0), # 58
(24, 12, 15, 17, 18, 8, 6, 5, 1, 5, 1, 3, 0, 15, 16, 12, 6, 17, 7, 4, 5, 5, 5, 4, 0, 0), # 59
(20, 17, 11, 9, 17, 7, 7, 3, 7, 3, 3, 1, 0, 21, 9, 14, 10, 20, 9, 11, 3, 5, 8, 2, 2, 0), # 60
(14, 14, 11, 10, 18, 4, 7, 7, 9, 2, 3, 2, 0, 13, 16, 17, 8, 20, 8, 7, 7, 7, 9, 2, 2, 0), # 61
(12, 18, 20, 21, 13, 5, 5, 5, 10, 1, 0, 2, 0, 22, 21, 10, 11, 14, 7, 8, 2, 13, 3, 1, 1, 0), # 62
(23, 14, 11, 12, 14, 8, 10, 2, 8, 3, 4, 0, 0, 15, 9, 10, 9, 14, 4, 7, 4, 9, 4, 2, 0, 0), # 63
(18, 20, 10, 15, 26, 6, 8, 10, 6, 4, 5, 0, 0, 17, 14, 13, 6, 14, 8, 6, 4, 6, 6, 3, 1, 0), # 64
(23, 16, 11, 16, 11, 9, 2, 11, 6, 3, 3, 2, 0, 18, 14, 11, 9, 12, 12, 7, 4, 4, 5, 0, 1, 0), # 65
(18, 20, 15, 15, 8, 2, 6, 6, 10, 2, 4, 0, 0, 25, 8, 15, 10, 9, 8, 7, 5, 8, 5, 2, 2, 0), # 66
(18, 11, 15, 15, 13, 8, 6, 5, 9, 5, 3, 0, 0, 18, 19, 14, 7, 6, 12, 9, 2, 7, 10, 3, 1, 0), # 67
(13, 17, 14, 13, 15, 4, 5, 4, 3, 1, 4, 2, 0, 22, 15, 19, 8, 17, 9, 6, 4, 7, 5, 2, 1, 0), # 68
(19, 18, 17, 19, 10, 9, 4, 4, 7, 1, 3, 2, 0, 18, 13, 17, 9, 9, 4, 11, 3, 4, 5, 1, 1, 0), # 69
(26, 15, 8, 15, 9, 11, 6, 2, 6, 3, 2, 1, 0, 18, 12, 8, 13, 8, 5, 6, 6, 6, 11, 2, 2, 0), # 70
(21, 10, 10, 12, 15, 3, 5, 5, 9, 2, 1, 1, 0, 8, 14, 9, 9, 10, 11, 4, 8, 8, 5, 4, 2, 0), # 71
(15, 10, 24, 14, 12, 8, 3, 10, 4, 2, 1, 3, 0, 21, 20, 5, 8, 12, 8, 5, 3, 8, 5, 2, 1, 0), # 72
(20, 18, 19, 16, 11, 4, 8, 7, 8, 1, 5, 2, 0, 18, 13, 15, 12, 19, 9, 4, 6, 5, 8, 3, 2, 0), # 73
(28, 20, 15, 12, 15, 7, 3, 1, 4, 1, 1, 1, 0, 14, 18, 10, 8, 11, 10, 1, 4, 10, 3, 2, 1, 0), # 74
(18, 15, 23, 12, 7, 4, 5, 7, 7, 3, 3, 0, 0, 25, 12, 9, 6, 12, 6, 7, 3, 3, 8, 2, 2, 0), # 75
(15, 17, 20, 13, 12, 7, 8, 4, 11, 7, 2, 0, 0, 15, 14, 7, 11, 12, 6, 4, 5, 7, 4, 4, 0, 0), # 76
(21, 13, 19, 21, 12, 10, 10, 4, 4, 3, 2, 2, 0, 15, 9, 12, 3, 14, 6, 9, 5, 4, 6, 2, 1, 0), # 77
(24, 13, 10, 13, 13, 10, 5, 3, 6, 5, 2, 2, 0, 11, 7, 12, 13, 16, 7, 5, 10, 5, 11, 3, 2, 0), # 78
(18, 14, 13, 19, 11, 11, 6, 9, 6, 3, 3, 0, 0, 24, 14, 11, 8, 13, 7, 7, 8, 10, 3, 6, 3, 0), # 79
(17, 13, 15, 15, 13, 3, 9, 9, 3, 2, 6, 1, 0, 19, 16, 14, 9, 8, 7, 7, 4, 3, 5, 1, 1, 0), # 80
(12, 22, 26, 14, 14, 6, 4, 5, 7, 1, 2, 3, 0, 20, 14, 9, 7, 9, 5, 5, 5, 6, 2, 2, 3, 0), # 81
(23, 10, 18, 7, 15, 5, 7, 7, 5, 2, 4, 1, 0, 18, 10, 16, 13, 18, 2, 5, 8, 9, 3, 1, 1, 0), # 82
(18, 18, 10, 26, 13, 7, 9, 3, 9, 4, 0, 2, 0, 10, 12, 9, 15, 12, 8, 5, 6, 6, 4, 1, 0, 0), # 83
(25, 21, 10, 13, 13, 9, 8, 4, 6, 5, 2, 3, 0, 22, 12, 13, 9, 14, 10, 10, 5, 10, 6, 5, 2, 0), # 84
(24, 13, 19, 15, 10, 7, 8, 6, 5, 5, 1, 2, 0, 15, 13, 7, 8, 12, 4, 4, 7, 9, 11, 3, 2, 0), # 85
(10, 12, 14, 11, 21, 8, 8, 6, 10, 3, 4, 1, 0, 16, 11, 11, 7, 2, 10, 5, 6, 10, 3, 3, 0, 0), # 86
(19, 14, 21, 16, 13, 10, 12, 7, 9, 4, 3, 1, 0, 13, 15, 13, 9, 12, 5, 7, 4, 10, 4, 2, 0, 0), # 87
(25, 18, 12, 18, 15, 9, 7, 7, 6, 5, 2, 4, 0, 14, 21, 9, 5, 8, 6, 7, 10, 10, 3, 4, 3, 0), # 88
(19, 10, 20, 15, 12, 12, 6, 5, 5, 4, 3, 1, 0, 14, 24, 10, 13, 10, 10, 5, 3, 5, 8, 3, 3, 0), # 89
(18, 12, 9, 16, 6, 6, 8, 3, 4, 4, 2, 2, 0, 22, 12, 9, 6, 11, 6, 9, 4, 8, 5, 3, 3, 0), # 90
(23, 9, 13, 17, 10, 7, 8, 4, 6, 2, 0, 1, 0, 19, 18, 8, 5, 13, 8, 10, 4, 8, 7, 7, 0, 0), # 91
(22, 21, 14, 16, 23, 3, 8, 4, 5, 4, 1, 0, 0, 18, 14, 9, 9, 11, 10, 10, 4, 3, 3, 1, 6, 0), # 92
(15, 13, 17, 14, 9, 6, 6, 4, 6, 5, 1, 0, 0, 17, 19, 6, 13, 12, 7, 3, 1, 8, 7, 1, 1, 0), # 93
(20, 15, 18, 13, 13, 11, 1, 5, 7, 2, 7, 1, 0, 19, 11, 17, 15, 10, 8, 7, 4, 11, 7, 2, 1, 0), # 94
(15, 8, 16, 17, 17, 6, 9, 1, 6, 4, 0, 0, 0, 18, 16, 6, 11, 8, 7, 2, 4, 8, 4, 3, 1, 0), # 95
(14, 14, 18, 10, 8, 6, 6, 6, 8, 2, 0, 1, 0, 19, 7, 9, 15, 10, 12, 2, 3, 6, 3, 0, 2, 0), # 96
(19, 12, 12, 21, 12, 8, 7, 3, 6, 0, 2, 0, 0, 12, 12, 6, 4, 18, 8, 8, 6, 11, 4, 2, 4, 0), # 97
(19, 14, 13, 14, 20, 8, 7, 8, 7, 3, 1, 1, 0, 14, 11, 8, 7, 8, 7, 7, 4, 8, 3, 2, 1, 0), # 98
(17, 20, 13, 16, 12, 7, 5, 7, 6, 5, 2, 1, 0, 16, 15, 6, 10, 17, 8, 8, 7, 8, 1, 6, 1, 0), # 99
(15, 14, 17, 22, 13, 7, 5, 6, 12, 2, 4, 1, 0, 19, 14, 9, 6, 16, 8, 4, 6, 6, 3, 5, 0, 0), # 100
(17, 13, 18, 23, 16, 6, 4, 4, 8, 2, 3, 0, 0, 16, 10, 13, 11, 12, 4, 2, 8, 6, 9, 4, 0, 0), # 101
(18, 13, 7, 21, 16, 3, 7, 3, 6, 1, 0, 1, 0, 20, 16, 11, 7, 13, 9, 10, 6, 8, 5, 4, 1, 0), # 102
(13, 19, 16, 19, 9, 9, 13, 10, 7, 1, 1, 4, 0, 19, 13, 12, 15, 12, 6, 2, 3, 6, 6, 3, 3, 0), # 103
(15, 14, 11, 15, 16, 10, 4, 7, 4, 2, 1, 1, 0, 18, 18, 5, 12, 13, 5, 7, 4, 4, 9, 4, 1, 0), # 104
(10, 17, 13, 14, 7, 5, 2, 2, 9, 2, 2, 1, 0, 12, 11, 10, 13, 10, 10, 9, 2, 7, 1, 5, 2, 0), # 105
(21, 13, 15, 20, 15, 3, 6, 5, 7, 4, 2, 0, 0, 16, 16, 6, 8, 15, 4, 7, 2, 6, 2, 1, 1, 0), # 106
(19, 13, 14, 15, 14, 3, 6, 3, 10, 2, 0, 1, 0, 14, 14, 5, 10, 17, 10, 1, 4, 7, 5, 2, 1, 0), # 107
(16, 14, 17, 13, 13, 6, 9, 4, 10, 3, 2, 0, 0, 15, 9, 10, 4, 10, 10, 7, 6, 7, 7, 1, 0, 0), # 108
(13, 16, 17, 6, 15, 6, 9, 4, 9, 0, 3, 1, 0, 27, 12, 10, 7, 12, 6, 9, 6, 5, 6, 4, 2, 0), # 109
(14, 7, 13, 12, 13, 9, 5, 4, 8, 4, 1, 1, 0, 13, 9, 11, 9, 16, 5, 7, 6, 5, 3, 2, 1, 0), # 110
(14, 13, 10, 16, 17, 10, 7, 5, 5, 1, 1, 2, 0, 8, 13, 11, 4, 12, 7, 7, 4, 10, 7, 4, 0, 0), # 111
(27, 10, 11, 17, 12, 6, 8, 3, 8, 2, 1, 3, 0, 17, 16, 15, 7, 14, 3, 4, 3, 8, 10, 0, 1, 0), # 112
(25, 12, 10, 17, 10, 8, 4, 4, 5, 1, 0, 0, 0, 21, 10, 7, 8, 20, 7, 5, 4, 6, 5, 0, 2, 0), # 113
(20, 10, 16, 10, 12, 6, 4, 3, 8, 3, 4, 3, 0, 16, 16, 11, 8, 16, 8, 3, 3, 7, 8, 4, 0, 0), # 114
(19, 13, 16, 27, 11, 3, 4, 7, 3, 1, 1, 3, 0, 16, 12, 13, 8, 18, 9, 6, 10, 4, 5, 4, 1, 0), # 115
(16, 16, 11, 17, 11, 7, 5, 4, 12, 5, 1, 3, 0, 18, 13, 12, 6, 20, 4, 7, 5, 5, 9, 4, 1, 0), # 116
(21, 14, 16, 22, 15, 12, 7, 3, 10, 4, 3, 2, 0, 19, 9, 3, 7, 17, 5, 9, 8, 5, 2, 3, 1, 0), # 117
(18, 11, 15, 19, 20, 4, 6, 4, 6, 1, 4, 0, 0, 26, 8, 9, 3, 20, 9, 2, 6, 5, 4, 2, 0, 0), # 118
(17, 12, 17, 14, 10, 3, 9, 4, 6, 3, 2, 1, 0, 7, 23, 5, 7, 16, 3, 6, 5, 5, 4, 1, 2, 0), # 119
(22, 10, 15, 14, 15, 7, 2, 3, 6, 1, 4, 2, 0, 14, 21, 7, 7, 9, 7, 2, 2, 10, 3, 6, 2, 0), # 120
(21, 16, 14, 13, 7, 9, 4, 2, 6, 2, 6, 0, 0, 9, 11, 16, 10, 14, 6, 4, 2, 3, 5, 3, 2, 0), # 121
(14, 11, 15, 13, 10, 9, 3, 7, 4, 4, 2, 0, 0, 14, 14, 9, 8, 11, 2, 10, 6, 6, 4, 4, 2, 0), # 122
(17, 12, 18, 10, 8, 2, 6, 3, 11, 1, 2, 1, 0, 17, 22, 16, 6, 12, 6, 0, 3, 4, 5, 3, 0, 0), # 123
(18, 14, 15, 10, 9, 8, 9, 4, 5, 3, 1, 1, 0, 28, 12, 9, 6, 6, 5, 8, 4, 5, 5, 3, 1, 0), # 124
(18, 14, 14, 14, 9, 3, 3, 5, 6, 2, 2, 1, 0, 16, 15, 10, 6, 12, 10, 2, 5, 2, 3, 4, 1, 0), # 125
(5, 16, 15, 10, 11, 14, 4, 4, 7, 2, 2, 0, 0, 13, 12, 11, 8, 10, 9, 7, 1, 7, 9, 1, 0, 0), # 126
(14, 16, 9, 13, 12, 6, 2, 4, 10, 1, 1, 0, 0, 21, 15, 11, 6, 10, 9, 7, 6, 7, 6, 5, 2, 0), # 127
(18, 9, 10, 20, 15, 6, 6, 7, 6, 2, 1, 1, 0, 13, 9, 6, 7, 11, 9, 6, 4, 4, 4, 5, 1, 0), # 128
(14, 14, 15, 11, 10, 4, 5, 5, 8, 4, 5, 2, 0, 21, 15, 5, 8, 15, 6, 5, 3, 4, 5, 1, 1, 0), # 129
(11, 15, 16, 11, 11, 8, 6, 8, 5, 3, 3, 3, 0, 13, 4, 15, 6, 18, 10, 1, 3, 7, 3, 3, 0, 0), # 130
(20, 8, 11, 18, 7, 5, 5, 7, 6, 7, 5, 1, 0, 12, 17, 7, 6, 11, 3, 6, 3, 8, 6, 1, 0, 0), # 131
(18, 12, 12, 13, 12, 8, 4, 3, 5, 1, 0, 1, 0, 18, 18, 12, 8, 12, 6, 2, 1, 6, 3, 3, 3, 0), # 132
(19, 8, 13, 15, 17, 11, 3, 5, 6, 1, 1, 0, 0, 16, 4, 10, 14, 8, 6, 4, 4, 4, 6, 3, 2, 0), # 133
(16, 12, 14, 17, 9, 9, 4, 4, 9, 0, 2, 1, 0, 14, 16, 8, 7, 5, 1, 10, 2, 6, 3, 1, 5, 0), # 134
(19, 14, 11, 19, 10, 9, 6, 7, 4, 3, 3, 0, 0, 11, 14, 11, 6, 13, 6, 8, 4, 4, 4, 4, 2, 0), # 135
(15, 8, 11, 12, 14, 8, 6, 5, 9, 2, 3, 0, 0, 18, 11, 11, 8, 9, 6, 7, 5, 5, 5, 0, 0, 0), # 136
(14, 13, 17, 10, 13, 4, 7, 3, 7, 0, 4, 0, 0, 15, 10, 10, 12, 12, 11, 4, 4, 8, 4, 1, 0, 0), # 137
(11, 7, 14, 11, 13, 4, 5, 2, 7, 1, 2, 1, 0, 17, 13, 7, 7, 10, 7, 6, 5, 8, 4, 2, 0, 0), # 138
(13, 10, 7, 9, 14, 3, 11, 8, 5, 6, 2, 1, 0, 14, 14, 12, 8, 16, 11, 5, 3, 5, 4, 0, 1, 0), # 139
(14, 10, 18, 9, 18, 3, 4, 4, 5, 2, 4, 2, 0, 13, 21, 5, 8, 14, 12, 4, 2, 4, 3, 4, 0, 0), # 140
(23, 13, 14, 12, 7, 4, 4, 3, 4, 3, 2, 1, 0, 15, 10, 10, 9, 13, 5, 3, 7, 6, 5, 5, 2, 0), # 141
(18, 9, 16, 10, 11, 5, 4, 4, 4, 0, 2, 1, 0, 15, 17, 8, 8, 13, 5, 6, 5, 7, 5, 0, 0, 0), # 142
(12, 14, 14, 14, 11, 3, 7, 6, 7, 2, 1, 6, 0, 14, 15, 11, 4, 11, 5, 2, 2, 7, 5, 2, 2, 0), # 143
(21, 10, 13, 17, 10, 5, 8, 6, 6, 2, 6, 3, 0, 13, 16, 10, 10, 15, 4, 5, 6, 4, 3, 4, 1, 0), # 144
(12, 5, 13, 16, 11, 4, 6, 6, 10, 1, 2, 1, 0, 15, 17, 9, 4, 20, 6, 5, 4, 8, 5, 2, 0, 0), # 145
(20, 13, 16, 23, 11, 5, 7, 10, 6, 1, 2, 0, 0, 22, 12, 6, 10, 13, 4, 4, 4, 3, 6, 2, 0, 0), # 146
(12, 7, 12, 19, 12, 10, 4, 3, 7, 2, 1, 0, 0, 20, 10, 7, 6, 7, 6, 3, 6, 7, 5, 5, 0, 0), # 147
(20, 7, 11, 10, 7, 9, 3, 5, 12, 3, 2, 4, 0, 14, 18, 10, 7, 12, 6, 6, 0, 4, 2, 3, 1, 0), # 148
(8, 12, 11, 8, 8, 6, 7, 6, 3, 1, 1, 1, 0, 9, 14, 8, 11, 19, 6, 5, 1, 6, 8, 2, 1, 0), # 149
(17, 12, 11, 17, 18, 9, 4, 5, 7, 1, 2, 0, 0, 18, 12, 9, 8, 10, 7, 4, 5, 7, 3, 2, 3, 0), # 150
(9, 16, 12, 17, 10, 4, 4, 9, 5, 1, 0, 2, 0, 12, 11, 5, 9, 6, 9, 5, 3, 4, 4, 1, 0, 0), # 151
(7, 11, 15, 13, 10, 4, 3, 4, 6, 1, 2, 0, 0, 11, 9, 7, 10, 13, 4, 5, 4, 3, 1, 1, 2, 0), # 152
(14, 10, 18, 8, 14, 6, 8, 1, 4, 2, 1, 1, 0, 13, 11, 9, 7, 12, 2, 3, 5, 8, 5, 0, 4, 0), # 153
(15, 6, 12, 16, 12, 2, 2, 1, 8, 4, 0, 2, 0, 12, 13, 9, 13, 16, 4, 5, 3, 6, 5, 2, 1, 0), # 154
(10, 12, 15, 11, 8, 3, 5, 6, 5, 4, 5, 1, 0, 10, 11, 14, 2, 13, 4, 4, 6, 7, 8, 1, 1, 0), # 155
(14, 12, 13, 14, 13, 6, 5, 6, 7, 1, 4, 1, 0, 13, 9, 5, 10, 11, 3, 5, 4, 3, 7, 2, 1, 0), # 156
(10, 10, 15, 16, 8, 4, 8, 1, 1, 6, 2, 1, 0, 16, 13, 6, 9, 13, 3, 5, 6, 9, 1, 3, 0, 0), # 157
(16, 10, 14, 14, 14, 4, 4, 5, 8, 1, 2, 1, 0, 11, 8, 12, 5, 14, 7, 6, 6, 3, 7, 3, 0, 0), # 158
(9, 12, 12, 10, 9, 5, 7, 2, 2, 0, 2, 0, 0, 15, 9, 4, 10, 12, 8, 6, 2, 4, 1, 2, 1, 0), # 159
(9, 8, 10, 5, 8, 4, 5, 3, 2, 2, 1, 1, 0, 12, 14, 9, 7, 8, 6, 8, 2, 5, 4, 3, 2, 0), # 160
(17, 13, 9, 17, 14, 7, 7, 0, 5, 2, 0, 1, 0, 15, 12, 3, 7, 14, 9, 3, 3, 4, 3, 0, 1, 0), # 161
(15, 10, 14, 5, 11, 5, 1, 6, 6, 1, 0, 1, 0, 10, 11, 13, 8, 8, 6, 5, 4, 4, 4, 1, 0, 0), # 162
(16, 8, 17, 12, 11, 10, 7, 3, 5, 2, 2, 2, 0, 15, 8, 9, 8, 9, 8, 1, 5, 7, 2, 2, 0, 0), # 163
(10, 6, 12, 11, 4, 2, 5, 4, 7, 4, 2, 1, 0, 16, 6, 8, 13, 16, 7, 3, 0, 5, 3, 2, 0, 0), # 164
(11, 5, 8, 10, 14, 2, 7, 6, 8, 0, 0, 0, 0, 17, 11, 10, 7, 10, 6, 5, 2, 5, 3, 5, 0, 0), # 165
(11, 11, 10, 8, 6, 1, 3, 1, 5, 2, 0, 0, 0, 15, 13, 9, 7, 10, 5, 6, 2, 2, 4, 2, 2, 0), # 166
(11, 8, 14, 7, 8, 3, 2, 8, 5, 1, 2, 1, 0, 6, 6, 11, 4, 7, 5, 3, 1, 5, 2, 2, 0, 0), # 167
(10, 3, 7, 12, 12, 2, 3, 5, 1, 1, 2, 1, 0, 15, 14, 7, 4, 7, 2, 7, 0, 0, 3, 3, 1, 0), # 168
(8, 13, 8, 15, 9, 5, 4, 6, 4, 1, 1, 1, 0, 7, 12, 3, 6, 14, 8, 2, 4, 4, 2, 7, 1, 0), # 169
(9, 10, 13, 7, 8, 1, 5, 3, 7, 0, 2, 0, 0, 11, 9, 5, 5, 11, 6, 5, 3, 4, 3, 2, 1, 0), # 170
(19, 7, 14, 2, 9, 6, 2, 6, 8, 2, 0, 0, 0, 13, 8, 4, 5, 10, 9, 2, 4, 8, 3, 3, 0, 0), # 171
(11, 7, 11, 6, 11, 5, 2, 1, 1, 0, 0, 0, 0, 12, 5, 10, 3, 13, 5, 0, 2, 6, 5, 2, 0, 0), # 172
(5, 1, 6, 12, 9, 4, 4, 1, 6, 0, 0, 0, 0, 9, 5, 2, 10, 7, 1, 5, 5, 3, 2, 0, 2, 0), # 173
(6, 5, 13, 5, 6, 3, 1, 2, 4, 2, 0, 1, 0, 16, 6, 6, 6, 4, 3, 4, 1, 6, 1, 0, 1, 0), # 174
(6, 4, 14, 9, 7, 2, 3, 0, 2, 1, 2, 0, 0, 9, 8, 3, 2, 10, 6, 3, 4, 2, 3, 1, 0, 0), # 175
(6, 7, 3, 5, 5, 1, 6, 4, 5, 0, 0, 3, 0, 8, 4, 6, 2, 6, 2, 3, 1, 3, 3, 0, 0, 0), # 176
(7, 9, 4, 5, 6, 4, 1, 2, 6, 1, 0, 3, 0, 6, 8, 5, 5, 4, 6, 2, 1, 5, 4, 2, 0, 0), # 177
(9, 7, 8, 6, 7, 3, 1, 3, 0, 1, 1, 2, 0, 5, 12, 4, 6, 8, 4, 4, 2, 4, 4, 3, 1, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(9.037558041069182, 9.9455194074477, 9.380309813302512, 11.18640199295418, 9.998434093697302, 5.64957887766721, 7.462864107673047, 8.375717111362961, 10.962178311902413, 7.124427027940266, 7.569477294994085, 8.816247140951113, 9.150984382641052), # 0
(9.637788873635953, 10.602109249460566, 9.999623864394273, 11.925259655897909, 10.660482607453627, 6.0227704512766005, 7.955044094274649, 8.927124701230275, 11.686041587399236, 7.59416524609887, 8.069573044721038, 9.398189989465838, 9.755624965391739), # 1
(10.236101416163518, 11.256093307603763, 10.616476113985344, 12.66117786839663, 11.320133352749538, 6.3944732061224006, 8.445273314329269, 9.476325446227955, 12.407016252379588, 8.062044795036982, 8.567681667797364, 9.9778187736955, 10.357856690777442), # 2
(10.830164027663812, 11.904876903485604, 11.228419564775738, 13.391237533557733, 11.974791016803424, 6.763213120653203, 8.93160655496632, 10.021142083490112, 13.122243289657968, 8.526208857167125, 9.061827141289289, 10.55283423287483, 10.955291051257605), # 3
(11.417645067148767, 12.545865358714394, 11.833007219465467, 14.112519554488625, 12.621860286833686, 7.127516173317602, 9.412098603315226, 10.559397350150848, 13.828863682048873, 8.984800614901822, 9.550033442263036, 11.120937106238575, 11.54553953929167), # 4
(11.996212893630318, 13.176463994898459, 12.427792080754532, 14.822104834296708, 13.258745850058704, 7.485908342564186, 9.884804246505404, 11.088913983344266, 14.524018412366805, 9.435963250653593, 10.030324547784838, 11.679828133021466, 12.126213647339089), # 5
(12.5635358661204, 13.794078133646101, 13.010327151342958, 15.517074276089375, 13.882852393696878, 7.836915606841555, 10.347778271666273, 11.60751472020448, 15.204848463426268, 9.877839946834966, 10.500724434920908, 12.227208052458254, 12.694924867859292), # 6
(13.117282343630944, 14.396113096565637, 13.578165433930742, 16.194508782974033, 14.491584604966597, 8.179063944598298, 10.799075465927253, 12.113022297865593, 15.868494818041759, 10.308573885858456, 10.959257080737483, 12.760777603783673, 13.249284693311735), # 7
(13.655120685173882, 14.979974205265378, 14.128859931217914, 16.85148925805807, 15.082347171086255, 8.510879334283002, 11.236750616417757, 12.603259453461705, 16.512098459027772, 10.726308250136594, 11.403946462300778, 13.278237526232465, 13.786904616155851), # 8
(14.174719249761154, 15.543066781353641, 14.659963645904467, 17.485096604448906, 15.652544779274237, 8.830887754344271, 11.658858510267216, 13.076048924126933, 17.132800369198815, 11.129186222081895, 11.83281655667702, 13.777288559039365, 14.305396128851092), # 9
(14.673746396404677, 16.082796146438728, 15.169029580690424, 18.092411725253918, 16.199582116748942, 9.137615183230693, 12.063453934605038, 13.52921344699538, 17.727741531369386, 11.515350984106886, 12.243891340932432, 14.255631441439114, 14.802370723856898), # 10
(15.149870484116411, 16.596567622128973, 15.653610738275788, 18.670515523580516, 16.72086387072876, 9.429587599390864, 12.44859167656065, 13.960575759201147, 18.294062928353988, 11.882945718624095, 12.635194792133248, 14.710966912666459, 15.2754398936327), # 11
(15.600759871908263, 17.081786530032655, 16.111260121360573, 19.216488902536103, 17.21379472843208, 9.705330981273365, 12.812326523263462, 14.367958597878339, 18.82890554296712, 12.23011360804603, 13.004750887345683, 15.140995711956123, 15.722215130637963), # 12
(16.02408291879218, 17.535858191758116, 16.539530732644792, 19.727412765228078, 17.675779377077284, 9.963371307326803, 13.152713261842901, 14.749184700161067, 19.329410358023278, 12.554997834785228, 13.350583603635965, 15.543418578542857, 16.140307927332124), # 13
(16.41750798378009, 17.95618792891366, 16.935975574828465, 20.20036801476383, 18.10422250388278, 10.202234555999762, 13.46780667942839, 15.102076803183444, 19.79271835633696, 12.855741581254202, 13.670716918070312, 15.915936251661408, 16.527329776174614), # 14
(16.77870342588394, 18.34018106310759, 17.298147650611575, 20.632435554250776, 18.496528796066954, 10.420446705740842, 13.755661563149326, 15.424457644079562, 20.215970520722674, 13.130488029865482, 13.963174807714955, 16.256249470546507, 16.880892169624886), # 15
(17.10533760411564, 18.685242915948237, 17.623599962694165, 21.02069628679629, 18.8501029408482, 10.616533734998628, 14.014332700135158, 15.71414995998353, 20.596307833994917, 13.377380363031593, 14.225981249636122, 16.56205897443289, 17.198606600142384), # 16
(17.395078877487137, 18.988778809043904, 17.909885513776235, 21.362231115507804, 19.162349625444907, 10.789021622221714, 14.24187487751528, 15.968976488029472, 20.930871278968173, 13.594561763165041, 14.457160220900038, 16.8310655025553, 17.47808456018655), # 17
(17.645595605010367, 19.248194064002895, 18.154557306557784, 21.654120943492703, 19.43067353707546, 10.936436345858706, 14.436342882419133, 16.18675996535147, 21.216801838456973, 13.780175412678366, 14.654735698572916, 17.060969794148487, 17.716937542216822), # 18
(17.85455614569726, 19.46089400243354, 18.355168343738843, 21.893446673858367, 19.65247936295826, 11.057303884358175, 14.59579150197611, 16.36532312908364, 21.4512404952758, 13.93236449398409, 14.81673165972098, 17.249472588447173, 17.912777038692653), # 19
(18.01962885855975, 19.624283945944132, 18.509271628019405, 22.077289209712237, 19.8251717903117, 11.150150216168733, 14.718275523315652, 16.50248871636009, 21.631328232239156, 14.049272189494726, 14.94117208141047, 17.394274624686105, 18.063214542073485), # 20
(18.13848210260976, 19.735769216143005, 18.614420162099496, 22.202729454161673, 19.94615550635416, 11.213501319738963, 14.801849733567167, 16.596079464314922, 21.754206032161537, 14.1290416816228, 15.026080940707608, 17.49307664210003, 18.165861544818743), # 21
(18.20878423685924, 19.792755134638462, 18.668166948679115, 22.266848310314106, 20.012835198304035, 11.245883173517461, 14.844568919860079, 16.643918110082247, 21.81701487785745, 14.169816152780836, 15.069482214678613, 17.54357937992368, 18.218329539387888), # 22
(18.23470805401675, 19.799502469135803, 18.674861728395065, 22.274875462962967, 20.029917700858675, 11.25, 14.84964720406681, 16.64908888888889, 21.824867222222224, 14.17462609053498, 15.074924466891131, 17.549815637860082, 18.225), # 23
(18.253822343461476, 19.79556666666667, 18.673766666666666, 22.273887500000004, 20.039593704506736, 11.25, 14.8468568627451, 16.6419, 21.823815, 14.17167111111111, 15.074324242424245, 17.548355555555556, 18.225), # 24
(18.272533014380844, 19.78780864197531, 18.671604938271606, 22.27193287037037, 20.049056902070106, 11.25, 14.841358024691358, 16.62777777777778, 21.82173611111111, 14.16585390946502, 15.073134118967452, 17.545473251028806, 18.225), # 25
(18.290838634286462, 19.776346913580248, 18.668406172839507, 22.269033796296295, 20.05830696315799, 11.25, 14.833236092955698, 16.60698888888889, 21.81865722222222, 14.157271275720165, 15.07136487093154, 17.54120823045268, 18.225), # 26
(18.308737770689945, 19.7613, 18.6642, 22.265212499999997, 20.067343557379587, 11.25, 14.822576470588237, 16.579800000000002, 21.814605, 14.146019999999998, 15.069027272727272, 17.535600000000002, 18.225), # 27
(18.3262289911029, 19.742786419753084, 18.659016049382718, 22.260491203703705, 20.076166354344124, 11.25, 14.809464560639071, 16.54647777777778, 21.809606111111112, 14.132196872427985, 15.066132098765433, 17.528688065843625, 18.225), # 28
(18.34331086303695, 19.720924691358025, 18.652883950617287, 22.25489212962963, 20.084775023660796, 11.25, 14.793985766158318, 16.507288888888887, 21.803687222222223, 14.115898683127574, 15.06269012345679, 17.520511934156378, 18.225), # 29
(18.359981954003697, 19.695833333333333, 18.645833333333332, 22.2484375, 20.093169234938827, 11.25, 14.776225490196078, 16.4625, 21.796875, 14.097222222222223, 15.058712121212121, 17.51111111111111, 18.225), # 30
(18.376240831514746, 19.667630864197534, 18.637893827160497, 22.241149537037035, 20.101348657787415, 11.25, 14.756269135802471, 16.412377777777778, 21.78919611111111, 14.07626427983539, 15.054208866442199, 17.500525102880662, 18.225), # 31
(18.392086063081717, 19.636435802469137, 18.629095061728393, 22.233050462962964, 20.10931296181577, 11.25, 14.734202106027599, 16.357188888888892, 21.780677222222224, 14.053121646090535, 15.0491911335578, 17.48879341563786, 18.225), # 32
(18.407516216216216, 19.602366666666665, 18.619466666666668, 22.2241625, 20.117061816633115, 11.25, 14.710109803921569, 16.2972, 21.771345, 14.027891111111112, 15.043669696969696, 17.475955555555554, 18.225), # 33
(18.422529858429858, 19.56554197530864, 18.609038271604938, 22.21450787037037, 20.12459489184864, 11.25, 14.684077632534496, 16.232677777777777, 21.761226111111114, 14.000669465020577, 15.037655331088663, 17.462051028806584, 18.225), # 34
(18.437125557234253, 19.52608024691358, 18.597839506172843, 22.204108796296293, 20.131911857071568, 11.25, 14.656190994916486, 16.163888888888888, 21.750347222222224, 13.971553497942386, 15.031158810325476, 17.447119341563788, 18.225), # 35
(18.45130188014101, 19.484099999999998, 18.5859, 22.192987499999997, 20.139012381911105, 11.25, 14.626535294117646, 16.0911, 21.738735, 13.94064, 15.024190909090908, 17.431200000000004, 18.225), # 36
(18.46505739466174, 19.43971975308642, 18.57324938271605, 22.181166203703704, 20.145896135976457, 11.25, 14.595195933188089, 16.014577777777777, 21.72641611111111, 13.908025761316873, 15.016762401795738, 17.414332510288066, 18.225), # 37
(18.47839066830806, 19.39305802469136, 18.559917283950615, 22.168667129629632, 20.152562788876843, 11.25, 14.562258315177923, 15.934588888888891, 21.713417222222223, 13.873807572016462, 15.00888406285073, 17.396556378600824, 18.225), # 38
(18.491300268591576, 19.34423333333333, 18.545933333333334, 22.1555125, 20.159012010221467, 11.25, 14.527807843137257, 15.8514, 21.699765000000003, 13.838082222222223, 15.000566666666668, 17.37791111111111, 18.225), # 39
(18.503784763023894, 19.293364197530863, 18.531327160493827, 22.14172453703704, 20.165243469619533, 11.25, 14.491929920116196, 15.765277777777781, 21.685486111111114, 13.800946502057615, 14.99182098765432, 17.358436213991773, 18.225), # 40
(18.51584271911663, 19.24056913580247, 18.51612839506173, 22.127325462962965, 20.171256836680264, 11.25, 14.454709949164851, 15.67648888888889, 21.67060722222222, 13.76249720164609, 14.982657800224468, 17.338171193415636, 18.225), # 41
(18.527472704381402, 19.18596666666667, 18.500366666666668, 22.112337500000002, 20.177051781012857, 11.25, 14.416233333333333, 15.5853, 21.655155000000004, 13.72283111111111, 14.97308787878788, 17.317155555555555, 18.225), # 42
(18.538673286329807, 19.12967530864198, 18.484071604938272, 22.096782870370372, 20.182627972226527, 11.25, 14.37658547567175, 15.491977777777779, 21.63915611111111, 13.682045020576133, 14.96312199775533, 17.295428806584365, 18.225), # 43
(18.54944303247347, 19.071813580246914, 18.467272839506176, 22.0806837962963, 20.18798507993048, 11.25, 14.335851779230211, 15.396788888888892, 21.62263722222222, 13.64023572016461, 14.952770931537597, 17.2730304526749, 18.225), # 44
(18.55978051032399, 19.0125, 18.45, 22.064062500000002, 20.193122773733933, 11.25, 14.294117647058824, 15.3, 21.605625, 13.597500000000002, 14.942045454545454, 17.25, 18.225), # 45
(18.569684287392985, 18.951853086419753, 18.432282716049382, 22.046941203703703, 20.198040723246088, 11.25, 14.251468482207699, 15.20187777777778, 21.588146111111108, 13.553934650205761, 14.930956341189674, 17.226376954732512, 18.225), # 46
(18.579152931192063, 18.88999135802469, 18.41415061728395, 22.02934212962963, 20.202738598076163, 11.25, 14.207989687726945, 15.102688888888888, 21.570227222222226, 13.50963646090535, 14.919514365881032, 17.20220082304527, 18.225), # 47
(18.588185009232834, 18.827033333333333, 18.395633333333333, 22.0112875, 20.20721606783336, 11.25, 14.163766666666668, 15.0027, 21.551895000000002, 13.464702222222222, 14.907730303030302, 17.177511111111112, 18.225), # 48
(18.596779089026917, 18.763097530864197, 18.376760493827163, 21.99279953703704, 20.211472802126895, 11.25, 14.118884822076978, 14.902177777777778, 21.53317611111111, 13.419228724279836, 14.895614927048262, 17.152347325102884, 18.225), # 49
(18.604933738085908, 18.698302469135808, 18.357561728395066, 21.973900462962963, 20.21550847056597, 11.25, 14.073429557007989, 14.801388888888889, 21.514097222222222, 13.373312757201646, 14.883179012345678, 17.126748971193418, 18.225), # 50
(18.61264752392144, 18.63276666666667, 18.338066666666666, 21.9546125, 20.219322742759797, 11.25, 14.027486274509805, 14.7006, 21.494685000000004, 13.32705111111111, 14.870433333333335, 17.10075555555556, 18.225), # 51
(18.619919014045102, 18.56660864197531, 18.318304938271606, 21.934957870370372, 20.222915288317584, 11.25, 13.981140377632535, 14.600077777777777, 21.47496611111111, 13.280540576131688, 14.857388664421999, 17.074406584362144, 18.225), # 52
(18.626746775968517, 18.49994691358025, 18.29830617283951, 21.914958796296297, 20.226285776848552, 11.25, 13.93447726942629, 14.50008888888889, 21.454967222222226, 13.233877942386831, 14.844055780022448, 17.04774156378601, 18.225), # 53
(18.63312937720329, 18.432900000000004, 18.2781, 21.8946375, 20.229433877961906, 11.25, 13.887582352941177, 14.400899999999998, 21.434715, 13.18716, 14.830445454545453, 17.0208, 18.225), # 54
(18.63906538526104, 18.365586419753086, 18.25771604938272, 21.874016203703704, 20.232359261266843, 11.25, 13.840541031227307, 14.302777777777777, 21.414236111111112, 13.140483539094651, 14.816568462401795, 16.993621399176956, 18.225), # 55
(18.64455336765337, 18.298124691358026, 18.237183950617286, 21.85311712962963, 20.235061596372585, 11.25, 13.793438707334786, 14.20598888888889, 21.393557222222224, 13.09394534979424, 14.802435578002246, 16.96624526748971, 18.225), # 56
(18.649591891891887, 18.230633333333333, 18.216533333333334, 21.8319625, 20.23754055288834, 11.25, 13.746360784313726, 14.110800000000001, 21.372705, 13.047642222222223, 14.788057575757577, 16.93871111111111, 18.225), # 57
(18.654179525488225, 18.163230864197534, 18.195793827160493, 21.810574537037034, 20.239795800423316, 11.25, 13.699392665214235, 14.017477777777778, 21.35170611111111, 13.001670946502058, 14.773445230078567, 16.91105843621399, 18.225), # 58
(18.658314835953966, 18.096035802469135, 18.174995061728396, 21.788975462962963, 20.24182700858672, 11.25, 13.65261975308642, 13.92628888888889, 21.330587222222224, 12.956128312757203, 14.758609315375981, 16.883326748971193, 18.225), # 59
(18.661996390800738, 18.02916666666667, 18.154166666666665, 21.767187500000002, 20.243633846987766, 11.25, 13.606127450980392, 13.8375, 21.309375000000003, 12.911111111111111, 14.743560606060607, 16.855555555555558, 18.225), # 60
(18.665222757540146, 17.962741975308646, 18.13333827160494, 21.74523287037037, 20.24521598523566, 11.25, 13.560001161946259, 13.751377777777778, 21.288096111111113, 12.866716131687244, 14.728309876543209, 16.82778436213992, 18.225), # 61
(18.66799250368381, 17.89688024691358, 18.112539506172844, 21.7231337962963, 20.246573092939624, 11.25, 13.514326289034132, 13.66818888888889, 21.266777222222224, 12.823040164609054, 14.712867901234567, 16.80005267489712, 18.225), # 62
(18.670304196743327, 17.831699999999998, 18.0918, 21.7009125, 20.24770483970884, 11.25, 13.469188235294117, 13.5882, 21.245445, 12.78018, 14.697245454545456, 16.7724, 18.225), # 63
(18.672156404230314, 17.767319753086422, 18.071149382716047, 21.678591203703704, 20.24861089515255, 11.25, 13.424672403776325, 13.511677777777779, 21.22412611111111, 12.738232427983538, 14.681453310886642, 16.7448658436214, 18.225), # 64
(18.67354769365639, 17.703858024691357, 18.05061728395062, 21.65619212962963, 20.24929092887994, 11.25, 13.380864197530865, 13.438888888888888, 21.202847222222225, 12.697294238683126, 14.665502244668913, 16.717489711934153, 18.225), # 65
(18.674476632533153, 17.641433333333335, 18.030233333333335, 21.6337375, 20.249744610500233, 11.25, 13.337849019607843, 13.3701, 21.181635000000004, 12.657462222222222, 14.649403030303029, 16.690311111111114, 18.225), # 66
(18.674941788372227, 17.580164197530863, 18.010027160493827, 21.611249537037036, 20.249971609622634, 11.25, 13.29571227305737, 13.30557777777778, 21.16051611111111, 12.618833168724281, 14.633166442199778, 16.6633695473251, 18.225), # 67
(18.674624906065485, 17.519847550776582, 17.989930709876543, 21.588555132850242, 20.249780319535223, 11.24979122085048, 13.254327350693364, 13.245018930041153, 21.13935812757202, 12.5813167949649, 14.616514779372677, 16.636554039419536, 18.22477527006173), # 68
(18.671655072463768, 17.458641935483872, 17.969379166666666, 21.564510326086953, 20.248039215686273, 11.248140740740741, 13.212482726423904, 13.185177777777778, 21.11723611111111, 12.543851503267971, 14.597753110047847, 16.608994152046783, 18.222994791666668), # 69
(18.665794417606012, 17.39626642771804, 17.948283179012343, 21.538956823671498, 20.244598765432098, 11.244890260631001, 13.169988242210465, 13.125514403292183, 21.09402520576132, 12.506255144032922, 14.576667995746943, 16.580560970327056, 18.219478202160495), # 70
(18.657125389157272, 17.332758303464754, 17.92665015432099, 21.51193230676329, 20.239502541757446, 11.240092455418381, 13.12686298717018, 13.066048559670783, 21.06975997942387, 12.46852864681675, 14.553337267410951, 16.551275286982886, 18.21427179783951), # 71
(18.64573043478261, 17.268154838709677, 17.9044875, 21.48347445652174, 20.23279411764706, 11.2338, 13.083126050420168, 13.0068, 21.044475000000002, 12.43067294117647, 14.527838755980863, 16.52115789473684, 18.207421875), # 72
(18.631692002147076, 17.20249330943847, 17.88180262345679, 21.45362095410628, 20.224517066085692, 11.226065569272976, 13.038796521077565, 12.947788477366256, 21.01820483539095, 12.392688956669087, 14.50025029239766, 16.490229586311454, 18.198974729938275), # 73
(18.61509253891573, 17.1358109916368, 17.858602932098762, 21.42240948067633, 20.214714960058096, 11.216941838134431, 12.9938934882595, 12.889033744855967, 20.990984053497943, 12.354577622851611, 14.470649707602341, 16.45851115442928, 18.18897665895062), # 74
(18.59601449275362, 17.06814516129032, 17.83489583333333, 21.389877717391304, 20.203431372549023, 11.206481481481482, 12.9484360410831, 12.830555555555556, 20.96284722222222, 12.316339869281046, 14.439114832535884, 16.426023391812866, 18.177473958333334), # 75
(18.57454031132582, 16.99953309438471, 17.8106887345679, 21.35606334541063, 20.19070987654321, 11.19473717421125, 12.902443268665492, 12.772373662551441, 20.93382890946502, 12.277976625514404, 14.405723498139285, 16.392787091184747, 18.164512924382716), # 76
(18.55075244229737, 16.93001206690562, 17.785989043209874, 21.32100404589372, 20.176594045025414, 11.18176159122085, 12.855934260123803, 12.714507818930043, 20.90396368312757, 12.239488821108692, 14.370553535353537, 16.358823045267492, 18.150139853395064), # 77
(18.524733333333334, 16.859619354838713, 17.760804166666667, 21.2847375, 20.16112745098039, 11.167607407407406, 12.808928104575164, 12.65697777777778, 20.87328611111111, 12.200877385620915, 14.333682775119618, 16.324152046783627, 18.134401041666667), # 78
(18.496565432098766, 16.788392234169656, 17.735141512345677, 21.24730138888889, 20.144353667392885, 11.152327297668037, 12.761443891136702, 12.59980329218107, 20.84183076131687, 12.162143248608086, 14.29518904837852, 16.28879488845571, 18.117342785493825), # 79
(18.466331186258724, 16.71636798088411, 17.70900848765432, 21.208733393719807, 20.126316267247642, 11.135973936899862, 12.713500708925546, 12.543004115226339, 20.809632201646092, 12.123287339627208, 14.255150186071239, 16.252772363006283, 18.09901138117284), # 80
(18.434113043478263, 16.643583870967742, 17.682412499999998, 21.169071195652176, 20.10705882352941, 11.118599999999999, 12.665117647058823, 12.486600000000001, 20.776725, 12.084310588235295, 14.213644019138757, 16.216105263157896, 18.079453124999997), # 81
(18.399993451422436, 16.570077180406216, 17.655360956790126, 21.12835247584541, 20.086624909222948, 11.10025816186557, 12.616313794653665, 12.430610699588478, 20.743143724279836, 12.045213923989348, 14.170748378522063, 16.178814381633096, 18.058714313271608), # 82
(18.364054857756308, 16.495885185185184, 17.6278612654321, 21.086614915458934, 20.065058097313, 11.08100109739369, 12.567108240827196, 12.37505596707819, 20.70892294238683, 12.00599827644638, 14.12654109516215, 16.14092051115443, 18.036841242283952), # 83
(18.326379710144927, 16.421045161290323, 17.599920833333332, 21.043896195652174, 20.042401960784314, 11.060881481481482, 12.517520074696545, 12.319955555555556, 20.674097222222223, 11.9666645751634, 14.0811, 16.102444444444444, 18.013880208333333), # 84
(18.287050456253354, 16.345594384707287, 17.571547067901232, 21.000233997584544, 20.01870007262164, 11.039951989026063, 12.467568385378843, 12.265329218106997, 20.63870113168724, 11.92721374969741, 14.034502923976609, 16.06340697422569, 17.989877507716052), # 85
(18.246149543746643, 16.269570131421744, 17.54274737654321, 20.955666002415462, 19.99399600580973, 11.018265294924555, 12.417272261991217, 12.21119670781893, 20.60276923868313, 11.887646729605423, 13.986827698032961, 16.02382889322071, 17.964879436728395), # 86
(18.203759420289852, 16.193009677419354, 17.513529166666665, 20.910229891304347, 19.968333333333337, 10.995874074074074, 12.366650793650793, 12.157577777777778, 20.566336111111116, 11.847964444444443, 13.938152153110048, 15.983730994152046, 17.938932291666667), # 87
(18.159962533548043, 16.11595029868578, 17.483899845679012, 20.86396334541063, 19.941755628177198, 10.972831001371743, 12.315723069474704, 12.104492181069958, 20.52943631687243, 11.808167823771482, 13.888554120148857, 15.943134069742257, 17.912082368827164), # 88
(18.11484133118626, 16.03842927120669, 17.453866820987656, 20.81690404589372, 19.91430646332607, 10.94918875171468, 12.264508178580074, 12.051959670781894, 20.492104423868312, 11.76825779714355, 13.838111430090379, 15.902058912713883, 17.884375964506173), # 89
(18.068478260869565, 15.960483870967742, 17.423437500000002, 20.769089673913047, 19.886029411764707, 10.925, 12.213025210084034, 12.0, 20.454375000000002, 11.728235294117647, 13.786901913875598, 15.860526315789475, 17.855859375), # 90
(18.020955770263015, 15.8821513739546, 17.392619290123456, 20.720557910628024, 19.85696804647785, 10.900317421124829, 12.161293253103711, 11.9486329218107, 20.41628261316873, 11.688101244250786, 13.735003402445509, 15.818557071691574, 17.826578896604936), # 91
(17.97235630703167, 15.80346905615293, 17.361419598765433, 20.671346437198068, 19.827165940450254, 10.875193689986283, 12.109331396756236, 11.897878189300412, 20.377861831275723, 11.647856577099976, 13.682493726741095, 15.776171973142736, 17.796580825617283), # 92
(17.92276231884058, 15.724474193548389, 17.329845833333334, 20.621492934782612, 19.796666666666667, 10.84968148148148, 12.057158730158731, 11.847755555555556, 20.339147222222223, 11.607502222222221, 13.62945071770335, 15.733391812865497, 17.76591145833333), # 93
(17.872256253354806, 15.645204062126643, 17.29790540123457, 20.571035084541062, 19.765513798111837, 10.823833470507545, 12.00479434242833, 11.798284773662553, 20.300173353909464, 11.567039109174534, 13.575952206273259, 15.690237383582414, 17.734617091049383), # 94
(17.820920558239397, 15.56569593787336, 17.265605709876546, 20.52001056763285, 19.733750907770517, 10.797702331961592, 11.95225732268216, 11.749485596707821, 20.260974794238685, 11.526468167513919, 13.522076023391813, 15.646729478016026, 17.70274402006173), # 95
(17.76883768115942, 15.485987096774197, 17.23295416666667, 20.468457065217393, 19.701421568627453, 10.77134074074074, 11.899566760037347, 11.701377777777779, 20.221586111111108, 11.485790326797385, 13.4679, 15.602888888888891, 17.67033854166667), # 96
(17.716090069779927, 15.406114814814819, 17.199958179012345, 20.416412258454105, 19.668569353667394, 10.744801371742112, 11.846741743611025, 11.65398106995885, 20.182041872427984, 11.445006516581941, 13.413501967038808, 15.558736408923545, 17.637446952160495), # 97
(17.66276017176597, 15.326116367980884, 17.166625154320986, 20.363913828502415, 19.635237835875095, 10.718136899862827, 11.793801362520316, 11.607315226337448, 20.142376646090533, 11.404117666424595, 13.35895975544923, 15.514292830842535, 17.604115547839505), # 98
(17.608930434782607, 15.246029032258065, 17.1329625, 20.31099945652174, 19.601470588235298, 10.6914, 11.740764705882354, 11.5614, 20.102625, 11.363124705882353, 13.304351196172249, 15.469578947368422, 17.570390625), # 99
(17.5546833064949, 15.165890083632016, 17.09897762345679, 20.257706823671498, 19.567311183732752, 10.664643347050754, 11.687650862814262, 11.516255144032922, 20.062821502057616, 11.322028564512225, 13.249754120148857, 15.42461555122374, 17.536318479938274), # 100
(17.500101234567904, 15.085736798088412, 17.064677932098768, 20.204073611111113, 19.532803195352216, 10.637919615912208, 11.634478922433171, 11.471900411522633, 20.02300072016461, 11.280830171871218, 13.195246358320043, 15.379423435131034, 17.501945408950615), # 101
(17.44526666666667, 15.005606451612904, 17.030070833333333, 20.1501375, 19.497990196078433, 10.611281481481482, 11.58126797385621, 11.428355555555555, 19.98319722222222, 11.239530457516341, 13.140905741626794, 15.334023391812867, 17.467317708333336), # 102
(17.390262050456254, 14.92553632019116, 16.9951637345679, 20.095936171497584, 19.462915758896152, 10.584781618655693, 11.528037106200506, 11.385640329218107, 19.943445576131687, 11.1981303510046, 13.086810101010101, 15.28843621399177, 17.432481674382714), # 103
(17.335169833601718, 14.845563679808842, 16.959964043209876, 20.041507306763286, 19.427623456790123, 10.558472702331962, 11.474805408583187, 11.343774485596708, 19.90378034979424, 11.156630781893005, 13.03303726741095, 15.242682694390297, 17.397483603395063), # 104
(17.280072463768114, 14.765725806451613, 16.924479166666668, 19.98688858695652, 19.392156862745097, 10.532407407407408, 11.421591970121383, 11.302777777777779, 19.86423611111111, 11.115032679738563, 12.979665071770334, 15.196783625730996, 17.362369791666666), # 105
(17.225052388620504, 14.686059976105138, 16.888716512345678, 19.932117693236716, 19.356559549745825, 10.50663840877915, 11.36841587993222, 11.262669958847736, 19.82484742798354, 11.07333697409828, 12.92677134502924, 15.15075980073641, 17.327186535493826), # 106
(17.17019205582394, 14.606603464755079, 16.852683487654325, 19.877232306763286, 19.32087509077705, 10.48121838134431, 11.31529622713283, 11.223470781893006, 19.78564886831276, 11.03154459452917, 12.874433918128654, 15.104632012129088, 17.29198013117284), # 107
(17.11557391304348, 14.5273935483871, 16.8163875, 19.822270108695655, 19.28514705882353, 10.4562, 11.262252100840335, 11.185200000000002, 19.746675000000003, 10.989656470588237, 12.82273062200957, 15.05842105263158, 17.256796875000003), # 108
(17.061280407944178, 14.448467502986858, 16.779835956790127, 19.767268780193234, 19.249419026870008, 10.431635939643346, 11.209302590171871, 11.147877366255145, 19.707960390946504, 10.947673531832486, 12.771739287612972, 15.012147714966428, 17.221683063271605), # 109
(17.007393988191087, 14.369862604540026, 16.743036265432103, 19.71226600241546, 19.213734567901238, 10.407578875171467, 11.15646678424456, 11.111522633744855, 19.669539609053498, 10.90559670781893, 12.72153774587985, 14.965832791856185, 17.18668499228395), # 110
(16.953997101449275, 14.29161612903226, 16.705995833333336, 19.65729945652174, 19.178137254901962, 10.384081481481482, 11.103763772175537, 11.076155555555555, 19.631447222222224, 10.863426928104575, 12.672203827751195, 14.919497076023394, 17.151848958333336), # 111
(16.90117219538379, 14.213765352449222, 16.66872206790124, 19.602406823671497, 19.142670660856936, 10.361196433470509, 11.051212643081925, 11.041795884773663, 19.593717798353907, 10.821165122246429, 12.623815364167996, 14.873161360190599, 17.11722125771605), # 112
(16.84890760266548, 14.136477513814715, 16.631312090853726, 19.547700988485673, 19.10731622431267, 10.338965584586125, 10.998946734582185, 11.00853462380509, 19.556483060265517, 10.778948525902914, 12.57646303107516, 14.826947285707972, 17.0827990215178), # 113
(16.796665616220118, 14.060514930345965, 16.594282215038913, 19.493620958299207, 19.071708038219388, 10.317338295353823, 10.947632775139043, 10.976780267109216, 19.52031426428351, 10.73756730224301, 12.530239806803754, 14.781441909803354, 17.048295745488062), # 114
(16.744292825407193, 13.985904957629483, 16.55765447887317, 19.440152109327204, 19.035733820199482, 10.296258322497776, 10.89730737034481, 10.946524777701677, 19.485224961603823, 10.697085590378538, 12.485078120568769, 14.736667648605932, 17.013611936988678), # 115
(16.691723771827743, 13.912538906325063, 16.521357941970972, 19.38719907047953, 18.999339347490803, 10.275675979116777, 10.847888671550209, 10.917684563218188, 19.451126410610094, 10.657428045209185, 12.440890676288666, 14.692541755477222, 16.978693067560602), # 116
(16.63889299708279, 13.840308087092497, 16.485321663946774, 19.33466647066604, 18.9624703973312, 10.255541578309604, 10.799294830105955, 10.890176031294454, 19.417929869685967, 10.618519321634633, 12.39759017788191, 14.64898148377875, 16.943484608744804), # 117
(16.58573504277338, 13.769103810591583, 16.44947470441506, 19.2824589387966, 18.925072746958516, 10.235805433175049, 10.751443997362767, 10.863915589566174, 19.385546597215082, 10.580284074554568, 12.355089329266963, 14.60590408687203, 16.907932032082243), # 118
(16.532184450500534, 13.698817387482112, 16.413746122990304, 19.23048110378107, 18.887092173610597, 10.2164178568119, 10.70425432467136, 10.838819645669062, 19.353887851581078, 10.54264695886867, 12.31330083436229, 14.563226818118581, 16.87198080911388), # 119
(16.47817576186529, 13.629340128423884, 16.37806497928697, 19.17863759452931, 18.848474454525295, 10.197329162318939, 10.657643963382455, 10.814804607238818, 19.322864891167605, 10.50553262947663, 12.272137397086349, 14.520866930879935, 16.835576411380675), # 120
(16.423643518468683, 13.560563344076693, 16.342360332919537, 19.12683303995118, 18.809165366940455, 10.178489662794956, 10.611531064846766, 10.791786881911152, 19.2923889743583, 10.468865741278133, 12.23151172135761, 14.4787416785176, 16.79866431042359), # 121
(16.36852226191174, 13.49237834510033, 16.30656124350248, 19.07497206895654, 18.76911068809392, 10.159849671338735, 10.565833780415012, 10.769682877321769, 19.2623713595368, 10.43257094917286, 12.191336511094532, 14.436768314393102, 16.761189977783587), # 122
(16.312746533795494, 13.424676442154594, 16.270596770650265, 19.02295931045525, 18.728256195223544, 10.141359501049065, 10.52047026143791, 10.74840900110637, 19.232723305086758, 10.396572908060497, 12.151524470215579, 14.394864091867959, 16.72309888500163), # 123
(16.256250875720976, 13.357348945899277, 16.234395973977367, 18.970699393357176, 18.68654766556717, 10.12296946502473, 10.475358659266176, 10.727881660900668, 19.20335606939181, 10.36079627284073, 12.111988302639215, 14.352946264303695, 16.68433650361868), # 124
(16.198969829289226, 13.290287166994178, 16.197887913098263, 18.91809694657217, 18.643930876362642, 10.104629876364521, 10.43041712525053, 10.708017264340365, 19.174180910835588, 10.32516569841324, 12.072640712283903, 14.310932085061827, 16.644848305175692), # 125
(16.14083793610127, 13.22338241609909, 16.16100164762742, 18.8650565990101, 18.60035160484781, 10.086291048167222, 10.385563810741687, 10.688732219061166, 19.145109087801753, 10.289605839677717, 12.033394403068103, 14.268738807503881, 16.604579761213643), # 126
(16.08178973775815, 13.156526003873804, 16.123666237179307, 18.81148297958082, 18.555755628260517, 10.067903293531618, 10.34071686709037, 10.669942932698781, 19.116051858673934, 10.254041351533843, 11.994162078910282, 14.226283684991369, 16.56347634327348), # 127
(16.021759775860883, 13.089609240978122, 16.08581074136841, 18.7572807171942, 18.51008872383862, 10.0494169255565, 10.295794445647289, 10.651565812888913, 19.086920481835772, 10.218396888881303, 11.954856443728904, 14.183483970885819, 16.521483522896165), # 128
(15.960682592010507, 13.022523438071834, 16.047364219809193, 18.702354440760086, 18.46329666881996, 10.03078225734065, 10.250714697763163, 10.633517267267269, 19.057626215670915, 10.182597106619781, 11.915390201442428, 14.140256918548745, 16.478546771622668), # 129
(15.89849272780806, 12.955159905814739, 16.008255732116123, 18.646608779188355, 18.415325240442385, 10.011949601982854, 10.205395774788713, 10.61571370346955, 19.028080318563003, 10.146566659648963, 11.87567605596932, 14.096519781341675, 16.434611560993947), # 130
(15.83512472485457, 12.887409954866628, 15.968414337903685, 18.589948361388856, 18.36612021594374, 9.992869272581904, 10.159755828074656, 10.59807152913147, 18.998194048895677, 10.110230202868534, 11.835626711228041, 14.052189812626125, 16.38962336255096), # 131
(15.770513124751067, 12.8191648958873, 15.927769096786342, 18.532277816271456, 18.315627372561877, 9.973491582236585, 10.113713008971706, 10.580507151888732, 18.967878665052577, 10.073512391178177, 11.795154871137056, 14.007184265763614, 16.343527647834676), # 132
(15.704592469098595, 12.750316039536544, 15.88624906837857, 18.473501772746012, 18.263792487534637, 9.95376684404568, 10.06718546883058, 10.562936979377039, 18.93704542541735, 10.036337879477578, 11.754173239614829, 13.961420394115667, 16.296269888386057), # 133
(15.63729729949817, 12.68075469647416, 15.843783312294848, 18.413524859722386, 18.210561338099865, 9.933645371107978, 10.020091359002002, 10.545277419232098, 18.905605588373632, 9.998631322666423, 11.712594520579822, 13.914815451043799, 16.24779555574605), # 134
(15.568562157550836, 12.610372177359944, 15.800300888149636, 18.352251706110444, 18.15587970149542, 9.913077476522266, 9.972348830836681, 10.527444879089616, 18.873470412305064, 9.960317375644397, 11.670331417950496, 13.867286689909534, 16.198050121455637), # 135
(15.498321584857623, 12.539059792853687, 15.755730855557415, 18.28958694082003, 18.09969335495913, 9.892013473387332, 9.923876035685343, 10.509355766585298, 18.840551155595293, 9.92132069331118, 11.627296635645319, 13.818751364074394, 16.146979057055766), # 136
(15.426510123019561, 12.466708853615184, 15.710002274132659, 18.225435192761026, 18.04194807572886, 9.870403674801956, 9.8745911248987, 10.490926489354854, 18.80675907662796, 9.881565930566463, 11.583402877582751, 13.769126726899895, 16.094527834087398), # 137
(15.353062313637686, 12.393210670304235, 15.66304420348983, 18.159701090843274, 17.982589641042455, 9.848198393864935, 9.824412249827468, 10.472073455033982, 18.772005433786706, 9.840977742309924, 11.538562847681254, 13.718330031747561, 16.040641924091503), # 138
(15.277912698313022, 12.31845655358063, 15.614785703243411, 18.092289263976646, 17.921563828137746, 9.825347943675048, 9.773257561822367, 10.452713071258394, 18.73620148545517, 9.799480783441254, 11.492689249859293, 13.66627853197891, 15.985266798609034), # 139
(15.200995818646616, 12.242337814104165, 15.565155833007877, 18.023104341071, 17.858816414252605, 9.801802637331082, 9.721045212234115, 10.432761745663793, 18.699258490016998, 9.756999708860134, 11.445694788035329, 13.612889480955465, 15.928347929180966), # 140
(15.122246216239494, 12.164745762534638, 15.514083652397689, 17.952050951036195, 17.794293176624855, 9.777512787931828, 9.667693352413432, 10.412135885885887, 18.661087705855824, 9.713459173466253, 11.39749216612783, 13.558080132038745, 15.869830787348244), # 141
(15.041598432692682, 12.08557170953184, 15.461498221027327, 17.879033722782097, 17.727939892492355, 9.752428708576069, 9.613120133711027, 10.39075189956038, 18.621600391355297, 9.66878383215929, 11.347994088055255, 13.50176773859027, 15.80966084465184), # 142
(14.958987009607215, 12.004706965755565, 15.407328598511267, 17.803957285218555, 17.659702339092952, 9.726500712362592, 9.557243707477623, 10.368526194322978, 18.580707804899063, 9.622898339838935, 11.297113257736068, 13.443869553971561, 15.747783572632711), # 143
(14.874346488584132, 11.922042841865615, 15.35150384446397, 17.72672626725544, 17.58952629366449, 9.699679112390184, 9.499982225063938, 10.34537517780939, 18.53832120487076, 9.575727351404868, 11.244762379088732, 13.384302831544138, 15.684144442831826), # 144
(14.787611411224459, 11.837470648521778, 15.29395301849992, 17.64724529780261, 17.51735753344482, 9.671914221757634, 9.441253837820689, 10.321215257655316, 18.494351849654016, 9.527195521756779, 11.190854156031712, 13.322984824669524, 15.618688926790139), # 145
(14.69871631912923, 11.750881696383855, 15.23460518023359, 17.565419005769925, 17.443141835671785, 9.643156353563725, 9.380976697098594, 10.295962841496468, 18.448710997632492, 9.477227505794348, 11.135301292483467, 13.259832786709236, 15.551362496048613), # 146
(14.607595753899481, 11.662167296111635, 15.173389389279437, 17.481152020067245, 17.36682497758323, 9.613355820907245, 9.319068954248365, 10.269534336968547, 18.401309907189823, 9.425747958417263, 11.078016492362465, 13.194763971024798, 15.482110622148213), # 147
(14.51418425713624, 11.571218758364918, 15.11023470525195, 17.394348969604433, 17.28835273641701, 9.582462936886982, 9.255448760620729, 10.241846151707264, 18.352059836709653, 9.372681534525205, 11.018912459587169, 13.127695630977726, 15.410878776629895), # 148
(14.418416370440541, 11.477927393803494, 15.045070187765598, 17.304914483291345, 17.207670889410966, 9.550428014601719, 9.190034267566393, 10.21281469334832, 18.30087204457561, 9.317952889017864, 10.957901898076038, 13.058545019929545, 15.337612431034628), # 149
(14.320226635413416, 11.382184513087163, 14.97782489643485, 17.212753190037848, 17.124725213802947, 9.517201367150248, 9.122743626436081, 10.182356369527422, 18.247657789171353, 9.261486676794918, 10.894897511747537, 12.987229391241772, 15.262257056903364), # 150
(14.219549593655895, 11.283881426875716, 14.908427890874176, 17.117769718753795, 17.0394614868308, 9.48273330763135, 9.05349498858051, 10.150387587880278, 18.19232832888052, 9.20320755275606, 10.829812004520129, 12.91366599827593, 15.184758125777073), # 151
(14.116319786769019, 11.182909445828951, 14.836808230698063, 17.019868698349054, 16.951825485732364, 9.446974149143815, 8.982206505350396, 10.116824756042595, 18.134794922086748, 9.143040171800969, 10.762558080312278, 12.837772094393538, 15.105061109196717), # 152
(14.010471756353809, 11.079159880606662, 14.762894975520963, 16.91895475773348, 16.8617629877455, 9.409874204786428, 8.908796328096455, 10.081584281650072, 18.07496882717368, 9.080909188829333, 10.693048443042448, 12.759464932956115, 15.02311147870325), # 153
(13.901940044011312, 10.972524041868644, 14.686617184957365, 16.81493252581694, 16.769219770108045, 9.371383787657978, 8.83318260816941, 10.044582572338422, 18.01276130252496, 9.016739258740834, 10.6211957966291, 12.678661767325185, 14.938854705837642), # 154
(13.790659191342543, 10.86289324027469, 14.607903918621735, 16.707706631509282, 16.674141610057855, 9.331453210857248, 8.75528349691997, 10.005736035743345, 17.948083606524232, 8.950455036435159, 10.5469128449907, 12.595279850862267, 14.852236262140847), # 155
(13.676563739948545, 10.750158786484597, 14.526684236128547, 16.597181703720377, 16.576474284832766, 9.29003278748303, 8.67501714569886, 9.964961079500554, 17.88084699755513, 8.88198117681199, 10.470112292045709, 12.50923643692888, 14.763201619153833), # 156
(13.559588231430352, 10.634211991158162, 14.442887197092272, 16.483262371360087, 16.476163571670632, 9.247072830634105, 8.592301705856794, 9.922174111245749, 17.8109627340013, 8.811242334771014, 10.39070684171259, 12.420448778886547, 14.671696248417557), # 157
(13.43642570352943, 10.512815617390064, 14.352465517024239, 16.36158524697224, 16.368625990567796, 9.199844057370798, 8.505192097670143, 9.87443451422887, 17.732991764878374, 8.73605864932406, 10.306072354570096, 12.32567921554981, 14.573674546947622), # 158
(13.288116180561124, 10.37351757527906, 14.232128073125379, 16.207158885819215, 16.22734435760693, 9.132641366412786, 8.40278297409429, 9.804984358975888, 17.61556907019986, 8.644105789377742, 10.20135048411419, 12.206452542629595, 14.445769764456351), # 159
(13.112769770827757, 10.215174111373285, 14.0794577243206, 16.017439518735948, 16.04955623642423, 9.043814332885832, 8.284038747090811, 9.712078541149223, 17.455365409011574, 8.534170173353209, 10.075067115497172, 12.060903507998123, 14.285557096008445), # 160
(12.911799698254727, 10.038817562544844, 13.896084549438555, 15.79423050676211, 15.837107623707803, 8.934439034826566, 8.149826602812377, 9.596880959597605, 17.254493580598233, 8.407184747707687, 9.928334978279473, 11.890381444033627, 14.094673280674375), # 161
(12.686619186767443, 9.84548026566583, 13.683638627307893, 15.539335210937388, 15.591844516145768, 8.80559155027162, 8.001013727411657, 9.460555513169764, 17.015066384244545, 8.264082458898416, 9.762266802021516, 11.696235683114327, 13.874755057524599), # 162
(12.438641460291295, 9.636194557608343, 13.443750036757264, 15.254556992301481, 15.315612910426239, 8.65834795725763, 7.838467307041322, 9.304266100714425, 16.73919661923523, 8.105796253382625, 9.577975316283736, 11.479815557618458, 13.627439165629584), # 163
(12.16927974275169, 9.411992775244478, 13.178048856615318, 14.941699211894072, 15.01025880323734, 8.493784333821234, 7.663054527854039, 9.129176621080324, 16.428997084855002, 7.933259077617543, 9.376573250626553, 11.242470399924246, 13.35436234405979), # 164
(11.879947258074031, 9.173907255446338, 12.888165165710705, 14.602565230754854, 14.677628191267182, 8.312976757999055, 7.475642576002479, 8.936450973116184, 16.086580580388564, 7.747403878060404, 9.1591733346104, 10.985549542409915, 13.057161331885686), # 165
(11.572057230183715, 8.922970335086019, 12.57572904287207, 14.238958409923503, 14.319567071203886, 8.117001307827735, 7.277098637639315, 8.727253055670738, 15.714059905120632, 7.549163601168441, 8.926888297795703, 10.710402317453703, 12.737472868177733), # 166
(11.24702288300614, 8.660214351035616, 12.242370566928068, 13.852682110439718, 13.937921439735565, 7.906934061343905, 7.0682898989172145, 8.502746767592717, 15.31354785833592, 7.339471193398886, 8.680830869742888, 10.418378057433825, 12.396933692006392), # 167
(10.906257440466712, 8.386671640167231, 11.889719816707347, 13.445539693343184, 13.534537293550335, 7.683851096584198, 6.850083545988848, 8.264096007730847, 14.887157239319139, 7.11925960120897, 8.422113780012385, 10.11082609472852, 12.037180542442131), # 168
(10.551174126490828, 8.103374539352963, 11.519406871038555, 13.019334519673588, 13.111260629336316, 7.4488284915852505, 6.623346765006885, 8.012464674933861, 14.437000847355009, 6.889461771055926, 8.151849758164623, 9.78909576171601, 11.659850158555415), # 169
(10.18318616500389, 7.811355385464907, 11.133061808750343, 12.575869950470615, 12.66993744378162, 7.2029423243836925, 6.388946742123995, 7.749016668050485, 13.96519148172823, 6.6510106493969845, 7.871151533760029, 9.454536390774527, 11.2665792794167), # 170
(9.8037067799313, 7.511646515375161, 10.73231470867136, 12.116949346773964, 12.21241373357437, 6.947268673016157, 6.147750663492849, 7.47491588592945, 13.47384194172352, 6.404839182689379, 7.581131836359027, 9.108497314282296, 10.859004644096458), # 171
(9.414149195198457, 7.205280265955825, 10.318795649630257, 11.644376069623315, 11.740535495402677, 6.682883615519281, 5.900625715266118, 7.191326227419487, 12.965065026625595, 6.151880317390344, 7.282903395522049, 8.752327864617548, 10.438762991665145), # 172
(9.015926634730764, 6.893288974078996, 9.894134710455681, 11.159953480058356, 11.256148725954663, 6.410863229929695, 5.64843908359647, 6.899411591369322, 12.440973535719161, 5.893066999957107, 6.97757894080952, 8.387377374158506, 10.007491061193234), # 173
(8.610452322453618, 6.576704976616772, 9.459961969976282, 10.665484939118773, 10.76109942191844, 6.132283594284034, 5.3920579546365754, 6.600335876627689, 11.903680268288936, 5.629332176846904, 6.66627120178187, 8.014995175283403, 9.566825591751181), # 174
(8.19913948229242, 6.256560610441251, 9.017907507020714, 10.162773807844262, 10.257233579982124, 5.848220786618931, 5.132349514539104, 6.295262982043313, 11.35529802361963, 5.361608794516964, 6.3500929079995245, 7.636530600370466, 9.118403322409455), # 175
(7.783401338172574, 5.933888212424531, 8.569601400417621, 9.653623447274505, 9.746397196833835, 5.55975088497102, 4.870180949456727, 5.985356806464928, 10.797939600995955, 5.090829799424521, 6.0301567890229135, 7.253332981797922, 8.663860992238513), # 176
(7.364651114019479, 5.6097201194387125, 8.116673728995655, 9.13983721844919, 9.230436269161691, 5.267949967376934, 4.606419445542112, 5.671781248741259, 10.233717799702626, 4.817928138026804, 5.7075755744124645, 6.866751651944002, 8.204835340308824), # 177
(6.944302033758534, 5.285088668355891, 7.660754571583465, 8.623218482408008, 8.711196793653805, 4.973894111873309, 4.341932188947932, 5.355700207721038, 9.664745419024355, 4.54383675678105, 5.383461993728603, 6.478135943186929, 7.742963105690853), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(11, 11, 6, 9, 7, 3, 2, 1, 2, 1, 5, 2, 0, 9, 6, 8, 9, 8, 6, 5, 8, 3, 4, 2, 1, 0), # 0
(18, 18, 10, 19, 15, 10, 8, 3, 3, 3, 6, 3, 0, 20, 17, 16, 16, 10, 11, 11, 10, 6, 5, 3, 1, 0), # 1
(30, 24, 15, 25, 20, 12, 11, 6, 8, 4, 7, 3, 0, 32, 30, 26, 23, 21, 15, 15, 13, 6, 7, 5, 3, 0), # 2
(40, 29, 19, 37, 28, 14, 16, 10, 11, 6, 9, 4, 0, 46, 38, 34, 31, 28, 20, 20, 20, 8, 11, 5, 3, 0), # 3
(50, 40, 28, 48, 34, 18, 23, 13, 20, 9, 10, 5, 0, 59, 49, 44, 36, 37, 28, 22, 21, 11, 12, 7, 3, 0), # 4
(59, 52, 39, 55, 40, 22, 28, 16, 21, 12, 11, 5, 0, 75, 60, 49, 49, 52, 39, 25, 26, 16, 14, 8, 4, 0), # 5
(70, 73, 48, 64, 47, 26, 35, 24, 26, 16, 11, 6, 0, 88, 71, 62, 55, 63, 46, 33, 30, 18, 23, 11, 5, 0), # 6
(84, 84, 61, 78, 57, 36, 41, 30, 29, 16, 11, 8, 0, 104, 79, 72, 59, 75, 54, 42, 32, 23, 27, 13, 7, 0), # 7
(98, 97, 69, 90, 62, 44, 50, 34, 31, 24, 11, 8, 0, 121, 91, 86, 67, 79, 61, 51, 36, 25, 34, 13, 10, 0), # 8
(115, 110, 80, 101, 70, 49, 57, 40, 43, 27, 12, 10, 0, 133, 100, 99, 79, 87, 69, 56, 40, 28, 38, 22, 10, 0), # 9
(133, 126, 94, 120, 80, 55, 64, 44, 45, 28, 13, 11, 0, 148, 110, 106, 86, 99, 76, 61, 47, 31, 43, 26, 11, 0), # 10
(142, 139, 104, 135, 94, 59, 67, 49, 50, 32, 14, 11, 0, 178, 123, 115, 97, 114, 81, 64, 50, 34, 48, 30, 11, 0), # 11
(150, 153, 120, 156, 104, 68, 72, 57, 55, 36, 19, 11, 0, 188, 132, 130, 107, 130, 89, 73, 54, 39, 55, 30, 11, 0), # 12
(159, 172, 126, 177, 116, 76, 82, 65, 60, 36, 20, 12, 0, 192, 146, 143, 119, 146, 95, 80, 59, 44, 58, 30, 12, 0), # 13
(172, 186, 134, 193, 126, 79, 89, 67, 67, 37, 22, 13, 0, 215, 160, 154, 132, 158, 105, 96, 62, 50, 66, 35, 13, 0), # 14
(190, 204, 147, 208, 135, 87, 97, 72, 74, 42, 22, 14, 0, 229, 179, 164, 141, 170, 113, 105, 67, 52, 71, 37, 14, 0), # 15
(215, 223, 164, 219, 148, 98, 105, 74, 77, 43, 24, 14, 0, 244, 190, 178, 157, 182, 126, 111, 71, 62, 76, 40, 14, 0), # 16
(234, 239, 174, 240, 157, 104, 112, 84, 83, 49, 27, 15, 0, 256, 208, 188, 167, 194, 140, 117, 76, 66, 84, 42, 15, 0), # 17
(250, 254, 181, 252, 172, 113, 115, 88, 90, 52, 28, 18, 0, 278, 222, 191, 172, 205, 152, 122, 77, 73, 90, 47, 16, 0), # 18
(271, 273, 198, 268, 184, 117, 123, 92, 93, 53, 30, 18, 0, 298, 239, 206, 182, 222, 169, 131, 84, 77, 92, 47, 17, 0), # 19
(285, 293, 211, 284, 199, 120, 133, 95, 98, 58, 30, 18, 0, 317, 253, 223, 194, 236, 178, 137, 92, 85, 98, 50, 19, 0), # 20
(302, 311, 219, 305, 216, 123, 138, 102, 103, 63, 33, 22, 0, 335, 260, 242, 199, 247, 196, 143, 97, 93, 99, 54, 20, 0), # 21
(316, 327, 233, 315, 232, 129, 144, 113, 108, 67, 36, 23, 0, 356, 276, 255, 212, 269, 205, 150, 102, 96, 105, 54, 22, 0), # 22
(336, 339, 244, 334, 247, 138, 152, 123, 110, 72, 39, 23, 0, 376, 293, 270, 226, 279, 215, 157, 109, 100, 115, 56, 24, 0), # 23
(352, 357, 261, 348, 260, 145, 159, 128, 117, 77, 43, 23, 0, 390, 315, 290, 241, 292, 221, 166, 113, 109, 123, 59, 25, 0), # 24
(386, 375, 278, 368, 278, 157, 163, 139, 123, 80, 46, 23, 0, 408, 330, 302, 251, 308, 231, 173, 117, 116, 130, 59, 25, 0), # 25
(403, 390, 296, 383, 286, 161, 170, 145, 127, 80, 50, 24, 0, 425, 351, 312, 260, 314, 243, 183, 119, 119, 130, 61, 26, 0), # 26
(416, 409, 314, 404, 301, 165, 177, 149, 133, 84, 53, 25, 0, 457, 370, 330, 272, 323, 255, 192, 123, 128, 140, 63, 27, 0), # 27
(430, 429, 336, 427, 315, 175, 186, 156, 137, 91, 58, 26, 0, 477, 387, 342, 289, 332, 260, 198, 126, 138, 148, 68, 28, 0), # 28
(454, 442, 346, 451, 335, 182, 193, 161, 145, 93, 60, 27, 0, 496, 398, 357, 299, 351, 269, 210, 128, 148, 151, 71, 28, 0), # 29
(475, 459, 357, 468, 349, 191, 199, 171, 151, 93, 61, 27, 0, 510, 414, 369, 307, 364, 278, 222, 130, 157, 159, 73, 30, 0), # 30
(502, 472, 371, 483, 363, 197, 206, 177, 156, 96, 65, 28, 0, 530, 430, 387, 325, 376, 290, 228, 134, 163, 163, 73, 30, 0), # 31
(517, 491, 386, 501, 380, 204, 216, 182, 163, 98, 67, 31, 0, 549, 451, 406, 336, 390, 298, 240, 138, 168, 170, 76, 30, 0), # 32
(529, 515, 404, 528, 394, 210, 227, 189, 176, 99, 70, 33, 0, 563, 460, 418, 345, 404, 310, 247, 144, 174, 174, 80, 31, 0), # 33
(552, 532, 419, 548, 408, 217, 234, 196, 182, 103, 70, 34, 0, 586, 476, 427, 357, 418, 320, 254, 148, 184, 180, 83, 37, 0), # 34
(571, 554, 431, 566, 419, 223, 241, 203, 187, 107, 71, 36, 0, 609, 493, 440, 365, 429, 329, 261, 155, 189, 186, 86, 38, 0), # 35
(589, 566, 446, 584, 421, 236, 249, 210, 197, 109, 73, 38, 0, 634, 514, 452, 380, 444, 340, 270, 159, 197, 190, 90, 38, 0), # 36
(605, 584, 460, 602, 435, 241, 254, 218, 204, 113, 77, 38, 0, 647, 540, 467, 386, 453, 349, 275, 161, 208, 197, 95, 41, 0), # 37
(627, 600, 470, 619, 448, 249, 263, 222, 219, 116, 79, 39, 0, 663, 564, 476, 397, 475, 358, 286, 162, 215, 200, 96, 41, 0), # 38
(651, 616, 487, 635, 459, 254, 274, 230, 229, 119, 79, 42, 0, 682, 587, 487, 406, 492, 367, 298, 167, 226, 206, 102, 44, 0), # 39
(672, 631, 498, 656, 483, 262, 281, 235, 239, 120, 79, 42, 0, 702, 599, 495, 412, 502, 376, 307, 169, 232, 212, 105, 46, 0), # 40
(690, 644, 515, 667, 495, 269, 288, 241, 251, 123, 84, 43, 0, 720, 613, 516, 424, 519, 386, 312, 175, 237, 215, 106, 48, 0), # 41
(708, 662, 541, 686, 513, 276, 293, 250, 259, 126, 86, 44, 0, 735, 630, 525, 434, 534, 401, 316, 186, 245, 224, 108, 48, 0), # 42
(725, 673, 553, 695, 534, 282, 302, 255, 265, 127, 89, 45, 0, 761, 646, 539, 438, 547, 414, 329, 191, 250, 228, 113, 48, 0), # 43
(739, 686, 568, 718, 549, 289, 311, 259, 276, 130, 92, 48, 0, 782, 663, 550, 449, 561, 428, 335, 197, 261, 231, 116, 48, 0), # 44
(757, 703, 584, 730, 574, 294, 317, 266, 282, 138, 96, 49, 0, 798, 680, 561, 458, 576, 436, 341, 201, 270, 236, 119, 51, 0), # 45
(772, 733, 597, 749, 593, 297, 327, 270, 288, 143, 96, 52, 0, 812, 697, 576, 467, 591, 441, 346, 206, 278, 239, 122, 52, 0), # 46
(794, 752, 617, 765, 607, 303, 335, 278, 296, 145, 97, 54, 0, 827, 715, 588, 479, 604, 449, 356, 214, 282, 246, 126, 55, 0), # 47
(809, 771, 629, 786, 624, 312, 340, 283, 302, 149, 99, 54, 0, 851, 727, 599, 494, 621, 459, 362, 217, 289, 249, 129, 55, 0), # 48
(833, 792, 642, 804, 638, 321, 345, 291, 309, 149, 100, 55, 0, 862, 743, 613, 503, 633, 473, 369, 222, 296, 255, 131, 59, 0), # 49
(856, 810, 667, 826, 651, 322, 351, 299, 319, 155, 100, 58, 0, 886, 761, 625, 519, 642, 487, 376, 224, 302, 260, 135, 62, 0), # 50
(873, 828, 688, 848, 661, 332, 356, 302, 326, 156, 101, 58, 0, 908, 785, 643, 535, 654, 497, 380, 232, 311, 266, 136, 62, 0), # 51
(890, 845, 710, 857, 669, 343, 367, 308, 333, 159, 102, 59, 0, 920, 799, 653, 547, 668, 501, 384, 237, 319, 273, 139, 62, 0), # 52
(904, 861, 730, 870, 686, 348, 374, 317, 339, 161, 106, 59, 0, 939, 814, 662, 555, 679, 510, 390, 243, 326, 277, 142, 63, 0), # 53
(927, 879, 741, 888, 697, 356, 380, 321, 345, 163, 107, 59, 0, 959, 829, 672, 563, 691, 519, 399, 248, 331, 287, 149, 64, 0), # 54
(949, 893, 757, 906, 712, 360, 393, 330, 352, 168, 108, 59, 0, 977, 841, 681, 572, 706, 528, 408, 254, 338, 293, 150, 66, 0), # 55
(972, 906, 776, 924, 725, 367, 401, 339, 361, 179, 109, 61, 0, 990, 851, 689, 578, 719, 535, 413, 256, 343, 297, 154, 68, 0), # 56
(994, 920, 790, 937, 736, 375, 413, 342, 369, 181, 112, 66, 0, 1004, 871, 701, 586, 745, 542, 425, 264, 349, 301, 156, 70, 0), # 57
(1012, 930, 803, 959, 747, 381, 422, 347, 374, 184, 115, 68, 0, 1017, 887, 711, 596, 753, 551, 433, 269, 353, 310, 158, 74, 0), # 58
(1036, 942, 818, 976, 765, 389, 428, 352, 375, 189, 116, 71, 0, 1032, 903, 723, 602, 770, 558, 437, 274, 358, 315, 162, 74, 0), # 59
(1056, 959, 829, 985, 782, 396, 435, 355, 382, 192, 119, 72, 0, 1053, 912, 737, 612, 790, 567, 448, 277, 363, 323, 164, 76, 0), # 60
(1070, 973, 840, 995, 800, 400, 442, 362, 391, 194, 122, 74, 0, 1066, 928, 754, 620, 810, 575, 455, 284, 370, 332, 166, 78, 0), # 61
(1082, 991, 860, 1016, 813, 405, 447, 367, 401, 195, 122, 76, 0, 1088, 949, 764, 631, 824, 582, 463, 286, 383, 335, 167, 79, 0), # 62
(1105, 1005, 871, 1028, 827, 413, 457, 369, 409, 198, 126, 76, 0, 1103, 958, 774, 640, 838, 586, 470, 290, 392, 339, 169, 79, 0), # 63
(1123, 1025, 881, 1043, 853, 419, 465, 379, 415, 202, 131, 76, 0, 1120, 972, 787, 646, 852, 594, 476, 294, 398, 345, 172, 80, 0), # 64
(1146, 1041, 892, 1059, 864, 428, 467, 390, 421, 205, 134, 78, 0, 1138, 986, 798, 655, 864, 606, 483, 298, 402, 350, 172, 81, 0), # 65
(1164, 1061, 907, 1074, 872, 430, 473, 396, 431, 207, 138, 78, 0, 1163, 994, 813, 665, 873, 614, 490, 303, 410, 355, 174, 83, 0), # 66
(1182, 1072, 922, 1089, 885, 438, 479, 401, 440, 212, 141, 78, 0, 1181, 1013, 827, 672, 879, 626, 499, 305, 417, 365, 177, 84, 0), # 67
(1195, 1089, 936, 1102, 900, 442, 484, 405, 443, 213, 145, 80, 0, 1203, 1028, 846, 680, 896, 635, 505, 309, 424, 370, 179, 85, 0), # 68
(1214, 1107, 953, 1121, 910, 451, 488, 409, 450, 214, 148, 82, 0, 1221, 1041, 863, 689, 905, 639, 516, 312, 428, 375, 180, 86, 0), # 69
(1240, 1122, 961, 1136, 919, 462, 494, 411, 456, 217, 150, 83, 0, 1239, 1053, 871, 702, 913, 644, 522, 318, 434, 386, 182, 88, 0), # 70
(1261, 1132, 971, 1148, 934, 465, 499, 416, 465, 219, 151, 84, 0, 1247, 1067, 880, 711, 923, 655, 526, 326, 442, 391, 186, 90, 0), # 71
(1276, 1142, 995, 1162, 946, 473, 502, 426, 469, 221, 152, 87, 0, 1268, 1087, 885, 719, 935, 663, 531, 329, 450, 396, 188, 91, 0), # 72
(1296, 1160, 1014, 1178, 957, 477, 510, 433, 477, 222, 157, 89, 0, 1286, 1100, 900, 731, 954, 672, 535, 335, 455, 404, 191, 93, 0), # 73
(1324, 1180, 1029, 1190, 972, 484, 513, 434, 481, 223, 158, 90, 0, 1300, 1118, 910, 739, 965, 682, 536, 339, 465, 407, 193, 94, 0), # 74
(1342, 1195, 1052, 1202, 979, 488, 518, 441, 488, 226, 161, 90, 0, 1325, 1130, 919, 745, 977, 688, 543, 342, 468, 415, 195, 96, 0), # 75
(1357, 1212, 1072, 1215, 991, 495, 526, 445, 499, 233, 163, 90, 0, 1340, 1144, 926, 756, 989, 694, 547, 347, 475, 419, 199, 96, 0), # 76
(1378, 1225, 1091, 1236, 1003, 505, 536, 449, 503, 236, 165, 92, 0, 1355, 1153, 938, 759, 1003, 700, 556, 352, 479, 425, 201, 97, 0), # 77
(1402, 1238, 1101, 1249, 1016, 515, 541, 452, 509, 241, 167, 94, 0, 1366, 1160, 950, 772, 1019, 707, 561, 362, 484, 436, 204, 99, 0), # 78
(1420, 1252, 1114, 1268, 1027, 526, 547, 461, 515, 244, 170, 94, 0, 1390, 1174, 961, 780, 1032, 714, 568, 370, 494, 439, 210, 102, 0), # 79
(1437, 1265, 1129, 1283, 1040, 529, 556, 470, 518, 246, 176, 95, 0, 1409, 1190, 975, 789, 1040, 721, 575, 374, 497, 444, 211, 103, 0), # 80
(1449, 1287, 1155, 1297, 1054, 535, 560, 475, 525, 247, 178, 98, 0, 1429, 1204, 984, 796, 1049, 726, 580, 379, 503, 446, 213, 106, 0), # 81
(1472, 1297, 1173, 1304, 1069, 540, 567, 482, 530, 249, 182, 99, 0, 1447, 1214, 1000, 809, 1067, 728, 585, 387, 512, 449, 214, 107, 0), # 82
(1490, 1315, 1183, 1330, 1082, 547, 576, 485, 539, 253, 182, 101, 0, 1457, 1226, 1009, 824, 1079, 736, 590, 393, 518, 453, 215, 107, 0), # 83
(1515, 1336, 1193, 1343, 1095, 556, 584, 489, 545, 258, 184, 104, 0, 1479, 1238, 1022, 833, 1093, 746, 600, 398, 528, 459, 220, 109, 0), # 84
(1539, 1349, 1212, 1358, 1105, 563, 592, 495, 550, 263, 185, 106, 0, 1494, 1251, 1029, 841, 1105, 750, 604, 405, 537, 470, 223, 111, 0), # 85
(1549, 1361, 1226, 1369, 1126, 571, 600, 501, 560, 266, 189, 107, 0, 1510, 1262, 1040, 848, 1107, 760, 609, 411, 547, 473, 226, 111, 0), # 86
(1568, 1375, 1247, 1385, 1139, 581, 612, 508, 569, 270, 192, 108, 0, 1523, 1277, 1053, 857, 1119, 765, 616, 415, 557, 477, 228, 111, 0), # 87
(1593, 1393, 1259, 1403, 1154, 590, 619, 515, 575, 275, 194, 112, 0, 1537, 1298, 1062, 862, 1127, 771, 623, 425, 567, 480, 232, 114, 0), # 88
(1612, 1403, 1279, 1418, 1166, 602, 625, 520, 580, 279, 197, 113, 0, 1551, 1322, 1072, 875, 1137, 781, 628, 428, 572, 488, 235, 117, 0), # 89
(1630, 1415, 1288, 1434, 1172, 608, 633, 523, 584, 283, 199, 115, 0, 1573, 1334, 1081, 881, 1148, 787, 637, 432, 580, 493, 238, 120, 0), # 90
(1653, 1424, 1301, 1451, 1182, 615, 641, 527, 590, 285, 199, 116, 0, 1592, 1352, 1089, 886, 1161, 795, 647, 436, 588, 500, 245, 120, 0), # 91
(1675, 1445, 1315, 1467, 1205, 618, 649, 531, 595, 289, 200, 116, 0, 1610, 1366, 1098, 895, 1172, 805, 657, 440, 591, 503, 246, 126, 0), # 92
(1690, 1458, 1332, 1481, 1214, 624, 655, 535, 601, 294, 201, 116, 0, 1627, 1385, 1104, 908, 1184, 812, 660, 441, 599, 510, 247, 127, 0), # 93
(1710, 1473, 1350, 1494, 1227, 635, 656, 540, 608, 296, 208, 117, 0, 1646, 1396, 1121, 923, 1194, 820, 667, 445, 610, 517, 249, 128, 0), # 94
(1725, 1481, 1366, 1511, 1244, 641, 665, 541, 614, 300, 208, 117, 0, 1664, 1412, 1127, 934, 1202, 827, 669, 449, 618, 521, 252, 129, 0), # 95
(1739, 1495, 1384, 1521, 1252, 647, 671, 547, 622, 302, 208, 118, 0, 1683, 1419, 1136, 949, 1212, 839, 671, 452, 624, 524, 252, 131, 0), # 96
(1758, 1507, 1396, 1542, 1264, 655, 678, 550, 628, 302, 210, 118, 0, 1695, 1431, 1142, 953, 1230, 847, 679, 458, 635, 528, 254, 135, 0), # 97
(1777, 1521, 1409, 1556, 1284, 663, 685, 558, 635, 305, 211, 119, 0, 1709, 1442, 1150, 960, 1238, 854, 686, 462, 643, 531, 256, 136, 0), # 98
(1794, 1541, 1422, 1572, 1296, 670, 690, 565, 641, 310, 213, 120, 0, 1725, 1457, 1156, 970, 1255, 862, 694, 469, 651, 532, 262, 137, 0), # 99
(1809, 1555, 1439, 1594, 1309, 677, 695, 571, 653, 312, 217, 121, 0, 1744, 1471, 1165, 976, 1271, 870, 698, 475, 657, 535, 267, 137, 0), # 100
(1826, 1568, 1457, 1617, 1325, 683, 699, 575, 661, 314, 220, 121, 0, 1760, 1481, 1178, 987, 1283, 874, 700, 483, 663, 544, 271, 137, 0), # 101
(1844, 1581, 1464, 1638, 1341, 686, 706, 578, 667, 315, 220, 122, 0, 1780, 1497, 1189, 994, 1296, 883, 710, 489, 671, 549, 275, 138, 0), # 102
(1857, 1600, 1480, 1657, 1350, 695, 719, 588, 674, 316, 221, 126, 0, 1799, 1510, 1201, 1009, 1308, 889, 712, 492, 677, 555, 278, 141, 0), # 103
(1872, 1614, 1491, 1672, 1366, 705, 723, 595, 678, 318, 222, 127, 0, 1817, 1528, 1206, 1021, 1321, 894, 719, 496, 681, 564, 282, 142, 0), # 104
(1882, 1631, 1504, 1686, 1373, 710, 725, 597, 687, 320, 224, 128, 0, 1829, 1539, 1216, 1034, 1331, 904, 728, 498, 688, 565, 287, 144, 0), # 105
(1903, 1644, 1519, 1706, 1388, 713, 731, 602, 694, 324, 226, 128, 0, 1845, 1555, 1222, 1042, 1346, 908, 735, 500, 694, 567, 288, 145, 0), # 106
(1922, 1657, 1533, 1721, 1402, 716, 737, 605, 704, 326, 226, 129, 0, 1859, 1569, 1227, 1052, 1363, 918, 736, 504, 701, 572, 290, 146, 0), # 107
(1938, 1671, 1550, 1734, 1415, 722, 746, 609, 714, 329, 228, 129, 0, 1874, 1578, 1237, 1056, 1373, 928, 743, 510, 708, 579, 291, 146, 0), # 108
(1951, 1687, 1567, 1740, 1430, 728, 755, 613, 723, 329, 231, 130, 0, 1901, 1590, 1247, 1063, 1385, 934, 752, 516, 713, 585, 295, 148, 0), # 109
(1965, 1694, 1580, 1752, 1443, 737, 760, 617, 731, 333, 232, 131, 0, 1914, 1599, 1258, 1072, 1401, 939, 759, 522, 718, 588, 297, 149, 0), # 110
(1979, 1707, 1590, 1768, 1460, 747, 767, 622, 736, 334, 233, 133, 0, 1922, 1612, 1269, 1076, 1413, 946, 766, 526, 728, 595, 301, 149, 0), # 111
(2006, 1717, 1601, 1785, 1472, 753, 775, 625, 744, 336, 234, 136, 0, 1939, 1628, 1284, 1083, 1427, 949, 770, 529, 736, 605, 301, 150, 0), # 112
(2031, 1729, 1611, 1802, 1482, 761, 779, 629, 749, 337, 234, 136, 0, 1960, 1638, 1291, 1091, 1447, 956, 775, 533, 742, 610, 301, 152, 0), # 113
(2051, 1739, 1627, 1812, 1494, 767, 783, 632, 757, 340, 238, 139, 0, 1976, 1654, 1302, 1099, 1463, 964, 778, 536, 749, 618, 305, 152, 0), # 114
(2070, 1752, 1643, 1839, 1505, 770, 787, 639, 760, 341, 239, 142, 0, 1992, 1666, 1315, 1107, 1481, 973, 784, 546, 753, 623, 309, 153, 0), # 115
(2086, 1768, 1654, 1856, 1516, 777, 792, 643, 772, 346, 240, 145, 0, 2010, 1679, 1327, 1113, 1501, 977, 791, 551, 758, 632, 313, 154, 0), # 116
(2107, 1782, 1670, 1878, 1531, 789, 799, 646, 782, 350, 243, 147, 0, 2029, 1688, 1330, 1120, 1518, 982, 800, 559, 763, 634, 316, 155, 0), # 117
(2125, 1793, 1685, 1897, 1551, 793, 805, 650, 788, 351, 247, 147, 0, 2055, 1696, 1339, 1123, 1538, 991, 802, 565, 768, 638, 318, 155, 0), # 118
(2142, 1805, 1702, 1911, 1561, 796, 814, 654, 794, 354, 249, 148, 0, 2062, 1719, 1344, 1130, 1554, 994, 808, 570, 773, 642, 319, 157, 0), # 119
(2164, 1815, 1717, 1925, 1576, 803, 816, 657, 800, 355, 253, 150, 0, 2076, 1740, 1351, 1137, 1563, 1001, 810, 572, 783, 645, 325, 159, 0), # 120
(2185, 1831, 1731, 1938, 1583, 812, 820, 659, 806, 357, 259, 150, 0, 2085, 1751, 1367, 1147, 1577, 1007, 814, 574, 786, 650, 328, 161, 0), # 121
(2199, 1842, 1746, 1951, 1593, 821, 823, 666, 810, 361, 261, 150, 0, 2099, 1765, 1376, 1155, 1588, 1009, 824, 580, 792, 654, 332, 163, 0), # 122
(2216, 1854, 1764, 1961, 1601, 823, 829, 669, 821, 362, 263, 151, 0, 2116, 1787, 1392, 1161, 1600, 1015, 824, 583, 796, 659, 335, 163, 0), # 123
(2234, 1868, 1779, 1971, 1610, 831, 838, 673, 826, 365, 264, 152, 0, 2144, 1799, 1401, 1167, 1606, 1020, 832, 587, 801, 664, 338, 164, 0), # 124
(2252, 1882, 1793, 1985, 1619, 834, 841, 678, 832, 367, 266, 153, 0, 2160, 1814, 1411, 1173, 1618, 1030, 834, 592, 803, 667, 342, 165, 0), # 125
(2257, 1898, 1808, 1995, 1630, 848, 845, 682, 839, 369, 268, 153, 0, 2173, 1826, 1422, 1181, 1628, 1039, 841, 593, 810, 676, 343, 165, 0), # 126
(2271, 1914, 1817, 2008, 1642, 854, 847, 686, 849, 370, 269, 153, 0, 2194, 1841, 1433, 1187, 1638, 1048, 848, 599, 817, 682, 348, 167, 0), # 127
(2289, 1923, 1827, 2028, 1657, 860, 853, 693, 855, 372, 270, 154, 0, 2207, 1850, 1439, 1194, 1649, 1057, 854, 603, 821, 686, 353, 168, 0), # 128
(2303, 1937, 1842, 2039, 1667, 864, 858, 698, 863, 376, 275, 156, 0, 2228, 1865, 1444, 1202, 1664, 1063, 859, 606, 825, 691, 354, 169, 0), # 129
(2314, 1952, 1858, 2050, 1678, 872, 864, 706, 868, 379, 278, 159, 0, 2241, 1869, 1459, 1208, 1682, 1073, 860, 609, 832, 694, 357, 169, 0), # 130
(2334, 1960, 1869, 2068, 1685, 877, 869, 713, 874, 386, 283, 160, 0, 2253, 1886, 1466, 1214, 1693, 1076, 866, 612, 840, 700, 358, 169, 0), # 131
(2352, 1972, 1881, 2081, 1697, 885, 873, 716, 879, 387, 283, 161, 0, 2271, 1904, 1478, 1222, 1705, 1082, 868, 613, 846, 703, 361, 172, 0), # 132
(2371, 1980, 1894, 2096, 1714, 896, 876, 721, 885, 388, 284, 161, 0, 2287, 1908, 1488, 1236, 1713, 1088, 872, 617, 850, 709, 364, 174, 0), # 133
(2387, 1992, 1908, 2113, 1723, 905, 880, 725, 894, 388, 286, 162, 0, 2301, 1924, 1496, 1243, 1718, 1089, 882, 619, 856, 712, 365, 179, 0), # 134
(2406, 2006, 1919, 2132, 1733, 914, 886, 732, 898, 391, 289, 162, 0, 2312, 1938, 1507, 1249, 1731, 1095, 890, 623, 860, 716, 369, 181, 0), # 135
(2421, 2014, 1930, 2144, 1747, 922, 892, 737, 907, 393, 292, 162, 0, 2330, 1949, 1518, 1257, 1740, 1101, 897, 628, 865, 721, 369, 181, 0), # 136
(2435, 2027, 1947, 2154, 1760, 926, 899, 740, 914, 393, 296, 162, 0, 2345, 1959, 1528, 1269, 1752, 1112, 901, 632, 873, 725, 370, 181, 0), # 137
(2446, 2034, 1961, 2165, 1773, 930, 904, 742, 921, 394, 298, 163, 0, 2362, 1972, 1535, 1276, 1762, 1119, 907, 637, 881, 729, 372, 181, 0), # 138
(2459, 2044, 1968, 2174, 1787, 933, 915, 750, 926, 400, 300, 164, 0, 2376, 1986, 1547, 1284, 1778, 1130, 912, 640, 886, 733, 372, 182, 0), # 139
(2473, 2054, 1986, 2183, 1805, 936, 919, 754, 931, 402, 304, 166, 0, 2389, 2007, 1552, 1292, 1792, 1142, 916, 642, 890, 736, 376, 182, 0), # 140
(2496, 2067, 2000, 2195, 1812, 940, 923, 757, 935, 405, 306, 167, 0, 2404, 2017, 1562, 1301, 1805, 1147, 919, 649, 896, 741, 381, 184, 0), # 141
(2514, 2076, 2016, 2205, 1823, 945, 927, 761, 939, 405, 308, 168, 0, 2419, 2034, 1570, 1309, 1818, 1152, 925, 654, 903, 746, 381, 184, 0), # 142
(2526, 2090, 2030, 2219, 1834, 948, 934, 767, 946, 407, 309, 174, 0, 2433, 2049, 1581, 1313, 1829, 1157, 927, 656, 910, 751, 383, 186, 0), # 143
(2547, 2100, 2043, 2236, 1844, 953, 942, 773, 952, 409, 315, 177, 0, 2446, 2065, 1591, 1323, 1844, 1161, 932, 662, 914, 754, 387, 187, 0), # 144
(2559, 2105, 2056, 2252, 1855, 957, 948, 779, 962, 410, 317, 178, 0, 2461, 2082, 1600, 1327, 1864, 1167, 937, 666, 922, 759, 389, 187, 0), # 145
(2579, 2118, 2072, 2275, 1866, 962, 955, 789, 968, 411, 319, 178, 0, 2483, 2094, 1606, 1337, 1877, 1171, 941, 670, 925, 765, 391, 187, 0), # 146
(2591, 2125, 2084, 2294, 1878, 972, 959, 792, 975, 413, 320, 178, 0, 2503, 2104, 1613, 1343, 1884, 1177, 944, 676, 932, 770, 396, 187, 0), # 147
(2611, 2132, 2095, 2304, 1885, 981, 962, 797, 987, 416, 322, 182, 0, 2517, 2122, 1623, 1350, 1896, 1183, 950, 676, 936, 772, 399, 188, 0), # 148
(2619, 2144, 2106, 2312, 1893, 987, 969, 803, 990, 417, 323, 183, 0, 2526, 2136, 1631, 1361, 1915, 1189, 955, 677, 942, 780, 401, 189, 0), # 149
(2636, 2156, 2117, 2329, 1911, 996, 973, 808, 997, 418, 325, 183, 0, 2544, 2148, 1640, 1369, 1925, 1196, 959, 682, 949, 783, 403, 192, 0), # 150
(2645, 2172, 2129, 2346, 1921, 1000, 977, 817, 1002, 419, 325, 185, 0, 2556, 2159, 1645, 1378, 1931, 1205, 964, 685, 953, 787, 404, 192, 0), # 151
(2652, 2183, 2144, 2359, 1931, 1004, 980, 821, 1008, 420, 327, 185, 0, 2567, 2168, 1652, 1388, 1944, 1209, 969, 689, 956, 788, 405, 194, 0), # 152
(2666, 2193, 2162, 2367, 1945, 1010, 988, 822, 1012, 422, 328, 186, 0, 2580, 2179, 1661, 1395, 1956, 1211, 972, 694, 964, 793, 405, 198, 0), # 153
(2681, 2199, 2174, 2383, 1957, 1012, 990, 823, 1020, 426, 328, 188, 0, 2592, 2192, 1670, 1408, 1972, 1215, 977, 697, 970, 798, 407, 199, 0), # 154
(2691, 2211, 2189, 2394, 1965, 1015, 995, 829, 1025, 430, 333, 189, 0, 2602, 2203, 1684, 1410, 1985, 1219, 981, 703, 977, 806, 408, 200, 0), # 155
(2705, 2223, 2202, 2408, 1978, 1021, 1000, 835, 1032, 431, 337, 190, 0, 2615, 2212, 1689, 1420, 1996, 1222, 986, 707, 980, 813, 410, 201, 0), # 156
(2715, 2233, 2217, 2424, 1986, 1025, 1008, 836, 1033, 437, 339, 191, 0, 2631, 2225, 1695, 1429, 2009, 1225, 991, 713, 989, 814, 413, 201, 0), # 157
(2731, 2243, 2231, 2438, 2000, 1029, 1012, 841, 1041, 438, 341, 192, 0, 2642, 2233, 1707, 1434, 2023, 1232, 997, 719, 992, 821, 416, 201, 0), # 158
(2740, 2255, 2243, 2448, 2009, 1034, 1019, 843, 1043, 438, 343, 192, 0, 2657, 2242, 1711, 1444, 2035, 1240, 1003, 721, 996, 822, 418, 202, 0), # 159
(2749, 2263, 2253, 2453, 2017, 1038, 1024, 846, 1045, 440, 344, 193, 0, 2669, 2256, 1720, 1451, 2043, 1246, 1011, 723, 1001, 826, 421, 204, 0), # 160
(2766, 2276, 2262, 2470, 2031, 1045, 1031, 846, 1050, 442, 344, 194, 0, 2684, 2268, 1723, 1458, 2057, 1255, 1014, 726, 1005, 829, 421, 205, 0), # 161
(2781, 2286, 2276, 2475, 2042, 1050, 1032, 852, 1056, 443, 344, 195, 0, 2694, 2279, 1736, 1466, 2065, 1261, 1019, 730, 1009, 833, 422, 205, 0), # 162
(2797, 2294, 2293, 2487, 2053, 1060, 1039, 855, 1061, 445, 346, 197, 0, 2709, 2287, 1745, 1474, 2074, 1269, 1020, 735, 1016, 835, 424, 205, 0), # 163
(2807, 2300, 2305, 2498, 2057, 1062, 1044, 859, 1068, 449, 348, 198, 0, 2725, 2293, 1753, 1487, 2090, 1276, 1023, 735, 1021, 838, 426, 205, 0), # 164
(2818, 2305, 2313, 2508, 2071, 1064, 1051, 865, 1076, 449, 348, 198, 0, 2742, 2304, 1763, 1494, 2100, 1282, 1028, 737, 1026, 841, 431, 205, 0), # 165
(2829, 2316, 2323, 2516, 2077, 1065, 1054, 866, 1081, 451, 348, 198, 0, 2757, 2317, 1772, 1501, 2110, 1287, 1034, 739, 1028, 845, 433, 207, 0), # 166
(2840, 2324, 2337, 2523, 2085, 1068, 1056, 874, 1086, 452, 350, 199, 0, 2763, 2323, 1783, 1505, 2117, 1292, 1037, 740, 1033, 847, 435, 207, 0), # 167
(2850, 2327, 2344, 2535, 2097, 1070, 1059, 879, 1087, 453, 352, 200, 0, 2778, 2337, 1790, 1509, 2124, 1294, 1044, 740, 1033, 850, 438, 208, 0), # 168
(2858, 2340, 2352, 2550, 2106, 1075, 1063, 885, 1091, 454, 353, 201, 0, 2785, 2349, 1793, 1515, 2138, 1302, 1046, 744, 1037, 852, 445, 209, 0), # 169
(2867, 2350, 2365, 2557, 2114, 1076, 1068, 888, 1098, 454, 355, 201, 0, 2796, 2358, 1798, 1520, 2149, 1308, 1051, 747, 1041, 855, 447, 210, 0), # 170
(2886, 2357, 2379, 2559, 2123, 1082, 1070, 894, 1106, 456, 355, 201, 0, 2809, 2366, 1802, 1525, 2159, 1317, 1053, 751, 1049, 858, 450, 210, 0), # 171
(2897, 2364, 2390, 2565, 2134, 1087, 1072, 895, 1107, 456, 355, 201, 0, 2821, 2371, 1812, 1528, 2172, 1322, 1053, 753, 1055, 863, 452, 210, 0), # 172
(2902, 2365, 2396, 2577, 2143, 1091, 1076, 896, 1113, 456, 355, 201, 0, 2830, 2376, 1814, 1538, 2179, 1323, 1058, 758, 1058, 865, 452, 212, 0), # 173
(2908, 2370, 2409, 2582, 2149, 1094, 1077, 898, 1117, 458, 355, 202, 0, 2846, 2382, 1820, 1544, 2183, 1326, 1062, 759, 1064, 866, 452, 213, 0), # 174
(2914, 2374, 2423, 2591, 2156, 1096, 1080, 898, 1119, 459, 357, 202, 0, 2855, 2390, 1823, 1546, 2193, 1332, 1065, 763, 1066, 869, 453, 213, 0), # 175
(2920, 2381, 2426, 2596, 2161, 1097, 1086, 902, 1124, 459, 357, 205, 0, 2863, 2394, 1829, 1548, 2199, 1334, 1068, 764, 1069, 872, 453, 213, 0), # 176
(2927, 2390, 2430, 2601, 2167, 1101, 1087, 904, 1130, 460, 357, 208, 0, 2869, 2402, 1834, 1553, 2203, 1340, 1070, 765, 1074, 876, 455, 213, 0), # 177
(2936, 2397, 2438, 2607, 2174, 1104, 1088, 907, 1130, 461, 358, 210, 0, 2874, 2414, 1838, 1559, 2211, 1344, 1074, 767, 1078, 880, 458, 214, 0), # 178
(2936, 2397, 2438, 2607, 2174, 1104, 1088, 907, 1130, 461, 358, 210, 0, 2874, 2414, 1838, 1559, 2211, 1344, 1074, 767, 1078, 880, 458, 214, 0), # 179
)
passenger_arriving_rate = (
(9.037558041069182, 9.116726123493724, 7.81692484441876, 8.389801494715634, 6.665622729131535, 3.295587678639206, 3.7314320538365235, 3.4898821297345672, 3.654059437300804, 1.781106756985067, 1.261579549165681, 0.7346872617459261, 0.0, 9.150984382641052, 8.081559879205185, 6.307897745828405, 5.3433202709552, 7.308118874601608, 4.885834981628395, 3.7314320538365235, 2.3539911990280045, 3.3328113645657673, 2.7966004982385453, 1.5633849688837522, 0.828793283953975, 0.0), # 0
(9.637788873635953, 9.718600145338852, 8.333019886995228, 8.943944741923431, 7.106988404969084, 3.5132827632446837, 3.9775220471373247, 3.7196352921792815, 3.8953471957997454, 1.8985413115247178, 1.3449288407868398, 0.7831824991221532, 0.0, 9.755624965391739, 8.615007490343684, 6.724644203934198, 5.695623934574153, 7.790694391599491, 5.207489409050994, 3.9775220471373247, 2.509487688031917, 3.553494202484542, 2.9813149139744777, 1.6666039773990458, 0.883509104121714, 0.0), # 1
(10.236101416163518, 10.318085531970116, 8.847063428321121, 9.495883401297473, 7.546755568499692, 3.7301093702380674, 4.222636657164634, 3.948468935928315, 4.135672084126529, 2.015511198759246, 1.4279469446328943, 0.8314848978079584, 0.0, 10.357856690777442, 9.14633387588754, 7.13973472316447, 6.046533596277737, 8.271344168253059, 5.527856510299641, 4.222636657164634, 2.6643638358843336, 3.773377784249846, 3.1652944670991583, 1.7694126856642243, 0.938007775633647, 0.0), # 2
(10.830164027663812, 10.912803828195138, 9.357016303979782, 10.0434281501683, 7.983194011202283, 3.9452076537143688, 4.46580327748316, 4.175475868120881, 4.374081096552656, 2.1315522142917818, 1.5103045235482149, 0.8794028527395692, 0.0, 10.955291051257605, 9.67343138013526, 7.551522617741075, 6.3946566428753435, 8.748162193105312, 5.845666215369232, 4.46580327748316, 2.818005466938835, 3.9915970056011414, 3.3478093833894342, 1.8714032607959565, 0.9920730752904672, 0.0), # 3
(11.417645067148767, 11.500376578821527, 9.860839349554556, 10.584389665866468, 8.41457352455579, 4.1577177677686015, 4.706049301657613, 4.399748895896186, 4.609621227349624, 2.246200153725456, 1.5916722403771728, 0.9267447588532147, 0.0, 11.54553953929167, 10.19419234738536, 7.958361201885864, 6.738600461176366, 9.219242454699248, 6.159648454254661, 4.706049301657613, 2.969798405549001, 4.207286762277895, 3.528129888622157, 1.9721678699109113, 1.0454887798928663, 0.0), # 4
(11.996212893630318, 12.07842532865692, 10.356493400628777, 11.11657862572253, 8.839163900039136, 4.366779866495776, 4.942402123252702, 4.620380826393444, 4.841339470788935, 2.3589908126633987, 1.67172075796414, 0.9733190110851223, 0.0, 12.126213647339089, 10.706509121936344, 8.358603789820698, 7.076972437990195, 9.68267894157787, 6.468533156950822, 4.942402123252702, 3.119128476068411, 4.419581950019568, 3.705526208574178, 2.071298680125756, 1.0980386662415385, 0.0), # 5
(12.5635358661204, 12.644571622508925, 10.8419392927858, 11.63780570706703, 9.255234929131252, 4.571534103990907, 5.173889135833137, 4.836464466751867, 5.068282821142089, 2.469459986708742, 1.750120739153485, 1.0189340043715214, 0.0, 12.694924867859292, 11.208274048086732, 8.750603695767424, 7.408379960126224, 10.136565642284179, 6.771050253452613, 5.173889135833137, 3.265381502850648, 4.627617464565626, 3.8792685690223445, 2.16838785855716, 1.1495065111371752, 0.0), # 6
(13.117282343630944, 13.196437005185167, 11.315137861608953, 12.145881587230525, 9.661056403311065, 4.771120634349007, 5.399537732963626, 5.047092624110664, 5.289498272680586, 2.5771434714646144, 1.8265428467895808, 1.0633981336486396, 0.0, 13.249284693311735, 11.697379470135033, 9.132714233947903, 7.7314304143938415, 10.578996545361171, 7.06592967375493, 5.399537732963626, 3.4079433102492906, 4.830528201655532, 4.048627195743509, 2.2630275723217905, 1.1996760913804698, 0.0), # 7
(13.655120685173882, 13.731643021493262, 11.774049942681595, 12.638616943543553, 10.054898114057503, 4.964679611665085, 5.618375308208878, 5.251358105609044, 5.504032819675924, 2.681577062534149, 1.9006577437167966, 1.1065197938527056, 0.0, 13.786904616155851, 12.171717732379758, 9.503288718583983, 8.044731187602444, 11.008065639351848, 7.351901347852662, 5.618375308208878, 3.5461997226179176, 5.027449057028751, 4.212872314514518, 2.3548099885363194, 1.248331183772115, 0.0), # 8
(14.174719249761154, 14.247811216240837, 12.216636371587056, 13.11382245333668, 10.43502985284949, 5.151351190034158, 5.829429255133608, 5.4483537183862225, 5.710933456399605, 2.782296555520474, 1.9721360927795035, 1.1481073799199473, 0.0, 14.305396128851092, 12.629181179119417, 9.860680463897518, 8.34688966656142, 11.42186691279921, 7.627695205740712, 5.829429255133608, 3.679536564310113, 5.217514926424745, 4.371274151112227, 2.4433272743174115, 1.2952555651128035, 0.0), # 9
(14.673746396404677, 14.7425631342355, 12.640857983908687, 13.569308793940438, 10.799721411165962, 5.330275523551238, 6.031726967302519, 5.637172269581408, 5.909247177123128, 2.878837746026722, 2.0406485568220725, 1.187969286786593, 0.0, 14.802370723856898, 13.06766215465252, 10.20324278411036, 8.636513238080164, 11.818494354246257, 7.892041177413972, 6.031726967302519, 3.8073396596794558, 5.399860705582981, 4.52310293131348, 2.5281715967817378, 1.3402330122032275, 0.0), # 10
(15.149870484116411, 15.213520320284891, 13.044675615229824, 14.002886642685386, 11.14724258048584, 5.500592766311337, 6.224295838280325, 5.816906566333811, 6.098020976117995, 2.970736429656024, 2.105865798688875, 1.2259139093888718, 0.0, 15.2754398936327, 13.485053003277587, 10.529328993444373, 8.912209288968072, 12.19604195223599, 8.143669192867335, 6.224295838280325, 3.9289948330795266, 5.57362129024292, 4.66762888089513, 2.6089351230459648, 1.3830473018440812, 0.0), # 11
(15.600759871908263, 15.6583043191966, 13.42605010113381, 14.412366676902078, 11.475863152288053, 5.6614430724094635, 6.406163261631731, 5.986649415782641, 6.276301847655707, 3.0575284020115086, 2.1674584812242808, 1.2617496426630104, 0.0, 15.722215130637963, 13.879246069293112, 10.837292406121403, 9.172585206034523, 12.552603695311413, 8.381309182095698, 6.406163261631731, 4.043887908863902, 5.737931576144026, 4.804122225634027, 2.6852100202267626, 1.4234822108360548, 0.0), # 12
(16.02408291879218, 16.074536675778273, 13.782942277203993, 14.795559573921057, 11.783852918051522, 5.8119665959406355, 6.576356630921451, 6.145493625067111, 6.443136786007759, 3.138749458696308, 2.225097267272661, 1.2952848815452382, 0.0, 16.140307927332124, 14.248133696997618, 11.125486336363304, 9.416248376088921, 12.886273572015519, 8.603691075093955, 6.576356630921451, 4.151404711386168, 5.891926459025761, 4.93185319130702, 2.756588455440799, 1.4613215159798432, 0.0), # 13
(16.41750798378009, 16.45983893483752, 14.113312979023721, 15.150276011072872, 12.069481669255186, 5.9513034909998614, 6.733903339714195, 6.292532001326435, 6.597572785445653, 3.2139353953135514, 2.2784528196783858, 1.3263280209717843, 0.0, 16.527329776174614, 14.589608230689624, 11.392264098391927, 9.641806185940652, 13.195145570891306, 8.80954480185701, 6.733903339714195, 4.250931064999901, 6.034740834627593, 5.050092003690958, 2.8226625958047444, 1.4963489940761385, 0.0), # 14
(16.77870342588394, 16.811832641181958, 14.415123042176313, 15.474326665688082, 12.33101919737797, 6.078593911682158, 6.877830781574663, 6.426857351699818, 6.738656840240891, 3.2826220074663714, 2.3271958012858263, 1.3546874558788757, 0.0, 16.880892169624886, 14.90156201466763, 11.63597900642913, 9.847866022399112, 13.477313680481782, 8.997600292379746, 6.877830781574663, 4.341852794058684, 6.165509598688985, 5.158108888562695, 2.883024608435263, 1.5283484219256327, 0.0), # 15
(17.10533760411564, 17.128139339619217, 14.686333302245139, 15.765522215097217, 12.566735293898798, 6.192978012082533, 7.007166350067579, 6.547562483326471, 6.865435944664972, 3.344345090757899, 2.370996874939354, 1.380171581202741, 0.0, 17.198606600142384, 15.181887393230149, 11.85498437469677, 10.033035272273695, 13.730871889329944, 9.16658747665706, 7.007166350067579, 4.423555722916095, 6.283367646949399, 5.255174071699074, 2.9372666604490276, 1.55710357632902, 0.0), # 16
(17.395078877487137, 17.406380574956913, 14.92490459481353, 16.021673336630855, 12.774899750296605, 6.2935959462960005, 7.12093743875764, 6.653740203345614, 6.976957092989391, 3.398640440791261, 2.40952670348334, 1.4025887918796085, 0.0, 17.47808456018655, 15.428476710675692, 12.047633517416699, 10.195921322373781, 13.953914185978782, 9.31523628468386, 7.12093743875764, 4.4954256759257145, 6.387449875148302, 5.340557778876952, 2.984980918962706, 1.5823982340869922, 0.0), # 17
(17.645595605010367, 17.644177892002652, 15.12879775546482, 16.24059070761953, 12.953782358050306, 6.379587868417579, 7.2181714412095666, 6.744483318896446, 7.072267279485658, 3.4450438531695924, 2.4424559497621527, 1.4217474828457075, 0.0, 17.716937542216822, 15.63922231130278, 12.212279748810763, 10.335131559508774, 14.144534558971316, 9.442276646455024, 7.2181714412095666, 4.556848477441128, 6.476891179025153, 5.413530235873177, 3.0257595510929645, 1.6040161720002415, 0.0), # 18
(17.85455614569726, 17.83915283556408, 15.29597361978237, 16.420085005393776, 13.10165290863884, 6.450093932542269, 7.297895750988055, 6.818884637118185, 7.150413498425267, 3.4830911234960236, 2.4694552766201636, 1.4374560490372645, 0.0, 17.912777038692653, 15.812016539409907, 12.347276383100818, 10.449273370488068, 14.300826996850533, 9.546438491965459, 7.297895750988055, 4.607209951815906, 6.55082645431942, 5.473361668464593, 3.059194723956474, 1.621741166869462, 0.0), # 19
(18.01962885855975, 17.988926950448786, 15.424393023349506, 16.55796690728418, 13.216781193541133, 6.504254292765094, 7.359137761657826, 6.876036965150038, 7.210442744079718, 3.5123180473736824, 2.490195346901745, 1.4495228853905089, 0.0, 18.063214542073485, 15.944751739295596, 12.450976734508725, 10.536954142121044, 14.420885488159437, 9.626451751210054, 7.359137761657826, 4.645895923403639, 6.608390596770566, 5.51932230242806, 3.084878604669901, 1.6353569954953444, 0.0), # 20
(18.13848210260976, 18.09112178146442, 15.51201680174958, 16.652047090621256, 13.297437004236105, 6.541209103181062, 7.400924866783583, 6.915033110131218, 7.251402010720512, 3.532260420405701, 2.5043468234512685, 1.4577563868416692, 0.0, 18.165861544818743, 16.03532025525836, 12.52173411725634, 10.5967812612171, 14.502804021441024, 9.681046354183705, 7.400924866783583, 4.672292216557902, 6.648718502118053, 5.550682363540419, 3.1024033603499164, 1.644647434678584, 0.0), # 21
(18.20878423685924, 18.143358873418588, 15.55680579056593, 16.70013623273558, 13.341890132202689, 6.560098517885186, 7.422284459930039, 6.934965879200936, 7.27233829261915, 3.54245403819521, 2.5115803691131027, 1.4619649483269737, 0.0, 18.218329539387888, 16.08161443159671, 12.557901845565512, 10.627362114585626, 14.5446765852383, 9.70895223088131, 7.422284459930039, 4.6857846556322755, 6.6709450661013445, 5.5667120775785275, 3.111361158113186, 1.649396261219872, 0.0), # 22
(18.23470805401675, 18.14954393004115, 15.562384773662554, 16.706156597222225, 13.353278467239116, 6.5625, 7.424823602033405, 6.937120370370371, 7.274955740740741, 3.543656522633746, 2.512487411148522, 1.4624846364883404, 0.0, 18.225, 16.08733100137174, 12.56243705574261, 10.630969567901236, 14.549911481481482, 9.71196851851852, 7.424823602033405, 4.6875, 6.676639233619558, 5.568718865740743, 3.1124769547325113, 1.6499585390946503, 0.0), # 23
(18.253822343461476, 18.145936111111112, 15.561472222222221, 16.705415625000004, 13.359729136337823, 6.5625, 7.42342843137255, 6.934125, 7.274604999999999, 3.5429177777777783, 2.5123873737373743, 1.462362962962963, 0.0, 18.225, 16.085992592592593, 12.561936868686871, 10.628753333333332, 14.549209999999999, 9.707775, 7.42342843137255, 4.6875, 6.679864568168911, 5.568471875000002, 3.1122944444444447, 1.649630555555556, 0.0), # 24
(18.272533014380844, 18.138824588477366, 15.559670781893006, 16.70394965277778, 13.366037934713404, 6.5625, 7.420679012345679, 6.928240740740742, 7.273912037037037, 3.541463477366256, 2.512189019827909, 1.4621227709190674, 0.0, 18.225, 16.08335048010974, 12.560945099139545, 10.624390432098766, 14.547824074074073, 9.69953703703704, 7.420679012345679, 4.6875, 6.683018967356702, 5.567983217592594, 3.1119341563786014, 1.6489840534979427, 0.0), # 25
(18.290838634286462, 18.128318004115226, 15.557005144032923, 16.70177534722222, 13.372204642105325, 6.5625, 7.416618046477849, 6.919578703703704, 7.27288574074074, 3.539317818930042, 2.511894145155257, 1.4617673525377233, 0.0, 18.225, 16.079440877914955, 12.559470725776283, 10.617953456790124, 14.54577148148148, 9.687410185185186, 7.416618046477849, 4.6875, 6.686102321052663, 5.567258449074075, 3.111401028806585, 1.648028909465021, 0.0), # 26
(18.308737770689945, 18.114524999999997, 15.553500000000001, 16.698909375, 13.378229038253057, 6.5625, 7.411288235294118, 6.908250000000002, 7.271535, 3.5365050000000005, 2.5115045454545455, 1.4613000000000003, 0.0, 18.225, 16.0743, 12.557522727272728, 10.609514999999998, 14.54307, 9.671550000000002, 7.411288235294118, 4.6875, 6.689114519126528, 5.566303125, 3.1107000000000005, 1.646775, 0.0), # 27
(18.3262289911029, 18.097554218106993, 15.549180041152265, 16.695368402777778, 13.384110902896083, 6.5625, 7.404732280319536, 6.894365740740742, 7.269868703703704, 3.533049218106997, 2.5110220164609056, 1.4607240054869688, 0.0, 18.225, 16.067964060356655, 12.555110082304529, 10.599147654320989, 14.539737407407408, 9.652112037037039, 7.404732280319536, 4.6875, 6.6920554514480415, 5.565122800925927, 3.1098360082304533, 1.6452322016460905, 0.0), # 28
(18.34331086303695, 18.077514300411522, 15.54406995884774, 16.69116909722222, 13.389850015773863, 6.5625, 7.396992883079159, 6.8780370370370365, 7.267895740740741, 3.5289746707818943, 2.510448353909465, 1.4600426611796984, 0.0, 18.225, 16.06046927297668, 12.552241769547326, 10.58692401234568, 14.535791481481482, 9.629251851851851, 7.396992883079159, 4.6875, 6.694925007886932, 5.563723032407409, 3.1088139917695483, 1.6434103909465023, 0.0), # 29
(18.359981954003697, 18.054513888888888, 15.538194444444445, 16.686328125000003, 13.395446156625884, 6.5625, 7.388112745098039, 6.859375, 7.265625, 3.5243055555555567, 2.509785353535354, 1.4592592592592593, 0.0, 18.225, 16.05185185185185, 12.548926767676768, 10.572916666666668, 14.53125, 9.603125, 7.388112745098039, 4.6875, 6.697723078312942, 5.562109375000001, 3.107638888888889, 1.6413194444444446, 0.0), # 30
(18.376240831514746, 18.028661625514406, 15.531578189300415, 16.680862152777777, 13.400899105191609, 6.5625, 7.378134567901236, 6.838490740740741, 7.26306537037037, 3.5190660699588485, 2.5090348110737, 1.458377091906722, 0.0, 18.225, 16.04214801097394, 12.5451740553685, 10.557198209876542, 14.52613074074074, 9.573887037037037, 7.378134567901236, 4.6875, 6.7004495525958045, 5.56028738425926, 3.106315637860083, 1.638969238683128, 0.0), # 31
(18.392086063081717, 18.000066152263376, 15.524245884773661, 16.674787847222223, 13.406208641210513, 6.5625, 7.3671010530137995, 6.815495370370372, 7.260225740740741, 3.5132804115226346, 2.5081985222596335, 1.4573994513031552, 0.0, 18.225, 16.031393964334704, 12.540992611298167, 10.539841234567902, 14.520451481481482, 9.541693518518521, 7.3671010530137995, 4.6875, 6.703104320605257, 5.558262615740742, 3.1048491769547324, 1.6363696502057616, 0.0), # 32
(18.407516216216216, 17.96883611111111, 15.516222222222224, 16.668121874999997, 13.411374544422076, 6.5625, 7.355054901960784, 6.790500000000001, 7.257115, 3.506972777777779, 2.507278282828283, 1.4563296296296298, 0.0, 18.225, 16.019625925925926, 12.536391414141413, 10.520918333333334, 14.51423, 9.5067, 7.355054901960784, 4.6875, 6.705687272211038, 5.5560406250000005, 3.103244444444445, 1.6335305555555555, 0.0), # 33
(18.422529858429858, 17.93508014403292, 15.507531893004115, 16.660880902777777, 13.41639659456576, 6.5625, 7.342038816267248, 6.7636157407407405, 7.253742037037037, 3.500167366255145, 2.5062758885147773, 1.4551709190672155, 0.0, 18.225, 16.006880109739367, 12.531379442573886, 10.500502098765432, 14.507484074074075, 9.469062037037038, 7.342038816267248, 4.6875, 6.70819829728288, 5.553626967592593, 3.1015063786008232, 1.6304618312757202, 0.0), # 34
(18.437125557234253, 17.898906893004114, 15.49819958847737, 16.65308159722222, 13.421274571381044, 6.5625, 7.328095497458243, 6.734953703703703, 7.250115740740741, 3.4928883744855974, 2.5051931350542462, 1.4539266117969825, 0.0, 18.225, 15.993192729766804, 12.52596567527123, 10.47866512345679, 14.500231481481482, 9.428935185185185, 7.328095497458243, 4.6875, 6.710637285690522, 5.551027199074074, 3.099639917695474, 1.627173353909465, 0.0), # 35
(18.45130188014101, 17.860424999999996, 15.488249999999999, 16.644740624999997, 13.426008254607403, 6.5625, 7.313267647058823, 6.704625000000001, 7.246244999999999, 3.485160000000001, 2.504031818181818, 1.4526000000000006, 0.0, 18.225, 15.978600000000004, 12.520159090909091, 10.45548, 14.492489999999998, 9.386475, 7.313267647058823, 4.6875, 6.7130041273037016, 5.548246875, 3.0976500000000002, 1.623675, 0.0), # 36
(18.46505739466174, 17.819743106995883, 15.477707818930043, 16.63587465277778, 13.430597423984304, 6.5625, 7.2975979665940445, 6.672740740740741, 7.242138703703703, 3.477006440329219, 2.502793733632623, 1.451194375857339, 0.0, 18.225, 15.963138134430727, 12.513968668163116, 10.431019320987655, 14.484277407407406, 9.341837037037038, 7.2975979665940445, 4.6875, 6.715298711992152, 5.545291550925927, 3.0955415637860084, 1.619976646090535, 0.0), # 37
(18.47839066830806, 17.776969855967078, 15.466597736625513, 16.626500347222226, 13.435041859251228, 6.5625, 7.281129157588961, 6.639412037037038, 7.237805740740741, 3.4684518930041164, 2.5014806771417883, 1.4497130315500688, 0.0, 18.225, 15.946843347050754, 12.507403385708942, 10.405355679012347, 14.475611481481481, 9.295176851851854, 7.281129157588961, 4.6875, 6.717520929625614, 5.542166782407409, 3.0933195473251027, 1.61608816872428, 0.0), # 38
(18.491300268591576, 17.732213888888886, 15.454944444444445, 16.616634375, 13.439341340147644, 6.5625, 7.2639039215686285, 6.60475, 7.233255000000001, 3.4595205555555566, 2.500094444444445, 1.4481592592592594, 0.0, 18.225, 15.92975185185185, 12.500472222222223, 10.378561666666666, 14.466510000000001, 9.24665, 7.2639039215686285, 4.6875, 6.719670670073822, 5.538878125000001, 3.0909888888888895, 1.6120194444444444, 0.0), # 39
(18.503784763023894, 17.685583847736623, 15.442772633744857, 16.60629340277778, 13.443495646413021, 6.5625, 7.245964960058098, 6.568865740740742, 7.228495370370371, 3.4502366255144046, 2.49863683127572, 1.4465363511659812, 0.0, 18.225, 15.911899862825791, 12.4931841563786, 10.350709876543212, 14.456990740740743, 9.196412037037039, 7.245964960058098, 4.6875, 6.721747823206511, 5.535431134259261, 3.0885545267489714, 1.6077803497942387, 0.0), # 40
(18.51584271911663, 17.637188374485596, 15.430106995884776, 16.595494097222222, 13.447504557786843, 6.5625, 7.2273549745824255, 6.531870370370371, 7.22353574074074, 3.4406243004115233, 2.4971096333707448, 1.4448475994513033, 0.0, 18.225, 15.893323593964332, 12.485548166853723, 10.321872901234567, 14.44707148148148, 9.14461851851852, 7.2273549745824255, 4.6875, 6.723752278893421, 5.531831365740742, 3.0860213991769556, 1.6033807613168727, 0.0), # 41
(18.527472704381402, 17.587136111111114, 15.416972222222224, 16.584253125000004, 13.45136785400857, 6.5625, 7.208116666666666, 6.493875, 7.218385000000001, 3.4307077777777786, 2.4955146464646467, 1.4430962962962963, 0.0, 18.225, 15.874059259259258, 12.477573232323234, 10.292123333333333, 14.436770000000003, 9.091425000000001, 7.208116666666666, 4.6875, 6.725683927004285, 5.5280843750000015, 3.083394444444445, 1.598830555555556, 0.0), # 42
(18.538673286329807, 17.53553569958848, 15.403393004115227, 16.57258715277778, 13.455085314817683, 6.5625, 7.188292737835875, 6.454990740740741, 7.213052037037036, 3.420511255144034, 2.4938536662925554, 1.4412857338820306, 0.0, 18.225, 15.854143072702334, 12.469268331462775, 10.2615337654321, 14.426104074074072, 9.036987037037038, 7.188292737835875, 4.6875, 6.727542657408842, 5.524195717592594, 3.080678600823046, 1.5941396090534983, 0.0), # 43
(18.54944303247347, 17.482495781893004, 15.389394032921814, 16.560512847222224, 13.458656719953654, 6.5625, 7.1679258896151055, 6.415328703703706, 7.2075457407407395, 3.4100589300411532, 2.4921284885895996, 1.439419204389575, 0.0, 18.225, 15.833611248285322, 12.460642442947998, 10.230176790123457, 14.415091481481479, 8.981460185185188, 7.1679258896151055, 4.6875, 6.729328359976827, 5.520170949074076, 3.077878806584363, 1.5893177983539097, 0.0), # 44
(18.55978051032399, 17.428124999999998, 15.375, 16.548046875, 13.462081849155954, 6.5625, 7.147058823529412, 6.375000000000001, 7.201874999999999, 3.3993750000000014, 2.4903409090909094, 1.4375000000000002, 0.0, 18.225, 15.8125, 12.451704545454545, 10.198125000000001, 14.403749999999999, 8.925, 7.147058823529412, 4.6875, 6.731040924577977, 5.516015625000001, 3.075, 1.584375, 0.0), # 45
(18.569684287392985, 17.372531995884774, 15.360235596707819, 16.535205902777776, 13.465360482164058, 6.5625, 7.125734241103849, 6.334115740740741, 7.196048703703703, 3.388483662551441, 2.4884927235316128, 1.4355314128943761, 0.0, 18.225, 15.790845541838134, 12.442463617658062, 10.16545098765432, 14.392097407407405, 8.86776203703704, 7.125734241103849, 4.6875, 6.732680241082029, 5.511735300925927, 3.072047119341564, 1.5793210905349795, 0.0), # 46
(18.579152931192063, 17.31582541152263, 15.345125514403293, 16.522006597222223, 13.46849239871744, 6.5625, 7.103994843863473, 6.292787037037037, 7.190075740740742, 3.3774091152263384, 2.486585727646839, 1.4335167352537728, 0.0, 18.225, 15.768684087791497, 12.432928638234193, 10.132227345679013, 14.380151481481484, 8.809901851851851, 7.103994843863473, 4.6875, 6.73424619935872, 5.507335532407408, 3.069025102880659, 1.5741659465020577, 0.0), # 47
(18.588185009232834, 17.258113888888886, 15.329694444444444, 16.508465625, 13.471477378555573, 6.5625, 7.081883333333334, 6.251125000000001, 7.183965000000001, 3.3661755555555564, 2.4846217171717173, 1.4314592592592594, 0.0, 18.225, 15.746051851851853, 12.423108585858586, 10.098526666666666, 14.367930000000001, 8.751575, 7.081883333333334, 4.6875, 6.735738689277786, 5.502821875000001, 3.065938888888889, 1.5689194444444445, 0.0), # 48
(18.596779089026917, 17.199506069958847, 15.313967078189304, 16.49459965277778, 13.47431520141793, 6.5625, 7.059442411038489, 6.209240740740741, 7.17772537037037, 3.35480718106996, 2.4826024878413775, 1.4293622770919072, 0.0, 18.225, 15.722985048010976, 12.413012439206886, 10.064421543209878, 14.35545074074074, 8.692937037037037, 7.059442411038489, 4.6875, 6.737157600708965, 5.498199884259261, 3.0627934156378607, 1.5635914609053498, 0.0), # 49
(18.604933738085908, 17.140110596707824, 15.297968106995889, 16.480425347222223, 13.477005647043978, 6.5625, 7.0367147785039945, 6.16724537037037, 7.1713657407407405, 3.3433281893004123, 2.480529835390947, 1.427229080932785, 0.0, 18.225, 15.699519890260632, 12.402649176954732, 10.029984567901234, 14.342731481481481, 8.634143518518519, 7.0367147785039945, 4.6875, 6.738502823521989, 5.4934751157407415, 3.059593621399178, 1.5581918724279842, 0.0), # 50
(18.61264752392144, 17.080036111111113, 15.281722222222223, 16.465959375, 13.479548495173198, 6.5625, 7.013743137254902, 6.12525, 7.164895000000001, 3.3317627777777785, 2.478405555555556, 1.4250629629629634, 0.0, 18.225, 15.675692592592595, 12.392027777777779, 9.995288333333333, 14.329790000000003, 8.57535, 7.013743137254902, 4.6875, 6.739774247586599, 5.488653125000001, 3.0563444444444445, 1.552730555555556, 0.0), # 51
(18.619919014045102, 17.019391255144033, 15.26525411522634, 16.45121840277778, 13.481943525545056, 6.5625, 6.9905701888162675, 6.08336574074074, 7.158322037037037, 3.320135144032923, 2.4762314440703332, 1.4228672153635122, 0.0, 18.225, 15.651539368998632, 12.381157220351666, 9.960405432098767, 14.316644074074073, 8.516712037037037, 6.9905701888162675, 4.6875, 6.740971762772528, 5.483739467592594, 3.0530508230452678, 1.547217386831276, 0.0), # 52
(18.626746775968517, 16.958284670781893, 15.248588477366258, 16.43621909722222, 13.484190517899034, 6.5625, 6.967238634713145, 6.041703703703704, 7.1516557407407415, 3.3084694855967087, 2.4740092966704084, 1.4206451303155008, 0.0, 18.225, 15.627096433470507, 12.37004648335204, 9.925408456790123, 14.303311481481483, 8.458385185185186, 6.967238634713145, 4.6875, 6.742095258949517, 5.478739699074075, 3.049717695473252, 1.5416622427983542, 0.0), # 53
(18.63312937720329, 16.896825000000003, 15.23175, 16.420978125, 13.486289251974604, 6.5625, 6.943791176470588, 6.000374999999999, 7.144905, 3.296790000000001, 2.4717409090909093, 1.4184000000000003, 0.0, 18.225, 15.602400000000001, 12.358704545454545, 9.89037, 14.28981, 8.400525, 6.943791176470588, 4.6875, 6.743144625987302, 5.473659375000001, 3.04635, 1.5360750000000005, 0.0), # 54
(18.63906538526104, 16.835120884773662, 15.2147633744856, 16.405512152777778, 13.488239507511228, 6.5625, 6.9202705156136535, 5.9594907407407405, 7.1380787037037035, 3.2851208847736637, 2.4694280770669663, 1.4161351165980798, 0.0, 18.225, 15.577486282578874, 12.34714038533483, 9.855362654320988, 14.276157407407407, 8.343287037037037, 6.9202705156136535, 4.6875, 6.744119753755614, 5.468504050925927, 3.04295267489712, 1.530465534979424, 0.0), # 55
(18.64455336765337, 16.77328096707819, 15.197653292181073, 16.389837847222225, 13.49004106424839, 6.5625, 6.896719353667393, 5.9191620370370375, 7.131185740740741, 3.2734863374485608, 2.467072596333708, 1.4138537722908093, 0.0, 18.225, 15.5523914951989, 12.335362981668538, 9.82045901234568, 14.262371481481482, 8.286826851851853, 6.896719353667393, 4.6875, 6.745020532124195, 5.463279282407409, 3.0395306584362145, 1.5248437242798356, 0.0), # 56
(18.649591891891887, 16.711413888888888, 15.180444444444445, 16.373971875, 13.49169370192556, 6.5625, 6.873180392156863, 5.879500000000001, 7.124235, 3.2619105555555565, 2.4646762626262633, 1.4115592592592594, 0.0, 18.225, 15.527151851851851, 12.323381313131314, 9.785731666666667, 14.24847, 8.231300000000001, 6.873180392156863, 4.6875, 6.74584685096278, 5.457990625000001, 3.0360888888888895, 1.5192194444444447, 0.0), # 57
(18.654179525488225, 16.64962829218107, 15.163161522633745, 16.357930902777774, 13.49319720028221, 6.5625, 6.849696332607118, 5.840615740740741, 7.11723537037037, 3.2504177366255154, 2.4622408716797612, 1.4092548696844995, 0.0, 18.225, 15.501803566529492, 12.311204358398806, 9.751253209876543, 14.23447074074074, 8.176862037037038, 6.849696332607118, 4.6875, 6.746598600141105, 5.4526436342592595, 3.032632304526749, 1.5136025720164612, 0.0), # 58
(18.658314835953966, 16.58803281893004, 15.145829218106996, 16.34173159722222, 13.494551339057814, 6.5625, 6.82630987654321, 5.802620370370371, 7.110195740740741, 3.2390320781893016, 2.4597682192293306, 1.4069438957475995, 0.0, 18.225, 15.476382853223592, 12.298841096146651, 9.717096234567903, 14.220391481481482, 8.12366851851852, 6.82630987654321, 4.6875, 6.747275669528907, 5.447243865740742, 3.0291658436213997, 1.5080029835390947, 0.0), # 59
(18.661996390800738, 16.526736111111113, 15.128472222222221, 16.325390625, 13.495755897991843, 6.5625, 6.803063725490196, 5.765625, 7.103125, 3.2277777777777787, 2.4572601010101014, 1.40462962962963, 0.0, 18.225, 15.450925925925928, 12.286300505050505, 9.683333333333334, 14.20625, 8.071875, 6.803063725490196, 4.6875, 6.747877948995922, 5.441796875000001, 3.0256944444444445, 1.502430555555556, 0.0), # 60
(18.665222757540146, 16.465846810699592, 15.111115226337452, 16.308924652777776, 13.496810656823772, 6.5625, 6.780000580973129, 5.729740740740741, 7.0960320370370376, 3.216679032921812, 2.4547183127572016, 1.40231536351166, 0.0, 18.225, 15.425468998628258, 12.273591563786008, 9.650037098765434, 14.192064074074075, 8.021637037037038, 6.780000580973129, 4.6875, 6.748405328411886, 5.436308217592593, 3.0222230452674905, 1.496895164609054, 0.0), # 61
(18.66799250368381, 16.40547355967078, 15.093782921810703, 16.292350347222225, 13.497715395293081, 6.5625, 6.757163144517066, 5.695078703703705, 7.088925740740741, 3.2057600411522644, 2.4521446502057613, 1.4000043895747603, 0.0, 18.225, 15.40004828532236, 12.260723251028807, 9.61728012345679, 14.177851481481483, 7.973110185185186, 6.757163144517066, 4.6875, 6.748857697646541, 5.430783449074076, 3.018756584362141, 1.4914066872427985, 0.0), # 62
(18.670304196743327, 16.345724999999998, 15.0765, 16.275684375, 13.498469893139227, 6.5625, 6.734594117647059, 5.6617500000000005, 7.081815, 3.195045000000001, 2.4495409090909095, 1.3977000000000002, 0.0, 18.225, 15.3747, 12.247704545454548, 9.585135, 14.16363, 7.926450000000001, 6.734594117647059, 4.6875, 6.749234946569613, 5.425228125000001, 3.0153000000000003, 1.485975, 0.0), # 63
(18.672156404230314, 16.286709773662555, 15.059291152263373, 16.258943402777778, 13.499073930101698, 6.5625, 6.712336201888163, 5.629865740740741, 7.0747087037037035, 3.1845581069958855, 2.446908885147774, 1.3954054869684502, 0.0, 18.225, 15.34946035665295, 12.23454442573887, 9.553674320987653, 14.149417407407407, 7.881812037037038, 6.712336201888163, 4.6875, 6.749536965050849, 5.419647800925927, 3.011858230452675, 1.4806099794238687, 0.0), # 64
(18.67354769365639, 16.228536522633743, 15.042181069958849, 16.242144097222223, 13.49952728591996, 6.5625, 6.690432098765433, 5.599537037037037, 7.067615740740742, 3.1743235596707824, 2.4442503741114856, 1.3931241426611796, 0.0, 18.225, 15.324365569272972, 12.221251870557428, 9.522970679012344, 14.135231481481483, 7.839351851851852, 6.690432098765433, 4.6875, 6.74976364295998, 5.4140480324074085, 3.00843621399177, 1.4753215020576131, 0.0), # 65
(18.674476632533153, 16.17131388888889, 15.025194444444447, 16.225303125, 13.499829740333489, 6.5625, 6.668924509803921, 5.570875000000001, 7.060545000000001, 3.1643655555555563, 2.4415671717171716, 1.3908592592592597, 0.0, 18.225, 15.299451851851854, 12.207835858585858, 9.493096666666666, 14.121090000000002, 7.799225000000001, 6.668924509803921, 4.6875, 6.749914870166744, 5.408434375000001, 3.0050388888888895, 1.4701194444444448, 0.0), # 66
(18.674941788372227, 16.11515051440329, 15.00835596707819, 16.208437152777776, 13.499981073081756, 6.5625, 6.647856136528685, 5.543990740740742, 7.05350537037037, 3.154708292181071, 2.438861073699963, 1.3886141289437586, 0.0, 18.225, 15.274755418381341, 12.194305368499816, 9.464124876543211, 14.10701074074074, 7.761587037037039, 6.647856136528685, 4.6875, 6.749990536540878, 5.40281238425926, 3.001671193415638, 1.465013683127572, 0.0), # 67
(18.674624906065485, 16.059860254878533, 14.99160892489712, 16.19141634963768, 13.499853546356814, 6.56237821216278, 6.627163675346682, 5.518757887517148, 7.046452709190673, 3.145329198741226, 2.436085796562113, 1.3863795032849615, 0.0, 18.22477527006173, 15.250174536134574, 12.180428982810565, 9.435987596223676, 14.092905418381346, 7.726261042524007, 6.627163675346682, 4.6874130086877, 6.749926773178407, 5.3971387832125615, 2.998321784979424, 1.4599872958980487, 0.0), # 68
(18.671655072463768, 16.00375510752688, 14.974482638888889, 16.173382744565217, 13.498692810457515, 6.561415432098766, 6.606241363211952, 5.493824074074074, 7.039078703703703, 3.1359628758169937, 2.4329588516746417, 1.3840828460038987, 0.0, 18.222994791666668, 15.224911306042884, 12.164794258373206, 9.407888627450978, 14.078157407407407, 7.6913537037037045, 6.606241363211952, 4.686725308641976, 6.749346405228757, 5.391127581521739, 2.994896527777778, 1.4548868279569895, 0.0), # 69
(18.665794417606012, 15.946577558741536, 14.956902649176953, 16.154217617753623, 13.496399176954732, 6.559519318701418, 6.5849941211052325, 5.468964334705077, 7.031341735253773, 3.1265637860082314, 2.429444665957824, 1.3817134141939216, 0.0, 18.219478202160495, 15.198847556133135, 12.147223329789119, 9.379691358024692, 14.062683470507546, 7.656550068587107, 6.5849941211052325, 4.685370941929584, 6.748199588477366, 5.384739205917875, 2.9913805298353906, 1.4496888689765035, 0.0), # 70
(18.657125389157272, 15.888361778176023, 14.938875128600824, 16.133949230072467, 13.493001694504963, 6.556720598994056, 6.56343149358509, 5.444186899862826, 7.023253326474624, 3.1171321617041885, 2.425556211235159, 1.3792729405819073, 0.0, 18.21427179783951, 15.172002346400978, 12.127781056175793, 9.351396485112563, 14.046506652949247, 7.621861659807958, 6.56343149358509, 4.683371856424325, 6.746500847252482, 5.377983076690823, 2.987775025720165, 1.4443965252887296, 0.0), # 71
(18.64573043478261, 15.82914193548387, 14.92040625, 16.112605842391304, 13.488529411764706, 6.553050000000001, 6.541563025210084, 5.4195, 7.014825, 3.1076682352941183, 2.421306459330144, 1.376763157894737, 0.0, 18.207421875, 15.144394736842104, 12.10653229665072, 9.323004705882353, 14.02965, 7.587300000000001, 6.541563025210084, 4.680750000000001, 6.744264705882353, 5.370868614130436, 2.98408125, 1.4390129032258066, 0.0), # 72
(18.631692002147076, 15.768952200318596, 14.90150218621399, 16.09021571557971, 13.483011377390461, 6.548538248742569, 6.519398260538782, 5.394911865569274, 7.006068278463649, 3.0981722391672726, 2.4167083820662767, 1.374185798859288, 0.0, 18.198974729938275, 15.116043787452165, 12.083541910331384, 9.294516717501814, 14.012136556927299, 7.552876611796983, 6.519398260538782, 4.677527320530407, 6.741505688695231, 5.363405238526571, 2.9803004372427986, 1.4335411091198726, 0.0), # 73
(18.61509253891573, 15.707826742333731, 14.882169110082302, 16.06680711050725, 13.47647664003873, 6.543216072245086, 6.49694674412975, 5.37043072702332, 6.996994684499314, 3.0886444057129037, 2.411774951267057, 1.3715425962024403, 0.0, 18.18897665895062, 15.086968558226841, 12.058874756335285, 9.26593321713871, 13.993989368998628, 7.518603017832648, 6.49694674412975, 4.673725765889347, 6.738238320019365, 5.355602370169083, 2.976433822016461, 1.4279842493030668, 0.0), # 74
(18.59601449275362, 15.645799731182793, 14.862413194444443, 16.04240828804348, 13.468954248366014, 6.537114197530865, 6.47421802054155, 5.346064814814815, 6.98761574074074, 3.0790849673202625, 2.406519138755981, 1.3688352826510723, 0.0, 18.177473958333334, 15.057188109161793, 12.032595693779903, 9.237254901960785, 13.97523148148148, 7.484490740740742, 6.47421802054155, 4.669367283950618, 6.734477124183007, 5.347469429347827, 2.9724826388888888, 1.422345430107527, 0.0), # 75
(18.57454031132582, 15.582905336519316, 14.842240612139918, 16.01704750905797, 13.460473251028805, 6.53026335162323, 6.451221634332746, 5.321822359396434, 6.977942969821673, 3.069494156378602, 2.400953916356548, 1.3660655909320625, 0.0, 18.164512924382716, 15.026721500252684, 12.004769581782737, 9.208482469135802, 13.955885939643347, 7.450551303155008, 6.451221634332746, 4.664473822588021, 6.730236625514403, 5.339015836352658, 2.9684481224279837, 1.4166277578653925, 0.0), # 76
(18.55075244229737, 15.519177727996816, 14.821657536008228, 15.99075303442029, 13.451062696683609, 6.522694261545496, 6.4279671300619015, 5.2977115912208514, 6.967987894375857, 3.059872205277174, 2.3950922558922563, 1.3632352537722912, 0.0, 18.150139853395064, 14.9955877914952, 11.975461279461282, 9.179616615831518, 13.935975788751714, 7.416796227709193, 6.4279671300619015, 4.659067329675354, 6.725531348341804, 5.330251011473431, 2.964331507201646, 1.4108343389088016, 0.0), # 77
(18.524733333333334, 15.45465107526882, 14.80067013888889, 15.963553124999999, 13.440751633986928, 6.514437654320987, 6.404464052287582, 5.273740740740742, 6.957762037037036, 3.0502193464052296, 2.388947129186603, 1.3603460038986357, 0.0, 18.134401041666667, 14.963806042884991, 11.944735645933015, 9.150658039215687, 13.915524074074073, 7.383237037037039, 6.404464052287582, 4.653169753086419, 6.720375816993464, 5.3211843750000005, 2.960134027777778, 1.404968279569893, 0.0), # 78
(18.496565432098766, 15.389359547988851, 14.779284593621398, 15.935476041666668, 13.429569111595256, 6.505524256973022, 6.380721945568351, 5.249918038408779, 6.947276920438957, 3.0405358121520223, 2.382531508063087, 1.3573995740379758, 0.0, 18.117342785493825, 14.931395314417731, 11.912657540315433, 9.121607436456063, 13.894553840877913, 7.349885253772292, 6.380721945568351, 4.646803040695016, 6.714784555797628, 5.311825347222223, 2.95585691872428, 1.399032686180805, 0.0), # 79
(18.466331186258724, 15.323337315810434, 14.757507073045266, 15.906550045289855, 13.417544178165095, 6.49598479652492, 6.356750354462773, 5.226251714677641, 6.9365440672153635, 3.030821834906803, 2.375858364345207, 1.3543976969171905, 0.0, 18.09901138117284, 14.898374666089092, 11.879291821726033, 9.092465504720405, 13.873088134430727, 7.316752400548698, 6.356750354462773, 4.639989140374943, 6.708772089082547, 5.302183348429953, 2.9515014146090537, 1.3930306650736761, 0.0), # 80
(18.434113043478263, 15.256618548387095, 14.735343749999998, 15.876803396739131, 13.404705882352939, 6.48585, 6.3325588235294115, 5.202750000000001, 6.925574999999999, 3.0210776470588248, 2.36894066985646, 1.3513421052631582, 0.0, 18.079453124999997, 14.864763157894737, 11.844703349282298, 9.063232941176471, 13.851149999999999, 7.283850000000001, 6.3325588235294115, 4.63275, 6.7023529411764695, 5.292267798913045, 2.94706875, 1.3869653225806453, 0.0), # 81
(18.399993451422436, 15.189237415372364, 14.712800797325105, 15.846264356884058, 13.391083272815298, 6.475150594421583, 6.308156897326833, 5.179421124828533, 6.914381241426612, 3.011303480997338, 2.3617913964203443, 1.3482345318027582, 0.0, 18.058714313271608, 14.830579849830338, 11.80895698210172, 9.03391044299201, 13.828762482853223, 7.2511895747599455, 6.308156897326833, 4.625107567443988, 6.695541636407649, 5.2820881189613536, 2.9425601594650215, 1.3808397650338515, 0.0), # 82
(18.364054857756308, 15.121228086419752, 14.689884387860083, 15.8149611865942, 13.376705398208665, 6.463917306812986, 6.283554120413598, 5.156273319615913, 6.902974314128944, 3.001499569111596, 2.3544235158603586, 1.3450767092628693, 0.0, 18.036841242283952, 14.79584380189156, 11.772117579301792, 9.004498707334786, 13.805948628257887, 7.218782647462278, 6.283554120413598, 4.617083790580704, 6.688352699104333, 5.2716537288647345, 2.9379768775720168, 1.374657098765432, 0.0), # 83
(18.326379710144927, 15.052624731182796, 14.666600694444444, 15.78292214673913, 13.361601307189542, 6.452180864197532, 6.258760037348273, 5.133314814814815, 6.89136574074074, 2.9916661437908503, 2.3468500000000003, 1.3418703703703705, 0.0, 18.013880208333333, 14.760574074074073, 11.73425, 8.97499843137255, 13.78273148148148, 7.186640740740741, 6.258760037348273, 4.608700617283951, 6.680800653594771, 5.260974048913044, 2.933320138888889, 1.3684204301075271, 0.0), # 84
(18.287050456253354, 14.983461519315012, 14.642955889917694, 15.750175498188408, 13.345800048414427, 6.439971993598538, 6.233784192689422, 5.110553840877915, 6.879567043895747, 2.981803437424353, 2.3390838206627684, 1.338617247852141, 0.0, 17.989877507716052, 14.724789726373547, 11.69541910331384, 8.945410312273058, 13.759134087791494, 7.154775377229082, 6.233784192689422, 4.5999799954275264, 6.672900024207213, 5.250058499396137, 2.928591177983539, 1.362132865392274, 0.0), # 85
(18.246149543746643, 14.913772620469931, 14.618956147119343, 15.716749501811597, 13.32933067053982, 6.427321422039324, 6.208636130995608, 5.087998628257887, 6.86758974622771, 2.9719116824013563, 2.3311379496721605, 1.3353190744350594, 0.0, 17.964879436728395, 14.68850981878565, 11.655689748360802, 8.915735047204068, 13.73517949245542, 7.123198079561043, 6.208636130995608, 4.590943872885232, 6.66466533526991, 5.2389165006038665, 2.923791229423869, 1.3557975109518121, 0.0), # 86
(18.203759420289852, 14.843592204301075, 14.594607638888888, 15.68267241847826, 13.312222222222225, 6.41425987654321, 6.1833253968253965, 5.065657407407408, 6.855445370370372, 2.9619911111111112, 2.323025358851675, 1.3319775828460039, 0.0, 17.938932291666667, 14.651753411306041, 11.615126794258373, 8.885973333333332, 13.710890740740744, 7.091920370370371, 6.1833253968253965, 4.581614197530865, 6.656111111111112, 5.227557472826088, 2.9189215277777776, 1.3494174731182798, 0.0), # 87
(18.159962533548043, 14.772954440461966, 14.569916538065844, 15.647972509057974, 13.294503752118132, 6.400818084133517, 6.157861534737352, 5.043538408779149, 6.843145438957476, 2.952041955942871, 2.31475902002481, 1.328594505811855, 0.0, 17.912082368827164, 14.614539563930402, 11.573795100124048, 8.856125867828611, 13.686290877914953, 7.06095377229081, 6.157861534737352, 4.572012917238227, 6.647251876059066, 5.215990836352659, 2.913983307613169, 1.3429958582238153, 0.0), # 88
(18.11484133118626, 14.701893498606132, 14.544889017489714, 15.612678034420288, 13.276204308884047, 6.387026771833563, 6.132254089290037, 5.0216498628257895, 6.830701474622771, 2.942064449285888, 2.3063519050150636, 1.3251715760594904, 0.0, 17.884375964506173, 14.576887336654393, 11.531759525075316, 8.826193347857663, 13.661402949245542, 7.0303098079561055, 6.132254089290037, 4.562161979881116, 6.638102154442024, 5.2042260114734304, 2.908977803497943, 1.3365357726005578, 0.0), # 89
(18.068478260869565, 14.630443548387097, 14.519531250000002, 15.576817255434786, 13.257352941176471, 6.372916666666668, 6.106512605042017, 5.0, 6.818125, 2.9320588235294123, 2.2978169856459334, 1.3217105263157898, 0.0, 17.855859375, 14.538815789473684, 11.489084928229666, 8.796176470588236, 13.63625, 7.0, 6.106512605042017, 4.552083333333334, 6.6286764705882355, 5.192272418478263, 2.903906250000001, 1.3300403225806454, 0.0), # 90
(18.020955770263015, 14.558638759458383, 14.493849408436214, 15.540418432971018, 13.237978697651899, 6.35851849565615, 6.0806466265518555, 4.978597050754459, 6.80542753772291, 2.922025311062697, 2.2891672337409186, 1.3182130893076314, 0.0, 17.826578896604936, 14.500343982383942, 11.445836168704592, 8.76607593318809, 13.61085507544582, 6.9700358710562424, 6.0806466265518555, 4.541798925468679, 6.6189893488259495, 5.180139477657007, 2.898769881687243, 1.3235126144962168, 0.0), # 91
(17.97235630703167, 14.486513301473519, 14.467849665637862, 15.50350982789855, 13.218110626966835, 6.343862985825332, 6.054665698378118, 4.957449245541839, 6.7926206104252405, 2.9119641442749944, 2.2804156211235163, 1.3146809977618947, 0.0, 17.796580825617283, 14.46149097538084, 11.40207810561758, 8.735892432824983, 13.585241220850481, 6.940428943758574, 6.054665698378118, 4.531330704160951, 6.609055313483418, 5.167836609299518, 2.8935699331275724, 1.3169557546794108, 0.0), # 92
(17.92276231884058, 14.414101344086022, 14.441538194444446, 15.46611970108696, 13.197777777777777, 6.328980864197531, 6.0285793650793655, 4.936564814814815, 6.779715740740741, 2.9018755555555558, 2.2715751196172254, 1.3111159844054583, 0.0, 17.76591145833333, 14.422275828460037, 11.357875598086125, 8.705626666666666, 13.559431481481482, 6.911190740740742, 6.0285793650793655, 4.520700617283951, 6.598888888888888, 5.155373233695654, 2.888307638888889, 1.3103728494623659, 0.0), # 93
(17.872256253354806, 14.341437056949422, 14.414921167695475, 15.428276313405796, 13.177009198741224, 6.313902857796068, 6.002397171214165, 4.915951989026064, 6.766724451303155, 2.891759777293634, 2.2626587010455435, 1.3075197819652014, 0.0, 17.734617091049383, 14.382717601617212, 11.313293505227715, 8.675279331880901, 13.53344890260631, 6.88233278463649, 6.002397171214165, 4.509930612711477, 6.588504599370612, 5.1427587711352665, 2.882984233539095, 1.3037670051772203, 0.0), # 94
(17.820920558239397, 14.268554609717246, 14.388004758230455, 15.390007925724635, 13.155833938513677, 6.298659693644262, 5.97612866134108, 4.895618998628259, 6.753658264746228, 2.88161704187848, 2.253679337231969, 1.3038941231680024, 0.0, 17.70274402006173, 14.342835354848022, 11.268396686159845, 8.644851125635439, 13.507316529492456, 6.853866598079563, 5.97612866134108, 4.49904263831733, 6.577916969256838, 5.130002641908213, 2.8776009516460914, 1.2971413281561135, 0.0), # 95
(17.76883768115942, 14.195488172043014, 14.360795138888891, 15.351342798913045, 13.134281045751635, 6.283282098765432, 5.9497833800186735, 4.875574074074075, 6.740528703703703, 2.8714475816993468, 2.2446500000000005, 1.300240740740741, 0.0, 17.67033854166667, 14.30264814814815, 11.22325, 8.614342745098039, 13.481057407407405, 6.825803703703705, 5.9497833800186735, 4.488058641975309, 6.5671405228758175, 5.117114266304349, 2.8721590277777787, 1.2904989247311833, 0.0), # 96
(17.716090069779927, 14.12227191358025, 14.333298482510289, 15.31230919384058, 13.112379569111596, 6.267800800182899, 5.9233708718055125, 4.855825445816188, 6.727347290809328, 2.8612516291454857, 2.235583661173135, 1.2965613674102956, 0.0, 17.637446952160495, 14.262175041513249, 11.177918305865674, 8.583754887436456, 13.454694581618655, 6.798155624142662, 5.9233708718055125, 4.477000571559214, 6.556189784555798, 5.104103064613527, 2.8666596965020577, 1.2838429012345685, 0.0), # 97
(17.66276017176597, 14.048940003982477, 14.305520961934155, 15.27293537137681, 13.090158557250064, 6.252246524919983, 5.896900681260158, 4.83638134430727, 6.714125548696844, 2.851029416606149, 2.226493292574872, 1.2928577359035447, 0.0, 17.604115547839505, 14.22143509493899, 11.13246646287436, 8.553088249818446, 13.428251097393687, 6.770933882030178, 5.896900681260158, 4.465890374942845, 6.545079278625032, 5.090978457125605, 2.8611041923868314, 1.277176363998407, 0.0), # 98
(17.608930434782607, 13.975526612903225, 14.277468750000002, 15.233249592391303, 13.067647058823532, 6.23665, 5.870382352941177, 4.8172500000000005, 6.700875, 2.8407811764705886, 2.2173918660287084, 1.2891315789473687, 0.0, 17.570390625, 14.180447368421053, 11.086959330143541, 8.522343529411764, 13.40175, 6.744150000000001, 5.870382352941177, 4.45475, 6.533823529411766, 5.0777498641304355, 2.8554937500000004, 1.2705024193548389, 0.0), # 99
(17.5546833064949, 13.902065909996015, 14.249148019547325, 15.193280117753623, 13.044874122488501, 6.2210419524462734, 5.843825431407131, 4.798439643347051, 6.687607167352539, 2.8305071411280567, 2.2082923533581433, 1.285384629268645, 0.0, 17.536318479938274, 14.139230921955095, 11.041461766790714, 8.49152142338417, 13.375214334705078, 6.717815500685871, 5.843825431407131, 4.443601394604481, 6.522437061244251, 5.064426705917875, 2.8498296039094653, 1.2638241736360014, 0.0), # 100
(17.500101234567904, 13.828592064914377, 14.22056494341564, 15.153055208333335, 13.021868796901476, 6.205453109282122, 5.817239461216586, 4.7799585048010975, 6.674333573388203, 2.820207542967805, 2.1992077263866743, 1.281618619594253, 0.0, 17.501945408950615, 14.097804815536781, 10.99603863193337, 8.460622628903414, 13.348667146776407, 6.691941906721536, 5.817239461216586, 4.432466506630087, 6.510934398450738, 5.051018402777779, 2.8441129886831282, 1.2571447331740344, 0.0), # 101
(17.44526666666667, 13.755139247311828, 14.191725694444445, 15.112603125, 12.998660130718955, 6.189914197530865, 5.790633986928105, 4.761814814814815, 6.66106574074074, 2.809882614379086, 2.1901509569377993, 1.2778352826510724, 0.0, 17.467317708333336, 14.056188109161795, 10.950754784688995, 8.429647843137257, 13.32213148148148, 6.666540740740741, 5.790633986928105, 4.421367283950618, 6.499330065359477, 5.037534375000001, 2.838345138888889, 1.2504672043010754, 0.0), # 102
(17.390262050456254, 13.681741626841896, 14.16263644547325, 15.071952128623188, 12.975277172597433, 6.174455944215821, 5.764018553100253, 4.7440168038408785, 6.647815192043895, 2.7995325877511505, 2.181135016835017, 1.2740363511659811, 0.0, 17.432481674382714, 14.014399862825789, 10.905675084175085, 8.39859776325345, 13.29563038408779, 6.64162352537723, 5.764018553100253, 4.410325674439872, 6.487638586298717, 5.023984042874397, 2.8325272890946502, 1.2437946933492634, 0.0), # 103
(17.335169833601718, 13.608433373158105, 14.133303369341563, 15.031130480072465, 12.951748971193414, 6.159109076360311, 5.737402704291593, 4.7265727023319615, 6.634593449931413, 2.7891576954732518, 2.1721728779018252, 1.2702235578658583, 0.0, 17.397483603395063, 13.972459136524439, 10.860864389509127, 8.367473086419754, 13.269186899862826, 6.617201783264746, 5.737402704291593, 4.399363625971651, 6.475874485596707, 5.010376826690822, 2.826660673868313, 1.237130306650737, 0.0), # 104
(17.280072463768114, 13.535248655913978, 14.103732638888891, 14.99016644021739, 12.928104575163397, 6.143904320987655, 5.710795985060692, 4.709490740740741, 6.621412037037037, 2.7787581699346413, 2.1632775119617227, 1.2663986354775831, 0.0, 17.362369791666666, 13.930384990253412, 10.816387559808613, 8.336274509803923, 13.242824074074074, 6.5932870370370384, 5.710795985060692, 4.388503086419754, 6.464052287581699, 4.996722146739131, 2.820746527777778, 1.2304771505376346, 0.0), # 105
(17.225052388620504, 13.462221644763043, 14.073930426954732, 14.949088269927536, 12.904373033163882, 6.128872405121171, 5.68420793996611, 4.6927791495198905, 6.608282475994512, 2.7683342435245706, 2.1544618908382067, 1.2625633167280343, 0.0, 17.327186535493826, 13.888196484008375, 10.772309454191033, 8.30500273057371, 13.216564951989024, 6.5698908093278465, 5.68420793996611, 4.377766003657979, 6.452186516581941, 4.98302942330918, 2.8147860853909465, 1.223838331342095, 0.0), # 106
(17.17019205582394, 13.389386509358822, 14.043902906378605, 14.907924230072464, 12.880583393851367, 6.114044055784181, 5.657648113566415, 4.6764461591220865, 6.595216289437586, 2.7578861486322928, 2.145738986354776, 1.2587193343440908, 0.0, 17.29198013117284, 13.845912677784996, 10.728694931773878, 8.273658445896878, 13.190432578875171, 6.547024622770921, 5.657648113566415, 4.367174325560129, 6.440291696925684, 4.969308076690822, 2.808780581275721, 1.2172169553962566, 0.0), # 107
(17.11557391304348, 13.31677741935484, 14.013656250000002, 14.866702581521741, 12.856764705882352, 6.099450000000001, 5.631126050420168, 4.660500000000001, 6.582225000000001, 2.7474141176470597, 2.1371217703349283, 1.2548684210526317, 0.0, 17.256796875000003, 13.803552631578947, 10.685608851674642, 8.242242352941178, 13.164450000000002, 6.524700000000001, 5.631126050420168, 4.356750000000001, 6.428382352941176, 4.955567527173915, 2.8027312500000003, 1.2106161290322583, 0.0), # 108
(17.061280407944178, 13.24442854440462, 13.983196630658439, 14.825451585144926, 12.832946017913338, 6.085120964791952, 5.604651295085936, 4.644948902606311, 6.569320130315501, 2.736918382958122, 2.1286232146021624, 1.2510123095805359, 0.0, 17.221683063271605, 13.761135405385891, 10.64311607301081, 8.210755148874364, 13.138640260631002, 6.502928463648835, 5.604651295085936, 4.346514974851394, 6.416473008956669, 4.941817195048309, 2.796639326131688, 1.2040389585822384, 0.0), # 109
(17.007393988191087, 13.17237405416169, 13.95253022119342, 14.784199501811596, 12.809156378600825, 6.071087677183356, 5.57823339212228, 4.62980109739369, 6.556513203017833, 2.726399176954733, 2.120256290979975, 1.2471527326546823, 0.0, 17.18668499228395, 13.718680059201501, 10.601281454899876, 8.179197530864197, 13.113026406035665, 6.4817215363511655, 5.57823339212228, 4.336491197988112, 6.404578189300413, 4.928066500603866, 2.790506044238684, 1.1974885503783357, 0.0), # 110
(16.953997101449275, 13.10064811827957, 13.921663194444447, 14.742974592391306, 12.785424836601308, 6.0573808641975315, 5.551881886087768, 4.615064814814815, 6.543815740740741, 2.715856732026144, 2.1120339712918663, 1.2432914230019496, 0.0, 17.151848958333336, 13.676205653021444, 10.56016985645933, 8.147570196078432, 13.087631481481482, 6.461090740740741, 5.551881886087768, 4.326700617283951, 6.392712418300654, 4.914324864130436, 2.78433263888889, 1.1909680107526885, 0.0), # 111
(16.90117219538379, 13.029284906411787, 13.890601723251033, 14.701805117753622, 12.76178044057129, 6.044031252857797, 5.5256063215409625, 4.60074828532236, 6.531239266117969, 2.7052912805616076, 2.103969227361333, 1.2394301133492167, 0.0, 17.11722125771605, 13.633731246841382, 10.519846136806663, 8.115873841684822, 13.062478532235938, 6.441047599451304, 5.5256063215409625, 4.3171651806127125, 6.380890220285645, 4.900601705917875, 2.778120344650207, 1.1844804460374354, 0.0), # 112
(16.84890760266548, 12.958437720996821, 13.859426742378105, 14.660775741364255, 12.738210816208445, 6.03106325767524, 5.499473367291093, 4.586889426585454, 6.518827686755172, 2.694737131475729, 2.0960771718458604, 1.2355789404756645, 0.0, 17.0827990215178, 13.591368345232306, 10.480385859229301, 8.084211394427186, 13.037655373510344, 6.421645197219636, 5.499473367291093, 4.307902326910885, 6.369105408104223, 4.886925247121419, 2.7718853484756214, 1.178039792817893, 0.0), # 113
(16.796665616220118, 12.888805352817133, 13.828568512532428, 14.620215718724406, 12.71447202547959, 6.018447338956397, 5.473816387569522, 4.57365844462884, 6.506771421427836, 2.684391825560753, 2.0883733011339594, 1.2317868258169462, 0.0, 17.048295745488062, 13.549655083986407, 10.441866505669795, 8.053175476682258, 13.013542842855673, 6.403121822480377, 5.473816387569522, 4.298890956397426, 6.357236012739795, 4.873405239574803, 2.7657137025064857, 1.1717095775288306, 0.0), # 114
(16.744292825407193, 12.820412877827026, 13.798045399060976, 14.580114081995404, 12.690489213466321, 6.006150688123703, 5.448653685172405, 4.561051990709032, 6.495074987201274, 2.674271397594635, 2.0808463534281283, 1.2280556373838278, 0.0, 17.013611936988678, 13.508612011222104, 10.404231767140642, 8.022814192783905, 12.990149974402549, 6.385472786992645, 5.448653685172405, 4.290107634374073, 6.345244606733161, 4.860038027331802, 2.7596090798121957, 1.165492079802457, 0.0), # 115
(16.691723771827743, 12.753160664131308, 13.767798284975811, 14.540399302859647, 12.666226231660534, 5.994144321151453, 5.423944335775104, 4.549035234674245, 6.483708803536698, 2.6643570113022967, 2.0734817793814444, 1.224378479623102, 0.0, 16.978693067560602, 13.46816327585412, 10.367408896907222, 7.9930710339068884, 12.967417607073395, 6.368649328543944, 5.423944335775104, 4.281531657965324, 6.333113115830267, 4.846799767619883, 2.7535596569951624, 1.1593782421937553, 0.0), # 116
(16.63889299708279, 12.686949079834788, 13.73776805328898, 14.50099985299953, 12.641646931554131, 5.982399254013936, 5.399647415052978, 4.537573346372689, 6.472643289895322, 2.6546298304086586, 2.0662650296469853, 1.2207484569815625, 0.0, 16.943484608744804, 13.428233026797187, 10.331325148234924, 7.963889491225975, 12.945286579790643, 6.352602684921765, 5.399647415052978, 4.2731423242956685, 6.320823465777066, 4.833666617666511, 2.747553610657796, 1.1533590072577082, 0.0), # 117
(16.58573504277338, 12.621678493042284, 13.707895587012551, 14.461844204097451, 12.616715164639011, 5.970886502685445, 5.375721998681383, 4.526631495652572, 6.461848865738361, 2.6450710186386424, 2.0591815548778274, 1.2171586739060027, 0.0, 16.907932032082243, 13.388745412966028, 10.295907774389137, 7.935213055915925, 12.923697731476722, 6.337284093913602, 5.375721998681383, 4.264918930489604, 6.3083575823195055, 4.820614734699151, 2.74157911740251, 1.1474253175492988, 0.0), # 118
(16.532184450500534, 12.557249271858602, 13.678121769158587, 14.422860827835802, 12.591394782407065, 5.9595770831402755, 5.35212716233568, 4.516174852362109, 6.451295950527026, 2.6356617397171678, 2.0522168057270487, 1.2136022348432152, 0.0, 16.87198080911388, 13.349624583275366, 10.261084028635242, 7.906985219151502, 12.902591901054052, 6.322644793306953, 5.35212716233568, 4.256840773671625, 6.295697391203532, 4.807620275945268, 2.7356243538317178, 1.1415681156235096, 0.0), # 119
(16.47817576186529, 12.49356178438856, 13.648387482739144, 14.383978195896983, 12.565649636350196, 5.948442011352714, 5.3288219816912274, 4.506168586349507, 6.440954963722534, 2.626383157369158, 2.045356232847725, 1.2100722442399947, 0.0, 16.835576411380675, 13.31079468663994, 10.226781164238623, 7.879149472107472, 12.881909927445069, 6.308636020889311, 5.3288219816912274, 4.248887150966224, 6.282824818175098, 4.794659398632328, 2.7296774965478288, 1.1357783440353237, 0.0), # 120
(16.423643518468683, 12.430516398736968, 13.618633610766281, 14.345124779963385, 12.539443577960302, 5.937452303297058, 5.305765532423383, 4.49657786746298, 6.430796324786099, 2.6172164353195337, 2.038585286892935, 1.2065618065431336, 0.0, 16.79866431042359, 13.272179871974467, 10.192926434464676, 7.8516493059586, 12.861592649572199, 6.295209014448172, 5.305765532423383, 4.2410373594978985, 6.269721788980151, 4.781708259987796, 2.7237267221532564, 1.1300469453397246, 0.0), # 121
(16.36852226191174, 12.368013483008635, 13.588801036252066, 14.306229051717406, 12.51274045872928, 5.926578974947596, 5.282916890207506, 4.487367865550737, 6.420790453178933, 2.6081427372932153, 2.0318894185157554, 1.2030640261994254, 0.0, 16.761189977783587, 13.233704288193676, 10.159447092578777, 7.824428211879645, 12.841580906357866, 6.282315011771032, 5.282916890207506, 4.2332706963911395, 6.25637022936464, 4.768743017239136, 2.7177602072504135, 1.1243648620916942, 0.0), # 122
(16.312746533795494, 12.305953405308378, 13.558830642208555, 14.267219482841437, 12.485504130149028, 5.915793042278621, 5.260235130718955, 4.478503750460988, 6.410907768362252, 2.5991432270151247, 2.0252540783692634, 1.1995720076556633, 0.0, 16.72309888500163, 13.195292084212294, 10.126270391846315, 7.797429681045372, 12.821815536724504, 6.269905250645383, 5.260235130718955, 4.225566458770444, 6.242752065074514, 4.755739827613813, 2.711766128441711, 1.1187230368462162, 0.0), # 123
(16.256250875720976, 12.244236533741004, 13.528663311647806, 14.228024545017881, 12.457698443711445, 5.905065521264426, 5.237679329633088, 4.469950692041945, 6.401118689797269, 2.590199068210183, 2.018664717106536, 1.1960788553586414, 0.0, 16.68433650361868, 13.156867408945052, 10.09332358553268, 7.770597204630548, 12.802237379594539, 6.257930968858723, 5.237679329633088, 4.217903943760304, 6.2288492218557225, 4.742674848339295, 2.7057326623295617, 1.1131124121582732, 0.0), # 124
(16.198969829289226, 12.18276323641133, 13.498239927581887, 14.188572709929128, 12.429287250908427, 5.894367427879304, 5.215208562625265, 4.461673860141818, 6.391393636945196, 2.5812914246033105, 2.012106785380651, 1.1925776737551523, 0.0, 16.644848305175692, 13.118354411306674, 10.060533926903252, 7.74387427380993, 12.782787273890392, 6.246343404198546, 5.215208562625265, 4.210262448485217, 6.2146436254542134, 4.7295242366430434, 2.6996479855163775, 1.1075239305828484, 0.0), # 125
(16.14083793610127, 12.121433881424165, 13.46750137302285, 14.148792449257574, 12.400234403231872, 5.883669778097547, 5.192781905370843, 4.453638424608819, 6.381703029267251, 2.57240145991943, 2.005565733844684, 1.1890615672919902, 0.0, 16.604579761213643, 13.079677240211891, 10.02782866922342, 7.717204379758288, 12.763406058534501, 6.235093794452347, 5.192781905370843, 4.202621270069677, 6.200117201615936, 4.716264149752526, 2.69350027460457, 1.1019485346749243, 0.0), # 126
(16.08178973775815, 12.06014883688432, 13.436388530982757, 14.108612234685616, 12.370503752173677, 5.872943587893444, 5.170358433545185, 4.445809555291159, 6.3720172862246445, 2.563510337883461, 1.9990270131517138, 1.1855236404159475, 0.0, 16.56347634327348, 13.040760044575421, 9.99513506575857, 7.690531013650382, 12.744034572449289, 6.224133377407623, 5.170358433545185, 4.194959705638174, 6.185251876086839, 4.702870744895206, 2.6872777061965514, 1.0963771669894837, 0.0), # 127
(16.021759775860883, 11.998808470896611, 13.404842284473675, 14.06796053789565, 12.340059149225747, 5.862159873241292, 5.147897222823644, 4.438152422037048, 6.362306827278591, 2.554599222220326, 1.9924760739548175, 1.1819569975738184, 0.0, 16.521483522896165, 13.001526973312, 9.962380369774086, 7.663797666660978, 12.724613654557182, 6.2134133908518665, 5.147897222823644, 4.187257052315209, 6.170029574612873, 4.689320179298551, 2.680968456894735, 1.0908007700815103, 0.0), # 128
(15.960682592010507, 11.937313151565847, 13.37280351650766, 14.026765830570064, 12.308864445879973, 5.85128965011538, 5.125357348881582, 4.430632194694696, 6.352542071890305, 2.5456492766549457, 1.9858983669070716, 1.1783547432123955, 0.0, 16.478546771622668, 12.96190217533635, 9.929491834535357, 7.636947829964836, 12.70508414378061, 6.202885072572574, 5.125357348881582, 4.179492607225272, 6.154432222939986, 4.675588610190022, 2.6745607033015326, 1.0852102865059863, 0.0), # 129
(15.89849272780806, 11.875563246996844, 13.34021311009677, 13.984956584391266, 12.276883493628256, 5.840303934489999, 5.102697887394356, 4.423214043112313, 6.342693439521001, 2.536641664912241, 1.9792793426615536, 1.174709981778473, 0.0, 16.434611560993947, 12.921809799563201, 9.896396713307768, 7.609924994736723, 12.685386879042001, 6.192499660357238, 5.102697887394356, 4.171645667492856, 6.138441746814128, 4.66165219479709, 2.668042622019354, 1.0795966588178951, 0.0), # 130
(15.83512472485457, 11.81345912529441, 13.307011948253072, 13.942461271041642, 12.244080143962494, 5.829173742339445, 5.079877914037328, 4.415863137138113, 6.332731349631892, 2.527557550717134, 1.9726044518713404, 1.1710158177188439, 0.0, 16.38962336255096, 12.88117399490728, 9.863022259356702, 7.5826726521514, 12.665462699263784, 6.182208391993358, 5.079877914037328, 4.16369553024246, 6.122040071981247, 4.647487090347215, 2.6614023896506143, 1.073950829572219, 0.0), # 131
(15.770513124751067, 11.750901154563357, 13.27314091398862, 13.899208362203591, 12.210418248374584, 5.817870089638008, 5.056856504485853, 4.408544646620305, 6.322626221684192, 2.5183780977945447, 1.9658591451895095, 1.1672653554803014, 0.0, 16.343527647834676, 12.839918910283313, 9.829295725947548, 7.555134293383633, 12.645252443368385, 6.171962505268427, 5.056856504485853, 4.155621492598577, 6.105209124187292, 4.633069454067865, 2.654628182797724, 1.0682637413239418, 0.0), # 132
(15.704592469098595, 11.687789702908498, 13.238540890315475, 13.855126329559509, 12.175861658356425, 5.80636399235998, 5.03359273441529, 4.4012237414071, 6.312348475139116, 2.509084469869395, 1.9590288732691383, 1.1634516995096391, 0.0, 16.296269888386057, 12.797968694606027, 9.795144366345692, 7.527253409608184, 12.624696950278231, 6.1617132379699395, 5.03359273441529, 4.1474028516857, 6.087930829178212, 4.618375443186504, 2.647708178063095, 1.0625263366280455, 0.0), # 133
(15.63729729949817, 11.624025138434646, 13.203152760245707, 13.81014364479179, 12.14037422539991, 5.794626466479654, 5.010045679501001, 4.3938655913467075, 6.301868529457877, 2.499657830666606, 1.952099086763304, 1.1595679542536501, 0.0, 16.24779555574605, 12.755247496790147, 9.76049543381652, 7.498973491999817, 12.603737058915755, 6.151411827885391, 5.010045679501001, 4.139018904628324, 6.070187112699955, 4.6033812149305975, 2.6406305520491418, 1.0567295580395135, 0.0), # 134
(15.568562157550836, 11.559507829246614, 13.166917406791363, 13.764188779582833, 12.103919800996945, 5.7826285279713225, 4.986174415418341, 4.3864353662873405, 6.291156804101687, 2.4900793439110998, 1.945055236325083, 1.155607224159128, 0.0, 16.198050121455637, 12.711679465750406, 9.725276181625414, 7.470238031733298, 12.582313608203375, 6.141009512802277, 4.986174415418341, 4.130448948550945, 6.051959900498472, 4.588062926527612, 2.633383481358273, 1.0508643481133288, 0.0), # 135
(15.498321584857623, 11.494138143449213, 13.129775712964513, 13.717190205615022, 12.066462236639419, 5.770341192809277, 4.961938017842671, 4.378898236077208, 6.280183718531764, 2.4803301733277956, 1.9378827726075534, 1.1515626136728663, 0.0, 16.146979057055766, 12.667188750401527, 9.689413863037766, 7.4409905199833855, 12.560367437063528, 6.130457530508091, 4.961938017842671, 4.121672280578055, 6.033231118319709, 4.572396735205008, 2.6259551425929026, 1.044921649404474, 0.0), # 136
(15.426510123019561, 11.427816449147253, 13.091668561777217, 13.66907639457077, 12.02796538381924, 5.757735476967808, 4.93729556244935, 4.371219370564522, 6.2689196922093195, 2.4703914826416162, 1.930567146263792, 1.1474272272416581, 0.0, 16.094527834087398, 12.621699499658236, 9.652835731318959, 7.411174447924847, 12.537839384418639, 6.119707118790331, 4.93729556244935, 4.112668197834148, 6.01398269190962, 4.556358798190257, 2.6183337123554433, 1.0388924044679322, 0.0), # 137
(15.353062313637686, 11.360443114445548, 13.052536836241526, 13.619775818132457, 11.988393094028304, 5.744782396421213, 4.912206124913734, 4.363363939597493, 6.257335144595569, 2.4602444355774815, 1.9230938079468758, 1.143194169312297, 0.0, 16.040641924091503, 12.575135862435264, 9.615469039734378, 7.380733306732443, 12.514670289191137, 6.10870951543649, 4.912206124913734, 4.103415997443723, 5.994196547014152, 4.5399252727108195, 2.6105073672483052, 1.0327675558586864, 0.0), # 138
(15.277912698313022, 11.29191850744891, 13.01232141936951, 13.569216947982484, 11.947709218758497, 5.731452967143778, 4.886628780911184, 4.355297113024331, 6.245400495151722, 2.449870195860314, 1.9154482083098823, 1.1388565443315761, 0.0, 15.985266798609034, 12.527421987647335, 9.577241041549412, 7.3496105875809405, 12.490800990303445, 6.0974159582340635, 4.886628780911184, 4.093894976531271, 5.973854609379249, 4.523072315994162, 2.602464283873902, 1.0265380461317193, 0.0), # 139
(15.200995818646616, 11.22214299626215, 12.970963194173232, 13.51732825580325, 11.905877609501736, 5.717718205109798, 4.860522606117057, 4.346984060693248, 6.233086163338999, 2.439249927215034, 1.9076157980058883, 1.134407456746289, 0.0, 15.928347929180966, 12.478482024209175, 9.538078990029442, 7.3177497816451, 12.466172326677999, 6.085777684970546, 4.860522606117057, 4.084084432221284, 5.952938804750868, 4.505776085267751, 2.5941926388346466, 1.020194817842014, 0.0), # 140
(15.122246216239494, 11.151016948990085, 12.92840304366474, 13.464038213277146, 11.862862117749902, 5.7035491262935665, 4.833846676206716, 4.338389952452453, 6.220362568618608, 2.4283647933665637, 1.8995820276879718, 1.129840011003229, 0.0, 15.869830787348244, 12.428240121035515, 9.497910138439858, 7.2850943800996895, 12.440725137237216, 6.073745933433434, 4.833846676206716, 4.0739636616382615, 5.931431058874951, 4.48801273775905, 2.5856806087329485, 1.0137288135445532, 0.0), # 141
(15.041598432692682, 11.07844073373752, 12.884581850856106, 13.409275292086573, 11.818626594994903, 5.688916746669374, 4.806560066855513, 4.329479958150158, 6.207200130451765, 2.417195958039823, 1.8913323480092095, 1.1251473115491895, 0.0, 15.80966084465184, 12.37662042704108, 9.456661740046046, 7.251587874119467, 12.41440026090353, 6.061271941410222, 4.806560066855513, 4.063511961906696, 5.909313297497452, 4.469758430695525, 2.5769163701712214, 1.00713097579432, 0.0), # 142
(14.958987009607215, 11.004314718609267, 12.839440498759389, 13.352967963913915, 11.773134892728635, 5.673792082211512, 4.778621853738811, 4.320219247634575, 6.1935692682996875, 2.405724584959734, 1.8828522096226783, 1.1203224628309636, 0.0, 15.747783572632711, 12.323547091140597, 9.41426104811339, 7.217173754879202, 12.387138536599375, 6.048306946688404, 4.778621853738811, 4.05270863015108, 5.886567446364317, 4.45098932130464, 2.5678880997518783, 1.0003922471462972, 0.0), # 143
(14.874346488584132, 10.928539271710147, 12.792919870386642, 13.29504470044158, 11.726350862442994, 5.658146148894274, 4.749991112531969, 4.310572990753912, 6.1794404016235855, 2.3939318378512175, 1.8741270631814555, 1.115358569295345, 0.0, 15.684144442831826, 12.268944262248793, 9.370635315907277, 7.181795513553651, 12.358880803247171, 6.034802187055478, 4.749991112531969, 4.04153296349591, 5.863175431221497, 4.431681566813861, 2.5585839740773286, 0.993503570155468, 0.0), # 144
(14.787611411224459, 10.851014761144963, 12.744960848749933, 13.235433973351956, 11.67823835562988, 5.641949962691953, 4.7206269189103445, 4.300506357356382, 6.164783949884672, 2.381798880439195, 1.865142359338619, 1.110248735389127, 0.0, 15.618688926790139, 12.212736089280396, 9.325711796693094, 7.145396641317584, 12.329567899769344, 6.020708900298935, 4.7206269189103445, 4.029964259065681, 5.83911917781494, 4.411811324450653, 2.548992169749987, 0.986455887376815, 0.0), # 145
(14.69871631912923, 10.771641555018533, 12.695504316861326, 13.174064254327444, 11.62876122378119, 5.62517453957884, 4.690488348549297, 4.289984517290195, 6.1495703325441635, 2.3693068764485874, 1.8558835487472447, 1.104986065559103, 0.0, 15.551362496048613, 12.154846721150133, 9.279417743736223, 7.107920629345761, 12.299140665088327, 6.005978324206273, 4.690488348549297, 4.0179818139848855, 5.814380611890595, 4.391354751442482, 2.539100863372265, 0.9792401413653213, 0.0), # 146
(14.607595753899481, 10.690320021435666, 12.644491157732865, 13.110864015050435, 11.577883318388821, 5.607790895529226, 4.659534477124183, 4.278972640403562, 6.133769969063274, 2.3564369896043162, 1.846336082060411, 1.0995636642520668, 0.0, 15.482110622148213, 12.095200306772732, 9.231680410302054, 7.069310968812948, 12.267539938126548, 5.990561696564987, 4.659534477124183, 4.005564925378019, 5.7889416591944105, 4.370288005016812, 2.5288982315465733, 0.9718472746759697, 0.0), # 147
(14.51418425713624, 10.606950528501175, 12.591862254376625, 13.045761727203324, 11.525568490944673, 5.5897700465174065, 4.627724380310364, 4.2674358965446935, 6.1173532789032175, 2.3431703836313016, 1.836485409931195, 1.0939746359148106, 0.0, 15.410878776629895, 12.033720995062914, 9.182427049655974, 7.029511150893903, 12.234706557806435, 5.974410255162571, 4.627724380310364, 3.9926928903695758, 5.762784245472337, 4.348587242401109, 2.5183724508753254, 0.9642682298637433, 0.0), # 148
(14.418416370440541, 10.52143344431987, 12.537558489804665, 12.97868586246851, 11.471780592940643, 5.57108300851767, 4.595017133783196, 4.255339455561801, 6.100290681525203, 2.3294882222544664, 1.8263169830126733, 1.0882120849941288, 0.0, 15.337612431034628, 11.970332934935415, 9.131584915063366, 6.988464666763398, 12.200581363050405, 5.957475237786521, 4.595017133783196, 3.9793450060840496, 5.735890296470322, 4.326228620822837, 2.507511697960933, 0.9564939494836247, 0.0), # 149
(14.320226635413416, 10.433669136996565, 12.481520747029043, 12.909564892528387, 11.416483475868631, 5.551700797504312, 4.561371813218041, 4.242648487303093, 6.0825525963904505, 2.31537166919873, 1.815816251957923, 1.0822691159368145, 0.0, 15.262257056903364, 11.904960275304958, 9.079081259789614, 6.946115007596189, 12.165105192780901, 5.93970788222433, 4.561371813218041, 3.9655005696459367, 5.7082417379343156, 4.303188297509463, 2.4963041494058085, 0.948515376090597, 0.0), # 150
(14.219549593655895, 10.343557974636072, 12.423689909061814, 12.838327289065347, 11.359640991220532, 5.531594429451621, 4.526747494290255, 4.229328161616783, 6.064109442960174, 2.3008018881890155, 1.8049686674200216, 1.0761388331896609, 0.0, 15.184758125777073, 11.837527165086268, 9.024843337100108, 6.902405664567045, 12.128218885920347, 5.921059426263496, 4.526747494290255, 3.951138878179729, 5.679820495610266, 4.27944242968845, 2.484737981812363, 0.9403234522396431, 0.0), # 151
(14.116319786769019, 10.251000325343204, 12.364006858915053, 12.76490152376179, 11.301216990488243, 5.510734920333892, 4.491103252675198, 4.215343648351081, 6.044931640695582, 2.2857600429502427, 1.7937596800520466, 1.0698143411994616, 0.0, 15.105061109196717, 11.767957753194075, 8.968798400260232, 6.857280128850727, 12.089863281391164, 5.901481107691514, 4.491103252675198, 3.936239228809923, 5.650608495244121, 4.254967174587264, 2.4728013717830106, 0.931909120485746, 0.0), # 152
(14.010471756353809, 10.155896557222773, 12.302412479600802, 12.68921606830011, 11.241175325163667, 5.489093286125417, 4.454398164048228, 4.200660117354197, 6.024989609057894, 2.2702272972073336, 1.782174740507075, 1.0632887444130097, 0.0, 15.02311147870325, 11.696176188543106, 8.910873702535374, 6.810681891622, 12.049979218115787, 5.880924164295876, 4.454398164048228, 3.920780918661012, 5.620587662581833, 4.229738689433371, 2.4604824959201608, 0.9232633233838886, 0.0), # 153
(13.901940044011312, 10.05814703837959, 12.238847654131138, 12.611199394362703, 11.179479846738696, 5.466640542800487, 4.416591304084705, 4.185242738474343, 6.00425376750832, 2.254184814685209, 1.7701992994381837, 1.0565551472770989, 0.0, 14.938854705837642, 11.622106620048086, 8.850996497190918, 6.762554444055626, 12.00850753501664, 5.85933983386408, 4.416591304084705, 3.904743244857491, 5.589739923369348, 4.203733131454236, 2.447769530826228, 0.9143770034890537, 0.0), # 154
(13.790659191342543, 9.957652136918465, 12.173253265518113, 12.530779973631962, 11.116094406705237, 5.443347706333395, 4.377641748459985, 4.169056681559727, 5.982694535508077, 2.23761375910879, 1.7578188074984502, 1.0496066542385225, 0.0, 14.852236262140847, 11.545673196623744, 8.789094037492251, 6.712841277326369, 11.965389071016155, 5.836679354183619, 4.377641748459985, 3.8881055045238533, 5.5580472033526185, 4.176926657877321, 2.4346506531036227, 0.9052411033562243, 0.0), # 155
(13.676563739948545, 9.854312220944214, 12.10557019677379, 12.447886277790282, 11.050982856555176, 5.419185792698435, 4.33750857284943, 4.152067116458564, 5.960282332518376, 2.220495294202998, 1.7450187153409518, 1.0424363697440735, 0.0, 14.763201619153833, 11.466800067184806, 8.725093576704758, 6.661485882608993, 11.920564665036752, 5.81289396304199, 4.33750857284943, 3.870846994784596, 5.525491428277588, 4.149295425930095, 2.4211140393547583, 0.8958465655403832, 0.0), # 156
(13.559588231430352, 9.748027658561648, 12.035739330910227, 12.362446778520066, 10.984109047780422, 5.394125817869895, 4.296150852928397, 4.134239213019062, 5.9369875780004335, 2.202810583692754, 1.731784473618765, 1.0350373982405456, 0.0, 14.671696248417557, 11.385411380646001, 8.658922368093824, 6.60843175107826, 11.873975156000867, 5.787934898226687, 4.296150852928397, 3.8529470127642105, 5.492054523890211, 4.120815592840023, 2.407147866182046, 0.8861843325965136, 0.0), # 157
(13.43642570352943, 9.636747649274225, 11.960387930853534, 12.27118893522918, 10.912417327045198, 5.366575700132966, 4.252596048835072, 4.1143477142620295, 5.910997254959458, 2.1840146623310153, 1.717678725761683, 1.027139934629151, 0.0, 14.573674546947622, 11.298539280920659, 8.588393628808413, 6.552043986993045, 11.821994509918916, 5.7600867999668415, 4.252596048835072, 3.833268357237833, 5.456208663522599, 4.090396311743061, 2.3920775861707066, 0.8760679681158388, 0.0), # 158
(13.288116180561124, 9.509057777339137, 11.860106727604483, 12.155369164364412, 10.818229571737954, 5.327374130407459, 4.201391487047145, 4.085410149573287, 5.871856356733287, 2.161026447344436, 1.7002250806856987, 1.0172043785524665, 0.0, 14.445769764456351, 11.189248164077128, 8.501125403428492, 6.483079342033307, 11.743712713466573, 5.719574209402602, 4.201391487047145, 3.8052672360053275, 5.409114785868977, 4.051789721454805, 2.372021345520897, 0.8644597979399218, 0.0), # 159
(13.112769770827757, 9.363909602092178, 11.732881436933834, 12.013079639051961, 10.699704157616154, 5.275558360850069, 4.142019373545406, 4.04669939214551, 5.818455136337191, 2.1335425433383026, 1.6791778525828622, 1.0050752923331772, 0.0, 14.285557096008445, 11.055828215664945, 8.39588926291431, 6.400627630014906, 11.636910272674381, 5.665379149003714, 4.142019373545406, 3.7682559720357633, 5.349852078808077, 4.004359879683988, 2.346576287386767, 0.8512645092811072, 0.0), # 160
(12.911799698254727, 9.202249432332774, 11.580070457865464, 11.845672880071582, 10.558071749138534, 5.21175610364883, 4.0749133014061885, 3.9987003998323356, 5.751497860199411, 2.101796186926922, 1.6547224963799123, 0.9908651203361357, 0.0, 14.094673280674375, 10.899516323697492, 8.273612481899562, 6.305388560780765, 11.502995720398822, 5.59818055976527, 4.0749133014061885, 3.722682931177736, 5.279035874569267, 3.9485576266905285, 2.3160140915730927, 0.8365681302120704, 0.0), # 161
(12.686619186767443, 9.025023576860344, 11.403032189423245, 11.654501408203041, 10.394563010763845, 5.1365950709917785, 4.000506863705828, 3.941898130487402, 5.6716887947481816, 2.0660206147246045, 1.6270444670035862, 0.9746863069261941, 0.0, 13.874755057524599, 10.721549376188133, 8.13522233501793, 6.198061844173813, 11.343377589496363, 5.518657382682362, 4.000506863705828, 3.668996479279842, 5.197281505381922, 3.884833802734348, 2.280606437884649, 0.8204566888054858, 0.0), # 162
(12.438641460291295, 8.833178344474314, 11.203125030631053, 11.44091774422611, 10.210408606950825, 5.050702975066952, 3.919233653520661, 3.876777541964344, 5.579732206411743, 2.0264490633456567, 1.5963292193806227, 0.956651296468205, 0.0, 13.627439165629584, 10.523164261150253, 7.9816460969031136, 6.079347190036969, 11.159464412823485, 5.427488558750082, 3.919233653520661, 3.6076449821906795, 5.105204303475412, 3.813639248075371, 2.2406250061262107, 0.8030162131340287, 0.0), # 163
(12.16927974275169, 8.627660043974105, 10.981707380512765, 11.206274408920553, 10.006839202158226, 4.954707528062387, 3.8315272639270197, 3.8038235921168018, 5.476332361618334, 1.9833147694043862, 1.562762208437759, 0.9368725333270206, 0.0, 13.35436234405979, 10.305597866597225, 7.813811042188794, 5.949944308213158, 10.952664723236667, 5.325353028963523, 3.8315272639270197, 3.5390768057588473, 5.003419601079113, 3.735424802973519, 2.1963414761025533, 0.7843327312703733, 0.0), # 164
(11.879947258074031, 8.409414984159142, 10.740137638092254, 10.95192392306614, 9.785085460844787, 4.849236442166116, 3.7378212880012396, 3.7235212387984102, 5.3621935267961875, 1.9368509695151015, 1.5265288891017337, 0.915462461867493, 0.0, 13.057161331885686, 10.070087080542422, 7.632644445508667, 5.810552908545303, 10.724387053592375, 5.2129297343177745, 3.7378212880012396, 3.4637403158329394, 4.892542730422393, 3.6506413076887143, 2.148027527618451, 0.7644922712871949, 0.0), # 165
(11.572057230183715, 8.17938947382885, 10.479774202393392, 10.679218807442627, 9.546378047469258, 4.734917429566179, 3.6385493188196576, 3.636355439862808, 5.2380199683735436, 1.8872909002921108, 1.4878147162992839, 0.8925335264544754, 0.0, 12.737472868177733, 9.817868790999228, 7.4390735814964195, 5.661872700876331, 10.476039936747087, 5.090897615807931, 3.6385493188196576, 3.3820838782615565, 4.773189023734629, 3.5597396024808767, 2.0959548404786785, 0.7435808612571683, 0.0), # 166
(11.24702288300614, 7.938529821782648, 10.201975472440058, 10.389511582829789, 9.291947626490376, 4.6123782024506115, 3.5341449494586072, 3.542811153163632, 5.104515952778639, 1.834867798349722, 1.4468051449571482, 0.8681981714528189, 0.0, 12.396933692006392, 9.550179885981006, 7.23402572478574, 5.504603395049164, 10.209031905557278, 4.959935614429085, 3.5341449494586072, 3.2945558588932937, 4.645973813245188, 3.4631705276099303, 2.040395094488012, 0.7216845292529681, 0.0), # 167
(10.906257440466712, 7.687782336819962, 9.908099847256123, 10.084154770007387, 9.023024862366888, 4.482246473007449, 3.425041772994424, 3.44337333655452, 4.962385746439713, 1.779814900302243, 1.4036856300020644, 0.8425688412273767, 0.0, 12.037180542442131, 9.268257253501142, 7.018428150010321, 5.339444700906728, 9.924771492879426, 4.820722671176328, 3.425041772994424, 3.2016046235767495, 4.511512431183444, 3.361384923335797, 1.9816199694512246, 0.6988893033472693, 0.0), # 168
(10.551174126490828, 7.428093327740216, 9.599505725865463, 9.76450088975519, 8.740840419557543, 4.3451499534247295, 3.3116733825034426, 3.338526947889109, 4.812333615785002, 1.7223654427639818, 1.3586416263607706, 0.8157579801430009, 0.0, 11.659850158555415, 8.97333778157301, 6.793208131803853, 5.167096328291944, 9.624667231570005, 4.673937727044753, 3.3116733825034426, 3.103678538160521, 4.370420209778771, 3.254833629918398, 1.9199011451730927, 0.675281211612747, 0.0), # 169
(10.18318616500389, 7.160409103342831, 9.277551507291953, 9.43190246285296, 8.44662496252108, 4.201716355890488, 3.1944733710619975, 3.228756945021036, 4.655063827242743, 1.6627526623492466, 1.311858588960005, 0.7878780325645439, 0.0, 11.2665792794167, 8.666658358209983, 6.559292944800025, 4.988257987047739, 9.310127654485486, 4.52025972302945, 3.1944733710619975, 3.0012259684932054, 4.22331248126054, 3.1439674876176547, 1.8555103014583907, 0.6509462821220756, 0.0), # 170
(9.8037067799313, 6.88567597242723, 8.943595590559468, 9.087712010080473, 8.141609155716246, 4.052573392592758, 3.0738753317464247, 3.1145482858039375, 4.491280647241173, 1.6012097956723452, 1.2635219727265048, 0.759041442856858, 0.0, 10.859004644096458, 8.349455871425437, 6.317609863632523, 4.803629387017034, 8.982561294482347, 4.360367600125513, 3.0738753317464247, 2.8946952804233987, 4.070804577858123, 3.029237336693492, 1.7887191181118935, 0.6259705429479302, 0.0), # 171
(9.414149195198457, 6.604840243792839, 8.59899637469188, 8.733282052217486, 7.827023663601784, 3.898348775719581, 2.950312857633059, 2.996385928091453, 4.321688342208532, 1.5379700793475863, 1.2138172325870082, 0.7293606553847958, 0.0, 10.438762991665145, 8.022967209232752, 6.069086162935041, 4.613910238042758, 8.643376684417063, 4.194940299328034, 2.950312857633059, 2.7845348397997007, 3.913511831800892, 2.911094017405829, 1.7197992749383764, 0.6004400221629854, 0.0), # 172
(9.015926634730764, 6.31884822623908, 8.245112258713068, 8.369965110043767, 7.504099150636442, 3.739670217458989, 2.824219541798235, 2.874754829737218, 4.146991178573053, 1.4732667499892769, 1.1629298234682535, 0.6989481145132089, 0.0, 10.007491061193234, 7.6884292596452966, 5.8146491173412675, 4.41980024996783, 8.293982357146106, 4.024656761632105, 2.824219541798235, 2.6711930124707064, 3.752049575318221, 2.7899883700145893, 1.6490224517426137, 0.5744407478399164, 0.0), # 173
(8.610452322453618, 6.028646228565374, 7.883301641646902, 7.99911370433908, 7.174066281278959, 3.57716542999902, 2.6960289773182877, 2.7501399485948705, 3.9678934227629785, 1.4073330442117262, 1.1110452002969786, 0.6679162646069503, 0.0, 9.566825591751181, 7.347078910676452, 5.555226001484892, 4.221999132635178, 7.935786845525957, 3.850195928032819, 2.6960289773182877, 2.5551181642850143, 3.5870331406394795, 2.6663712347796937, 1.5766603283293805, 0.5480587480513978, 0.0), # 174
(8.19913948229242, 5.7351805595711465, 7.514922922517262, 7.622080355883197, 6.838155719988082, 3.41146212552771, 2.566174757269552, 2.623026242518047, 3.7850993412065432, 1.3404021986292411, 1.058348817999921, 0.6363775500308723, 0.0, 9.118403322409455, 7.000153050339593, 5.291744089999604, 4.021206595887723, 7.5701986824130865, 3.6722367395252657, 2.566174757269552, 2.4367586610912215, 3.419077859994041, 2.540693451961066, 1.5029845845034526, 0.5213800508701043, 0.0), # 175
(7.783401338172574, 5.43939752805582, 7.141334500348018, 7.240217585455879, 6.497598131222556, 3.2431880162330953, 2.4350904747283635, 2.493898669360387, 3.5993132003319848, 1.2727074498561304, 1.0050261315038191, 0.6044444151498269, 0.0, 8.663860992238513, 6.648888566648095, 5.025130657519095, 3.8181223495683905, 7.1986264006639695, 3.4914581371045417, 2.4350904747283635, 2.3165628687379254, 3.248799065611278, 2.4134058618186267, 1.4282669000696038, 0.49449068436871096, 0.0), # 176
(7.364651114019479, 5.1422434428188195, 6.763894774163046, 6.8548779138368925, 6.1536241794411275, 3.0729708143032117, 2.303209722771056, 2.3632421869755245, 3.411239266567542, 1.2044820345067013, 0.9512625957354108, 0.5722293043286669, 0.0, 8.204835340308824, 6.2945223476153345, 4.756312978677054, 3.6134461035201033, 6.822478533135084, 3.3085390617657344, 2.303209722771056, 2.1949791530737226, 3.0768120897205637, 2.284959304612298, 1.3527789548326095, 0.4674766766198928, 0.0), # 177
(6.944302033758534, 4.8446646126595665, 6.383962142986221, 6.467413861806007, 5.807464529102536, 2.901438231926097, 2.170966094473966, 2.2315417532170994, 3.2215818063414514, 1.1359591891952627, 0.897243665621434, 0.5398446619322442, 0.0, 7.742963105690853, 5.938291281254685, 4.486218328107169, 3.4078775675857873, 6.443163612682903, 3.1241584545039394, 2.170966094473966, 2.072455879947212, 2.903732264551268, 2.1558046206020025, 1.2767924285972443, 0.44042405569632426, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 139
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 141
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 142
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 143
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
94, # 1
)
| 278.381818
| 490
| 0.771314
| 32,987
| 260,287
| 6.085791
| 0.234213
| 0.355065
| 0.340719
| 0.645573
| 0.366318
| 0.361052
| 0.360554
| 0.360554
| 0.360554
| 0.360554
| 0
| 0.851074
| 0.095026
| 260,287
| 934
| 491
| 278.679872
| 0.001184
| 0.01541
| 0
| 0.200873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.005459
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2dee1c6da90c06c11b1886c3fcc895736a955350
| 79
|
py
|
Python
|
neuralqa/expander/__init__.py
|
thecloudcircle/neuralqa
|
4bf8a01c75af747053bf52d1dd8f0cc23daf1d58
|
[
"MIT"
] | 220
|
2020-06-30T16:16:41.000Z
|
2022-03-21T08:01:13.000Z
|
neuralqa/expander/__init__.py
|
thecloudcircle/neuralqa
|
4bf8a01c75af747053bf52d1dd8f0cc23daf1d58
|
[
"MIT"
] | 36
|
2020-06-15T16:28:04.000Z
|
2022-02-27T09:59:57.000Z
|
neuralqa/expander/__init__.py
|
thecloudcircle/neuralqa
|
4bf8a01c75af747053bf52d1dd8f0cc23daf1d58
|
[
"MIT"
] | 33
|
2020-08-01T05:33:37.000Z
|
2021-11-29T18:31:10.000Z
|
from .expander import *
from .mlmexpander import *
from .expanderpool import *
| 19.75
| 27
| 0.772152
| 9
| 79
| 6.777778
| 0.555556
| 0.327869
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151899
| 79
| 3
| 28
| 26.333333
| 0.910448
| 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
|
93014a77dcdc2c2462410c7dbf5509dc46159217
| 20,128
|
py
|
Python
|
rem_backend/query_data.py
|
danieldUKIM/rem_backend
|
8c590d5288f578f76e265bb8bc6651ff6bd0ec63
|
[
"Apache-2.0"
] | null | null | null |
rem_backend/query_data.py
|
danieldUKIM/rem_backend
|
8c590d5288f578f76e265bb8bc6651ff6bd0ec63
|
[
"Apache-2.0"
] | null | null | null |
rem_backend/query_data.py
|
danieldUKIM/rem_backend
|
8c590d5288f578f76e265bb8bc6651ff6bd0ec63
|
[
"Apache-2.0"
] | 1
|
2020-06-05T08:23:12.000Z
|
2020-06-05T08:23:12.000Z
|
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import date, datetime, timedelta
import mysql.connector
import os
import rem_backend.interpolation as interpolation
import rem_backend.localization as localization
import math
__author__ = "Valentin Rakovic"
__copyright__ = "Copyright (c) 2017, Faculty of Electrical Engineering and Information Technologies, UKIM, Skopje, Macedonia"
__version__ = "0.1.0"
__email__ = "{valentin}@feit.ukim.edu.mk"
'''
query data Module
Main REM interfacing function. Should be used by any RRM that connects to the platform
'''
def get_device(mac_add):
'''
Returs information for a specific device
Args:
mac_add: Mac address of the device
Returns:
device: Dictionary of device information. (channel capabilities, location, mode of operation, stauts, channel)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
query = ("select chan_capab, x_coord, y_coord, global_loc_id, floor, mode, status, active_channel, active_channel_sup from devices where mac_address = '"+mac_add+"';")
cursor.execute(query)
device = dict();
rows = cursor.fetchone()
if rows is None:
device = None
else:
device['chan_capab'] = rows[0]
device['x_coord'] = rows[1]
device['y_coord'] = rows[2]
device['global_location'] = rows[3]
device['floor'] = rows[4]
device['mode'] = rows[5]
device['status'] = rows[6]
device['channel'] = rows[7]
device['channel_sup'] = rows[8]
cursor.close()
cnx.close()
return device;
def get_pathloss_model(channel):
'''
Returs the path loss model for a specific channel
Args:
channel: the channel of interest
Returns:
device: Dictionary of channel_model. (L0, alpha, sigma, d0)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
query = ("select L0, alpha, sigma, d0 from propagation_model where channel = " +str(channel)+ " order by timestamp DESC limit 1")
cursor.execute(query)
channel_model = dict();
rows = cursor.fetchone()
if rows is None:
channel_model['L0'] = 'none'
else:
channel_model['L0'] = rows[0]
channel_model['alpha'] = rows[1]
channel_model['sigma'] = rows[2]
channel_model['d0'] = rows[3]
cursor.close()
cnx.close()
return channel_model;
def get_channel_model(links, channel, timespan):
'''
Returs the channel model for a specific number of links
Comment: Obsolete for now. maybe can be used for the future, if per device filtering is required
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select if (sum_cnt>"+str(links)+", sum_cnt, 0) from (select sum(cnt) as sum_cnt from (select tx_mac_address, count(distinct rx_mac_address) as cnt from (select * from propagation_model where channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmptb group by tx_mac_address) temtab) sumtab;")
cursor.execute(query)
sum_cnt = cursor.fetchone();
channel_model = dict();
if sum_cnt[0] > 0:
query = ("select avg(L0), avg(alpha) from propagation_model;")
cursor.execute(query)
rows = cursor.fetchone()
if rows is None:
channel_model['L0'] = 'none'
else:
channel_model['L0'] = rows[0]
channel_model['alpha'] = rows[1]
else:
print("nema uslovi")
channel_model['L0'] = 'none'
cursor.close()
cnx.close()
return channel_model;
def get_tx_locations(channel, floor, timespan):
'''
Returs the tx locations for a specific channel floor and timespan
Args:
channel: the channel of interest
floor: the floor of interest
timepsan: the timespan of interest
Returns:
tx_loc: List of location tupples. (mac address, x and y coordinates, global location id, tx power)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select tx_mac_address, x_coord, y_coord, global_loc_id, tx_power from estimated_locations where floor = "+str(floor)+" and channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"';")
cursor.execute(query)
tx_loc = cursor.fetchall()
cursor.close()
cnx.close()
return tx_loc;
def get_channel_status(channel, threshold, timespan):
'''
Returs the channel status for a specific channel and timespan. Efectively cooperative spectrum sensing based on hard decision combining
Args:
channel: the channel of interest
threshold: duty cycle threshold
timepsan: the timespan of interest
Returns:
status: channel status (0--> free, 1--> ocupied)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select if(avg_val>"+str(threshold)+",1,0) from (select avg(value) as avg_val from duty_cycle where channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb;")
cursor.execute(query)
status = cursor.fetchone()
cursor.close()
cnx.close()
return status;
def get_channel_status_by_area(channel, threshold, timespan, ulx=0, uly=1000, drx=1000, dry=0):
'''
Returs the channel status for a specific channel, area and timespan. Efectively cooperative spectrum sensing based on hard decision combining
Args:
channel: the channel of interest
threshold: duty cycle threshold
timepsan: the timespan of interest
ulx, uly, drx, dry: upper left and lower right corner coordinates, of the area of interest
Returns:
status: channel status (0--> free, 1--> ocupied)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select if(avg_val>"+str(threshold)+",1,0) from (select avg(value) as avg_val from duty_cycle, (select mac_address as addr from (select mac_address, ST_GeomFromText(CONCAT('Point(',x_coord,' ', y_coord,')')) as point from devices) tmpdb where MBRContains(ST_GeomFromText('Polygon(("+str(ulx)+" "+str(dry)+","+str(ulx)+" "+str(uly)+","+str(drx)+" "+str(uly)+","+str(drx)+" "+str(dry)+","+str(ulx)+" "+str(dry)+"))'),point)) tmp where channel = "+str(channel)+" and rx_mac_address = addr and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpavg;")
cursor.execute(query)
status = cursor.fetchone()
cursor.close()
cnx.close()
return status;
def get_channel_status_by_device(channel, rx_add, threshold, timespan):
'''
Returs the channel status for a specific channel, device and timespan.
Args:
channel: the channel of interest
rx_add: mac addres of the device
threshold: duty cycle threshold
timepsan: the timespan of interest
Returns:
status: channel status (0--> free, 1--> ocupied)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select if(avg_val>"+str(threshold)+",1,0) from (select avg(value) as avg_val from duty_cycle where rx_mac_address = '"+rx_add+"' and channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb;")
cursor.execute(query)
status = cursor.fetchone()
cursor.close()
cnx.close()
return status;
def get_channel_status_all_by_device(rx_add, threshold, timespan):
'''
Returs the list of channel status for all channels, specific device and timespan.
Args:
rx_add: the channel of interest
threshold: duty cycle threshold
timepsan: the timespan of interest
Returns:
dc: list of tuple (channel, channel status) (0--> free, 1--> ocupied)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select channel, if(avg_val>"+str(threshold)+",1,0) from (select channel, avg(value) as avg_val from (select * from duty_cycle where rx_mac_address = '"+str(rx_add)+"' and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb group by channel) tempdb;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_channel_status_all(threshold, timespan):
'''
Returs the list of channel status for all channels, and timespan. Efectively cooperative spectrum sensing based on hard decision combining
Args:
threshold: duty cycle threshold
timepsan: the timespan of interest
Returns:
dc: list of tuple (channel, channel status) (0--> free, 1--> ocupied)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select channel, if(avg_val>"+str(threshold)+",1,0) from (select channel, avg(value) as avg_val from (select * from duty_cycle where timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb group by channel) tempdb;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle(channel, timespan):
'''
Returs the duty cycle for a channel and timespan of interest
Args:
channel: channel of interest
timepsan: the timespan of interest
Returns:
dc: the duty cycle value
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select avg(value) from duty_cycle where channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"';")
cursor.execute(query)
dc = cursor.fetchone()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle_by_area(channel, timespan, ulx=0, uly=1000, drx=1000, dry=0):
'''
Returs the duty cycle for a channel, area and timespan of interest
Args:
channel: channel of interest
timepsan: the timespan of interest
ulx, uly, drx, dry: upper left and lower right corner coordinates, of the area of interest
Returns:
dc: the duty cycle value
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select avg(value) as avg_val from duty_cycle, (select mac_address as addr from (select mac_address, ST_GeomFromText(CONCAT('Point(',x_coord,' ', y_coord,')')) as point from devices) tmpdb where MBRContains(ST_GeomFromText('Polygon(("+str(ulx)+" "+str(dry)+","+str(ulx)+" "+str(uly)+","+str(drx)+" "+str(uly)+","+str(drx)+" "+str(dry)+","+str(ulx)+" "+str(dry)+"))'),point)) tmp where channel = "+str(channel)+" and rx_mac_address = addr and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"';")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle_by_device(channel, rx_add, timespan):
'''
Returs the duty cycle for a channel, device and timespan of interest
Args:
channel: channel of interest
rx_add: the mac address of the device
timepsan: the timespan of interest
Returns:
dc: the duty cycle value
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select value from duty_cycle where rx_mac_address = '"+rx_add+"' and channel = "+str(channel)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"';")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle_all_channels_by_device(rx_add, timespan):
'''
Returs the duty cycle for all channels, for a given device and timespan of interest
Args:
rx_add: the mac address of the device
timepsan: the timespan of interest
Returns:
dc: list of tupple (channel,duty cycle)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select channel, avg(value) from (select * from duty_cycle where rx_mac_address = '"+str(rx_add)+"' and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb group by channel;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle_all_channels(timespan):
'''
Returs the duty cycle for all channels, and timespan of interest
Args:
timepsan: the timespan of interest
Returns:
dc: list of tupple (channel,duty cycle)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select channel, avg(value) from (select * from duty_cycle where timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmpdb group by channel;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_duty_cycle_heat_map(channel, timespan, nx=50, ny=50, ulx=0, uly=1000, drx=1000, dry=0, intp=1):
'''
Returs the duty cycle heatmap for a specific channel, area and timespan of interest
Args:
channel: the channel of interest
timepsan: the timespan of interest
ulx, uly, drx, dry: upper left and lower right corner coordinates, of the area of interest
nx,ny: grid resolution of heat map
intp: interpolation type. Please check interpoaltion module for more information
Returns:
val: vector of calculated/interpolated values
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select max(x_coord) as x, max(y_coord) as y, avg(value) as avg_val from duty_cycle, (select x_coord, y_coord, mac_address as addr from (select x_coord, y_coord, mac_address, ST_GeomFromText(CONCAT('Point(',x_coord,' ', y_coord,')')) as point from devices) tmpdb where MBRContains(ST_GeomFromText('Polygon(("+str(ulx)+" "+str(dry)+","+str(ulx)+" "+str(uly)+","+str(drx)+" "+str(uly)+","+str(drx)+" "+str(dry)+","+str(ulx)+" "+str(dry)+"))'),point)) tmp where channel = "+str(channel)+" and rx_mac_address = addr and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"' group by rx_mac_address;")
cursor.execute(query)
dc = cursor.fetchall()
x = []
y = []
val = []
for row in dc:
x.append(row[0])
y.append(row[1])
val.append(row[2])
val = interpolation(x,y,val,ulx,dry,drx,uly,nx,ny,intp)
cursor.close()
cnx.close()
return val;
def estimate_tx_location(addr, timespan=60, ulx=0, uly=15, drx=32, dry=0, nx=50, ny=50, nz=50):
'''
Returs the estimated location of a tx of interest
Args:
addr: the mac address of the localized device
timepsan: the timespan of interest
ulx, uly, drx, dry: upper left and lower right corner coordinates, of the area of interest
nx,ny,nz: grid resolution of the localization algorithm
Returns:
val: tuple consisted of estimated x,y,z coordinates and respective estimated tx power (x,y,z,txpow)
'''
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select x_coord, y_coord, z_coord, value from devices,(select value, rx_mac_address as addr from rssi_meas where tx_mac_address = '"+str(addr)+"' and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"') tmp where mac_address = addr;")
cursor.execute(query)
dc = cursor.fetchall()
x = []
y = []
z = []
val = []
for row in dc:
x.append(row[0])
y.append(row[1])
z.append(row[2])
val.append(row[3])
val = localization.ML_grid(x,y,z,val,ulx,dry,drx,uly,nx,ny,nz)
cursor.close()
cnx.close()
return val;
#get list of occupied channels from DB
def get_occupied_channels():
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
query = ("select active_channel from devices where status = 2 group by active_channel;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
#get list of occupied channels from DB
def get_occupied_channels_count():
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
query = ("select count(active_channel), active_channel from devices where status = 2 group by active_channel;")
cursor.execute(query)
dc = cursor.fetchall()
cursor.close()
cnx.close()
return dc;
def get_ap_statistics(timespan=1):
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select ap_mac_address, avg(total_tx_retries) as avg_tx_retries, avg(total_tx_failed) as avg_tx_failed, avg(total_tx_throughput) as avg_tx_throughput, avg(total_rx_throughput) as avg_rx_throughput, avg(total_tx_activity) as avg_tx_activity, avg(total_rx_activity) as avg_rx_activity from ap_statistics where timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"' group by ap_mac_address;")
cursor.execute(query)
apstats = cursor.fetchall()
cursor.close()
cnx.close()
return apstats;
def get_ap_degraded_retries(timespan=1, retries_threshold=10):
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select ap_mac_address, avg_tx_retries from (select ap_mac_address, avg(total_tx_retries) as avg_tx_retries from ap_statistics where timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"' group by ap_mac_address) tmp where avg_tx_retries >= "+str(retries_threshold)+";")
cursor.execute(query)
apdeg = cursor.fetchall()
cursor.close()
cnx.close()
return apdeg;
#get all active devices form DB on a given channel and timespan
def get_all_active_devices_on_channel(chann, timespan=10):
host_env = os.getenv('MYSQL_ENV', 'localhost')
cnx = mysql.connector.connect(user='root',password='rem', host=host_env,database='remdb')
cursor = cnx.cursor()
stopdate = datetime.now()
startdate = stopdate-timedelta(minutes=timespan)
query = ("select tx_mac_address from rssi_meas where active_channel = "+str(chann)+" and timestamp between '"+str(startdate)+"' and '"+str(stopdate)+"' group by tx_mac_address;")
cursor.execute(query)
rows = cursor.fetchall()
cursor.close()
cnx.close()
return rows;
| 30.918587
| 615
| 0.718005
| 2,886
| 20,128
| 4.886348
| 0.093902
| 0.020848
| 0.028081
| 0.022337
| 0.781804
| 0.762303
| 0.751454
| 0.724578
| 0.688271
| 0.672741
| 0
| 0.007102
| 0.139557
| 20,128
| 650
| 616
| 30.966154
| 0.807148
| 0.236387
| 0
| 0.71987
| 0
| 0.032573
| 0.318798
| 0.027253
| 0
| 0
| 0
| 0
| 0
| 1
| 0.068404
| false
| 0.068404
| 0.022801
| 0
| 0.159609
| 0.006515
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
9332873a6f20ea552294df94ef7b7cc019afc51b
| 86
|
py
|
Python
|
expand_string/__main__.py
|
ja-odur/exapnd-string
|
c85de2089d6aa0921f7385274e45d29e9c3d8f02
|
[
"MIT"
] | null | null | null |
expand_string/__main__.py
|
ja-odur/exapnd-string
|
c85de2089d6aa0921f7385274e45d29e9c3d8f02
|
[
"MIT"
] | null | null | null |
expand_string/__main__.py
|
ja-odur/exapnd-string
|
c85de2089d6aa0921f7385274e45d29e9c3d8f02
|
[
"MIT"
] | null | null | null |
import sys
from expand_string import expand_string
print(expand_string(sys.argv[1]))
| 17.2
| 39
| 0.825581
| 14
| 86
| 4.857143
| 0.571429
| 0.529412
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012821
| 0.093023
| 86
| 4
| 40
| 21.5
| 0.858974
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0.333333
| 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
|
935c116eaec5b6713075db0176147e54dcdd393c
| 123
|
py
|
Python
|
program/assignment/__init__.py
|
mmsbrggr/polar
|
34348baf6992232e47cee7a4d56b5a96567c50b8
|
[
"MIT"
] | 2
|
2021-10-06T13:29:24.000Z
|
2021-11-11T19:42:43.000Z
|
program/assignment/__init__.py
|
mmsbrggr/polar
|
34348baf6992232e47cee7a4d56b5a96567c50b8
|
[
"MIT"
] | 1
|
2022-01-26T15:58:28.000Z
|
2022-01-28T13:47:28.000Z
|
program/assignment/__init__.py
|
mmsbrggr/polar
|
34348baf6992232e47cee7a4d56b5a96567c50b8
|
[
"MIT"
] | 2
|
2021-10-01T15:08:52.000Z
|
2022-03-15T14:10:06.000Z
|
from .assignment import Assignment
from .dist_assignment import DistAssignment
from .poly_assignment import PolyAssignment
| 30.75
| 43
| 0.878049
| 14
| 123
| 7.571429
| 0.5
| 0.45283
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097561
| 123
| 3
| 44
| 41
| 0.954955
| 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
|
fa8a45d9c7b47f7eef32e8dc41b9918c1f0f2e6c
| 737
|
py
|
Python
|
caffe-onnx/onnx_caffe/layers.py
|
jwj04ok/ONNX_Convertor
|
067a17e16dfc8aa80e36f44c4523959daf7359f5
|
[
"MIT"
] | 33
|
2020-06-09T21:05:35.000Z
|
2022-02-24T01:48:45.000Z
|
caffe-onnx/onnx_caffe/layers.py
|
jwj04ok/ONNX_Convertor
|
067a17e16dfc8aa80e36f44c4523959daf7359f5
|
[
"MIT"
] | 17
|
2020-07-14T19:44:09.000Z
|
2022-02-10T10:03:01.000Z
|
caffe-onnx/onnx_caffe/layers.py
|
jwj04ok/ONNX_Convertor
|
067a17e16dfc8aa80e36f44c4523959daf7359f5
|
[
"MIT"
] | 16
|
2020-06-17T22:56:11.000Z
|
2021-12-21T05:44:32.000Z
|
# This file is generated by generate_layers.py
from .merg_layers import Eltwise
from .merg_layers import Concat
from .mystery_layers import Mystery
from .norm_layers import BatchNorm
from .norm_layers import Power
from .norm_layers import Normalize
from .conv_layers import Convolution
from .conv_layers import DepthwiseConvolution
from .conv_layers import Deconvolution
from .pool_layers import Pooling
from .pool_layers import ROIPooling
from .core_layers import InnerProduct
from .core_layers import Dropout
from .core_layers import Reshape
from .core_layers import Flatten
from .core_layers import Permute
from .aact_layers import ReLU
from .aact_layers import PReLU
from .aact_layers import Softmax
from .aact_layers import Sigmoid
| 33.5
| 46
| 0.852103
| 108
| 737
| 5.62037
| 0.333333
| 0.395387
| 0.115321
| 0.164745
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.118046
| 737
| 21
| 47
| 35.095238
| 0.933846
| 0.059701
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
fae3755b6088dd5738ba505be944d136b67a45df
| 9,873
|
py
|
Python
|
paper_evaluation/IV.F_sensitivity_geographic_location/dataLoader.py
|
rafaspadilha/timestampVerificationTIFS
|
bad325e676b0b6087e54f2e280c3600c3b0b767f
|
[
"MIT"
] | 5
|
2022-03-11T18:08:32.000Z
|
2022-03-31T13:47:49.000Z
|
paper_evaluation/IV.F_sensitivity_geographic_location/dataLoader.py
|
rafaspadilha/timestampVerificationTIFS
|
bad325e676b0b6087e54f2e280c3600c3b0b767f
|
[
"MIT"
] | null | null | null |
paper_evaluation/IV.F_sensitivity_geographic_location/dataLoader.py
|
rafaspadilha/timestampVerificationTIFS
|
bad325e676b0b6087e54f2e280c3600c3b0b767f
|
[
"MIT"
] | null | null | null |
#####################################################
# Content-Aware Detection of Timestamp Manipulation #
# IEEE Trans. on Information Forensics and Security #
# R. Padilha, T. Salem, S. Workman, #
# F. A. Andalo, A. Rocha, N. Jacobs #
#####################################################
##### DESCRIPTION
import numpy as np
import random
"""
Extending the dataLoader class, focusing on the Location Errors
"""
import sys
sys.path.append("../../../datasets")
from dataLoader import *
def randomPlusOrMinus():
return 1 if random.random() < 0.5 else -1
class DataLoaderWithLocError(DataLoader):
def __init__(self, setToLoad="train", includeSatellite=True, outputTransientAttributes=True):
DataLoader.__init__(self, setToLoad,
includeLocation=True,
includeSatellite=includeSatellite,
outputTransientAttributes=outputTransientAttributes)
#### Method used to generate batches of data with Location Augmentation
def loadImagesInBatchesWithLocAug(self, batchSize):
# Initialize arrays to store each input modality and labels
inputBatch, outputBatch = [], []
gBatch, aBatch, tBatch, lBatch, labels, transAtt = [], [], [], [], [], []
# Count the number of samples in the batch
nInBatch = 0
### Epoch loop
while 1:
## Shuffle data at each epoch
rndIdx = np.arange(self.nPairsInSet)
np.random.shuffle(rndIdx)
for idx in rndIdx:
# Load and preprocess ground-level image
try:
gImg = load_preprocess_groundImg(self.groundPaths[idx])
except (OSError, IOError) as e:
#If error in loading, go to the next sample
continue
# Load and preprocess satellite image
if self.includeSatellite:
try:
aImg = load_preprocess_aerialImg(self.aerialPaths[idx])
except (OSError, IOError) as e:
#If error in loading, go to the next sample
continue
# Include each image in the batch twice (consistent and inconsistent)
gBatch += [gImg, gImg]
if self.includeSatellite:
aBatch += [aImg, aImg]
#Augment location
originalLoc = np.array(self.locLabels[idx])
if random.choice([0, 1]) == 1:
errorPercentage = random.choice(
[0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
originalLoc[0] *= 1 + (errorPercentage * randomPlusOrMinus())
originalLoc[0] = max(-90.0, min(originalLoc[0], 90.0))
if random.choice([0, 1]) == 1:
errorPercentage = random.choice(
[0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
originalLoc[1] *= 1 + (errorPercentage * randomPlusOrMinus())
originalLoc[1] = max(-180.0, min(originalLoc[1], 180.0))
#Add the location information to the batch
loc = preprocess_loc(originalLoc)
lBatch += [loc, loc]
#Process time info and tamper the time for one pair
time = preprocess_time(self.timeLabels[idx])
fakeTime = self.fakeTime(self.timeLabels[idx])
tBatch += [time, fakeTime]
#Label 0 = real/consistent tuple
#Label 1 = tampered/inconsistent tuple
labels += [to_categorical(0, num_classes=2),
to_categorical(1, num_classes=2)]
# Add the transient attributes to the output
if self.outputTA:
transAtt += [self.transientAttributes[idx],
self.transientAttributes[idx]]
nInBatch += 2
if nInBatch >= batchSize:
inputBatch = [np.array(gBatch)]
inputBatch += [np.array(aBatch)
] if self.includeSatellite else []
inputBatch += [np.array(lBatch)
] if self.includeLocation else []
inputBatch += [np.array(tBatch)]
outputBatch = [np.array(labels)]
outputBatch += [np.array(transAtt),
np.array(transAtt)] if self.outputTA else []
yield inputBatch, outputBatch
gBatch, aBatch, tBatch, lBatch, labels, transAtt = [], [], [], [], [], []
inputBatch, outputBatch = [], []
nInBatch = 0
def loadTestDataInBatchesWithLocError(self, batchSize, errorType, absoluteError, seed=42):
# Initialize arrays to store each input modality and labels
inputBatch, outputBatch = [], []
gBatch, aBatch, tBatch, lBatch, labels, transAtt = [], [], [], [], [], []
# Count the number of samples in the batch
nInBatch = 0
# Set the seed
random.seed(seed)
idxList = range(len(self.groundPaths))
for idx in idxList:
# Load and preprocess ground-level image
try:
gImg = load_preprocess_groundImg(self.groundPaths[idx])
except (OSError, IOError) as e:
#If error in loading, go to the next sample
continue
# Load and preprocess satellite image
if self.includeSatellite:
try:
aImg = load_preprocess_aerialImg(self.aerialPaths[idx])
except (OSError, IOError) as e:
#If error in loading, go to the next sample
continue
# Include each image in the batch twice (consistent and inconsistent)
gBatch += [gImg, gImg]
if self.includeSatellite:
aBatch += [aImg, aImg]
### Perturbing the location
originalLoc = list(self.locLabels[idx])
if errorType == "lat":
originalLoc[0] += (absoluteError * randomPlusOrMinus())
originalLoc[0] = max(-90.0, min(originalLoc[0], 90.0))
elif errorType == "lon":
originalLoc[1] += (absoluteError * randomPlusOrMinus())
originalLoc[1] = max(-180.0, min(originalLoc[1], 180.0))
elif errorType == "both":
originalLoc[0] += (absoluteError * randomPlusOrMinus())
originalLoc[0] = max(-90.0, min(originalLoc[0], 90.0))
originalLoc[1] += (absoluteError * randomPlusOrMinus())
originalLoc[1] = max(-180.0, min(originalLoc[1], 180.0))
else:
raise Exception()
#Add the location information to the batch
loc = preprocess_loc(originalLoc)
lBatch += [loc, loc]
#Process time info and tamper the time for one pair
time = preprocess_time(self.timeLabels[idx])
fakeTime = self.fakeTime(self.timeLabels[idx])
tBatch += [time, fakeTime]
#Label 0 = real/consistent tuple
#Label 1 = tampered/inconsistent tuple
labels += [to_categorical(0, num_classes=2),
to_categorical(1, num_classes=2)]
# Add the transient attributes to the output
if self.outputTA:
transAtt += [self.transientAttributes[idx],
self.transientAttributes[idx]]
nInBatch += 2
if nInBatch >= batchSize:
inputBatch = [np.array(gBatch)]
inputBatch += [np.array(aBatch)
] if self.includeSatellite else []
inputBatch += [np.array(lBatch)
] if self.includeLocation else []
inputBatch += [np.array(tBatch)]
outputBatch = [np.array(labels)]
outputBatch += [np.array(transAtt),
np.array(transAtt)] if self.outputTA else []
yield inputBatch, outputBatch
gBatch, aBatch, tBatch, lBatch, labels, transAtt = [], [], [], [], [], []
inputBatch, outputBatch = [], []
nInBatch = 0
#Yield the final batch, if smaller than batchSize
if nInBatch > 0:
inputBatch = [np.array(gBatch)]
inputBatch += [np.array(aBatch)
] if self.includeSatellite else []
inputBatch += [np.array(lBatch)
] if self.includeLocation else []
inputBatch += [np.array(tBatch)]
outputBatch = [np.array(labels)]
outputBatch += [np.array(transAtt),
np.array(transAtt)] if self.outputTA else []
yield inputBatch, outputBatch
##################################################
# Sanity Check #
##################################################
if __name__ == '__main__':
ld = DataLoaderWithLocError("test")
for batch, gt in ld.loadTestDataInBatches(1, "both", 0.1):
print(len(batch), [x.shape for x in batch])
print(len(gt), [x.shape for x in gt])
print(gt[1][0])
break
| 36.839552
| 97
| 0.492353
| 885
| 9,873
| 5.451977
| 0.224859
| 0.031917
| 0.04228
| 0.026114
| 0.732021
| 0.727047
| 0.727047
| 0.727047
| 0.727047
| 0.727047
| 0
| 0.023205
| 0.393295
| 9,873
| 267
| 98
| 36.977528
| 0.782304
| 0.152335
| 0
| 0.731034
| 0
| 0
| 0.005988
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.027586
| false
| 0
| 0.027586
| 0.006897
| 0.068966
| 0.02069
| 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
|
faf95fbc1259b26f3393cf097994d4f77c62b0c1
| 86
|
py
|
Python
|
megatron/utils/__init__.py
|
ntaylorwss/megatron
|
a6c572e04583e21715f3eaf35630cb4d75f686f7
|
[
"MIT"
] | 9
|
2018-08-21T21:30:08.000Z
|
2021-12-29T06:39:05.000Z
|
megatron/utils/__init__.py
|
ntaylorwss/megatron
|
a6c572e04583e21715f3eaf35630cb4d75f686f7
|
[
"MIT"
] | 6
|
2018-08-23T18:30:48.000Z
|
2020-03-30T22:08:13.000Z
|
megatron/utils/__init__.py
|
ntaylorwss/megatron
|
a6c572e04583e21715f3eaf35630cb4d75f686f7
|
[
"MIT"
] | 3
|
2018-08-26T15:53:57.000Z
|
2020-07-21T12:06:55.000Z
|
from . import pipeline
from . import hash
from . import errors
from .generic import *
| 17.2
| 22
| 0.755814
| 12
| 86
| 5.416667
| 0.5
| 0.461538
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.186047
| 86
| 4
| 23
| 21.5
| 0.928571
| 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
|
877bf205cfc40705c23a6a25ec2b52d90252aad5
| 1,148
|
py
|
Python
|
mat.py
|
256dpi/art32
|
95e76b4c519df4baa4cfd98bef89abe6bf884186
|
[
"MIT"
] | 1
|
2019-04-24T19:03:27.000Z
|
2019-04-24T19:03:27.000Z
|
mat.py
|
256dpi/art32
|
95e76b4c519df4baa4cfd98bef89abe6bf884186
|
[
"MIT"
] | null | null | null |
mat.py
|
256dpi/art32
|
95e76b4c519df4baa4cfd98bef89abe6bf884186
|
[
"MIT"
] | null | null | null |
import numpy as np
np.set_printoptions(precision=4, suppress=True)
print("--- test 2x2")
print(np.linalg.pinv(np.array([
[0.1, 0.2],
[0.3, 0.4],
])))
# [[-20. 10.]
# [ 15. -5.]]
print("--- test 2x3")
print(np.linalg.pinv(np.array([
[0.1, 0.2, 0.1],
[0.3, 0.4, 0.1],
])))
# [[-8.3333 5. ]
# [ 3.3333 0. ]
# [11.6667 -5. ]]
print("--- test 3x3")
print(np.linalg.pinv(np.array([
[0.1, 0.2, 0.1],
[0.3, 0.4, 0.1],
[0.1, 0.2, 0.3],
])))
# [[-25. 10. 5.]
# [ 20. -5. -5.]
# [ -5. 0. 5.]]
print("--- test 5x3z")
print(np.linalg.pinv(np.array([
[0.0, 0.1, 0.2, 0.0, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.3, 0.4, 0.0, 0.1],
])))
# [[ 0. 0. -0. ]
# [-8.3333 0. 5. ]
# [ 3.3333 0. 0. ]
# [ 0. 0. 0. ]
# [11.6667 0. -5. ]]
print("--- test 3x5z")
print(np.linalg.pinv(np.array([
[0.0, 0.0, 0.0],
[0.1, 0.0, 0.3],
[0.2, 0.0, 0.4],
[0.0, 0.0, 0.0],
[0.1, 0.0, 0.1],
])))
# [[ 0. -8.3333 3.3333 0. 11.6667]
# [ 0. 0. 0. 0. 0. ]
# [-0. 5. 0. 0. -5. ]]
| 19.133333
| 47
| 0.373693
| 212
| 1,148
| 2.018868
| 0.15566
| 0.224299
| 0.245327
| 0.214953
| 0.595794
| 0.492991
| 0.462617
| 0.436916
| 0.436916
| 0.317757
| 0
| 0.262274
| 0.325784
| 1,148
| 59
| 48
| 19.457627
| 0.290698
| 0.352787
| 0
| 0.5
| 0
| 0
| 0.085399
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.03125
| 0
| 0.03125
| 0.34375
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
87d5a575ec4f3cc588eda7485f87e5491edf1085
| 35
|
py
|
Python
|
jupyter/__index__.py
|
cipherkit/PowershellFileScripts
|
7b2589ee1f38d9fee178b891c450a2f4eae0cac0
|
[
"MIT"
] | null | null | null |
jupyter/__index__.py
|
cipherkit/PowershellFileScripts
|
7b2589ee1f38d9fee178b891c450a2f4eae0cac0
|
[
"MIT"
] | null | null | null |
jupyter/__index__.py
|
cipherkit/PowershellFileScripts
|
7b2589ee1f38d9fee178b891c450a2f4eae0cac0
|
[
"MIT"
] | null | null | null |
from migration.py import Migration
| 17.5
| 34
| 0.857143
| 5
| 35
| 6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 35
| 1
| 35
| 35
| 0.967742
| 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
|
e207790411566b950f145becc6c0493509b2a70f
| 114
|
py
|
Python
|
utils/voc_classname_encoder.py
|
DLH06/CenterNet-tensorflow
|
cca054182842c2745f9113b8cd6d1b73afd129f2
|
[
"MIT"
] | null | null | null |
utils/voc_classname_encoder.py
|
DLH06/CenterNet-tensorflow
|
cca054182842c2745f9113b8cd6d1b73afd129f2
|
[
"MIT"
] | null | null | null |
utils/voc_classname_encoder.py
|
DLH06/CenterNet-tensorflow
|
cca054182842c2745f9113b8cd6d1b73afd129f2
|
[
"MIT"
] | null | null | null |
classname_to_ids = {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4, '5': 5, '6': 6, '7': 7, '8': 8, '9': 9, 'colon': 10}
| 57
| 113
| 0.377193
| 25
| 114
| 1.64
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.247191
| 0.219298
| 114
| 1
| 114
| 114
| 0.213483
| 0
| 0
| 0
| 0
| 0
| 0.131579
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
35813341566cf40be4dde7b3088b4be881589bec
| 248
|
py
|
Python
|
gbvision/utils/continuity/__init__.py
|
computerboy0555/GBVision
|
79fc9ba09865bfd9c7a39abaa3980c46ce090b07
|
[
"Apache-2.0"
] | 16
|
2019-04-15T18:52:58.000Z
|
2022-02-13T23:00:46.000Z
|
gbvision/utils/continuity/__init__.py
|
computerboy0555/GBVision
|
79fc9ba09865bfd9c7a39abaa3980c46ce090b07
|
[
"Apache-2.0"
] | 2
|
2019-04-15T19:00:05.000Z
|
2019-04-19T15:47:21.000Z
|
gbvision/utils/continuity/__init__.py
|
computerboy0555/GBVision
|
79fc9ba09865bfd9c7a39abaa3980c46ce090b07
|
[
"Apache-2.0"
] | 3
|
2019-05-03T13:48:25.000Z
|
2019-09-22T14:03:49.000Z
|
from .continues_circle import ContinuesCircle
from .continues_rect import ContinuesRect
from .continues_rotated_rect import ContinuesRotatedRect
from .continues_shape import ContinuesShape
from .continues_shape_wrapper import ContinuesShapeWrapper
| 41.333333
| 58
| 0.899194
| 27
| 248
| 8
| 0.481481
| 0.300926
| 0.166667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080645
| 248
| 5
| 59
| 49.6
| 0.947368
| 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
|
35b041a58d062dbc5306bd5900ff350ac9cfcc1b
| 5,420
|
py
|
Python
|
tests/test_loan_transition_to_item_on_loan.py
|
blankoworld/invenio-circulation
|
7ec41adb96ab3780029ae3a378f3fbc7a4d6b77b
|
[
"MIT"
] | null | null | null |
tests/test_loan_transition_to_item_on_loan.py
|
blankoworld/invenio-circulation
|
7ec41adb96ab3780029ae3a378f3fbc7a4d6b77b
|
[
"MIT"
] | null | null | null |
tests/test_loan_transition_to_item_on_loan.py
|
blankoworld/invenio-circulation
|
7ec41adb96ab3780029ae3a378f3fbc7a4d6b77b
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2018-2019 CERN.
# Copyright (C) 2018-2019 RERO.
#
# Invenio-Circulation is free software; you can redistribute it and/or modify
# it under the terms of the MIT License; see LICENSE file for more details.
"""Tests for loan states."""
from datetime import timedelta
import arrow
import pytest
from invenio_circulation.errors import ItemNotAvailableError, \
TransitionConstraintsViolationError
from invenio_circulation.proxies import current_circulation
from .helpers import SwappedConfig
def test_created_to_item_on_loan_available_item_with_default_location(
loan_created, params, mock_is_item_available_for_checkout
):
"""Test direct checkout on available item with default location."""
mock_is_item_available_for_checkout.return_value = True
assert loan_created["state"] == "CREATED"
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout")
)
assert loan["state"] == "ITEM_ON_LOAN"
assert loan["pickup_location_pid"] == "pickup_location_pid"
assert loan["item_pid"] == "item_pid"
def test_created_to_item_on_loan_available_item_with_specified_location(
loan_created, params, mock_is_item_available_for_checkout
):
"""Test direct checkout on available item with different location."""
mock_is_item_available_for_checkout.return_value = True
assert loan_created["state"] == "CREATED"
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout",
pickup_location_pid="other_location_pid")
)
assert loan["state"] == "ITEM_ON_LOAN"
assert loan["pickup_location_pid"] == "other_location_pid"
assert loan["item_pid"] == "item_pid"
def test_created_to_item_on_loan_unavailable_item(
loan_created, params, mock_ensure_item_is_available_for_checkout
):
"""Test direct checkout on unavailable item."""
assert loan_created["state"] == "CREATED"
mock_ensure_item_is_available_for_checkout.side_effect = \
ItemNotAvailableError(description="Item Not Available")
with pytest.raises(ItemNotAvailableError):
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout",
pickup_location_pid="other_location_pid")
)
def test_created_to_item_on_loan_available_item_with_invalid_duration(
loan_created, params, mock_is_item_available_for_checkout
):
"""Test direct checkout on available item with invalid duration."""
mock_is_item_available_for_checkout.return_value = True
assert loan_created["state"] == "CREATED"
params["start_date"] = arrow.get("2018-01-01")
params["end_date"] = params["start_date"] + timedelta(days=60)
with pytest.raises(TransitionConstraintsViolationError):
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout")
)
def test_created_to_item_on_loan_available_item_with_valid_duration(
loan_created, params, mock_ensure_item_is_available_for_checkout
):
"""Test direct checkout on available item with valid duration."""
mock_ensure_item_is_available_for_checkout.side_effect = None
assert loan_created["state"] == "CREATED"
params["transaction_date"] = arrow.utcnow()
params["start_date"] = arrow.get("2018-01-01")
params["end_date"] = params["start_date"] + timedelta(days=59)
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout")
)
assert loan["state"] == "ITEM_ON_LOAN"
assert loan["pickup_location_pid"] == "pickup_location_pid"
assert loan["item_pid"] == "item_pid"
assert loan["transaction_date"] == params["transaction_date"].isoformat()
def test_pending_to_item_on_loan_available_item(
loan_created, params, mock_ensure_item_is_available_for_checkout
):
"""Test direct checkout on available item."""
mock_ensure_item_is_available_for_checkout.side_effect = None
with SwappedConfig(
"CIRCULATION_ITEM_LOCATION_RETRIEVER",
lambda x: "pickup_location_pid"
):
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="request")
)
assert loan["state"] == "PENDING"
loan = current_circulation.circulation.trigger(
loan_created, **dict(params, trigger="checkout",
pickup_location_pid="other_location_pid")
)
assert loan["state"] == "ITEM_ON_LOAN"
assert loan["pickup_location_pid"] == "other_location_pid"
assert loan["item_pid"] == "item_pid"
| 33.251534
| 78
| 0.7
| 625
| 5,420
| 5.6912
| 0.1616
| 0.06185
| 0.07169
| 0.070846
| 0.796458
| 0.768625
| 0.752882
| 0.752882
| 0.752882
| 0.739949
| 0
| 0.008585
| 0.204797
| 5,420
| 162
| 79
| 33.45679
| 0.816705
| 0.107934
| 0
| 0.707547
| 0
| 0
| 0.193824
| 0.043814
| 0
| 0
| 0
| 0
| 0.179245
| 1
| 0.056604
| false
| 0
| 0.056604
| 0
| 0.113208
| 0
| 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
|
35be5dd7b7bd2344fc9b40d90dc192d149e820f6
| 9,214
|
py
|
Python
|
episode_dataloader.py
|
lovelyqian/AMeFu-Net
|
1c1dedd0390fe35cf5e73ac1f6f9f8a9ae7595c2
|
[
"MIT"
] | 24
|
2021-05-13T13:25:29.000Z
|
2022-01-18T08:49:02.000Z
|
episode_dataloader.py
|
lovelyqian/AMeFu-Net
|
1c1dedd0390fe35cf5e73ac1f6f9f8a9ae7595c2
|
[
"MIT"
] | 8
|
2021-05-27T05:12:54.000Z
|
2021-12-22T03:25:10.000Z
|
episode_dataloader.py
|
lovelyqian/AMeFu-Net
|
1c1dedd0390fe35cf5e73ac1f6f9f8a9ae7595c2
|
[
"MIT"
] | 3
|
2021-05-15T07:23:00.000Z
|
2021-11-24T01:58:02.000Z
|
import torch
from utils import *
class EpisodeDataloader():
'''
get_episode: return episode
shuffle label every episode
'''
def __init__(self, params):
self.dataset = params.dataset
self.mode = params.mode
self.n_way = params.n_way
self.k_shot = params.k_shot
self.video_frames = params.VIDEO_FRAMES
self.frame_dir = FRAME_DIR[self.dataset]
if(self.mode == 'train'):
self.dataset_list = TRAIN_LIST[self.dataset]
elif(self.mode == 'val'):
self.dataset_list = VAL_LIST[self.dataset]
elif(self.mode == 'test'):
self.dataset_list = TEST_LIST[self.dataset]
self.data = open(self.dataset_list).readlines()
def get_episode(self):
'''
:return: support_x = n_way * k_shot * video, support_y = n_way * k_shot * y,;
:return: query_x = 1* video , query_y = 1 * y
'''
# handle dataset_info
dict = {}
for line in self.data:
line = line.strip('\n')
class_name = line.split('/')[0]
if class_name not in dict.keys():
dict[class_name] = [line]
else:
dict[class_name].append(line)
# sample n-way class_name and shuffle then
aim_class_names = random.sample(dict.keys(), self.n_way)
# sample 1 query_name
aim_query_name = random.sample(aim_class_names,1)[0]
# sample n-way * k_shot support sets and 1 quey video
support_x = []
support_x_depth = []
support_y = []
query_x = []
query_x_depth = []
query_y = []
# for visulization
support_y_global = []
query_y_global = []
for class_name in aim_class_names:
# get the additional one for query
if (class_name == aim_query_name):
aim_video_infos = random.sample(dict[class_name], self.k_shot +1 )
video_info = aim_video_infos[0]
video, video_depth = get_video_fusion_from_video_info_rgb_depth_object(video_info, mode= self.mode, video_frames = self.video_frames, frame_dir = self.frame_dir)
video_class = get_classname_from_video_info(video_info)
video_y = aim_class_names.index(video_class)
query_x.append(video)
query_x_depth.append(video_depth)
query_y.append(video_y)
# for visulization
video_label=get_label_from_video_info(video_info,self.dataset_list)
query_y_global.append(video_label)
aim_video_infos=aim_video_infos[1:]
else:
aim_video_infos = random.sample(dict[class_name],self.k_shot)
# sample support set
for video_info in aim_video_infos:
video, video_depth = get_video_fusion_from_video_info_rgb_depth_object(video_info, mode= self.mode, video_frames = self.video_frames, frame_dir = self.frame_dir)
video_class = get_classname_from_video_info(video_info)
video_y = aim_class_names.index(video_class)
support_x.append(video)
support_x_depth.append(video_depth)
support_y.append(video_y)
# for visulization
video_label=get_label_from_video_info(video_info,self.dataset_list)
support_y_global.append(video_label)
support_x = torch.stack(support_x)
support_x = torch.FloatTensor(support_x)
support_x_depth = torch.stack(support_x_depth)
support_x_depth = torch.FloatTensor(support_x_depth)
support_y = torch.FloatTensor(support_y)
query_x = torch.stack(query_x)
query_x = torch.FloatTensor(query_x)
query_x_depth = torch.stack(query_x_depth)
query_x_depth = torch.FloatTensor(query_x_depth)
query_y = torch.FloatTensor(query_y)
support_y_global = torch.FloatTensor(support_y_global)
query_y_global = torch.FloatTensor(query_y_global)
return ({'support_x': support_x, 'support_y': support_y, 'query_x': query_x, 'query_y': query_y, 'support_x_depth':support_x_depth, 'query_x_depth':query_x_depth, 'support_y_global': support_y_global, 'query_y_global': query_y_global})
def get_episode_multi_depth(self):
'''
:return: support_x = n_way * k_shot * video, support_y = n_way * k_shot * y,;
:return: query_x = 1* video , query_y = 1 * y
'''
# handle dataset_info
dict = {}
for line in self.data:
line = line.strip('\n')
class_name = line.split('/')[0]
if class_name not in dict.keys():
dict[class_name] = [line]
else:
dict[class_name].append(line)
# sample n-way class_name and shuffle then
aim_class_names = random.sample(dict.keys(), self.n_way)
# sample 1 query_name
aim_query_name = random.sample(aim_class_names,1)[0]
# sample n-way * k_shot support sets and 1 quey video
support_x = []
support_x_depth1 = []
support_x_depth2 = []
support_x_depth3 = []
support_y = []
query_x = []
query_x_depth1 = []
query_x_depth2 = []
query_x_depth3 = []
query_y = []
# for visulization
support_y_global = []
query_y_global = []
for class_name in aim_class_names:
# get the additional one for query
if (class_name == aim_query_name):
aim_video_infos = random.sample(dict[class_name], self.k_shot +1 )
video_info = aim_video_infos[0]
video, video_depth = get_video_fusion_from_video_info_rgb_depth_object_multi_depth(video_info, mode= self.mode, video_frames = self.video_frames, frame_dir = self.frame_dir)
video_class = get_classname_from_video_info(video_info)
video_y = aim_class_names.index(video_class)
query_x.append(video)
query_x_depth1.append(video_depth[0])
query_x_depth2.append(video_depth[1])
query_x_depth3.append(video_depth[2])
query_y.append(video_y)
# for visulization
video_label=get_label_from_video_info(video_info,self.dataset_list)
query_y_global.append(video_label)
aim_video_infos=aim_video_infos[1:]
else:
aim_video_infos = random.sample(dict[class_name], self.k_shot)
# sample support set
for video_info in aim_video_infos:
# support depends on it's mode
video, video_depth = get_video_fusion_from_video_info_rgb_depth_object_multi_depth(video_info, mode= self.mode, video_frames = self.video_frames, frame_dir = self.frame_dir)
video_class = get_classname_from_video_info(video_info)
video_y = aim_class_names.index(video_class)
support_x.append(video)
support_x_depth1.append(video_depth[0])
support_x_depth2.append(video_depth[1])
support_x_depth3.append(video_depth[2])
support_y.append(video_y)
# for visulization
video_label=get_label_from_video_info(video_info,self.dataset_list)
support_y_global.append(video_label)
support_x = torch.stack(support_x)
support_x = torch.FloatTensor(support_x)
support_x_depth1 = torch.stack(support_x_depth1)
support_x_depth1 = torch.FloatTensor(support_x_depth1)
support_x_depth2 = torch.stack(support_x_depth2)
support_x_depth2 = torch.FloatTensor(support_x_depth2)
support_x_depth3 = torch.stack(support_x_depth3)
support_x_depth3 = torch.FloatTensor(support_x_depth3)
support_x_depth = [support_x_depth1, support_x_depth2, support_x_depth3]
support_y = torch.FloatTensor(support_y)
query_x = torch.stack(query_x)
query_x = torch.FloatTensor(query_x)
query_x_depth1 = torch.stack(query_x_depth1)
query_x_depth1 = torch.FloatTensor(query_x_depth1)
query_x_depth2 = torch.stack(query_x_depth2)
query_x_depth2 = torch.FloatTensor(query_x_depth2)
query_x_depth3 = torch.stack(query_x_depth3)
query_x_depth3 = torch.FloatTensor(query_x_depth3)
query_x_depth = [query_x_depth1, query_x_depth2, query_x_depth3]
query_y = torch.FloatTensor(query_y)
support_y_global = torch.FloatTensor(support_y_global)
query_y_global = torch.FloatTensor(query_y_global)
return ({'support_x': support_x, 'support_y': support_y, 'query_x': query_x, 'query_y': query_y, 'support_x_depth':support_x_depth, 'query_x_depth':query_x_depth, 'support_y_global': support_y_global, 'query_y_global': query_y_global})
| 43.056075
| 244
| 0.617104
| 1,188
| 9,214
| 4.37037
| 0.072391
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| 0.862866
| 0.811248
| 0.739214
| 0.739214
| 0.739214
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| 0
| 0.010533
| 0.299327
| 9,214
| 213
| 245
| 43.258216
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0
| 6
|
35d473e4e55aad67a646e907790ffd9dcb62b3b8
| 7,700
|
py
|
Python
|
convert_nii_to_tfrecords.py
|
zhongzisha/ISBI2018_PETCT_Segmentation
|
4501d368e430096cd97b046a607737cc675d58a0
|
[
"MIT"
] | 15
|
2018-06-14T07:00:56.000Z
|
2022-03-30T11:24:55.000Z
|
convert_nii_to_tfrecords.py
|
SSurprising/ISBI2018_PETCT_Segmentation
|
d403df7cd6e284711a8cbdc9dc13d4df2bb47746
|
[
"MIT"
] | 3
|
2018-06-22T13:06:01.000Z
|
2020-11-26T14:54:01.000Z
|
convert_nii_to_tfrecords.py
|
zhongzisha/ISBI2018_PETCT_Segmentation
|
4501d368e430096cd97b046a607737cc675d58a0
|
[
"MIT"
] | 2
|
2018-12-28T08:12:56.000Z
|
2021-08-29T13:10:56.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 30 07:22:32 2017
@author: ziszhong
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import glob
import tensorflow as tf
import numpy as np
import nibabel as nib
import pandas as pd
import SimpleITK as sitk
from myconfig import *
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def convert_oneset(filenames):
image_sum = np.zeros((DEPTH, HEIGHT, WIDTH, 2), dtype=np.float64)
for f in filenames:
img_fn = f[1]
case_name = img_fn.split('/')[-1]
filename = os.path.join(str(os.path.join(img_fn, 'data.tfrecords')))
writer = tf.python_io.TFRecordWriter(filename)
ct_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputCT_ROI.nii.gz')))
ct = sitk.GetArrayFromImage(ct_sitk).astype((np.float32))
ptsuv_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputPET_SUV_ROI.nii.gz')))
ptsuv = sitk.GetArrayFromImage(ptsuv_sitk).astype((np.float32))
lbl_ct = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_CT.nii.gz')))).astype(np.uint8)
lbl_pt = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_PET.nii.gz')))).astype(np.uint8)
ct[ct>200.] = 200.
ct[ct<-500.] = -500.
ct = 255*(ct+500)/(700.)
ct = ct.astype(np.uint8)
ptsuv[ptsuv<0.01]=0.01
ptsuv[ptsuv>20.]=20.
ptsuv = 255*(ptsuv-0.01)/(19.99)
ptsuv = ptsuv.astype(np.uint8)
image_raw = np.concatenate((ct[...,np.newaxis],ptsuv[...,np.newaxis]),axis=3)
label_raw = np.concatenate((lbl_ct[...,np.newaxis],lbl_pt[...,np.newaxis]),axis=3)
depth, height, width, channels = image_raw.shape
example = tf.train.Example(features=tf.train.Features(feature={
'case_name': _bytes_feature(case_name),
'label_raw': _bytes_feature(label_raw.tostring()),
'image_raw': _bytes_feature(image_raw.tostring())}))
writer.write(example.SerializeToString())
print(filename)
writer.close()
image_sum += image_raw.astype(np.float64)
print('number of files: ', len(filenames))
print(np.sum(image_sum, axis=(0,1,2))/(HEIGHT*WIDTH*DEPTH*len(filenames)))
def convert_oneset_for_str(filenames):
image_sum = np.zeros((DEPTH, HEIGHT, WIDTH, 2), dtype=np.float64)
for f in filenames:
img_fn = f[1]
case_name = img_fn.split('/')[-1]
filename = os.path.join(str(os.path.join(img_fn, 'data2.tfrecords')))
writer = tf.python_io.TFRecordWriter(filename)
ct_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputCT_ROI.nii.gz')))
ct = sitk.GetArrayFromImage(ct_sitk).astype((np.float32))
ptsuv_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputPET_SUV_ROI.nii.gz')))
ptsuv = sitk.GetArrayFromImage(ptsuv_sitk).astype((np.float32))
lbl_ct = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_CT{}.nii.gz'.format(GT_POSTFIX))))).astype(np.uint8)
lbl_pt = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_PET{}.nii.gz'.format(GT_POSTFIX))))).astype(np.uint8)
ct[ct>200.] = 200.
ct[ct<-500.] = -500.
ct = 255*(ct+500)/(700.)
ct = ct.astype(np.uint8)
ptsuv[ptsuv<0.01]=0.01
ptsuv[ptsuv>20.]=20.
ptsuv = 255*(ptsuv-0.01)/(19.99)
ptsuv = ptsuv.astype(np.uint8)
image_raw = np.concatenate((ct[...,np.newaxis],ptsuv[...,np.newaxis]),axis=3)
label_raw = np.concatenate((lbl_ct[...,np.newaxis],lbl_pt[...,np.newaxis]),axis=3)
depth, height, width, channels = image_raw.shape
example = tf.train.Example(features=tf.train.Features(feature={
'case_name': _bytes_feature(tf.compat.as_bytes(case_name)),
'label_raw': _bytes_feature(label_raw.tostring()),
'image_raw': _bytes_feature(image_raw.tostring())}))
writer.write(example.SerializeToString())
# print(filename)
writer.close()
image_sum += image_raw.astype(np.float64)
print('number of files: ', len(filenames))
print(np.sum(image_sum, axis=(0,1,2))/(HEIGHT*WIDTH*DEPTH*len(filenames)))
def convert_oneset_2d(filenames):
s = np.zeros((HEIGHT,WIDTH,2), dtype=np.float32)
count = 0
for f in filenames:
img_fn = f[1]
case_name = img_fn.split('/')[-1]
filename = os.path.join(str(os.path.join(img_fn, 'data_2d.tfrecords')))
writer = tf.python_io.TFRecordWriter(filename)
ct_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputCT_ROI.nii.gz')))
ct = sitk.GetArrayFromImage(ct_sitk).astype((np.float32))
ptsuv_sitk = sitk.ReadImage(str(os.path.join(img_fn, 'InputPET_SUV_ROI.nii.gz')))
ptsuv = sitk.GetArrayFromImage(ptsuv_sitk).astype((np.float32))
lbl_ct = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_CT{}.nii.gz'.format(GT_POSTFIX))))).astype(np.uint8)
lbl_pt = sitk.GetArrayFromImage(sitk.ReadImage(str(os.path.join(
img_fn, 'GTV_Primary_ROI_PET{}.nii.gz'.format(GT_POSTFIX))))).astype(np.uint8)
ct[ct>200.] = 200.
ct[ct<-500.] = -500.
ct = 255*(ct+500)/(700.)
ct = ct.astype(np.uint8)
ptsuv[ptsuv<0.01]=0.01
ptsuv[ptsuv>20.]=20.
ptsuv = 255*(ptsuv-0.01)/(19.99)
ptsuv = ptsuv.astype(np.uint8)
ctpt_and = np.logical_and(lbl_ct==1, lbl_pt==1).astype(np.uint8)
for i in range(ctpt_and.shape[0]):
if np.count_nonzero(ctpt_and[i,:,:])>20:
image_raw = np.concatenate((ct[i,:,:,np.newaxis],ptsuv[i,:,:,np.newaxis]),axis=2)
s += image_raw.astype(np.float32)
label_raw = np.concatenate((lbl_ct[i,:,:,np.newaxis],lbl_pt[i,:,:,np.newaxis]),axis=2)
example = tf.train.Example(features=tf.train.Features(feature={
'case_name': _bytes_feature(tf.compat.as_bytes('{}_{}'.format(case_name,i))),
'label_raw': _bytes_feature(label_raw.tostring()),
'image_raw': _bytes_feature(image_raw.tostring())}))
writer.write(example.SerializeToString())
count += 1
# print(filename)
writer.close()
print(count, np.sum(s,axis=(0,1))/HEIGHT/WIDTH/count)
if __name__ == '__main__':
train_filenames = pd.read_csv(
TRAIN_FILENAME,
dtype=object,
keep_default_na=False,
na_values=[]).as_matrix()
val_filenames = pd.read_csv(
VAL_FILENAME,
dtype=object,
keep_default_na=False,
na_values=[]).as_matrix()
test_filenames = pd.read_csv(
TEST_FILENAME,
dtype=object,
keep_default_na=False,
na_values=[]).as_matrix()
# convert_oneset(train_filenames)
# convert_oneset(val_filenames)
# convert_oneset(test_filenames)
convert_oneset_for_str(train_filenames)
convert_oneset_for_str(val_filenames)
convert_oneset_for_str(test_filenames)
# convert_oneset_2d(train_filenames)
# convert_oneset_2d(val_filenames)
# convert_oneset_2d(test_filenames)
| 37.378641
| 102
| 0.622208
| 1,044
| 7,700
| 4.37069
| 0.14272
| 0.038571
| 0.039448
| 0.042735
| 0.800789
| 0.766601
| 0.746877
| 0.746877
| 0.746877
| 0.746877
| 0
| 0.036074
| 0.225974
| 7,700
| 205
| 103
| 37.560976
| 0.72953
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| 0
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| 0.062551
| 0.031208
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| 0.034722
| false
| 0
| 0.083333
| 0.013889
| 0.131944
| 0.048611
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| null | 0
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|
0
| 6
|
ea031102a48020afa88b1ca0dd34739e8c80b935
| 2,252
|
py
|
Python
|
migrations/versions/2020_06_28_ff4a8e283767_remove_unneeded_server_defaults.py
|
MobidX/Ultimate-Poll-Bot
|
5d525dcccf7cd81582c653ff21dfb1d8b1f89c09
|
[
"MIT"
] | 1
|
2020-08-10T08:07:34.000Z
|
2020-08-10T08:07:34.000Z
|
migrations/versions/2020_06_28_ff4a8e283767_remove_unneeded_server_defaults.py
|
MobidX/Ultimate-Poll-Bot
|
5d525dcccf7cd81582c653ff21dfb1d8b1f89c09
|
[
"MIT"
] | null | null | null |
migrations/versions/2020_06_28_ff4a8e283767_remove_unneeded_server_defaults.py
|
MobidX/Ultimate-Poll-Bot
|
5d525dcccf7cd81582c653ff21dfb1d8b1f89c09
|
[
"MIT"
] | null | null | null |
"""Remove unneeded server defaults
Revision ID: ff4a8e283767
Revises: 1095210c0ba3
Create Date: 2020-06-28 16:40:52.974552
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'ff4a8e283767'
down_revision = '1095210c0ba3'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('daily_statistic', 'notifications',
existing_type=sa.INTEGER(),
server_default=None,
existing_nullable=False)
op.alter_column('poll', 'allow_sharing',
existing_type=sa.BOOLEAN(),
server_default=None,
existing_nullable=False)
op.alter_column('poll', 'show_option_votes',
existing_type=sa.BOOLEAN(),
server_default=None,
existing_nullable=False)
op.alter_column('user', 'banned',
existing_type=sa.BOOLEAN(),
server_default=None,
existing_nullable=False)
op.alter_column('user', 'deleted',
existing_type=sa.BOOLEAN(),
server_default=None,
existing_nullable=False)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('user', 'deleted',
existing_type=sa.BOOLEAN(),
server_default=sa.text('false'),
existing_nullable=False)
op.alter_column('user', 'banned',
existing_type=sa.BOOLEAN(),
server_default=sa.text('false'),
existing_nullable=False)
op.alter_column('poll', 'show_option_votes',
existing_type=sa.BOOLEAN(),
server_default=sa.text('true'),
existing_nullable=False)
op.alter_column('poll', 'allow_sharing',
existing_type=sa.BOOLEAN(),
server_default=sa.text('false'),
existing_nullable=False)
op.alter_column('daily_statistic', 'notifications',
existing_type=sa.INTEGER(),
server_default=sa.text('0'),
existing_nullable=False)
# ### end Alembic commands ###
| 33.61194
| 65
| 0.600355
| 234
| 2,252
| 5.559829
| 0.286325
| 0.053805
| 0.099923
| 0.14143
| 0.787856
| 0.787856
| 0.744043
| 0.744043
| 0.739431
| 0.739431
| 0
| 0.034161
| 0.28508
| 2,252
| 66
| 66
| 34.121212
| 0.773913
| 0.138988
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| 0.114616
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| 0.041667
| false
| 0
| 0.041667
| 0
| 0.083333
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| null | 0
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| 1
| 1
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| 1
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| null | 0
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| 0
|
0
| 6
|
ea8b19f3e24d22583bb4dcbf2fa78005c5df8c3d
| 89
|
py
|
Python
|
apps/bouygue/models.py
|
GuillaumeM92/La-Bouygue
|
a402efbc9746acb51cd7fc66ccdac4a45b854a22
|
[
"MIT"
] | null | null | null |
apps/bouygue/models.py
|
GuillaumeM92/La-Bouygue
|
a402efbc9746acb51cd7fc66ccdac4a45b854a22
|
[
"MIT"
] | null | null | null |
apps/bouygue/models.py
|
GuillaumeM92/La-Bouygue
|
a402efbc9746acb51cd7fc66ccdac4a45b854a22
|
[
"MIT"
] | null | null | null |
# """Bouygue models."""
# from django.db import models
# from django.urls import reverse
| 22.25
| 33
| 0.719101
| 12
| 89
| 5.333333
| 0.666667
| 0.3125
| 0.5
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| 0.146067
| 89
| 3
| 34
| 29.666667
| 0.842105
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| null | true
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| null | 0
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| 0
|
0
| 6
|
ea9e21e70f36a21d3e4d0041ef447c49998e78a9
| 24
|
py
|
Python
|
itemmodelmixins/__init__.py
|
jamathews/MPE_Util
|
cd99a25fbb8cb665f36a9a52a7641107cadb8161
|
[
"MIT"
] | 1
|
2021-03-02T11:42:49.000Z
|
2021-03-02T11:42:49.000Z
|
itemmodelmixins/__init__.py
|
jamathews/MPE_Util
|
cd99a25fbb8cb665f36a9a52a7641107cadb8161
|
[
"MIT"
] | null | null | null |
itemmodelmixins/__init__.py
|
jamathews/MPE_Util
|
cd99a25fbb8cb665f36a9a52a7641107cadb8161
|
[
"MIT"
] | null | null | null |
from MPEMixIns import *
| 12
| 23
| 0.791667
| 3
| 24
| 6.333333
| 1
| 0
| 0
| 0
| 0
| 0
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| 0.166667
| 24
| 1
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| 24
| 0.95
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| 1
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| 1
| 0
|
0
| 6
|
577f620a48c2ec3ddd3ded67ccc2889a8953b3dd
| 16,423
|
py
|
Python
|
equinox/nn/conv.py
|
marcelroed/equinox
|
3804a8d60217bde685bee0a893a7bd55b1e63c26
|
[
"Apache-2.0"
] | null | null | null |
equinox/nn/conv.py
|
marcelroed/equinox
|
3804a8d60217bde685bee0a893a7bd55b1e63c26
|
[
"Apache-2.0"
] | null | null | null |
equinox/nn/conv.py
|
marcelroed/equinox
|
3804a8d60217bde685bee0a893a7bd55b1e63c26
|
[
"Apache-2.0"
] | null | null | null |
import itertools as it
from typing import Any, Optional, Sequence, Tuple, Union
import jax
import jax.lax as lax
import jax.numpy as jnp
import jax.random as jrandom
import numpy as np
from ..custom_types import Array
from ..module import Module, static_field
def _ntuple(n: int) -> callable:
def parse(x: Any) -> tuple:
if isinstance(x, Sequence):
if len(x) == n:
return tuple(x)
else:
raise ValueError(
f"Length of {x} (length = {len(x)}) is not equal to {n}"
)
else:
return tuple(it.repeat(x, n))
return parse
class Conv(Module):
"""General N-dimensional convolution."""
num_spatial_dims: int = static_field()
weight: Array
bias: Optional[Array]
in_channels: int = static_field()
out_channels: int = static_field()
kernel_size: Tuple[int, ...] = static_field()
stride: Tuple[int, ...] = static_field()
padding: Tuple[Tuple[int, int], ...] = static_field()
dilation: Tuple[int, ...] = static_field()
use_bias: bool = static_field()
def __init__(
self,
num_spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Sequence[int]],
stride: Union[int, Sequence[int]] = 1,
padding: Union[int, Sequence[int]] = 0,
dilation: Union[int, Sequence[int]] = 1,
use_bias: bool = True,
*,
key: "jax.random.PRNGKey",
**kwargs,
):
"""**Arguments:**
- `num_spatial_dims`: The number of spatial dimensions. For example traditional
convolutions for image processing have this set to `2`.
- `in_channels`: The number of input channels.
- `out_channels`: The number of output channels.
- `kernel_size`: The size of the convolutional kernel.
- `stride`: The stride of the convolution.
- `padding`: The amount of padding to apply before and after each spatial
dimension. The same amount of padding is applied both before and after.
- `dilation`: The dilation of the convolution.
- `use_bias`: Whether to add on a bias after the convolution.
- `key`: A `jax.random.PRNGKey` used to provide randomness for parameter
initialisation. (Keyword only argument.)
!!! info
All of `kernel_size`, `stride`, `padding`, `dilation` can be either an
integer or a sequence of integers. If they are a sequence then the sequence
should be of length equal to `num_spatial_dims`, and specify the value of
each property down each spatial dimension in turn. If they are an integer
then the same kernel size / stride / padding / dilation will be used along
every spatial dimension.
"""
super().__init__(**kwargs)
wkey, bkey = jrandom.split(key, 2)
parse = _ntuple(num_spatial_dims)
kernel_size = parse(kernel_size)
stride = parse(stride)
dilation = parse(dilation)
lim = 1 / np.sqrt(in_channels * np.prod(kernel_size))
self.weight = jrandom.uniform(
wkey,
(out_channels, in_channels) + kernel_size,
minval=-lim,
maxval=lim,
)
if use_bias:
self.bias = jrandom.uniform(
bkey,
(out_channels,) + (1,) * num_spatial_dims,
minval=-lim,
maxval=lim,
)
else:
self.bias = None
self.num_spatial_dims = num_spatial_dims
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
if isinstance(padding, int):
self.padding = tuple((padding, padding) for _ in range(num_spatial_dims))
elif isinstance(padding, Sequence) and len(padding) == num_spatial_dims:
self.padding = tuple((p, p) for p in padding)
else:
raise ValueError(
"`padding` must either be an int or tuple of length "
f"{num_spatial_dims}."
)
self.dilation = dilation
self.use_bias = use_bias
def __call__(
self, x: Array, *, key: Optional["jax.random.PRNGKey"] = None
) -> Array:
"""**Arguments:**
- `x`: The input. Should be a JAX array of shape `(in_channels, dim_1, ..., dim_N)`, where
`N = num_spatial_dims`.
- `key`: Ignored; provided for compatibility with the rest of the Equinox API.
(Keyword only argument.)
**Returns:**
A JAX array of shape `(out_channels, new_dim_1, ..., new_dim_N)`.
"""
unbatched_rank = self.num_spatial_dims + 1
if x.ndim != unbatched_rank:
raise ValueError(
f"Input to `Conv` needs to have rank {unbatched_rank},",
f" but input has shape {x.shape}.",
)
x = jnp.expand_dims(x, axis=0)
x = lax.conv_general_dilated(
lhs=x,
rhs=self.weight,
window_strides=self.stride,
padding=self.padding,
rhs_dilation=self.dilation,
)
if self.use_bias:
x = x + self.bias
x = jnp.squeeze(x, axis=0)
return x
class Conv1d(Conv):
"""As [`equinox.nn.Conv`][] with `num_spatial_dims=1`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=1,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
class Conv2d(Conv):
"""As [`equinox.nn.Conv`][] with `num_spatial_dims=2`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=(1, 1),
padding=(0, 0),
dilation=(1, 1),
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=2,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
class Conv3d(Conv):
"""As [`equinox.nn.Conv`][] with `num_spatial_dims=3`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=(1, 1, 1),
padding=(0, 0, 0),
dilation=(1, 1, 1),
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=3,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
class ConvTranspose(Module):
"""General N-dimensional transposed convolution."""
num_spatial_dims: int = static_field()
weight: Array
bias: Optional[Array]
in_channels: int = static_field()
out_channels: int = static_field()
kernel_size: Tuple[int, ...] = static_field()
stride: Tuple[int, ...] = static_field()
padding: Tuple[Tuple[int, int], ...] = static_field()
output_padding: Tuple[int, ...] = static_field()
dilation: Tuple[int, ...] = static_field()
use_bias: bool = static_field()
def __init__(
self,
num_spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Sequence[int]],
stride: Union[int, Sequence[int]] = 1,
padding: Union[int, Sequence[int]] = 0,
output_padding: Union[int, Sequence[int]] = 0,
dilation: Union[int, Sequence[int]] = 1,
use_bias: bool = True,
*,
key: "jax.random.PRNGKey",
**kwargs,
):
"""**Arguments:**
- `num_spatial_dims`: The number of spatial dimensions. For example traditional
convolutions for image processing have this set to `2`.
- `in_channels`: The number of input channels.
- `out_channels`: The number of output channels.
- `kernel_size`: The size of the transposed convolutional kernel.
- `stride`: The stride used on the equivalent [`equinox.nn.Conv`][].
- `padding`: The amount of padding used on the equivalent [`equinox.nn.Conv`][].
- `output_padding`: Additional padding for the output shape.
- `dilation`: The spacing between kernel points.
- `use_bias`: Whether to add on a bias after the transposed convolution.
- `key`: A `jax.random.PRNGKey` used to provide randomness for parameter
initialisation. (Keyword only argument.)
!!! info
All of `kernel_size`, `stride`, `padding`, `output_padding`, `dilation` can
be either an integer or a sequence of integers. If they are a sequence then
the sequence should be of length equal to `num_spatial_dims`, and specify
the value of each property down each spatial dimension in turn.. If they
are an integer then the same kernel size / stride / padding / dilation will
be used along every spatial dimension.
!!! tip
Transposed convolutions are often used to go in the "opposite direction" to
a normal convolution. That is, from something with the shape of the output
of a convolution to something with the shape of the input to a convolution.
Moreover, to do so with the same "connectivity", i.e. which inputs can
affect which outputs.
Relative to an [`equinox.nn.Conv`][] layer, this can be accomplished by
switching the values of `in_channels` and `out_channels`, whilst keeping
`kernel_size`, `stride, `padding`, and `dilation` the same.
When `stride > 1` then [`equinox.nn.Conv`][] maps multiple input shapes to the
same output shape. `output_padding` is provided to resolve this ambiguity,
by adding a little extra padding to just the bottom/right edges of the
input.
See [these animations](https://github.com/vdumoulin/conv_arithmetic/blob/af6f818b0bb396c26da79899554682a8a499101d/README.md#transposed-convolution-animations)
and [this report](https://arxiv.org/abs/1603.07285) for a nice reference.
""" # noqa: E501
super().__init__(**kwargs)
wkey, bkey = jrandom.split(key, 2)
parse = _ntuple(num_spatial_dims)
kernel_size = parse(kernel_size)
stride = parse(stride)
output_padding = parse(output_padding)
dilation = parse(dilation)
for s, o in zip(stride, output_padding):
if output_padding >= stride:
raise ValueError("Must have `output_padding < stride` (elementwise).")
lim = 1 / np.sqrt(in_channels * np.prod(kernel_size))
self.weight = jrandom.uniform(
wkey,
(out_channels, in_channels) + kernel_size,
minval=-lim,
maxval=lim,
)
if use_bias:
self.bias = jrandom.uniform(
bkey,
(out_channels,) + (1,) * num_spatial_dims,
minval=-lim,
maxval=lim,
)
else:
self.bias = None
self.num_spatial_dims = num_spatial_dims
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
if isinstance(padding, int):
self.padding = tuple((padding, padding) for _ in range(num_spatial_dims))
elif isinstance(padding, Sequence) and len(padding) == num_spatial_dims:
self.padding = tuple((p, p) for p in padding)
else:
raise ValueError(
"`padding` must either be an int or tuple of length "
f"{num_spatial_dims}."
)
self.output_padding = output_padding
self.dilation = dilation
self.use_bias = use_bias
def __call__(
self, x: Array, *, key: Optional["jax.random.PRNGKey"] = None
) -> Array:
"""**Arguments:**
- `x`: The input. Should be a JAX array of shape `(in_channels, dim_1, ..., dim_N)`, where
`N = num_spatial_dims`.
- `key`: Ignored; provided for compatibility with the rest of the Equinox API.
(Keyword only argument.)
**Returns:**
A JAX array of shape `(out_channels, new_dim_1, ..., new_dim_N)`.
"""
unbatched_rank = self.num_spatial_dims + 1
if x.ndim != unbatched_rank:
raise ValueError(
f"Input to `ConvTranspose` needs to have rank {unbatched_rank},",
f" but input has shape {x.shape}.",
)
x = jnp.expand_dims(x, axis=0)
# Given by Relationship 14 of https://arxiv.org/abs/1603.07285
padding = tuple(
(d * (k - 1) - p0, d * (k - 1) - p1 + o)
for k, (p0, p1), o, d in zip(
self.kernel_size, self.padding, self.output_padding, self.dilation
)
)
x = lax.conv_general_dilated(
lhs=x,
rhs=self.weight,
window_strides=(1,) * self.num_spatial_dims,
padding=padding,
lhs_dilation=self.stride,
rhs_dilation=self.dilation,
)
if self.use_bias:
x = x + self.bias
x = jnp.squeeze(x, axis=0)
return x
class ConvTranspose1d(ConvTranspose):
"""As [`equinox.nn.ConvTranspose`][] with `num_spatial_dims=1`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
output_padding=0,
padding=0,
dilation=1,
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=1,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
output_padding=output_padding,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
class ConvTranspose2d(ConvTranspose):
"""As [`equinox.nn.ConvTranspose`][] with `num_spatial_dims=2`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=(1, 1),
output_padding=(0, 0),
padding=(0, 0),
dilation=(1, 1),
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=2,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
output_padding=output_padding,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
class ConvTranspose3d(ConvTranspose):
"""As [`equinox.nn.ConvTranspose`][] with `num_spatial_dims=3`."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=(1, 1, 1),
output_padding=(0, 0, 0),
padding=(0, 0, 0),
dilation=(1, 1, 1),
use_bias=True,
*,
key,
**kwargs,
):
super().__init__(
num_spatial_dims=3,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
output_padding=output_padding,
padding=padding,
dilation=dilation,
use_bias=use_bias,
key=key,
**kwargs,
)
| 32.32874
| 170
| 0.560982
| 1,898
| 16,423
| 4.660169
| 0.138567
| 0.047484
| 0.06173
| 0.028491
| 0.781458
| 0.768005
| 0.753081
| 0.745845
| 0.745845
| 0.726851
| 0
| 0.013212
| 0.336358
| 16,423
| 507
| 171
| 32.392505
| 0.79833
| 0.278512
| 0
| 0.824658
| 0
| 0
| 0.043633
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032877
| false
| 0
| 0.024658
| 0
| 0.150685
| 0
| 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
|
17b58c5f773e39bd680224e954ccb7e7e1aa6d48
| 41
|
py
|
Python
|
rdap_query/__init__.py
|
BrandonNX01/rdap_query
|
36496358e78cbac0766095941a47fbe8c7951cf1
|
[
"MIT"
] | null | null | null |
rdap_query/__init__.py
|
BrandonNX01/rdap_query
|
36496358e78cbac0766095941a47fbe8c7951cf1
|
[
"MIT"
] | null | null | null |
rdap_query/__init__.py
|
BrandonNX01/rdap_query
|
36496358e78cbac0766095941a47fbe8c7951cf1
|
[
"MIT"
] | null | null | null |
from .rdap import Rdap
from . import cli
| 13.666667
| 22
| 0.756098
| 7
| 41
| 4.428571
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195122
| 41
| 2
| 23
| 20.5
| 0.939394
| 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
|
17ce1efad8ddd883d8df2b22a9e3f6829b71397e
| 4,842
|
py
|
Python
|
neuroio/groups/v1.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
neuroio/groups/v1.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | 6
|
2021-09-06T08:23:09.000Z
|
2021-11-10T16:19:20.000Z
|
neuroio/groups/v1.py
|
neuroio/neuroio-python
|
160f96515877e5e2ee0e888b7424c77cb2d7496a
|
[
"MIT"
] | null | null | null |
from typing import List, Union
from httpx import Response
from neuroio.base import APIBase, APIBaseAsync, APIBaseBase
from neuroio.constants import sentinel
from neuroio.utils import request_query_processing
class GroupsBase(APIBaseBase):
def get_url(self, key: str = None) -> str:
if key:
return self.base_url + f"/v1/groups/persons/{key}/"
else:
return self.base_url + "/v1/groups/persons/"
class Impl(APIBase, GroupsBase):
def create(self, name: str) -> Response:
data = {"name": name}
with self.get_client() as client:
return client.post(url=self.get_url(), json=data)
def list(
self,
q: Union[str, object] = sentinel,
pids_include: Union[List[str], object] = sentinel,
pids_exclude: Union[List[str], object] = sentinel,
groups_ids: Union[List[int], object] = sentinel,
spaces_ids: Union[List[int], object] = sentinel,
limit: int = 20,
offset: int = 0,
) -> Response:
data = request_query_processing(locals(), ["self"])
with self.get_client() as client:
return client.get(url=self.get_url(), params=data)
def get(self, id: int) -> Response:
with self.get_client() as client:
return client.get(url=self.get_url(f"{id}"))
def update(self, id: int, name: str) -> Response:
data = {"name": name}
with self.get_client() as client:
return client.patch(url=self.get_url(f"{id}"), json=data)
def delete(self, id: int) -> Response:
with self.get_client() as client:
return client.delete(url=self.get_url(f"{id}"))
def persons(
self,
id: int,
pids: Union[List[str], object] = sentinel,
limit: int = 20,
offset: int = 0,
) -> Response:
data = request_query_processing(locals(), ["self", "id"])
with self.get_client() as client:
return client.get(url=self.get_url(f"{id}/pids"), params=data)
def add(self, pids: List[str], groups_ids: List[int]) -> Response:
data = {"pids": pids, "groups_ids": groups_ids}
with self.get_client() as client:
return client.post(url=self.get_url("pids"), json=data)
def remove(self, pids: List[str], groups_ids: List[int]) -> Response:
data = {"pids": pids, "groups_ids": groups_ids}
with self.get_client() as client:
return client.request(
"DELETE", url=self.get_url("pids"), json=data
)
class ImplAsync(APIBaseAsync, GroupsBase):
async def create(self, name: str) -> Response:
data = {"name": name}
async with self.get_client() as client:
return await client.post(url=self.get_url(), json=data)
async def list(
self,
q: Union[str, object] = sentinel,
pids_include: Union[List[str], object] = sentinel,
pids_exclude: Union[List[str], object] = sentinel,
groups_ids: Union[List[int], object] = sentinel,
spaces_ids: Union[List[int], object] = sentinel,
limit: int = 20,
offset: int = 0,
) -> Response:
data = request_query_processing(locals(), ["self"])
async with self.get_client() as client:
return await client.get(url=self.get_url(), params=data)
async def get(self, id: int) -> Response:
async with self.get_client() as client:
return await client.get(url=self.get_url(f"{id}"))
async def update(self, id: int, name: str) -> Response:
data = {"name": name}
async with self.get_client() as client:
return await client.patch(url=self.get_url(f"{id}"), json=data)
async def delete(self, id: int) -> Response:
async with self.get_client() as client:
return await client.delete(url=self.get_url(f"{id}"))
async def persons(
self,
id: int,
pids: Union[List[str], object] = sentinel,
limit: int = 20,
offset: int = 0,
) -> Response:
data = request_query_processing(locals(), ["self", "id"])
async with self.get_client() as client:
return await client.get(
url=self.get_url(f"{id}/pids"), params=data
)
async def add(self, pids: List[str], groups_ids: List[int]) -> Response:
data = {"pids": pids, "groups_ids": groups_ids}
async with self.get_client() as client:
return await client.post(url=self.get_url("pids"), json=data)
async def remove(self, pids: List[str], groups_ids: List[int]) -> Response:
data = {"pids": pids, "groups_ids": groups_ids}
async with self.get_client() as client:
return await client.request(
"DELETE", url=self.get_url("pids"), json=data
)
| 36.406015
| 79
| 0.593763
| 630
| 4,842
| 4.460317
| 0.104762
| 0.079715
| 0.062633
| 0.096797
| 0.858363
| 0.858363
| 0.847687
| 0.847687
| 0.815658
| 0.797865
| 0
| 0.00396
| 0.26993
| 4,842
| 132
| 80
| 36.681818
| 0.790948
| 0
| 0
| 0.550459
| 0
| 0
| 0.042544
| 0.005163
| 0
| 0
| 0
| 0
| 0
| 1
| 0.082569
| false
| 0
| 0.045872
| 0
| 0.321101
| 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
|
17e2ab9758fb2ffba8031d55ab98d474c8e0ee32
| 48
|
py
|
Python
|
segmentation_models_pytorch/experiments/__init__.py
|
utegulovalmat/segmentation_models.pytorch
|
01fb07e9b3d91b7643394ffb1631cb3a9fc214a4
|
[
"MIT"
] | null | null | null |
segmentation_models_pytorch/experiments/__init__.py
|
utegulovalmat/segmentation_models.pytorch
|
01fb07e9b3d91b7643394ffb1631cb3a9fc214a4
|
[
"MIT"
] | null | null | null |
segmentation_models_pytorch/experiments/__init__.py
|
utegulovalmat/segmentation_models.pytorch
|
01fb07e9b3d91b7643394ffb1631cb3a9fc214a4
|
[
"MIT"
] | null | null | null |
from . import helpers
from . import train_model
| 16
| 25
| 0.791667
| 7
| 48
| 5.285714
| 0.714286
| 0.540541
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 48
| 2
| 26
| 24
| 0.925
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
aa06e883fc5e91f12ec094ab9e3f605e09d30cc4
| 320
|
py
|
Python
|
ignite/contrib/handlers/__init__.py
|
maaario/ignite
|
4dfd75ced07f4b45f0b6de6e247353aa317d7e93
|
[
"BSD-3-Clause"
] | null | null | null |
ignite/contrib/handlers/__init__.py
|
maaario/ignite
|
4dfd75ced07f4b45f0b6de6e247353aa317d7e93
|
[
"BSD-3-Clause"
] | null | null | null |
ignite/contrib/handlers/__init__.py
|
maaario/ignite
|
4dfd75ced07f4b45f0b6de6e247353aa317d7e93
|
[
"BSD-3-Clause"
] | null | null | null |
from ignite.contrib.handlers.param_scheduler import LinearCyclicalScheduler, CosineAnnealingScheduler, \
ConcatScheduler, LRScheduler, create_lr_scheduler_with_warmup, PiecewiseLinear
from ignite.contrib.handlers.tqdm_logger import ProgressBar
from ignite.contrib.handlers.custom_events import CustomPeriodicEvent
| 45.714286
| 104
| 0.878125
| 33
| 320
| 8.30303
| 0.666667
| 0.109489
| 0.186131
| 0.273723
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 320
| 6
| 105
| 53.333333
| 0.925676
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.75
| 0
| 0.75
| 0
| 0
| 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
| 1
| 0
|
0
| 6
|
351ec81dddb1743798128f1fc2062aafdbad5900
| 5,200
|
py
|
Python
|
tests/test_paillier.py
|
guypod/PySyft
|
bb3fa00840f5bc73e462ead50220334e55dae0c4
|
[
"Apache-2.0"
] | null | null | null |
tests/test_paillier.py
|
guypod/PySyft
|
bb3fa00840f5bc73e462ead50220334e55dae0c4
|
[
"Apache-2.0"
] | null | null | null |
tests/test_paillier.py
|
guypod/PySyft
|
bb3fa00840f5bc73e462ead50220334e55dae0c4
|
[
"Apache-2.0"
] | 1
|
2020-05-27T10:20:40.000Z
|
2020-05-27T10:20:40.000Z
|
from syft.he.paillier import KeyPair, PaillierTensor
from syft.he.keys import Paillier
from syft import TensorBase
import unittest
import numpy as np
import syft as sy
# Here's our "unit tests".
class DimTests(unittest.TestCase):
def test_dim_one(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
self.assertTrue(x.dim() == 1)
class DotTests(unittest.TestCase):
def test_dot_product(self):
pk, sk = Paillier()
x = pk.ones(10)
y = sy.ones(10)
out1 = y.dot(x).decrypt(sk)
out2 = x.dot(y).decrypt(sk)
self.assertEqual(out1, 10)
self.assertEqual(out2, 10)
class AddTests(unittest.TestCase):
def test_simple(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = PaillierTensor(p, np.array([3, 4, 5, 6, 7.]))
y = (x + x2).decrypt(s)
self.assertTrue(y == np.array([4., 6., 8., 10., 12.]))
def test_simple_reversed(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = PaillierTensor(p, np.array([3, 4, 5, 6, 7.]))
y = (x2 + x).decrypt(s)
self.assertTrue(y == np.array([4., 6., 8., 10., 12.]))
def test_scalar_in_place(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x += 1
self.assertTrue(s.decrypt(x) == np.array([2., 3., 4., 5., 6.]))
def test_in_place(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = PaillierTensor(p, np.array([3, 4, 5, 6, 7.]))
x += x2
self.assertTrue(s.decrypt(x) == np.array([4., 6., 8., 10., 12.]))
def test_in_place_reversed(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = PaillierTensor(p, np.array([3, 4, 5, 6, 7.]))
x2 += x
self.assertTrue(s.decrypt(x2) == np.array([4., 6., 8., 10., 12.]))
def test_scalar(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
y = x + 40
self.assertTrue(s.decrypt(y) == np.array([41., 42., 43., 44., 45.]))
def test_in_place_plain_text(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x += x2
self.assertTrue(s.decrypt(x) == np.array([4., 6., 8., 10., 12.]))
def test_add_depth(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x += x2
self.assertEqual(x._add_depth, 1)
class MulTests(unittest.TestCase):
def test_basic(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
y = x * x2
self.assertTrue(y.decrypt(s) == np.array([3., 8., 15., 24., 35.]))
def test_basic_reversed(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
y = x2 * x
self.assertTrue(y.decrypt(s) == np.array([3., 8., 15., 24., 35.]))
def test_inline(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x *= x2
self.assertTrue(x.decrypt(s) == np.array([3., 8., 15., 24., 35.]))
def test_inline_reversed(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x2 *= x
self.assertTrue(x2.decrypt(s) == np.array([3., 8., 15., 24., 35.]))
def test_scalar(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x *= 2
self.assertTrue(s.decrypt(x) == np.array([2., 4., 6., 8., 10.]))
def test_mul_depth(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([1, 2, 3, 4, 5.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x *= x2
self.assertEqual(x._mul_depth, 1)
class DivTests(unittest.TestCase):
def test_basic(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([3., 8., 15., 24., 35.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
y = x / x2
print(y.decrypt(s))
self.assertTrue(y.decrypt(s) == np.array([1., 2., 3., 4., 5.]))
def test_inline(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([3., 8., 15., 24., 35.]))
x2 = TensorBase(np.array([3, 4, 5, 6, 7.]))
x /= x2
self.assertTrue(x.decrypt(s) == np.array([1., 2., 3., 4., 5.]))
def test_scalar(self):
p, s = KeyPair().generate()
x = PaillierTensor(p, np.array([2., 4., 6., 8., 10.]))
x /= 2
self.assertTrue(s.decrypt(x) == np.array([1, 2, 3, 4, 5.]))
| 27.368421
| 76
| 0.513077
| 788
| 5,200
| 3.337563
| 0.100254
| 0.122433
| 0.036502
| 0.18403
| 0.775665
| 0.772624
| 0.772624
| 0.760837
| 0.748669
| 0.724715
| 0
| 0.083712
| 0.280962
| 5,200
| 189
| 77
| 27.513228
| 0.619684
| 0.004615
| 0
| 0.540984
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163934
| 1
| 0.155738
| false
| 0
| 0.04918
| 0
| 0.245902
| 0.008197
| 0
| 0
| 0
| null | 0
| 0
| 1
| 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
|
1048ee6e3b715d3e90ef04e1c3c4f195d6bcf230
| 44
|
py
|
Python
|
examples/modules/motion_detector/__init__.py
|
jagin/dvg-utils
|
a7d19ead75398b09a9f1e146464cf4227f06a476
|
[
"MIT"
] | 7
|
2020-09-02T08:39:22.000Z
|
2021-10-13T18:13:04.000Z
|
examples/modules/motion_detector/__init__.py
|
jagin/dvg-utils
|
a7d19ead75398b09a9f1e146464cf4227f06a476
|
[
"MIT"
] | null | null | null |
examples/modules/motion_detector/__init__.py
|
jagin/dvg-utils
|
a7d19ead75398b09a9f1e146464cf4227f06a476
|
[
"MIT"
] | null | null | null |
from .motion_detector import MotionDetector
| 22
| 43
| 0.886364
| 5
| 44
| 7.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 44
| 1
| 44
| 44
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
108592a0092a48323baae7f7089db0692e968145
| 114
|
py
|
Python
|
metrics/__init__.py
|
caixin1998/pl-template
|
6918f0289ab2b32d107e5722617d25c9a683399c
|
[
"BSD-3-Clause"
] | 1
|
2021-08-31T04:03:44.000Z
|
2021-08-31T04:03:44.000Z
|
metrics/__init__.py
|
caixin1998/Gaze-Estimation
|
17524aab7c7d7b43b855967782dc8f33b9bf4a32
|
[
"BSD-3-Clause"
] | null | null | null |
metrics/__init__.py
|
caixin1998/Gaze-Estimation
|
17524aab7c7d7b43b855967782dc8f33b9bf4a32
|
[
"BSD-3-Clause"
] | null | null | null |
from metrics.mean_angular_error import MeanAngularError
from metrics.mean_distance_error import MeanDistanceError
| 38
| 57
| 0.912281
| 14
| 114
| 7.142857
| 0.642857
| 0.22
| 0.3
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070175
| 114
| 2
| 58
| 57
| 0.943396
| 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
|
1087086ea6005facbdcc1b8828648bc3436b8423
| 11,436
|
py
|
Python
|
Estimation/auxiliary_functions_likelihood.py
|
vpnsctl/pybetareg
|
9f75dcb9972cf57dc0e62965d117ac70d0fdb308
|
[
"MIT"
] | null | null | null |
Estimation/auxiliary_functions_likelihood.py
|
vpnsctl/pybetareg
|
9f75dcb9972cf57dc0e62965d117ac70d0fdb308
|
[
"MIT"
] | null | null | null |
Estimation/auxiliary_functions_likelihood.py
|
vpnsctl/pybetareg
|
9f75dcb9972cf57dc0e62965d117ac70d0fdb308
|
[
"MIT"
] | null | null | null |
import numpy as np
from scipy.special import gammaln, digamma
from scipy.optimize import minimize
from auxiliary_functions_estimation import (
correct_dimension,
estimate_mean,
estimate_precision,
)
def loglikelihood_function_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision,
):
"""
Obtain the log-likelihood function for the beta regression model.
For more details we refer to:
Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression
for modeling rates and proportions. J. Appl. Statist. 31, 799–815
Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved
estimators for a general class of beta regression models. Computational
Statistics and Data Analysis, 54, 348–366
We return the value with the minus sign since we want to maximize
the log-likelihood function and we will utilize the minimize function to
optimizate.
:param endog (array_like): 1d array of endogenous response variable.
:param exog_mean (array_like): A nobs x k array where nobs is the number
of observations and k is the number of mean regressors. An intercept is
not included by default and should be added by the user.
:param exog_precision (array_like): A nobs x q array where nobs is the
number of observations and q is the number of precision regressors.
An intercept is not included by default and should be added by the user.
:param bounded_reg_link: An instance of BoundedRegLink. Recall that
the default mean link is 'logit' and that the default precision link
is None.
:param param_mean: 1d array of mean regression parameters.
:param param_precision: 1d array of precision regression parameters.
"""
estimated_mean = estimate_mean(exog_mean, param_mean, bounded_reg_link)
estimated_precision = estimate_precision(
exog_precision, param_precision, bounded_reg_link
)
loglik = -np.sum(
gammaln(estimated_precision)
- gammaln(estimated_mean * estimated_precision)
- gammaln((1 - estimated_mean) * estimated_precision)
+ (estimated_mean * estimated_precision -1) * np.log(endog)
+ ((1 - estimated_mean) * estimated_precision - 1) * np.log(1 - endog)
)
return loglik
def score_mean_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision,
):
"""
Computes the score vector with respect to the mean regression parameters.
For more details we refer to:
Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression
for modeling rates and proportions. J. Appl. Statist. 31, 799–815
Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved
estimators for a general class of beta regression models. Computational
Statistics and Data Analysis, 54, 348–366
:param endog (array_like): 1d array of endogenous response variable.
:param exog_mean (array_like): A nobs x k array where nobs is the number
of observations and k is the number of mean regressors. An intercept is
not included by default and should be added by the user.
:param exog_precision (array_like): A nobs x q array where nobs is the
number of observations and q is the number of precision regressors.
An intercept is not included by default and should be added by the user.
:param bounded_reg_link: An instance of BoundedRegLink. Recall that
the default mean link is 'logit' and that the default precision link
is None.
:param param_mean: 1d array of mean regression parameters.
:param param_precision: 1d array of precision regression parameters.
"""
estimated_mean = estimate_mean(exog_mean, param_mean, bounded_reg_link)
estimated_precision = estimate_precision(
exog_precision, param_precision, bounded_reg_link
)
score_mean = np.matmul(
exog_mean.T,
(
np.log(endog) - np.log(1 - endog)
- digamma(estimated_mean * estimated_precision)
+ digamma((1 - estimated_mean) * estimated_precision)
)
* bounded_reg_link.dmudeta(estimated_mean)
* estimated_precision,
)
return correct_dimension(score_mean)
def score_precision_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision,
):
"""
Computes the score vector with respect to the precision regression
parameters.
For more details we refer to:
Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression
for modeling rates and proportions. J. Appl. Statist. 31, 799–815
Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved
estimators for a general class of beta regression models. Computational
Statistics and Data Analysis, 54, 348–366
:param endog (array_like): 1d array of endogenous response variable.
:param exog_mean (array_like): A nobs x k array where nobs is the number
of observations and k is the number of mean regressors. An intercept is
not included by default and should be added by the user.
:param exog_precision (array_like): A nobs x q array where nobs is the
number of observations and q is the number of precision regressors.
An intercept is not included by default and should be added by the user.
:param bounded_reg_link: An instance of BoundedRegLink. Recall that
the default mean link is 'logit' and that the default precision link
is None.
:param param_mean: 1d array of mean regression parameters.
:param param_precision: 1d array of precision regression parameters.
"""
estimated_mean = estimate_mean(exog_mean, param_mean, bounded_reg_link)
estimated_precision = estimate_precision(
exog_precision, param_precision, bounded_reg_link
)
if exog_precision is None:
exog_precision = param_precision * np.ones_like(estimated_mean)
score_precision = np.matmul(
exog_precision.T,
(
estimated_mean * (
np.log(endog) - np.log(1 - endog)
- digamma(estimated_mean * estimated_precision)
+ digamma((1 - estimated_mean) * estimated_precision)
)
+ digamma(estimated_precision)
- digamma((1 - estimated_mean) * estimated_precision)
+ np.log(1 - endog)
)
* bounded_reg_link.dphideta(estimated_precision),
)
return correct_dimension(score_precision)
def score_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision
):
"""
Return minus score vector of the beta regression model.
For more details we refer to:
Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression
for modeling rates and proportions. J. Appl. Statist. 31, 799–815
Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved
estimators for a general class of beta regression models. Computational
Statistics and Data Analysis, 54, 348–366
:param endog (array_like): 1d array of endogenous response variable.
:param exog_mean (array_like): A nobs x k array where nobs is the number
of observations and k is the number of mean regressors. An intercept is
not included by default and should be added by the user.
:param exog_precision (array_like): A nobs x q array where nobs is the
number of observations and q is the number of precision regressors.
An intercept is not included by default and should be added by the user.
:param bounded_reg_link: An instance of BoundedRegLink. Recall that
the default mean link is 'logit' and that the default precision link
is None.
:param param_mean: 1d array of mean regression parameters.
:param param_precision: 1d array of precision regression parameters.
:param previous_precision: 1d array of the regression parameters
related to the precision in the previous EM-step.
"""
score_mean = score_mean_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision,
)
score_precision = score_precision_beta(
endog,
exog_mean,
exog_precision,
bounded_reg_link,
param_mean,
param_precision,
)
score = np.concatenate([-score_mean, -score_precision])
return np.ndarray.flatten(score)
def maximize_loglikelihood_beta(
param_mean_start,
param_precision_start,
endog,
exog_mean,
bounded_reg_link,
method,
exog_precision=None,
**kwargs,
):
"""
Maximize the loglikelihood function.
For more details we refer to:
Ferrari, S. L. P., Cribari-Neto, F. (2004). Beta regression
for modeling rates and proportions. J. Appl. Statist. 31, 799–815
Simas, A. B., Barreto-Souza, W., Rocha, A. V. (2010). Improved
estimators for a general class of beta regression models. Computational
Statistics and Data Analysis, 54, 348–366
:param param_mean_start (array_like): 1d array of initial guesses for
the mean regression parameters.
:param param_precision_start (array_like): 1d array of initial guesses
for the precision regression parameters.
:param endog (array_like): 1d array of endogenous response variable.
:param exog_mean (array_like): A nobs x k array where nobs is the number
of observations and k is the number of mean regressors. An intercept is
not included by default and should be added by the user.
:param exog_precision (array_like): A nobs x q array where nobs is the
number of observations and q is the number of precision regressors.
An intercept is not included by default and should be added by the user.
:param bounded_reg_link: An instance of BoundedRegLink. Recall that
the default mean link is 'logit' and that the default precision link
is None.
:param em_optim_params (dict): A dictionary of parameters related
to optimization:
em_tolerance: the error tolerance for convergence in the EM procedure.
max_em_iterations: the maximum number of iterations in the EM
procedure.
**kwargs: additional parameters to be passed to the minimize function
from the scipy.optimize module.
"""
k = exog_mean.shape[1]
if exog_precision is None:
q = 1
else:
q = exog_precision.shape[1]
param_start = np.concatenate([param_mean_start, param_precision_start])
fit = minimize(
lambda x: loglikelihood_function_beta(
endog = endog,
exog_mean = exog_mean,
exog_precision=exog_precision,
bounded_reg_link=bounded_reg_link,
param_mean=x[:k],
param_precision=x[k : (k + q)],
),
param_start,
jac=lambda x: score_beta(
endog = endog,
exog_mean = exog_mean,
exog_precision=exog_precision,
bounded_reg_link=bounded_reg_link,
param_mean=x[:k],
param_precision=x[k : (k + q)],
),
method=method,
**kwargs,
)
return fit
| 34.549849
| 78
| 0.676898
| 1,549
| 11,436
| 4.855391
| 0.111039
| 0.041484
| 0.044675
| 0.03457
| 0.807074
| 0.796171
| 0.769711
| 0.759872
| 0.752294
| 0.752294
| 0
| 0.017741
| 0.260668
| 11,436
| 331
| 79
| 34.549849
| 0.870609
| 0.574939
| 0
| 0.527778
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 1
| 0.034722
| false
| 0
| 0.027778
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| 0.097222
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| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
10a078e519f747fbbd3eabaf38b2fac0078acce6
| 117
|
py
|
Python
|
3GPP Meeting Helper/config/tdoc_regex_matching.py
|
telekom/3gpp-meeting-tools
|
1276a62835fd595487aa817c9500c42c3f5e35f3
|
[
"MIT"
] | null | null | null |
3GPP Meeting Helper/config/tdoc_regex_matching.py
|
telekom/3gpp-meeting-tools
|
1276a62835fd595487aa817c9500c42c3f5e35f3
|
[
"MIT"
] | 1
|
2020-09-04T06:26:41.000Z
|
2020-09-04T06:26:41.000Z
|
3GPP Meeting Helper/config/tdoc_regex_matching.py
|
telekom/3gpp-meeting-tools
|
1276a62835fd595487aa817c9500c42c3f5e35f3
|
[
"MIT"
] | 3
|
2020-06-12T02:09:48.000Z
|
2021-08-30T10:36:37.000Z
|
import re
tdoc_regex = re.compile(r'(?P<group>[S\d]*)-(?P<year>\d\d)(?P<tdoc_number>[\d]+)(?P<revision>r[\d][\d])?')
| 39
| 106
| 0.581197
| 24
| 117
| 2.75
| 0.541667
| 0.090909
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.042735
| 117
| 3
| 106
| 39
| 0.589286
| 0
| 0
| 0
| 0
| 0.5
| 0.661017
| 0.661017
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
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| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
10a370cdcf9a97d6b9894b4b082e30d25e91233b
| 9,923
|
py
|
Python
|
NeoML/Python/neoml/Dnn/Concat.py
|
ndrewl/neoml
|
c87361fa8489c28a672cb8e1a447f47ba4c1dbc5
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
NeoML/Python/neoml/Dnn/Concat.py
|
ndrewl/neoml
|
c87361fa8489c28a672cb8e1a447f47ba4c1dbc5
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
NeoML/Python/neoml/Dnn/Concat.py
|
ndrewl/neoml
|
c87361fa8489c28a672cb8e1a447f47ba4c1dbc5
|
[
"ECL-2.0",
"Apache-2.0"
] | null | null | null |
""" Copyright (c) 2017-2020 ABBYY Production LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--------------------------------------------------------------------------------------------------------------*/
"""
import neoml.PythonWrapper as PythonWrapper
from .Dnn import Layer
from neoml.Utils import check_input_layers
class ConcatChannels(Layer):
"""The layer that concatenates several blobs into one
along the Channels dimension.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Width, Depth equal for all inputs
- Channels dimension may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Width, Depth equal to the inputs' dimensions
- Channels equal to the sum of all inputs' Channels
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatChannels:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `ConcatChannels` can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatChannels(str(name), layers, outputs)
super().__init__(internal)
# ----------------------------------------------------------------------------------------------------------------------
class ConcatDepth(Layer):
"""The layer that concatenates several blobs into one
along the Depth dimension.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Width, Channels equal for all inputs
- Depth dimension may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Width, Channels equal to the inputs' dimensions
- Depth equal to the sum of all inputs' Depth
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatDepth:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `ConcatDepth can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatDepth(str(name), layers, outputs)
super().__init__(internal)
# ----------------------------------------------------------------------------------------------------------------------
class ConcatWidth(Layer):
"""The layer that concatenates several blobs into one
along the Width dimension.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Depth, Channels equal for all inputs
- Width dimension may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Height, Depth, Channels equal to the inputs' dimensions
- Width equal to the sum of all inputs' Width
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatWidth:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `ConcatWidth can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatWidth(str(name), layers, outputs)
super().__init__(internal)
# ----------------------------------------------------------------------------------------------------------------------
class ConcatHeight(Layer):
"""The layer that concatenates several blobs into one
along the Height dimension.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Width, Depth, Channels equal for all inputs
- Height dimension may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, BatchWidth, ListSize,
Width, Depth, Channels equal to the inputs' dimensions
- Height equal to the sum of all inputs' Height
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatHeight:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `ConcatHeight can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatHeight(str(name), layers, outputs)
super().__init__(internal)
# ----------------------------------------------------------------------------------------------------------------------
class ConcatBatchWidth(Layer):
"""The layer that concatenates several blobs into one
along the BatchWidth dimension.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, ListSize, Height,
Width, Depth, Channels equal for all inputs
- BatchWidth dimension may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, ListSize, Height,
Width, Depth, Channels equal to the inputs' dimensions
- BatchWidth equal to the sum of all inputs' BatchWidth
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatBatchWidth:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `BatchWidth can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatBatchWidth(str(name), layers, outputs)
super().__init__(internal)
# ----------------------------------------------------------------------------------------------------------------------
class ConcatObject(Layer):
"""The layer that concatenates several blobs into one
along the Height, Width, Depth, and Channels dimensions.
Layer inputs
----------
The layer accepts an arbitrary number of inputs.
The dimensions:
- BatchLength, BatchWidth, ListSize equal for all inputs
- Height, Width, Depth, Channels dimensions may vary
Layer outputs
----------
#1: a blob with the result of concatenation.
The dimensions:
- BatchLength, BatchWidth, ListSize equal to the inputs' dimensions
- Height, Width, Depth equal to 1
- Channels equal to the sum of
Height * Width * Depth * Channels over all inputs
Parameters
----------
input_layers : array of (object, int) tuples and objects
The input layers to be connected.
The integer in each tuple specifies the number of the output.
If not set, the first output will be used.
name : str, default=None
The layer name.
"""
def __init__(self, input_layers, name=None):
if type(input_layers) is PythonWrapper.ConcatObject:
super().__init__(input_layers)
return
if len(input_layers) > 32:
raise ValueError('The `ConcatObject can merge no more than 32 blobs.')
layers, outputs = check_input_layers(input_layers, 0)
internal = PythonWrapper.ConcatObject(str(name), layers, outputs)
super().__init__(internal)
| 34.454861
| 120
| 0.607679
| 1,134
| 9,923
| 5.209877
| 0.133157
| 0.091232
| 0.048747
| 0.057549
| 0.819397
| 0.806872
| 0.788761
| 0.75457
| 0.711916
| 0.677725
| 0
| 0.00655
| 0.246095
| 9,923
| 287
| 121
| 34.574913
| 0.783184
| 0.615338
| 0
| 0.571429
| 0
| 0
| 0.095803
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.095238
| false
| 0
| 0.047619
| 0
| 0.333333
| 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
|
10b32f03bdcdb48a6ca476dbdcd58f93f3ea5284
| 44
|
py
|
Python
|
tests/tests_unit/test_api/bad_function_code2/handler.py
|
AlexThunder/cognite-sdk-python-experimental
|
468d29e7809793ed45cef5da25dca22418839972
|
[
"Apache-2.0"
] | null | null | null |
tests/tests_unit/test_api/bad_function_code2/handler.py
|
AlexThunder/cognite-sdk-python-experimental
|
468d29e7809793ed45cef5da25dca22418839972
|
[
"Apache-2.0"
] | null | null | null |
tests/tests_unit/test_api/bad_function_code2/handler.py
|
AlexThunder/cognite-sdk-python-experimental
|
468d29e7809793ed45cef5da25dca22418839972
|
[
"Apache-2.0"
] | null | null | null |
def xyz(data):
return {"assetId": 1234}
| 14.666667
| 28
| 0.613636
| 6
| 44
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 0.204545
| 44
| 2
| 29
| 22
| 0.657143
| 0
| 0
| 0
| 0
| 0
| 0.159091
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
52ca1cfec266dd9d40dd15daa3862025a7bbf535
| 24
|
py
|
Python
|
eddl/utils/__init__.py
|
salvacarrion/pyeddl
|
56d1e4378844d12c064f168f4541900684079c4b
|
[
"MIT"
] | null | null | null |
eddl/utils/__init__.py
|
salvacarrion/pyeddl
|
56d1e4378844d12c064f168f4541900684079c4b
|
[
"MIT"
] | null | null | null |
eddl/utils/__init__.py
|
salvacarrion/pyeddl
|
56d1e4378844d12c064f168f4541900684079c4b
|
[
"MIT"
] | null | null | null |
from .np_utils import *
| 12
| 23
| 0.75
| 4
| 24
| 4.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 1
| 24
| 24
| 0.85
| 0
| 0
| 0
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| true
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| null | 0
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| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5e03b3dc8eaaad57f2b1e76df934ee3182b1484e
| 327
|
py
|
Python
|
tetrominos_handler/__init__.py
|
RahulShagri/OG-Tetris-Game
|
0a746f5a10e9278ef42d579c394d4b684ec9cc5a
|
[
"MIT"
] | 8
|
2021-08-06T16:29:22.000Z
|
2021-12-12T16:23:32.000Z
|
tetrominos_handler/__init__.py
|
RahulShagri/OG-Tetris-Game
|
0a746f5a10e9278ef42d579c394d4b684ec9cc5a
|
[
"MIT"
] | 1
|
2021-10-31T16:41:22.000Z
|
2021-11-07T18:53:37.000Z
|
tetrominos_handler/__init__.py
|
RahulShagri/OG-Tetris-Game
|
0a746f5a10e9278ef42d579c394d4b684ec9cc5a
|
[
"MIT"
] | 1
|
2021-08-09T09:41:18.000Z
|
2021-08-09T09:41:18.000Z
|
from tetrominos_handler.tetrominosAPI import *
from tetrominos_handler.IBlock import *
from tetrominos_handler.JBlock import *
from tetrominos_handler.LBlock import *
from tetrominos_handler.OBlock import *
from tetrominos_handler.SBlock import *
from tetrominos_handler.TBlock import *
from tetrominos_handler.ZBlock import *
| 36.333333
| 46
| 0.853211
| 40
| 327
| 6.775
| 0.3
| 0.413284
| 0.619926
| 0.697417
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.097859
| 327
| 8
| 47
| 40.875
| 0.918644
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
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| 0
| 0
| 1
| 0
| true
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| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
5e0d0c7b44c3578b1694d0b848a2a384ab2a1042
| 13,858
|
py
|
Python
|
surv_ci_info/survci_info_api.py
|
muskang48/SurvCI
|
8bd778f288ebb9664e61b354bbf07b2a23d1e952
|
[
"MIT"
] | 1
|
2022-03-08T09:42:35.000Z
|
2022-03-08T09:42:35.000Z
|
surv_ci_info/survci_info_api.py
|
muskang48/SurvCI
|
8bd778f288ebb9664e61b354bbf07b2a23d1e952
|
[
"MIT"
] | null | null | null |
surv_ci_info/survci_info_api.py
|
muskang48/SurvCI
|
8bd778f288ebb9664e61b354bbf07b2a23d1e952
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""csa_api.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1OfXVnydZA5UonDed1gTcBbFmpMLW4YTY
"""
from surv_ci_info.train_utils import train_survci_info
from surv_ci_info.train_utils import _get_padded_features, _get_padded_targets
from surv_ci_info.train_utils import _reshape_tensor_with_nans
from surv_ci_info.utilities import get_parameters, softmax_out, sample_weibull, sample_lognormal,auc
from surv_ci_info.model import survci_info
from lifelines.utils import concordance_index
from surv_ci_info.losses import conditional_loss,unconditional_loss,mse_loss,imb_loss,l2_loss
import pdb
import torch
import numpy as np
np.random.seed(1234)
torch.manual_seed(seed=1234)
if torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
class survci_infoBase():
def __init__(self, k=3, layers=None, distribution="Weibull",
temp=1000., discount=1.0,imb_func='lin_disc',p_ipm=0.5,p_alpha=1e-2,p_beta =1e-4,p_gamma=1e-1,p_lamda=1e-2):
self.k = k
self.layers = layers
self.dist = distribution
self.temp = temp
self.discount = discount
self.fitted = False
self.imb_func = imb_func
self.p_ipm = p_ipm
self.p_alpha = p_alpha
self.p_beta = p_beta
self.p_gamma = p_gamma #change
self.p_lamda = p_lamda #change
def _gen_torch_model(self, inputdim, optimizer, num_treatments):
"""Helper function to return a torch model."""
return survci_info(inputdim,k=self.k, layers=self.layers,
dist=self.dist,
temp=self.temp,
discount=self.discount,
optimizer=optimizer,
num_treatments=2,imb_func=self.imb_func,p_ipm=self.p_ipm,p_alpha=self.p_alpha, p_beta = self.p_beta,p_gamma=self.p_gamma,p_lamda=self.p_lamda)
#def fit(self, x, t,c, e,w, vsize=0.15, val_data=None,
# iters=1, learning_rate=1e-3, batch_size=100,
# elbo=True, optimizer="Adam", random_state=1234):
def fit(self,x,y,e,w,vsize=0.5,val_data=None,
iters=1,learning_rate=1e-3,batch_size=100,
elbo=True,optimizer='Adam',random_state=1234):
#processed_data = self._prepocess_training_data(x, t, c,e,w,
#vsize, val_data,
#random_state)
#x_train, t_train, c_train, e_train,w_train, x_val, t_val, c_val,e_val,w_val = processed_data
processed_data = self._prepocess_training_data(x, y,e,w,
vsize, val_data,
random_state)
x_train ,y_train, e_train,w_train, x_val, y_val,e_val,w_val = processed_data
inputdim = x_train.shape[-1]
model = self._gen_torch_model(inputdim, optimizer, num_treatments=2)
# model, _ = train_survci_info(model,
# x_train, t_train,c_train, e_train,w_train,
# x_val, t_val, c_val, e_val,w_val,
# n_iter=iters,
# lr=learning_rate,
# elbo=elbo,
# bs=batch_size)
model,_ = train_survci_info(model,
x_train,y_train, e_train,w_train,
x_val, y_val, e_val,w_val,
n_iter=iters,
lr=learning_rate,
elbo=elbo,
bs=batch_size)
#pdb.set_trace()
self.torch_model = model.eval()
self.fitted = True
return self
#def compute_loss(self, x, t, c,e,w):
def compute_loss(self, x , y, e,w):
if not self.fitted:
raise Exception("The model has not been fitted yet. Please fit the " +
"model using the `fit` method on some training data " +
"before calling `_eval_nll`.")
processed_data = self._prepocess_training_data(x, y,e,w, 0, None, 0)
_, _,_, _,_, x_val, y_val, e_val,w_val = processed_data
x_val, y_val,e_val,w_val = x_val,\
_reshape_tensor_with_nans(y_val),\
_reshape_tensor_with_nans(e_val),\
_reshape_tensor_with_nans(w_val)
loss = 0
ll_loss = float(conditional_loss(self.torch_model,x_val, y_val, e_val,w_val, elbo=False))
imb = imb_loss(self.torch_model,x_val,w_val)
mse = mse_loss(self.torch_model,x_val,y_val,e_val,w_val)
l_f = factual_loss(self.torch_model,x_val,y_val,e_val,w_val)
l2 = l2_loss(self.torch_model) #change
processed_data = self._prepocess_training_data(x, y,e,w, 0, None, 0)
_, _,_, _,_, x_val, t_val,c_val, e_val,w_val = processed_data
# x_val, t_val, c_val,e_val,w_val = x_val,\
# _reshape_tensor_with_nans(t_val),\
# _reshape_tensor_with_nans(c_val),\
# _reshape_tensor_with_nans(e_val),\
# _reshape_tensor_with_nans(w_val)
# loss = 0
# ll_loss = float(conditional_loss(self.torch_model,x_val, t_val,c_val, e_val,w_val, elbo=False))
# imb = imb_loss(self.torch_model,x_val,w_val)
# mse = mse_loss(self.torch_model,x_val,t_val,c_val,w_val)
# l_f = factual_loss(self.torch_model,x_val,t_val,c_val,e_val,w_val)
## mse = mse_total(self.torch_model,x_val,t_val,e_val,w_val)
loss = self.torch_model.p_gamma*ll_loss + self.torch_model.p_alpha*imb + self.torch_model.p_beta*mse +self.torch_model.p_lamda*l2_loss
##loss = ll_loss+self.torch_model.p_alpha*imb + self.torch_model.p_beta*l_f
return loss.detach().numpy()
#def compute_mse_factual(self, x, t,c, e,w):
def compute_mse_factual(self, x, y, e,w):
if not self.fitted:
raise Exception("The model has not been fitted yet. Please fit the " +
"model using the `fit` method on some training data " +
"before calling `_eval_nll`.")
# processed_data = self._prepocess_training_data(x, t, c, e,w, 0, None, 0)
# _, _,_, _,_, x_val, t_val,c_val, e_val,w_val = processed_data
# x_val, t_val, c_val, e_val,w_val = x_val,\
# _reshape_tensor_with_nans(t_val),\
# _reshape_tensor_with_nans(c_val),\
# _reshape_tensor_with_nans(e_val),\
# _reshape_tensor_with_nans(w_val)
# #loss = 0
# #ll_loss = float(conditional_loss(self.torch_model,x_val, t_val, e_val,w_val, elbo=False))
# #imb = imb_loss(self.torch_model,x_val,w_val)
# mse = mse_loss(self.torch_model,x_val,t_val,c_val,w_val)
# # mse = mse_total(self.torch_model,x_val,t_val,e_val,w_val)
# #loss = ll_loss + self.torch_model.p_alpha*imb + self.torch_model.p_beta*mse
processed_data = self._prepocess_training_data(x, y, e,w, 0, None, 0)
_, _,_, _, x_val, y_val, e_val,w_val = processed_data
x_val, y_val, e_val,w_val = x_val,\
_reshape_tensor_with_nans(y_val),\
_reshape_tensor_with_nans(e_val),\
_reshape_tensor_with_nans(w_val)
#loss = 0
#ll_loss = float(conditional_loss(self.torch_model,x_val, t_val, e_val,w_val, elbo=False))
#imb = imb_loss(self.torch_model,x_val,w_val)
mse = mse_loss(self.torch_model,x_val,y_val,e_val,w_val)
# mse = mse_total(self.torch_model,x_val,t_val,e_val,w_val)
#loss = ll_loss + self.torch_model.p_alpha*imb + self.torch_model.p_beta*mse
return mse.detach().numpy()
def _prepocess_test_data(self, x):
return torch.from_numpy(x)
# def _prepocess_training_data(self, x, t,c, e, w,vsize, val_data, random_state):
# idx = list(range(x.shape[0]))
# np.random.seed(random_state)
# np.random.shuffle(idx)
# x_train, t_train, c_train, e_train,w_train = x[idx], t[idx], c[idx] , e[idx], w[idx]
# x_train = torch.from_numpy(x_train).double()
# t_train = torch.from_numpy(t_train).double()
# c_train = torch.from_numpy(c_train).double()
# e_train = torch.from_numpy(e_train).double()
# w_train = torch.from_numpy(w_train).double()
# if val_data is None:
# vsize = int(vsize*x_train.shape[0])
# x_val, t_val, c_val, e_val,w_val = x_train[-vsize:], t_train[-vsize:], c_train[-vsize:],e_train[-vsize:], w_train[-vsize:]
# x_train = x_train[:-vsize]
# t_train = t_train[:-vsize]
# c_train = c_train[:-vsize]
# e_train = e_train[:-vsize]
# w_train = w_train[:-vsize]
# else:
# x_val, t_val, c_val, e_val,w_val = val_data
# x_val = torch.from_numpy(x_val).double()
# t_val = torch.from_numpy(t_val).double()
# c_val = torch.from_numpy(c_val).double()
# e_val = torch.from_numpy(e_val).double()
# w_val = torch.from_numpy(w_val).double()
# return (x_train, t_train,c_train, e_train,w_train,
# x_val, t_val, c_val, e_val,w_val)
def _prepocess_training_data(self, x, y, e, w,vsize, val_data, random_state):
idx = list(range(x.shape[0]))
np.random.seed(random_state)
np.random.shuffle(idx)
x_train, y_train, e_train,w_train = x[idx], y[idx] , e[idx], w[idx]
x_train = torch.from_numpy(x_train).double()
y_train = torch.from_numpy(y_train).double()
#c_train = torch.from_numpy(c_train).double()
e_train = torch.from_numpy(e_train).double()
w_train = torch.from_numpy(w_train).double()
if val_data is None:
vsize = int(vsize*x_train.shape[0])
x_val, y_val, e_val,w_val = x_train[-vsize:], y_train[-vsize:],e_train[-vsize:], w_train[-vsize:]
x_train = x_train[:-vsize]
y_train = y_train[:-vsize]
e_train = e_train[:-vsize]
w_train = w_train[:-vsize]
else:
x_val, y_val, e_val,w_val = val_data
x_val = torch.from_numpy(x_val).double()
y_val = torch.from_numpy(y_val).double()
e_val = torch.from_numpy(e_val).double()
w_val = torch.from_numpy(w_val).double()
return (x_train, y_train, e_train,w_train,
x_val, y_val, e_val,w_val)
def predict_dist_parameters(self,x,w):
x = self._prepocess_test_data(x)
if self.fitted:
return get_parameters(self.torch_model,x,w)
else:
raise Exception("The model has not been fitted yet. Please fit the " +
"model using the `fit` method on some training data " +
"before calling `predict_time`.")
# def compute_ci(self,x,t,c,e,w):
# processed_data = self._prepocess_training_data(x, t,c, e,w, 0, None, 0)
# _, _, _,_,_, x_val, t_val,c_val, e_val,w_val = processed_data
# x_val, t_val, c_val,e_val,w_val = x_val,\
# _reshape_tensor_with_nans(t_val),\
# _reshape_tensor_with_nans(c_val),\
# _reshape_tensor_with_nans(e_val),\
# _reshape_tensor_with_nans(w_val)
# if self.fitted:
# treated_idx = torch.where(w_val>0)[0]
# control_idx = torch.where(w_val<1)[0]
# shape_co,scale_co,logits_co, shape_tr,scale_tr, logits_tr,shape_co_c,scale_co_c,logits_co_c, shape_tr_c,scale_tr_c, logits_tr_c = get_parameters(self.torch_model,x_val,w_val) #Predicted Factual Parameters
# t_pred_f_co = auc(self.torch_model, t_val,shape_co,scale_co, logits_co )
# t_pred_f_tr = auc(self.torch_model,t_val,shape_tr,scale_tr, logits_tr)
# c_index_co = concordance_index(event_times=t_val[control_idx],predicted_scores=t_pred_f_co.detach().numpy(),event_observed=e_val[control_idx])
# c_index_tr = concordance_index(event_times=t_val[treated_idx],predicted_scores=t_pred_f_tr.detach().numpy(),event_observed=e_val[treated_idx])
# ci_index = (c_index_co + c_index_tr) * 0.5
# return ci_index
# else:
# raise Exception("The model has not been fitted yet. Please fit the " +
# "model using the `fit` method on some training data " +
# "before calling `predict_time`.")
def compute_ci(self,x,y,e,w):
processed_data = self._prepocess_training_data(x, y, e,w, 0, None, 0)
_, _, _,_, x_val, y_val, e_val,w_val = processed_data
x_val, y_val,e_val,w_val = x_val,\
_reshape_tensor_with_nans(y_val),\
_reshape_tensor_with_nans(e_val),\
_reshape_tensor_with_nans(w_val)
if self.fitted:
treated_idx = torch.where(w_val>0)[0]
control_idx = torch.where(w_val<1)[0]
shape_co,scale_co,logits_co, shape_tr,scale_tr, logits_tr,shape_co_c,scale_co_c,logits_co_c, shape_tr_c,scale_tr_c, logits_tr_c = get_parameters(self.torch_model,x_val,w_val) #Predicted Factual Parameters
t_pred_f_co = auc(self.torch_model, y_val,shape_co,scale_co, logits_co )
t_pred_f_tr = auc(self.torch_model, y_val, shape_tr,scale_tr,logits_tr)
c_pred_f_co = auc(self.torch_model, y_val, shape_co_c, scale_co_c, logits_co_c)
c_pred_f_tr = auc(self.torch_model, y_val,shape_tr_c, scale_tr_c, logits_tr_c)
y_pred_f_co = e_val[w_val==0]*t_pred_f_co + (1-e_val[w_val==0])*c_pred_f_co
y_pred_f_tr = e_val[w_val==1]*t_pred_f_tr + (1-e_val[w_val==1])*c_pred_f_tr
# y_pred_f_co = auc(self.torch_model, y_val,shape_co,scale_co, logits_co )
# y_pred_f_tr = auc(self.torch_model,y_val,shape_tr,scale_tr, logits_tr)
c_index_co = concordance_index(event_times=y_val[control_idx],predicted_scores=y_pred_f_co.detach().numpy(),event_observed=e_val[control_idx])
c_index_tr = concordance_index(event_times=y_val[treated_idx],predicted_scores=y_pred_f_tr.detach().numpy(),event_observed=e_val[treated_idx])
ci_index = (c_index_co + c_index_tr) * 0.5
return ci_index
else:
raise Exception("The model has not been fitted yet. Please fit the " +
"model using the `fit` method on some training data " +
"before calling `predict_time`.")
| 43.442006
| 215
| 0.652475
| 2,272
| 13,858
| 3.584507
| 0.084067
| 0.028978
| 0.038679
| 0.036346
| 0.787083
| 0.76805
| 0.741036
| 0.720653
| 0.713409
| 0.701498
| 0
| 0.009352
| 0.228388
| 13,858
| 319
| 216
| 43.442006
| 0.752268
| 0.38779
| 0
| 0.266667
| 1
| 0
| 0.065056
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.06
| false
| 0
| 0.066667
| 0.006667
| 0.186667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
5e330f7e1745717c98bc320386640a68edcb6ac6
| 1,039
|
py
|
Python
|
dae/dae/backends/tests/test_fixtures_query_by_variant_type.py
|
iossifovlab/gpf
|
e556243d29666179dbcb72859845b4d6c011af2b
|
[
"MIT"
] | null | null | null |
dae/dae/backends/tests/test_fixtures_query_by_variant_type.py
|
iossifovlab/gpf
|
e556243d29666179dbcb72859845b4d6c011af2b
|
[
"MIT"
] | 82
|
2019-07-22T11:44:23.000Z
|
2022-01-13T15:27:33.000Z
|
dae/dae/backends/tests/test_fixtures_query_by_variant_type.py
|
iossifovlab/gpf
|
e556243d29666179dbcb72859845b4d6c011af2b
|
[
"MIT"
] | null | null | null |
"""
Created on Jul 5, 2018
@author: lubo
"""
import pytest
@pytest.mark.parametrize("variants", ["variants_impala", "variants_vcf"])
@pytest.mark.parametrize(
"variant_type,count",
[(None, 10), ("sub", 9), ("del", 1), ("sub or del", 10), ],
)
def test_single_alt_allele_variant_types(
variants_impl, variants, variant_type, count
):
fvars = variants_impl(variants)("backends/effects_trio")
vs = list(fvars.query_variants(variant_type=variant_type,))
for v in vs:
print(v.variant_types)
assert len(vs) == count
@pytest.mark.parametrize("variants", ["variants_impala", "variants_vcf"])
@pytest.mark.parametrize(
"variant_type,count",
[(None, 3), ("sub", 3), ("del", 1), ("del or sub", 3)],
)
def test_multi_alt_allele_variant_types(
variants_impl, variants, variant_type, count
):
fvars = variants_impl(variants)("backends/effects_trio_multi")
vs = list(fvars.query_variants(variant_type=variant_type,))
for v in vs:
print(v.variant_types)
assert len(vs) == count
| 28.081081
| 73
| 0.681424
| 140
| 1,039
| 4.821429
| 0.328571
| 0.13037
| 0.124444
| 0.085926
| 0.841481
| 0.841481
| 0.841481
| 0.841481
| 0.841481
| 0.841481
| 0
| 0.017162
| 0.158807
| 1,039
| 36
| 74
| 28.861111
| 0.755149
| 0.035611
| 0
| 0.666667
| 0
| 0
| 0.187123
| 0.04829
| 0
| 0
| 0
| 0
| 0.074074
| 1
| 0.074074
| false
| 0
| 0.037037
| 0
| 0.111111
| 0.074074
| 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
|
eaaa3c4b68fba871db587db58a33ac52aa131d3f
| 32,957
|
py
|
Python
|
e3nn/o3/_tensor_product/_codegen.py
|
claycurry34/e3nn
|
3cfbf679d10781a01d9c83b04a2e7d79d4914c23
|
[
"MIT"
] | null | null | null |
e3nn/o3/_tensor_product/_codegen.py
|
claycurry34/e3nn
|
3cfbf679d10781a01d9c83b04a2e7d79d4914c23
|
[
"MIT"
] | null | null | null |
e3nn/o3/_tensor_product/_codegen.py
|
claycurry34/e3nn
|
3cfbf679d10781a01d9c83b04a2e7d79d4914c23
|
[
"MIT"
] | null | null | null |
from collections import OrderedDict
from math import sqrt
from typing import List
import torch
from e3nn import o3
from e3nn.util import prod
from opt_einsum_fx import optimize_einsums_full
from torch import fx
from ._instruction import Instruction
def _sum_tensors(xs: List[torch.Tensor], shape: torch.Size, like: torch.Tensor):
if len(xs) > 0:
out = xs[0]
for x in xs[1:]:
out = out + x
return out
return like.new_zeros(shape)
def codegen_tensor_product_left_right(
irreps_in1: o3.Irreps,
irreps_in2: o3.Irreps,
irreps_out: o3.Irreps,
instructions: List[Instruction],
shared_weights: bool = False,
specialized_code: bool = True,
optimize_einsums: bool = True,
) -> fx.GraphModule:
graph = fx.Graph()
# = Function definitions =
tracer = fx.proxy.GraphAppendingTracer(graph)
constants = OrderedDict()
x1s = fx.Proxy(graph.placeholder('x1', torch.Tensor), tracer=tracer)
x2s = fx.Proxy(graph.placeholder('x2', torch.Tensor), tracer=tracer)
weights = fx.Proxy(graph.placeholder('w', torch.Tensor), tracer=tracer)
empty = fx.Proxy(graph.call_function(torch.empty, ((),), dict(device='cpu')), tracer=tracer)
if shared_weights:
output_shape = torch.broadcast_tensors(empty.expand(x1s.shape[:-1]), empty.expand(x2s.shape[:-1]))[0].shape
else:
output_shape = torch.broadcast_tensors(empty.expand(x1s.shape[:-1]), empty.expand(x2s.shape[:-1]), empty.expand(weights.shape[:-1]))[0].shape
del empty
# = Short-circut for zero dimensional =
# We produce no code for empty instructions
instructions = [ins for ins in instructions if 0 not in ins.path_shape]
if len(instructions) == 0:
outputs = x1s.new_zeros(output_shape + (irreps_out.dim,))
graph.output(outputs.node, torch.Tensor)
# Short circut
return fx.GraphModule({}, graph, "tp_forward")
# = Broadcast inputs =
if shared_weights:
x1s, x2s = x1s.broadcast_to(output_shape + (-1,)), x2s.broadcast_to(output_shape + (-1,))
else:
x1s, x2s, weights = x1s.broadcast_to(output_shape + (-1,)), x2s.broadcast_to(output_shape + (-1,)), weights.broadcast_to(output_shape + (-1,))
output_shape = output_shape + (irreps_out.dim,)
x1s = x1s.reshape(-1, irreps_in1.dim)
x2s = x2s.reshape(-1, irreps_in2.dim)
batch_numel = x1s.shape[0]
# = Determine number of weights and reshape weights ==
weight_numel = sum(prod(ins.path_shape) for ins in instructions if ins.has_weight)
if weight_numel > 0:
weights = weights.reshape(-1, weight_numel)
del weight_numel
# = extract individual input irreps =
# If only one input irrep, can avoid creating a view
if len(irreps_in1) == 1:
x1_list = [x1s.reshape(batch_numel, irreps_in1[0].mul, irreps_in1[0].ir.dim)]
else:
x1_list = [
x1s[:, i].reshape(batch_numel, mul_ir.mul, mul_ir.ir.dim)
for i, mul_ir in zip(irreps_in1.slices(), irreps_in1)
]
x2_list = []
# If only one input irrep, can avoid creating a view
if len(irreps_in2) == 1:
x2_list.append(
x2s.reshape(batch_numel, irreps_in2[0].mul, irreps_in2[0].ir.dim)
)
else:
for i, mul_ir in zip(irreps_in2.slices(), irreps_in2):
x2_list.append(
x2s[:, i].reshape(batch_numel, mul_ir.mul, mul_ir.ir.dim)
)
# The einsum string index to prepend to the weights if the weights are not shared and have a batch dimension
z = '' if shared_weights else 'z'
# Cache of input irrep pairs whose outer products (xx) have already been computed
xx_dict = dict()
# Current index in the flat weight tensor
flat_weight_index = 0
outputs = []
for ins in instructions:
mul_ir_in1 = irreps_in1[ins.i_in1]
mul_ir_in2 = irreps_in2[ins.i_in2]
mul_ir_out = irreps_out[ins.i_out]
assert mul_ir_in1.ir.p * mul_ir_in2.ir.p == mul_ir_out.ir.p
assert abs(mul_ir_in1.ir.l - mul_ir_in2.ir.l) <= mul_ir_out.ir.l <= mul_ir_in1.ir.l + mul_ir_in2.ir.l
if mul_ir_in1.dim == 0 or mul_ir_in2.dim == 0 or mul_ir_out.dim == 0:
continue
x1 = x1_list[ins.i_in1]
x2 = x2_list[ins.i_in2]
assert ins.connection_mode in ['uvw', 'uvu', 'uvv', 'uuw', 'uuu', 'uvuv', 'uvu<v', 'u<vw']
if ins.has_weight:
# Extract the weight from the flattened weight tensor
w = weights[:, flat_weight_index:flat_weight_index + prod(ins.path_shape)].reshape((() if shared_weights else (-1,)) + tuple(ins.path_shape))
flat_weight_index += prod(ins.path_shape)
# Construct the general xx in case this instruction isn't specialized
# If this isn't used, the dead code will get removed
key = (ins.i_in1, ins.i_in2, ins.connection_mode[:2])
if key not in xx_dict:
if ins.connection_mode[:2] == 'uu':
xx_dict[key] = torch.einsum('zui,zuj->zuij', x1, x2)
else:
xx_dict[key] = torch.einsum('zui,zvj->zuvij', x1, x2)
xx = xx_dict[key]
del key
# Create a proxy & request for the relevant wigner w3j
# If not used (because of specialized code), will get removed later.
w3j_name = f"_w3j_{mul_ir_in1.ir.l}_{mul_ir_in2.ir.l}_{mul_ir_out.ir.l}"
w3j = fx.Proxy(graph.get_attr(w3j_name), tracer=tracer)
l1l2l3 = (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l)
if ins.connection_mode == 'uvw':
assert ins.has_weight
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum(f"{z}uvw,zu,zv->zw", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uvw,zu,zvj->zwj", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uvw,zui,zv->zwi", w, x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uvw,zui,zvi->zw", w, x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uvw,ijk,zuvij->zwk", w, w3j, xx)
if ins.connection_mode == 'uvu':
assert mul_ir_in1.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum(f"{z}uv,zu,zv->zu", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uv,zu,zvj->zuj", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uv,zui,zv->zui", w, x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uv,zui,zvi->zu", w, x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uv,ijk,zuvij->zuk", w, w3j, xx)
else:
# not so useful operation because v is summed
result = torch.einsum("ijk,zuvij->zuk", w3j, xx)
if ins.connection_mode == 'uvv':
assert mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum(f"{z}uv,zu,zv->zv", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uv,zu,zvj->zvj", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uv,zui,zv->zvi", w, x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uv,zui,zvi->zv", w, x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uv,ijk,zuvij->zvk", w, w3j, xx)
else:
# not so useful operation because u is summed
# only specialize out for this path
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum("zu,zv->zv", x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum("zu,zvj->zvj", x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum("zui,zv->zvi", x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum("zui,zvi->zv", x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum("ijk,zuvij->zvk", w3j, xx)
if ins.connection_mode == 'uuw':
assert mul_ir_in1.mul == mul_ir_in2.mul
if ins.has_weight:
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum(f"{z}uw,zu,zu->zw", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uw,zu,zuj->zwj", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uw,zui,zu->zwi", w, x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uw,zui,zui->zw", w, x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uw,ijk,zuij->zwk", w, w3j, xx)
else:
# equivalent to tp(x, y, 'uuu').sum('u')
assert mul_ir_out.mul == 1
result = torch.einsum("ijk,zuij->zk", w3j, xx)
if ins.connection_mode == 'uuu':
assert mul_ir_in1.mul == mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum(f"{z}u,zu,zu->zu", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and l1l2l3 == (1, 1, 1):
result = torch.einsum(
f"{z}u,zui->zui",
w,
torch.cross(x1, x2, dim=2)
) / sqrt(2*3)
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}u,zu,zuj->zuj", w, x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}u,zui,zu->zui", w, x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}u,zui,zui->zu", w, x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}u,ijk,zuij->zuk", w, w3j, xx)
else:
if specialized_code and l1l2l3 == (0, 0, 0):
result = torch.einsum("zu,zu->zu", x1.reshape(batch_numel, mul_ir_in1.dim), x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and l1l2l3 == (1, 1, 1):
result = torch.cross(x1, x2, dim=2) * (1.0 / sqrt(2*3))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum("zu,zuj->zuj", x1.reshape(batch_numel, mul_ir_in1.dim), x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum("zui,zu->zui", x1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum("zui,zui->zu", x1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum("ijk,zuij->zuk", w3j, xx)
if ins.connection_mode == 'uvuv':
assert mul_ir_in1.mul * mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
# TODO implement specialized code
result = torch.einsum(f"{z}uv,ijk,zuvij->zuvk", w, w3j, xx)
else:
# TODO implement specialized code
result = torch.einsum("ijk,zuvij->zuvk", w3j, xx)
if ins.connection_mode == 'uvu<v':
assert mul_ir_in1.mul == mul_ir_in2.mul
assert mul_ir_in1.mul * (mul_ir_in1.mul - 1) // 2 == mul_ir_out.mul
name = f"_triu_indices_{mul_ir_in1.mul}"
constants[name] = torch.triu_indices(mul_ir_in1.mul, mul_ir_in1.mul, 1)
i = fx.Proxy(graph.get_attr(name), tracer=tracer)
xx = xx[:, i[0], i[1]] # zuvij -> zwij
if ins.has_weight:
# TODO implement specialized code
result = torch.einsum(f"{z}w,ijk,zwij->zwk", w, w3j, xx)
else:
# TODO implement specialized code
result = torch.einsum("ijk,zwij->zwk", w3j, xx)
if ins.connection_mode == 'u<vw':
assert mul_ir_in1.mul == mul_ir_in2.mul
assert ins.has_weight
name = f"_triu_indices_{mul_ir_in1.mul}"
constants[name] = torch.triu_indices(mul_ir_in1.mul, mul_ir_in1.mul, 1)
i = fx.Proxy(graph.get_attr(name), tracer=tracer)
xx = xx[:, i[0], i[1]] # zuvij -> zqij
# TODO implement specialized code
result = torch.einsum(f"{z}qw,ijk,zqij->zwk", w, w3j, xx)
result = ins.path_weight * result
outputs += [result.reshape(batch_numel, mul_ir_out.dim)]
# Remove unused w3js:
if len(w3j.node.users) == 0:
# The w3j nodes are reshapes, so we have to remove them from the graph
# Although they are dead code, they try to reshape to dimensions that don't exist
# (since the corresponding w3js are not in w3j)
# so they screw up the shape propagation, even though they would be removed later as dead code by TorchScript.
graph.erase_node(w3j.node)
else:
if w3j_name not in constants:
constants[w3j_name] = o3.wigner_3j(mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l)
# = Return the result =
outputs = [
_sum_tensors(
[out for ins, out in zip(instructions, outputs) if ins.i_out == i_out],
shape=(batch_numel, mul_ir_out.dim),
like=x1s
)
for i_out, mul_ir_out in enumerate(irreps_out)
if mul_ir_out.mul > 0
]
if len(outputs) > 1:
outputs = torch.cat(outputs, dim=1)
else:
# Avoid an unnecessary copy in a size one torch.cat
outputs = outputs[0]
outputs = outputs.reshape(output_shape)
graph.output(outputs.node, torch.Tensor)
# check graphs
graph.lint()
# Make GraphModules
# By putting the constants in a Module rather than a dict,
# we force FX to copy them as buffers instead of as attributes.
#
# FX seems to have resolved this issue for dicts in 1.9, but we support all the way back to 1.8.0.
constants_root = torch.nn.Module()
for key, value in constants.items():
constants_root.register_buffer(key, value)
graphmod = fx.GraphModule(constants_root, graph, class_name="tp_forward")
# == Optimize ==
# TODO: when eliminate_dead_code() is in PyTorch stable, use that
if optimize_einsums:
# Note that for our einsums, we can optimize _once_ for _any_ batch dimension
# and still get the right path for _all_ batch dimensions.
# This is because our einsums are essentially of the form:
# zuvw,ijk,zuvij->zwk OR uvw,ijk,zuvij->zwk
# In the first case, all but one operands have the batch dimension
# => The first contraction gains the batch dimension
# => All following contractions have batch dimension
# => All possible contraction paths have cost that scales linearly in batch size
# => The optimal path is the same for all batch sizes
# For the second case, this logic follows as long as the first contraction is not between the first two operands. Since those two operands do not share any indexes, contracting them first is a rare pathological case. See
# https://github.com/dgasmith/opt_einsum/issues/158
# for more details.
#
# TODO: consider the impact maximum intermediate result size on this logic
# \- this is the `memory_limit` option in opt_einsum
# TODO: allow user to choose opt_einsum parameters?
#
# We use float32 and zeros to save memory and time, since opt_einsum_fx looks only at traced shapes, not values or dtypes.
batchdim = 4
example_inputs = (
torch.zeros((batchdim, irreps_in1.dim)),
torch.zeros((batchdim, irreps_in2.dim)),
torch.zeros(
1 if shared_weights else batchdim,
flat_weight_index,
),
)
graphmod = optimize_einsums_full(graphmod, example_inputs)
return graphmod
def codegen_tensor_product_right(
irreps_in1: o3.Irreps,
irreps_in2: o3.Irreps,
irreps_out: o3.Irreps,
instructions: List[Instruction],
shared_weights: bool = False,
specialized_code: bool = True,
optimize_einsums: bool = True,
) -> fx.GraphModule:
graph = fx.Graph()
# = Function definitions =
tracer = fx.proxy.GraphAppendingTracer(graph)
constants = OrderedDict()
x2s = fx.Proxy(graph.placeholder('x2', torch.Tensor), tracer=tracer)
weights = fx.Proxy(graph.placeholder('w', torch.Tensor), tracer=tracer)
empty = fx.Proxy(graph.call_function(torch.empty, ((),), dict(device='cpu')), tracer=tracer)
if shared_weights:
output_shape = x2s.shape[:-1]
else:
output_shape = torch.broadcast_tensors(empty.expand(x2s.shape[:-1]), empty.expand(weights.shape[:-1]))[0].shape
del empty
# = Short-circut for zero dimensional =
# We produce no code for empty instructions
instructions = [ins for ins in instructions if 0 not in ins.path_shape]
if len(instructions) == 0:
outputs = x2s.new_zeros(output_shape + (irreps_in1.dim, irreps_out.dim,))
graph.output(outputs.node, torch.Tensor)
# Short circut
return fx.GraphModule({}, graph, "tp_right")
# = Broadcast inputs =
if not shared_weights:
x2s, weights = x2s.broadcast_to(output_shape + (-1,)), weights.broadcast_to(output_shape + (-1,))
output_shape = output_shape + (irreps_in1.dim, irreps_out.dim,)
x2s = x2s.reshape(-1, irreps_in2.dim)
batch_numel = x2s.shape[0]
# = Determine number of weights and reshape weights ==
weight_numel = sum(prod(ins.path_shape) for ins in instructions if ins.has_weight)
if weight_numel > 0:
weights = weights.reshape(-1, weight_numel)
del weight_numel
# = book-keeping for wigners =
# = extract individual input irreps =
# If only one input irrep, can avoid creating a view
x2_list = []
# If only one input irrep, can avoid creating a view
if len(irreps_in2) == 1:
x2_list.append(
x2s.reshape(batch_numel, irreps_in2[0].mul, irreps_in2[0].ir.dim)
)
else:
for i, mul_ir in zip(irreps_in2.slices(), irreps_in2):
x2_list.append(
x2s[:, i].reshape(batch_numel, mul_ir.mul, mul_ir.ir.dim)
)
# The einsum string index to prepend to the weights if the weights are not shared and have a batch dimension
z = '' if shared_weights else 'z'
# Current index in the flat weight tensor
flat_weight_index = 0
outputs = []
for ins in instructions:
mul_ir_in1 = irreps_in1[ins.i_in1]
mul_ir_in2 = irreps_in2[ins.i_in2]
mul_ir_out = irreps_out[ins.i_out]
assert mul_ir_in1.ir.p * mul_ir_in2.ir.p == mul_ir_out.ir.p
assert abs(mul_ir_in1.ir.l - mul_ir_in2.ir.l) <= mul_ir_out.ir.l <= mul_ir_in1.ir.l + mul_ir_in2.ir.l
if mul_ir_in1.dim == 0 or mul_ir_in2.dim == 0 or mul_ir_out.dim == 0:
continue
x2 = x2_list[ins.i_in2]
e1 = fx.Proxy(graph.call_function(torch.eye, (mul_ir_in1.mul,), dict(dtype=x2s.dtype.node, device=x2s.device.node)), tracer=tracer)
e2 = fx.Proxy(graph.call_function(torch.eye, (mul_ir_in2.mul,), dict(dtype=x2s.dtype.node, device=x2s.device.node)), tracer=tracer)
i1 = fx.Proxy(graph.call_function(torch.eye, (mul_ir_in1.ir.dim,), dict(dtype=x2s.dtype.node, device=x2s.device.node)), tracer=tracer)
assert ins.connection_mode in ['uvw', 'uvu', 'uvv', 'uuw', 'uuu', 'uvuv', 'uvu<v', 'u<vw']
if ins.has_weight:
# Extract the weight from the flattened weight tensor
w = weights[:, flat_weight_index:flat_weight_index + prod(ins.path_shape)].reshape((() if shared_weights else (-1,)) + tuple(ins.path_shape))
flat_weight_index += prod(ins.path_shape)
# Create a proxy & request for the relevant wigner w3j
# If not used (because of specialized code), will get removed later.
w3j_name = f"_w3j_{mul_ir_in1.ir.l}_{mul_ir_in2.ir.l}_{mul_ir_out.ir.l}"
w3j = fx.Proxy(graph.get_attr(w3j_name), tracer=tracer)
if ins.connection_mode == 'uvw':
assert ins.has_weight
if specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (0, 0, 0):
result = torch.einsum(f"{z}uvw,zv->zuw", w, x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uvw,zvi->zuwi", w, x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uvw,ij,zv->zuiwj", w, i1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uvw,zvi->zuiw", w, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uvw,ijk,zvj->zuiwk", w, w3j, x2)
if ins.connection_mode == 'uvu':
assert mul_ir_in1.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (0, 0, 0):
result = torch.einsum(f"{z}uv,uw,zv->zuw", w, e1, x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uv,uw,zvi->zuwi", w, e1, x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uv,ij,uw,zv->zuiwj", w, i1, e1, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uv,uw,zvi->zuiw", w, e1, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uv,ijk,uw,zvj->zuiwk", w, w3j, e1, x2)
else:
# not so useful operation because v is summed
result = torch.einsum("ijk,uw,zvj->zuiwk", w3j, e1, x2)
if ins.connection_mode == 'uvv':
assert mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (0, 0, 0):
result = torch.einsum(f"{z}uv,vw,zv->zuw", w, e2, x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}uv,vw,zvi->zuwi", w, e2, x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}uv,ij,vw,zv->zuiwj", w, i1, e2, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}uv,vw,zvi->zuiw", w, e2, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}uv,ijk,zvj->zuivk", w, w3j, x2)
else:
# not so useful operation because u is summed
# only specialize out for this path
s2ones = fx.Proxy(graph.call_function(torch.ones, (mul_ir_in1.mul,), dict(device=x2.device.node, dtype=x2.dtype.node)), tracer=tracer)
result = torch.einsum("u,ijk,zvj->zuivk", s2ones, w3j, x2)
if ins.connection_mode == 'uuw':
assert mul_ir_in1.mul == mul_ir_in2.mul
if ins.has_weight:
# TODO: specialize right()
result = torch.einsum(f"{z}uw,ijk,zuj->zuiwk", w, w3j, x2)
else:
# equivalent to tp(x, y, 'uuu').sum('u')
assert mul_ir_out.mul == 1
result = torch.einsum("ijk,zuj->zuik", w3j, x2)
if ins.connection_mode == 'uuu':
assert mul_ir_in1.mul == mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
if specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (0, 0, 0):
result = torch.einsum(f"{z}u,uw,zu->zuw", w, e2, x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (1, 1, 1):
# For cross product, use the general case right()
result = torch.einsum(f"{z}u,ijk,uw,zuj->zuiwk", w, w3j, e1, x2)
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum(f"{z}u,uw,zui->zuwi", w, e2, x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum(f"{z}u,ij,uw,zu->zuiwj", w, i1, e2, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum(f"{z}u,uw,zui->zuiw", w, e2, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum(f"{z}u,ijk,uw,zuj->zuiwk", w, w3j, e1, x2)
else:
if specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (0, 0, 0):
result = torch.einsum("uw,zu->zuw", e2, x2.reshape(batch_numel, mul_ir_in2.dim))
elif specialized_code and (mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l) == (1, 1, 1):
# For cross product, use the general case right()
result = torch.einsum("ijk,uw,zuj->zuiwk", w3j, e1, x2)
elif specialized_code and mul_ir_in1.ir.l == 0:
result = torch.einsum("uw,zui->zuwi", e2, x2) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_in2.ir.l == 0:
result = torch.einsum("ij,uw,zu->zuiwj", i1, e2, x2.reshape(batch_numel, mul_ir_in2.dim)) / sqrt(mul_ir_out.ir.dim)
elif specialized_code and mul_ir_out.ir.l == 0:
result = torch.einsum("uw,zui->zuiw", e2, x2) / sqrt(mul_ir_in1.ir.dim)
else:
result = torch.einsum("ijk,uw,zuj->zuiwk", w3j, e1, x2)
if ins.connection_mode == 'uvuv':
assert mul_ir_in1.mul * mul_ir_in2.mul == mul_ir_out.mul
if ins.has_weight:
# TODO implement specialized code
result = torch.einsum(f"{z}uv,ijk,uw,zvj->zuiwvk", w, w3j, e1, x2)
else:
# TODO implement specialized code
result = torch.einsum("ijk,uw,zvj->zuiwvk", w3j, e1, x2)
if ins.connection_mode == 'uvu<v':
raise NotImplementedError
if ins.connection_mode == 'u<vw':
raise NotImplementedError
result = ins.path_weight * result
outputs += [result.reshape(batch_numel, mul_ir_in1.dim, mul_ir_out.dim)]
# Remove unused w3js:
if len(w3j.node.users) == 0:
# The w3j nodes are reshapes, so we have to remove them from the graph
# Although they are dead code, they try to reshape to dimensions that don't exist
# (since the corresponding w3js are not in w3j)
# so they screw up the shape propagation, even though they would be removed later as dead code by TorchScript.
graph.erase_node(w3j.node)
else:
if w3j_name not in constants:
constants[w3j_name] = o3.wigner_3j(mul_ir_in1.ir.l, mul_ir_in2.ir.l, mul_ir_out.ir.l)
# = Return the result =
outputs = [
torch.cat([
_sum_tensors(
[out for ins, out in zip(instructions, outputs) if (ins.i_in1, ins.i_out) == (i_in1, i_out)],
shape=(batch_numel, mul_ir_in1.dim, mul_ir_out.dim),
like=x2s
)
for i_out, mul_ir_out in enumerate(irreps_out)
if mul_ir_out.mul > 0
], dim=2)
for i_in1, mul_ir_in1 in enumerate(irreps_in1)
if mul_ir_in1.mul > 0
]
if len(outputs) > 1:
outputs = torch.cat(outputs, dim=1)
else:
outputs = outputs[0]
outputs = outputs.reshape(output_shape)
graph.output(outputs.node, torch.Tensor)
# check graphs
graph.lint()
# Make GraphModules
# By putting the constants in a Module rather than a dict,
# we force FX to copy them as buffers instead of as attributes.
#
# FX seems to have resolved this issue for dicts in 1.9, but we support all the way back to 1.8.0.
constants_root = torch.nn.Module()
for key, value in constants.items():
constants_root.register_buffer(key, value)
graphmod = fx.GraphModule(constants_root, graph, class_name="tp_right")
# == Optimize ==
# TODO: when eliminate_dead_code() is in PyTorch stable, use that
if optimize_einsums:
# Note that for our einsums, we can optimize _once_ for _any_ batch dimension
# and still get the right path for _all_ batch dimensions.
# This is because our einsums are essentially of the form:
# zuvw,ijk,zuvij->zwk OR uvw,ijk,zuvij->zwk
# In the first case, all but one operands have the batch dimension
# => The first contraction gains the batch dimension
# => All following contractions have batch dimension
# => All possible contraction paths have cost that scales linearly in batch size
# => The optimal path is the same for all batch sizes
# For the second case, this logic follows as long as the first contraction is not between the first two operands. Since those two operands do not share any indexes, contracting them first is a rare pathological case. See
# https://github.com/dgasmith/opt_einsum/issues/158
# for more details.
#
# TODO: consider the impact maximum intermediate result size on this logic
# \- this is the `memory_limit` option in opt_einsum
# TODO: allow user to choose opt_einsum parameters?
#
# We use float32 and zeros to save memory and time, since opt_einsum_fx looks only at traced shapes, not values or dtypes.
batchdim = 4
example_inputs = (
torch.zeros((batchdim, irreps_in1.dim)),
torch.zeros((batchdim, irreps_in2.dim)),
torch.zeros(
1 if shared_weights else batchdim,
flat_weight_index,
),
)
graphmod = optimize_einsums_full(graphmod, example_inputs[1:])
return graphmod
| 50.162861
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| 1
| 0.006536
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| 0.019608
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| null | 0
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0
| 6
|
d802710486b7a2c4c2b659b24fb0dce7b460973d
| 16,547
|
py
|
Python
|
Old MRI segmentation code/Hist-seg-WES_010.py
|
akac0297/PETLAB
|
950cc153ce230d12d752ad0d11111e7fc22d9e7d
|
[
"MIT"
] | null | null | null |
Old MRI segmentation code/Hist-seg-WES_010.py
|
akac0297/PETLAB
|
950cc153ce230d12d752ad0d11111e7fc22d9e7d
|
[
"MIT"
] | null | null | null |
Old MRI segmentation code/Hist-seg-WES_010.py
|
akac0297/PETLAB
|
950cc153ce230d12d752ad0d11111e7fc22d9e7d
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#import modules
import SimpleITK as sitk
from platipy.imaging.visualisation.tools import ImageVisualiser
from platipy.imaging.utils.tools import get_com
import matplotlib.pyplot as plt
import numpy as np
get_ipython().run_line_magic('matplotlib', 'notebook')
# In[4]:
#add segs tp4
seg_B50T=sitk.ReadImage("test_label_threshold_010_4_B50T_hist.nii.gz")
seg_B800T=sitk.ReadImage("test_label_threshold_010_4_B800T_hist.nii.gz")
seg_T2=sitk.ReadImage("test_label_threshold_010_4_T2w_hist.nii.gz")
seg_MPE=sitk.ReadImage("test_label_threshold_010_4_MPE_hist.nii.gz")
seg_B50T=sitk.Resample(seg_B50T,seg_T2)
seg_B800T=sitk.Resample(seg_B800T,seg_T2)
seg_MPE=sitk.Resample(seg_MPE,seg_T2)
new_seg_T2=sitk.LabelMapToBinary(sitk.Cast(seg_T2, sitk.sitkLabelUInt8))
new_seg_B50T=sitk.LabelMapToBinary(sitk.Cast(seg_B50T, sitk.sitkLabelUInt8))
new_seg_B800T=sitk.LabelMapToBinary(sitk.Cast(seg_B800T, sitk.sitkLabelUInt8))
new_seg_MPE=sitk.LabelMapToBinary(sitk.Cast(seg_MPE, sitk.sitkLabelUInt8))
new_TRACE_seg=(new_seg_B50T+new_seg_B800T)/2#sitk.Cast((new_seg_B50T+new_seg_B800T)/2,sitk.sitkUInt8)
new_seg_1=(sitk.Cast(new_seg_T2,sitk.sitkFloat64)+new_TRACE_seg+sitk.Cast(new_seg_MPE,sitk.sitkFloat64)) #need to threshold this somehow
vis=ImageVisualiser(new_seg_1, cut=get_com(new_seg_1), window=[0,3])
fig=vis.show()
# In[5]:
new_seg_1_1=sitk.BinaryThreshold(new_seg_1, lowerThreshold=2)
vis=ImageVisualiser(new_seg_1_1, cut=get_com(new_seg_1), window=[0,1])
fig=vis.show()
# In[6]:
sitk.WriteImage(new_seg_1_1,"new_seg_010_4_mri.nii.gz")
# In[7]:
R_breast=sitk.ReadImage("/home/alicja/Downloads/Segmentation.nii.gz")
# In[8]:
WES_010_4_B50T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_4_20180829_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B50T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_MIX_5.nii.gz")
WES_010_4_B800T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_4_20180829_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B800T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_MIX_5.nii.gz")
# In[9]:
from platipy.imaging.visualisation.tools import ImageVisualiser
from platipy.imaging.registration.registration import (
initial_registration,
fast_symmetric_forces_demons_registration,
transform_propagation,
apply_field
)
# In[10]:
#DIR to tp5
WES_010_5_B50T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_5_20181010_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B50T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_MIX_6.nii.gz")
image_to_0_rigid, tfm_to_0_rigid = initial_registration(
WES_010_5_B50T,
WES_010_4_B50T,
options={
'shrink_factors': [8,4],
'smooth_sigmas': [0,0],
'sampling_rate': 0.5,
'final_interp': 2,
'metric': 'mean_squares',
'optimiser': 'gradient_descent_line_search',
'number_of_iterations': 25},
reg_method='Rigid')
image_to_0_dir, tfm_to_0_dir = fast_symmetric_forces_demons_registration(
WES_010_5_B50T,
image_to_0_rigid,
resolution_staging=[4,2],
iteration_staging=[10,10]
)
R_breast_to_0_rigid = transform_propagation(
WES_010_5_B50T,
R_breast,
tfm_to_0_rigid,
structure=True
)
R_breast_to_0_dir = apply_field(
R_breast_to_0_rigid,
tfm_to_0_dir,
structure=True
)
# In[11]:
vis = ImageVisualiser(WES_010_5_B50T, axis='z', cut=get_com(R_breast_to_0_dir), window=[-250, 500])
vis.add_contour(R_breast_to_0_dir, name='BREAST', color='g')
fig = vis.show()
# In[12]:
breast_contour_dilate=sitk.BinaryDilate(R_breast_to_0_dir, (2,2,2))
# In[14]:
vis = ImageVisualiser(WES_010_5_B50T, axis='z', cut=get_com(R_breast_to_0_dir), window=[-250, 500])
vis.add_contour(breast_contour_dilate, name='BREAST', color='g')
fig = vis.show()
# In[15]:
masked_R_breast = sitk.Mask(WES_010_5_B50T, breast_contour_dilate)
# In[20]:
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(500,3000,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[22]:
image_mri=WES_010_5_B50T
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=950, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_5_B50T_hist.nii.gz")
# In[18]:
def estimate_tumour_vol(img_mri, lowerthreshold=300, upperthreshold=3000, hole_size=1):
label_threshold = sitk.BinaryThreshold(img_mri, lowerThreshold=lowerthreshold, upperThreshold=upperthreshold)
label_threshold_cc = sitk.RelabelComponent(sitk.ConnectedComponent(label_threshold))
label_threshold_cc_x = (label_threshold_cc==1)
label_threshold_cc_x_f = sitk.BinaryMorphologicalClosing(label_threshold_cc_x, (hole_size,hole_size,hole_size))
return(label_threshold_cc_x_f)
# In[23]:
WES_010_5_B800T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_5_20181010_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B800T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_MIX_6.nii.gz")
WES_010_5_T2w=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_5_20181010_MR_T2_TSE_TRA_SPAIR_TSE2D1_11_T2_TSE_TRA_SPAIR_3.nii.gz")
WES_010_5_MPE=sitk.ReadImage("MPE_sub_WES_010_5.nii.gz")
masked_R_breast = sitk.Mask(WES_010_5_B800T, breast_contour_dilate)
# In[31]:
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(200,750,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[33]:
image_mri=WES_010_5_B800T
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=400, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_5_B800T_hist.nii.gz") #ok but picks up fibro
# In[49]:
WES_010_5_T2w=sitk.Resample(WES_010_5_B50T)
masked_R_breast = sitk.Mask(WES_010_5_T2w, breast_contour_dilate)
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(200,750,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[51]:
image_mri=WES_010_5_B800T
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=440, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_5_T2w_hist.nii.gz") #picks up fibro
# In[38]:
WES_010_5_MPE=sitk.Resample(WES_010_5_B50T)
masked_R_breast = sitk.Mask(WES_010_5_MPE, breast_contour_dilate)
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(1,750,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[42]:
image_mri=WES_010_5_MPE
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=640, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_5_MPE_hist.nii.gz") #okay but not ideal
# In[52]:
#add segs tp4
seg_B50T=sitk.ReadImage("test_label_threshold_010_5_B50T_hist.nii.gz")
seg_B800T=sitk.ReadImage("test_label_threshold_010_5_B800T_hist.nii.gz")
seg_T2=sitk.ReadImage("test_label_threshold_010_5_T2w_hist.nii.gz")
seg_MPE=sitk.ReadImage("test_label_threshold_010_5_MPE_hist.nii.gz")
seg_B50T=sitk.Resample(seg_B50T,seg_T2)
seg_B800T=sitk.Resample(seg_B800T,seg_T2)
seg_MPE=sitk.Resample(seg_MPE,seg_T2)
new_seg_T2=sitk.LabelMapToBinary(sitk.Cast(seg_T2, sitk.sitkLabelUInt8))
new_seg_B50T=sitk.LabelMapToBinary(sitk.Cast(seg_B50T, sitk.sitkLabelUInt8))
new_seg_B800T=sitk.LabelMapToBinary(sitk.Cast(seg_B800T, sitk.sitkLabelUInt8))
new_seg_MPE=sitk.LabelMapToBinary(sitk.Cast(seg_MPE, sitk.sitkLabelUInt8))
new_TRACE_seg=(new_seg_B50T+new_seg_B800T)/2#sitk.Cast((new_seg_B50T+new_seg_B800T)/2,sitk.sitkUInt8)
new_seg_1=(sitk.Cast(new_seg_T2,sitk.sitkFloat64)+new_TRACE_seg+sitk.Cast(new_seg_MPE,sitk.sitkFloat64)) #need to threshold this somehow
vis=ImageVisualiser(new_seg_1, cut=get_com(new_seg_1), window=[0,3])
fig=vis.show()
# In[53]:
new_seg_1_1=sitk.BinaryThreshold(new_seg_1, lowerThreshold=2)
vis=ImageVisualiser(new_seg_1_1, cut=get_com(new_seg_1), window=[0,1])
fig=vis.show()
# In[54]:
sitk.WriteImage(new_seg_1_1,"new_seg_010_5_mri.nii.gz") #not good but okay
# In[62]:
#DIR to tp6
WES_010_6_B50T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_6_20190301_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B50T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_5.nii.gz")
WES_010_6_B50T=sitk.Resample(WES_010_6_B50T,WES_010_5_B50T)
image_to_0_rigid, tfm_to_0_rigid = initial_registration(
WES_010_6_B50T,
WES_010_4_B50T,
options={
'shrink_factors': [8,4],
'smooth_sigmas': [0,0],
'sampling_rate': 0.5,
'final_interp': 2,
'metric': 'mean_squares',
'optimiser': 'gradient_descent_line_search',
'number_of_iterations': 25},
reg_method='Rigid')
image_to_0_dir, tfm_to_0_dir = fast_symmetric_forces_demons_registration(
WES_010_6_B50T,
image_to_0_rigid,
resolution_staging=[4,2],
iteration_staging=[10,10]
)
R_breast_to_0_rigid = transform_propagation(
WES_010_6_B50T,
R_breast,
tfm_to_0_rigid,
structure=True
)
R_breast_to_0_dir = apply_field(
R_breast_to_0_rigid,
tfm_to_0_dir,
structure=True
)
# In[63]:
vis = ImageVisualiser(WES_010_6_B50T, axis='z', cut=get_com(R_breast_to_0_dir), window=[-250, 500])
vis.add_contour(R_breast_to_0_dir, name='BREAST', color='g')
fig = vis.show()
# In[64]:
breast_contour_dilate=sitk.BinaryDilate(R_breast_to_0_dir, (2,2,2))
# In[65]:
vis = ImageVisualiser(WES_010_5_B50T, axis='z', cut=get_com(R_breast_to_0_dir), window=[-250, 500])
vis.add_contour(breast_contour_dilate, name='BREAST', color='g')
fig = vis.show()
# In[66]:
masked_R_breast = sitk.Mask(WES_010_6_B50T, breast_contour_dilate)
# In[72]:
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(1,600,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[73]:
image_mri=WES_010_6_B50T
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=405, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_6_B50T_hist.nii.gz") #is okay
# In[79]:
WES_010_6_B800T=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_6_20190301_MR_EP2D_DIFF_TRA_SPAIR_ZOOMIT_EZ_B800T_EP2D_DIFF_TRA_SPAIR_ZOOMIT_TRACEW_DFC_5.nii.gz")
WES_010_6_B800T=sitk.Resample(WES_010_6_B800T,WES_010_6_B50T)
masked_R_breast = sitk.Mask(WES_010_6_B800T, breast_contour_dilate)
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(100,400,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[82]:
image_mri=WES_010_6_B50T
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=330, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_6_B800T_hist.nii.gz") #okay but no time
# In[105]:
WES_010_6_T2w=sitk.ReadImage("/home/alicja/Documents/WES_010/IMAGES/WES_010_6_20190301_MR_T2_TSE_TRA_SPAIR_TSE2D1_11_T2_TSE_TRA_SPAIR_3.nii.gz")
WES_010_6_T2w=sitk.Resample(WES_010_6_T2w,WES_010_6_B50T)
masked_R_breast = sitk.Mask(WES_010_6_B800T, breast_contour_dilate)
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(1,400,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[109]:
image_mri=WES_010_6_T2w
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
arr_mri[:,:,:177] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
image_mri_masked=sitk.Mask(image_mri_masked, breast_contour_dilate)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=100, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_6_T2w_hist.nii.gz")#this one doesnt work
# In[111]:
WES_010_6_MPE=sitk.ReadImage("MPE_sub_WES_010_6.nii.gz")
WES_010_6_MPE=sitk.Resample(WES_010_6_MPE,WES_010_6_B50T)
masked_R_breast = sitk.Mask(WES_010_6_MPE, breast_contour_dilate)
values = sitk.GetArrayViewFromImage(masked_R_breast).flatten()
fig, ax = plt.subplots(1,1)
ax.hist(values, bins=np.linspace(1,400,50), histtype='stepfilled', lw=2)
#ax.set_yscale('log')
ax.grid()
ax.set_axisbelow(True)
ax.set_xlabel('Intensity')
ax.set_ylabel('Frequency')
fig.show()
# In[123]:
image_mri=WES_010_6_MPE
arr_mri = sitk.GetArrayFromImage(image_mri)
arr_mri[:,:,arr_mri.shape[2]//2:] = 0
arr_mri[:,:,:100] = 0
image_mri_masked=sitk.GetImageFromArray(arr_mri)
image_mri_masked.CopyInformation(image_mri)
image_mri_masked=sitk.Mask(image_mri_masked, breast_contour_dilate)
label_threshold_cc_x_f=estimate_tumour_vol(image_mri_masked, lowerthreshold=85, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_6_MPE_hist.nii.gz") #doesnt work
# In[126]:
#add segs tp4
seg_B50T=sitk.ReadImage("test_label_threshold_010_6_B50T_hist.nii.gz")
seg_B800T=sitk.ReadImage("test_label_threshold_010_6_B800T_hist.nii.gz")
seg_B800T=sitk.Resample(seg_B800T,seg_B50T)
new_seg_B50T=sitk.LabelMapToBinary(sitk.Cast(seg_B50T, sitk.sitkLabelUInt8))
new_seg_B800T=sitk.LabelMapToBinary(sitk.Cast(seg_B800T, sitk.sitkLabelUInt8))
new_TRACE_seg=(new_seg_B50T+new_seg_B800T)/2#sitk.Cast((new_seg_B50T+new_seg_B800T)/2,sitk.sitkUInt8)
new_seg_1=(sitk.Cast(new_TRACE_seg,sitk.sitkFloat64)) #need to threshold this somehow
vis=ImageVisualiser(new_seg_1, cut=get_com(new_seg_1), window=[0,3])
fig=vis.show()
# In[127]:
new_seg_1_1=sitk.BinaryThreshold(new_seg_1, lowerThreshold=1)
vis=ImageVisualiser(new_seg_1_1, cut=get_com(new_seg_1), window=[0,1])
fig=vis.show()
# In[128]:
sitk.WriteImage(new_seg_1_1,"new_seg_010_6_mri.nii.gz") #very bad
# In[130]:
image_mri_masked=sitk.Mask(WES_010_6_MPE,new_seg_1_1)
arr_mri_masked=sitk.GetArrayFromImage(image_mri_masked)
arr_mri_masked[arr_mri_masked<120]=0
tum_MPE=sitk.GetImageFromArray(arr_mri_masked)
tum_MPE.CopyInformation(image_mri_masked)
# In[131]:
label_threshold_cc_x_f=estimate_tumour_vol(tum_MPE, lowerthreshold=150, upperthreshold=5000, hole_size=1)
sitk.WriteImage(label_threshold_cc_x_f,"test_label_threshold_010_6_MPE_hist_new.nii.gz") #doesnt work either
# In[2]:
#date order: 29/08, 10/10, 01/03 (next year)
#volumes
img1=sitk.ReadImage("new_seg_010_4_mri.nii.gz")
img2=sitk.ReadImage("new_seg_010_5_mri.nii.gz")
img3=sitk.ReadImage("new_seg_010_6_mri.nii.gz")
arr1=sitk.GetArrayFromImage(img1)
arr2=sitk.GetArrayFromImage(img2)
arr3=sitk.GetArrayFromImage(img3)
vol1=np.sum(arr1==1)
vol2=np.sum(arr2==1)
vol3=np.sum(arr3==1)
# In[3]:
print(vol1, vol2, vol3)
# In[ ]:
| 26.949511
| 180
| 0.794223
| 2,817
| 16,547
| 4.257366
| 0.103301
| 0.036521
| 0.018678
| 0.031185
| 0.881097
| 0.843992
| 0.841491
| 0.827149
| 0.816226
| 0.8003
| 0
| 0.080417
| 0.077899
| 16,547
| 613
| 181
| 26.993475
| 0.705597
| 0.065813
| 0
| 0.655844
| 0
| 0
| 0.17419
| 0.142839
| 0
| 0
| 0
| 0
| 0
| 1
| 0.003247
| false
| 0
| 0.022727
| 0
| 0.025974
| 0.003247
| 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
|
d83667512f38d1603941dbb2e7bbb98003a4e531
| 22
|
py
|
Python
|
src/deeply/model/pranet.py
|
achillesrasquinha/deeply
|
fd1ce32da130591fc92df8df89e07f1497b2b902
|
[
"MIT"
] | 2
|
2021-10-05T16:37:30.000Z
|
2021-10-11T21:31:43.000Z
|
src/deeply/model/pranet.py
|
achillesrasquinha/deeply
|
fd1ce32da130591fc92df8df89e07f1497b2b902
|
[
"MIT"
] | null | null | null |
src/deeply/model/pranet.py
|
achillesrasquinha/deeply
|
fd1ce32da130591fc92df8df89e07f1497b2b902
|
[
"MIT"
] | 1
|
2021-07-16T02:23:37.000Z
|
2021-07-16T02:23:37.000Z
|
def PraNet():
pass
| 11
| 13
| 0.590909
| 3
| 22
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.272727
| 22
| 2
| 14
| 11
| 0.8125
| 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
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
dc43e5b59eafdcf8e26c10f0cbf396eaf9993fe5
| 713
|
bzl
|
Python
|
test/com/facebook/buck/apple/testdata/apple_binary_with_conditional_relinking/Apps/Libraries/defs.bzl
|
jasonnam/buck
|
1ddbbf986312b30413aa36cac337267536a11f04
|
[
"Apache-2.0"
] | null | null | null |
test/com/facebook/buck/apple/testdata/apple_binary_with_conditional_relinking/Apps/Libraries/defs.bzl
|
jasonnam/buck
|
1ddbbf986312b30413aa36cac337267536a11f04
|
[
"Apache-2.0"
] | null | null | null |
test/com/facebook/buck/apple/testdata/apple_binary_with_conditional_relinking/Apps/Libraries/defs.bzl
|
jasonnam/buck
|
1ddbbf986312b30413aa36cac337267536a11f04
|
[
"Apache-2.0"
] | null | null | null |
LOCATION_PREPROCESSOR_FLAGS = []
CONTACTS_PREPROCESSOR_FLAGS = []
def _update_fields():
if native.read_config("test", "swap_symbols", None):
LOCATION_PREPROCESSOR_FLAGS.append("-DTEST_IMPLEMENT_CONTACTS_FUNCTION=1")
CONTACTS_PREPROCESSOR_FLAGS.append("-DTEST_IMPLEMENT_LOCATION_FUNCTION=1")
else:
LOCATION_PREPROCESSOR_FLAGS.append("-DTEST_IMPLEMENT_LOCATION_FUNCTION=1")
CONTACTS_PREPROCESSOR_FLAGS.append("-DTEST_IMPLEMENT_CONTACTS_FUNCTION=1")
if native.read_config("test", "add_symbols", None):
LOCATION_PREPROCESSOR_FLAGS.append("-DTEST_USE_STATIC_FUNCTION=1")
CONTACTS_PREPROCESSOR_FLAGS.append("-DTEST_USE_STATIC_FUNCTION=1")
_update_fields()
| 41.941176
| 82
| 0.774194
| 82
| 713
| 6.219512
| 0.292683
| 0.266667
| 0.270588
| 0.329412
| 0.827451
| 0.741176
| 0.72549
| 0.635294
| 0
| 0
| 0
| 0.009585
| 0.12202
| 713
| 16
| 83
| 44.5625
| 0.805112
| 0
| 0
| 0
| 0
| 0
| 0.323983
| 0.280505
| 0
| 0
| 0
| 0
| 0
| 1
| 0.076923
| false
| 0
| 0
| 0
| 0.076923
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dc9001065c036f4e9017109d1ee4ddc870dadd55
| 7,882
|
py
|
Python
|
fmspy/rtmp/tests/test_packets.py
|
smira/fmspy
|
85260f4ebe8ccb17b0c755f8631f15af848b1707
|
[
"MIT"
] | 11
|
2015-01-06T09:43:26.000Z
|
2022-02-02T14:30:42.000Z
|
fmspy/rtmp/tests/test_packets.py
|
smira/fmspy
|
85260f4ebe8ccb17b0c755f8631f15af848b1707
|
[
"MIT"
] | null | null | null |
fmspy/rtmp/tests/test_packets.py
|
smira/fmspy
|
85260f4ebe8ccb17b0c755f8631f15af848b1707
|
[
"MIT"
] | 3
|
2015-07-13T03:18:21.000Z
|
2020-07-14T07:06:26.000Z
|
# FMSPy - Copyright (c) 2009 Andrey Smirnov.
#
# See COPYRIGHT for details.
"""
Tests for L{fmspy.rtmp.packets}.
"""
import unittest
import pyamf
from pyamf.util import BufferedByteStream
from fmspy.rtmp.header import RTMPHeader
from fmspy.rtmp.packets import Packet, DataPacket, Invoke, BytesRead, Ping
class DataPacketTestCase(unittest.TestCase):
"""
Test case for L{fmspy.rtmp.packets.DataPacket}.
"""
def setUp(self):
self.p1 = DataPacket(RTMPHeader(3, 1, 0, 0x14, 0), "aaaa")
self.p2 = DataPacket(RTMPHeader(2, 1, 0, 0x14, 0), "dddddd")
self.p3 = DataPacket(RTMPHeader(3, 1, 0, 0x14, 0), "aaaa")
def test_eq(self):
self.failUnlessEqual(self.p1, self.p3)
self.failIfEqual(self.p1, self.p2)
def test_repr(self):
self.failUnlessEqual("<DataPacket(header=<RTMPHeader(object_id=3, timestamp=1, length=4, type=0x14, stream_id=0)>, data='aaaa')>", repr(self.p1))
class InvokeTestCase(unittest.TestCase):
"""
Test case for L{fmspy.rtmp.packets.Invoke}.
"""
data = [
(
{ 'header' : RTMPHeader(object_id=3, timestamp=0, length=235, type=0x14, stream_id=0L),
'buf' : BufferedByteStream('\x02\x00\x07connect\x00?\xf0\x00\x00\x00\x00\x00\x00\x03\x00\x03app\x02\x00\x04echo\x00\x08flashVer\x02\x00\rLNX 10,0,20,7\x00\x06swfUrl\x06\x00\x05tcUrl\x02\x00\x15rtmp://localhost/echo\x00\x04fpad\x01\x00\x00\x0ccapabilities\x00@.\x00\x00\x00\x00\x00\x00\x00\x0baudioCodecs\x00@\xa8\xee\x00\x00\x00\x00\x00\x00\x0bvideoCodecs\x00@o\x80\x00\x00\x00\x00\x00\x00\rvideoFunction\x00?\xf0\x00\x00\x00\x00\x00\x00\x00\x07pageUrl\x06\x00\x0eobjectEncoding\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\t'),
},
Invoke(name=u'connect', argv=({'videoCodecs': 252, 'audioCodecs': 3191, 'flashVer': u'LNX 10,0,20,7', 'app': u'echo',
'tcUrl': u'rtmp://localhost/echo', 'videoFunction': 1, 'capabilities': 15, 'pageUrl': pyamf.Undefined, 'fpad': False,
'swfUrl': pyamf.Undefined, 'objectEncoding': 0},), id=1, header=RTMPHeader(object_id=3, timestamp=0, length=235, type=0x14, stream_id=0L)),
False
),
(
{ 'header' : RTMPHeader(object_id=3, timestamp=0, length=0, type=0x14, stream_id=0L),
'buf' : BufferedByteStream('\x02\x00\x07destroy\x00@@\x80\x00\x00\x00\x00\x00\x03\x00\x0bvideoCodecs\x00@o\x80\x00\x00\x00\x00\x00\x00\x00\t'),
},
Invoke(name=u'destroy', argv=({'videoCodecs': 252},), id=33, header=RTMPHeader(object_id=3, timestamp=0, length=0, type=0x14, stream_id=0L)),
True
),
]
def test_eq(self):
self.failUnlessEqual(Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)), Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)))
self.failIfEqual(Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)), Invoke(name='b', argv=(), id=35.0, header=RTMPHeader(object_id=3)))
self.failIfEqual(Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)), Invoke(name='a', argv=('a'), id=35.0, header=RTMPHeader(object_id=3)))
self.failIfEqual(Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)), Invoke(name='a', argv=(), id=36.0, header=RTMPHeader(object_id=3)))
self.failIfEqual(Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=3)), Invoke(name='a', argv=(), id=35.0, header=RTMPHeader(object_id=4)))
def test_repr(self):
self.failUnlessEqual("<Invoke(name=u'destroy', argv=({'videoCodecs': 252},), id=33, header=<RTMPHeader(object_id=3, timestamp=0, length=0, type=0x14, stream_id=0L)>)>",
repr(Invoke(name=u'destroy', argv=({'videoCodecs': 252},), id=33, header=RTMPHeader(object_id=3, timestamp=0, length=0, type=0x14, stream_id=0L))))
def test_read(self):
for fixture in self.data:
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[1], Invoke.read(**fixture[0]))
def test_write(self):
for fixture in self.data:
if not fixture[2]:
continue
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[0]['buf'].read(), fixture[1].write())
class BytesReadTestCase(unittest.TestCase):
"""
Test case for L{fmspy.rtmp.packets.BytesRead}.
"""
data = [
(
{ 'header' : RTMPHeader(object_id=2, timestamp=0, length=4, type=0x03, stream_id=0L),
'buf' : BufferedByteStream('\x00\x00\x00\x89'),
},
BytesRead( bytes=137,
header=RTMPHeader(object_id=2, timestamp=0, length=4, type=0x03, stream_id=0L)),
),
]
def test_eq(self):
self.failUnlessEqual(BytesRead(bytes=5, header=RTMPHeader(object_id=3)), BytesRead(bytes=5, header=RTMPHeader(object_id=3)))
self.failIfEqual(BytesRead(bytes=5, header=RTMPHeader(object_id=4)), BytesRead(bytes=5, header=RTMPHeader(object_id=3)))
self.failIfEqual(BytesRead(bytes=6, header=RTMPHeader(object_id=3)), BytesRead(bytes=5, header=RTMPHeader(object_id=3)))
def test_read(self):
for fixture in self.data:
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[1], BytesRead.read(**fixture[0]))
def test_write(self):
for fixture in self.data:
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[0]['buf'].read(), fixture[1].write())
class PingTestCase(unittest.TestCase):
"""
Test case for L{fmspy.rtmp.packets.Ping}.
"""
data = [
(
{ 'header' : RTMPHeader(object_id=2, timestamp=0, length=6, type=0x04, stream_id=0L),
'buf' : BufferedByteStream('\x00\x06\x00\x00\x00\x89'),
},
Ping( event=6, data=[137],
header=RTMPHeader(object_id=2, timestamp=0, length=6, type=0x04, stream_id=0L)),
),
(
{ 'header' : RTMPHeader(object_id=2, timestamp=0, length=10, type=0x04, stream_id=0L),
'buf' : BufferedByteStream('\x00\x06\x00\x00\x00\x89\x00\x00\x00\x0e'),
},
Ping( event=6, data=[137, 14],
header=RTMPHeader(object_id=2, timestamp=0, length=10, type=0x04, stream_id=0L)),
),
(
{ 'header' : RTMPHeader(object_id=2, timestamp=0, length=14, type=0x04, stream_id=0L),
'buf' : BufferedByteStream('\x00\x06\x00\x00\x00\x89\x00\x00\x00\x0e\x00\x00\x03y'),
},
Ping( event=6, data=[137, 14, 889],
header=RTMPHeader(object_id=2, timestamp=0, length=14, type=0x04, stream_id=0L)),
),
]
def test_eq(self):
self.failUnlessEqual(Ping(event=5, data=[3], header=RTMPHeader(object_id=3)), Ping(event=5, data=[3], header=RTMPHeader(object_id=3)))
self.failIfEqual(Ping(event=5, data=[3], header=RTMPHeader(object_id=3)), Ping(event=5, data=[3], header=RTMPHeader(object_id=4)))
self.failIfEqual(Ping(event=5, data=[3], header=RTMPHeader(object_id=3)), Ping(event=5, data=[3, 4], header=RTMPHeader(object_id=3)))
self.failIfEqual(Ping(event=5, data=[3], header=RTMPHeader(object_id=3)), Ping(event=6, data=[3], header=RTMPHeader(object_id=3)))
def test_read(self):
for fixture in self.data:
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[1], Ping.read(**fixture[0]))
def test_write(self):
for fixture in self.data:
fixture[0]['buf'].seek(0)
self.failUnlessEqual(fixture[0]['buf'].read(), fixture[1].write())
| 50.851613
| 548
| 0.604161
| 1,051
| 7,882
| 4.468126
| 0.135109
| 0.079216
| 0.08816
| 0.199319
| 0.790886
| 0.782155
| 0.72977
| 0.717632
| 0.685477
| 0.619889
| 0
| 0.094639
| 0.223801
| 7,882
| 154
| 549
| 51.181818
| 0.672932
| 0.008754
| 0
| 0.321429
| 0
| 0.044643
| 0.168969
| 0.114772
| 0
| 0
| 0.009564
| 0
| 0
| 0
| null | null | 0
| 0.044643
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
dcaedd21e3f5e832cce2cad517e82ebb31e79253
| 45
|
py
|
Python
|
scripts/qgis_fixes/fix_input.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | null | null | null |
scripts/qgis_fixes/fix_input.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | null | null | null |
scripts/qgis_fixes/fix_input.py
|
dyna-mis/Hilabeling
|
cb7d5d4be29624a20c8a367162dbc6fd779b2b52
|
[
"MIT"
] | 1
|
2021-12-25T08:40:30.000Z
|
2021-12-25T08:40:30.000Z
|
from lib2to3.fixes.fix_input import FixInput
| 22.5
| 44
| 0.866667
| 7
| 45
| 5.428571
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.04878
| 0.088889
| 45
| 1
| 45
| 45
| 0.878049
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 1
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| true
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| 1
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| null | 0
| 0
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| 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
|
f4991febc78ec59287ddc161d889c4318d46db96
| 187
|
py
|
Python
|
django/contrib/gis/geos/base.py
|
Yoann-Vie/esgi-hearthstone
|
115d03426c7e8e80d89883b78ac72114c29bed12
|
[
"PSF-2.0",
"BSD-3-Clause"
] | null | null | null |
django/contrib/gis/geos/base.py
|
Yoann-Vie/esgi-hearthstone
|
115d03426c7e8e80d89883b78ac72114c29bed12
|
[
"PSF-2.0",
"BSD-3-Clause"
] | null | null | null |
django/contrib/gis/geos/base.py
|
Yoann-Vie/esgi-hearthstone
|
115d03426c7e8e80d89883b78ac72114c29bed12
|
[
"PSF-2.0",
"BSD-3-Clause"
] | null | null | null |
from django.contrib.gis.geos.error import GEOSException
from django.contrib.gis.ptr import CPointerBase
class GEOSBase(CPointerBase):
null_ptr_exception_class = GEOSException
| 26.714286
| 56
| 0.807487
| 23
| 187
| 6.434783
| 0.608696
| 0.135135
| 0.22973
| 0.27027
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.13369
| 187
| 6
| 57
| 31.166667
| 0.91358
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f4e96a5a45c36e1ed802ad6105344f8efe720bdd
| 7,405
|
gyp
|
Python
|
ppapi/native_client/native_client.gyp
|
1065672644894730302/Chromium
|
239dd49e906be4909e293d8991e998c9816eaa35
|
[
"BSD-3-Clause"
] | 1
|
2019-04-23T15:57:04.000Z
|
2019-04-23T15:57:04.000Z
|
ppapi/native_client/native_client.gyp
|
1065672644894730302/Chromium
|
239dd49e906be4909e293d8991e998c9816eaa35
|
[
"BSD-3-Clause"
] | null | null | null |
ppapi/native_client/native_client.gyp
|
1065672644894730302/Chromium
|
239dd49e906be4909e293d8991e998c9816eaa35
|
[
"BSD-3-Clause"
] | null | null | null |
# Copyright (c) 2011 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'includes': [
'../../native_client/build/common.gypi',
],
'conditions': [
['disable_nacl==0 and disable_nacl_untrusted==0', {
'targets': [
{
'target_name': 'ppapi_lib',
'type': 'none',
'dependencies': [
'../../native_client/src/untrusted/pthread/pthread.gyp:pthread_lib',
'../../native_client/src/untrusted/irt_stub/irt_stub.gyp:ppapi_stub_lib',
],
'copies': [
{
'destination': '<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32',
'files': [
'<(DEPTH)/native_client/src/untrusted/irt_stub/libppapi.a',
],
},
{
'destination': '<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64',
'files': [
'<(DEPTH)/native_client/src/untrusted/irt_stub/libppapi.a',
],
},
{
'destination': '<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm',
'files': [
'<(DEPTH)/native_client/src/untrusted/irt_stub/libppapi.a',
],
},
],
},
{
'target_name': 'nacl_irt',
'type': 'none',
'variables': {
'nexe_target': 'nacl_irt',
'out64': '<(PRODUCT_DIR)/nacl_irt_x86_64.nexe',
'out32': '<(PRODUCT_DIR)/nacl_irt_x86_32.nexe',
'out_arm': '<(PRODUCT_DIR)/nacl_irt_arm.nexe',
'build_glibc': 0,
'build_newlib': 1,
'include_dirs': [
'lib/gl/include',
'..',
],
'link_flags': [
'-Wl,--start-group',
'-lirt_browser',
'-lppruntime',
'-lsrpc',
'-limc_syscalls',
'-lplatform',
'-lgio',
'-Wl,--end-group',
'-lm',
],
# See http://code.google.com/p/nativeclient/issues/detail?id=2691.
# The PNaCl linker (gold) does not implement the "-Ttext-segment"
# option. However, with the linker for x86, the "-Ttext" option
# does not affect the executable's base address.
# TODO(olonho): simplify flags handling and avoid duplication
# with NaCl logic.
'conditions': [
['target_arch!="arm"',
{
'link_flags': [
'-Wl,--section-start,.rodata=<(NACL_IRT_DATA_START)',
'-Wl,-Ttext-segment=<(NACL_IRT_TEXT_START)',
]
}, { # target_arch == "arm"
'link_flags': [
'-Wl,--section-start,.rodata=<(NACL_IRT_DATA_START)',
'-Wl,-Ttext=<(NACL_IRT_TEXT_START)',
'--pnacl-allow-native',
'-arch', 'arm',
'-Wt,-mtls-use-call',
],
},
],
],
'sources': [
],
'extra_args': [
'--strip-debug',
],
# TODO(bradchen): get rid of extra_deps64 and extra_deps32
# once native_client/build/untrusted.gypi no longer needs them.
'extra_deps64': [
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libgio.a',
],
'extra_deps32': [
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libgio.a',
],
'extra_deps_newlib64': [
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib64/libgio.a',
],
'extra_deps_newlib32': [
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/lib32/libgio.a',
],
'extra_deps_glibc64': [
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib64/libgio.a',
],
'extra_deps_glibc32': [
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_glibc/lib32/libgio.a',
],
'extra_deps_arm': [
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libppruntime.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libirt_browser.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libsrpc.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libplatform.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libimc_syscalls.a',
'<(SHARED_INTERMEDIATE_DIR)/tc_newlib/libarm/libgio.a',
],
},
'dependencies': [
'src/shared/ppapi_proxy/ppapi_proxy_untrusted.gyp:ppruntime_lib',
'../../native_client/src/untrusted/irt/irt.gyp:irt_browser_lib',
'../../native_client/src/shared/srpc/srpc.gyp:srpc_lib',
'../../native_client/src/shared/platform/platform.gyp:platform_lib',
'../../native_client/src/untrusted/nacl/nacl.gyp:imc_syscalls_lib',
'../../native_client/src/shared/gio/gio.gyp:gio_lib',
],
},
],
}],
],
}
| 44.608434
| 86
| 0.544767
| 739
| 7,405
| 5.136671
| 0.230041
| 0.213383
| 0.248946
| 0.272655
| 0.676238
| 0.630137
| 0.574552
| 0.462856
| 0.389884
| 0.389884
| 0
| 0.023151
| 0.311681
| 7,405
| 165
| 87
| 44.878788
| 0.721601
| 0.083052
| 0
| 0.418301
| 0
| 0
| 0.601446
| 0.510846
| 0
| 0
| 0
| 0.006061
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 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
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
7612cd34848cec45d650330a637352bd9a244b7a
| 64
|
py
|
Python
|
adn/utils/__init__.py
|
SunYH66/adn-master
|
d69a73e2f9cf2a4472c1d97f7347677b1947543a
|
[
"BSD-2-Clause"
] | null | null | null |
adn/utils/__init__.py
|
SunYH66/adn-master
|
d69a73e2f9cf2a4472c1d97f7347677b1947543a
|
[
"BSD-2-Clause"
] | null | null | null |
adn/utils/__init__.py
|
SunYH66/adn-master
|
d69a73e2f9cf2a4472c1d97f7347677b1947543a
|
[
"BSD-2-Clause"
] | null | null | null |
from .misc import *
from .torch import *
from .log import Logger
| 21.333333
| 23
| 0.75
| 10
| 64
| 4.8
| 0.6
| 0.416667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.171875
| 64
| 3
| 23
| 21.333333
| 0.90566
| 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
|
52035bda987af8d5570d35cc65bf8d3cdd828cb2
| 27
|
py
|
Python
|
goto_cloud/source/public.py
|
jdepoix/goto_cloud
|
59bb9923026e1b1dc6e8e08fb6b21300c8e8854a
|
[
"MIT"
] | 2
|
2018-02-04T23:22:17.000Z
|
2019-04-15T12:06:04.000Z
|
goto_cloud/source/public.py
|
jdepoix/goto_cloud
|
59bb9923026e1b1dc6e8e08fb6b21300c8e8854a
|
[
"MIT"
] | null | null | null |
goto_cloud/source/public.py
|
jdepoix/goto_cloud
|
59bb9923026e1b1dc6e8e08fb6b21300c8e8854a
|
[
"MIT"
] | null | null | null |
from .models import Source
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 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
|
520374dfc467ee85ef925c1e408d7eaca7b7e2a0
| 242
|
py
|
Python
|
torch_gists/models/__init__.py
|
rishikanthc/torch-snippets
|
b836c2c2fffbc5be1f08a1adae4b48473ad1fd60
|
[
"MIT"
] | null | null | null |
torch_gists/models/__init__.py
|
rishikanthc/torch-snippets
|
b836c2c2fffbc5be1f08a1adae4b48473ad1fd60
|
[
"MIT"
] | null | null | null |
torch_gists/models/__init__.py
|
rishikanthc/torch-snippets
|
b836c2c2fffbc5be1f08a1adae4b48473ad1fd60
|
[
"MIT"
] | null | null | null |
from torch_gists.models.resnet import ResNet18, ResNet34, ResNet50, ResNet152, ResNet101
from torch_gists.models.mobilenetv2 import MobileNetV2
from torch_gists.models.vgg import VGG
from torch_gists.models.resnetsignals import ResNetSignals
| 48.4
| 88
| 0.867769
| 32
| 242
| 6.4375
| 0.4375
| 0.174757
| 0.271845
| 0.38835
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063063
| 0.082645
| 242
| 4
| 89
| 60.5
| 0.864865
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 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
|
5246bf0aa4ae6a3cf0669bb52c2ab38c2e77ea7f
| 46
|
py
|
Python
|
PyCAD/test.py
|
danheeks/HeeksCAM
|
eb54f5c402effd74aa97fee4041ab303bafd1a7e
|
[
"BSD-2-Clause"
] | 5
|
2015-12-07T15:27:43.000Z
|
2021-03-03T15:51:52.000Z
|
PyCAD/test.py
|
danheeks/HeeksCAM
|
eb54f5c402effd74aa97fee4041ab303bafd1a7e
|
[
"BSD-2-Clause"
] | null | null | null |
PyCAD/test.py
|
danheeks/HeeksCAM
|
eb54f5c402effd74aa97fee4041ab303bafd1a7e
|
[
"BSD-2-Clause"
] | 4
|
2017-02-12T10:30:28.000Z
|
2020-06-15T12:18:21.000Z
|
import sys
from HeeksCAD import HeeksCADapp
| 15.333333
| 33
| 0.826087
| 6
| 46
| 6.333333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 46
| 2
| 34
| 23
| 1
| 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
|
52847d7a9d2b0253bebd0aaf378d34496306fb91
| 25
|
py
|
Python
|
tests/example/tests/__init__.py
|
frankhood/django-self-aware-model
|
837ef249c218e5cd7078f3ef25de6e11da1bb8b6
|
[
"MIT"
] | null | null | null |
tests/example/tests/__init__.py
|
frankhood/django-self-aware-model
|
837ef249c218e5cd7078f3ef25de6e11da1bb8b6
|
[
"MIT"
] | null | null | null |
tests/example/tests/__init__.py
|
frankhood/django-self-aware-model
|
837ef249c218e5cd7078f3ef25de6e11da1bb8b6
|
[
"MIT"
] | null | null | null |
from . import test_models
| 25
| 25
| 0.84
| 4
| 25
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12
| 25
| 1
| 25
| 25
| 0.909091
| 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
|
876207c549549cbdfe0fbc690792b23f8d765f43
| 4,039
|
py
|
Python
|
tests/functionnal/test_example_4.py
|
docker-leash/docker-leash
|
d98c0a98ddecac2c9775e839d1e64382b811a3cf
|
[
"MIT"
] | 1
|
2018-01-15T12:29:20.000Z
|
2018-01-15T12:29:20.000Z
|
tests/functionnal/test_example_4.py
|
docker-leash/docker-leash
|
d98c0a98ddecac2c9775e839d1e64382b811a3cf
|
[
"MIT"
] | 92
|
2018-01-12T21:04:42.000Z
|
2018-04-08T17:25:26.000Z
|
tests/functionnal/test_example_4.py
|
docker-leash/docker-leash
|
d98c0a98ddecac2c9775e839d1e64382b811a3cf
|
[
"MIT"
] | 2
|
2018-01-13T16:52:54.000Z
|
2020-04-24T22:45:46.000Z
|
# vim:set ts=4 sw=4 et:
'''
ValidateExample4Functionnal
---------------------------
'''
from . import is_success, post
from .test_base import LeashServerFunctionnalBaseTests
class ValidateExample4Functionnal(LeashServerFunctionnalBaseTests):
"""Functionnal validation of the documented example 4
"""
@staticmethod
def set_conf_files(application):
"""Define config file to read
:param `Flask` application: The current flask application
"""
example_dir = "./docs/examples/configs/example_4"
application.config['GROUPS_FILE'] = example_dir + "/groups.yml"
application.config['POLICIES_FILE'] = example_dir + "/policies.yml"
def test_servers_1(self):
"""Servers are restricted to admin only: admin
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "mal",
"Host": "srv01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertTrue(is_success(response))
def test_servers_2(self):
"""Servers are restricted to admin only: user
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "jre",
"Host": "srv01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
def test_servers_3(self):
"""Servers are restricted to admin only: user not from any group
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "vol",
"Host": "srv01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
def test_servers_4(self):
"""Servers are restricted to admin only: anonymous
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"Host": "srv01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
def test_other_1(self):
"""All other hosts are open to group, else deny: admin
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "mal",
"Host": "other01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
def test_other_2(self):
"""All other hosts are open to group, else deny: user from any group
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "jre",
"Host": "other01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertTrue(is_success(response))
def test_other_3(self):
"""All other hosts are open to group, else deny: user not from group
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"User": "vol",
"Host": "other01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
def test_other_4(self):
"""All other hosts are open to group, else deny: anonymous
"""
payload = {
"RequestMethod": "GET",
"RequestUri": "/v1.32/containers/json",
"Host": "other01",
}
response = post(self.app, payload)
self.assertEqual(response.status_code, 200)
self.assertFalse(is_success(response))
| 30.141791
| 76
| 0.562763
| 407
| 4,039
| 5.481572
| 0.206388
| 0.036307
| 0.082474
| 0.118333
| 0.779471
| 0.779471
| 0.774092
| 0.742716
| 0.707754
| 0.707754
| 0
| 0.027788
| 0.305026
| 4,039
| 133
| 77
| 30.368421
| 0.767011
| 0.18049
| 0
| 0.72093
| 0
| 0
| 0.181453
| 0.064606
| 0
| 0
| 0
| 0
| 0.186047
| 1
| 0.104651
| false
| 0
| 0.023256
| 0
| 0.139535
| 0
| 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
|
5eb175318ff0c7dd1f610844789ac09024b5c50a
| 36
|
py
|
Python
|
ra-gym/ra_gym/envs/__init__.py
|
RickyMexx/DeepRL-LTLf
|
24cb3ac49e5bb9e07c37644d7226201ccb2b59a4
|
[
"Apache-2.0"
] | 6
|
2020-12-07T23:47:44.000Z
|
2022-02-14T13:27:45.000Z
|
ra-gym/ra_gym/envs/__init__.py
|
RickyMexx/DeepRL-LTLf
|
24cb3ac49e5bb9e07c37644d7226201ccb2b59a4
|
[
"Apache-2.0"
] | 1
|
2021-05-06T11:38:33.000Z
|
2021-05-10T18:06:33.000Z
|
ra-gym/ra_gym/envs/__init__.py
|
RickyMexx/DeepRL-LTLf
|
24cb3ac49e5bb9e07c37644d7226201ccb2b59a4
|
[
"Apache-2.0"
] | 1
|
2021-01-09T02:32:10.000Z
|
2021-01-09T02:32:10.000Z
|
from ra_gym.envs.ra_env import RAEnv
| 36
| 36
| 0.861111
| 8
| 36
| 3.625
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 1
| 36
| 36
| 0.878788
| 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
|
0d7a94f1620dea529b89f88c2c59bc5be223eb57
| 191
|
py
|
Python
|
list.py
|
sshan0509/day3
|
21cb110f28346e5171252a1ecd37b48526e7afd6
|
[
"Apache-2.0"
] | null | null | null |
list.py
|
sshan0509/day3
|
21cb110f28346e5171252a1ecd37b48526e7afd6
|
[
"Apache-2.0"
] | null | null | null |
list.py
|
sshan0509/day3
|
21cb110f28346e5171252a1ecd37b48526e7afd6
|
[
"Apache-2.0"
] | null | null | null |
fruits = ['apple', 'banana', 'watermelon', 'orange']
print(fruits)
fruits.append('melon')
print(fruits)
print(fruits[0])
del fruits[0]
print(fruits)
fruits.append('apple'[0:5])
print(fruits)
| 19.1
| 52
| 0.706806
| 27
| 191
| 5
| 0.407407
| 0.407407
| 0.251852
| 0.340741
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022727
| 0.078534
| 191
| 9
| 53
| 21.222222
| 0.744318
| 0
| 0
| 0.444444
| 0
| 0
| 0.193717
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.555556
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
0d97cb009e70f6cac1939c6e51d07fc204743b39
| 23
|
py
|
Python
|
tardis/apps/related_info/__init__.py
|
keithschulze/mytardis
|
8ed3562574ce990d42bfe96133185a82c31c27d4
|
[
"Apache-2.0"
] | 42
|
2020-03-02T17:48:36.000Z
|
2022-02-05T17:44:15.000Z
|
tardis/apps/related_info/__init__.py
|
keithschulze/mytardis
|
8ed3562574ce990d42bfe96133185a82c31c27d4
|
[
"Apache-2.0"
] | 238
|
2019-09-04T14:37:54.000Z
|
2020-04-15T16:24:24.000Z
|
tardis/apps/related_info/__init__.py
|
keithschulze/mytardis
|
8ed3562574ce990d42bfe96133185a82c31c27d4
|
[
"Apache-2.0"
] | 10
|
2020-04-13T20:43:08.000Z
|
2022-01-11T09:29:37.000Z
|
from . import settings
| 11.5
| 22
| 0.782609
| 3
| 23
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 23
| 1
| 23
| 23
| 0.947368
| 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
|
0d9bcd711035b4abcad54478fbd03562313596af
| 6,391
|
py
|
Python
|
auth-api/tests/unit/services/test_membership.py
|
karthik-aot/sbc-auth
|
f24028040fda67d4f10ae9b608b8832c15d2a8ad
|
[
"Apache-2.0"
] | null | null | null |
auth-api/tests/unit/services/test_membership.py
|
karthik-aot/sbc-auth
|
f24028040fda67d4f10ae9b608b8832c15d2a8ad
|
[
"Apache-2.0"
] | null | null | null |
auth-api/tests/unit/services/test_membership.py
|
karthik-aot/sbc-auth
|
f24028040fda67d4f10ae9b608b8832c15d2a8ad
|
[
"Apache-2.0"
] | null | null | null |
# Copyright © 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the Membership service.
Test suite to ensure that the Membership service routines are working as expected.
"""
from auth_api.models import MembershipStatusCode as MembershipStatusCodeModel
from auth_api.services import Membership as MembershipService
from auth_api.services import Org as OrgService
from auth_api.services.keycloak import KeycloakService
from auth_api.utils.constants import GROUP_ACCOUNT_HOLDERS
from auth_api.utils.enums import ProductCode, Status
from tests.utilities.factory_scenarios import KeycloakScenario, TestOrgInfo, TestUserInfo
from tests.utilities.factory_utils import factory_membership_model, factory_product_model, factory_user_model
def test_accept_invite_adds_group_to_the_user(session, monkeypatch): # pylint:disable=unused-argument
"""Assert that accepting an invite adds group to the user."""
# Create a user in keycloak
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
keycloak_service.add_user(request, return_if_exists=True)
kc_user = keycloak_service.get_user_by_username(request.user_name)
user = factory_user_model(TestUserInfo.get_user_with_kc_guid(kc_guid=kc_user.id))
# Patch token info
def token_info(): # pylint: disable=unused-argument; mocks of library methods
return {
'sub': str(kc_user.id),
'username': 'public_user',
'realm_access': {
'roles': [
'edit'
]
},
'product_code': ProductCode.BUSINESS.value
}
monkeypatch.setattr('auth_api.services.keycloak.KeycloakService._get_token_info', token_info)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user.id)
# Create another user
request = KeycloakScenario.create_user_request()
keycloak_service.add_user(request, return_if_exists=True)
kc_user2 = keycloak_service.get_user_by_username(request.user_name)
user2 = factory_user_model(TestUserInfo.get_user_with_kc_guid(kc_guid=kc_user2.id))
# Add a membership to the user for the org created
factory_membership_model(user2.id, org.as_dict().get('id'), member_type='COORDINATOR', member_status=4)
# Add a product to org
factory_product_model(org.as_dict().get('id'), product_code=ProductCode.BUSINESS.value)
# Find the membership and update to ACTIVE
membership = MembershipService.get_membership_for_org_and_user(org.as_dict().get('id'), user2.id)
active_membership_status = MembershipStatusCodeModel.get_membership_status_by_code(Status.ACTIVE.name)
updated_fields = {'membership_status': active_membership_status}
MembershipService(membership).update_membership(updated_fields=updated_fields, token_info=token_info())
user_groups = keycloak_service.get_user_groups(user_id=kc_user2.id)
groups = []
for group in user_groups:
groups.append(group.get('name'))
assert GROUP_ACCOUNT_HOLDERS in groups
def test_remove_member_removes_group_to_the_user(session, monkeypatch): # pylint:disable=unused-argument
"""Assert that accepting an invite adds group to the user."""
# Create a user in keycloak
keycloak_service = KeycloakService()
request = KeycloakScenario.create_user_request()
keycloak_service.add_user(request, return_if_exists=True)
kc_user = keycloak_service.get_user_by_username(request.user_name)
user = factory_user_model(TestUserInfo.get_user_with_kc_guid(kc_guid=kc_user.id))
# Patch token info
def token_info(): # pylint: disable=unused-argument; mocks of library methods
return {
'sub': str(kc_user.id),
'username': 'public_user',
'realm_access': {
'roles': [
'edit'
]
},
'product_code': ProductCode.BUSINESS.value
}
monkeypatch.setattr('auth_api.services.keycloak.KeycloakService._get_token_info', token_info)
org = OrgService.create_org(TestOrgInfo.org1, user_id=user.id)
# Create another user
request = KeycloakScenario.create_user_request()
keycloak_service.add_user(request, return_if_exists=True)
kc_user2 = keycloak_service.get_user_by_username(request.user_name)
user2 = factory_user_model(TestUserInfo.get_user_with_kc_guid(kc_guid=kc_user2.id))
# Add a membership to the user for the org created
factory_membership_model(user2.id, org.as_dict().get('id'), member_type='COORDINATOR', member_status=4)
# Add a product to org
factory_product_model(org.as_dict().get('id'), product_code=ProductCode.BUSINESS.value)
# Find the membership and update to ACTIVE
membership = MembershipService.get_membership_for_org_and_user(org.as_dict().get('id'), user2.id)
active_membership_status = MembershipStatusCodeModel.get_membership_status_by_code(Status.ACTIVE.name)
updated_fields = {'membership_status': active_membership_status}
MembershipService(membership).update_membership(updated_fields=updated_fields, token_info=token_info())
user_groups = keycloak_service.get_user_groups(user_id=kc_user2.id)
groups = []
for group in user_groups:
groups.append(group.get('name'))
assert GROUP_ACCOUNT_HOLDERS in groups
# Find the membership and update to INACTIVE
active_membership_status = MembershipStatusCodeModel.get_membership_status_by_code(Status.INACTIVE.name)
updated_fields = {'membership_status': active_membership_status}
MembershipService(membership).update_membership(updated_fields=updated_fields, token_info=token_info())
user_groups = keycloak_service.get_user_groups(user_id=kc_user2.id)
groups = []
for group in user_groups:
groups.append(group.get('name'))
assert GROUP_ACCOUNT_HOLDERS not in groups
| 46.992647
| 109
| 0.752151
| 843
| 6,391
| 5.421115
| 0.196916
| 0.027571
| 0.014004
| 0.033698
| 0.771116
| 0.760175
| 0.75186
| 0.75186
| 0.75186
| 0.75186
| 0
| 0.004683
| 0.164763
| 6,391
| 135
| 110
| 47.340741
| 0.851255
| 0.213895
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| 0
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| 0.064886
| 0.023303
| 0
| 0
| 0
| 0
| 0.035714
| 1
| 0.047619
| false
| 0
| 0.095238
| 0.02381
| 0.166667
| 0
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| 0
| null | 0
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| 0
| 0
| 0
|
0
| 6
|
0dd26418ddd25e40ef983e63d8b6a1765c383946
| 117
|
py
|
Python
|
app/app/views.py
|
ronnyrules/Django-Docker-4-Dummies
|
bc616c71a27553d7566acaf9a6c2aa6389f3c79e
|
[
"MIT"
] | 7
|
2022-03-14T00:42:28.000Z
|
2022-03-21T11:21:58.000Z
|
app/app/views.py
|
ronnyrules/Django-Docker-4-Dummies
|
bc616c71a27553d7566acaf9a6c2aa6389f3c79e
|
[
"MIT"
] | null | null | null |
app/app/views.py
|
ronnyrules/Django-Docker-4-Dummies
|
bc616c71a27553d7566acaf9a6c2aa6389f3c79e
|
[
"MIT"
] | 2
|
2022-03-14T05:16:35.000Z
|
2022-03-14T05:23:12.000Z
|
from django.http import HttpResponse
def index(request):
return HttpResponse("WELCOME TO A NEW DJANGO PROJECT")
| 23.4
| 58
| 0.777778
| 16
| 117
| 5.6875
| 0.875
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| 0.153846
| 117
| 4
| 59
| 29.25
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| 0
|
0
| 6
|
2195b412287eff0336f494cf1485b2d46c3842ee
| 27,034
|
py
|
Python
|
annotation/migrations/0002_auto_20200929_1503.py
|
SACGF/variantgrid
|
515195e2f03a0da3a3e5f2919d8e0431babfd9c9
|
[
"RSA-MD"
] | 5
|
2021-01-14T03:34:42.000Z
|
2022-03-07T15:34:18.000Z
|
annotation/migrations/0002_auto_20200929_1503.py
|
SACGF/variantgrid
|
515195e2f03a0da3a3e5f2919d8e0431babfd9c9
|
[
"RSA-MD"
] | 551
|
2020-10-19T00:02:38.000Z
|
2022-03-30T02:18:22.000Z
|
annotation/migrations/0002_auto_20200929_1503.py
|
SACGF/variantgrid
|
515195e2f03a0da3a3e5f2919d8e0431babfd9c9
|
[
"RSA-MD"
] | null | null | null |
# Generated by Django 3.1 on 2020-09-29 05:33
import django.db.models.deletion
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
('annotation', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('patients', '0001_initial'),
('snpdb', '0001_initial'),
('genes', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='varianttranscriptannotation',
name='gene',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='varianttranscriptannotation',
name='transcript',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.transcript'),
),
migrations.AddField(
model_name='varianttranscriptannotation',
name='transcript_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.transcriptversion'),
),
migrations.AddField(
model_name='varianttranscriptannotation',
name='variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variant'),
),
migrations.AddField(
model_name='varianttranscriptannotation',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='variantgeneoverlap',
name='annotation_run',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.annotationrun'),
),
migrations.AddField(
model_name='variantgeneoverlap',
name='gene',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='variantgeneoverlap',
name='variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variant'),
),
migrations.AddField(
model_name='variantgeneoverlap',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='variantannotationversion',
name='gene_annotation_release',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.geneannotationrelease'),
),
migrations.AddField(
model_name='variantannotationversion',
name='genome_build',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.genomebuild'),
),
migrations.AddField(
model_name='variantannotation',
name='annotation_run',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.annotationrun'),
),
migrations.AddField(
model_name='variantannotation',
name='gene',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='variantannotation',
name='transcript',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.transcript'),
),
migrations.AddField(
model_name='variantannotation',
name='transcript_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.transcriptversion'),
),
migrations.AddField(
model_name='variantannotation',
name='variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variant'),
),
migrations.AddField(
model_name='variantannotation',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='textphenotypesentence',
name='phenotype_description',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.phenotypedescription'),
),
migrations.AddField(
model_name='textphenotypesentence',
name='text_phenotype',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.textphenotype'),
),
migrations.AddField(
model_name='textphenotypematch',
name='gene_symbol',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='genes.genesymbol'),
),
migrations.AddField(
model_name='textphenotypematch',
name='hpo',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.humanphenotypeontology'),
),
migrations.AddField(
model_name='textphenotypematch',
name='omim_alias',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.mimmorbidalias'),
),
migrations.AddField(
model_name='textphenotypematch',
name='text_phenotype',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.textphenotype'),
),
migrations.AddField(
model_name='samplevariantannotationstatspassingfilter',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='samplevariantannotationstatspassingfilter',
name='variant_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='samplevariantannotationstats',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='samplevariantannotationstats',
name='variant_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='sampleensemblgeneannotationstatspassingfilter',
name='ensembl_gene_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.ensemblgeneannotationversion'),
),
migrations.AddField(
model_name='sampleensemblgeneannotationstatspassingfilter',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='sampleensemblgeneannotationstats',
name='ensembl_gene_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.ensemblgeneannotationversion'),
),
migrations.AddField(
model_name='sampleensemblgeneannotationstats',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='sampleclinvarannotationstatspassingfilter',
name='clinvar_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.clinvarversion'),
),
migrations.AddField(
model_name='sampleclinvarannotationstatspassingfilter',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='sampleclinvarannotationstats',
name='clinvar_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.clinvarversion'),
),
migrations.AddField(
model_name='sampleclinvarannotationstats',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='phenotypemim',
name='hpo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.humanphenotypeontology'),
),
migrations.AddField(
model_name='phenotypemim',
name='mim_morbid',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.mimmorbid'),
),
migrations.AddField(
model_name='patienttextphenotype',
name='approved_by',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='patienttextphenotype',
name='patient',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='patient_text_phenotype', to='patients.patient'),
),
migrations.AddField(
model_name='patienttextphenotype',
name='phenotype_description',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='annotation.phenotypedescription'),
),
migrations.AddField(
model_name='mimmorbidalias',
name='mim_morbid',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.mimmorbid'),
),
migrations.AddField(
model_name='mimgene',
name='gene',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='mimgene',
name='mim_morbid',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.mimmorbid'),
),
migrations.AddField(
model_name='manualvariantentrycollection',
name='genome_build',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.genomebuild'),
),
migrations.AddField(
model_name='manualvariantentrycollection',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='manualvariantentry',
name='manual_variant_entry_collection',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.manualvariantentrycollection'),
),
migrations.AddField(
model_name='humanproteinatlasannotation',
name='gene',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='humanproteinatlasannotation',
name='tissue_sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.humanproteinatlastissuesample'),
),
migrations.AddField(
model_name='humanproteinatlasannotation',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.humanproteinatlasannotationversion'),
),
migrations.AddField(
model_name='humanphenotypeontology',
name='children',
field=models.ManyToManyField(blank=True, related_name='_parents', through='annotation.HPOEdge', to='annotation.HumanPhenotypeOntology'),
),
migrations.AddField(
model_name='hposynonym',
name='hpo',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.humanphenotypeontology'),
),
migrations.AddField(
model_name='hpoedge',
name='child',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='humanphenotypeontology_parent', to='annotation.humanphenotypeontology'),
),
migrations.AddField(
model_name='hpoedge',
name='parent',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='humanphenotypeontology_child', to='annotation.humanphenotypeontology'),
),
migrations.AddField(
model_name='genevaluecountcollection',
name='gene_count_type',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genecounttype'),
),
migrations.AddField(
model_name='genevaluecountcollection',
name='source',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantsource'),
),
migrations.AddField(
model_name='genevaluecount',
name='collection',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genevaluecountcollection'),
),
migrations.AddField(
model_name='genevaluecount',
name='gene',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='genevaluecount',
name='value',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genevalue'),
),
migrations.AddField(
model_name='genevalue',
name='gene_count_type',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genecounttype'),
),
migrations.AddField(
model_name='genesymbolpubmedcount',
name='gene_symbol',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='genes.genesymbol'),
),
migrations.AddField(
model_name='genesymbolcitation',
name='citation',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.citation'),
),
migrations.AddField(
model_name='genesymbolcitation',
name='gene_symbol',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.genesymbol'),
),
migrations.AddField(
model_name='genediseasevalidityevidence',
name='citation',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.citation'),
),
migrations.AddField(
model_name='genediseasevalidityevidence',
name='gene_disease',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genediseasevalidity'),
),
migrations.AddField(
model_name='genediseasevalidity',
name='disease_validity',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.diseasevalidity'),
),
migrations.AddField(
model_name='genediseasevalidity',
name='gene_symbol',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.genesymbol'),
),
migrations.AddField(
model_name='genediseasecurator',
name='cached_web_resource',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.cachedwebresource'),
),
migrations.AddField(
model_name='genediseasecurator',
name='user',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL),
),
migrations.AddField(
model_name='ensemblgeneannotationversion',
name='genome_build',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.genomebuild'),
),
migrations.AddField(
model_name='ensemblgeneannotation',
name='gene',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='genes.gene'),
),
migrations.AddField(
model_name='ensemblgeneannotation',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.ensemblgeneannotationversion'),
),
migrations.AddField(
model_name='diseasevalidity',
name='gene_disease_curator',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genediseasecurator'),
),
migrations.AddField(
model_name='diseasevalidity',
name='hpo_synonym',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='annotation.hposynonym'),
),
migrations.AddField(
model_name='diseasevalidity',
name='mim_morbid_alias',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='annotation.mimmorbidalias'),
),
migrations.AddField(
model_name='diseasevalidity',
name='mondo',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='annotation.monarchdiseaseontology'),
),
migrations.AddField(
model_name='createdmanualvariant',
name='manual_variant_entry',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.manualvariantentry'),
),
migrations.AddField(
model_name='createdmanualvariant',
name='variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variant'),
),
migrations.AddField(
model_name='columnvepfield',
name='variant_grid_column',
field=models.OneToOneField(null=True, on_delete=django.db.models.deletion.SET_NULL, to='snpdb.variantgridcolumn'),
),
migrations.AddField(
model_name='columnvcfinfo',
name='column',
field=models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variantgridcolumn'),
),
migrations.AddField(
model_name='cohortgenecounts',
name='cohort',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.cohort'),
),
migrations.AddField(
model_name='cohortgenecounts',
name='gene_count_type',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.genecounttype'),
),
migrations.AddField(
model_name='cohortgenecounts',
name='variant_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='clinvarversion',
name='genome_build',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.genomebuild'),
),
migrations.AddField(
model_name='clinvarcitation',
name='citation',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.citation'),
),
migrations.AddField(
model_name='clinvarcitation',
name='clinvar_citations_collection',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.clinvarcitationscollection'),
),
migrations.AddField(
model_name='clinvar',
name='variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.variant'),
),
migrations.AddField(
model_name='clinvar',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.clinvarversion'),
),
migrations.AlterUniqueTogether(
name='citation',
unique_together={('citation_source', 'citation_id')},
),
migrations.AddField(
model_name='cachedcitation',
name='citation',
field=models.OneToOneField(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.citation'),
),
migrations.AddField(
model_name='annotationversion',
name='clinvar_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='annotation.clinvarversion'),
),
migrations.AddField(
model_name='annotationversion',
name='ensembl_gene_annotation_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='annotation.ensemblgeneannotationversion'),
),
migrations.AddField(
model_name='annotationversion',
name='genome_build',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.genomebuild'),
),
migrations.AddField(
model_name='annotationversion',
name='human_protein_atlas_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='annotation.humanproteinatlasannotationversion'),
),
migrations.AddField(
model_name='annotationversion',
name='variant_annotation_version',
field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='annotationrun',
name='annotation_range_lock',
field=models.OneToOneField(null=True, on_delete=django.db.models.deletion.CASCADE, to='annotation.annotationrangelock'),
),
migrations.AddField(
model_name='annotationrangelock',
name='max_variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='max_variant', to='snpdb.variant'),
),
migrations.AddField(
model_name='annotationrangelock',
name='min_variant',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='min_variant', to='snpdb.variant'),
),
migrations.AddField(
model_name='annotationrangelock',
name='version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AlterUniqueTogether(
name='varianttranscriptannotation',
unique_together={('version', 'variant', 'transcript_version')},
),
migrations.AlterUniqueTogether(
name='variantgeneoverlap',
unique_together={('version', 'variant', 'annotation_run', 'gene')},
),
migrations.AddField(
model_name='variantannotationversiondiff',
name='version_from',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='version_diff_from', to='annotation.variantannotationversion'),
),
migrations.AddField(
model_name='variantannotationversiondiff',
name='version_to',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='version_diff_to', to='annotation.variantannotationversion'),
),
migrations.AlterUniqueTogether(
name='variantannotation',
unique_together={('version', 'variant')},
),
migrations.AddField(
model_name='sampleannotationversionvariantsource',
name='sample',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='snpdb.sample'),
),
migrations.AddField(
model_name='sampleannotationversionvariantsource',
name='variant_annotation_version',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='annotation.variantannotationversion'),
),
migrations.AlterUniqueTogether(
name='phenotypemim',
unique_together={('hpo', 'mim_morbid')},
),
migrations.AlterUniqueTogether(
name='mimgene',
unique_together={('mim_morbid', 'gene')},
),
migrations.AlterUniqueTogether(
name='hposynonym',
unique_together={('hpo', 'name')},
),
migrations.AlterUniqueTogether(
name='genevaluecount',
unique_together={('collection', 'gene', 'value')},
),
migrations.AlterUniqueTogether(
name='genevalue',
unique_together={('gene_count_type', 'label')},
),
migrations.AlterUniqueTogether(
name='genesymbolcitation',
unique_together={('gene_symbol', 'citation')},
),
migrations.AddField(
model_name='ensemblgeneannotationversiondiff',
name='version_from',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='version_diff_from', to='annotation.ensemblgeneannotationversion'),
),
migrations.AddField(
model_name='ensemblgeneannotationversiondiff',
name='version_to',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='version_diff_to', to='annotation.ensemblgeneannotationversion'),
),
migrations.AlterUniqueTogether(
name='ensemblgeneannotation',
unique_together={('version', 'gene')},
),
migrations.AlterUniqueTogether(
name='sampleannotationversionvariantsource',
unique_together={('sample', 'variant_annotation_version')},
),
]
| 46.211966
| 167
| 0.636532
| 2,411
| 27,034
| 6.99046
| 0.070925
| 0.049365
| 0.085558
| 0.134449
| 0.870239
| 0.86478
| 0.72333
| 0.66726
| 0.66637
| 0.657648
| 0
| 0.001473
| 0.246393
| 27,034
| 584
| 168
| 46.291096
| 0.825799
| 0.001591
| 0
| 0.792028
| 1
| 0
| 0.241321
| 0.133017
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.010399
| 0.005199
| 0
| 0.012132
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
219835c4456616cd2ca6c2bfcf3040f3bec217da
| 349
|
py
|
Python
|
src/aptitude/nodes/literary_devices/characterization/characterization.py
|
ThatAquarel/Aptitude
|
1311b6a1a284c5f84ae5ec5707b1f896ac7bc710
|
[
"MIT"
] | null | null | null |
src/aptitude/nodes/literary_devices/characterization/characterization.py
|
ThatAquarel/Aptitude
|
1311b6a1a284c5f84ae5ec5707b1f896ac7bc710
|
[
"MIT"
] | null | null | null |
src/aptitude/nodes/literary_devices/characterization/characterization.py
|
ThatAquarel/Aptitude
|
1311b6a1a284c5f84ae5ec5707b1f896ac7bc710
|
[
"MIT"
] | null | null | null |
from aptitude.nodes.literary_devices.metaphor.metaphor import Metaphor
from aptitude.nodes.literary_devices.simile.simile import Simile
from aptitude.pipeline.parser import Parser
class Characterization(Parser):
@staticmethod
def get_dependencies() -> list[type]:
return [Metaphor, Simile]
def parse(self, data):
pass
| 26.846154
| 70
| 0.756447
| 41
| 349
| 6.365854
| 0.560976
| 0.137931
| 0.130268
| 0.191571
| 0.245211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163324
| 349
| 12
| 71
| 29.083333
| 0.893836
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.222222
| false
| 0.111111
| 0.333333
| 0.111111
| 0.777778
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
|
0
| 6
|
0dfe4aae3ddeb363f3d0d606cdeaf47e8fa17002
| 171
|
py
|
Python
|
angrmanagement/ui/toolbars/__init__.py
|
DennyDai/angr-management
|
8a4ba5dafbf2f4d2ba558528a0d1ae099a199a04
|
[
"BSD-2-Clause"
] | null | null | null |
angrmanagement/ui/toolbars/__init__.py
|
DennyDai/angr-management
|
8a4ba5dafbf2f4d2ba558528a0d1ae099a199a04
|
[
"BSD-2-Clause"
] | null | null | null |
angrmanagement/ui/toolbars/__init__.py
|
DennyDai/angr-management
|
8a4ba5dafbf2f4d2ba558528a0d1ae099a199a04
|
[
"BSD-2-Clause"
] | null | null | null |
from .file_toolbar import FileToolbar
from .function_table_toolbar import FunctionTableToolbar
from .nav_toolbar import NavToolbar
from .debug_toolbar import DebugToolbar
| 34.2
| 56
| 0.883041
| 21
| 171
| 6.952381
| 0.571429
| 0.356164
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093567
| 171
| 4
| 57
| 42.75
| 0.941935
| 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 | 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
|
df267c6adde430320bce250928b5d8430b63bea7
| 11,655
|
py
|
Python
|
ws2122-lspm/Lib/site-packages/pm4py/algo/organizational_mining/resource_profiles/algorithm.py
|
Malekhy/ws2122-lspm
|
e4dc8b801d12f862b8ef536a0f125f346f085a00
|
[
"MIT"
] | 1
|
2022-01-19T04:02:46.000Z
|
2022-01-19T04:02:46.000Z
|
ws2122-lspm/Lib/site-packages/pm4py/algo/organizational_mining/resource_profiles/algorithm.py
|
Malekhy/ws2122-lspm
|
e4dc8b801d12f862b8ef536a0f125f346f085a00
|
[
"MIT"
] | 1
|
2021-11-19T07:21:48.000Z
|
2021-11-19T07:21:48.000Z
|
ws2122-lspm/Lib/site-packages/pm4py/algo/organizational_mining/resource_profiles/algorithm.py
|
Malekhy/ws2122-lspm
|
e4dc8b801d12f862b8ef536a0f125f346f085a00
|
[
"MIT"
] | 1
|
2022-01-14T17:15:38.000Z
|
2022-01-14T17:15:38.000Z
|
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.algo.organizational_mining.resource_profiles.variants import pandas, log
import pandas as pd
from pm4py.objects.log.obj import EventLog
from typing import Union, Optional, Dict, Any
from datetime import datetime
def distinct_activities(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> int:
"""
Number of distinct activities done by a resource in a given time interval [t1, t2)
Metric RBI 1.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
-----------------
distinct_activities
Distinct activities
"""
if type(log_obj) is pd.DataFrame:
return pandas.distinct_activities(log_obj, t1, t2, r, parameters=parameters)
else:
return log.distinct_activities(log_obj, t1, t2, r, parameters=parameters)
def activity_frequency(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
Fraction of completions of a given activity a, by a given resource r, during a given time slot, [t1, t2),
with respect to the total number of activity completions by resource r during [t1, t2)
Metric RBI 1.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
a
Activity
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.activity_frequency(log_obj, t1, t2, r, a, parameters=parameters)
else:
return log.activity_frequency(log_obj, t1, t2, r, a, parameters=parameters)
def activity_completions(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> int:
"""
The number of activity instances completed by a given resource during a given time slot.
Metric RBI 2.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.activity_completions(log_obj, t1, t2, r, parameters=parameters)
else:
return log.activity_completions(log_obj, t1, t2, r, parameters=parameters)
def case_completions(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> int:
"""
The number of cases completed during a given time slot in which a given resource was involved.
Metric RBI 2.2 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.case_completions(log_obj, t1, t2, r, parameters=parameters)
else:
return log.case_completions(log_obj, t1, t2, r, parameters=parameters)
def fraction_case_completions(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The fraction of cases completed during a given time slot in which a given resource was involved with respect to the
total number of cases completed during the time slot.
Metric RBI 2.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.fraction_case_completions(log_obj, t1, t2, r, parameters=parameters)
else:
return log.fraction_case_completions(log_obj, t1, t2, r, parameters=parameters)
def average_workload(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The average number of activities started by a given resource but not completed at a moment in time.
Metric RBI 2.4 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.average_workload(log_obj, t1, t2, r, parameters=parameters)
else:
return log.average_workload(log_obj, t1, t2, r, parameters=parameters)
def multitasking(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The fraction of active time during which a given resource is involved in more than one activity with respect
to the resource's active time.
Metric RBI 3.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.multitasking(log_obj, t1, t2, r, parameters=parameters)
else:
return log.multitasking(log_obj, t1, t2, r, parameters=parameters)
def average_duration_activity(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str, a: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The average duration of instances of a given activity completed during a given time slot by a given resource.
Metric RBI 4.3 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
a
Activity
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.average_duration_activity(log_obj, t1, t2, r, a, parameters=parameters)
else:
return log.average_duration_activity(log_obj, t1, t2, r, a, parameters=parameters)
def average_case_duration(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The average duration of cases completed during a given time slot in which a given resource was involved.
Metric RBI 4.4 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.average_case_duration(log_obj, t1, t2, r, parameters=parameters)
else:
return log.average_case_duration(log_obj, t1, t2, r, parameters=parameters)
def interaction_two_resources(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r1: str, r2: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The number of cases completed during a given time slot in which two given resources were involved.
Metric RBI 5.1 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r1
Resource 1
r2
Resource 2
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.interaction_two_resources(log_obj, t1, t2, r1, r2, parameters=parameters)
else:
return log.interaction_two_resources(log_obj, t1, t2, r1, r2, parameters=parameters)
def social_position(log_obj: Union[pd.DataFrame, EventLog], t1: Union[datetime, str], t2: Union[datetime, str], r: str,
parameters: Optional[Dict[Any, Any]] = None) -> float:
"""
The fraction of resources involved in the same cases with a given resource during a given time slot with
respect to the total number of resources active during the time slot.
Metric RBI 5.2 in Pika, Anastasiia, et al.
"Mining resource profiles from event logs." ACM Transactions on Management Information Systems (TMIS) 8.1 (2017): 1-30.
Parameters
-----------------
log_obj
Log object
t1
Left interval
t2
Right interval
r
Resource
Returns
----------------
metric
Value of the metric
"""
if type(log_obj) is pd.DataFrame:
return pandas.social_position(log_obj, t1, t2, r, parameters=parameters)
else:
return log.social_position(log_obj, t1, t2, r, parameters=parameters)
| 32.196133
| 139
| 0.63329
| 1,537
| 11,655
| 4.73715
| 0.111906
| 0.046148
| 0.048345
| 0.030216
| 0.822277
| 0.79783
| 0.779014
| 0.767065
| 0.753193
| 0.704162
| 0
| 0.027024
| 0.260232
| 11,655
| 361
| 140
| 32.285319
| 0.817444
| 0.492578
| 0
| 0.464789
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15493
| false
| 0
| 0.070423
| 0
| 0.535211
| 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
|
df3069f0cb3b40d0f7f9d3dd48527ddaa8d3be57
| 7,804
|
py
|
Python
|
registry/tests/profile_test.py
|
jbn/quilt
|
67960d2739ce5ea34c05febbe8f2bb9f75e211a8
|
[
"Apache-2.0"
] | null | null | null |
registry/tests/profile_test.py
|
jbn/quilt
|
67960d2739ce5ea34c05febbe8f2bb9f75e211a8
|
[
"Apache-2.0"
] | null | null | null |
registry/tests/profile_test.py
|
jbn/quilt
|
67960d2739ce5ea34c05febbe8f2bb9f75e211a8
|
[
"Apache-2.0"
] | null | null | null |
# Copyright (c) 2017 Quilt Data, Inc. All rights reserved.
"""
Profile tests
"""
import json
from unittest.mock import patch
import requests
from quilt_server.const import PUBLIC, TEAM
from quilt_server.core import RootNode
from .utils import QuiltTestCase
class ProfileTestCase(QuiltTestCase):
"""
Test the profile endpoint
"""
@patch('quilt_server.views.ALLOW_ANONYMOUS_ACCESS', True)
def testProfile(self):
"""
List all accessible packages.
"""
user = "test_user"
pkg = "pkg"
public_pkg = "publicpkg"
self.put_package(user, pkg, RootNode(children=dict()))
self.put_package(user, public_pkg, RootNode(children=dict()), is_public=True)
# The user can see own packages.
resp = self.app.get(
'/api/profile',
headers={
'Authorization': user
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == [
dict(owner=user, name=pkg, is_public=False, is_team=False),
dict(owner=user, name=public_pkg, is_public=True, is_team=False),
]
assert data['shared'] == []
# Other users can't see anything.
sharewith = "share_with"
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == []
# Users can see shared packages.
resp = self._share_package(user, pkg, sharewith)
assert resp.status_code == requests.codes.ok
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == [dict(owner=user, name=pkg, is_public=False, is_team=False)]
# Packages that are both public and shared show up under "shared".
resp = self._share_package(user, pkg, PUBLIC)
assert resp.status_code == requests.codes.ok
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == [dict(owner=user, name=pkg, is_public=True, is_team=False)]
@patch('quilt_server.views.ALLOW_ANONYMOUS_ACCESS', False)
@patch('quilt_server.views.ALLOW_TEAM_ACCESS', True)
def testTeamProfile(self):
"""
Test the profile endpoint but with teams and no public access.
"""
user = "test_user"
pkg = "pkg"
team_pkg = "teampkg"
self.put_package(user, pkg, RootNode(children=dict()))
self.put_package(user, team_pkg, RootNode(children=dict()), is_team=True)
# The user can see own packages.
resp = self.app.get(
'/api/profile',
headers={
'Authorization': user
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == [
dict(owner=user, name=pkg, is_public=False, is_team=False),
dict(owner=user, name=team_pkg, is_public=False, is_team=True),
]
assert data['shared'] == []
# Other users can't see anything.
sharewith = "share_with"
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == []
# Users can see shared packages.
resp = self._share_package(user, pkg, sharewith)
assert resp.status_code == requests.codes.ok
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == [dict(owner=user, name=pkg, is_public=False, is_team=False)]
# Packages that are both team and shared show up under "shared".
resp = self._share_package(user, pkg, TEAM)
assert resp.status_code == requests.codes.ok
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == [dict(owner=user, name=pkg, is_public=False, is_team=True)]
@patch('quilt_server.views.ALLOW_ANONYMOUS_ACCESS', True)
@patch('quilt_server.views.ALLOW_TEAM_ACCESS', True)
def testTeamProfileWithPublic(self):
"""
Test the profile endpoint but with teams *AND* public packages.
"""
user = "test_user"
self.put_package(user, 'pkg0', RootNode(children=dict()))
self.put_package(user, 'pkg1', RootNode(children=dict()), is_team=True)
self.put_package(user, 'pkg2', RootNode(children=dict()), is_public=True)
self.put_package(user, 'pkg3', RootNode(children=dict()), is_team=True, is_public=True)
# The user can see own packages.
resp = self.app.get(
'/api/profile',
headers={
'Authorization': user
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == [
dict(owner=user, name='pkg0', is_public=False, is_team=False),
dict(owner=user, name='pkg1', is_public=False, is_team=True),
dict(owner=user, name='pkg2', is_public=True, is_team=False),
dict(owner=user, name='pkg3', is_public=True, is_team=True),
]
assert data['shared'] == []
# Other users can't see anything.
sharewith = "share_with"
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == []
# Packages that are both team and shared show up under "shared".
resp = self._share_package(user, 'pkg1', sharewith)
assert resp.status_code == requests.codes.ok
resp = self._share_package(user, 'pkg3', sharewith)
assert resp.status_code == requests.codes.ok
resp = self.app.get(
'/api/profile',
headers={
'Authorization': sharewith
}
)
assert resp.status_code == requests.codes.ok
data = json.loads(resp.data.decode('utf8'))['packages']
assert data['own'] == []
assert data['shared'] == [
dict(owner=user, name='pkg1', is_public=False, is_team=True),
dict(owner=user, name='pkg3', is_public=True, is_team=True),
]
| 30.84585
| 95
| 0.564198
| 879
| 7,804
| 4.895336
| 0.108077
| 0.051127
| 0.063212
| 0.079015
| 0.884034
| 0.862189
| 0.826168
| 0.791076
| 0.77016
| 0.731118
| 0
| 0.004962
| 0.302793
| 7,804
| 252
| 96
| 30.968254
| 0.785885
| 0.089185
| 0
| 0.668639
| 0
| 0
| 0.118201
| 0.027837
| 0
| 0
| 0
| 0
| 0.230769
| 1
| 0.017751
| false
| 0
| 0.035503
| 0
| 0.059172
| 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
|
df4d0d36522801fde9a6639c3c149f922b2e7276
| 6,379
|
py
|
Python
|
day3.py
|
gitkoogie/AdventOfCode2021
|
416408c22bc704abc95ed46105d086d08e116e1d
|
[
"Apache-2.0"
] | null | null | null |
day3.py
|
gitkoogie/AdventOfCode2021
|
416408c22bc704abc95ed46105d086d08e116e1d
|
[
"Apache-2.0"
] | null | null | null |
day3.py
|
gitkoogie/AdventOfCode2021
|
416408c22bc704abc95ed46105d086d08e116e1d
|
[
"Apache-2.0"
] | null | null | null |
import time
start = time.perf_counter()
with open('inputs/day3.txt') as f:
temp = f.readlines()
# fix input
data = []
for i in range(len(temp)):
data.append(temp[i].strip("\n"))
import numpy
#data = numpy.loadtxt("inputs/day3.txt")
#data = ["00100", "11110", "10110", "10111", "10101", "01111", "00111", "11100", "10000", "11001", "00010", "01010"]
gamma_rate = []
epsilon_rate = []
# func 1
def day1func1(data):
for i in range(len(data[0])):
sum_zero = 0
sum_one = 0
for j in range(len(data)):
if int(data[j][i]) == 0:
sum_zero += 1
else:
sum_one += 1
if sum_zero > sum_one:
gamma_rate.append("0")
epsilon_rate.append("1")
else:
gamma_rate.append("1")
epsilon_rate.append("0")
t1 = "0b"
t2 = "0b"
for i in range(len(gamma_rate)):
t1 += gamma_rate[i]
t2 += epsilon_rate[i]
return int(t1, 2) * int(t2, 2)
# helper find 1 or 0 most common
def find(data, pos, v):
sum_zero = 0
sum_one = 0
for i in range(len(data)):
if int(data[i][pos]) == 0:
sum_zero += 1
else:
sum_one += 1
if v == 0:
if sum_one >= sum_zero:
return 1
else:
return 0
elif v == 1:
if sum_one < sum_zero:
return 1
else:
return 0
# helper oxy
def oxygen(data):
ret = ""
ret += str(find(data, 0, 0))
new = []
for i in range(len(data)):
if data[i][0] == ret[0]:
new.append(data[i])
if(len(new) == 1):
return new
ret += str(find(new, 1, 0))
new2 = []
for i in range(len(new)):
if new[i][1] == ret[1]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 2, 0))
new = []
for i in range(len(new2)):
if new2[i][2] == ret[2]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 3, 0))
new2 = []
for i in range(len(new)):
if new[i][3] == ret[3]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 4, 0))
new = []
for i in range(len(new2)):
if new2[i][4] == ret[4]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 5, 0))
new2 = []
for i in range(len(new)):
if new[i][5] == ret[5]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 6, 0))
new = []
for i in range(len(new2)):
if new2[i][6] == ret[6]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 7, 0))
new2 = []
for i in range(len(new)):
if new[i][7] == ret[7]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 8, 0))
new = []
for i in range(len(new2)):
if new2[i][8] == ret[8]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 9, 0))
new2 = []
for i in range(len(new)):
if new[i][9] == ret[9]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 10, 0))
new = []
for i in range(len(new2)):
if new2[i][10] == ret[10]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 11, 0))
new2 = []
for i in range(len(new)):
if new[i][11] == ret[11]:
new2.append(new[i])
if(len(new2) == 1):
return new2
# func 2
def carbon(data):
ret = ""
ret += str(find(data, 0, 1))
new = []
for i in range(len(data)):
if data[i][0] == ret[0]:
new.append(data[i])
if(len(new) == 1):
return new
ret += str(find(new, 1, 1))
new2 = []
for i in range(len(new)):
if new[i][1] == ret[1]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 2, 1))
new = []
for i in range(len(new2)):
if new2[i][2] == ret[2]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 3, 1))
new2 = []
for i in range(len(new)):
if new[i][3] == ret[3]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 4, 1))
new = []
for i in range(len(new2)):
if new2[i][4] == ret[4]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 5, 1))
new2 = []
for i in range(len(new)):
if new[i][5] == ret[5]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 6, 1))
new = []
for i in range(len(new2)):
if new2[i][6] == ret[6]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 7, 1))
new2 = []
for i in range(len(new)):
if new[i][7] == ret[7]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 8, 1))
new = []
for i in range(len(new2)):
if new2[i][8] == ret[8]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 9, 1))
new2 = []
for i in range(len(new)):
if new[i][9] == ret[9]:
new2.append(new[i])
if(len(new2) == 1):
return new2
ret += str(find(new2, 10, 1))
new = []
for i in range(len(new2)):
if new2[i][10] == ret[10]:
new.append(new2[i])
if(len(new) == 1):
return new
ret += str(find(new, 11, 1))
new2 = []
for i in range(len(new)):
if new[i][11] == ret[11]:
new2.append(new[i])
if(len(new2) == 1):
return new2
def day1func2(data):
oxy = oxygen(data)
t = "0b"
for i in range(len(oxy)):
t += oxy[i]
oxy = int(t, 2)
co = carbon(data)
t = "0b"
for i in range(len(co)):
t += co[i]
co = int(t, 2)
return co * oxy
print(day1func1(data))
print(day1func2(data))
print((time.perf_counter() - start)*1000, "ms")
| 22.620567
| 116
| 0.45446
| 967
| 6,379
| 2.972079
| 0.092037
| 0.075505
| 0.107864
| 0.114823
| 0.786708
| 0.786708
| 0.767223
| 0.722338
| 0.707724
| 0.692415
| 0
| 0.081758
| 0.361499
| 6,379
| 282
| 117
| 22.620567
| 0.623864
| 0.034802
| 0
| 0.70339
| 0
| 0
| 0.005041
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.021186
| false
| 0
| 0.008475
| 0
| 0.15678
| 0.012712
| 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
|
10e2f0961c766351ca007e3738763ca8811238a3
| 36
|
py
|
Python
|
TurtleBop/models/__init__.py
|
ThatGuyJustin/TurtleBop
|
40b18ad524f297b9fda02bab11173b61b6fa4099
|
[
"MIT"
] | null | null | null |
TurtleBop/models/__init__.py
|
ThatGuyJustin/TurtleBop
|
40b18ad524f297b9fda02bab11173b61b6fa4099
|
[
"MIT"
] | null | null | null |
TurtleBop/models/__init__.py
|
ThatGuyJustin/TurtleBop
|
40b18ad524f297b9fda02bab11173b61b6fa4099
|
[
"MIT"
] | null | null | null |
from TurtleBop.models.guild import *
| 36
| 36
| 0.833333
| 5
| 36
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083333
| 36
| 1
| 36
| 36
| 0.909091
| 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
|
10f076ae907c96c4683235252866134c0cfb0d3b
| 123
|
py
|
Python
|
torch_mimicry/nets/sngan/__init__.py
|
houliangict/mimicry
|
d9e43940254de4a85c78e644f2d2b1135de4b50d
|
[
"MIT"
] | 560
|
2020-03-31T07:07:26.000Z
|
2022-03-15T08:29:37.000Z
|
torch_mimicry/nets/sngan/__init__.py
|
houliangict/mimicry
|
d9e43940254de4a85c78e644f2d2b1135de4b50d
|
[
"MIT"
] | 34
|
2020-03-31T02:42:16.000Z
|
2021-12-10T15:47:30.000Z
|
torch_mimicry/nets/sngan/__init__.py
|
houliangict/mimicry
|
d9e43940254de4a85c78e644f2d2b1135de4b50d
|
[
"MIT"
] | 63
|
2020-04-04T09:56:22.000Z
|
2022-03-15T02:34:58.000Z
|
from .sngan_128 import *
from .sngan_32 import *
from .sngan_48 import *
from .sngan_64 import *
from .sngan_base import *
| 20.5
| 25
| 0.756098
| 20
| 123
| 4.4
| 0.4
| 0.511364
| 0.681818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.087379
| 0.162602
| 123
| 5
| 26
| 24.6
| 0.76699
| 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
|
802575b4e4c05a5e38167bcb911569003f0bad6b
| 174
|
py
|
Python
|
domain/useCases/__init__.py
|
JVGC/MyFinancesPython
|
5e4ac02ea00c4ddab688dd0093eed3f3fb2ad826
|
[
"MIT"
] | null | null | null |
domain/useCases/__init__.py
|
JVGC/MyFinancesPython
|
5e4ac02ea00c4ddab688dd0093eed3f3fb2ad826
|
[
"MIT"
] | null | null | null |
domain/useCases/__init__.py
|
JVGC/MyFinancesPython
|
5e4ac02ea00c4ddab688dd0093eed3f3fb2ad826
|
[
"MIT"
] | null | null | null |
from .AddNewDebt import *
from .GetDebtById import *
from .PayDebtPart import *
from .UpdateDebtDescription import *
from .DeleteDebtById import *
from .GetAllDebts import *
| 24.857143
| 36
| 0.793103
| 18
| 174
| 7.666667
| 0.444444
| 0.362319
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 174
| 6
| 37
| 29
| 0.92
| 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
|
3378601f401cc65b610da88dac290768b028faa7
| 15,284
|
py
|
Python
|
sau/sau.py
|
mwhit74/sau
|
2278f0d34ac609aaa8360b99b34eb433e124e3c0
|
[
"MIT"
] | null | null | null |
sau/sau.py
|
mwhit74/sau
|
2278f0d34ac609aaa8360b99b34eb433e124e3c0
|
[
"MIT"
] | null | null | null |
sau/sau.py
|
mwhit74/sau
|
2278f0d34ac609aaa8360b99b34eb433e124e3c0
|
[
"MIT"
] | 2
|
2020-11-14T04:42:29.000Z
|
2022-02-09T18:51:08.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 14 08:39:58 2020
@author: mwhitten
"""
"""
Functions ac1 - ac45 are taken from AISC
Functions rc1 - rc20 are taken from Roarks' Forumlas for Stress-Strain
Signularity function for multiple point loads on a simply-supported beam
Singularity function for multiple uniformly distributed loads on a
simply-supported beam
"""
import math
import numpy as np
import tabulate as tabulate
np.set_printoptions(suppress=True)
def ac1_vx(w, l, x):
"""Shear at x - Beam simply supported - Uniformly dist. loads
Calculates the shear in the beam at any location, x, along the beam due
to a uniformly distributed load.
v = w*(l/2-x)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
x (float): distance along beam from left support
Returns:
v (tuple(float, str)): shear at x
Notes:
1. Consistent units are the responsibility of the user.
2. For the reaction at the support use x = 0.0 or x = L.
"""
v = w*(l/2.0-x)
text = (f'v = w*(l/2.0-x) \n' +
f'v = {w:.3f}*({l:.2f}/2.0-{x:.2f}) \n' +
f'v = {v:.2f}')
return v, text
def ac1_mx(w, l, x):
"""Moment at x - Beam simply supported - Uniformly dist. loads
Calculates the moment in the beam at any location, x, along the
beam due to a uniformly distributed load.
m = w*x/2*(l-x)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
m (tuple(float, str)): maximum positive moment at midspan
Notes:
1. Consistent units are the responsibility of the user.
3. For maximum positive moment use x = L/2.
"""
m = w*x/2.*(l-x)
text = (f'm = w*x/2*(l-x) \n' +
f'm = {w:.2f}*{x:.2f}/2*({l:.2f}-{x:.2f}) \n' +
f'm = {m:.2f}')
return m, text
def ac1_defl(w, l, E, I, x):
"""Deflection at x - Beam simply supported - Uniformly dist. loads
Calculates the deflection in the beam at any location, x, along the
beam due to a uniformly distributed load.
d = w*x/(24*E*I)*(math.pow(l,3) - 2*l*math.pow(x,2) + math.pow(x,3))
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
d (tuple(float, str)): deflection at x
Notes:
1. Consistent units are the responsibility of the user.
"""
d = w*x/(24*E*I)*(math.pow(l,3) - 2*l*math.pow(x,2) + math.pow(x,3))
text = (f'd = w*x/(24*E*I)*(math.pow(l,3) - 2*l*math.pow(x,2) + ' +
f'math.pow(x,3)) \n' +
f'd = {w:.2f}*{x:.1f}/(24*{e:.1}*{i:.1f})*(math.pow({l:.1f},3) - '+
f'2*{l:.1f}*math.pow({x:.1f},2) + math.pow({x:.1f},3)) \n' +
f'd = {d:.3f}')
return d, text
def ac15_vx(w, l, x):
"""Shear at x - Beam fixed at both ends - Uniformly dist. loads
Calculates the shear in the beam at any location, x, along the
beam due to a uniformly distributed load.
v = w*(l/2.0-x)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
x (float): distance along beam from left support
Returns:
v (tuple(float, str)): shear at x
Notes:
1. Consistent units are the responsibility of the user.
2. For the reaction at the support use x = 0.0 or x = L.
"""
v = w*(l/2.0-x)
text = (f'v = w*(l/2.0-x) \n' +
f'v = {w:.3f}*({l:.2f}/2.0-{x:.2f})' +
f'v = {v:.2f}')
return v, text
def ac15_mx(w, l, x):
"""Moment at x - Beam fixed at both ends - Uniformly dist. loads
Calculates the moment in the beam at any location, x, along the
beam due to a uniformly distributed load.
m = w/12.0*(6*l*x - math.pow(l,2) - 6*math.pow(x,2))
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
m (tuple(float, str)): maximum positive moment at midspan
Notes:
1. Consistent units are the responsibility of the user.
2. For maximum negative moment use x = 0.0 or x = L.
3. For maximum positive moment use x = L/2.
"""
m = w/12.0*(6*l*x - math.pow(l,2) - 6*math.pow(x,2))
text = (f'm = w/12.0*(6*l*x - math.pow(l,2) - 6*math.pow(x,2)) \n' +
f'm = {w:.3f}/12.0*(6.0*{l:.2f}*{x:.2f} - math.pow({l:.2f},2) - '
f'6.0*math.pow({x:.2f},2)) \n' +
f'm = {m:.2f}')
return m, text
def ac15_defl(w, l, E, I, x):
"""Deflection at x - Beam fixed at both ends - Uniformly dist. loads
Calculates the deflection in the beam at any location, x, along the
beam due to a uniformly distributed load.
d = w*math.pow(x,2)/(24.0*E*I)*math.pow(l-x,2)
Args:
w (float): uniformly distributed load
l (float): length of beam between supports
E (float): modulus of elasticity
I (float): section modulus
x (float): distance along beam from left support
Returns:
d (tuple(float, str)): deflection at x
Notes:
1. Consistent units are the responsibility of the user.
"""
d = d = w*math.pow(x,2)/(24.0*E*I)*math.pow(l-x,2)
text = (f'd = w*math.pow(x,2)/(24.0*E*I)*math.pow(l-x,2) \n' +
f'd = {w:.3f}*math.pow({x:.2f},2)/(24.0*{E:.1f}*{I:.1f})'
f'*math.pow({l:.2f}-{x:.2f},2) \n' +
f'd = {d:.3f}')
return d, text
def rc8a_stress_at_edge(a, b, q, e, t):
"""
"""
pass
def rc8a_stress_at_center(a, b, q, e, t):
"""
"""
pass
def ra8a_defl_at_center(a, b, q, e, t):
pass
def mplob(l, loads, locs, e = 0.0, i = 0.0, defl_factor = 1.0, j = 21):
"""Multiple point loads on a beam
Calculates the shear, moment, and deflection along a simply-supported
beam for any number of point loads applied to the beam.
Args:
l (float): length of beam
loads (list): magnitude of loads
locs (list): location of each load from left end of beam
e (float, optional): elastic modulus; defaults to 0.0
i (float, optional): second moment of area; defaults to 0.0
defl_factor (float, optional): scale factor for deflection results;
defaults to 1.0
j (int, optional): number of analysis points; defaults to 21
Returns:
(dls, v, m, y, text) (tuple(float, float, float, float, str)):
dls - location of analysis points w.r.t. left support
v - shear at each analysis point
m - moment at each analysis point
y - vertical deflection at each analysis point
text - user input and formatted table of results
Notes:
1. Units are the responsibility of the user.
Internal units are consistent.
2. Recommended units are kips and feet with a defl_factor = 12. This
will provide shear in kips, moment in kip-ft, and deflection in
inches. This means that E [ksf] and I [ft^4].
3. If e = 0.0 or i = 0.0 no deflection will be calculated
4. Elastic analysis, no shear deflection.
5. Algorithm uses singularity functions to calculate values.
"""
def v_conc(ra, p, x, a, l):
if p == 0.0:
return 0.0
else:
return ra * sf(x, 0, 0) - p * sf(x, a, 0)
def m_conc(ra, p, x, a, l):
if p == 0.0:
return 0.0
else:
return ra * sf(x, 0, 1) - p * sf(x, a, 1)
def y_conc(ra, p, x, a, l):
if p == 0.0:
return 0.0
else:
return (ra/6 * sf(x, 0, 3) - p/6 * sf(x,a,3) +
(p/6 * math.pow(l-a,3) - ra/6 * math.pow(l,3))/l*x)
def sf(x, a, n):
if x < a:
return 0
else:
return math.pow(x-a,n)
locs = np.asarray(locs, dtype=np.float32)
loads = np.asarray(loads, dtype=np.float32)
a_locs = locs
b_locs = l - locs
ras = loads*b_locs/l
rbs = loads*a_locs/l
dls = np.linspace(0,l,j)
vs = np.empty([j], dtype=np.float32)
ms = np.empty([j], dtype=np.float32)
ys = np.empty([j], dtype=np.float32)
k = 0
for dl in dls:
sum_v = 0.
sum_m = 0.
sum_y = 0.
for ra, a, load in zip(ras, a_locs, loads):
sum_v = sum_v + v_conc(ra, load, dl, a, l)
sum_m = sum_m + m_conc(ra, load, dl, a, l)
sum_y = sum_y + y_conc(ra, load, dl, a, l)
vs[k] = sum_v
ms[k] = sum_m
ys[k] = sum_y
k += 1
if e == 0.0 or i == 0.0:
ys = ys*0.0
else:
ys = ys/e/i*defl_factor
text1 = (f'Multiple Point Loads on Simply Supported Beam Analysis \n' +
f'(Using singularity functions) \n' +
f'------------------------------------------------------ \n\n' +
f'l = {l:.2f}, E = {e:.1f}, I = {i:.3f}, ' +
f'Defl Factor = {defl_factor:.1f}, Num Nodes = {j:d} \n\n')
text2 = tabulate.tabulate({f'Load':loads, 'Location':locs},
headers='keys',
floatfmt=(".2f", ".2f"))
text3 = tabulate.tabulate({f"L/{j:d}":dls,
f"V":vs,
f"M":ms,
f"Y*{defl_factor:.2f}":ys},
headers='keys',
floatfmt=(".2f", ".2f", ".2f", ".4f"))
text = text1 + text2 + "\n\n" + text3
return dls, vs, ms, ys, text
def mdlob(l, loads, locs, e = 0.0, i = 0.0, defl_factor = 1.0, j = 21):
"""Multiple point loads on a beam
Calculates the shear, moment, and deflection along a simply-supported
beam for any number of point loads applied to the beam.
Args:
l (float): length of beam
loads (list): magnitude of loads
locs (list of tubles): start and end of each load from left end of beam
e (float, optional): elastic modulus; defaults to 0.0
i (float, optional): second moment of area; defaults to 0.0
defl_factor (float, optional): scale factor for deflection results;
defaults to 1.0
j (int, optional): number of analysis points; defaults to 21
Returns:
(dls, v, m, y, text) (tuple(float, float, float, float, str)):
dls - location of analysis points w.r.t. left support
v - shear at each analysis point
m - moment at each analysis point
y - vertical deflection at each analysis point
text - user input and formatted table of results
Notes:
1. Units are the responsibility of the user. Internal units
are consistent.
2. Recommended units are kips and feet with a defl_factor = 12. This
will provide shear in kips, moment in kip-ft, and deflection in
inches. This means that E [ksf] and I [ft^4].
3. If e = 0.0 or i = 0.0 no deflection will be calculated
4. Elastic analysis, no shear deflection.
5. Algorithm uses singularity functions to calculate values.
"""
def v_dist(ra, w, x, a, b):
if w == 0.0:
return 0.0
else:
return ra * sf(x, 0, 0) - w * sf(x, a, 1) + w * sf(x, b, 1)
def m_dist(ra, w, x, a, b):
if w == 0.0:
return 0.0
else:
return ra * sf(x, 0, 1) - w/2 * sf(x, a, 2) + w/2 * sf(x, b, 2)
def y_dist(ra, w, x, a, b, l):
if w == 0.0:
return 0.0
else:
return (ra/6 * sf(x, 0, 3) - w/24 * sf(x, a, 4) +
w/24 * sf(x, b, 4) + (w/24 * math.pow(l-a,4) -
w/24 * math.pow(l-b,4) - ra/6*math.pow(l,3))/l*x)
def sf(x, a, n):
if x < a:
return 0
else:
return math.pow(x-a,n)
locs = np.asarray(locs, dtype=np.float32)
loads = np.asarray(loads, dtype=np.float32)
s_locs = locs[...,0]
e_locs = locs[...,1]
a_locs = locs[...,0]
b_locs = locs[...,1] - a_locs
c_locs = l - a_locs - b_locs
ras = loads*b_locs/(2*l)*(2*c_locs + b_locs)
rbs = loads*b_locs/(2*l)*(2*c_locs + b_locs)
dls = np.linspace(0,l,j)
vs = np.empty([j], dtype=np.float32)
ms = np.empty([j], dtype=np.float32)
ys = np.empty([j], dtype=np.float32)
k = 0
for dl in dls:
sum_v = 0.
sum_m = 0.
sum_y = 0.
for ra, a, b, load in zip(ras, s_locs, e_locs, loads):
sum_v = sum_v + v_dist(ra, load, dl, a, b)
sum_m = sum_m + m_dist(ra, load, dl, a, b)
sum_y = sum_y + y_dist(ra, load, dl, a, b, l)
vs[k] = sum_v
ms[k] = sum_m
ys[k] = sum_y
k += 1
if e == 0.0 or i == 0.0:
ys = ys*0.0
else:
ys = ys/e/i*defl_factor
text1 = (f'Multiple Distributed Loads on Simply Supported Beam ' +
f'Analysis \n' +
f'(Using singularity functions) \n' +
f'------------------------------------------------------ \n\n' +
f'l = {l:.2f}, E = {e:.1f}, I = {i:.3f}, ' +
f'Defl Factor = {defl_factor:.1f}, Num Nodes = {j:d} \n\n')
text2 = tabulate.tabulate({f'Load':loads, 'Start':a_locs, 'End':b_locs},
headers='keys',
floatfmt=(".2f", ".2f", ".2f"))
text3 = tabulate.tabulate({f"L/{j:d}":dls,
f"V":vs,
f"M":ms,
f"Y*{defl_factor:.2f}":ys},
headers='keys',
floatfmt=(".2f", ".2f", ".2f", ".4f"))
text = text1 + text2 + "\n\n" + text3
return dls, vs, ms, ys, text
| 30.205534
| 79
| 0.489728
| 2,283
| 15,284
| 3.240473
| 0.09505
| 0.009462
| 0.019465
| 0.010949
| 0.892944
| 0.87037
| 0.846445
| 0.829413
| 0.8186
| 0.802379
| 0
| 0.040644
| 0.37379
| 15,284
| 506
| 80
| 30.205534
| 0.732316
| 0.432348
| 0
| 0.6
| 0
| 0.059459
| 0.178994
| 0.065896
| 0
| 0
| 0
| 0
| 0
| 1
| 0.102703
| false
| 0.016216
| 0.016216
| 0
| 0.248649
| 0.005405
| 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
|
338289597b6c8354f0d1713c6f0ced5c4b49ad04
| 2,318
|
py
|
Python
|
tests/core/pyspec/eth2spec/test/altair/transition/test_leaking.py
|
sifraitech/eth2.0-specs
|
1bfefe301da592375e2e02f65849a96aadec1936
|
[
"CC0-1.0"
] | 497
|
2021-08-19T01:22:07.000Z
|
2022-03-30T21:40:40.000Z
|
tests/core/pyspec/eth2spec/test/altair/transition/test_leaking.py
|
sifraitech/eth2.0-specs
|
1bfefe301da592375e2e02f65849a96aadec1936
|
[
"CC0-1.0"
] | 133
|
2021-08-18T16:47:29.000Z
|
2022-03-31T22:31:56.000Z
|
tests/core/pyspec/eth2spec/test/altair/transition/test_leaking.py
|
sifraitech/eth2.0-specs
|
1bfefe301da592375e2e02f65849a96aadec1936
|
[
"CC0-1.0"
] | 98
|
2021-08-31T09:19:27.000Z
|
2022-03-27T05:07:04.000Z
|
from eth2spec.test.context import (
ForkMeta,
with_fork_metas,
)
from eth2spec.test.helpers.constants import (
ALL_PRE_POST_FORKS,
)
from eth2spec.test.helpers.fork_transition import (
do_fork,
transition_until_fork,
transition_to_next_epoch_and_append_blocks,
)
@with_fork_metas([ForkMeta(pre_fork_name=pre, post_fork_name=post, fork_epoch=7) for pre, post in ALL_PRE_POST_FORKS])
def test_transition_with_leaking_pre_fork(state, fork_epoch, spec, post_spec, pre_tag, post_tag):
"""
Leaking starts at epoch 6 (MIN_EPOCHS_TO_INACTIVITY_PENALTY + 2).
The leaking starts before the fork transition in this case.
"""
transition_until_fork(spec, state, fork_epoch)
assert spec.is_in_inactivity_leak(state)
assert spec.get_current_epoch(state) < fork_epoch
yield "pre", state
# irregular state transition to handle fork:
blocks = []
state, block = do_fork(state, spec, post_spec, fork_epoch)
blocks.append(post_tag(block))
# check post transition state
assert spec.is_in_inactivity_leak(state)
# continue regular state transition with new spec into next epoch
transition_to_next_epoch_and_append_blocks(post_spec, state, post_tag, blocks, only_last_block=True)
yield "blocks", blocks
yield "post", state
@with_fork_metas([ForkMeta(pre_fork_name=pre, post_fork_name=post, fork_epoch=6) for pre, post in ALL_PRE_POST_FORKS])
def test_transition_with_leaking_at_fork(state, fork_epoch, spec, post_spec, pre_tag, post_tag):
"""
Leaking starts at epoch 6 (MIN_EPOCHS_TO_INACTIVITY_PENALTY + 2).
The leaking starts at the fork transition in this case.
"""
transition_until_fork(spec, state, fork_epoch)
assert not spec.is_in_inactivity_leak(state)
assert spec.get_current_epoch(state) < fork_epoch
yield "pre", state
# irregular state transition to handle fork:
blocks = []
state, block = do_fork(state, spec, post_spec, fork_epoch)
blocks.append(post_tag(block))
# check post transition state
assert spec.is_in_inactivity_leak(state)
# continue regular state transition with new spec into next epoch
transition_to_next_epoch_and_append_blocks(post_spec, state, post_tag, blocks, only_last_block=True)
yield "blocks", blocks
yield "post", state
| 33.594203
| 118
| 0.75151
| 343
| 2,318
| 4.749271
| 0.183673
| 0.055249
| 0.051565
| 0.044199
| 0.883978
| 0.883978
| 0.883978
| 0.861878
| 0.861878
| 0.861878
| 0
| 0.004678
| 0.169974
| 2,318
| 68
| 119
| 34.088235
| 0.841996
| 0.223469
| 0
| 0.552632
| 0
| 0
| 0.014806
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 1
| 0.052632
| false
| 0
| 0.078947
| 0
| 0.131579
| 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
|
33e1ce7265f000ab487068fc79f10413b37e1780
| 78
|
py
|
Python
|
jacdac/braille_display/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | 1
|
2022-02-15T21:30:36.000Z
|
2022-02-15T21:30:36.000Z
|
jacdac/braille_display/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | null | null | null |
jacdac/braille_display/__init__.py
|
microsoft/jacdac-python
|
712ad5559e29065f5eccb5dbfe029c039132df5a
|
[
"MIT"
] | 1
|
2022-02-08T19:32:45.000Z
|
2022-02-08T19:32:45.000Z
|
# Autogenerated file.
from .client import BrailleDisplayClient # type: ignore
| 26
| 55
| 0.807692
| 8
| 78
| 7.875
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.128205
| 78
| 2
| 56
| 39
| 0.926471
| 0.410256
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
33e3706bc8f8d5b4803acc1c238fa2680a135709
| 73
|
py
|
Python
|
libg3n/modules/python/__init__.py
|
jhkloss/libg3n
|
f7394b39c51bb199a1307edc2fcba1bd2ba0d0dd
|
[
"Unlicense"
] | null | null | null |
libg3n/modules/python/__init__.py
|
jhkloss/libg3n
|
f7394b39c51bb199a1307edc2fcba1bd2ba0d0dd
|
[
"Unlicense"
] | null | null | null |
libg3n/modules/python/__init__.py
|
jhkloss/libg3n
|
f7394b39c51bb199a1307edc2fcba1bd2ba0d0dd
|
[
"Unlicense"
] | null | null | null |
from .python_class import PythonClass
from .python_file import PythonFile
| 36.5
| 37
| 0.876712
| 10
| 73
| 6.2
| 0.7
| 0.322581
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09589
| 73
| 2
| 38
| 36.5
| 0.939394
| 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
|
d509e61de87cbad77d72a02bd8c29b7a969a8eaf
| 2,311
|
py
|
Python
|
tests/test_inline_functions/test_query.py
|
JourneyG/pymapdl
|
23fdc008c151c0546504e4ef8257a64f5f169100
|
[
"MIT"
] | 1
|
2021-07-28T00:42:53.000Z
|
2021-07-28T00:42:53.000Z
|
tests/test_inline_functions/test_query.py
|
JourneyG/pymapdl
|
23fdc008c151c0546504e4ef8257a64f5f169100
|
[
"MIT"
] | null | null | null |
tests/test_inline_functions/test_query.py
|
JourneyG/pymapdl
|
23fdc008c151c0546504e4ef8257a64f5f169100
|
[
"MIT"
] | null | null | null |
import pytest
class TestParseParameter:
@pytest.mark.parametrize('values', [('PARAMETER test = 4', 4.),
('PARAMETER=4', 4.),
('PARAMETER WARNING = 4', 4.),
('PARAMETER = _=4', 4.),
('WARNING = PARAMETER = 4', 4.),
('PARAMETER = .4', .4)])
def test_parse_float(self, values, query):
input_, output = values
assert query._parse_parameter_float_response(input_) == output
@pytest.mark.parametrize('values', [('PARAMETER test = 4', 4),
('PARAMETER=4', 4),
('PARAMETER WARNING = 4', 4),
('PARAMETER = _=4', 4),
('WARNING = PARAMETER = 4', 4),
('PARAMETER = .4', 0)])
def test_parse_int(self, values, query):
input_, output = values
assert query._parse_parameter_integer_response(input_) == output
def test_parse_float_type_warning(self, query):
input_ = 'WARNING PARAMETER = 4'
with pytest.warns(UserWarning):
query._parse_parameter_float_response(input_)
def test_parse_int_type_warning(self, query):
input_ = 'WARNING PARAMETER = 4'
with pytest.warns(UserWarning):
query._parse_parameter_integer_response(input_)
@pytest.mark.parametrize('value', ['parameter test = 4',
'PARAMETER 4',
'WARNING = 4',
''])
def test_parse_float_type_error(self, value, query):
input_ = value
with pytest.raises(TypeError):
query._parse_parameter_float_response(input_)
@pytest.mark.parametrize('value', ['parameter test = 4',
'PARAMETER 4',
'WARNING = 4',
''])
def test_parse_int_type_error(self, value, query):
input_ = value
with pytest.raises(TypeError):
query._parse_parameter_integer_response(input_)
| 42.796296
| 72
| 0.473821
| 198
| 2,311
| 5.237374
| 0.156566
| 0.115718
| 0.08486
| 0.069431
| 0.939248
| 0.883317
| 0.798457
| 0.798457
| 0.798457
| 0.798457
| 0
| 0.023952
| 0.421895
| 2,311
| 53
| 73
| 43.603774
| 0.752246
| 0
| 0
| 0.5
| 0
| 0
| 0.150715
| 0
| 0
| 0
| 0
| 0
| 0.045455
| 1
| 0.136364
| false
| 0
| 0.022727
| 0
| 0.181818
| 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
|
1d40d2f0b1384b460edcfed384a44c4355b9bed7
| 186
|
py
|
Python
|
pytest_cases/tests/cases/doc/test_doc_cases.py
|
chinghwayu/python-pytest-cases
|
a95f2a50c201a10c6a2aa2544bd1ea39aab23a47
|
[
"BSD-3-Clause"
] | null | null | null |
pytest_cases/tests/cases/doc/test_doc_cases.py
|
chinghwayu/python-pytest-cases
|
a95f2a50c201a10c6a2aa2544bd1ea39aab23a47
|
[
"BSD-3-Clause"
] | null | null | null |
pytest_cases/tests/cases/doc/test_doc_cases.py
|
chinghwayu/python-pytest-cases
|
a95f2a50c201a10c6a2aa2544bd1ea39aab23a47
|
[
"BSD-3-Clause"
] | null | null | null |
def case_two_positive_ints():
""" Inputs are two positive integers """
return 1, 2
def case_two_negative_ints():
""" Inputs are two negative integers """
return -1, -2
| 20.666667
| 44
| 0.655914
| 26
| 186
| 4.461538
| 0.461538
| 0.12069
| 0.172414
| 0.275862
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027778
| 0.225806
| 186
| 8
| 45
| 23.25
| 0.777778
| 0.354839
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 6
|
1d4fc4004cd2f3bac5863c2b2ce662eca826939c
| 21
|
py
|
Python
|
hw1/__init__.py
|
Xanonymous-GitHub/data-structure
|
96f9d09bacd986dcfd40fd0b16e179a5da96f888
|
[
"MIT"
] | null | null | null |
hw1/__init__.py
|
Xanonymous-GitHub/data-structure
|
96f9d09bacd986dcfd40fd0b16e179a5da96f888
|
[
"MIT"
] | null | null | null |
hw1/__init__.py
|
Xanonymous-GitHub/data-structure
|
96f9d09bacd986dcfd40fd0b16e179a5da96f888
|
[
"MIT"
] | null | null | null |
from .hw1 import run
| 10.5
| 20
| 0.761905
| 4
| 21
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0.190476
| 21
| 1
| 21
| 21
| 0.882353
| 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
|
1d713bc1ccdc48d522e885667d74569e9f0bd641
| 304
|
py
|
Python
|
sabot/decorators.py
|
Make-Munich/SaBoT
|
cabc7e2f5e0f7166d94d2ef683f75d8d3be02834
|
[
"MIT"
] | 19
|
2016-04-09T10:13:26.000Z
|
2020-06-21T23:14:16.000Z
|
sabot/decorators.py
|
Make-Munich/SaBoT
|
cabc7e2f5e0f7166d94d2ef683f75d8d3be02834
|
[
"MIT"
] | 13
|
2017-01-14T20:42:45.000Z
|
2019-08-10T22:48:44.000Z
|
sabot/decorators.py
|
Make-Munich/SaBoT
|
cabc7e2f5e0f7166d94d2ef683f75d8d3be02834
|
[
"MIT"
] | 9
|
2016-04-09T12:52:48.000Z
|
2018-08-16T19:08:16.000Z
|
from django.contrib.auth.decorators import user_passes_test, login_required
def user_is_staff(func):
return user_passes_test(lambda u: u.is_staff)(login_required(func))
def user_is_finance(func):
return user_passes_test(lambda u: u.is_staff and u.groups.filter(name="finance"))(login_required(func))
| 38
| 104
| 0.815789
| 51
| 304
| 4.568627
| 0.45098
| 0.128755
| 0.180258
| 0.171674
| 0.334764
| 0.334764
| 0.334764
| 0.334764
| 0.334764
| 0.334764
| 0
| 0
| 0.075658
| 304
| 7
| 105
| 43.428571
| 0.829181
| 0
| 0
| 0
| 0
| 0
| 0.023026
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.6
| 0.2
| 0.4
| 1
| 0
| 0
| 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
| 1
| 0
| 1
| 0
| 1
| 0
| 0
|
0
| 6
|
d53bc4df42733552f59d73a18b51b12461bdf781
| 24
|
py
|
Python
|
exts/omni.warp/omni/warp/tests/__init__.py
|
addy1997/warp
|
1c231e3eda88a39ce8142b9727e918d2a3e4a4b1
|
[
"MIT",
"Unlicense",
"Apache-2.0",
"0BSD"
] | 306
|
2022-03-21T23:24:13.000Z
|
2022-03-31T21:11:28.000Z
|
exts/omni.warp/omni/warp/tests/__init__.py
|
addy1997/warp
|
1c231e3eda88a39ce8142b9727e918d2a3e4a4b1
|
[
"MIT",
"Unlicense",
"Apache-2.0",
"0BSD"
] | 11
|
2022-03-23T06:23:25.000Z
|
2022-03-31T22:17:18.000Z
|
exts/omni.warp/omni/warp/tests/__init__.py
|
addy1997/warp
|
1c231e3eda88a39ce8142b9727e918d2a3e4a4b1
|
[
"MIT",
"Unlicense",
"Apache-2.0",
"0BSD"
] | 18
|
2022-03-22T16:27:21.000Z
|
2022-03-30T20:07:47.000Z
|
from .test_ext import *
| 12
| 23
| 0.75
| 4
| 24
| 4.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 1
| 24
| 24
| 0.85
| 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
|
d55495b429b19a34fbd8f9c2a3e62a8cc94671af
| 97
|
py
|
Python
|
pyflu/__init__.py
|
flupke/pyflu
|
8856759ced5367fc8439a418b3ce6570b82707ce
|
[
"BSD-3-Clause"
] | 1
|
2017-07-17T06:50:24.000Z
|
2017-07-17T06:50:24.000Z
|
pyflu/__init__.py
|
flupke/pyflu
|
8856759ced5367fc8439a418b3ce6570b82707ce
|
[
"BSD-3-Clause"
] | null | null | null |
pyflu/__init__.py
|
flupke/pyflu
|
8856759ced5367fc8439a418b3ce6570b82707ce
|
[
"BSD-3-Clause"
] | null | null | null |
"""
A collection of general purpose reusable utilities.
"""
def version():
return "0.9.7"
| 10.777778
| 51
| 0.649485
| 13
| 97
| 4.846154
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038961
| 0.206186
| 97
| 8
| 52
| 12.125
| 0.779221
| 0.525773
| 0
| 0
| 0
| 0
| 0.135135
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 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
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
d56e576e10564f2434ba37108989a908e82d6505
| 11,433
|
py
|
Python
|
sendit/apps/parcels/tests/test_parcels.py
|
dbytecoderc/sendit-api
|
8ccb4729fc3123581794b547ec8937804e95687a
|
[
"MIT"
] | 2
|
2020-04-12T14:55:08.000Z
|
2021-11-19T22:14:06.000Z
|
sendit/apps/parcels/tests/test_parcels.py
|
dbytecoderc/sendit-api
|
8ccb4729fc3123581794b547ec8937804e95687a
|
[
"MIT"
] | 6
|
2020-04-12T15:17:48.000Z
|
2021-09-22T18:40:48.000Z
|
sendit/apps/parcels/tests/test_parcels.py
|
dbytecoderc/sendit-api
|
8ccb4729fc3123581794b547ec8937804e95687a
|
[
"MIT"
] | null | null | null |
from rest_framework.views import status
from sendit.apps.core.tests.base_test import TestBaseCase
class TestCreateParcelOrder(TestBaseCase):
def base_parcels(self, message, response):
self.assertIn(message.encode(), response.content)
def regular_authorized_post_request(self, url):
return self.client.post(
url,
self.parcel,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token(),
)
def regular_get_request(self, url):
return self.client.get(
url, format="json", HTTP_AUTHORIZATION="Token " + self.regular_token()
)
def admin_get_request(self, url):
return self.client.get(
url, format="json", HTTP_AUTHORIZATION="Token " + self.admin_token()
)
def http_200_ok(self, response):
self.assertEqual(response.status_code, status.HTTP_200_OK)
def http_403_forbidden(self, response):
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_create_parcel_order(self):
"""
This method checks if a user can create a parcel order
"""
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.base_parcels("Pickup location", response)
self.base_parcels("Destination yup", response)
self.base_parcels("Present location", response)
self.base_parcels("50", response)
self.base_parcels("503", response)
def test_user_can_create_parcel_order_without_present_location(self):
"""
This method checks if a user can create a parcel order without present location
"""
self.parcel["parcel"].pop("present_location")
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.base_parcels("Pickup location", response)
self.base_parcels("Destination yup", response)
self.base_parcels("50", response)
self.base_parcels("503", response)
def test_create_parcels_unauthorized(self):
"""
This method tests that unathorized user cannot create parcel deliveries
"""
response = self.client.post(
self.create_list_parcel_url, self.parcel, format="json"
)
self.http_403_forbidden(response)
self.base_parcels("Authentication credentials were not provided.", response)
def test_user_cannot_create_without_pickup_location(self):
"""
This method makes sure a parcel delivery cannot be created without pick up location input
"""
self.parcel["parcel"].pop("pickup_location")
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.base_parcels("Pickup location is required", response)
def test_user_cannot_create_without_destination(self):
"""
This method makes sure a parcel delivery cannot be created without destination input
"""
self.parcel["parcel"].pop("destination")
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.base_parcels("Destination is required", response)
def test_user_cannot_create_without_weight(self):
"""
This method makes sure a parcel delivery cannot be created without weight input
"""
self.parcel["parcel"].pop("weight")
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.base_parcels("Weight is required", response)
def test_user_cannot_create_without_quote(self):
"""
This method makes sure a parcel delivery cannot be created without quote input
"""
self.parcel["parcel"].pop("quote")
response = self.regular_authorized_post_request(self.create_list_parcel_url)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.base_parcels("Quote is required", response)
def test_admin_get_parcels(self):
"""
This method tests that only an admin can successfully retrieve all parcel deliveries
"""
response = self.admin_get_request(self.create_list_parcel_url)
self.http_200_ok(response)
def test_get_parcels_unauthorized(self):
"""
This method tests that unathorized user cannot retrieve parcel deliveries
"""
response = self.client.get(
self.create_list_parcel_url, self.parcel, format="json"
)
self.http_403_forbidden(response)
self.base_parcels("Authentication credentials were not provided.", response)
def test_get_parcels_non_admin_user(self):
"""
This method tests that a non-admin user cannot retrieve parcel deliveries
"""
response = self.regular_get_request(self.create_list_parcel_url)
self.http_403_forbidden(response)
self.base_parcels(
"You do not have permission to perform this action.", response
)
def test_user_can_update(self):
"""
This method checks if a user can update an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.put(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token(),
)
self.base_parcels("Updated Pickup location", response)
self.http_200_ok(response)
self.base_parcels("Pickup location", created_article)
def test_admin_can_update(self):
"""
This method checks if a admin can update an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.put(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.admin_token(),
)
self.base_parcels("Updated Pickup location", response)
self.http_200_ok(response)
self.base_parcels("Pickup location", created_article)
def test_non_creator_cant_update(self):
"""
This method checks that a non-creator of a resource can't update it
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.put(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token2(),
)
self.http_403_forbidden(response)
self.base_parcels(
"You do not have permission to perform this action.", response
)
def test_user_can_retrieve(self):
"""
This method checks if a user can retrieve an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.get(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token(),
)
self.http_200_ok(response)
self.base_parcels("Pickup location", response)
self.base_parcels("Destination yup", response)
self.base_parcels("50", response)
self.base_parcels("503", response)
def test_admin_can_retrieve(self):
"""
This method checks if an admin can retrieve an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.get(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.admin_token(),
)
self.http_200_ok(response)
self.base_parcels("Pickup location", response)
self.base_parcels("Destination yup", response)
self.base_parcels("50", response)
self.base_parcels("503", response)
def test_non_creator_cant_retrieve(self):
"""
This method checks that a non-creator of a resource can't retrieve it
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.get(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token2(),
)
self.http_403_forbidden(response)
self.base_parcels(
"You do not have permission to perform this action.", response
)
def test_user_can_delete(self):
"""
This method checks if a user can delete an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.delete(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token(),
)
self.http_200_ok(response)
self.base_parcels("Parcel Deleted Successfully", response)
def test_amin_can_delete(self):
"""
This method checks if an admin can delete an existing parcel delivery
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.delete(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.admin_token(),
)
self.http_200_ok(response)
self.base_parcels("Parcel Deleted Successfully", response)
def test_non_creator_cant_delete(self):
"""
This method checks that a non-creator of a resource can't delete it
"""
created_article = self.regular_authorized_post_request(
self.create_list_parcel_url
)
url = self.single_parcel_url(created_article.data["id"])
response = self.client.delete(
url,
self.update_parcel_data,
format="json",
HTTP_AUTHORIZATION="Token " + self.regular_token2(),
)
self.http_403_forbidden(response)
self.base_parcels(
"You do not have permission to perform this action.", response
)
| 37.12013
| 97
| 0.647774
| 1,330
| 11,433
| 5.293985
| 0.089474
| 0.083511
| 0.070303
| 0.081665
| 0.912939
| 0.865076
| 0.848459
| 0.828718
| 0.802159
| 0.746201
| 0
| 0.010969
| 0.266422
| 11,433
| 307
| 98
| 37.241042
| 0.828544
| 0.122103
| 0
| 0.646226
| 0
| 0
| 0.092728
| 0
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| 0
| 0
| 0
| 0.042453
| 1
| 0.117925
| false
| 0
| 0.009434
| 0.014151
| 0.146226
| 0
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| null | 0
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| 1
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| null | 0
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| 0
| 0
| 0
|
0
| 6
|
8951be043183271b7dcedae2bb22b13db36c442a
| 88
|
py
|
Python
|
src/lib/application/cliApp/command.py
|
kolesa-team/python-ms-skeleton
|
b0ac658a0539ce18bb4384f96dc7f7473e701111
|
[
"MIT"
] | null | null | null |
src/lib/application/cliApp/command.py
|
kolesa-team/python-ms-skeleton
|
b0ac658a0539ce18bb4384f96dc7f7473e701111
|
[
"MIT"
] | 1
|
2021-06-01T22:48:10.000Z
|
2021-06-01T22:48:10.000Z
|
src/lib/application/cliApp/command.py
|
kolesa-team/python-ms-skeleton
|
b0ac658a0539ce18bb4384f96dc7f7473e701111
|
[
"MIT"
] | 1
|
2018-10-12T11:40:55.000Z
|
2018-10-12T11:40:55.000Z
|
from src.lib.application.component import Component
class Command(Component):
pass
| 17.6
| 51
| 0.795455
| 11
| 88
| 6.363636
| 0.818182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 88
| 5
| 52
| 17.6
| 0.921053
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
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| 0.666667
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| 1
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| null | 0
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| 0
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| 0
| 0
| 1
| 0
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| 0
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| 0
| 0
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| 0
| null | 0
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| 0
| 0
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| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
899172b6047a11d17295877d165526883115b65a
| 58
|
py
|
Python
|
rplanpy/__init__.py
|
unaisaralegui/rplanpy
|
eebdfde4e523c085e6309f5a35f2d2234806d898
|
[
"MIT"
] | 1
|
2021-04-27T14:27:01.000Z
|
2021-04-27T14:27:01.000Z
|
rplanpy/__init__.py
|
unaisaralegui/rplanpy
|
eebdfde4e523c085e6309f5a35f2d2234806d898
|
[
"MIT"
] | null | null | null |
rplanpy/__init__.py
|
unaisaralegui/rplanpy
|
eebdfde4e523c085e6309f5a35f2d2234806d898
|
[
"MIT"
] | 1
|
2021-06-25T10:20:58.000Z
|
2021-06-25T10:20:58.000Z
|
from . import data
from . import utils
from . import plot
| 14.5
| 19
| 0.741379
| 9
| 58
| 4.777778
| 0.555556
| 0.697674
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| 0.206897
| 58
| 3
| 20
| 19.333333
| 0.934783
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| true
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| 0
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| 0
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| null | 0
| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9849bac8c775ea6afe2ade0bd6dba3f475fb3512
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/yapftests/file_resources_test.py
|
GiulianaPola/select_repeats
|
17a0d053d4f874e42cf654dd142168c2ec8fbd11
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/yapftests/file_resources_test.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/yapftests/file_resources_test.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/3f/27/12/fe7b25a27b163474da08cb24039e40460b00dccde40d392a7f260611fc
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.4375
| 0
| 96
| 1
| 96
| 96
| 0.458333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 1
| 0
| 0
| 1
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| 0
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| null | 1
| 0
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| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
985bd1b038339285f12f5bfff0e63c341065dc6b
| 20,172
|
py
|
Python
|
seqdb_py/tools/test/test_pull_seqdb_seqs.py
|
AAFC-BICoE/seqdb-py
|
7bd2c3ee53af42149bc287c8087cdbea79b043c6
|
[
"MIT"
] | null | null | null |
seqdb_py/tools/test/test_pull_seqdb_seqs.py
|
AAFC-BICoE/seqdb-py
|
7bd2c3ee53af42149bc287c8087cdbea79b043c6
|
[
"MIT"
] | null | null | null |
seqdb_py/tools/test/test_pull_seqdb_seqs.py
|
AAFC-BICoE/seqdb-py
|
7bd2c3ee53af42149bc287c8087cdbea79b043c6
|
[
"MIT"
] | null | null | null |
'''
Created on Oct 16, 2015
@author: korolo
'''
import os.path
import unittest
from config import config_root
from tools import pull_seqdb_seqs
class TestPullSeqdbSeqs(unittest.TestCase):
@classmethod
def setUpClass(self):
self.output_file_name = "test_output_file."
self.output_fasta_file_name = self.output_file_name + "fasta"
self.output_fastq_file_name = self.output_file_name + "fastq"
self.output_taxon_file_name = "test_output_taxonomy.txt"
@classmethod
def tearDownClass(self):
if os.path.isfile(self.output_taxon_file_name):
os.remove(self.output_taxon_file_name)
if os.path.isfile(self.output_fasta_file_name):
os.remove(self.output_fasta_file_name)
if os.path.isfile(self.output_fastq_file_name):
os.remove(self.output_fastq_file_name)
### TESTING FASTA FILE CREATION
def test_execute_script_consensus_fasta(self):
# Time: 86.143s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "consensus"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
# Note that the number of all consensus sequences you get in SeqDB UI is 15037. This is a bug in
# SeqDB that there are some sequences that are not deleted properly, so they are reported as there,
# but they don't have any sequence information.
self.assertEqual(15037, count, "Expected 15037 sequences but got {}".format(count))
self.assertIn('>seqdb|358301', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|4823203', idList, "Expected sequence ID 4823203 is not found in the file")
self.assertIn('>seqdb|4829279', idList, "Expected sequence ID 4829279 is not found in the file")
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "consensus", "--geneRegion", "28s"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(1499, count, "Expected 1499 sequences but got {}".format(count))
self.assertIn('>seqdb|1582548', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|4825579', idList, "Expected sequence ID 4823203 is not found in the file")
self.assertIn('>seqdb|4827758', idList, "Expected sequence ID 4829279 is not found in the file")
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "consensus", "--specNums", "4405,4264"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(3, count, "Expected 3 sequences but got {}".format(count))
self.assertIn('>seqdb|358301', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|358302', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|4825628', idList, "Expected sequence ID 358301 is not found in the file")
def test_execute_script_raw_fasta(self):
'''
#Getting all Raw Sequences. Time: TOO LONG
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "raw"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
self.assertEqual(480088, count, "Expected 480,088 sequences but got {}".format(count))
'''
# Time: 10.5s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "raw", "--seqName", "S-SH-"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(134, count, "Expected 134 sequences but got {}".format(count))
self.assertIn('>seqdb|1', idList, "Expected sequence ID 1 is not found in the file")
self.assertIn('>seqdb|79390', idList, "Expected sequence ID 79390 is not found in the file")
self.assertIn('>seqdb|126059', idList, "Expected sequence ID 126059 is not found in the file")
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "raw", "--collectionCode", "pm"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(148, count, "Expected 148 sequences but got {}".format(count))
self.assertIn('>seqdb|268749', idList, "Expected sequence ID 1 is not found in the file")
self.assertIn('>seqdb|308734', idList, "Expected sequence ID 79390 is not found in the file")
self.assertIn('>seqdb|356572', idList, "Expected sequence ID 126059 is not found in the file")
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "raw", "--specNums", "4405,4264"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(33, count, "Expected 33 sequences but got {}".format(count))
self.assertIn('>seqdb|27755', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|155033', idList, "Expected sequence ID 358301 is not found in the file")
self.assertIn('>seqdb|239733', idList, "Expected sequence ID 358301 is not found in the file")
def test_execute_script_all_fasta(self):
'''
#Getting All Sequences. Time: TOO LONG
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "all"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
self.assertEqual(485643, count, "Expected 485,643 sequences but got {}".format(count))
'''
# Time: 32.8s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "all", "--geneRegion", "EF-1a"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(492, count, "Expected 492 sequences but got {}".format(count))
self.assertIn('>seqdb|1689', idList, "Expected sequence ID 1689 is not found in the file")
self.assertIn('>seqdb|103372', idList, "Expected sequence ID 103372 is not found in the file")
self.assertIn('>seqdb|149807', idList, "Expected sequence ID 149807 is not found in the file")
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "all", "--projectName", "Pythium Type Specimens"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(4373, count, "Expected 4,373 sequences but got {}".format(count))
self.assertIn('>seqdb|358305', idList, "Expected sequence ID 1689 is not found in the file")
self.assertIn('>seqdb|196715', idList, "Expected sequence ID 103372 is not found in the file")
self.assertIn('>seqdb|356858', idList, "Expected sequence ID 149807 is not found in the file")
### TESTING FASTQ FILE CREATION
def test_execute_script_raw_fastq(self):
# Filtering on Sample Name. Time: 4.6s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fastq", "raw", "--sampleName", "LEV6103"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fastq_file_name), "Fastq file was not created.")
self.assertFalse(os.path.isfile(self.output_fasta_file_name), "Fasta file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fastq_file_name) as f:
for line in f:
if line.startswith('@'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(60, count, "Expected 60 sequences but got {}".format(count))
self.assertIn('@seqdb|266400', idList, "Expected sequence ID 266400 is not found in the file")
self.assertIn('@seqdb|301609', idList, "Expected sequence ID 301609 is not found in the file")
self.assertIn('@seqdb|331086', idList, "Expected sequence ID 331086 is not found in the file")
### TESTING TAXONOMY FILE CREATION
def test_execute_script_consensus_taxonomy(self):
# Filtering on Sequence Name. Time: 1.06s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "-t", "consensus", "--seqName", "Pyt_arrhenomanes_"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertTrue(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
count = 0
idList = []
with open(self.output_taxon_file_name) as f:
for line in f:
count = count + 1
idList.append(line.split()[0])
self.assertEqual(5, count, "Expected 5 sequence but got {}".format(count))
self.assertIn('358301', idList, "Expected taxonomy ID 358301 is not found in the file")
self.assertIn('358327', idList, "Expected taxonomy ID 358327 is not found in the file")
self.assertIn('358485', idList, "Expected taxonomy ID 358485 is not found in the file")
# Filtering on Taxonomy Rank and Taxonomy Value. Time: 2.49s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "-t", "consensus", "--taxRank", "species", "--taxValue", "megasperma"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertTrue(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
count = 0
idList = []
with open(self.output_taxon_file_name) as f:
for line in f:
count = count + 1
idList.append(line.split()[0])
self.assertEqual(3, count, "Expected 3 sequence but got {}".format(count))
self.assertIn('358368', idList, "Expected taxonomy ID 358301 is not found in the file")
self.assertIn('358385', idList, "Expected taxonomy ID 358327 is not found in the file")
self.assertIn('358394', idList, "Expected taxonomy ID 358485 is not found in the file")
def test_execute_script_raw_taxonomy(self):
# Filtering on Sample Name. Time: 3.40s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "raw", "-t", "--sampleName", "INVITRO221"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertTrue(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was not created.")
count = 0
idList = []
with open(self.output_taxon_file_name) as f:
for line in f:
count = count + 1
idList.append(line.split()[0])
self.assertEqual(6, count, "Expected 6 sequences but got {}".format(count))
self.assertIn('961', idList, "Expected taxonomy ID 961 is not the found in the file")
self.assertIn('97830', idList, "Expected taxonomy ID 97830 is not found in the file")
self.assertIn('97847', idList, "Expected taxonomy ID 97847 is not found in the file")
def test_execute_script_all_taxonomy(self):
# Filtering on Gene Region Name. Time: 124.98s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "-t", "all", "--geneRegion", "ACA"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertTrue(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
count = 0
idList = []
with open(self.output_taxon_file_name) as f:
for line in f:
count = count + 1
idList.append(line.split()[0])
self.assertEqual(1041, count, "Expected 1,041 sequences but got {}".format(count))
self.assertIn('358301', idList, "Expected taxonomy ID 358301 is not found in the file")
self.assertIn('37674', idList, "Expected taxonomy ID 37674 is not found in the file")
self.assertIn('148710', idList, "Expected taxonomy ID 148710 is not found in the file")
### TESTING ITS SEQUENCES
def test_execute_script_its(self):
# Getting all ITS Sequences. Time: 963.44s
pull_seqdb_seqs.execute_script(["-c", config_root.path() + '/config4tests.yaml', "-r", "fasta", "its"],
self.output_file_name, self.output_taxon_file_name)
self.assertTrue(os.path.isfile(self.output_fasta_file_name), "Fasta file was not created.")
self.assertFalse(os.path.isfile(self.output_fastq_file_name), "Fastq file was created.")
self.assertFalse(os.path.isfile(self.output_taxon_file_name), "Taxonomy file was created.")
count = 0
idList = []
with open(self.output_fasta_file_name) as f:
for line in f:
if line.startswith('>'):
count = count + 1
idList.append(line.split()[0])
self.assertEqual(23517, count, "Expected 23517 sequences but got {}".format(count))
self.assertIn('>seqdb|131072', idList, "Expected sequence ID 131072 is not found in the file")
self.assertIn('>seqdb|111872', idList, "Expected sequence ID 11187 is not found in the file")
self.assertIn('>seqdb|131058', idList, "Expected sequence ID 131071 is not found in the file")
if __name__ == "__main__":
unittest.main()
| 57.144476
| 176
| 0.629437
| 2,612
| 20,172
| 4.701378
| 0.083461
| 0.087948
| 0.049837
| 0.06645
| 0.849267
| 0.842427
| 0.817752
| 0.798779
| 0.755537
| 0.743811
| 0
| 0.047044
| 0.252875
| 20,172
| 353
| 177
| 57.144476
| 0.767766
| 0.106088
| 0
| 0.606426
| 0
| 0
| 0.278234
| 0.00135
| 0
| 0
| 0
| 0
| 0.393574
| 1
| 0.040161
| false
| 0
| 0.016064
| 0
| 0.060241
| 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
|
986c7d0f12959bc1eeb84cc3f29793648aa5e24a
| 33
|
py
|
Python
|
my_package/analysis/__init__.py
|
kaushal-banthia/Image-Detector-Using-OOPs
|
d9d4add55ffaa3097ddd1494a86805e134e009af
|
[
"MIT"
] | null | null | null |
my_package/analysis/__init__.py
|
kaushal-banthia/Image-Detector-Using-OOPs
|
d9d4add55ffaa3097ddd1494a86805e134e009af
|
[
"MIT"
] | null | null | null |
my_package/analysis/__init__.py
|
kaushal-banthia/Image-Detector-Using-OOPs
|
d9d4add55ffaa3097ddd1494a86805e134e009af
|
[
"MIT"
] | null | null | null |
from .visualize import plot_boxes
| 33
| 33
| 0.878788
| 5
| 33
| 5.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090909
| 33
| 1
| 33
| 33
| 0.933333
| 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
|
986d21480de15efb35b4c2a8b57dd46792d2fac7
| 175
|
py
|
Python
|
mmdet/utils/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | null | null | null |
mmdet/utils/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | null | null | null |
mmdet/utils/__init__.py
|
witnessai/GRAN
|
952c2b08a58f3b0087f0f18fd48f8e385e45908b
|
[
"Apache-2.0"
] | null | null | null |
from .flops_counter import get_model_complexity_info
from .registry import Registry, build_from_cfg
__all__ = ['Registry', 'build_from_cfg', 'get_model_complexity_info']
| 35
| 70
| 0.811429
| 24
| 175
| 5.291667
| 0.5
| 0.125984
| 0.283465
| 0.346457
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.108571
| 175
| 4
| 71
| 43.75
| 0.814103
| 0
| 0
| 0
| 0
| 0
| 0.274854
| 0.146199
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
98844812557b9426f944a36c844f7323a1159e30
| 48
|
py
|
Python
|
cartomap/__init__.py
|
gregstarr/cartomap
|
46f0917c4315dede1a12a663de80cdde0ae73393
|
[
"MIT"
] | 5
|
2019-06-21T01:18:20.000Z
|
2021-03-21T22:17:40.000Z
|
cartomap/__init__.py
|
gregstarr/cartomap
|
46f0917c4315dede1a12a663de80cdde0ae73393
|
[
"MIT"
] | 1
|
2019-06-10T13:05:18.000Z
|
2019-06-10T13:05:18.000Z
|
cartomap/__init__.py
|
gregstarr/cartomap
|
46f0917c4315dede1a12a663de80cdde0ae73393
|
[
"MIT"
] | 4
|
2018-08-29T00:08:39.000Z
|
2020-06-02T21:51:19.000Z
|
from .geogmap import plotCartoMap # noqa: F401
| 24
| 47
| 0.770833
| 6
| 48
| 6.166667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.075
| 0.166667
| 48
| 1
| 48
| 48
| 0.85
| 0.208333
| 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
|
7f2cf29b0eadc8431d14e75ac9dfe17a900e9837
| 75
|
py
|
Python
|
src/Debug/__init__.py
|
AndrewGrim/MonsterHunterWorldDatabase
|
a904647f5499926e46a64d884a2ffebe38dd5407
|
[
"MIT"
] | 1
|
2020-02-17T00:16:01.000Z
|
2020-02-17T00:16:01.000Z
|
src/Debug/__init__.py
|
AndrewGrim/MonsterHunterWorldDatabase
|
a904647f5499926e46a64d884a2ffebe38dd5407
|
[
"MIT"
] | null | null | null |
src/Debug/__init__.py
|
AndrewGrim/MonsterHunterWorldDatabase
|
a904647f5499926e46a64d884a2ffebe38dd5407
|
[
"MIT"
] | 1
|
2020-06-26T06:54:00.000Z
|
2020-06-26T06:54:00.000Z
|
from .RedirectText import *
from .DebugWindow import *
from .debug import *
| 25
| 27
| 0.773333
| 9
| 75
| 6.444444
| 0.555556
| 0.344828
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146667
| 75
| 3
| 28
| 25
| 0.90625
| 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
|
7f408a8ef5ef4701dbba9ac015da005f59bbe55e
| 3,791
|
py
|
Python
|
logdeep/models/lstm.py
|
zaihanLit/logbert
|
8128ffc1544d537e41bf178bbca7f5086f910da0
|
[
"MIT"
] | null | null | null |
logdeep/models/lstm.py
|
zaihanLit/logbert
|
8128ffc1544d537e41bf178bbca7f5086f910da0
|
[
"MIT"
] | null | null | null |
logdeep/models/lstm.py
|
zaihanLit/logbert
|
8128ffc1544d537e41bf178bbca7f5086f910da0
|
[
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
class Deeplog(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, vocab_size, embedding_dim):
super(Deeplog, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding_dim = embedding_dim
self.vocab_size = vocab_size
self.embedding = nn.Embedding(self.vocab_size, self.embedding_dim)
torch.nn.init.uniform_(self.embedding.weight)
self.embedding.weight.requires_grad = True
self.lstm = nn.LSTM(self.embedding_dim,
hidden_size,
num_layers,
batch_first=True)
self.fc0 = nn.Linear(hidden_size, vocab_size)
def forward(self, features, device):
input0 = features[0]
embed0 = self.embedding(input0)
h0 = torch.zeros(self.num_layers, embed0.size(0),
self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, embed0.size(0),
self.hidden_size).to(device)
out, _ = self.lstm(embed0, (h0, c0))
out0 = self.fc0(out[:, -1, :])
return out0
class Robustlog(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_keys):
super(Robustlog, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size,
hidden_size,
num_layers,
batch_first=True)
self.fc = nn.Linear(hidden_size, num_keys)
def forward(self, features, device):
input0 = features[0]
h0 = torch.zeros(self.num_layers, input0.size(0),
self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers, input0.size(0),
self.hidden_size).to(device)
out, _ = self.lstm(input0, (h0, c0))
out = self.fc(out[:, -1, :])
return out
#log key add embedding
class Loganomaly(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, vocab_size, embedding_dim):
super(Loganomaly, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.embedding_dim = embedding_dim
self.embedding_size = vocab_size
self.embedding = nn.Embedding(self.embedding_size, self.embedding_dim)
torch.nn.init.uniform_(self.embedding.weight)
self.embedding.weight.requires_grad = True
self.lstm0 = nn.LSTM(self.embedding_dim,
hidden_size,
num_layers,
batch_first=True)
self.lstm1 = nn.LSTM(input_size,
hidden_size,
num_layers,
batch_first=True)
self.fc = nn.Linear(2 * hidden_size, vocab_size)
def forward(self, features, device):
input0, input1 = features[0], features[1]
embed0 = self.embedding(input0)
h0_0 = torch.zeros(self.num_layers, embed0.size(0),
self.hidden_size).to(device)
c0_0 = torch.zeros(self.num_layers, embed0.size(0),
self.hidden_size).to(device)
out0, _ = self.lstm0(embed0, (h0_0, c0_0))
h0_1 = torch.zeros(self.num_layers, input1.size(0),
self.hidden_size).to(device)
c0_1 = torch.zeros(self.num_layers, input1.size(0),
self.hidden_size).to(device)
out1, _ = self.lstm1(input1, (h0_1, c0_1))
multi_out = torch.cat((out0[:, -1, :], out1[:, -1, :]), -1)
out = self.fc(multi_out)
return out
| 36.451923
| 87
| 0.566869
| 456
| 3,791
| 4.453947
| 0.125
| 0.118168
| 0.075825
| 0.066962
| 0.805022
| 0.779419
| 0.77745
| 0.77745
| 0.710487
| 0.710487
| 0
| 0.028997
| 0.326827
| 3,791
| 103
| 88
| 36.805825
| 0.76685
| 0.005539
| 0
| 0.530864
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.074074
| false
| 0
| 0.024691
| 0
| 0.17284
| 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
|
7fa6e7bd16e31496925e02517152f073e731744b
| 71
|
py
|
Python
|
abi2solc/__init__.py
|
iamdefinitelyahuman/abi2solc
|
65526eced1888d10067d14099b5668c14a0bfbcd
|
[
"MIT"
] | 8
|
2019-09-30T11:51:17.000Z
|
2021-12-30T14:30:48.000Z
|
abi2solc/__init__.py
|
iamdefinitelyahuman/abi2solc
|
65526eced1888d10067d14099b5668c14a0bfbcd
|
[
"MIT"
] | null | null | null |
abi2solc/__init__.py
|
iamdefinitelyahuman/abi2solc
|
65526eced1888d10067d14099b5668c14a0bfbcd
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
from .main import generate_interface # NOQA: F401
| 17.75
| 50
| 0.746479
| 10
| 71
| 5.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.065574
| 0.140845
| 71
| 3
| 51
| 23.666667
| 0.786885
| 0.394366
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
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