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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d994174906b6b4cc6b80d4fa5355f83fbb836e40
| 116
|
py
|
Python
|
{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/__init__.py
|
karec/cookiecutter-graphene-flask
|
4940195e9c3e3789543bf25cc790156daa051519
|
[
"MIT"
] | 7
|
2016-09-18T16:27:01.000Z
|
2018-07-18T19:33:19.000Z
|
{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/__init__.py
|
karec/cookiecutter-graphene-flask
|
4940195e9c3e3789543bf25cc790156daa051519
|
[
"MIT"
] | null | null | null |
{{cookiecutter.project_name}}/{{cookiecutter.app_name}}/models/__init__.py
|
karec/cookiecutter-graphene-flask
|
4940195e9c3e3789543bf25cc790156daa051519
|
[
"MIT"
] | 3
|
2017-11-21T21:44:47.000Z
|
2018-10-18T21:02:50.000Z
|
from {{cookiecutter.app_name}}.models.user import User
from {{cookiecutter.app_name}}.models.article import Article
| 38.666667
| 60
| 0.810345
| 16
| 116
| 5.75
| 0.5
| 0.347826
| 0.413043
| 0.5
| 0.630435
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068966
| 116
| 2
| 61
| 58
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 1
| null | null | 0
| 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
796baa747e4ac40035021b6dd136f824bd960ec0
| 3,167
|
py
|
Python
|
simple_permutator.py
|
Archheretic/wordlist-utils
|
c3a4740516ab02487207dd73ab4a9cc6381922be
|
[
"MIT"
] | null | null | null |
simple_permutator.py
|
Archheretic/wordlist-utils
|
c3a4740516ab02487207dd73ab4a9cc6381922be
|
[
"MIT"
] | null | null | null |
simple_permutator.py
|
Archheretic/wordlist-utils
|
c3a4740516ab02487207dd73ab4a9cc6381922be
|
[
"MIT"
] | null | null | null |
def permute(xs, low=0):
if low + 1 >= len(xs):
yield xs
else:
for p in permute(xs, low + 1):
yield p
for i in range(low + 1, len(xs)):
xs[low], xs[i] = xs[i], xs[low]
for p in permute(xs, low + 1):
yield p
xs[low], xs[i] = xs[i], xs[low]
out = open("permutations.txt", "w+")
for p in permute(["Henry", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "June", " ", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", " ", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", " ", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "June", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "June", " ", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "06", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "june", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["henry", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "06", " ", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "06.", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "Juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Henry", "juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["henry", "Juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "June", " ", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "June", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "June", " ", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "06", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "june", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["arne", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "Juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["arne", "Juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "juni", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "06", " ", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Arne", "06.", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["June", "10", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["June", "10.", "1975"]):
out.write("".join(p) + '\n')
for p in permute(["Juni", "10", "1975"]):
out.write("".join(p) + '\n')
| 26.838983
| 56
| 0.470793
| 481
| 3,167
| 3.099792
| 0.06237
| 0.093897
| 0.140845
| 0.305164
| 0.945674
| 0.933602
| 0.933602
| 0.933602
| 0.896043
| 0.862508
| 0
| 0.085645
| 0.214714
| 3,167
| 118
| 57
| 26.838983
| 0.513872
| 0
| 0
| 0.551282
| 0
| 0
| 0.170455
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.012821
| false
| 0
| 0
| 0
| 0.012821
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 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
| 7
|
79a55ae0ae1f0263db73a776fd00f4335bc3cb49
| 4,373
|
py
|
Python
|
models/video_generator.py
|
wilson1yan/tgan
|
33d0f687505f32774b8a731c18e5f88370afaaa0
|
[
"MIT"
] | 76
|
2017-10-05T07:19:57.000Z
|
2022-03-25T13:00:15.000Z
|
models/video_generator.py
|
jrdeco560/tgan
|
53fde1bcb59e2a57f76b9b26097226ec0423811f
|
[
"MIT"
] | 12
|
2017-10-05T07:42:20.000Z
|
2020-11-04T10:24:45.000Z
|
models/video_generator.py
|
jrdeco560/tgan
|
53fde1bcb59e2a57f76b9b26097226ec0423811f
|
[
"MIT"
] | 39
|
2017-10-05T07:20:04.000Z
|
2022-03-01T18:27:47.000Z
|
import chainer
import chainer.functions as F
import chainer.links as L
class VideoGeneratorInitUniform(chainer.Chain):
def __init__(self, z_slow_dim, z_fast_dim, out_channels, bottom_width,
conv_ch=512, wscale=0.01):
self.ch = conv_ch
self.bottom_width = bottom_width
slow_mid_dim = bottom_width * bottom_width * conv_ch // 2
fast_mid_dim = bottom_width * bottom_width * conv_ch // 2
super(VideoGeneratorInitUniform, self).__init__()
w = chainer.initializers.Uniform(wscale)
with self.init_scope():
self.l0s = L.Linear(z_slow_dim, slow_mid_dim, initialW=w, nobias=True)
self.l0f = L.Linear(z_fast_dim, fast_mid_dim, initialW=w, nobias=True)
self.dc1 = L.Deconvolution2D(conv_ch, conv_ch // 2, 4, 2, 1, initialW=w, nobias=True)
self.dc2 = L.Deconvolution2D(conv_ch // 2, conv_ch // 4, 4, 2, 1, initialW=w, nobias=True)
self.dc3 = L.Deconvolution2D(conv_ch // 4, conv_ch // 8, 4, 2, 1, initialW=w, nobias=True)
self.dc4 = L.Deconvolution2D(conv_ch // 8, conv_ch // 16, 4, 2, 1, initialW=w, nobias=True)
self.dc5 = L.Deconvolution2D(conv_ch // 16, out_channels, 3, 1, 1, initialW=w, nobias=False)
self.bn0s = L.BatchNormalization(slow_mid_dim)
self.bn0f = L.BatchNormalization(fast_mid_dim)
self.bn1 = L.BatchNormalization(conv_ch // 2)
self.bn2 = L.BatchNormalization(conv_ch // 4)
self.bn3 = L.BatchNormalization(conv_ch // 8)
self.bn4 = L.BatchNormalization(conv_ch // 16)
def __call__(self, z_slow, z_fast):
n = z_slow.shape[0]
h_slow = F.reshape(F.relu(self.bn0s(self.l0s(z_slow))),
(n, self.ch // 2, self.bottom_width, self.bottom_width))
h_fast = F.reshape(F.relu(self.bn0f(self.l0f(z_fast))),
(n, self.ch // 2, self.bottom_width, self.bottom_width))
h = F.concat([h_slow, h_fast], axis=1)
h = F.relu(self.bn1(self.dc1(h)))
h = F.relu(self.bn2(self.dc2(h)))
h = F.relu(self.bn3(self.dc3(h)))
h = F.relu(self.bn4(self.dc4(h)))
x = F.tanh(self.dc5(h))
return x
class VideoGeneratorInitDefault(chainer.Chain):
def __init__(self, z_slow_dim, z_fast_dim, out_channels, bottom_width,
conv_ch=512, wscale=0.02):
self.ch = conv_ch
self.bottom_width = bottom_width
slow_mid_dim = bottom_width * bottom_width * conv_ch // 2
fast_mid_dim = bottom_width * bottom_width * conv_ch // 2
super(VideoGeneratorInitDefault, self).__init__()
w = None
with self.init_scope():
self.l0s = L.Linear(z_slow_dim, slow_mid_dim, initialW=w, nobias=True)
self.l0f = L.Linear(z_fast_dim, fast_mid_dim, initialW=w, nobias=True)
self.dc1 = L.Deconvolution2D(conv_ch, conv_ch // 2, 4, 2, 1, initialW=w, nobias=True)
self.dc2 = L.Deconvolution2D(conv_ch // 2, conv_ch // 4, 4, 2, 1, initialW=w, nobias=True)
self.dc3 = L.Deconvolution2D(conv_ch // 4, conv_ch // 8, 4, 2, 1, initialW=w, nobias=True)
self.dc4 = L.Deconvolution2D(conv_ch // 8, conv_ch // 16, 4, 2, 1, initialW=w, nobias=True)
self.dc5 = L.Deconvolution2D(conv_ch // 16, out_channels, 3, 1, 1, initialW=w, nobias=False)
self.bn0s = L.BatchNormalization(slow_mid_dim)
self.bn0f = L.BatchNormalization(fast_mid_dim)
self.bn1 = L.BatchNormalization(conv_ch // 2)
self.bn2 = L.BatchNormalization(conv_ch // 4)
self.bn3 = L.BatchNormalization(conv_ch // 8)
self.bn4 = L.BatchNormalization(conv_ch // 16)
def __call__(self, z_slow, z_fast):
n = z_slow.shape[0]
h_slow = F.reshape(F.relu(self.bn0s(self.l0s(z_slow))),
(n, self.ch // 2, self.bottom_width, self.bottom_width))
h_fast = F.reshape(F.relu(self.bn0f(self.l0f(z_fast))),
(n, self.ch // 2, self.bottom_width, self.bottom_width))
h = F.concat([h_slow, h_fast], axis=1)
h = F.relu(self.bn1(self.dc1(h)))
h = F.relu(self.bn2(self.dc2(h)))
h = F.relu(self.bn3(self.dc3(h)))
h = F.relu(self.bn4(self.dc4(h)))
x = F.tanh(self.dc5(h))
return x
| 52.059524
| 104
| 0.605305
| 655
| 4,373
| 3.821374
| 0.112977
| 0.081502
| 0.083899
| 0.091091
| 0.909309
| 0.909309
| 0.909309
| 0.909309
| 0.909309
| 0.909309
| 0
| 0.045076
| 0.259319
| 4,373
| 83
| 105
| 52.686747
| 0.727694
| 0
| 0
| 0.853333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.053333
| false
| 0
| 0.04
| 0
| 0.146667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
79ac6b78cb1b520ad42f27671e7550df5f809d81
| 169
|
py
|
Python
|
tests/test_metrics.py
|
iammrhelo/iscr-searchengine
|
6613e0d9e7a53fe0c55c4568950e1ec45af8a865
|
[
"MIT"
] | null | null | null |
tests/test_metrics.py
|
iammrhelo/iscr-searchengine
|
6613e0d9e7a53fe0c55c4568950e1ec45af8a865
|
[
"MIT"
] | null | null | null |
tests/test_metrics.py
|
iammrhelo/iscr-searchengine
|
6613e0d9e7a53fe0c55c4568950e1ec45af8a865
|
[
"MIT"
] | 1
|
2018-07-06T19:44:50.000Z
|
2018-07-06T19:44:50.000Z
|
from iscr.ranker import metrics
def test_cross_entropy():
assert metrics.cross_entropy(0.5, 0.3) == -0.25541281188299536
assert metrics.cross_entropy(0.2, 0.0) == 0.
| 28.166667
| 63
| 0.751479
| 28
| 169
| 4.392857
| 0.535714
| 0.292683
| 0.292683
| 0.406504
| 0.422764
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18
| 0.112426
| 169
| 5
| 64
| 33.8
| 0.64
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.25
| true
| 0
| 0.25
| 0
| 0.5
| 0
| 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
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
8db2434b2b6d214ef5902b64cd4f4d646ed8e1b6
| 11,079
|
py
|
Python
|
tests/test_controls.py
|
wuschi/mopidy-phoniebox-idletimer
|
9e1b63aefd91f4f5320f343a6548461fcf37ed79
|
[
"Apache-2.0"
] | 1
|
2020-07-15T15:09:47.000Z
|
2020-07-15T15:09:47.000Z
|
tests/test_controls.py
|
wuschi/mopidy-phoniebox
|
9e1b63aefd91f4f5320f343a6548461fcf37ed79
|
[
"Apache-2.0"
] | null | null | null |
tests/test_controls.py
|
wuschi/mopidy-phoniebox
|
9e1b63aefd91f4f5320f343a6548461fcf37ed79
|
[
"Apache-2.0"
] | null | null | null |
#
# Copyright 2019 Thomas Wunschel (https://github.com/wuschi)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import unicode_literals
import subprocess
import unittest
import mock
from mopidy.audio import PlaybackState
from mopidy.models import TlTrack
from mopidy_phoniebox.controls import PhonieboxControls
class PhonieboxControlsTest(unittest.TestCase):
def test_init(self):
core = mock.Mock()
ctrls = PhonieboxControls(core)
self.assertIs(core, ctrls.core)
def test_play_pause(self):
core = mock.Mock()
future = mock.Mock()
core.playback.get_state.return_value = future
ctrls = PhonieboxControls(core)
future.get.return_value = PlaybackState.PLAYING
ctrls.play_pause()
core.playback.pause.assert_called_once()
core.playback.resume.assert_not_called()
core.playback.play.assert_not_called()
core.reset_mock()
ctrls = PhonieboxControls(core)
future.get.return_value = PlaybackState.PAUSED
ctrls.play_pause()
core.playback.pause.assert_not_called()
core.playback.resume.assert_called_once()
core.playback.play.assert_not_called()
core.reset_mock()
ctrls = PhonieboxControls(core)
future.get.return_value = PlaybackState.STOPPED
ctrls.play_pause()
core.playback.pause.assert_not_called()
core.playback.resume.assert_not_called()
core.playback.play.assert_called_once()
def test_cd_previous(self):
core = mock.Mock()
future = mock.Mock()
core.playback.get_time_position.return_value = future
future_tl_track = mock.Mock()
core.playback.get_current_tl_track.return_value = future_tl_track
future_tl = mock.Mock()
core.tracklist.get_length.return_value = future_tl
future_tl.get.return_value = 3
ctrls = PhonieboxControls(core)
future.get.return_value = 1000
future_tl_track.get.return_value = TlTrack(2, None)
ctrls.cd_previous()
core.playback.previous.assert_called_once()
core.playback.seek.assert_not_called()
core.playback.play.assert_not_called()
core.reset_mock()
ctrls = PhonieboxControls(core)
future.get.return_value = 1000
future_tl_track.get.return_value = TlTrack(1, None)
ctrls.cd_previous()
core.playback.previous.assert_not_called()
core.playback.seek.assert_not_called()
core.playback.play.assert_called_with(tlid=3)
core.reset_mock()
future.get.return_value = 4000
future_tl_track.get.return_value = TlTrack(2, None)
ctrls.cd_previous()
core.playback.previous.assert_not_called()
core.playback.seek.assert_called_with(0)
core.playback.play.assert_not_called()
future_tl.get.return_value = 0
core.reset_mock()
future.get.return_value = 4000
future_tl_track.get.return_value = None
ctrls.cd_previous()
core.playback.previous.assert_not_called()
core.playback.seek.assert_not_called()
core.playback.play.assert_not_called()
future_tl.get.return_value = 0
core.reset_mock()
future.get.return_value = 4000
future_tl_track.get.return_value = TlTrack(1, None)
ctrls.cd_previous()
core.playback.previous.assert_not_called()
core.playback.seek.assert_not_called()
core.playback.play.assert_not_called()
future_tl.get.return_value = 3
core.reset_mock()
future.get.return_value = 4000
future_tl_track.get.return_value = None
ctrls.cd_previous()
core.playback.previous.assert_not_called()
core.playback.seek.assert_not_called()
core.playback.play.assert_called_with(tlid=3)
def test_previous(self):
core = mock.Mock()
future_tl_track = mock.Mock()
core.playback.get_current_tl_track.return_value = future_tl_track
future_tl = mock.Mock()
core.tracklist.get_length.return_value = future_tl
future_tl.get.return_value = 3
ctrls = PhonieboxControls(core)
future_tl_track.get.return_value = TlTrack(2, None)
ctrls.previous()
core.playback.previous.assert_called_once()
core.playback.seek.assert_not_called()
core.reset_mock()
future_tl_track.get.return_value = TlTrack(1, None)
ctrls.previous()
core.playback.previous.assert_not_called()
core.playback.play.assert_called_with(tlid=3)
future_tl.get.return_value = 0
core.reset_mock()
future_tl_track.get.return_value = None
ctrls.previous()
core.playback.previous.assert_not_called()
core.playback.play.assert_not_called()
future_tl.get.return_value = 0
core.reset_mock()
future_tl_track.get.return_value = TlTrack(1, None)
ctrls.previous()
core.playback.previous.assert_not_called()
core.playback.play.assert_not_called()
future_tl.get.return_value = 3
core.reset_mock()
future_tl_track.get.return_value = None
ctrls.previous()
core.playback.previous.assert_not_called()
core.playback.play.assert_called_with(tlid=3)
def test_next(self):
core = mock.Mock()
future_tl_track = mock.Mock()
future_tl = mock.Mock()
core.playback.get_current_tl_track.return_value = future_tl_track
core.tracklist.get_length.return_value = future_tl
future_tl.get.return_value = 3
ctrls = PhonieboxControls(core)
future_tl_track.get.return_value = TlTrack(2, None)
ctrls.next()
core.playback.next.assert_called_once()
core.playback.play.assert_not_called()
core.reset_mock()
future_tl_track.get.return_value = TlTrack(3, None)
ctrls.next()
core.playback.next.assert_not_called()
core.playback.play.assert_called_with(tlid=1)
future_tl.get.return_value = 1
core.reset_mock()
future_tl_track.get.return_value = None
ctrls.next()
core.playback.next.assert_not_called()
core.playback.play.assert_called_with(tlid=1)
future_tl.get.return_value = 0
core.reset_mock()
future_tl_track.get.return_value = None
ctrls.next()
core.playback.next.assert_not_called()
core.playback.play.assert_not_called()
future_tl.get.return_value = 0
core.reset_mock()
future_tl_track.get.return_value = TlTrack(1, None)
ctrls.next()
core.playback.next.assert_not_called()
core.playback.play.assert_not_called()
def test_seek_bwd(self):
core = mock.Mock()
future_pos = mock.Mock()
core.playback.get_time_position.return_value = future_pos
ctrls = PhonieboxControls(core)
future_pos.get.return_value = 6000
ctrls.seek_bwd()
core.playback.seek.assert_called_with(1000)
core.reset_mock()
future_pos.get.return_value = 1000
ctrls.seek_bwd()
core.playback.seek.assert_called_with(0)
core.reset_mock()
future_pos.get.return_value = 10000
ctrls.seek_bwd(seconds=3)
core.playback.seek.assert_called_with(7000)
def test_seek_fwd(self):
core = mock.Mock()
future_pos = mock.Mock()
core.playback.get_time_position.return_value = future_pos
ctrls = PhonieboxControls(core)
future_pos.get.return_value = 6000
ctrls.seek_fwd()
core.playback.seek.assert_called_with(11000)
core.reset_mock()
future_pos.get.return_value = 0
ctrls.seek_fwd()
core.playback.seek.assert_called_with(5000)
core.reset_mock()
future_pos.get.return_value = 10000
ctrls.seek_fwd(seconds=3)
core.playback.seek.assert_called_with(13000)
def test_volume_up(self):
core = mock.Mock()
future_vol = mock.Mock()
core.mixer.get_volume.return_value = future_vol
ctrls = PhonieboxControls(core)
future_vol.get.return_value = None
ctrls.volume_up()
core.mixer.set_volume.asert_called_with(55)
core.reset_mock()
future_vol.get.return_value = 80
ctrls.volume_up()
core.mixer.set_volume.assert_called_with(85)
core.reset_mock()
future_vol.get.return_value = 80
ctrls.volume_up(vol_step=3)
core.mixer.set_volume.assert_called_with(83)
core.reset_mock()
future_vol.get.return_value = 100
ctrls.volume_up()
core.mixer.set_volume.assert_called_with(100)
def test_volume_down(self):
core = mock.Mock()
future_vol = mock.Mock()
core.mixer.get_volume.return_value = future_vol
ctrls = PhonieboxControls(core)
future_vol.get.return_value = None
ctrls.volume_down()
core.mixer.set_volume.asert_called_with(45)
core.reset_mock()
future_vol.get.return_value = 80
ctrls.volume_down()
core.mixer.set_volume.assert_called_with(75)
core.reset_mock()
future_vol.get.return_value = 80
ctrls.volume_down(vol_step=1)
core.mixer.set_volume.assert_called_with(79)
core.reset_mock()
future_vol.get.return_value = 0
ctrls.volume_down()
core.mixer.set_volume.assert_called_with(0)
def test_mute_unmute(self):
core = mock.Mock()
future_muted = mock.Mock()
core.mixer.get_mute.return_value = future_muted
ctrls = PhonieboxControls(core)
future_muted.get.return_value = None
ctrls.mute_unmute()
core.mixer.set_mute.assert_called_with(False)
core.reset_mock()
future_muted.get.return_value = False
ctrls.mute_unmute()
core.mixer.set_mute.assert_called_with(True)
core.reset_mock()
future_muted.get.return_value = True
ctrls.mute_unmute()
core.mixer.set_mute.assert_called_with(False)
def test_shutdown(self):
core = mock.Mock()
ctrls = PhonieboxControls(core)
subprocess.call = self.return_ok
rc = ctrls.shutdown()
self.assertEqual(0, rc)
subprocess.call = self.return_notok
rc = ctrls.shutdown()
self.assertEqual(1, rc)
def return_ok(self, *args):
return 0
def return_notok(self, *args):
return 1
| 32.973214
| 75
| 0.665764
| 1,429
| 11,079
| 4.881735
| 0.114066
| 0.105648
| 0.108372
| 0.073538
| 0.835579
| 0.813933
| 0.81164
| 0.771359
| 0.717317
| 0.68707
| 0
| 0.017018
| 0.241538
| 11,079
| 335
| 76
| 33.071642
| 0.813162
| 0.053164
| 0
| 0.748092
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.248092
| 1
| 0.049618
| false
| 0
| 0.026718
| 0.007634
| 0.087786
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
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0
| 7
|
ebb72dbe74ee3c1515625cc0f0d60458cb8f1895
| 13,455
|
py
|
Python
|
src/features/build_features.py
|
arnaou/MLOPS-GNN
|
62a2482187688841ada75b69541e1de06df38c9d
|
[
"BSD-3-Clause"
] | null | null | null |
src/features/build_features.py
|
arnaou/MLOPS-GNN
|
62a2482187688841ada75b69541e1de06df38c9d
|
[
"BSD-3-Clause"
] | 2
|
2022-01-10T13:23:38.000Z
|
2022-01-13T06:54:57.000Z
|
src/features/build_features.py
|
arnaou/MLOPS-GNN
|
62a2482187688841ada75b69541e1de06df38c9d
|
[
"BSD-3-Clause"
] | null | null | null |
import os
import os.path as osp
import re
from rdkit import Chem
import torch
from torch_geometric.data import (InMemoryDataset, Data, download_url,
extract_gz)
class Gen_Afp_Features(object):
def __init__(self):
self.symbols = [
"B",
"C",
"N",
"O",
"F",
"Si",
"P",
"S",
"Cl",
"As",
"Se",
"Br",
"Te",
"I",
"At",
"other",
]
self.hybridizations = [
Chem.rdchem.HybridizationType.SP,
Chem.rdchem.HybridizationType.SP2,
Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D,
Chem.rdchem.HybridizationType.SP3D2,
"other",
]
self.stereos = [
Chem.rdchem.BondStereo.STEREONONE,
Chem.rdchem.BondStereo.STEREOANY,
Chem.rdchem.BondStereo.STEREOZ,
Chem.rdchem.BondStereo.STEREOE,
]
def __call__(self, data):
# Generate AttentiveFP features according to Table 1.
mol = Chem.MolFromSmiles(data.smiles)
xs = []
for atom in mol.GetAtoms():
symbol = [0.0] * len(self.symbols)
symbol[self.symbols.index(atom.GetSymbol())] = 1.0
degree = [0.0] * 6
degree[atom.GetDegree()] = 1.0
formal_charge = atom.GetFormalCharge()
radical_electrons = atom.GetNumRadicalElectrons()
hybridization = [0.0] * len(self.hybridizations)
hybridization[self.hybridizations.index(atom.GetHybridization())] = 1.0
aromaticity = 1.0 if atom.GetIsAromatic() else 0.0
hydrogens = [0.0] * 5
hydrogens[atom.GetTotalNumHs()] = 1.0
chirality = 1.0 if atom.HasProp("_ChiralityPossible") else 0.0
chirality_type = [0.0] * 2
if atom.HasProp("_CIPCode"):
chirality_type[["R", "S"].index(atom.GetProp("_CIPCode"))] = 1.0
x = torch.tensor(
symbol
+ degree
+ [formal_charge]
+ [radical_electrons]
+ hybridization
+ [aromaticity]
+ hydrogens
+ [chirality]
+ chirality_type
)
xs.append(x)
data.x = torch.stack(xs, dim=0)
edge_indices = []
edge_attrs = []
for bond in mol.GetBonds():
edge_indices += [[bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()]]
edge_indices += [[bond.GetEndAtomIdx(), bond.GetBeginAtomIdx()]]
bond_type = bond.GetBondType()
single = 1.0 if bond_type == Chem.rdchem.BondType.SINGLE else 0.0
double = 1.0 if bond_type == Chem.rdchem.BondType.DOUBLE else 0.0
triple = 1.0 if bond_type == Chem.rdchem.BondType.TRIPLE else 0.0
aromatic = 1.0 if bond_type == Chem.rdchem.BondType.AROMATIC else 0.0
conjugation = 1.0 if bond.GetIsConjugated() else 0.0
ring = 1.0 if bond.IsInRing() else 0.0
stereo = [0.0] * 4
stereo[self.stereos.index(bond.GetStereo())] = 1.0
edge_attr = torch.tensor(
[single, double, triple, aromatic, conjugation, ring] + stereo
)
edge_attrs += [edge_attr, edge_attr]
if len(edge_attrs) == 0:
data.edge_index = torch.zeros((2, 0), dtype=torch.long)
data.edge_attr = torch.zeros((0, 10), dtype=torch.float)
else:
data.edge_index = torch.tensor(edge_indices).t().contiguous()
data.edge_attr = torch.stack(edge_attrs, dim=0)
return data
def process_my_smiles(smiles):
from rdkit import Chem
x_map = {
'atomic_num':
list(range(0, 119)),
'chirality': [
'CHI_UNSPECIFIED',
'CHI_TETRAHEDRAL_CW',
'CHI_TETRAHEDRAL_CCW',
'CHI_OTHER',
],
'degree':
list(range(0, 11)),
'formal_charge':
list(range(-5, 7)),
'num_hs':
list(range(0, 9)),
'num_radical_electrons':
list(range(0, 5)),
'hybridization': [
'UNSPECIFIED',
'S',
'SP',
'SP2',
'SP3',
'SP3D',
'SP3D2',
'OTHER',
],
'is_aromatic': [False, True],
'is_in_ring': [False, True],
}
e_map = {
'bond_type': [
'misc',
'SINGLE',
'DOUBLE',
'TRIPLE',
'AROMATIC',
],
'stereo': [
'STEREONONE',
'STEREOZ',
'STEREOE',
'STEREOCIS',
'STEREOTRANS',
'STEREOANY',
],
'is_conjugated': [False, True],
}
#pdb.set_trace()
mol = Chem.MolFromSmiles(smiles)
assert mol != None, "the smiles provided could not be parsed bt RDKit"
# node featurization
xs = []
for atom in mol.GetAtoms():
x = []
x.append(x_map['atomic_num'].index(atom.GetAtomicNum()))
x.append(x_map['chirality'].index(str(atom.GetChiralTag())))
x.append(x_map['degree'].index(atom.GetTotalDegree()))
x.append(x_map['formal_charge'].index(atom.GetFormalCharge()))
x.append(x_map['num_hs'].index(atom.GetTotalNumHs()))
x.append(x_map['num_radical_electrons'].index(
atom.GetNumRadicalElectrons()))
x.append(x_map['hybridization'].index(
str(atom.GetHybridization())))
x.append(x_map['is_aromatic'].index(atom.GetIsAromatic()))
x.append(x_map['is_in_ring'].index(atom.IsInRing()))
xs.append(x)
x = torch.tensor(xs, dtype=torch.long).view(-1, 9)
edge_indices, edge_attrs = [], []
for bond in mol.GetBonds():
i = bond.GetBeginAtomIdx()
j = bond.GetEndAtomIdx()
e = []
e.append(e_map['bond_type'].index(str(bond.GetBondType())))
e.append(e_map['stereo'].index(str(bond.GetStereo())))
e.append(e_map['is_conjugated'].index(bond.GetIsConjugated()))
edge_indices += [[i, j], [j, i]]
edge_attrs += [e, e]
edge_index = torch.tensor(edge_indices)
edge_index = edge_index.t().to(torch.long).view(2, -1)
edge_attr = torch.tensor(edge_attrs, dtype=torch.long).view(-1, 3)
# Sort indices.
if edge_index.numel() > 0:
perm = (edge_index[0] * x.size(0) + edge_index[1]).argsort()
edge_index, edge_attr = edge_index[:, perm], edge_attr[perm]
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, smiles=smiles)
# attentive fp featurization
return data
def process_smiles(smiles):
from rdkit import Chem
x_map = {
'atomic_num':
list(range(0, 119)),
'chirality': [
'CHI_UNSPECIFIED',
'CHI_TETRAHEDRAL_CW',
'CHI_TETRAHEDRAL_CCW',
'CHI_OTHER',
],
'degree':
list(range(0, 11)),
'formal_charge':
list(range(-5, 7)),
'num_hs':
list(range(0, 9)),
'num_radical_electrons':
list(range(0, 5)),
'hybridization': [
'UNSPECIFIED',
'S',
'SP',
'SP2',
'SP3',
'SP3D',
'SP3D2',
'OTHER',
],
'is_aromatic': [False, True],
'is_in_ring': [False, True],
}
e_map = {
'bond_type': [
'misc',
'SINGLE',
'DOUBLE',
'TRIPLE',
'AROMATIC',
],
'stereo': [
'STEREONONE',
'STEREOZ',
'STEREOE',
'STEREOCIS',
'STEREOTRANS',
'STEREOANY',
],
'is_conjugated': [False, True],
}
#pdb.set_trace()
mol = Chem.MolFromSmiles(smiles)
assert mol != None, "the smiles provided could not be parsed bt RDKit"
# node featurization
xs = []
for atom in mol.GetAtoms():
x = []
x.append(x_map['atomic_num'].index(atom.GetAtomicNum()))
x.append(x_map['chirality'].index(str(atom.GetChiralTag())))
x.append(x_map['degree'].index(atom.GetTotalDegree()))
x.append(x_map['formal_charge'].index(atom.GetFormalCharge()))
x.append(x_map['num_hs'].index(atom.GetTotalNumHs()))
x.append(x_map['num_radical_electrons'].index(
atom.GetNumRadicalElectrons()))
x.append(x_map['hybridization'].index(
str(atom.GetHybridization())))
x.append(x_map['is_aromatic'].index(atom.GetIsAromatic()))
x.append(x_map['is_in_ring'].index(atom.IsInRing()))
xs.append(x)
x = torch.tensor(xs, dtype=torch.long).view(-1, 9)
edge_indices, edge_attrs = [], []
for bond in mol.GetBonds():
i = bond.GetBeginAtomIdx()
j = bond.GetEndAtomIdx()
e = []
e.append(e_map['bond_type'].index(str(bond.GetBondType())))
e.append(e_map['stereo'].index(str(bond.GetStereo())))
e.append(e_map['is_conjugated'].index(bond.GetIsConjugated()))
edge_indices += [[i, j], [j, i]]
edge_attrs += [e, e]
edge_index = torch.tensor(edge_indices)
edge_index = edge_index.t().to(torch.long).view(2, -1)
edge_attr = torch.tensor(edge_attrs, dtype=torch.long).view(-1, 3)
# Sort indices.
if edge_index.numel() > 0:
perm = (edge_index[0] * x.size(0) + edge_index[1]).argsort()
edge_index, edge_attr = edge_index[:, perm], edge_attr[perm]
data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, smiles=smiles)
# afp featurize
symbols = [
"B",
"C",
"N",
"O",
"F",
"Si",
"P",
"S",
"Cl",
"As",
"Se",
"Br",
"Te",
"I",
"At",
"other",
]
hybridizations = [
Chem.rdchem.HybridizationType.SP,
Chem.rdchem.HybridizationType.SP2,
Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D,
Chem.rdchem.HybridizationType.SP3D2,
"other",
]
stereos = [
Chem.rdchem.BondStereo.STEREONONE,
Chem.rdchem.BondStereo.STEREOANY,
Chem.rdchem.BondStereo.STEREOZ,
Chem.rdchem.BondStereo.STEREOE,
]
mol = Chem.MolFromSmiles(data.smiles)
xs = []
for atom in mol.GetAtoms():
symbol = [0.0] * len(symbols)
symbol[symbols.index(atom.GetSymbol())] = 1.0
degree = [0.0] * 6
degree[atom.GetDegree()] = 1.0
formal_charge = atom.GetFormalCharge()
radical_electrons = atom.GetNumRadicalElectrons()
hybridization = [0.0] * len(hybridizations)
hybridization[hybridizations.index(atom.GetHybridization())] = 1.0
aromaticity = 1.0 if atom.GetIsAromatic() else 0.0
hydrogens = [0.0] * 5
hydrogens[atom.GetTotalNumHs()] = 1.0
chirality = 1.0 if atom.HasProp("_ChiralityPossible") else 0.0
chirality_type = [0.0] * 2
if atom.HasProp("_CIPCode"):
chirality_type[["R", "S"].index(atom.GetProp("_CIPCode"))] = 1.0
x = torch.tensor(
symbol
+ degree
+ [formal_charge]
+ [radical_electrons]
+ hybridization
+ [aromaticity]
+ hydrogens
+ [chirality]
+ chirality_type
)
xs.append(x)
data.x = torch.stack(xs, dim=0)
edge_indices = []
edge_attrs = []
for bond in mol.GetBonds():
edge_indices += [[bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()]]
edge_indices += [[bond.GetEndAtomIdx(), bond.GetBeginAtomIdx()]]
bond_type = bond.GetBondType()
single = 1.0 if bond_type == Chem.rdchem.BondType.SINGLE else 0.0
double = 1.0 if bond_type == Chem.rdchem.BondType.DOUBLE else 0.0
triple = 1.0 if bond_type == Chem.rdchem.BondType.TRIPLE else 0.0
aromatic = 1.0 if bond_type == Chem.rdchem.BondType.AROMATIC else 0.0
conjugation = 1.0 if bond.GetIsConjugated() else 0.0
ring = 1.0 if bond.IsInRing() else 0.0
stereo = [0.0] * 4
stereo[stereos.index(bond.GetStereo())] = 1.0
edge_attr = torch.tensor(
[single, double, triple, aromatic, conjugation, ring] + stereo
)
edge_attrs += [edge_attr, edge_attr]
if len(edge_attrs) == 0:
data.edge_index = torch.zeros((2, 0), dtype=torch.long)
data.edge_attr = torch.zeros((0, 10), dtype=torch.float)
else:
data.edge_index = torch.tensor(edge_indices).t().contiguous()
data.edge_attr = torch.stack(edge_attrs, dim=0)
return data
#import pdb
#if __name__ == "__main__":
# process_smiles("CCCCC")
| 31.808511
| 84
| 0.517205
| 1,441
| 13,455
| 4.684247
| 0.133241
| 0.008296
| 0.021333
| 0.029333
| 0.934519
| 0.934519
| 0.934519
| 0.934519
| 0.934519
| 0.934519
| 0
| 0.023121
| 0.347454
| 13,455
| 423
| 85
| 31.808511
| 0.745672
| 0.018655
| 0
| 0.885154
| 0
| 0
| 0.085826
| 0.006578
| 0
| 0
| 0
| 0
| 0.005602
| 1
| 0.011204
| false
| 0
| 0.022409
| 0
| 0.044818
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
cce7bb8eceb61e2b2e8fc54f8d51ffea1780a951
| 2,773
|
py
|
Python
|
002-gates-unitary-representation.py
|
m-aryanpour/introduction-to-Cirq
|
4ad8d6a663f7b90b4ec8c1b16f945a6215f5e9a7
|
[
"Apache-2.0"
] | null | null | null |
002-gates-unitary-representation.py
|
m-aryanpour/introduction-to-Cirq
|
4ad8d6a663f7b90b4ec8c1b16f945a6215f5e9a7
|
[
"Apache-2.0"
] | null | null | null |
002-gates-unitary-representation.py
|
m-aryanpour/introduction-to-Cirq
|
4ad8d6a663f7b90b4ec8c1b16f945a6215f5e9a7
|
[
"Apache-2.0"
] | null | null | null |
import cirq
## list of gates
gates= ['H', 'T', 'X', 'Y', 'Z',
'CX', 'CCX',
'CNOT', 'CCNOT', 'SWAP',
'TOFFOLI'
]
##
print(' unitary representation of gates ')
for gate in gates:
print(" gate: ",gate)
print(cirq.unitary(eval('cirq.'+gate)))
## unitary representation of gates
## gate: H
##[[ 0.70710678+0.j 0.70710678+0.j]
## [ 0.70710678+0.j -0.70710678+0.j]]
## gate: S
##[[1.+0.j 0.+0.j]
## [0.+0.j 0.+1.j]]
## gate: T
##[[1. +0.j 0. +0.j ]
## [0. +0.j 0.70710678+0.70710678j]]
## gate: X
##[[0.+0.j 1.+0.j]
## [1.+0.j 0.+0.j]]
## gate: Y
##[[0.+0.j 0.-1.j]
## [0.+1.j 0.+0.j]]
## gate: Z
##[[ 1.+0.j 0.+0.j]
## [ 0.+0.j -1.+0.j]]
## gate: CX
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j]]
## gate: CCX
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j]]
## gate: CNOT
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j]]
## gate: CCNOT
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j]]
## gate: SWAP
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j]]
## gate: TOFFOLI
##[[1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j 0.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j]
## [0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 1.+0.j 0.+0.j]]
##
| 35.551282
| 62
| 0.393437
| 860
| 2,773
| 1.268605
| 0.033721
| 0.476627
| 0.5967
| 0.777269
| 0.791017
| 0.773602
| 0.762603
| 0.762603
| 0.762603
| 0.751604
| 0
| 0.26158
| 0.205914
| 2,773
| 77
| 63
| 36.012987
| 0.233878
| 0.805986
| 0
| 0
| 0
| 0
| 0.228659
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.1
| 0
| 0.1
| 0.3
| 0
| 0
| 1
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 12
|
ccff9c82875a3dfe06289946b9270cf0f57e55c3
| 826
|
py
|
Python
|
double_robot_project/build/aubo_melodic/aubo_kinematics/catkin_generated/pkg.installspace.context.pc.py
|
Xray0302/dual_arm_project
|
34fed790f5b00411f4f5125c3197655eebce2db5
|
[
"Apache-2.0"
] | null | null | null |
double_robot_project/build/aubo_melodic/aubo_kinematics/catkin_generated/pkg.installspace.context.pc.py
|
Xray0302/dual_arm_project
|
34fed790f5b00411f4f5125c3197655eebce2db5
|
[
"Apache-2.0"
] | null | null | null |
double_robot_project/build/aubo_melodic/aubo_kinematics/catkin_generated/pkg.installspace.context.pc.py
|
Xray0302/dual_arm_project
|
34fed790f5b00411f4f5125c3197655eebce2db5
|
[
"Apache-2.0"
] | null | null | null |
# generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "${prefix}/include".split(';') if "${prefix}/include" != "" else []
PROJECT_CATKIN_DEPENDS = "roscpp;geometry_msgs;moveit_core;moveit_ros_planning;pluginlib;tf_conversions".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-laubo_i3_kinematic;-laubo_i5_kinematic;-laubo_i10_kinematic;-laubo_i3_moveit_kinematic_plugin;-laubo_i5_moveit_kinematic_plugin;-laubo_i10_moveit_kinematic_plugin".split(';') if "-laubo_i3_kinematic;-laubo_i5_kinematic;-laubo_i10_kinematic;-laubo_i3_moveit_kinematic_plugin;-laubo_i5_moveit_kinematic_plugin;-laubo_i10_moveit_kinematic_plugin" != "" else []
PROJECT_NAME = "aubo_kinematics"
PROJECT_SPACE_DIR = "/home/xurui/double_robot_project/install"
PROJECT_VERSION = "0.3.7"
| 91.777778
| 394
| 0.824455
| 113
| 826
| 5.495575
| 0.460177
| 0.135266
| 0.202899
| 0.167472
| 0.431562
| 0.431562
| 0.431562
| 0.431562
| 0.431562
| 0.431562
| 0
| 0.024142
| 0.047216
| 826
| 8
| 395
| 103.25
| 0.76493
| 0.065375
| 0
| 0
| 1
| 0
| 0.650649
| 0.575325
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6906a3c99be92a73d503e065d92de53c260618cb
| 10,478
|
py
|
Python
|
tests/feed_spider/feed_info_parser_test.py
|
DBeath/feedsearch-crawler
|
91258ecf6c7c25808ac5d4b5e1c5a7b96981bbe9
|
[
"MIT"
] | 20
|
2019-11-02T18:46:11.000Z
|
2022-03-18T16:22:32.000Z
|
tests/feed_spider/feed_info_parser_test.py
|
DBeath/feedsearch-crawler
|
91258ecf6c7c25808ac5d4b5e1c5a7b96981bbe9
|
[
"MIT"
] | 8
|
2019-11-02T08:26:17.000Z
|
2021-05-07T15:11:17.000Z
|
tests/feed_spider/feed_info_parser_test.py
|
DBeath/feedsearch-crawler
|
91258ecf6c7c25808ac5d4b5e1c5a7b96981bbe9
|
[
"MIT"
] | 5
|
2020-06-18T15:44:48.000Z
|
2022-02-19T14:21:47.000Z
|
from datetime import datetime
from dateutil.tz import tzutc
from feedsearch_crawler.feed_spider.feed_info_parser import FeedInfoParser
def test_entry_velocity_no_dates():
dates = []
result = FeedInfoParser.entry_velocity(dates)
assert result == 0
def test_entry_velocity_identical_dates():
dates = [datetime(2020, 1, 1), datetime(2020, 1, 1), datetime(2020, 1, 1)]
result = FeedInfoParser.entry_velocity(dates)
assert result == 0
def test_entry_velocity():
dates = [
datetime(2019, 1, 1),
datetime(2019, 1, 2),
datetime(2019, 1, 3),
datetime(2019, 1, 4),
datetime(2019, 1, 5),
]
result = FeedInfoParser.entry_velocity(dates)
assert result == 1.0
dates = [
datetime(2019, 1, 1, 1),
datetime(2019, 1, 1, 2),
datetime(2019, 1, 1, 3),
datetime(2019, 1, 1, 4),
datetime(2019, 1, 1, 5),
]
result = FeedInfoParser.entry_velocity(dates)
assert result == 24
dates = [
datetime(2019, 1, 1),
datetime(2019, 1, 7),
datetime(2019, 1, 14),
datetime(2019, 1, 21),
datetime(2019, 1, 27),
]
dates = sorted(dates, reverse=True)
result = FeedInfoParser.entry_velocity(dates)
assert result == 0.154
dates = [
datetime(2019, 9, 21, 13, 10, 3, tzinfo=tzutc()),
datetime(2019, 9, 21, 13, 10, 3, tzinfo=tzutc()),
datetime(2019, 9, 21, 2, 20, 32, tzinfo=tzutc()),
datetime(2019, 9, 21, 2, 20, 32, tzinfo=tzutc()),
datetime(2019, 9, 20, 20, 15, 45, tzinfo=tzutc()),
datetime(2019, 9, 20, 20, 15, 45, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 40, 40, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 40, 40, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 38, 23, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 38, 23, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 23, 20, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 23, 20, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 8, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 8, tzinfo=tzutc()),
datetime(2019, 9, 20, 18, 41, 57, tzinfo=tzutc()),
datetime(2019, 9, 20, 18, 41, 57, tzinfo=tzutc()),
datetime(2019, 9, 20, 17, 36, 30, tzinfo=tzutc()),
datetime(2019, 9, 20, 17, 36, 30, tzinfo=tzutc()),
datetime(2019, 9, 20, 17, 18, 2, tzinfo=tzutc()),
datetime(2019, 9, 20, 17, 18, 2, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 35, 53, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 35, 53, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 25, 13, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 25, 13, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 0, 49, tzinfo=tzutc()),
datetime(2019, 9, 20, 16, 0, 49, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 35, 50, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 35, 50, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 31, 35, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 31, 35, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 30, 48, tzinfo=tzutc()),
datetime(2019, 9, 20, 15, 30, 48, tzinfo=tzutc()),
datetime(2019, 9, 20, 11, 0, 53, tzinfo=tzutc()),
datetime(2019, 9, 20, 11, 0, 53, tzinfo=tzutc()),
datetime(2019, 9, 20, 10, 45, 16, tzinfo=tzutc()),
datetime(2019, 9, 20, 10, 45, 16, tzinfo=tzutc()),
datetime(2019, 9, 20, 10, 0, 49, tzinfo=tzutc()),
datetime(2019, 9, 20, 10, 0, 49, tzinfo=tzutc()),
datetime(2019, 9, 19, 22, 6, 47, tzinfo=tzutc()),
datetime(2019, 9, 19, 22, 6, 47, tzinfo=tzutc()),
]
result = FeedInfoParser.entry_velocity(dates)
assert result == 11.676
dates = [
datetime(2019, 9, 16, 14, 8, 51, tzinfo=tzutc()),
datetime(2019, 9, 16, 14, 8, 51, tzinfo=tzutc()),
datetime(2019, 9, 18, 4, 44, 14, tzinfo=tzutc()),
datetime(2019, 9, 18, 4, 44, 14, tzinfo=tzutc()),
datetime(2019, 9, 18, 9, 0, 16, tzinfo=tzutc()),
datetime(2019, 9, 18, 9, 0, 16, tzinfo=tzutc()),
datetime(2019, 9, 19, 14, 1, 56, tzinfo=tzutc()),
datetime(2019, 9, 19, 14, 1, 56, tzinfo=tzutc()),
datetime(2019, 9, 19, 20, 58, 52, tzinfo=tzutc()),
datetime(2019, 9, 19, 20, 58, 52, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 41, 7, tzinfo=tzutc()),
datetime(2019, 9, 20, 19, 41, 7, tzinfo=tzutc()),
datetime(2019, 9, 20, 23, 2, 15, tzinfo=tzutc()),
datetime(2019, 9, 20, 23, 2, 15, tzinfo=tzutc()),
datetime(2019, 9, 21, 2, 53, 22, tzinfo=tzutc()),
datetime(2019, 9, 21, 2, 53, 22, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 28, 43, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 28, 43, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 28, 44, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 28, 44, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 38, 3, tzinfo=tzutc()),
datetime(2019, 9, 21, 5, 38, 3, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 0, 1, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 0, 1, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 36, 12, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 36, 12, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 39, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 39, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 54, 27, tzinfo=tzutc()),
datetime(2019, 9, 21, 9, 54, 27, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 24, 14, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 24, 14, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 54, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 54, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 11, 57, 55, tzinfo=tzutc()),
datetime(2019, 9, 21, 12, 50, 21, tzinfo=tzutc()),
datetime(2019, 9, 21, 12, 50, 21, tzinfo=tzutc()),
datetime(2019, 9, 21, 13, 26, 45, tzinfo=tzutc()),
datetime(2019, 9, 21, 13, 26, 45, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 7, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 7, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 7, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 7, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 50, 13, tzinfo=tzutc()),
datetime(2019, 9, 21, 17, 50, 13, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 9, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 10, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 10, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 10, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 10, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 12, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 3, 12, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 39, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 39, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 2, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 4, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 5, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 6, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 6, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 7, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 44, 7, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 50, 33, tzinfo=tzutc()),
datetime(2019, 9, 21, 18, 50, 33, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 32, 23, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 32, 23, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 32, 25, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 32, 25, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 21, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 21, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 22, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 22, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 42, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 42, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 44, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 44, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 45, tzinfo=tzutc()),
datetime(2019, 9, 21, 19, 50, 45, tzinfo=tzutc()),
]
result = FeedInfoParser.entry_velocity(dates)
assert result == 7.255
def test_is_podcast_no_data():
data = {}
result = FeedInfoParser.is_podcast(data)
assert result is False
def test_is_podcast_not_podcast():
data = {"entries": [{}]}
result = FeedInfoParser.is_podcast(data)
assert result is False
def test_is_podcast_no_namespace():
data = {"entries": [{"enclosures": [{"media": "file_url"}]}]}
result = FeedInfoParser.is_podcast(data)
assert result is False
def test_is_podcast_is_true():
data = {
"namespaces": {"itunes": "testing"},
"entries": [{"enclosures": [{"media": "file_url"}]}],
}
result = FeedInfoParser.is_podcast(data)
assert result is True
def test_is_podcast_no_enclosures():
data = {"namespaces": {"itunes": "testing"}, "entries": [{}]}
result = FeedInfoParser.is_podcast(data)
assert result is False
| 44.777778
| 78
| 0.557167
| 1,540
| 10,478
| 3.75974
| 0.064935
| 0.321244
| 0.314335
| 0.548187
| 0.94456
| 0.908117
| 0.908117
| 0.908117
| 0.881002
| 0.845769
| 0
| 0.237243
| 0.25377
| 10,478
| 233
| 79
| 44.969957
| 0.503261
| 0
| 0
| 0.781991
| 0
| 0
| 0.011453
| 0
| 0
| 0
| 0
| 0
| 0.056872
| 1
| 0.037915
| false
| 0
| 0.014218
| 0
| 0.052133
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 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
| 11
|
691777f32e6e47fa44ffcdec96c896ffb76a2542
| 15,329
|
py
|
Python
|
sdk/python/pulumi_docker/registry_image.py
|
stevesloka/pulumi-docker
|
6628c2dfab8ab3b6293da0b8f6befd143d19bd47
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_docker/registry_image.py
|
stevesloka/pulumi-docker
|
6628c2dfab8ab3b6293da0b8f6befd143d19bd47
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_docker/registry_image.py
|
stevesloka/pulumi-docker
|
6628c2dfab8ab3b6293da0b8f6befd143d19bd47
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
from . import outputs
from ._inputs import *
__all__ = ['RegistryImageArgs', 'RegistryImage']
@pulumi.input_type
class RegistryImageArgs:
def __init__(__self__, *,
build: Optional[pulumi.Input['RegistryImageBuildArgs']] = None,
insecure_skip_verify: Optional[pulumi.Input[bool]] = None,
keep_remotely: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a RegistryImage resource.
:param pulumi.Input['RegistryImageBuildArgs'] build: Definition for building the image
:param pulumi.Input[bool] insecure_skip_verify: If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
:param pulumi.Input[bool] keep_remotely: If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
:param pulumi.Input[str] name: The name of the Docker image.
"""
if build is not None:
pulumi.set(__self__, "build", build)
if insecure_skip_verify is not None:
pulumi.set(__self__, "insecure_skip_verify", insecure_skip_verify)
if keep_remotely is not None:
pulumi.set(__self__, "keep_remotely", keep_remotely)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter
def build(self) -> Optional[pulumi.Input['RegistryImageBuildArgs']]:
"""
Definition for building the image
"""
return pulumi.get(self, "build")
@build.setter
def build(self, value: Optional[pulumi.Input['RegistryImageBuildArgs']]):
pulumi.set(self, "build", value)
@property
@pulumi.getter(name="insecureSkipVerify")
def insecure_skip_verify(self) -> Optional[pulumi.Input[bool]]:
"""
If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
"""
return pulumi.get(self, "insecure_skip_verify")
@insecure_skip_verify.setter
def insecure_skip_verify(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "insecure_skip_verify", value)
@property
@pulumi.getter(name="keepRemotely")
def keep_remotely(self) -> Optional[pulumi.Input[bool]]:
"""
If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
"""
return pulumi.get(self, "keep_remotely")
@keep_remotely.setter
def keep_remotely(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "keep_remotely", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the Docker image.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class _RegistryImageState:
def __init__(__self__, *,
build: Optional[pulumi.Input['RegistryImageBuildArgs']] = None,
insecure_skip_verify: Optional[pulumi.Input[bool]] = None,
keep_remotely: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
sha256_digest: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering RegistryImage resources.
:param pulumi.Input['RegistryImageBuildArgs'] build: Definition for building the image
:param pulumi.Input[bool] insecure_skip_verify: If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
:param pulumi.Input[bool] keep_remotely: If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
:param pulumi.Input[str] name: The name of the Docker image.
:param pulumi.Input[str] sha256_digest: The sha256 digest of the image.
"""
if build is not None:
pulumi.set(__self__, "build", build)
if insecure_skip_verify is not None:
pulumi.set(__self__, "insecure_skip_verify", insecure_skip_verify)
if keep_remotely is not None:
pulumi.set(__self__, "keep_remotely", keep_remotely)
if name is not None:
pulumi.set(__self__, "name", name)
if sha256_digest is not None:
pulumi.set(__self__, "sha256_digest", sha256_digest)
@property
@pulumi.getter
def build(self) -> Optional[pulumi.Input['RegistryImageBuildArgs']]:
"""
Definition for building the image
"""
return pulumi.get(self, "build")
@build.setter
def build(self, value: Optional[pulumi.Input['RegistryImageBuildArgs']]):
pulumi.set(self, "build", value)
@property
@pulumi.getter(name="insecureSkipVerify")
def insecure_skip_verify(self) -> Optional[pulumi.Input[bool]]:
"""
If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
"""
return pulumi.get(self, "insecure_skip_verify")
@insecure_skip_verify.setter
def insecure_skip_verify(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "insecure_skip_verify", value)
@property
@pulumi.getter(name="keepRemotely")
def keep_remotely(self) -> Optional[pulumi.Input[bool]]:
"""
If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
"""
return pulumi.get(self, "keep_remotely")
@keep_remotely.setter
def keep_remotely(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "keep_remotely", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the Docker image.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="sha256Digest")
def sha256_digest(self) -> Optional[pulumi.Input[str]]:
"""
The sha256 digest of the image.
"""
return pulumi.get(self, "sha256_digest")
@sha256_digest.setter
def sha256_digest(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "sha256_digest", value)
class RegistryImage(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
build: Optional[pulumi.Input[pulumi.InputType['RegistryImageBuildArgs']]] = None,
insecure_skip_verify: Optional[pulumi.Input[bool]] = None,
keep_remotely: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
<!-- Bug: Type and Name are switched -->
Manages the lifecycle of docker image/tag in a registry means it can store one or more version of specific docker images and identified by their tags.
## Example Usage
To be able to update an image itself when an updated image arrives.
```python
import pulumi
import pulumi_docker as docker
helloworld = docker.RegistryImage("helloworld", build=docker.RegistryImageBuildArgs(
context=f"{path['cwd']}/absolutePathToContextFolder",
))
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['RegistryImageBuildArgs']] build: Definition for building the image
:param pulumi.Input[bool] insecure_skip_verify: If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
:param pulumi.Input[bool] keep_remotely: If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
:param pulumi.Input[str] name: The name of the Docker image.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[RegistryImageArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
<!-- Bug: Type and Name are switched -->
Manages the lifecycle of docker image/tag in a registry means it can store one or more version of specific docker images and identified by their tags.
## Example Usage
To be able to update an image itself when an updated image arrives.
```python
import pulumi
import pulumi_docker as docker
helloworld = docker.RegistryImage("helloworld", build=docker.RegistryImageBuildArgs(
context=f"{path['cwd']}/absolutePathToContextFolder",
))
```
:param str resource_name: The name of the resource.
:param RegistryImageArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(RegistryImageArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
build: Optional[pulumi.Input[pulumi.InputType['RegistryImageBuildArgs']]] = None,
insecure_skip_verify: Optional[pulumi.Input[bool]] = None,
keep_remotely: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = RegistryImageArgs.__new__(RegistryImageArgs)
__props__.__dict__["build"] = build
__props__.__dict__["insecure_skip_verify"] = insecure_skip_verify
__props__.__dict__["keep_remotely"] = keep_remotely
__props__.__dict__["name"] = name
__props__.__dict__["sha256_digest"] = None
super(RegistryImage, __self__).__init__(
'docker:index/registryImage:RegistryImage',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
build: Optional[pulumi.Input[pulumi.InputType['RegistryImageBuildArgs']]] = None,
insecure_skip_verify: Optional[pulumi.Input[bool]] = None,
keep_remotely: Optional[pulumi.Input[bool]] = None,
name: Optional[pulumi.Input[str]] = None,
sha256_digest: Optional[pulumi.Input[str]] = None) -> 'RegistryImage':
"""
Get an existing RegistryImage resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['RegistryImageBuildArgs']] build: Definition for building the image
:param pulumi.Input[bool] insecure_skip_verify: If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
:param pulumi.Input[bool] keep_remotely: If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
:param pulumi.Input[str] name: The name of the Docker image.
:param pulumi.Input[str] sha256_digest: The sha256 digest of the image.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _RegistryImageState.__new__(_RegistryImageState)
__props__.__dict__["build"] = build
__props__.__dict__["insecure_skip_verify"] = insecure_skip_verify
__props__.__dict__["keep_remotely"] = keep_remotely
__props__.__dict__["name"] = name
__props__.__dict__["sha256_digest"] = sha256_digest
return RegistryImage(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def build(self) -> pulumi.Output[Optional['outputs.RegistryImageBuild']]:
"""
Definition for building the image
"""
return pulumi.get(self, "build")
@property
@pulumi.getter(name="insecureSkipVerify")
def insecure_skip_verify(self) -> pulumi.Output[Optional[bool]]:
"""
If `true`, the verification of TLS certificates of the server/registry is disabled. Defaults to `false`
"""
return pulumi.get(self, "insecure_skip_verify")
@property
@pulumi.getter(name="keepRemotely")
def keep_remotely(self) -> pulumi.Output[Optional[bool]]:
"""
If true, then the Docker image won't be deleted on destroy operation. If this is false, it will delete the image from the docker registry on destroy operation. Defaults to `false`
"""
return pulumi.get(self, "keep_remotely")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The name of the Docker image.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="sha256Digest")
def sha256_digest(self) -> pulumi.Output[str]:
"""
The sha256 digest of the image.
"""
return pulumi.get(self, "sha256_digest")
| 44.175793
| 228
| 0.656272
| 1,807
| 15,329
| 5.360266
| 0.1057
| 0.070411
| 0.078464
| 0.042742
| 0.818294
| 0.799298
| 0.788561
| 0.776275
| 0.77101
| 0.749432
| 0
| 0.006058
| 0.2462
| 15,329
| 346
| 229
| 44.303468
| 0.832194
| 0.347381
| 0
| 0.703125
| 1
| 0
| 0.11255
| 0.028736
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15625
| false
| 0.005208
| 0.036458
| 0
| 0.286458
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
c619472e7613ee0bc1501541ab9ca9f1f28c55bb
| 1,581
|
py
|
Python
|
ccnpy/flic/EncryptedNode.py
|
mmosko/ccnpy
|
20d982e2e3845818fde7f3facdc8cbcdff323dbb
|
[
"Apache-2.0"
] | 1
|
2020-12-23T14:17:25.000Z
|
2020-12-23T14:17:25.000Z
|
ccnpy/flic/EncryptedNode.py
|
mmosko/ccnpy
|
20d982e2e3845818fde7f3facdc8cbcdff323dbb
|
[
"Apache-2.0"
] | 1
|
2019-07-01T18:19:05.000Z
|
2019-07-02T05:35:52.000Z
|
ccnpy/flic/EncryptedNode.py
|
mmosko/ccnpy
|
20d982e2e3845818fde7f3facdc8cbcdff323dbb
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2019 Marc Mosko
#
# 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.
#
# 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 ccnpy
import ccnpy.crypto
class EncryptedNode(ccnpy.Payload):
"""
EncryptedNode works just like ccnpy.Payload -- it stores a byte array.
"""
__T_ENC_NODE = 0x0004
@classmethod
def class_type(cls):
return cls.__T_ENC_NODE
def __init__(self, value):
ccnpy.Payload.__init__(self, value)
def __repr__(self):
return "EncNode: %r" % ccnpy.DisplayFormatter.hexlify(self._value)
| 32.9375
| 75
| 0.727388
| 230
| 1,581
| 4.904348
| 0.404348
| 0.106383
| 0.046099
| 0.056738
| 0.737589
| 0.737589
| 0.737589
| 0.737589
| 0.737589
| 0.737589
| 0
| 0.013428
| 0.199241
| 1,581
| 47
| 76
| 33.638298
| 0.877567
| 0.733713
| 0
| 0
| 0
| 0
| 0.029024
| 0
| 0
| 0
| 0.015831
| 0
| 0
| 1
| 0.272727
| false
| 0
| 0.181818
| 0.181818
| 0.818182
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 1
| 1
| 1
| 1
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| 0
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| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
d668ec0676106bd1ef81d7c6c45b848cc9cbb177
| 54
|
py
|
Python
|
Curso_Python.py/prueba20.py
|
elviscruz45/Python_avanzado
|
259c5f4dcccfe71fefcff86484f36672a22caefb
|
[
"MIT"
] | null | null | null |
Curso_Python.py/prueba20.py
|
elviscruz45/Python_avanzado
|
259c5f4dcccfe71fefcff86484f36672a22caefb
|
[
"MIT"
] | null | null | null |
Curso_Python.py/prueba20.py
|
elviscruz45/Python_avanzado
|
259c5f4dcccfe71fefcff86484f36672a22caefb
|
[
"MIT"
] | null | null | null |
my_set={3,4,5,6,5,5,5,6,7,8,9}
print("my_set=",my_set)
| 27
| 30
| 0.62963
| 18
| 54
| 1.722222
| 0.555556
| 0.483871
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.207547
| 0.018519
| 54
| 2
| 31
| 27
| 0.377358
| 0
| 0
| 0
| 0
| 0
| 0.127273
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
d691a2cbb0236c5e59196f6b180727a4d3db2e1e
| 1,343
|
py
|
Python
|
Tools/tdd/test/test_startTddTool.py
|
neubertm/TDD_framework
|
31e6df2422221035c86fdc743ca4da09cdfc3b50
|
[
"BSD-3-Clause"
] | 1
|
2021-09-23T13:02:10.000Z
|
2021-09-23T13:02:10.000Z
|
Tools/tdd/test/test_startTddTool.py
|
neubertm/TDD_framework
|
31e6df2422221035c86fdc743ca4da09cdfc3b50
|
[
"BSD-3-Clause"
] | null | null | null |
Tools/tdd/test/test_startTddTool.py
|
neubertm/TDD_framework
|
31e6df2422221035c86fdc743ca4da09cdfc3b50
|
[
"BSD-3-Clause"
] | null | null | null |
from .context import startTddTool
from .context import mainMenu
import unittest
from unittest.mock import patch
import sys
import os
class TestStartTddTool(unittest.TestCase):
def test_main_twoArgs(self):
test_sriptFldrPath = os.path.dirname(__file__)
test_envIniFilePath = os.path.abspath(os.path.join(test_sriptFldrPath, 'ini_files', 'envPath.ini'))
test_testSetupFilePath = os.path.abspath(os.path.join(test_sriptFldrPath, 'ini_files', 'testSetups.ini'))
testargs = ['prog', test_envIniFilePath, test_testSetupFilePath]
with patch.object(sys, 'argv', testargs):
with patch("mainMenu.MainMenu.createAndShow") as mock_createAndShow:
startTddTool.main()
pass
# def test_main_oneArgs(self):
# test_sriptFldrPath = os.path.dirname(__file__)
# test_envIniFilePath = os.path.abspath(os.path.join(test_sriptFldrPath, 'ini_files', 'envPath.ini'))
# test_testSetupFilePath = os.path.abspath(os.path.join(test_sriptFldrPath, 'ini_files', 'testSetups.ini'))
# testargs = ['prog', test_envIniFilePath]
# with patch.object(sys, 'argv', testargs):
# with patch("mainMenu.MainMenu.createAndShow") as mock_createAndShow:
# startTddTool.main()
# pass
if __name__ == '__main__':
unittest.main()
| 36.297297
| 114
| 0.694713
| 150
| 1,343
| 5.96
| 0.273333
| 0.067114
| 0.058166
| 0.067114
| 0.744966
| 0.744966
| 0.744966
| 0.744966
| 0.744966
| 0.744966
| 0
| 0
| 0.191363
| 1,343
| 36
| 115
| 37.305556
| 0.823204
| 0.39166
| 0
| 0
| 0
| 0
| 0.11194
| 0.038557
| 0
| 0
| 0
| 0
| 0
| 1
| 0.055556
| false
| 0.055556
| 0.333333
| 0
| 0.444444
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
ba41eb1c125ab89db78df85fb705093aacdf6fde
| 186
|
py
|
Python
|
lab1/e6.py
|
daem-uni/dagdim-lab-informatica
|
9a5c3f829e8372ef994efb28e81a2f7d77c88681
|
[
"MIT"
] | null | null | null |
lab1/e6.py
|
daem-uni/dagdim-lab-informatica
|
9a5c3f829e8372ef994efb28e81a2f7d77c88681
|
[
"MIT"
] | null | null | null |
lab1/e6.py
|
daem-uni/dagdim-lab-informatica
|
9a5c3f829e8372ef994efb28e81a2f7d77c88681
|
[
"MIT"
] | 1
|
2020-12-03T15:17:29.000Z
|
2020-12-03T15:17:29.000Z
|
n = 1
tot = 0
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
tot += n
n += 1
print(tot)
| 7.44
| 10
| 0.397849
| 46
| 186
| 1.608696
| 0.108696
| 0.297297
| 0.675676
| 0.810811
| 0.810811
| 0.810811
| 0.810811
| 0.810811
| 0.810811
| 0.810811
| 0
| 0.101695
| 0.365591
| 186
| 25
| 10
| 7.44
| 0.525424
| 0
| 0
| 0.869565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.043478
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 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
| 11
|
ba531b7c3298e59b3886a053733bc53bc94bf7c7
| 10,787
|
py
|
Python
|
backend/api/schedule/tests/test_update_schedule_invitation.py
|
patrick91/pycon
|
9d7e15f540adcf0eaceb61fdbf67206d6aef73ec
|
[
"MIT"
] | 2
|
2017-07-18T21:51:25.000Z
|
2017-12-23T11:08:39.000Z
|
backend/api/schedule/tests/test_update_schedule_invitation.py
|
patrick91/pycon
|
9d7e15f540adcf0eaceb61fdbf67206d6aef73ec
|
[
"MIT"
] | 23
|
2017-07-18T20:22:38.000Z
|
2018-01-05T05:45:15.000Z
|
backend/api/schedule/tests/test_update_schedule_invitation.py
|
patrick91/pycon
|
9d7e15f540adcf0eaceb61fdbf67206d6aef73ec
|
[
"MIT"
] | 2
|
2017-07-18T21:27:33.000Z
|
2017-07-18T22:07:03.000Z
|
from pytest import mark
from schedule.models import ScheduleItem
pytestmark = mark.django_db
@mark.parametrize(
"option,expected_status",
(
("CONFIRM", ScheduleItem.STATUS.confirmed),
("MAYBE", ScheduleItem.STATUS.maybe),
("REJECT", ScheduleItem.STATUS.rejected),
("CANT_ATTEND", ScheduleItem.STATUS.cant_attend),
),
)
def test_update_invitation_answer(
submission_factory,
graphql_client,
user,
schedule_item_factory,
slot_factory,
day_factory,
option,
expected_status,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(user)
submission = submission_factory(
speaker_id=user.id,
)
schedule_item = schedule_item_factory(
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
... on ScheduleInvitation {
option
notes
}
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": option,
"notes": "notes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitation",
"option": option,
"notes": "notes",
}
schedule_item.refresh_from_db()
assert schedule_item.status == expected_status
assert schedule_item.speaker_invitation_notes == "notes"
mock_event.assert_called_once()
def test_saving_the_same_answer_does_not_trigger_event(
submission_factory,
graphql_client,
user,
schedule_item_factory,
slot_factory,
day_factory,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(user)
submission = submission_factory(
speaker_id=user.id,
)
schedule_item = schedule_item_factory(
status=ScheduleItem.STATUS.confirmed,
speaker_invitation_notes="notes",
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
... on ScheduleInvitation {
option
notes
}
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "notes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitation",
"option": "CONFIRM",
"notes": "notes",
}
schedule_item.refresh_from_db()
assert schedule_item.status == ScheduleItem.STATUS.confirmed
assert schedule_item.speaker_invitation_notes == "notes"
mock_event.assert_not_called()
def test_changing_notes_triggers_a_new_event(
submission_factory,
graphql_client,
user,
schedule_item_factory,
slot_factory,
day_factory,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(user)
submission = submission_factory(
speaker_id=user.id,
)
schedule_item = schedule_item_factory(
status=ScheduleItem.STATUS.confirmed,
speaker_invitation_notes="notes",
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
... on ScheduleInvitation {
option
notes
}
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "newnotes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitation",
"option": "CONFIRM",
"notes": "newnotes",
}
schedule_item.refresh_from_db()
assert schedule_item.status == ScheduleItem.STATUS.confirmed
assert schedule_item.speaker_invitation_notes == "newnotes"
mock_event.assert_called()
def test_random_user_cannot_update_an_invitation(
submission_factory,
graphql_client,
user,
schedule_item_factory,
slot_factory,
day_factory,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(user)
submission = submission_factory(
speaker_id=5000,
)
schedule_item = schedule_item_factory(
status=ScheduleItem.STATUS.waiting_confirmation,
speaker_invitation_notes="",
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "newnotes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitationNotFound",
}
schedule_item.refresh_from_db()
assert schedule_item.status == ScheduleItem.STATUS.waiting_confirmation
assert schedule_item.speaker_invitation_notes == ""
mock_event.assert_not_called()
def test_cannot_update_schedule_if_submission_doesnt_have_a_matching_schedule(
submission_factory, graphql_client, user, mocker
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(user)
submission = submission_factory(
speaker_id=user.id,
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "newnotes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitationNotFound",
}
mock_event.assert_not_called()
def test_reqires_authentication(
submission_factory,
graphql_client,
schedule_item_factory,
slot_factory,
day_factory,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
submission = submission_factory(
speaker_id=5000,
)
schedule_item = schedule_item_factory(
status=ScheduleItem.STATUS.waiting_confirmation,
speaker_invitation_notes="",
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "newnotes",
}
},
)
assert response["errors"][0]["message"] == "User not logged in"
assert not response.get("data")
schedule_item.refresh_from_db()
assert schedule_item.status == ScheduleItem.STATUS.waiting_confirmation
assert schedule_item.speaker_invitation_notes == ""
mock_event.assert_not_called()
def test_staff_can_update_invitation_answer(
submission_factory,
graphql_client,
admin_user,
schedule_item_factory,
slot_factory,
day_factory,
mocker,
):
mock_event = mocker.patch(
"api.schedule.mutations.send_new_schedule_invitation_answer"
)
graphql_client.force_login(admin_user)
submission = submission_factory(
speaker_id=300000,
)
schedule_item = schedule_item_factory(
submission=submission,
type=ScheduleItem.TYPES.submission,
conference=submission.conference,
slot=slot_factory(
day=day_factory(conference=submission.conference), hour="10:00", duration=30
),
)
response = graphql_client.query(
"""mutation($input: UpdateScheduleInvitationInput!) {
updateScheduleInvitation(input: $input) {
__typename
... on ScheduleInvitation {
option
notes
}
}
}""",
variables={
"input": {
"submissionId": submission.hashid,
"option": "CONFIRM",
"notes": "notes",
}
},
)
assert not response.get("errors")
assert response["data"]["updateScheduleInvitation"] == {
"__typename": "ScheduleInvitation",
"option": "CONFIRM",
"notes": "notes",
}
schedule_item.refresh_from_db()
assert schedule_item.status == ScheduleItem.STATUS.confirmed
assert schedule_item.speaker_invitation_notes == "notes"
mock_event.assert_called_once()
| 26.700495
| 88
| 0.619171
| 937
| 10,787
| 6.824973
| 0.11206
| 0.067553
| 0.035653
| 0.032838
| 0.900547
| 0.895231
| 0.888976
| 0.872557
| 0.870055
| 0.870055
| 0
| 0.006555
| 0.278761
| 10,787
| 403
| 89
| 26.766749
| 0.815424
| 0
| 0
| 0.726073
| 0
| 0
| 0.140358
| 0.066553
| 0
| 0
| 0
| 0
| 0.108911
| 1
| 0.023102
| false
| 0
| 0.006601
| 0
| 0.029703
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
ba6e4601af851e5bbe609bcdf6221aa1c3ba6312
| 131
|
py
|
Python
|
knx_plugin/__init__.py
|
majamassarini/automate-knx-plugin
|
478cd8c00c36b0cb8d5a7363985c05a45afae2d5
|
[
"MIT"
] | null | null | null |
knx_plugin/__init__.py
|
majamassarini/automate-knx-plugin
|
478cd8c00c36b0cb8d5a7363985c05a45afae2d5
|
[
"MIT"
] | 1
|
2022-02-12T19:06:23.000Z
|
2022-02-12T19:06:23.000Z
|
knx_plugin/__init__.py
|
majamassarini/automate-knx-plugin
|
478cd8c00c36b0cb8d5a7363985c05a45afae2d5
|
[
"MIT"
] | null | null | null |
from knx_plugin import gateway, client
from knx_plugin.message import Description, Command
from knx_plugin import trigger, command
| 32.75
| 51
| 0.854962
| 19
| 131
| 5.736842
| 0.526316
| 0.192661
| 0.357798
| 0.348624
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114504
| 131
| 3
| 52
| 43.666667
| 0.939655
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ba7b47f1ba5bddc73803f6d22ac1c659506b449c
| 2,745
|
py
|
Python
|
filer/migrations/0012_auto_20200817_1655.py
|
LukaRiba/django-filer
|
9c2620e80550202f4a5a9f682e7f0f447845c1e7
|
[
"BSD-3-Clause"
] | null | null | null |
filer/migrations/0012_auto_20200817_1655.py
|
LukaRiba/django-filer
|
9c2620e80550202f4a5a9f682e7f0f447845c1e7
|
[
"BSD-3-Clause"
] | null | null | null |
filer/migrations/0012_auto_20200817_1655.py
|
LukaRiba/django-filer
|
9c2620e80550202f4a5a9f682e7f0f447845c1e7
|
[
"BSD-3-Clause"
] | null | null | null |
# Generated by Django 3.0.9 on 2020-08-17 14:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('filer', '0011_auto_20190418_0137'),
]
operations = [
migrations.AddField(
model_name='file',
name='description_en',
field=models.TextField(blank=True, null=True, verbose_name='description'),
),
migrations.AddField(
model_name='file',
name='description_hr',
field=models.TextField(blank=True, null=True, verbose_name='description'),
),
migrations.AddField(
model_name='file',
name='description_it',
field=models.TextField(blank=True, null=True, verbose_name='description'),
),
migrations.AddField(
model_name='file',
name='name_en',
field=models.CharField(blank=True, default='', max_length=255, null=True, verbose_name='name'),
),
migrations.AddField(
model_name='file',
name='name_hr',
field=models.CharField(blank=True, default='', max_length=255, null=True, verbose_name='name'),
),
migrations.AddField(
model_name='file',
name='name_it',
field=models.CharField(blank=True, default='', max_length=255, null=True, verbose_name='name'),
),
migrations.AddField(
model_name='image',
name='default_alt_text_en',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default alt text'),
),
migrations.AddField(
model_name='image',
name='default_alt_text_hr',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default alt text'),
),
migrations.AddField(
model_name='image',
name='default_alt_text_it',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default alt text'),
),
migrations.AddField(
model_name='image',
name='default_caption_en',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default caption'),
),
migrations.AddField(
model_name='image',
name='default_caption_hr',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default caption'),
),
migrations.AddField(
model_name='image',
name='default_caption_it',
field=models.CharField(blank=True, max_length=255, null=True, verbose_name='default caption'),
),
]
| 37.094595
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| 2,745
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| 0.138373
| 0.17681
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| 0.865471
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| 0
|
0
| 9
|
242b4e48f2cec21e317ff0f7cf5b6a3d7f2ce2a8
| 222
|
py
|
Python
|
Basic/13_module/kalkulator.py
|
sekilas13/Python
|
8b2c91cf0c90ebaba7a22e97bd69dae7a6564714
|
[
"MIT"
] | 79
|
2021-09-12T02:31:14.000Z
|
2022-03-29T08:46:53.000Z
|
Basic/13_module/kalkulator.py
|
sekilas13/Python
|
8b2c91cf0c90ebaba7a22e97bd69dae7a6564714
|
[
"MIT"
] | 121
|
2021-09-10T02:38:47.000Z
|
2022-03-30T03:30:35.000Z
|
Basic/13_module/kalkulator.py
|
sekilas13/Python
|
8b2c91cf0c90ebaba7a22e97bd69dae7a6564714
|
[
"MIT"
] | 76
|
2021-09-10T02:27:28.000Z
|
2022-03-28T10:24:12.000Z
|
def tambah(angka1, angka2):
return angka1 + angka2
def kurang(angka1, angka2):
return angka1 - angka2
def kali(angka1, angka2):
return angka1 * angka2
def bagi(angka1, angka2):
return angka1 / angka2
| 14.8
| 27
| 0.684685
| 28
| 222
| 5.428571
| 0.285714
| 0.631579
| 0.473684
| 0.631579
| 0.848684
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| 0.093023
| 0.225225
| 222
| 14
| 28
| 15.857143
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0
| 8
|
037900bb80a084af286d6872c59ff17ddbf0df31
| 14,277
|
py
|
Python
|
sdk/python/pulumi_azure/automation/module.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 109
|
2018-06-18T00:19:44.000Z
|
2022-02-20T05:32:57.000Z
|
sdk/python/pulumi_azure/automation/module.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 663
|
2018-06-18T21:08:46.000Z
|
2022-03-31T20:10:11.000Z
|
sdk/python/pulumi_azure/automation/module.py
|
henriktao/pulumi-azure
|
f1cbcf100b42b916da36d8fe28be3a159abaf022
|
[
"ECL-2.0",
"Apache-2.0"
] | 41
|
2018-07-19T22:37:38.000Z
|
2022-03-14T10:56:26.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['ModuleArgs', 'Module']
@pulumi.input_type
class ModuleArgs:
def __init__(__self__, *,
automation_account_name: pulumi.Input[str],
module_link: pulumi.Input['ModuleModuleLinkArgs'],
resource_group_name: pulumi.Input[str],
name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a Module resource.
:param pulumi.Input[str] automation_account_name: The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
:param pulumi.Input['ModuleModuleLinkArgs'] module_link: The published Module link.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
:param pulumi.Input[str] name: Specifies the name of the Module. Changing this forces a new resource to be created.
"""
pulumi.set(__self__, "automation_account_name", automation_account_name)
pulumi.set(__self__, "module_link", module_link)
pulumi.set(__self__, "resource_group_name", resource_group_name)
if name is not None:
pulumi.set(__self__, "name", name)
@property
@pulumi.getter(name="automationAccountName")
def automation_account_name(self) -> pulumi.Input[str]:
"""
The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "automation_account_name")
@automation_account_name.setter
def automation_account_name(self, value: pulumi.Input[str]):
pulumi.set(self, "automation_account_name", value)
@property
@pulumi.getter(name="moduleLink")
def module_link(self) -> pulumi.Input['ModuleModuleLinkArgs']:
"""
The published Module link.
"""
return pulumi.get(self, "module_link")
@module_link.setter
def module_link(self, value: pulumi.Input['ModuleModuleLinkArgs']):
pulumi.set(self, "module_link", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of the Module. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@pulumi.input_type
class _ModuleState:
def __init__(__self__, *,
automation_account_name: Optional[pulumi.Input[str]] = None,
module_link: Optional[pulumi.Input['ModuleModuleLinkArgs']] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering Module resources.
:param pulumi.Input[str] automation_account_name: The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
:param pulumi.Input['ModuleModuleLinkArgs'] module_link: The published Module link.
:param pulumi.Input[str] name: Specifies the name of the Module. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
if automation_account_name is not None:
pulumi.set(__self__, "automation_account_name", automation_account_name)
if module_link is not None:
pulumi.set(__self__, "module_link", module_link)
if name is not None:
pulumi.set(__self__, "name", name)
if resource_group_name is not None:
pulumi.set(__self__, "resource_group_name", resource_group_name)
@property
@pulumi.getter(name="automationAccountName")
def automation_account_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "automation_account_name")
@automation_account_name.setter
def automation_account_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "automation_account_name", value)
@property
@pulumi.getter(name="moduleLink")
def module_link(self) -> Optional[pulumi.Input['ModuleModuleLinkArgs']]:
"""
The published Module link.
"""
return pulumi.get(self, "module_link")
@module_link.setter
def module_link(self, value: Optional[pulumi.Input['ModuleModuleLinkArgs']]):
pulumi.set(self, "module_link", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Specifies the name of the Module. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "resource_group_name", value)
class Module(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
automation_account_name: Optional[pulumi.Input[str]] = None,
module_link: Optional[pulumi.Input[pulumi.InputType['ModuleModuleLinkArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Manages a Automation Module.
## Import
Automation Modules can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:automation/module:Module module1 /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Automation/automationAccounts/account1/modules/module1
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] automation_account_name: The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
:param pulumi.Input[pulumi.InputType['ModuleModuleLinkArgs']] module_link: The published Module link.
:param pulumi.Input[str] name: Specifies the name of the Module. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ModuleArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Manages a Automation Module.
## Import
Automation Modules can be imported using the `resource id`, e.g.
```sh
$ pulumi import azure:automation/module:Module module1 /subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/group1/providers/Microsoft.Automation/automationAccounts/account1/modules/module1
```
:param str resource_name: The name of the resource.
:param ModuleArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ModuleArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
automation_account_name: Optional[pulumi.Input[str]] = None,
module_link: Optional[pulumi.Input[pulumi.InputType['ModuleModuleLinkArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ModuleArgs.__new__(ModuleArgs)
if automation_account_name is None and not opts.urn:
raise TypeError("Missing required property 'automation_account_name'")
__props__.__dict__["automation_account_name"] = automation_account_name
if module_link is None and not opts.urn:
raise TypeError("Missing required property 'module_link'")
__props__.__dict__["module_link"] = module_link
__props__.__dict__["name"] = name
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
super(Module, __self__).__init__(
'azure:automation/module:Module',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
automation_account_name: Optional[pulumi.Input[str]] = None,
module_link: Optional[pulumi.Input[pulumi.InputType['ModuleModuleLinkArgs']]] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None) -> 'Module':
"""
Get an existing Module resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] automation_account_name: The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
:param pulumi.Input[pulumi.InputType['ModuleModuleLinkArgs']] module_link: The published Module link.
:param pulumi.Input[str] name: Specifies the name of the Module. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ModuleState.__new__(_ModuleState)
__props__.__dict__["automation_account_name"] = automation_account_name
__props__.__dict__["module_link"] = module_link
__props__.__dict__["name"] = name
__props__.__dict__["resource_group_name"] = resource_group_name
return Module(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="automationAccountName")
def automation_account_name(self) -> pulumi.Output[str]:
"""
The name of the automation account in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "automation_account_name")
@property
@pulumi.getter(name="moduleLink")
def module_link(self) -> pulumi.Output['outputs.ModuleModuleLink']:
"""
The published Module link.
"""
return pulumi.get(self, "module_link")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Specifies the name of the Module. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Output[str]:
"""
The name of the resource group in which the Module is created. Changing this forces a new resource to be created.
"""
return pulumi.get(self, "resource_group_name")
| 46.054839
| 212
| 0.669118
| 1,717
| 14,277
| 5.329645
| 0.093186
| 0.067315
| 0.062725
| 0.030161
| 0.823407
| 0.799585
| 0.787455
| 0.764834
| 0.752705
| 0.724292
| 0
| 0.006724
| 0.239616
| 14,277
| 309
| 213
| 46.203884
| 0.83622
| 0.334594
| 0
| 0.589888
| 1
| 0
| 0.136771
| 0.041926
| 0
| 0
| 0
| 0
| 0
| 1
| 0.151685
| false
| 0.005618
| 0.039326
| 0
| 0.280899
| 0
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| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3013bb3fc2580511bf83296e76282f72c25ea42e
| 794,188
|
py
|
Python
|
strongdm/drivers_pb2.py
|
strongdm/strongdm-sdk-python
|
6abe6fa5b2a6f268bc6983cdfe96a8481b734c97
|
[
"Apache-2.0"
] | 3
|
2020-10-22T04:54:22.000Z
|
2022-03-24T20:26:03.000Z
|
strongdm/drivers_pb2.py
|
strongdm/strongdm-sdk-python
|
6abe6fa5b2a6f268bc6983cdfe96a8481b734c97
|
[
"Apache-2.0"
] | 1
|
2021-12-02T17:08:36.000Z
|
2021-12-06T23:55:29.000Z
|
strongdm/drivers_pb2.py
|
strongdm/strongdm-sdk-python
|
6abe6fa5b2a6f268bc6983cdfe96a8481b734c97
|
[
"Apache-2.0"
] | 3
|
2020-04-28T22:07:27.000Z
|
2021-01-21T19:13:31.000Z
|
# Copyright 2020 StrongDM Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: drivers.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from . import options_pb2 as options__pb2
from . import tags_pb2 as tags__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='drivers.proto',
package='v1',
syntax='proto3',
serialized_options=b'\n\034com.strongdm.api.v1.plumbingB\017DriversPlumbingZ2github.com/strongdm/strongdm-sdk-go/internal/v1;v1',
create_key=_descriptor._internal_create_key,
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\x01(\tBy\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x1a\xfa\xf3\xb3\x07\x15healthcheck-namespace\xf2\xf8\xb3\x07\x19\x82\xf4\xb3\x07\x14healthcheckNamespace\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00:\xa0\x01\xfa\xf8\xb3\x07\x05\xa8\xf3\xb3\x07\x01\xfa\xf8\xb3\x07 \xda\xf3\xb3\x07\x1bkubernetesuserimpersonation\xfa\xf8\xb3\x07 \xe2\xf3\xb3\x07\x1bkubernetesuserimpersonation\xfa\xf8\xb3\x07 \xea\xf3\xb3\x07\x1bkubernetesuserimpersonation\xfa\xf8\xb3\x07\"\xfa\xf3\xb3\x07\x1dkubernetes_user_impersonation\"\xe4\x07\n\x13KubernetesBasicAuth\x12\x18\n\x02id\x18\x80\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12$\n\x04name\x18\x81\x80\x02 \x01(\tB\x14\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\x12\'\n\x07healthy\x18\x82\x80\x02 \x01(\x08\x42\x14\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x01\x12$\n\x04tags\x18\x83\x80\x02 \x01(\x0b\x32\x08.v1.TagsB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12%\n\x0fsecret_store_id\x18\x84\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12#\n\regress_filter\x18\x85\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12r\n\x08hostname\x18\x01 \x01(\tB`\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\r\xfa\xf3\xb3\x07\x08hostname\xf2\xf8\xb3\x07\r\x82\xf4\xb3\x07\x08hostname\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00\x12\x66\n\x04port\x18\x02 \x01(\x05\x42X\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\t\xfa\xf3\xb3\x07\x04port\xf2\xf8\xb3\x07\t\x82\xf4\xb3\x07\x04port\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00\x12{\n\x08username\x18\x03 \x01(\tBi\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x12\xfa\xf3\xb3\x07\rhttp-username\xf2\xf8\xb3\x07\x11\x82\xf4\xb3\x07\x0chttpUsername\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x01\x12{\n\x08password\x18\x04 \x01(\tBi\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x12\xfa\xf3\xb3\x07\rhttp-password\xf2\xf8\xb3\x07\x11\x82\xf4\xb3\x07\x0chttpPassword\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x01\x12\x98\x01\n\x15healthcheck_namespace\x18\x05 \x01(\tBy\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x1a\xfa\xf3\xb3\x07\x15healthcheck-namespace\xf2\xf8\xb3\x07\x19\x82\xf4\xb3\x07\x14healthcheckNamespace\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00:\x80\x01\xfa\xf8\xb3\x07\x05\xa8\xf3\xb3\x07\x01\xfa\xf8\xb3\x07\x18\xda\xf3\xb3\x07\x13kuberneteshttpbasic\xfa\xf8\xb3\x07\x18\xe2\xf3\xb3\x07\x13kuberneteshttpbasic\xfa\xf8\xb3\x07\x18\xea\xf3\xb3\x07\x13kuberneteshttpbasic\xfa\xf8\xb3\x07\x1a\xfa\xf3\xb3\x07\x15kubernetes_basic_auth\"\xf5\x06\n\x18KubernetesServiceAccount\x12\x18\n\x02id\x18\x80\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12$\n\x04name\x18\x81\x80\x02 \x01(\tB\x14\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\x12\'\n\x07healthy\x18\x82\x80\x02 \x01(\x08\x42\x14\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x01\x12$\n\x04tags\x18\x83\x80\x02 \x01(\x0b\x32\x08.v1.TagsB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12%\n\x0fsecret_store_id\x18\x84\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12#\n\regress_filter\x18\x85\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12r\n\x08hostname\x18\x01 \x01(\tB`\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\r\xfa\xf3\xb3\x07\x08hostname\xf2\xf8\xb3\x07\r\x82\xf4\xb3\x07\x08hostname\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00\x12\x66\n\x04port\x18\x02 \x01(\x05\x42X\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\t\xfa\xf3\xb3\x07\x04port\xf2\xf8\xb3\x07\t\x82\xf4\xb3\x07\x04port\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00\x12p\n\x05token\x18\x03 \x01(\tBa\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x0e\xfa\xf3\xb3\x07\tapi-token\xf2\xf8\xb3\x07\r\x82\xf4\xb3\x07\x08\x61piToken\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x01\x12\x98\x01\n\x15healthcheck_namespace\x18\x04 \x01(\tBy\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x1a\xfa\xf3\xb3\x07\x15healthcheck-namespace\xf2\xf8\xb3\x07\x19\x82\xf4\xb3\x07\x14healthcheckNamespace\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xda\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xe8\xf3\xb3\x07\x00\xf2\xf8\xb3\x07\x05\x98\xf4\xb3\x07\x00\xf2\xf8\xb3\x07\x05\xa0\xf4\xb3\x07\x00:\x94\x01\xfa\xf8\xb3\x07\x05\xa8\xf3\xb3\x07\x01\xfa\xf8\xb3\x07\x1d\xda\xf3\xb3\x07\x18kubernetesserviceaccount\xfa\xf8\xb3\x07\x1d\xe2\xf3\xb3\x07\x18kubernetesserviceaccount\xfa\xf8\xb3\x07\x1d\xea\xf3\xb3\x07\x18kubernetesserviceaccount\xfa\xf8\xb3\x07\x1f\xfa\xf3\xb3\x07\x1akubernetes_service_account\"\xcc\x07\n)KubernetesServiceAccountUserImpersonation\x12\x18\n\x02id\x18\x80\x80\x02 \x01(\tB\n\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\x12$\n\x04name\x18\x81\x80\x02 \x01(\tB\x14\xf2\xf8\xb3\x07\x05\xb0\xf3\xb3\x07\x01\xf2\xf8\xb3\x07\x05\xc0\xf3\xb3\x07\x01\x12\'\n\x07healthy\x18\x82\x80\x02 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,
dependencies=[options__pb2.DESCRIPTOR,tags__pb2.DESCRIPTOR,])
_RESOURCE = _descriptor.Descriptor(
name='Resource',
full_name='v1.Resource',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='rabbit_mqamqp_091', full_name='v1.Resource.rabbit_mqamqp_091', index=0,
number=2500, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\026\212\364\263\007\021rabbitmq-amqp-091', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='amazon_mqamqp_091', full_name='v1.Resource.amazon_mqamqp_091', index=1,
number=2501, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\026\212\364\263\007\021amazonmq-amqp-091', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='athena', full_name='v1.Resource.athena', index=2,
number=100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006athena', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aws', full_name='v1.Resource.aws', index=3,
number=2300, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\010\212\364\263\007\003aws', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='big_query', full_name='v1.Resource.big_query', index=4,
number=200, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010bigquery', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='cassandra', full_name='v1.Resource.cassandra', index=5,
number=300, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tcassandra', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='db_2_i', full_name='v1.Resource.db_2_i', index=6,
number=2400, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\t\212\364\263\007\004db2i', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='db_2_luw', full_name='v1.Resource.db_2_luw', index=7,
number=2200, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006db2luw', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='druid', full_name='v1.Resource.druid', index=8,
number=400, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005druid', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='dynamo_db', full_name='v1.Resource.dynamo_db', index=9,
number=500, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006dynamo', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='amazon_es', full_name='v1.Resource.amazon_es', index=10,
number=600, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010amazones', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='elastic', full_name='v1.Resource.elastic', index=11,
number=601, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\014\212\364\263\007\007elastic', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='http_basic_auth', full_name='v1.Resource.http_basic_auth', index=12,
number=700, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\thttpBasic', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='http_no_auth', full_name='v1.Resource.http_no_auth', index=13,
number=701, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\017\212\364\263\007\nhttpNoAuth', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='http_auth', full_name='v1.Resource.http_auth', index=14,
number=702, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010httpAuth', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='kubernetes', full_name='v1.Resource.kubernetes', index=15,
number=800, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\017\212\364\263\007\nkubernetes', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='kubernetes_user_impersonation', full_name='v1.Resource.kubernetes_user_impersonation', index=16,
number=812, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007 \212\364\263\007\033kubernetesuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='kubernetes_basic_auth', full_name='v1.Resource.kubernetes_basic_auth', index=17,
number=801, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\030\212\364\263\007\023kuberneteshttpbasic', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='kubernetes_service_account', full_name='v1.Resource.kubernetes_service_account', index=18,
number=804, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\035\212\364\263\007\030kubernetesserviceaccount', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='kubernetes_service_account_user_impersonation', full_name='v1.Resource.kubernetes_service_account_user_impersonation', index=19,
number=808, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007.\212\364\263\007)kubernetesserviceaccountuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='amazon_eks', full_name='v1.Resource.amazon_eks', index=20,
number=802, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tamazoneks', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='amazon_eks_user_impersonation', full_name='v1.Resource.amazon_eks_user_impersonation', index=21,
number=818, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\037\212\364\263\007\032amazoneksuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='google_gke', full_name='v1.Resource.google_gke', index=22,
number=803, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tgooglegke', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='google_gke_user_impersonation', full_name='v1.Resource.google_gke_user_impersonation', index=23,
number=816, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\037\212\364\263\007\032googlegkeuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aks', full_name='v1.Resource.aks', index=24,
number=805, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\010\212\364\263\007\003aks', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aks_user_impersonation', full_name='v1.Resource.aks_user_impersonation', index=25,
number=814, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\031\212\364\263\007\024aksuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aks_basic_auth', full_name='v1.Resource.aks_basic_auth', index=26,
number=806, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\021\212\364\263\007\014akshttpbasic', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aks_service_account', full_name='v1.Resource.aks_service_account', index=27,
number=807, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\026\212\364\263\007\021aksserviceaccount', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aks_service_account_user_impersonation', full_name='v1.Resource.aks_service_account_user_impersonation', index=28,
number=810, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\'\212\364\263\007\"aksserviceaccountuserimpersonation', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='memcached', full_name='v1.Resource.memcached', index=29,
number=900, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tmemcached', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mongo_legacy_host', full_name='v1.Resource.mongo_legacy_host', index=30,
number=1000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005mongo', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mongo_legacy_replicaset', full_name='v1.Resource.mongo_legacy_replicaset', index=31,
number=1001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\025\212\364\263\007\020mongo-replicaset', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mongo_host', full_name='v1.Resource.mongo_host', index=32,
number=1002, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tmongoHost', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mongo_replica_set', full_name='v1.Resource.mongo_replica_set', index=33,
number=1003, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\024\212\364\263\007\017mongoReplicaSet', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='mysql', full_name='v1.Resource.mysql', index=34,
number=1100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005mysql', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aurora_mysql', full_name='v1.Resource.aurora_mysql', index=35,
number=1101, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\021\212\364\263\007\014aurora-mysql', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='clustrix', full_name='v1.Resource.clustrix', index=36,
number=1102, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010clustrix', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='maria', full_name='v1.Resource.maria', index=37,
number=1103, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005maria', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='memsql', full_name='v1.Resource.memsql', index=38,
number=1104, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006memsql', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='single_store', full_name='v1.Resource.single_store', index=39,
number=1105, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\020\212\364\263\007\013singlestore', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='oracle', full_name='v1.Resource.oracle', index=40,
number=1200, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006oracle', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='postgres', full_name='v1.Resource.postgres', index=41,
number=1300, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010postgres', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='aurora_postgres', full_name='v1.Resource.aurora_postgres', index=42,
number=1301, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\024\212\364\263\007\017aurora-postgres', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='greenplum', full_name='v1.Resource.greenplum', index=43,
number=1302, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tgreenplum', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='cockroach', full_name='v1.Resource.cockroach', index=44,
number=1303, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tcockroach', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='redshift', full_name='v1.Resource.redshift', index=45,
number=1304, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010redshift', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='citus', full_name='v1.Resource.citus', index=46,
number=1305, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005citus', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='presto', full_name='v1.Resource.presto', index=47,
number=1400, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006presto', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='raw_tcp', full_name='v1.Resource.raw_tcp', index=48,
number=2600, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006rawtcp', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='rdp', full_name='v1.Resource.rdp', index=49,
number=1500, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\010\212\364\263\007\003rdp', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='redis', full_name='v1.Resource.redis', index=50,
number=1600, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005redis', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='elasticache_redis', full_name='v1.Resource.elasticache_redis', index=51,
number=1601, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\014\212\364\263\007\007ecredis', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='snowflake', full_name='v1.Resource.snowflake', index=52,
number=1700, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\016\212\364\263\007\tsnowflake', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='sql_server', full_name='v1.Resource.sql_server', index=53,
number=1800, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\n\212\364\263\007\005mssql', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ssh', full_name='v1.Resource.ssh', index=54,
number=1900, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\010\212\364\263\007\003ssh', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ssh_cert', full_name='v1.Resource.ssh_cert', index=55,
number=1901, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\014\212\364\263\007\007sshCert', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='ssh_customer_key', full_name='v1.Resource.ssh_customer_key', index=56,
number=1902, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\023\212\364\263\007\016sshCustomerKey', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='sybase', full_name='v1.Resource.sybase', index=57,
number=2000, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\013\212\364\263\007\006sybase', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='sybase_iq', full_name='v1.Resource.sybase_iq', index=58,
number=2001, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010sybaseIQ', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='teradata', full_name='v1.Resource.teradata', index=59,
number=2100, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\r\212\364\263\007\010teradata', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007T\302\363\263\007O\242\363\263\007!tf_examples/resource_resource.txt\252\363\263\007$tf_examples/resource_data_source.txt\372\370\263\007\005\250\363\263\007\001',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name='resource', full_name='v1.Resource.resource',
index=0, containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[], serialized_options=b'\252\370\263\007\t\252\370\263\007\004name\252\370\263\007\t\252\370\263\007\004tags\252\370\263\007\024\252\370\263\007\017secret_store_id\252\370\263\007\022\252\370\263\007\regress_filter'),
],
serialized_start=49,
serialized_end=4172,
)
_RABBITMQAMQP091 = _descriptor.Descriptor(
name='RabbitMQAMQP091',
full_name='v1.RabbitMQAMQP091',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.RabbitMQAMQP091.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.RabbitMQAMQP091.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.RabbitMQAMQP091.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.RabbitMQAMQP091.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.RabbitMQAMQP091.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.RabbitMQAMQP091.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.RabbitMQAMQP091.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.RabbitMQAMQP091.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.RabbitMQAMQP091.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.RabbitMQAMQP091.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.RabbitMQAMQP091.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.RabbitMQAMQP091.tls_required', index=11,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\026\332\363\263\007\021rabbitmq-amqp-091\372\370\263\007\026\342\363\263\007\021rabbitmq-amqp-091\372\370\263\007\026\352\363\263\007\021rabbitmq-amqp-091\372\370\263\007\026\372\363\263\007\021rabbitmq_amqp_091',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=4175,
serialized_end=5241,
)
_AMAZONMQAMQP091 = _descriptor.Descriptor(
name='AmazonMQAMQP091',
full_name='v1.AmazonMQAMQP091',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AmazonMQAMQP091.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AmazonMQAMQP091.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AmazonMQAMQP091.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AmazonMQAMQP091.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AmazonMQAMQP091.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AmazonMQAMQP091.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AmazonMQAMQP091.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.AmazonMQAMQP091.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AmazonMQAMQP091.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.AmazonMQAMQP091.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.AmazonMQAMQP091.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.AmazonMQAMQP091.tls_required', index=11,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\026\332\363\263\007\021amazonmq-amqp-091\372\370\263\007\026\342\363\263\007\021amazonmq-amqp-091\372\370\263\007\026\352\363\263\007\021amazonmq-amqp-091\372\370\263\007\026\372\363\263\007\021amazonmq_amqp_091',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=5244,
serialized_end=6310,
)
_ATHENA = _descriptor.Descriptor(
name='Athena',
full_name='v1.Athena',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Athena.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Athena.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Athena.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Athena.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Athena.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Athena.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.Athena.access_key', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021aws-access-key-id\362\370\263\007\016\202\364\263\007\taccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.Athena.secret_access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025aws-secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='output', full_name='v1.Athena.output', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022s3-output-location\362\370\263\007\013\202\364\263\007\006output\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Athena.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region', full_name='v1.Athena.region', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006region\362\370\263\007\013\202\364\263\007\006region\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.Athena.role_arn', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.Athena.role_external_id', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006athena\372\370\263\007\013\342\363\263\007\006athena\372\370\263\007\013\352\363\263\007\006athena\372\370\263\007\013\372\363\263\007\006athena',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=6313,
serialized_end=7515,
)
_AWS = _descriptor.Descriptor(
name='AWS',
full_name='v1.AWS',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AWS.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AWS.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AWS.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AWS.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AWS.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AWS.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.AWS.access_key', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\raccess-key-id\362\370\263\007\020\202\364\263\007\013accessKeyId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.AWS.secret_access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_region', full_name='v1.AWS.healthcheck_region', index=8,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022healthcheck-region\362\370\263\007\026\202\364\263\007\021healthcheckRegion\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.AWS.role_arn', index=9,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.AWS.role_external_id', index=10,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\010\332\363\263\007\003aws\372\370\263\007\010\342\363\263\007\003aws\372\370\263\007\010\352\363\263\007\003aws\372\370\263\007\010\372\363\263\007\003aws',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=7518,
serialized_end=8474,
)
_BIGQUERY = _descriptor.Descriptor(
name='BigQuery',
full_name='v1.BigQuery',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.BigQuery.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.BigQuery.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.BigQuery.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.BigQuery.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.BigQuery.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.BigQuery.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='private_key', full_name='v1.BigQuery.private_key', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013private-key\362\370\263\007\017\202\364\263\007\nprivateKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='project', full_name='v1.BigQuery.project', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\014\372\363\263\007\007project\362\370\263\007\014\202\364\263\007\007project\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.BigQuery.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.BigQuery.endpoint', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.BigQuery.username', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010bigquery\372\370\263\007\r\342\363\263\007\010bigquery\372\370\263\007\r\352\363\263\007\010bigquery\372\370\263\007\016\372\363\263\007\tbig_query',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=8477,
serialized_end=9399,
)
_CASSANDRA = _descriptor.Descriptor(
name='Cassandra',
full_name='v1.Cassandra',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Cassandra.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Cassandra.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Cassandra.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Cassandra.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Cassandra.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Cassandra.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Cassandra.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Cassandra.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Cassandra.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Cassandra.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Cassandra.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.Cassandra.tls_required', index=11,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tcassandra\372\370\263\007\016\342\363\263\007\tcassandra\372\370\263\007\016\352\363\263\007\tcassandra\372\370\263\007\016\372\363\263\007\tcassandra',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=9402,
serialized_end=10430,
)
_DB2I = _descriptor.Descriptor(
name='DB2I',
full_name='v1.DB2I',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.DB2I.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.DB2I.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.DB2I.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.DB2I.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.DB2I.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.DB2I.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.DB2I.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.DB2I.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.DB2I.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.DB2I.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.DB2I.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.DB2I.tls_required', index=11,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\t\332\363\263\007\004db2i\372\370\263\007\t\342\363\263\007\004db2i\372\370\263\007\t\352\363\263\007\004db2i\372\370\263\007\013\372\363\263\007\006db_2_i',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=10433,
serialized_end=11438,
)
_DB2LUW = _descriptor.Descriptor(
name='DB2LUW',
full_name='v1.DB2LUW',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.DB2LUW.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.DB2LUW.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.DB2LUW.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.DB2LUW.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.DB2LUW.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.DB2LUW.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.DB2LUW.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.DB2LUW.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.DB2LUW.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.DB2LUW.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.DB2LUW.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.DB2LUW.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006db2luw\372\370\263\007\013\342\363\263\007\006db2luw\372\370\263\007\013\352\363\263\007\006db2luw\372\370\263\007\r\372\363\263\007\010db_2_luw',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=11441,
serialized_end=12453,
)
_DRUID = _descriptor.Descriptor(
name='Druid',
full_name='v1.Druid',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Druid.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Druid.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Druid.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Druid.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Druid.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Druid.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Druid.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Druid.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Druid.username', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Druid.password', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Druid.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005druid\372\370\263\007\n\342\363\263\007\005druid\372\370\263\007\n\352\363\263\007\005druid\372\370\263\007\n\372\363\263\007\005druid',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=12456,
serialized_end=13345,
)
_DYNAMODB = _descriptor.Descriptor(
name='DynamoDB',
full_name='v1.DynamoDB',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.DynamoDB.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.DynamoDB.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.DynamoDB.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.DynamoDB.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.DynamoDB.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.DynamoDB.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.DynamoDB.access_key', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\raccess-key-id\362\370\263\007\020\202\364\263\007\013accessKeyId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.DynamoDB.secret_access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region', full_name='v1.DynamoDB.region', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006region\362\370\263\007\013\202\364\263\007\006region\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.DynamoDB.endpoint', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.DynamoDB.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.DynamoDB.role_arn', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.DynamoDB.role_external_id', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006dynamo\372\370\263\007\013\342\363\263\007\006dynamo\372\370\263\007\013\352\363\263\007\006dynamo\372\370\263\007\016\372\363\263\007\tdynamo_db',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=13348,
serialized_end=14543,
)
_AMAZONES = _descriptor.Descriptor(
name='AmazonES',
full_name='v1.AmazonES',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AmazonES.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AmazonES.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AmazonES.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AmazonES.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AmazonES.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AmazonES.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region', full_name='v1.AmazonES.region', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006region\362\370\263\007\013\202\364\263\007\006region\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.AmazonES.secret_access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.AmazonES.endpoint', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.AmazonES.access_key', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\raccess-key-id\362\370\263\007\020\202\364\263\007\013accessKeyId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.AmazonES.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.AmazonES.role_arn', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.AmazonES.role_external_id', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010amazones\372\370\263\007\r\342\363\263\007\010amazones\372\370\263\007\r\352\363\263\007\010amazones\372\370\263\007\016\372\363\263\007\tamazon_es',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=14546,
serialized_end=15747,
)
_ELASTIC = _descriptor.Descriptor(
name='Elastic',
full_name='v1.Elastic',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Elastic.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Elastic.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Elastic.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Elastic.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Elastic.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Elastic.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Elastic.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Elastic.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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_descriptor.FieldDescriptor(
name='password', full_name='v1.Elastic.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Elastic.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Elastic.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.Elastic.tls_required', index=11,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\014\332\363\263\007\007elastic\372\370\263\007\014\342\363\263\007\007elastic\372\370\263\007\014\352\363\263\007\007elastic\372\370\263\007\014\372\363\263\007\007elastic',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=15750,
serialized_end=16768,
)
_HTTPBASICAUTH = _descriptor.Descriptor(
name='HTTPBasicAuth',
full_name='v1.HTTPBasicAuth',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.HTTPBasicAuth.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.HTTPBasicAuth.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.HTTPBasicAuth.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.HTTPBasicAuth.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.HTTPBasicAuth.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.HTTPBasicAuth.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='url', full_name='v1.HTTPBasicAuth.url', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\010\372\363\263\007\003url\362\370\263\007\010\202\364\263\007\003url\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_path', full_name='v1.HTTPBasicAuth.healthcheck_path', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013health-path\362\370\263\007\017\202\364\263\007\nhealthPath\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.HTTPBasicAuth.username', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.HTTPBasicAuth.password', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='headers_blacklist', full_name='v1.HTTPBasicAuth.headers_blacklist', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021headers-blacklist\362\370\263\007\025\202\364\263\007\020headersBlacklist\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='default_path', full_name='v1.HTTPBasicAuth.default_path', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014default-path\362\370\263\007\020\202\364\263\007\013defaultPath\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='subdomain', full_name='v1.HTTPBasicAuth.subdomain', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\016\372\363\263\007\tsubdomain\362\370\263\007\016\202\364\263\007\tsubdomain\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\thttpBasic\372\370\263\007\016\342\363\263\007\thttpBasic\372\370\263\007\016\352\363\263\007\thttpBasic\372\370\263\007\024\372\363\263\007\017http_basic_auth',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=16771,
serialized_end=17950,
)
_HTTPNOAUTH = _descriptor.Descriptor(
name='HTTPNoAuth',
full_name='v1.HTTPNoAuth',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.HTTPNoAuth.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.HTTPNoAuth.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.HTTPNoAuth.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.HTTPNoAuth.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.HTTPNoAuth.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.HTTPNoAuth.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='url', full_name='v1.HTTPNoAuth.url', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\010\372\363\263\007\003url\362\370\263\007\010\202\364\263\007\003url\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_path', full_name='v1.HTTPNoAuth.healthcheck_path', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013health-path\362\370\263\007\017\202\364\263\007\nhealthPath\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='headers_blacklist', full_name='v1.HTTPNoAuth.headers_blacklist', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021headers-blacklist\362\370\263\007\025\202\364\263\007\020headersBlacklist\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='default_path', full_name='v1.HTTPNoAuth.default_path', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014default-path\362\370\263\007\020\202\364\263\007\013defaultPath\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='subdomain', full_name='v1.HTTPNoAuth.subdomain', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\017\332\363\263\007\nhttpNoAuth\372\370\263\007\017\342\363\263\007\nhttpNoAuth\372\370\263\007\017\352\363\263\007\nhttpNoAuth\372\370\263\007\021\372\363\263\007\014http_no_auth',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=17953,
serialized_end=18897,
)
_HTTPAUTH = _descriptor.Descriptor(
name='HTTPAuth',
full_name='v1.HTTPAuth',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.HTTPAuth.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.HTTPAuth.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.HTTPAuth.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.HTTPAuth.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.HTTPAuth.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.HTTPAuth.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='url', full_name='v1.HTTPAuth.url', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\010\372\363\263\007\003url\362\370\263\007\010\202\364\263\007\003url\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_path', full_name='v1.HTTPAuth.healthcheck_path', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013health-path\362\370\263\007\017\202\364\263\007\nhealthPath\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='auth_header', full_name='v1.HTTPAuth.auth_header', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013auth-header\362\370\263\007\017\202\364\263\007\nauthHeader\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='headers_blacklist', full_name='v1.HTTPAuth.headers_blacklist', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021headers-blacklist\362\370\263\007\025\202\364\263\007\020headersBlacklist\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='default_path', full_name='v1.HTTPAuth.default_path', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014default-path\362\370\263\007\020\202\364\263\007\013defaultPath\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='subdomain', full_name='v1.HTTPAuth.subdomain', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\016\372\363\263\007\tsubdomain\362\370\263\007\016\202\364\263\007\tsubdomain\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010httpAuth\372\370\263\007\r\342\363\263\007\010httpAuth\372\370\263\007\r\352\363\263\007\010httpAuth\372\370\263\007\016\372\363\263\007\thttp_auth',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=18900,
serialized_end=19957,
)
_KUBERNETES = _descriptor.Descriptor(
name='Kubernetes',
full_name='v1.Kubernetes',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Kubernetes.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Kubernetes.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Kubernetes.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Kubernetes.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Kubernetes.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Kubernetes.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Kubernetes.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Kubernetes.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.Kubernetes.certificate_authority', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_certificate', full_name='v1.Kubernetes.client_certificate', index=9,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022client-certificate\362\370\263\007\026\202\364\263\007\021clientCertificate\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_key', full_name='v1.Kubernetes.client_key', index=10,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\017\372\363\263\007\nclient-key\362\370\263\007\016\202\364\263\007\tclientKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.Kubernetes.healthcheck_namespace', index=11,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\017\332\363\263\007\nkubernetes\372\370\263\007\017\342\363\263\007\nkubernetes\372\370\263\007\017\352\363\263\007\nkubernetes\372\370\263\007\017\372\363\263\007\nkubernetes',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=19960,
serialized_end=21080,
)
_KUBERNETESUSERIMPERSONATION = _descriptor.Descriptor(
name='KubernetesUserImpersonation',
full_name='v1.KubernetesUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.KubernetesUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.KubernetesUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.KubernetesUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.KubernetesUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.KubernetesUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.KubernetesUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.KubernetesUserImpersonation.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.KubernetesUserImpersonation.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.KubernetesUserImpersonation.certificate_authority', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_certificate', full_name='v1.KubernetesUserImpersonation.client_certificate', index=9,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022client-certificate\362\370\263\007\026\202\364\263\007\021clientCertificate\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_key', full_name='v1.KubernetesUserImpersonation.client_key', index=10,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\017\372\363\263\007\nclient-key\362\370\263\007\016\202\364\263\007\tclientKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.KubernetesUserImpersonation.healthcheck_namespace', index=11,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007 \332\363\263\007\033kubernetesuserimpersonation\372\370\263\007 \342\363\263\007\033kubernetesuserimpersonation\372\370\263\007 \352\363\263\007\033kubernetesuserimpersonation\372\370\263\007\"\372\363\263\007\035kubernetes_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=21083,
serialized_end=22291,
)
_KUBERNETESBASICAUTH = _descriptor.Descriptor(
name='KubernetesBasicAuth',
full_name='v1.KubernetesBasicAuth',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.KubernetesBasicAuth.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.KubernetesBasicAuth.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.KubernetesBasicAuth.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.KubernetesBasicAuth.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.KubernetesBasicAuth.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.KubernetesBasicAuth.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.KubernetesBasicAuth.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.KubernetesBasicAuth.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.KubernetesBasicAuth.username', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rhttp-username\362\370\263\007\021\202\364\263\007\014httpUsername\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.KubernetesBasicAuth.password', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rhttp-password\362\370\263\007\021\202\364\263\007\014httpPassword\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.KubernetesBasicAuth.healthcheck_namespace', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\030\332\363\263\007\023kuberneteshttpbasic\372\370\263\007\030\342\363\263\007\023kuberneteshttpbasic\372\370\263\007\030\352\363\263\007\023kuberneteshttpbasic\372\370\263\007\032\372\363\263\007\025kubernetes_basic_auth',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=22294,
serialized_end=23290,
)
_KUBERNETESSERVICEACCOUNT = _descriptor.Descriptor(
name='KubernetesServiceAccount',
full_name='v1.KubernetesServiceAccount',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.KubernetesServiceAccount.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.KubernetesServiceAccount.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.KubernetesServiceAccount.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.KubernetesServiceAccount.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.KubernetesServiceAccount.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.KubernetesServiceAccount.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.KubernetesServiceAccount.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.KubernetesServiceAccount.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='v1.KubernetesServiceAccount.token', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\016\372\363\263\007\tapi-token\362\370\263\007\r\202\364\263\007\010apiToken\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.KubernetesServiceAccount.healthcheck_namespace', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\035\332\363\263\007\030kubernetesserviceaccount\372\370\263\007\035\342\363\263\007\030kubernetesserviceaccount\372\370\263\007\035\352\363\263\007\030kubernetesserviceaccount\372\370\263\007\037\372\363\263\007\032kubernetes_service_account',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=23293,
serialized_end=24178,
)
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION = _descriptor.Descriptor(
name='KubernetesServiceAccountUserImpersonation',
full_name='v1.KubernetesServiceAccountUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.KubernetesServiceAccountUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.KubernetesServiceAccountUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.KubernetesServiceAccountUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.KubernetesServiceAccountUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.KubernetesServiceAccountUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.KubernetesServiceAccountUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.KubernetesServiceAccountUserImpersonation.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.KubernetesServiceAccountUserImpersonation.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='v1.KubernetesServiceAccountUserImpersonation.token', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.KubernetesServiceAccountUserImpersonation.healthcheck_namespace', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007.\332\363\263\007)kubernetesserviceaccountuserimpersonation\372\370\263\007.\342\363\263\007)kubernetesserviceaccountuserimpersonation\372\370\263\007.\352\363\263\007)kubernetesserviceaccountuserimpersonation\372\370\263\0072\372\363\263\007-kubernetes_service_account_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=24181,
serialized_end=25153,
)
_AMAZONEKS = _descriptor.Descriptor(
name='AmazonEKS',
full_name='v1.AmazonEKS',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AmazonEKS.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AmazonEKS.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AmazonEKS.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AmazonEKS.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AmazonEKS.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AmazonEKS.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.AmazonEKS.endpoint', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.AmazonEKS.access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\raccess-key-id\362\370\263\007\020\202\364\263\007\013accessKeyId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.AmazonEKS.secret_access_key', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.AmazonEKS.certificate_authority', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region', full_name='v1.AmazonEKS.region', index=10,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006region\362\370\263\007\013\202\364\263\007\006region\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='cluster_name', full_name='v1.AmazonEKS.cluster_name', index=11,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014cluster-name\362\370\263\007\020\202\364\263\007\013clusterName\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.AmazonEKS.role_arn', index=12,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.AmazonEKS.role_external_id', index=13,
number=10, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AmazonEKS.healthcheck_namespace', index=14,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tamazoneks\372\370\263\007\016\342\363\263\007\tamazoneks\372\370\263\007\016\352\363\263\007\tamazoneks\372\370\263\007\017\372\363\263\007\namazon_eks',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=25156,
serialized_end=26660,
)
_AMAZONEKSUSERIMPERSONATION = _descriptor.Descriptor(
name='AmazonEKSUserImpersonation',
full_name='v1.AmazonEKSUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AmazonEKSUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AmazonEKSUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AmazonEKSUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AmazonEKSUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AmazonEKSUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AmazonEKSUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.AmazonEKSUserImpersonation.endpoint', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='access_key', full_name='v1.AmazonEKSUserImpersonation.access_key', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\raccess-key-id\362\370\263\007\020\202\364\263\007\013accessKeyId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_access_key', full_name='v1.AmazonEKSUserImpersonation.secret_access_key', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021secret-access-key\362\370\263\007\024\202\364\263\007\017secretAccessKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.AmazonEKSUserImpersonation.certificate_authority', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='region', full_name='v1.AmazonEKSUserImpersonation.region', index=10,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006region\362\370\263\007\013\202\364\263\007\006region\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='cluster_name', full_name='v1.AmazonEKSUserImpersonation.cluster_name', index=11,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014cluster-name\362\370\263\007\020\202\364\263\007\013clusterName\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_arn', full_name='v1.AmazonEKSUserImpersonation.role_arn', index=12,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010role-arn\362\370\263\007\014\202\364\263\007\007roleArn\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='role_external_id', full_name='v1.AmazonEKSUserImpersonation.role_external_id', index=13,
number=10, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\025\372\363\263\007\020role-external-id\362\370\263\007\023\202\364\263\007\016roleExternalId\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AmazonEKSUserImpersonation.healthcheck_namespace', index=14,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\037\332\363\263\007\032amazoneksuserimpersonation\372\370\263\007\037\342\363\263\007\032amazoneksuserimpersonation\372\370\263\007\037\352\363\263\007\032amazoneksuserimpersonation\372\370\263\007\"\372\363\263\007\035amazon_eks_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=26663,
serialized_end=28255,
)
_GOOGLEGKE = _descriptor.Descriptor(
name='GoogleGKE',
full_name='v1.GoogleGKE',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.GoogleGKE.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.GoogleGKE.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.GoogleGKE.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.GoogleGKE.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.GoogleGKE.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.GoogleGKE.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.GoogleGKE.endpoint', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.GoogleGKE.certificate_authority', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='service_account_key', full_name='v1.GoogleGKE.service_account_key', index=8,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\030\372\363\263\007\023service-account-key\362\370\263\007\030\202\364\263\007\023service-account-key\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.GoogleGKE.healthcheck_namespace', index=9,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tgooglegke\372\370\263\007\016\342\363\263\007\tgooglegke\372\370\263\007\016\352\363\263\007\tgooglegke\372\370\263\007\017\372\363\263\007\ngoogle_gke',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=28258,
serialized_end=29153,
)
_GOOGLEGKEUSERIMPERSONATION = _descriptor.Descriptor(
name='GoogleGKEUserImpersonation',
full_name='v1.GoogleGKEUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.GoogleGKEUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.GoogleGKEUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.GoogleGKEUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.GoogleGKEUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.GoogleGKEUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.GoogleGKEUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='endpoint', full_name='v1.GoogleGKEUserImpersonation.endpoint', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010endpoint\362\370\263\007\r\202\364\263\007\010endpoint\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.GoogleGKEUserImpersonation.certificate_authority', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='service_account_key', full_name='v1.GoogleGKEUserImpersonation.service_account_key', index=8,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\030\372\363\263\007\023service-account-key\362\370\263\007\030\202\364\263\007\023service-account-key\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.GoogleGKEUserImpersonation.healthcheck_namespace', index=9,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\037\332\363\263\007\032googlegkeuserimpersonation\372\370\263\007\037\342\363\263\007\032googlegkeuserimpersonation\372\370\263\007\037\352\363\263\007\032googlegkeuserimpersonation\372\370\263\007\"\372\363\263\007\035google_gke_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=29156,
serialized_end=30139,
)
_AKS = _descriptor.Descriptor(
name='AKS',
full_name='v1.AKS',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AKS.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AKS.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AKS.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AKS.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AKS.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AKS.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AKS.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AKS.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.AKS.certificate_authority', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_certificate', full_name='v1.AKS.client_certificate', index=9,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022client-certificate\362\370\263\007\026\202\364\263\007\021clientCertificate\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_key', full_name='v1.AKS.client_key', index=10,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\017\372\363\263\007\nclient-key\362\370\263\007\016\202\364\263\007\tclientKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AKS.healthcheck_namespace', index=11,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\010\332\363\263\007\003aks\372\370\263\007\010\342\363\263\007\003aks\372\370\263\007\010\352\363\263\007\003aks\372\370\263\007\010\372\363\263\007\003aks',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=30142,
serialized_end=31227,
)
_AKSUSERIMPERSONATION = _descriptor.Descriptor(
name='AKSUserImpersonation',
full_name='v1.AKSUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AKSUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AKSUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AKSUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AKSUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AKSUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AKSUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AKSUserImpersonation.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AKSUserImpersonation.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='certificate_authority', full_name='v1.AKSUserImpersonation.certificate_authority', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025certificate-authority\362\370\263\007\031\202\364\263\007\024certificateAuthority\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_certificate', full_name='v1.AKSUserImpersonation.client_certificate', index=9,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022client-certificate\362\370\263\007\026\202\364\263\007\021clientCertificate\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_key', full_name='v1.AKSUserImpersonation.client_key', index=10,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\017\372\363\263\007\nclient-key\362\370\263\007\016\202\364\263\007\tclientKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AKSUserImpersonation.healthcheck_namespace', index=11,
number=9, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\031\332\363\263\007\024aksuserimpersonation\372\370\263\007\031\342\363\263\007\024aksuserimpersonation\372\370\263\007\031\352\363\263\007\024aksuserimpersonation\372\370\263\007\033\372\363\263\007\026aks_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=31230,
serialized_end=32403,
)
_AKSBASICAUTH = _descriptor.Descriptor(
name='AKSBasicAuth',
full_name='v1.AKSBasicAuth',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AKSBasicAuth.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AKSBasicAuth.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AKSBasicAuth.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AKSBasicAuth.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AKSBasicAuth.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AKSBasicAuth.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AKSBasicAuth.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AKSBasicAuth.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.AKSBasicAuth.username', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rhttp-username\362\370\263\007\021\202\364\263\007\014httpUsername\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.AKSBasicAuth.password', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rhttp-password\362\370\263\007\021\202\364\263\007\014httpPassword\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AKSBasicAuth.healthcheck_namespace', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\021\332\363\263\007\014akshttpbasic\372\370\263\007\021\342\363\263\007\014akshttpbasic\372\370\263\007\021\352\363\263\007\014akshttpbasic\372\370\263\007\023\372\363\263\007\016aks_basic_auth',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=32406,
serialized_end=33366,
)
_AKSSERVICEACCOUNT = _descriptor.Descriptor(
name='AKSServiceAccount',
full_name='v1.AKSServiceAccount',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AKSServiceAccount.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AKSServiceAccount.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AKSServiceAccount.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AKSServiceAccount.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AKSServiceAccount.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AKSServiceAccount.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AKSServiceAccount.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AKSServiceAccount.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='v1.AKSServiceAccount.token', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\016\372\363\263\007\tapi-token\362\370\263\007\r\202\364\263\007\010apiToken\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AKSServiceAccount.healthcheck_namespace', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\026\332\363\263\007\021aksserviceaccount\372\370\263\007\026\342\363\263\007\021aksserviceaccount\372\370\263\007\026\352\363\263\007\021aksserviceaccount\372\370\263\007\030\372\363\263\007\023aks_service_account',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=33369,
serialized_end=34218,
)
_AKSSERVICEACCOUNTUSERIMPERSONATION = _descriptor.Descriptor(
name='AKSServiceAccountUserImpersonation',
full_name='v1.AKSServiceAccountUserImpersonation',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AKSServiceAccountUserImpersonation.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AKSServiceAccountUserImpersonation.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AKSServiceAccountUserImpersonation.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AKSServiceAccountUserImpersonation.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AKSServiceAccountUserImpersonation.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AKSServiceAccountUserImpersonation.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AKSServiceAccountUserImpersonation.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AKSServiceAccountUserImpersonation.port', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='v1.AKSServiceAccountUserImpersonation.token', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\016\372\363\263\007\tapi-token\362\370\263\007\r\202\364\263\007\010apiToken\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthcheck_namespace', full_name='v1.AKSServiceAccountUserImpersonation.healthcheck_namespace', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\032\372\363\263\007\025healthcheck-namespace\362\370\263\007\031\202\364\263\007\024healthcheckNamespace\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\'\332\363\263\007\"aksserviceaccountuserimpersonation\372\370\263\007\'\342\363\263\007\"aksserviceaccountuserimpersonation\372\370\263\007\'\352\363\263\007\"aksserviceaccountuserimpersonation\372\370\263\007+\372\363\263\007&aks_service_account_user_impersonation',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=34221,
serialized_end=35158,
)
_MEMCACHED = _descriptor.Descriptor(
name='Memcached',
full_name='v1.Memcached',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Memcached.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Memcached.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Memcached.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Memcached.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Memcached.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Memcached.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Memcached.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Memcached.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Memcached.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tmemcached\372\370\263\007\016\342\363\263\007\tmemcached\372\370\263\007\016\352\363\263\007\tmemcached\372\370\263\007\016\372\363\263\007\tmemcached',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=35161,
serialized_end=35838,
)
_MONGOLEGACYHOST = _descriptor.Descriptor(
name='MongoLegacyHost',
full_name='v1.MongoLegacyHost',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.MongoLegacyHost.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.MongoLegacyHost.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.MongoLegacyHost.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.MongoLegacyHost.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.MongoLegacyHost.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.MongoLegacyHost.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.MongoLegacyHost.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='auth_database', full_name='v1.MongoLegacyHost.auth_database', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rauth-database\362\370\263\007\021\202\364\263\007\014authDatabase\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.MongoLegacyHost.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.MongoLegacyHost.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.MongoLegacyHost.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.MongoLegacyHost.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='replica_set', full_name='v1.MongoLegacyHost.replica_set', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013replica-set\362\370\263\007\017\202\364\263\007\nreplicaSet\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.MongoLegacyHost.tls_required', index=13,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005mongo\372\370\263\007\n\342\363\263\007\005mongo\372\370\263\007\n\352\363\263\007\005mongo\372\370\263\007\026\372\363\263\007\021mongo_legacy_host',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=35841,
serialized_end=37126,
)
_MONGOLEGACYREPLICASET = _descriptor.Descriptor(
name='MongoLegacyReplicaset',
full_name='v1.MongoLegacyReplicaset',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.MongoLegacyReplicaset.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.MongoLegacyReplicaset.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.MongoLegacyReplicaset.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.MongoLegacyReplicaset.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.MongoLegacyReplicaset.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.MongoLegacyReplicaset.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.MongoLegacyReplicaset.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='auth_database', full_name='v1.MongoLegacyReplicaset.auth_database', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rauth-database\362\370\263\007\021\202\364\263\007\014authDatabase\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.MongoLegacyReplicaset.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.MongoLegacyReplicaset.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.MongoLegacyReplicaset.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.MongoLegacyReplicaset.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='replica_set', full_name='v1.MongoLegacyReplicaset.replica_set', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013replica-set\362\370\263\007\017\202\364\263\007\nreplicaSet\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='connect_to_replica', full_name='v1.MongoLegacyReplicaset.connect_to_replica', index=13,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022connect-to-replica\362\370\263\007\025\202\364\263\007\020connectToReplica\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.MongoLegacyReplicaset.tls_required', index=14,
number=9, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\025\332\363\263\007\020mongo-replicaset\372\370\263\007\025\342\363\263\007\020mongo-replicaset\372\370\263\007\025\352\363\263\007\020mongo-replicaset\372\370\263\007\034\372\363\263\007\027mongo_legacy_replicaset',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=37129,
serialized_end=38604,
)
_MONGOHOST = _descriptor.Descriptor(
name='MongoHost',
full_name='v1.MongoHost',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.MongoHost.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.MongoHost.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.MongoHost.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.MongoHost.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.MongoHost.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.MongoHost.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.MongoHost.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='auth_database', full_name='v1.MongoHost.auth_database', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rauth-database\362\370\263\007\021\202\364\263\007\014authDatabase\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.MongoHost.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.MongoHost.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.MongoHost.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.MongoHost.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.MongoHost.tls_required', index=12,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tmongoHost\372\370\263\007\016\342\363\263\007\tmongoHost\372\370\263\007\016\352\363\263\007\tmongoHost\372\370\263\007\017\372\363\263\007\nmongo_host',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=38607,
serialized_end=39767,
)
_MONGOREPLICASET = _descriptor.Descriptor(
name='MongoReplicaSet',
full_name='v1.MongoReplicaSet',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.MongoReplicaSet.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.MongoReplicaSet.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.MongoReplicaSet.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.MongoReplicaSet.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.MongoReplicaSet.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.MongoReplicaSet.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.MongoReplicaSet.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='auth_database', full_name='v1.MongoReplicaSet.auth_database', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rauth-database\362\370\263\007\021\202\364\263\007\014authDatabase\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.MongoReplicaSet.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.MongoReplicaSet.username', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.MongoReplicaSet.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.MongoReplicaSet.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='replica_set', full_name='v1.MongoReplicaSet.replica_set', index=12,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013replica-set\362\370\263\007\017\202\364\263\007\nreplicaSet\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='connect_to_replica', full_name='v1.MongoReplicaSet.connect_to_replica', index=13,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\027\372\363\263\007\022connect-to-replica\362\370\263\007\025\202\364\263\007\020connectToReplica\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.MongoReplicaSet.tls_required', index=14,
number=9, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\024\332\363\263\007\017mongoReplicaSet\372\370\263\007\024\342\363\263\007\017mongoReplicaSet\372\370\263\007\024\352\363\263\007\017mongoReplicaSet\372\370\263\007\026\372\363\263\007\021mongo_replica_set',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=39770,
serialized_end=41230,
)
_MYSQL = _descriptor.Descriptor(
name='Mysql',
full_name='v1.Mysql',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Mysql.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Mysql.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Mysql.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Mysql.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Mysql.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Mysql.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Mysql.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Mysql.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Mysql.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Mysql.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Mysql.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Mysql.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005mysql\372\370\263\007\n\342\363\263\007\005mysql\372\370\263\007\n\352\363\263\007\005mysql\372\370\263\007\n\372\363\263\007\005mysql',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=41233,
serialized_end=42238,
)
_AURORAMYSQL = _descriptor.Descriptor(
name='AuroraMysql',
full_name='v1.AuroraMysql',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AuroraMysql.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AuroraMysql.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AuroraMysql.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AuroraMysql.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AuroraMysql.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AuroraMysql.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AuroraMysql.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.AuroraMysql.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.AuroraMysql.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.AuroraMysql.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.AuroraMysql.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AuroraMysql.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\021\332\363\263\007\014aurora-mysql\372\370\263\007\021\342\363\263\007\014aurora-mysql\372\370\263\007\021\352\363\263\007\014aurora-mysql\372\370\263\007\021\372\363\263\007\014aurora_mysql',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=42241,
serialized_end=43280,
)
_CLUSTRIX = _descriptor.Descriptor(
name='Clustrix',
full_name='v1.Clustrix',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Clustrix.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Clustrix.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Clustrix.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Clustrix.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Clustrix.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Clustrix.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Clustrix.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Clustrix.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Clustrix.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Clustrix.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Clustrix.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Clustrix.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010clustrix\372\370\263\007\r\342\363\263\007\010clustrix\372\370\263\007\r\352\363\263\007\010clustrix\372\370\263\007\r\372\363\263\007\010clustrix',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=43283,
serialized_end=44303,
)
_MARIA = _descriptor.Descriptor(
name='Maria',
full_name='v1.Maria',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Maria.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Maria.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Maria.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Maria.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Maria.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Maria.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Maria.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Maria.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Maria.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Maria.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Maria.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Maria.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005maria\372\370\263\007\n\342\363\263\007\005maria\372\370\263\007\n\352\363\263\007\005maria\372\370\263\007\n\372\363\263\007\005maria',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=44306,
serialized_end=45311,
)
_MEMSQL = _descriptor.Descriptor(
name='Memsql',
full_name='v1.Memsql',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Memsql.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Memsql.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Memsql.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Memsql.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Memsql.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Memsql.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Memsql.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Memsql.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Memsql.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Memsql.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Memsql.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Memsql.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006memsql\372\370\263\007\013\342\363\263\007\006memsql\372\370\263\007\013\352\363\263\007\006memsql\372\370\263\007\013\372\363\263\007\006memsql',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=45314,
serialized_end=46324,
)
_SINGLESTORE = _descriptor.Descriptor(
name='SingleStore',
full_name='v1.SingleStore',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SingleStore.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SingleStore.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SingleStore.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SingleStore.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SingleStore.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SingleStore.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SingleStore.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SingleStore.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.SingleStore.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.SingleStore.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.SingleStore.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.SingleStore.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\020\332\363\263\007\013singlestore\372\370\263\007\020\342\363\263\007\013singlestore\372\370\263\007\020\352\363\263\007\013singlestore\372\370\263\007\021\372\363\263\007\014single_store',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=46327,
serialized_end=47363,
)
_ORACLE = _descriptor.Descriptor(
name='Oracle',
full_name='v1.Oracle',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Oracle.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Oracle.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Oracle.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Oracle.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Oracle.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Oracle.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Oracle.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Oracle.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Oracle.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Oracle.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Oracle.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Oracle.port_override', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.Oracle.tls_required', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006oracle\372\370\263\007\013\342\363\263\007\006oracle\372\370\263\007\013\352\363\263\007\006oracle\372\370\263\007\013\372\363\263\007\006oracle',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=47366,
serialized_end=48495,
)
_POSTGRES = _descriptor.Descriptor(
name='Postgres',
full_name='v1.Postgres',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Postgres.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Postgres.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Postgres.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Postgres.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Postgres.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Postgres.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Postgres.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Postgres.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Postgres.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Postgres.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Postgres.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Postgres.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.Postgres.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010postgres\372\370\263\007\r\342\363\263\007\010postgres\372\370\263\007\r\352\363\263\007\010postgres\372\370\263\007\r\372\363\263\007\010postgres',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=48498,
serialized_end=49661,
)
_AURORAPOSTGRES = _descriptor.Descriptor(
name='AuroraPostgres',
full_name='v1.AuroraPostgres',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.AuroraPostgres.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.AuroraPostgres.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.AuroraPostgres.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.AuroraPostgres.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.AuroraPostgres.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.AuroraPostgres.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.AuroraPostgres.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.AuroraPostgres.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.AuroraPostgres.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.AuroraPostgres.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.AuroraPostgres.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.AuroraPostgres.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.AuroraPostgres.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\024\332\363\263\007\017aurora-postgres\372\370\263\007\024\342\363\263\007\017aurora-postgres\372\370\263\007\024\352\363\263\007\017aurora-postgres\372\370\263\007\024\372\363\263\007\017aurora_postgres',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=49664,
serialized_end=50861,
)
_GREENPLUM = _descriptor.Descriptor(
name='Greenplum',
full_name='v1.Greenplum',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Greenplum.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Greenplum.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Greenplum.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Greenplum.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Greenplum.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Greenplum.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Greenplum.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Greenplum.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Greenplum.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Greenplum.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Greenplum.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Greenplum.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.Greenplum.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tgreenplum\372\370\263\007\016\342\363\263\007\tgreenplum\372\370\263\007\016\352\363\263\007\tgreenplum\372\370\263\007\016\372\363\263\007\tgreenplum',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=50864,
serialized_end=52032,
)
_COCKROACH = _descriptor.Descriptor(
name='Cockroach',
full_name='v1.Cockroach',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Cockroach.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Cockroach.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Cockroach.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Cockroach.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Cockroach.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Cockroach.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Cockroach.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Cockroach.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Cockroach.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Cockroach.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Cockroach.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Cockroach.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.Cockroach.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tcockroach\372\370\263\007\016\342\363\263\007\tcockroach\372\370\263\007\016\352\363\263\007\tcockroach\372\370\263\007\016\372\363\263\007\tcockroach',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=52035,
serialized_end=53203,
)
_REDSHIFT = _descriptor.Descriptor(
name='Redshift',
full_name='v1.Redshift',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Redshift.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Redshift.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Redshift.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Redshift.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Redshift.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Redshift.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Redshift.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Redshift.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Redshift.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Redshift.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Redshift.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Redshift.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.Redshift.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010redshift\372\370\263\007\r\342\363\263\007\010redshift\372\370\263\007\r\352\363\263\007\010redshift\372\370\263\007\r\372\363\263\007\010redshift',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=53206,
serialized_end=54369,
)
_CITUS = _descriptor.Descriptor(
name='Citus',
full_name='v1.Citus',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Citus.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Citus.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Citus.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Citus.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Citus.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Citus.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Citus.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Citus.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Citus.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Citus.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Citus.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Citus.port', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.Citus.override_database', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005citus\372\370\263\007\n\342\363\263\007\005citus\372\370\263\007\n\352\363\263\007\005citus\372\370\263\007\n\372\363\263\007\005citus',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=54372,
serialized_end=55520,
)
_PRESTO = _descriptor.Descriptor(
name='Presto',
full_name='v1.Presto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Presto.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Presto.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Presto.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Presto.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Presto.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Presto.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Presto.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Presto.password', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Presto.database', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Presto.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Presto.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Presto.username', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.Presto.tls_required', index=12,
number=7, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006presto\372\370\263\007\013\342\363\263\007\006presto\372\370\263\007\013\352\363\263\007\006presto\372\370\263\007\013\372\363\263\007\006presto',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=55523,
serialized_end=56652,
)
_RAWTCP = _descriptor.Descriptor(
name='RawTCP',
full_name='v1.RawTCP',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.RawTCP.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.RawTCP.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.RawTCP.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.RawTCP.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.RawTCP.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.RawTCP.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.RawTCP.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.RawTCP.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.RawTCP.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006rawtcp\372\370\263\007\013\342\363\263\007\006rawtcp\372\370\263\007\013\352\363\263\007\006rawtcp\372\370\263\007\014\372\363\263\007\007raw_tcp',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=56655,
serialized_end=57318,
)
_RDP = _descriptor.Descriptor(
name='RDP',
full_name='v1.RDP',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.RDP.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.RDP.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.RDP.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.RDP.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.RDP.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.RDP.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.RDP.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.RDP.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.RDP.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.RDP.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.RDP.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\010\332\363\263\007\003rdp\372\370\263\007\010\342\363\263\007\003rdp\372\370\263\007\010\352\363\263\007\003rdp\372\370\263\007\010\372\363\263\007\003rdp',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=57321,
serialized_end=58200,
)
_REDIS = _descriptor.Descriptor(
name='Redis',
full_name='v1.Redis',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Redis.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Redis.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Redis.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Redis.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Redis.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Redis.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Redis.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Redis.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Redis.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Redis.port', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005redis\372\370\263\007\n\342\363\263\007\005redis\372\370\263\007\n\352\363\263\007\005redis\372\370\263\007\n\372\363\263\007\005redis',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=58203,
serialized_end=58976,
)
_ELASTICACHEREDIS = _descriptor.Descriptor(
name='ElasticacheRedis',
full_name='v1.ElasticacheRedis',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.ElasticacheRedis.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.ElasticacheRedis.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.ElasticacheRedis.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.ElasticacheRedis.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.ElasticacheRedis.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.ElasticacheRedis.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.ElasticacheRedis.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.ElasticacheRedis.port_override', index=7,
number=2, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.ElasticacheRedis.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.ElasticacheRedis.port', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tls_required', full_name='v1.ElasticacheRedis.tls_required', index=10,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\021\372\363\263\007\014tls-required\362\370\263\007\010\202\364\263\007\003tls\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\014\332\363\263\007\007ecredis\372\370\263\007\014\342\363\263\007\007ecredis\372\370\263\007\014\352\363\263\007\007ecredis\372\370\263\007\026\372\363\263\007\021elasticache_redis',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=58979,
serialized_end=59900,
)
_SNOWFLAKE = _descriptor.Descriptor(
name='Snowflake',
full_name='v1.Snowflake',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Snowflake.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Snowflake.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Snowflake.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Snowflake.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Snowflake.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Snowflake.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Snowflake.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Snowflake.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Snowflake.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='database', full_name='v1.Snowflake.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010database\362\370\263\007\r\202\364\263\007\010database\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='schema', full_name='v1.Snowflake.schema', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\013\372\363\263\007\006schema\362\370\263\007\013\202\364\263\007\006schema\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Snowflake.port_override', index=11,
number=6, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\016\332\363\263\007\tsnowflake\372\370\263\007\016\342\363\263\007\tsnowflake\372\370\263\007\016\352\363\263\007\tsnowflake\372\370\263\007\016\372\363\263\007\tsnowflake',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=59903,
serialized_end=60934,
)
_SQLSERVER = _descriptor.Descriptor(
name='SQLServer',
full_name='v1.SQLServer',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SQLServer.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SQLServer.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SQLServer.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SQLServer.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SQLServer.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SQLServer.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SQLServer.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SQLServer.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.SQLServer.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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_descriptor.FieldDescriptor(
name='database', full_name='v1.SQLServer.database', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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_descriptor.FieldDescriptor(
name='port_override', full_name='v1.SQLServer.port_override', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='schema', full_name='v1.SQLServer.schema', index=11,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
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_descriptor.FieldDescriptor(
name='port', full_name='v1.SQLServer.port', index=12,
number=7, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='override_database', full_name='v1.SQLServer.override_database', index=13,
number=8, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\026\372\363\263\007\021override-database\362\370\263\007\025\202\364\263\007\020overrideDatabase\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\n\332\363\263\007\005mssql\372\370\263\007\n\342\363\263\007\005mssql\372\370\263\007\n\352\363\263\007\005mssql\372\370\263\007\017\372\363\263\007\nsql_server',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=60937,
serialized_end=62204,
)
_SSH = _descriptor.Descriptor(
name='SSH',
full_name='v1.SSH',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SSH.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SSH.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SSH.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SSH.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SSH.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SSH.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SSH.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SSH.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.SSH.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='public_key', full_name='v1.SSH.public_key', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\017\372\363\263\007\npublic-key\362\370\263\007\016\202\364\263\007\tpublicKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\001\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_forwarding', full_name='v1.SSH.port_forwarding', index=10,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\024\372\363\263\007\017port-forwarding\362\370\263\007\023\202\364\263\007\016portForwarding\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='allow_deprecated_key_exchanges', full_name='v1.SSH.allow_deprecated_key_exchanges', index=11,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007#\372\363\263\007\036allow-deprecated-key-exchanges\362\370\263\007 \202\364\263\007\033allowDeprecatedKeyExchanges\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\010\332\363\263\007\003ssh\372\370\263\007\010\342\363\263\007\003ssh\372\370\263\007\010\352\363\263\007\003ssh\372\370\263\007\010\372\363\263\007\003ssh',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=62207,
serialized_end=63278,
)
_SSHCERT = _descriptor.Descriptor(
name='SSHCert',
full_name='v1.SSHCert',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SSHCert.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SSHCert.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SSHCert.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SSHCert.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SSHCert.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SSHCert.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SSHCert.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SSHCert.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.SSHCert.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_forwarding', full_name='v1.SSHCert.port_forwarding', index=9,
number=4, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\024\372\363\263\007\017port-forwarding\362\370\263\007\023\202\364\263\007\016portForwarding\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='allow_deprecated_key_exchanges', full_name='v1.SSHCert.allow_deprecated_key_exchanges', index=10,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007#\372\363\263\007\036allow-deprecated-key-exchanges\362\370\263\007 \202\364\263\007\033allowDeprecatedKeyExchanges\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\014\332\363\263\007\007sshCert\372\370\263\007\014\342\363\263\007\007sshCert\372\370\263\007\014\352\363\263\007\007sshCert\372\370\263\007\r\372\363\263\007\010ssh_cert',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=63281,
serialized_end=64244,
)
_SSHCUSTOMERKEY = _descriptor.Descriptor(
name='SSHCustomerKey',
full_name='v1.SSHCustomerKey',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SSHCustomerKey.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SSHCustomerKey.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SSHCustomerKey.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SSHCustomerKey.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SSHCustomerKey.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SSHCustomerKey.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SSHCustomerKey.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SSHCustomerKey.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.SSHCustomerKey.port', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='private_key', full_name='v1.SSHCustomerKey.private_key', index=9,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\020\372\363\263\007\013private-key\362\370\263\007\017\202\364\263\007\nprivateKey\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_forwarding', full_name='v1.SSHCustomerKey.port_forwarding', index=10,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\024\372\363\263\007\017port-forwarding\362\370\263\007\023\202\364\263\007\016portForwarding\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='allow_deprecated_key_exchanges', full_name='v1.SSHCustomerKey.allow_deprecated_key_exchanges', index=11,
number=6, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007#\372\363\263\007\036allow-deprecated-key-exchanges\362\370\263\007 \202\364\263\007\033allowDeprecatedKeyExchanges\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\023\332\363\263\007\016sshCustomerKey\372\370\263\007\023\342\363\263\007\016sshCustomerKey\372\370\263\007\023\352\363\263\007\016sshCustomerKey\372\370\263\007\025\372\363\263\007\020ssh_customer_key',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=64247,
serialized_end=65370,
)
_SYBASE = _descriptor.Descriptor(
name='Sybase',
full_name='v1.Sybase',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Sybase.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Sybase.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Sybase.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Sybase.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Sybase.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Sybase.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Sybase.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Sybase.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Sybase.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Sybase.port', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Sybase.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\013\332\363\263\007\006sybase\372\370\263\007\013\342\363\263\007\006sybase\372\370\263\007\013\352\363\263\007\006sybase\372\370\263\007\013\372\363\263\007\006sybase',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=65373,
serialized_end=66267,
)
_SYBASEIQ = _descriptor.Descriptor(
name='SybaseIQ',
full_name='v1.SybaseIQ',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.SybaseIQ.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.SybaseIQ.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.SybaseIQ.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.SybaseIQ.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.SybaseIQ.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.SybaseIQ.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.SybaseIQ.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.SybaseIQ.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.SybaseIQ.port_override', index=8,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.SybaseIQ.port', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.SybaseIQ.password', index=10,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010sybaseIQ\372\370\263\007\r\342\363\263\007\010sybaseIQ\372\370\263\007\r\352\363\263\007\010sybaseIQ\372\370\263\007\016\372\363\263\007\tsybase_iq',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=66270,
serialized_end=67175,
)
_TERADATA = _descriptor.Descriptor(
name='Teradata',
full_name='v1.Teradata',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='id', full_name='v1.Teradata.id', index=0,
number=32768, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='v1.Teradata.name', index=1,
number=32769, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\300\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='healthy', full_name='v1.Teradata.healthy', index=2,
number=32770, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\005\230\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tags', full_name='v1.Teradata.tags', index=3,
number=32771, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='secret_store_id', full_name='v1.Teradata.secret_store_id', index=4,
number=32772, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='egress_filter', full_name='v1.Teradata.egress_filter', index=5,
number=32773, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='hostname', full_name='v1.Teradata.hostname', index=6,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010hostname\362\370\263\007\r\202\364\263\007\010hostname\362\370\263\007\005\300\363\263\007\001\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='username', full_name='v1.Teradata.username', index=7,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010username\362\370\263\007\r\202\364\263\007\010username\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='v1.Teradata.password', index=8,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\r\372\363\263\007\010password\362\370\263\007\r\202\364\263\007\010password\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\001\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\001', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port_override', full_name='v1.Teradata.port_override', index=9,
number=4, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\022\372\363\263\007\rport-override\362\370\263\007\021\202\364\263\007\014portOverride\362\370\263\007\005\300\363\263\007\000\362\370\263\007\r\332\363\263\007\010readonly\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='port', full_name='v1.Teradata.port', index=10,
number=5, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\362\370\263\007\005\260\363\263\007\001\362\370\263\007\t\372\363\263\007\004port\362\370\263\007\t\202\364\263\007\004port\362\370\263\007\005\300\363\263\007\000\362\370\263\007\005\332\363\263\007\000\362\370\263\007\005\350\363\263\007\000\362\370\263\007\005\230\364\263\007\000\362\370\263\007\005\240\364\263\007\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=b'\372\370\263\007\005\250\363\263\007\001\372\370\263\007\r\332\363\263\007\010teradata\372\370\263\007\r\342\363\263\007\010teradata\372\370\263\007\r\352\363\263\007\010teradata\372\370\263\007\r\372\363\263\007\010teradata',
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=67178,
serialized_end=68082,
)
_RESOURCE.fields_by_name['rabbit_mqamqp_091'].message_type = _RABBITMQAMQP091
_RESOURCE.fields_by_name['amazon_mqamqp_091'].message_type = _AMAZONMQAMQP091
_RESOURCE.fields_by_name['athena'].message_type = _ATHENA
_RESOURCE.fields_by_name['aws'].message_type = _AWS
_RESOURCE.fields_by_name['big_query'].message_type = _BIGQUERY
_RESOURCE.fields_by_name['cassandra'].message_type = _CASSANDRA
_RESOURCE.fields_by_name['db_2_i'].message_type = _DB2I
_RESOURCE.fields_by_name['db_2_luw'].message_type = _DB2LUW
_RESOURCE.fields_by_name['druid'].message_type = _DRUID
_RESOURCE.fields_by_name['dynamo_db'].message_type = _DYNAMODB
_RESOURCE.fields_by_name['amazon_es'].message_type = _AMAZONES
_RESOURCE.fields_by_name['elastic'].message_type = _ELASTIC
_RESOURCE.fields_by_name['http_basic_auth'].message_type = _HTTPBASICAUTH
_RESOURCE.fields_by_name['http_no_auth'].message_type = _HTTPNOAUTH
_RESOURCE.fields_by_name['http_auth'].message_type = _HTTPAUTH
_RESOURCE.fields_by_name['kubernetes'].message_type = _KUBERNETES
_RESOURCE.fields_by_name['kubernetes_user_impersonation'].message_type = _KUBERNETESUSERIMPERSONATION
_RESOURCE.fields_by_name['kubernetes_basic_auth'].message_type = _KUBERNETESBASICAUTH
_RESOURCE.fields_by_name['kubernetes_service_account'].message_type = _KUBERNETESSERVICEACCOUNT
_RESOURCE.fields_by_name['kubernetes_service_account_user_impersonation'].message_type = _KUBERNETESSERVICEACCOUNTUSERIMPERSONATION
_RESOURCE.fields_by_name['amazon_eks'].message_type = _AMAZONEKS
_RESOURCE.fields_by_name['amazon_eks_user_impersonation'].message_type = _AMAZONEKSUSERIMPERSONATION
_RESOURCE.fields_by_name['google_gke'].message_type = _GOOGLEGKE
_RESOURCE.fields_by_name['google_gke_user_impersonation'].message_type = _GOOGLEGKEUSERIMPERSONATION
_RESOURCE.fields_by_name['aks'].message_type = _AKS
_RESOURCE.fields_by_name['aks_user_impersonation'].message_type = _AKSUSERIMPERSONATION
_RESOURCE.fields_by_name['aks_basic_auth'].message_type = _AKSBASICAUTH
_RESOURCE.fields_by_name['aks_service_account'].message_type = _AKSSERVICEACCOUNT
_RESOURCE.fields_by_name['aks_service_account_user_impersonation'].message_type = _AKSSERVICEACCOUNTUSERIMPERSONATION
_RESOURCE.fields_by_name['memcached'].message_type = _MEMCACHED
_RESOURCE.fields_by_name['mongo_legacy_host'].message_type = _MONGOLEGACYHOST
_RESOURCE.fields_by_name['mongo_legacy_replicaset'].message_type = _MONGOLEGACYREPLICASET
_RESOURCE.fields_by_name['mongo_host'].message_type = _MONGOHOST
_RESOURCE.fields_by_name['mongo_replica_set'].message_type = _MONGOREPLICASET
_RESOURCE.fields_by_name['mysql'].message_type = _MYSQL
_RESOURCE.fields_by_name['aurora_mysql'].message_type = _AURORAMYSQL
_RESOURCE.fields_by_name['clustrix'].message_type = _CLUSTRIX
_RESOURCE.fields_by_name['maria'].message_type = _MARIA
_RESOURCE.fields_by_name['memsql'].message_type = _MEMSQL
_RESOURCE.fields_by_name['single_store'].message_type = _SINGLESTORE
_RESOURCE.fields_by_name['oracle'].message_type = _ORACLE
_RESOURCE.fields_by_name['postgres'].message_type = _POSTGRES
_RESOURCE.fields_by_name['aurora_postgres'].message_type = _AURORAPOSTGRES
_RESOURCE.fields_by_name['greenplum'].message_type = _GREENPLUM
_RESOURCE.fields_by_name['cockroach'].message_type = _COCKROACH
_RESOURCE.fields_by_name['redshift'].message_type = _REDSHIFT
_RESOURCE.fields_by_name['citus'].message_type = _CITUS
_RESOURCE.fields_by_name['presto'].message_type = _PRESTO
_RESOURCE.fields_by_name['raw_tcp'].message_type = _RAWTCP
_RESOURCE.fields_by_name['rdp'].message_type = _RDP
_RESOURCE.fields_by_name['redis'].message_type = _REDIS
_RESOURCE.fields_by_name['elasticache_redis'].message_type = _ELASTICACHEREDIS
_RESOURCE.fields_by_name['snowflake'].message_type = _SNOWFLAKE
_RESOURCE.fields_by_name['sql_server'].message_type = _SQLSERVER
_RESOURCE.fields_by_name['ssh'].message_type = _SSH
_RESOURCE.fields_by_name['ssh_cert'].message_type = _SSHCERT
_RESOURCE.fields_by_name['ssh_customer_key'].message_type = _SSHCUSTOMERKEY
_RESOURCE.fields_by_name['sybase'].message_type = _SYBASE
_RESOURCE.fields_by_name['sybase_iq'].message_type = _SYBASEIQ
_RESOURCE.fields_by_name['teradata'].message_type = _TERADATA
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['rabbit_mqamqp_091'])
_RESOURCE.fields_by_name['rabbit_mqamqp_091'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['amazon_mqamqp_091'])
_RESOURCE.fields_by_name['amazon_mqamqp_091'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['athena'])
_RESOURCE.fields_by_name['athena'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aws'])
_RESOURCE.fields_by_name['aws'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['big_query'])
_RESOURCE.fields_by_name['big_query'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['cassandra'])
_RESOURCE.fields_by_name['cassandra'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['db_2_i'])
_RESOURCE.fields_by_name['db_2_i'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['db_2_luw'])
_RESOURCE.fields_by_name['db_2_luw'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['druid'])
_RESOURCE.fields_by_name['druid'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['dynamo_db'])
_RESOURCE.fields_by_name['dynamo_db'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['amazon_es'])
_RESOURCE.fields_by_name['amazon_es'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['elastic'])
_RESOURCE.fields_by_name['elastic'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['http_basic_auth'])
_RESOURCE.fields_by_name['http_basic_auth'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['http_no_auth'])
_RESOURCE.fields_by_name['http_no_auth'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['http_auth'])
_RESOURCE.fields_by_name['http_auth'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['kubernetes'])
_RESOURCE.fields_by_name['kubernetes'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['kubernetes_user_impersonation'])
_RESOURCE.fields_by_name['kubernetes_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['kubernetes_basic_auth'])
_RESOURCE.fields_by_name['kubernetes_basic_auth'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['kubernetes_service_account'])
_RESOURCE.fields_by_name['kubernetes_service_account'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['kubernetes_service_account_user_impersonation'])
_RESOURCE.fields_by_name['kubernetes_service_account_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['amazon_eks'])
_RESOURCE.fields_by_name['amazon_eks'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['amazon_eks_user_impersonation'])
_RESOURCE.fields_by_name['amazon_eks_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['google_gke'])
_RESOURCE.fields_by_name['google_gke'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['google_gke_user_impersonation'])
_RESOURCE.fields_by_name['google_gke_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aks'])
_RESOURCE.fields_by_name['aks'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aks_user_impersonation'])
_RESOURCE.fields_by_name['aks_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aks_basic_auth'])
_RESOURCE.fields_by_name['aks_basic_auth'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aks_service_account'])
_RESOURCE.fields_by_name['aks_service_account'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aks_service_account_user_impersonation'])
_RESOURCE.fields_by_name['aks_service_account_user_impersonation'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['memcached'])
_RESOURCE.fields_by_name['memcached'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['mongo_legacy_host'])
_RESOURCE.fields_by_name['mongo_legacy_host'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['mongo_legacy_replicaset'])
_RESOURCE.fields_by_name['mongo_legacy_replicaset'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['mongo_host'])
_RESOURCE.fields_by_name['mongo_host'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['mongo_replica_set'])
_RESOURCE.fields_by_name['mongo_replica_set'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['mysql'])
_RESOURCE.fields_by_name['mysql'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aurora_mysql'])
_RESOURCE.fields_by_name['aurora_mysql'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['clustrix'])
_RESOURCE.fields_by_name['clustrix'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['maria'])
_RESOURCE.fields_by_name['maria'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['memsql'])
_RESOURCE.fields_by_name['memsql'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['single_store'])
_RESOURCE.fields_by_name['single_store'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['oracle'])
_RESOURCE.fields_by_name['oracle'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['postgres'])
_RESOURCE.fields_by_name['postgres'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['aurora_postgres'])
_RESOURCE.fields_by_name['aurora_postgres'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['greenplum'])
_RESOURCE.fields_by_name['greenplum'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['cockroach'])
_RESOURCE.fields_by_name['cockroach'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['redshift'])
_RESOURCE.fields_by_name['redshift'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['citus'])
_RESOURCE.fields_by_name['citus'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['presto'])
_RESOURCE.fields_by_name['presto'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['raw_tcp'])
_RESOURCE.fields_by_name['raw_tcp'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['rdp'])
_RESOURCE.fields_by_name['rdp'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['redis'])
_RESOURCE.fields_by_name['redis'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['elasticache_redis'])
_RESOURCE.fields_by_name['elasticache_redis'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['snowflake'])
_RESOURCE.fields_by_name['snowflake'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['sql_server'])
_RESOURCE.fields_by_name['sql_server'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['ssh'])
_RESOURCE.fields_by_name['ssh'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['ssh_cert'])
_RESOURCE.fields_by_name['ssh_cert'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['ssh_customer_key'])
_RESOURCE.fields_by_name['ssh_customer_key'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['sybase'])
_RESOURCE.fields_by_name['sybase'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['sybase_iq'])
_RESOURCE.fields_by_name['sybase_iq'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RESOURCE.oneofs_by_name['resource'].fields.append(
_RESOURCE.fields_by_name['teradata'])
_RESOURCE.fields_by_name['teradata'].containing_oneof = _RESOURCE.oneofs_by_name['resource']
_RABBITMQAMQP091.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AMAZONMQAMQP091.fields_by_name['tags'].message_type = tags__pb2._TAGS
_ATHENA.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AWS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_BIGQUERY.fields_by_name['tags'].message_type = tags__pb2._TAGS
_CASSANDRA.fields_by_name['tags'].message_type = tags__pb2._TAGS
_DB2I.fields_by_name['tags'].message_type = tags__pb2._TAGS
_DB2LUW.fields_by_name['tags'].message_type = tags__pb2._TAGS
_DRUID.fields_by_name['tags'].message_type = tags__pb2._TAGS
_DYNAMODB.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AMAZONES.fields_by_name['tags'].message_type = tags__pb2._TAGS
_ELASTIC.fields_by_name['tags'].message_type = tags__pb2._TAGS
_HTTPBASICAUTH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_HTTPNOAUTH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_HTTPAUTH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_KUBERNETES.fields_by_name['tags'].message_type = tags__pb2._TAGS
_KUBERNETESUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_KUBERNETESBASICAUTH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_KUBERNETESSERVICEACCOUNT.fields_by_name['tags'].message_type = tags__pb2._TAGS
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AMAZONEKS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AMAZONEKSUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_GOOGLEGKE.fields_by_name['tags'].message_type = tags__pb2._TAGS
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AKS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AKSUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AKSBASICAUTH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AKSSERVICEACCOUNT.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MEMCACHED.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MONGOLEGACYHOST.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MONGOLEGACYREPLICASET.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MONGOHOST.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MONGOREPLICASET.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MYSQL.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AURORAMYSQL.fields_by_name['tags'].message_type = tags__pb2._TAGS
_CLUSTRIX.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MARIA.fields_by_name['tags'].message_type = tags__pb2._TAGS
_MEMSQL.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SINGLESTORE.fields_by_name['tags'].message_type = tags__pb2._TAGS
_ORACLE.fields_by_name['tags'].message_type = tags__pb2._TAGS
_POSTGRES.fields_by_name['tags'].message_type = tags__pb2._TAGS
_AURORAPOSTGRES.fields_by_name['tags'].message_type = tags__pb2._TAGS
_GREENPLUM.fields_by_name['tags'].message_type = tags__pb2._TAGS
_COCKROACH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_REDSHIFT.fields_by_name['tags'].message_type = tags__pb2._TAGS
_CITUS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_PRESTO.fields_by_name['tags'].message_type = tags__pb2._TAGS
_RAWTCP.fields_by_name['tags'].message_type = tags__pb2._TAGS
_RDP.fields_by_name['tags'].message_type = tags__pb2._TAGS
_REDIS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_ELASTICACHEREDIS.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SNOWFLAKE.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SQLSERVER.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SSH.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SSHCERT.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SSHCUSTOMERKEY.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SYBASE.fields_by_name['tags'].message_type = tags__pb2._TAGS
_SYBASEIQ.fields_by_name['tags'].message_type = tags__pb2._TAGS
_TERADATA.fields_by_name['tags'].message_type = tags__pb2._TAGS
DESCRIPTOR.message_types_by_name['Resource'] = _RESOURCE
DESCRIPTOR.message_types_by_name['RabbitMQAMQP091'] = _RABBITMQAMQP091
DESCRIPTOR.message_types_by_name['AmazonMQAMQP091'] = _AMAZONMQAMQP091
DESCRIPTOR.message_types_by_name['Athena'] = _ATHENA
DESCRIPTOR.message_types_by_name['AWS'] = _AWS
DESCRIPTOR.message_types_by_name['BigQuery'] = _BIGQUERY
DESCRIPTOR.message_types_by_name['Cassandra'] = _CASSANDRA
DESCRIPTOR.message_types_by_name['DB2I'] = _DB2I
DESCRIPTOR.message_types_by_name['DB2LUW'] = _DB2LUW
DESCRIPTOR.message_types_by_name['Druid'] = _DRUID
DESCRIPTOR.message_types_by_name['DynamoDB'] = _DYNAMODB
DESCRIPTOR.message_types_by_name['AmazonES'] = _AMAZONES
DESCRIPTOR.message_types_by_name['Elastic'] = _ELASTIC
DESCRIPTOR.message_types_by_name['HTTPBasicAuth'] = _HTTPBASICAUTH
DESCRIPTOR.message_types_by_name['HTTPNoAuth'] = _HTTPNOAUTH
DESCRIPTOR.message_types_by_name['HTTPAuth'] = _HTTPAUTH
DESCRIPTOR.message_types_by_name['Kubernetes'] = _KUBERNETES
DESCRIPTOR.message_types_by_name['KubernetesUserImpersonation'] = _KUBERNETESUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['KubernetesBasicAuth'] = _KUBERNETESBASICAUTH
DESCRIPTOR.message_types_by_name['KubernetesServiceAccount'] = _KUBERNETESSERVICEACCOUNT
DESCRIPTOR.message_types_by_name['KubernetesServiceAccountUserImpersonation'] = _KUBERNETESSERVICEACCOUNTUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['AmazonEKS'] = _AMAZONEKS
DESCRIPTOR.message_types_by_name['AmazonEKSUserImpersonation'] = _AMAZONEKSUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['GoogleGKE'] = _GOOGLEGKE
DESCRIPTOR.message_types_by_name['GoogleGKEUserImpersonation'] = _GOOGLEGKEUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['AKS'] = _AKS
DESCRIPTOR.message_types_by_name['AKSUserImpersonation'] = _AKSUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['AKSBasicAuth'] = _AKSBASICAUTH
DESCRIPTOR.message_types_by_name['AKSServiceAccount'] = _AKSSERVICEACCOUNT
DESCRIPTOR.message_types_by_name['AKSServiceAccountUserImpersonation'] = _AKSSERVICEACCOUNTUSERIMPERSONATION
DESCRIPTOR.message_types_by_name['Memcached'] = _MEMCACHED
DESCRIPTOR.message_types_by_name['MongoLegacyHost'] = _MONGOLEGACYHOST
DESCRIPTOR.message_types_by_name['MongoLegacyReplicaset'] = _MONGOLEGACYREPLICASET
DESCRIPTOR.message_types_by_name['MongoHost'] = _MONGOHOST
DESCRIPTOR.message_types_by_name['MongoReplicaSet'] = _MONGOREPLICASET
DESCRIPTOR.message_types_by_name['Mysql'] = _MYSQL
DESCRIPTOR.message_types_by_name['AuroraMysql'] = _AURORAMYSQL
DESCRIPTOR.message_types_by_name['Clustrix'] = _CLUSTRIX
DESCRIPTOR.message_types_by_name['Maria'] = _MARIA
DESCRIPTOR.message_types_by_name['Memsql'] = _MEMSQL
DESCRIPTOR.message_types_by_name['SingleStore'] = _SINGLESTORE
DESCRIPTOR.message_types_by_name['Oracle'] = _ORACLE
DESCRIPTOR.message_types_by_name['Postgres'] = _POSTGRES
DESCRIPTOR.message_types_by_name['AuroraPostgres'] = _AURORAPOSTGRES
DESCRIPTOR.message_types_by_name['Greenplum'] = _GREENPLUM
DESCRIPTOR.message_types_by_name['Cockroach'] = _COCKROACH
DESCRIPTOR.message_types_by_name['Redshift'] = _REDSHIFT
DESCRIPTOR.message_types_by_name['Citus'] = _CITUS
DESCRIPTOR.message_types_by_name['Presto'] = _PRESTO
DESCRIPTOR.message_types_by_name['RawTCP'] = _RAWTCP
DESCRIPTOR.message_types_by_name['RDP'] = _RDP
DESCRIPTOR.message_types_by_name['Redis'] = _REDIS
DESCRIPTOR.message_types_by_name['ElasticacheRedis'] = _ELASTICACHEREDIS
DESCRIPTOR.message_types_by_name['Snowflake'] = _SNOWFLAKE
DESCRIPTOR.message_types_by_name['SQLServer'] = _SQLSERVER
DESCRIPTOR.message_types_by_name['SSH'] = _SSH
DESCRIPTOR.message_types_by_name['SSHCert'] = _SSHCERT
DESCRIPTOR.message_types_by_name['SSHCustomerKey'] = _SSHCUSTOMERKEY
DESCRIPTOR.message_types_by_name['Sybase'] = _SYBASE
DESCRIPTOR.message_types_by_name['SybaseIQ'] = _SYBASEIQ
DESCRIPTOR.message_types_by_name['Teradata'] = _TERADATA
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
Resource = _reflection.GeneratedProtocolMessageType('Resource', (_message.Message,), {
'DESCRIPTOR' : _RESOURCE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Resource)
})
_sym_db.RegisterMessage(Resource)
RabbitMQAMQP091 = _reflection.GeneratedProtocolMessageType('RabbitMQAMQP091', (_message.Message,), {
'DESCRIPTOR' : _RABBITMQAMQP091,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.RabbitMQAMQP091)
})
_sym_db.RegisterMessage(RabbitMQAMQP091)
AmazonMQAMQP091 = _reflection.GeneratedProtocolMessageType('AmazonMQAMQP091', (_message.Message,), {
'DESCRIPTOR' : _AMAZONMQAMQP091,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AmazonMQAMQP091)
})
_sym_db.RegisterMessage(AmazonMQAMQP091)
Athena = _reflection.GeneratedProtocolMessageType('Athena', (_message.Message,), {
'DESCRIPTOR' : _ATHENA,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Athena)
})
_sym_db.RegisterMessage(Athena)
AWS = _reflection.GeneratedProtocolMessageType('AWS', (_message.Message,), {
'DESCRIPTOR' : _AWS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AWS)
})
_sym_db.RegisterMessage(AWS)
BigQuery = _reflection.GeneratedProtocolMessageType('BigQuery', (_message.Message,), {
'DESCRIPTOR' : _BIGQUERY,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.BigQuery)
})
_sym_db.RegisterMessage(BigQuery)
Cassandra = _reflection.GeneratedProtocolMessageType('Cassandra', (_message.Message,), {
'DESCRIPTOR' : _CASSANDRA,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Cassandra)
})
_sym_db.RegisterMessage(Cassandra)
DB2I = _reflection.GeneratedProtocolMessageType('DB2I', (_message.Message,), {
'DESCRIPTOR' : _DB2I,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.DB2I)
})
_sym_db.RegisterMessage(DB2I)
DB2LUW = _reflection.GeneratedProtocolMessageType('DB2LUW', (_message.Message,), {
'DESCRIPTOR' : _DB2LUW,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.DB2LUW)
})
_sym_db.RegisterMessage(DB2LUW)
Druid = _reflection.GeneratedProtocolMessageType('Druid', (_message.Message,), {
'DESCRIPTOR' : _DRUID,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Druid)
})
_sym_db.RegisterMessage(Druid)
DynamoDB = _reflection.GeneratedProtocolMessageType('DynamoDB', (_message.Message,), {
'DESCRIPTOR' : _DYNAMODB,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.DynamoDB)
})
_sym_db.RegisterMessage(DynamoDB)
AmazonES = _reflection.GeneratedProtocolMessageType('AmazonES', (_message.Message,), {
'DESCRIPTOR' : _AMAZONES,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AmazonES)
})
_sym_db.RegisterMessage(AmazonES)
Elastic = _reflection.GeneratedProtocolMessageType('Elastic', (_message.Message,), {
'DESCRIPTOR' : _ELASTIC,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Elastic)
})
_sym_db.RegisterMessage(Elastic)
HTTPBasicAuth = _reflection.GeneratedProtocolMessageType('HTTPBasicAuth', (_message.Message,), {
'DESCRIPTOR' : _HTTPBASICAUTH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.HTTPBasicAuth)
})
_sym_db.RegisterMessage(HTTPBasicAuth)
HTTPNoAuth = _reflection.GeneratedProtocolMessageType('HTTPNoAuth', (_message.Message,), {
'DESCRIPTOR' : _HTTPNOAUTH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.HTTPNoAuth)
})
_sym_db.RegisterMessage(HTTPNoAuth)
HTTPAuth = _reflection.GeneratedProtocolMessageType('HTTPAuth', (_message.Message,), {
'DESCRIPTOR' : _HTTPAUTH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.HTTPAuth)
})
_sym_db.RegisterMessage(HTTPAuth)
Kubernetes = _reflection.GeneratedProtocolMessageType('Kubernetes', (_message.Message,), {
'DESCRIPTOR' : _KUBERNETES,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Kubernetes)
})
_sym_db.RegisterMessage(Kubernetes)
KubernetesUserImpersonation = _reflection.GeneratedProtocolMessageType('KubernetesUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _KUBERNETESUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.KubernetesUserImpersonation)
})
_sym_db.RegisterMessage(KubernetesUserImpersonation)
KubernetesBasicAuth = _reflection.GeneratedProtocolMessageType('KubernetesBasicAuth', (_message.Message,), {
'DESCRIPTOR' : _KUBERNETESBASICAUTH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.KubernetesBasicAuth)
})
_sym_db.RegisterMessage(KubernetesBasicAuth)
KubernetesServiceAccount = _reflection.GeneratedProtocolMessageType('KubernetesServiceAccount', (_message.Message,), {
'DESCRIPTOR' : _KUBERNETESSERVICEACCOUNT,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.KubernetesServiceAccount)
})
_sym_db.RegisterMessage(KubernetesServiceAccount)
KubernetesServiceAccountUserImpersonation = _reflection.GeneratedProtocolMessageType('KubernetesServiceAccountUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _KUBERNETESSERVICEACCOUNTUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.KubernetesServiceAccountUserImpersonation)
})
_sym_db.RegisterMessage(KubernetesServiceAccountUserImpersonation)
AmazonEKS = _reflection.GeneratedProtocolMessageType('AmazonEKS', (_message.Message,), {
'DESCRIPTOR' : _AMAZONEKS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AmazonEKS)
})
_sym_db.RegisterMessage(AmazonEKS)
AmazonEKSUserImpersonation = _reflection.GeneratedProtocolMessageType('AmazonEKSUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _AMAZONEKSUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AmazonEKSUserImpersonation)
})
_sym_db.RegisterMessage(AmazonEKSUserImpersonation)
GoogleGKE = _reflection.GeneratedProtocolMessageType('GoogleGKE', (_message.Message,), {
'DESCRIPTOR' : _GOOGLEGKE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.GoogleGKE)
})
_sym_db.RegisterMessage(GoogleGKE)
GoogleGKEUserImpersonation = _reflection.GeneratedProtocolMessageType('GoogleGKEUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _GOOGLEGKEUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.GoogleGKEUserImpersonation)
})
_sym_db.RegisterMessage(GoogleGKEUserImpersonation)
AKS = _reflection.GeneratedProtocolMessageType('AKS', (_message.Message,), {
'DESCRIPTOR' : _AKS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AKS)
})
_sym_db.RegisterMessage(AKS)
AKSUserImpersonation = _reflection.GeneratedProtocolMessageType('AKSUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _AKSUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AKSUserImpersonation)
})
_sym_db.RegisterMessage(AKSUserImpersonation)
AKSBasicAuth = _reflection.GeneratedProtocolMessageType('AKSBasicAuth', (_message.Message,), {
'DESCRIPTOR' : _AKSBASICAUTH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AKSBasicAuth)
})
_sym_db.RegisterMessage(AKSBasicAuth)
AKSServiceAccount = _reflection.GeneratedProtocolMessageType('AKSServiceAccount', (_message.Message,), {
'DESCRIPTOR' : _AKSSERVICEACCOUNT,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AKSServiceAccount)
})
_sym_db.RegisterMessage(AKSServiceAccount)
AKSServiceAccountUserImpersonation = _reflection.GeneratedProtocolMessageType('AKSServiceAccountUserImpersonation', (_message.Message,), {
'DESCRIPTOR' : _AKSSERVICEACCOUNTUSERIMPERSONATION,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AKSServiceAccountUserImpersonation)
})
_sym_db.RegisterMessage(AKSServiceAccountUserImpersonation)
Memcached = _reflection.GeneratedProtocolMessageType('Memcached', (_message.Message,), {
'DESCRIPTOR' : _MEMCACHED,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Memcached)
})
_sym_db.RegisterMessage(Memcached)
MongoLegacyHost = _reflection.GeneratedProtocolMessageType('MongoLegacyHost', (_message.Message,), {
'DESCRIPTOR' : _MONGOLEGACYHOST,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.MongoLegacyHost)
})
_sym_db.RegisterMessage(MongoLegacyHost)
MongoLegacyReplicaset = _reflection.GeneratedProtocolMessageType('MongoLegacyReplicaset', (_message.Message,), {
'DESCRIPTOR' : _MONGOLEGACYREPLICASET,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.MongoLegacyReplicaset)
})
_sym_db.RegisterMessage(MongoLegacyReplicaset)
MongoHost = _reflection.GeneratedProtocolMessageType('MongoHost', (_message.Message,), {
'DESCRIPTOR' : _MONGOHOST,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.MongoHost)
})
_sym_db.RegisterMessage(MongoHost)
MongoReplicaSet = _reflection.GeneratedProtocolMessageType('MongoReplicaSet', (_message.Message,), {
'DESCRIPTOR' : _MONGOREPLICASET,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.MongoReplicaSet)
})
_sym_db.RegisterMessage(MongoReplicaSet)
Mysql = _reflection.GeneratedProtocolMessageType('Mysql', (_message.Message,), {
'DESCRIPTOR' : _MYSQL,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Mysql)
})
_sym_db.RegisterMessage(Mysql)
AuroraMysql = _reflection.GeneratedProtocolMessageType('AuroraMysql', (_message.Message,), {
'DESCRIPTOR' : _AURORAMYSQL,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AuroraMysql)
})
_sym_db.RegisterMessage(AuroraMysql)
Clustrix = _reflection.GeneratedProtocolMessageType('Clustrix', (_message.Message,), {
'DESCRIPTOR' : _CLUSTRIX,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Clustrix)
})
_sym_db.RegisterMessage(Clustrix)
Maria = _reflection.GeneratedProtocolMessageType('Maria', (_message.Message,), {
'DESCRIPTOR' : _MARIA,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Maria)
})
_sym_db.RegisterMessage(Maria)
Memsql = _reflection.GeneratedProtocolMessageType('Memsql', (_message.Message,), {
'DESCRIPTOR' : _MEMSQL,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Memsql)
})
_sym_db.RegisterMessage(Memsql)
SingleStore = _reflection.GeneratedProtocolMessageType('SingleStore', (_message.Message,), {
'DESCRIPTOR' : _SINGLESTORE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SingleStore)
})
_sym_db.RegisterMessage(SingleStore)
Oracle = _reflection.GeneratedProtocolMessageType('Oracle', (_message.Message,), {
'DESCRIPTOR' : _ORACLE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Oracle)
})
_sym_db.RegisterMessage(Oracle)
Postgres = _reflection.GeneratedProtocolMessageType('Postgres', (_message.Message,), {
'DESCRIPTOR' : _POSTGRES,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Postgres)
})
_sym_db.RegisterMessage(Postgres)
AuroraPostgres = _reflection.GeneratedProtocolMessageType('AuroraPostgres', (_message.Message,), {
'DESCRIPTOR' : _AURORAPOSTGRES,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.AuroraPostgres)
})
_sym_db.RegisterMessage(AuroraPostgres)
Greenplum = _reflection.GeneratedProtocolMessageType('Greenplum', (_message.Message,), {
'DESCRIPTOR' : _GREENPLUM,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Greenplum)
})
_sym_db.RegisterMessage(Greenplum)
Cockroach = _reflection.GeneratedProtocolMessageType('Cockroach', (_message.Message,), {
'DESCRIPTOR' : _COCKROACH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Cockroach)
})
_sym_db.RegisterMessage(Cockroach)
Redshift = _reflection.GeneratedProtocolMessageType('Redshift', (_message.Message,), {
'DESCRIPTOR' : _REDSHIFT,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Redshift)
})
_sym_db.RegisterMessage(Redshift)
Citus = _reflection.GeneratedProtocolMessageType('Citus', (_message.Message,), {
'DESCRIPTOR' : _CITUS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Citus)
})
_sym_db.RegisterMessage(Citus)
Presto = _reflection.GeneratedProtocolMessageType('Presto', (_message.Message,), {
'DESCRIPTOR' : _PRESTO,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Presto)
})
_sym_db.RegisterMessage(Presto)
RawTCP = _reflection.GeneratedProtocolMessageType('RawTCP', (_message.Message,), {
'DESCRIPTOR' : _RAWTCP,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.RawTCP)
})
_sym_db.RegisterMessage(RawTCP)
RDP = _reflection.GeneratedProtocolMessageType('RDP', (_message.Message,), {
'DESCRIPTOR' : _RDP,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.RDP)
})
_sym_db.RegisterMessage(RDP)
Redis = _reflection.GeneratedProtocolMessageType('Redis', (_message.Message,), {
'DESCRIPTOR' : _REDIS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Redis)
})
_sym_db.RegisterMessage(Redis)
ElasticacheRedis = _reflection.GeneratedProtocolMessageType('ElasticacheRedis', (_message.Message,), {
'DESCRIPTOR' : _ELASTICACHEREDIS,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.ElasticacheRedis)
})
_sym_db.RegisterMessage(ElasticacheRedis)
Snowflake = _reflection.GeneratedProtocolMessageType('Snowflake', (_message.Message,), {
'DESCRIPTOR' : _SNOWFLAKE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Snowflake)
})
_sym_db.RegisterMessage(Snowflake)
SQLServer = _reflection.GeneratedProtocolMessageType('SQLServer', (_message.Message,), {
'DESCRIPTOR' : _SQLSERVER,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SQLServer)
})
_sym_db.RegisterMessage(SQLServer)
SSH = _reflection.GeneratedProtocolMessageType('SSH', (_message.Message,), {
'DESCRIPTOR' : _SSH,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SSH)
})
_sym_db.RegisterMessage(SSH)
SSHCert = _reflection.GeneratedProtocolMessageType('SSHCert', (_message.Message,), {
'DESCRIPTOR' : _SSHCERT,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SSHCert)
})
_sym_db.RegisterMessage(SSHCert)
SSHCustomerKey = _reflection.GeneratedProtocolMessageType('SSHCustomerKey', (_message.Message,), {
'DESCRIPTOR' : _SSHCUSTOMERKEY,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SSHCustomerKey)
})
_sym_db.RegisterMessage(SSHCustomerKey)
Sybase = _reflection.GeneratedProtocolMessageType('Sybase', (_message.Message,), {
'DESCRIPTOR' : _SYBASE,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Sybase)
})
_sym_db.RegisterMessage(Sybase)
SybaseIQ = _reflection.GeneratedProtocolMessageType('SybaseIQ', (_message.Message,), {
'DESCRIPTOR' : _SYBASEIQ,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.SybaseIQ)
})
_sym_db.RegisterMessage(SybaseIQ)
Teradata = _reflection.GeneratedProtocolMessageType('Teradata', (_message.Message,), {
'DESCRIPTOR' : _TERADATA,
'__module__' : 'drivers_pb2'
# @@protoc_insertion_point(class_scope:v1.Teradata)
})
_sym_db.RegisterMessage(Teradata)
DESCRIPTOR._options = None
_RESOURCE.oneofs_by_name['resource']._options = None
_RESOURCE.fields_by_name['rabbit_mqamqp_091']._options = None
_RESOURCE.fields_by_name['amazon_mqamqp_091']._options = None
_RESOURCE.fields_by_name['athena']._options = None
_RESOURCE.fields_by_name['aws']._options = None
_RESOURCE.fields_by_name['big_query']._options = None
_RESOURCE.fields_by_name['cassandra']._options = None
_RESOURCE.fields_by_name['db_2_i']._options = None
_RESOURCE.fields_by_name['db_2_luw']._options = None
_RESOURCE.fields_by_name['druid']._options = None
_RESOURCE.fields_by_name['dynamo_db']._options = None
_RESOURCE.fields_by_name['amazon_es']._options = None
_RESOURCE.fields_by_name['elastic']._options = None
_RESOURCE.fields_by_name['http_basic_auth']._options = None
_RESOURCE.fields_by_name['http_no_auth']._options = None
_RESOURCE.fields_by_name['http_auth']._options = None
_RESOURCE.fields_by_name['kubernetes']._options = None
_RESOURCE.fields_by_name['kubernetes_user_impersonation']._options = None
_RESOURCE.fields_by_name['kubernetes_basic_auth']._options = None
_RESOURCE.fields_by_name['kubernetes_service_account']._options = None
_RESOURCE.fields_by_name['kubernetes_service_account_user_impersonation']._options = None
_RESOURCE.fields_by_name['amazon_eks']._options = None
_RESOURCE.fields_by_name['amazon_eks_user_impersonation']._options = None
_RESOURCE.fields_by_name['google_gke']._options = None
_RESOURCE.fields_by_name['google_gke_user_impersonation']._options = None
_RESOURCE.fields_by_name['aks']._options = None
_RESOURCE.fields_by_name['aks_user_impersonation']._options = None
_RESOURCE.fields_by_name['aks_basic_auth']._options = None
_RESOURCE.fields_by_name['aks_service_account']._options = None
_RESOURCE.fields_by_name['aks_service_account_user_impersonation']._options = None
_RESOURCE.fields_by_name['memcached']._options = None
_RESOURCE.fields_by_name['mongo_legacy_host']._options = None
_RESOURCE.fields_by_name['mongo_legacy_replicaset']._options = None
_RESOURCE.fields_by_name['mongo_host']._options = None
_RESOURCE.fields_by_name['mongo_replica_set']._options = None
_RESOURCE.fields_by_name['mysql']._options = None
_RESOURCE.fields_by_name['aurora_mysql']._options = None
_RESOURCE.fields_by_name['clustrix']._options = None
_RESOURCE.fields_by_name['maria']._options = None
_RESOURCE.fields_by_name['memsql']._options = None
_RESOURCE.fields_by_name['single_store']._options = None
_RESOURCE.fields_by_name['oracle']._options = None
_RESOURCE.fields_by_name['postgres']._options = None
_RESOURCE.fields_by_name['aurora_postgres']._options = None
_RESOURCE.fields_by_name['greenplum']._options = None
_RESOURCE.fields_by_name['cockroach']._options = None
_RESOURCE.fields_by_name['redshift']._options = None
_RESOURCE.fields_by_name['citus']._options = None
_RESOURCE.fields_by_name['presto']._options = None
_RESOURCE.fields_by_name['raw_tcp']._options = None
_RESOURCE.fields_by_name['rdp']._options = None
_RESOURCE.fields_by_name['redis']._options = None
_RESOURCE.fields_by_name['elasticache_redis']._options = None
_RESOURCE.fields_by_name['snowflake']._options = None
_RESOURCE.fields_by_name['sql_server']._options = None
_RESOURCE.fields_by_name['ssh']._options = None
_RESOURCE.fields_by_name['ssh_cert']._options = None
_RESOURCE.fields_by_name['ssh_customer_key']._options = None
_RESOURCE.fields_by_name['sybase']._options = None
_RESOURCE.fields_by_name['sybase_iq']._options = None
_RESOURCE.fields_by_name['teradata']._options = None
_RESOURCE._options = None
_RABBITMQAMQP091.fields_by_name['id']._options = None
_RABBITMQAMQP091.fields_by_name['name']._options = None
_RABBITMQAMQP091.fields_by_name['healthy']._options = None
_RABBITMQAMQP091.fields_by_name['tags']._options = None
_RABBITMQAMQP091.fields_by_name['secret_store_id']._options = None
_RABBITMQAMQP091.fields_by_name['egress_filter']._options = None
_RABBITMQAMQP091.fields_by_name['hostname']._options = None
_RABBITMQAMQP091.fields_by_name['port_override']._options = None
_RABBITMQAMQP091.fields_by_name['port']._options = None
_RABBITMQAMQP091.fields_by_name['username']._options = None
_RABBITMQAMQP091.fields_by_name['password']._options = None
_RABBITMQAMQP091.fields_by_name['tls_required']._options = None
_RABBITMQAMQP091._options = None
_AMAZONMQAMQP091.fields_by_name['id']._options = None
_AMAZONMQAMQP091.fields_by_name['name']._options = None
_AMAZONMQAMQP091.fields_by_name['healthy']._options = None
_AMAZONMQAMQP091.fields_by_name['tags']._options = None
_AMAZONMQAMQP091.fields_by_name['secret_store_id']._options = None
_AMAZONMQAMQP091.fields_by_name['egress_filter']._options = None
_AMAZONMQAMQP091.fields_by_name['hostname']._options = None
_AMAZONMQAMQP091.fields_by_name['port_override']._options = None
_AMAZONMQAMQP091.fields_by_name['port']._options = None
_AMAZONMQAMQP091.fields_by_name['username']._options = None
_AMAZONMQAMQP091.fields_by_name['password']._options = None
_AMAZONMQAMQP091.fields_by_name['tls_required']._options = None
_AMAZONMQAMQP091._options = None
_ATHENA.fields_by_name['id']._options = None
_ATHENA.fields_by_name['name']._options = None
_ATHENA.fields_by_name['healthy']._options = None
_ATHENA.fields_by_name['tags']._options = None
_ATHENA.fields_by_name['secret_store_id']._options = None
_ATHENA.fields_by_name['egress_filter']._options = None
_ATHENA.fields_by_name['access_key']._options = None
_ATHENA.fields_by_name['secret_access_key']._options = None
_ATHENA.fields_by_name['output']._options = None
_ATHENA.fields_by_name['port_override']._options = None
_ATHENA.fields_by_name['region']._options = None
_ATHENA.fields_by_name['role_arn']._options = None
_ATHENA.fields_by_name['role_external_id']._options = None
_ATHENA._options = None
_AWS.fields_by_name['id']._options = None
_AWS.fields_by_name['name']._options = None
_AWS.fields_by_name['healthy']._options = None
_AWS.fields_by_name['tags']._options = None
_AWS.fields_by_name['secret_store_id']._options = None
_AWS.fields_by_name['egress_filter']._options = None
_AWS.fields_by_name['access_key']._options = None
_AWS.fields_by_name['secret_access_key']._options = None
_AWS.fields_by_name['healthcheck_region']._options = None
_AWS.fields_by_name['role_arn']._options = None
_AWS.fields_by_name['role_external_id']._options = None
_AWS._options = None
_BIGQUERY.fields_by_name['id']._options = None
_BIGQUERY.fields_by_name['name']._options = None
_BIGQUERY.fields_by_name['healthy']._options = None
_BIGQUERY.fields_by_name['tags']._options = None
_BIGQUERY.fields_by_name['secret_store_id']._options = None
_BIGQUERY.fields_by_name['egress_filter']._options = None
_BIGQUERY.fields_by_name['private_key']._options = None
_BIGQUERY.fields_by_name['project']._options = None
_BIGQUERY.fields_by_name['port_override']._options = None
_BIGQUERY.fields_by_name['endpoint']._options = None
_BIGQUERY.fields_by_name['username']._options = None
_BIGQUERY._options = None
_CASSANDRA.fields_by_name['id']._options = None
_CASSANDRA.fields_by_name['name']._options = None
_CASSANDRA.fields_by_name['healthy']._options = None
_CASSANDRA.fields_by_name['tags']._options = None
_CASSANDRA.fields_by_name['secret_store_id']._options = None
_CASSANDRA.fields_by_name['egress_filter']._options = None
_CASSANDRA.fields_by_name['hostname']._options = None
_CASSANDRA.fields_by_name['username']._options = None
_CASSANDRA.fields_by_name['password']._options = None
_CASSANDRA.fields_by_name['port_override']._options = None
_CASSANDRA.fields_by_name['port']._options = None
_CASSANDRA.fields_by_name['tls_required']._options = None
_CASSANDRA._options = None
_DB2I.fields_by_name['id']._options = None
_DB2I.fields_by_name['name']._options = None
_DB2I.fields_by_name['healthy']._options = None
_DB2I.fields_by_name['tags']._options = None
_DB2I.fields_by_name['secret_store_id']._options = None
_DB2I.fields_by_name['egress_filter']._options = None
_DB2I.fields_by_name['hostname']._options = None
_DB2I.fields_by_name['username']._options = None
_DB2I.fields_by_name['password']._options = None
_DB2I.fields_by_name['port_override']._options = None
_DB2I.fields_by_name['port']._options = None
_DB2I.fields_by_name['tls_required']._options = None
_DB2I._options = None
_DB2LUW.fields_by_name['id']._options = None
_DB2LUW.fields_by_name['name']._options = None
_DB2LUW.fields_by_name['healthy']._options = None
_DB2LUW.fields_by_name['tags']._options = None
_DB2LUW.fields_by_name['secret_store_id']._options = None
_DB2LUW.fields_by_name['egress_filter']._options = None
_DB2LUW.fields_by_name['hostname']._options = None
_DB2LUW.fields_by_name['username']._options = None
_DB2LUW.fields_by_name['password']._options = None
_DB2LUW.fields_by_name['database']._options = None
_DB2LUW.fields_by_name['port_override']._options = None
_DB2LUW.fields_by_name['port']._options = None
_DB2LUW._options = None
_DRUID.fields_by_name['id']._options = None
_DRUID.fields_by_name['name']._options = None
_DRUID.fields_by_name['healthy']._options = None
_DRUID.fields_by_name['tags']._options = None
_DRUID.fields_by_name['secret_store_id']._options = None
_DRUID.fields_by_name['egress_filter']._options = None
_DRUID.fields_by_name['hostname']._options = None
_DRUID.fields_by_name['port_override']._options = None
_DRUID.fields_by_name['username']._options = None
_DRUID.fields_by_name['password']._options = None
_DRUID.fields_by_name['port']._options = None
_DRUID._options = None
_DYNAMODB.fields_by_name['id']._options = None
_DYNAMODB.fields_by_name['name']._options = None
_DYNAMODB.fields_by_name['healthy']._options = None
_DYNAMODB.fields_by_name['tags']._options = None
_DYNAMODB.fields_by_name['secret_store_id']._options = None
_DYNAMODB.fields_by_name['egress_filter']._options = None
_DYNAMODB.fields_by_name['access_key']._options = None
_DYNAMODB.fields_by_name['secret_access_key']._options = None
_DYNAMODB.fields_by_name['region']._options = None
_DYNAMODB.fields_by_name['endpoint']._options = None
_DYNAMODB.fields_by_name['port_override']._options = None
_DYNAMODB.fields_by_name['role_arn']._options = None
_DYNAMODB.fields_by_name['role_external_id']._options = None
_DYNAMODB._options = None
_AMAZONES.fields_by_name['id']._options = None
_AMAZONES.fields_by_name['name']._options = None
_AMAZONES.fields_by_name['healthy']._options = None
_AMAZONES.fields_by_name['tags']._options = None
_AMAZONES.fields_by_name['secret_store_id']._options = None
_AMAZONES.fields_by_name['egress_filter']._options = None
_AMAZONES.fields_by_name['region']._options = None
_AMAZONES.fields_by_name['secret_access_key']._options = None
_AMAZONES.fields_by_name['endpoint']._options = None
_AMAZONES.fields_by_name['access_key']._options = None
_AMAZONES.fields_by_name['port_override']._options = None
_AMAZONES.fields_by_name['role_arn']._options = None
_AMAZONES.fields_by_name['role_external_id']._options = None
_AMAZONES._options = None
_ELASTIC.fields_by_name['id']._options = None
_ELASTIC.fields_by_name['name']._options = None
_ELASTIC.fields_by_name['healthy']._options = None
_ELASTIC.fields_by_name['tags']._options = None
_ELASTIC.fields_by_name['secret_store_id']._options = None
_ELASTIC.fields_by_name['egress_filter']._options = None
_ELASTIC.fields_by_name['hostname']._options = None
_ELASTIC.fields_by_name['username']._options = None
_ELASTIC.fields_by_name['password']._options = None
_ELASTIC.fields_by_name['port_override']._options = None
_ELASTIC.fields_by_name['port']._options = None
_ELASTIC.fields_by_name['tls_required']._options = None
_ELASTIC._options = None
_HTTPBASICAUTH.fields_by_name['id']._options = None
_HTTPBASICAUTH.fields_by_name['name']._options = None
_HTTPBASICAUTH.fields_by_name['healthy']._options = None
_HTTPBASICAUTH.fields_by_name['tags']._options = None
_HTTPBASICAUTH.fields_by_name['secret_store_id']._options = None
_HTTPBASICAUTH.fields_by_name['egress_filter']._options = None
_HTTPBASICAUTH.fields_by_name['url']._options = None
_HTTPBASICAUTH.fields_by_name['healthcheck_path']._options = None
_HTTPBASICAUTH.fields_by_name['username']._options = None
_HTTPBASICAUTH.fields_by_name['password']._options = None
_HTTPBASICAUTH.fields_by_name['headers_blacklist']._options = None
_HTTPBASICAUTH.fields_by_name['default_path']._options = None
_HTTPBASICAUTH.fields_by_name['subdomain']._options = None
_HTTPBASICAUTH._options = None
_HTTPNOAUTH.fields_by_name['id']._options = None
_HTTPNOAUTH.fields_by_name['name']._options = None
_HTTPNOAUTH.fields_by_name['healthy']._options = None
_HTTPNOAUTH.fields_by_name['tags']._options = None
_HTTPNOAUTH.fields_by_name['secret_store_id']._options = None
_HTTPNOAUTH.fields_by_name['egress_filter']._options = None
_HTTPNOAUTH.fields_by_name['url']._options = None
_HTTPNOAUTH.fields_by_name['healthcheck_path']._options = None
_HTTPNOAUTH.fields_by_name['headers_blacklist']._options = None
_HTTPNOAUTH.fields_by_name['default_path']._options = None
_HTTPNOAUTH.fields_by_name['subdomain']._options = None
_HTTPNOAUTH._options = None
_HTTPAUTH.fields_by_name['id']._options = None
_HTTPAUTH.fields_by_name['name']._options = None
_HTTPAUTH.fields_by_name['healthy']._options = None
_HTTPAUTH.fields_by_name['tags']._options = None
_HTTPAUTH.fields_by_name['secret_store_id']._options = None
_HTTPAUTH.fields_by_name['egress_filter']._options = None
_HTTPAUTH.fields_by_name['url']._options = None
_HTTPAUTH.fields_by_name['healthcheck_path']._options = None
_HTTPAUTH.fields_by_name['auth_header']._options = None
_HTTPAUTH.fields_by_name['headers_blacklist']._options = None
_HTTPAUTH.fields_by_name['default_path']._options = None
_HTTPAUTH.fields_by_name['subdomain']._options = None
_HTTPAUTH._options = None
_KUBERNETES.fields_by_name['id']._options = None
_KUBERNETES.fields_by_name['name']._options = None
_KUBERNETES.fields_by_name['healthy']._options = None
_KUBERNETES.fields_by_name['tags']._options = None
_KUBERNETES.fields_by_name['secret_store_id']._options = None
_KUBERNETES.fields_by_name['egress_filter']._options = None
_KUBERNETES.fields_by_name['hostname']._options = None
_KUBERNETES.fields_by_name['port']._options = None
_KUBERNETES.fields_by_name['certificate_authority']._options = None
_KUBERNETES.fields_by_name['client_certificate']._options = None
_KUBERNETES.fields_by_name['client_key']._options = None
_KUBERNETES.fields_by_name['healthcheck_namespace']._options = None
_KUBERNETES._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['id']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['name']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['healthy']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['tags']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['hostname']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['port']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['certificate_authority']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['client_certificate']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['client_key']._options = None
_KUBERNETESUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_KUBERNETESUSERIMPERSONATION._options = None
_KUBERNETESBASICAUTH.fields_by_name['id']._options = None
_KUBERNETESBASICAUTH.fields_by_name['name']._options = None
_KUBERNETESBASICAUTH.fields_by_name['healthy']._options = None
_KUBERNETESBASICAUTH.fields_by_name['tags']._options = None
_KUBERNETESBASICAUTH.fields_by_name['secret_store_id']._options = None
_KUBERNETESBASICAUTH.fields_by_name['egress_filter']._options = None
_KUBERNETESBASICAUTH.fields_by_name['hostname']._options = None
_KUBERNETESBASICAUTH.fields_by_name['port']._options = None
_KUBERNETESBASICAUTH.fields_by_name['username']._options = None
_KUBERNETESBASICAUTH.fields_by_name['password']._options = None
_KUBERNETESBASICAUTH.fields_by_name['healthcheck_namespace']._options = None
_KUBERNETESBASICAUTH._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['id']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['name']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['healthy']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['tags']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['secret_store_id']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['egress_filter']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['hostname']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['port']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['token']._options = None
_KUBERNETESSERVICEACCOUNT.fields_by_name['healthcheck_namespace']._options = None
_KUBERNETESSERVICEACCOUNT._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['id']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['name']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['healthy']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['tags']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['hostname']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['port']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['token']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_KUBERNETESSERVICEACCOUNTUSERIMPERSONATION._options = None
_AMAZONEKS.fields_by_name['id']._options = None
_AMAZONEKS.fields_by_name['name']._options = None
_AMAZONEKS.fields_by_name['healthy']._options = None
_AMAZONEKS.fields_by_name['tags']._options = None
_AMAZONEKS.fields_by_name['secret_store_id']._options = None
_AMAZONEKS.fields_by_name['egress_filter']._options = None
_AMAZONEKS.fields_by_name['endpoint']._options = None
_AMAZONEKS.fields_by_name['access_key']._options = None
_AMAZONEKS.fields_by_name['secret_access_key']._options = None
_AMAZONEKS.fields_by_name['certificate_authority']._options = None
_AMAZONEKS.fields_by_name['region']._options = None
_AMAZONEKS.fields_by_name['cluster_name']._options = None
_AMAZONEKS.fields_by_name['role_arn']._options = None
_AMAZONEKS.fields_by_name['role_external_id']._options = None
_AMAZONEKS.fields_by_name['healthcheck_namespace']._options = None
_AMAZONEKS._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['id']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['name']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['healthy']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['tags']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['endpoint']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['access_key']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['secret_access_key']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['certificate_authority']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['region']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['cluster_name']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['role_arn']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['role_external_id']._options = None
_AMAZONEKSUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_AMAZONEKSUSERIMPERSONATION._options = None
_GOOGLEGKE.fields_by_name['id']._options = None
_GOOGLEGKE.fields_by_name['name']._options = None
_GOOGLEGKE.fields_by_name['healthy']._options = None
_GOOGLEGKE.fields_by_name['tags']._options = None
_GOOGLEGKE.fields_by_name['secret_store_id']._options = None
_GOOGLEGKE.fields_by_name['egress_filter']._options = None
_GOOGLEGKE.fields_by_name['endpoint']._options = None
_GOOGLEGKE.fields_by_name['certificate_authority']._options = None
_GOOGLEGKE.fields_by_name['service_account_key']._options = None
_GOOGLEGKE.fields_by_name['healthcheck_namespace']._options = None
_GOOGLEGKE._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['id']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['name']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['healthy']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['tags']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['endpoint']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['certificate_authority']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['service_account_key']._options = None
_GOOGLEGKEUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_GOOGLEGKEUSERIMPERSONATION._options = None
_AKS.fields_by_name['id']._options = None
_AKS.fields_by_name['name']._options = None
_AKS.fields_by_name['healthy']._options = None
_AKS.fields_by_name['tags']._options = None
_AKS.fields_by_name['secret_store_id']._options = None
_AKS.fields_by_name['egress_filter']._options = None
_AKS.fields_by_name['hostname']._options = None
_AKS.fields_by_name['port']._options = None
_AKS.fields_by_name['certificate_authority']._options = None
_AKS.fields_by_name['client_certificate']._options = None
_AKS.fields_by_name['client_key']._options = None
_AKS.fields_by_name['healthcheck_namespace']._options = None
_AKS._options = None
_AKSUSERIMPERSONATION.fields_by_name['id']._options = None
_AKSUSERIMPERSONATION.fields_by_name['name']._options = None
_AKSUSERIMPERSONATION.fields_by_name['healthy']._options = None
_AKSUSERIMPERSONATION.fields_by_name['tags']._options = None
_AKSUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_AKSUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_AKSUSERIMPERSONATION.fields_by_name['hostname']._options = None
_AKSUSERIMPERSONATION.fields_by_name['port']._options = None
_AKSUSERIMPERSONATION.fields_by_name['certificate_authority']._options = None
_AKSUSERIMPERSONATION.fields_by_name['client_certificate']._options = None
_AKSUSERIMPERSONATION.fields_by_name['client_key']._options = None
_AKSUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_AKSUSERIMPERSONATION._options = None
_AKSBASICAUTH.fields_by_name['id']._options = None
_AKSBASICAUTH.fields_by_name['name']._options = None
_AKSBASICAUTH.fields_by_name['healthy']._options = None
_AKSBASICAUTH.fields_by_name['tags']._options = None
_AKSBASICAUTH.fields_by_name['secret_store_id']._options = None
_AKSBASICAUTH.fields_by_name['egress_filter']._options = None
_AKSBASICAUTH.fields_by_name['hostname']._options = None
_AKSBASICAUTH.fields_by_name['port']._options = None
_AKSBASICAUTH.fields_by_name['username']._options = None
_AKSBASICAUTH.fields_by_name['password']._options = None
_AKSBASICAUTH.fields_by_name['healthcheck_namespace']._options = None
_AKSBASICAUTH._options = None
_AKSSERVICEACCOUNT.fields_by_name['id']._options = None
_AKSSERVICEACCOUNT.fields_by_name['name']._options = None
_AKSSERVICEACCOUNT.fields_by_name['healthy']._options = None
_AKSSERVICEACCOUNT.fields_by_name['tags']._options = None
_AKSSERVICEACCOUNT.fields_by_name['secret_store_id']._options = None
_AKSSERVICEACCOUNT.fields_by_name['egress_filter']._options = None
_AKSSERVICEACCOUNT.fields_by_name['hostname']._options = None
_AKSSERVICEACCOUNT.fields_by_name['port']._options = None
_AKSSERVICEACCOUNT.fields_by_name['token']._options = None
_AKSSERVICEACCOUNT.fields_by_name['healthcheck_namespace']._options = None
_AKSSERVICEACCOUNT._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['id']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['name']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['healthy']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['tags']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['secret_store_id']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['egress_filter']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['hostname']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['port']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['token']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION.fields_by_name['healthcheck_namespace']._options = None
_AKSSERVICEACCOUNTUSERIMPERSONATION._options = None
_MEMCACHED.fields_by_name['id']._options = None
_MEMCACHED.fields_by_name['name']._options = None
_MEMCACHED.fields_by_name['healthy']._options = None
_MEMCACHED.fields_by_name['tags']._options = None
_MEMCACHED.fields_by_name['secret_store_id']._options = None
_MEMCACHED.fields_by_name['egress_filter']._options = None
_MEMCACHED.fields_by_name['hostname']._options = None
_MEMCACHED.fields_by_name['port_override']._options = None
_MEMCACHED.fields_by_name['port']._options = None
_MEMCACHED._options = None
_MONGOLEGACYHOST.fields_by_name['id']._options = None
_MONGOLEGACYHOST.fields_by_name['name']._options = None
_MONGOLEGACYHOST.fields_by_name['healthy']._options = None
_MONGOLEGACYHOST.fields_by_name['tags']._options = None
_MONGOLEGACYHOST.fields_by_name['secret_store_id']._options = None
_MONGOLEGACYHOST.fields_by_name['egress_filter']._options = None
_MONGOLEGACYHOST.fields_by_name['hostname']._options = None
_MONGOLEGACYHOST.fields_by_name['auth_database']._options = None
_MONGOLEGACYHOST.fields_by_name['port_override']._options = None
_MONGOLEGACYHOST.fields_by_name['username']._options = None
_MONGOLEGACYHOST.fields_by_name['password']._options = None
_MONGOLEGACYHOST.fields_by_name['port']._options = None
_MONGOLEGACYHOST.fields_by_name['replica_set']._options = None
_MONGOLEGACYHOST.fields_by_name['tls_required']._options = None
_MONGOLEGACYHOST._options = None
_MONGOLEGACYREPLICASET.fields_by_name['id']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['name']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['healthy']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['tags']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['secret_store_id']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['egress_filter']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['hostname']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['auth_database']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['port_override']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['username']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['password']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['port']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['replica_set']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['connect_to_replica']._options = None
_MONGOLEGACYREPLICASET.fields_by_name['tls_required']._options = None
_MONGOLEGACYREPLICASET._options = None
_MONGOHOST.fields_by_name['id']._options = None
_MONGOHOST.fields_by_name['name']._options = None
_MONGOHOST.fields_by_name['healthy']._options = None
_MONGOHOST.fields_by_name['tags']._options = None
_MONGOHOST.fields_by_name['secret_store_id']._options = None
_MONGOHOST.fields_by_name['egress_filter']._options = None
_MONGOHOST.fields_by_name['hostname']._options = None
_MONGOHOST.fields_by_name['auth_database']._options = None
_MONGOHOST.fields_by_name['port_override']._options = None
_MONGOHOST.fields_by_name['username']._options = None
_MONGOHOST.fields_by_name['password']._options = None
_MONGOHOST.fields_by_name['port']._options = None
_MONGOHOST.fields_by_name['tls_required']._options = None
_MONGOHOST._options = None
_MONGOREPLICASET.fields_by_name['id']._options = None
_MONGOREPLICASET.fields_by_name['name']._options = None
_MONGOREPLICASET.fields_by_name['healthy']._options = None
_MONGOREPLICASET.fields_by_name['tags']._options = None
_MONGOREPLICASET.fields_by_name['secret_store_id']._options = None
_MONGOREPLICASET.fields_by_name['egress_filter']._options = None
_MONGOREPLICASET.fields_by_name['hostname']._options = None
_MONGOREPLICASET.fields_by_name['auth_database']._options = None
_MONGOREPLICASET.fields_by_name['port_override']._options = None
_MONGOREPLICASET.fields_by_name['username']._options = None
_MONGOREPLICASET.fields_by_name['password']._options = None
_MONGOREPLICASET.fields_by_name['port']._options = None
_MONGOREPLICASET.fields_by_name['replica_set']._options = None
_MONGOREPLICASET.fields_by_name['connect_to_replica']._options = None
_MONGOREPLICASET.fields_by_name['tls_required']._options = None
_MONGOREPLICASET._options = None
_MYSQL.fields_by_name['id']._options = None
_MYSQL.fields_by_name['name']._options = None
_MYSQL.fields_by_name['healthy']._options = None
_MYSQL.fields_by_name['tags']._options = None
_MYSQL.fields_by_name['secret_store_id']._options = None
_MYSQL.fields_by_name['egress_filter']._options = None
_MYSQL.fields_by_name['hostname']._options = None
_MYSQL.fields_by_name['username']._options = None
_MYSQL.fields_by_name['password']._options = None
_MYSQL.fields_by_name['database']._options = None
_MYSQL.fields_by_name['port_override']._options = None
_MYSQL.fields_by_name['port']._options = None
_MYSQL._options = None
_AURORAMYSQL.fields_by_name['id']._options = None
_AURORAMYSQL.fields_by_name['name']._options = None
_AURORAMYSQL.fields_by_name['healthy']._options = None
_AURORAMYSQL.fields_by_name['tags']._options = None
_AURORAMYSQL.fields_by_name['secret_store_id']._options = None
_AURORAMYSQL.fields_by_name['egress_filter']._options = None
_AURORAMYSQL.fields_by_name['hostname']._options = None
_AURORAMYSQL.fields_by_name['username']._options = None
_AURORAMYSQL.fields_by_name['password']._options = None
_AURORAMYSQL.fields_by_name['database']._options = None
_AURORAMYSQL.fields_by_name['port_override']._options = None
_AURORAMYSQL.fields_by_name['port']._options = None
_AURORAMYSQL._options = None
_CLUSTRIX.fields_by_name['id']._options = None
_CLUSTRIX.fields_by_name['name']._options = None
_CLUSTRIX.fields_by_name['healthy']._options = None
_CLUSTRIX.fields_by_name['tags']._options = None
_CLUSTRIX.fields_by_name['secret_store_id']._options = None
_CLUSTRIX.fields_by_name['egress_filter']._options = None
_CLUSTRIX.fields_by_name['hostname']._options = None
_CLUSTRIX.fields_by_name['username']._options = None
_CLUSTRIX.fields_by_name['password']._options = None
_CLUSTRIX.fields_by_name['database']._options = None
_CLUSTRIX.fields_by_name['port_override']._options = None
_CLUSTRIX.fields_by_name['port']._options = None
_CLUSTRIX._options = None
_MARIA.fields_by_name['id']._options = None
_MARIA.fields_by_name['name']._options = None
_MARIA.fields_by_name['healthy']._options = None
_MARIA.fields_by_name['tags']._options = None
_MARIA.fields_by_name['secret_store_id']._options = None
_MARIA.fields_by_name['egress_filter']._options = None
_MARIA.fields_by_name['hostname']._options = None
_MARIA.fields_by_name['username']._options = None
_MARIA.fields_by_name['password']._options = None
_MARIA.fields_by_name['database']._options = None
_MARIA.fields_by_name['port_override']._options = None
_MARIA.fields_by_name['port']._options = None
_MARIA._options = None
_MEMSQL.fields_by_name['id']._options = None
_MEMSQL.fields_by_name['name']._options = None
_MEMSQL.fields_by_name['healthy']._options = None
_MEMSQL.fields_by_name['tags']._options = None
_MEMSQL.fields_by_name['secret_store_id']._options = None
_MEMSQL.fields_by_name['egress_filter']._options = None
_MEMSQL.fields_by_name['hostname']._options = None
_MEMSQL.fields_by_name['username']._options = None
_MEMSQL.fields_by_name['password']._options = None
_MEMSQL.fields_by_name['database']._options = None
_MEMSQL.fields_by_name['port_override']._options = None
_MEMSQL.fields_by_name['port']._options = None
_MEMSQL._options = None
_SINGLESTORE.fields_by_name['id']._options = None
_SINGLESTORE.fields_by_name['name']._options = None
_SINGLESTORE.fields_by_name['healthy']._options = None
_SINGLESTORE.fields_by_name['tags']._options = None
_SINGLESTORE.fields_by_name['secret_store_id']._options = None
_SINGLESTORE.fields_by_name['egress_filter']._options = None
_SINGLESTORE.fields_by_name['hostname']._options = None
_SINGLESTORE.fields_by_name['username']._options = None
_SINGLESTORE.fields_by_name['password']._options = None
_SINGLESTORE.fields_by_name['database']._options = None
_SINGLESTORE.fields_by_name['port_override']._options = None
_SINGLESTORE.fields_by_name['port']._options = None
_SINGLESTORE._options = None
_ORACLE.fields_by_name['id']._options = None
_ORACLE.fields_by_name['name']._options = None
_ORACLE.fields_by_name['healthy']._options = None
_ORACLE.fields_by_name['tags']._options = None
_ORACLE.fields_by_name['secret_store_id']._options = None
_ORACLE.fields_by_name['egress_filter']._options = None
_ORACLE.fields_by_name['hostname']._options = None
_ORACLE.fields_by_name['username']._options = None
_ORACLE.fields_by_name['password']._options = None
_ORACLE.fields_by_name['database']._options = None
_ORACLE.fields_by_name['port']._options = None
_ORACLE.fields_by_name['port_override']._options = None
_ORACLE.fields_by_name['tls_required']._options = None
_ORACLE._options = None
_POSTGRES.fields_by_name['id']._options = None
_POSTGRES.fields_by_name['name']._options = None
_POSTGRES.fields_by_name['healthy']._options = None
_POSTGRES.fields_by_name['tags']._options = None
_POSTGRES.fields_by_name['secret_store_id']._options = None
_POSTGRES.fields_by_name['egress_filter']._options = None
_POSTGRES.fields_by_name['hostname']._options = None
_POSTGRES.fields_by_name['username']._options = None
_POSTGRES.fields_by_name['password']._options = None
_POSTGRES.fields_by_name['database']._options = None
_POSTGRES.fields_by_name['port_override']._options = None
_POSTGRES.fields_by_name['port']._options = None
_POSTGRES.fields_by_name['override_database']._options = None
_POSTGRES._options = None
_AURORAPOSTGRES.fields_by_name['id']._options = None
_AURORAPOSTGRES.fields_by_name['name']._options = None
_AURORAPOSTGRES.fields_by_name['healthy']._options = None
_AURORAPOSTGRES.fields_by_name['tags']._options = None
_AURORAPOSTGRES.fields_by_name['secret_store_id']._options = None
_AURORAPOSTGRES.fields_by_name['egress_filter']._options = None
_AURORAPOSTGRES.fields_by_name['hostname']._options = None
_AURORAPOSTGRES.fields_by_name['username']._options = None
_AURORAPOSTGRES.fields_by_name['password']._options = None
_AURORAPOSTGRES.fields_by_name['database']._options = None
_AURORAPOSTGRES.fields_by_name['port_override']._options = None
_AURORAPOSTGRES.fields_by_name['port']._options = None
_AURORAPOSTGRES.fields_by_name['override_database']._options = None
_AURORAPOSTGRES._options = None
_GREENPLUM.fields_by_name['id']._options = None
_GREENPLUM.fields_by_name['name']._options = None
_GREENPLUM.fields_by_name['healthy']._options = None
_GREENPLUM.fields_by_name['tags']._options = None
_GREENPLUM.fields_by_name['secret_store_id']._options = None
_GREENPLUM.fields_by_name['egress_filter']._options = None
_GREENPLUM.fields_by_name['hostname']._options = None
_GREENPLUM.fields_by_name['username']._options = None
_GREENPLUM.fields_by_name['password']._options = None
_GREENPLUM.fields_by_name['database']._options = None
_GREENPLUM.fields_by_name['port_override']._options = None
_GREENPLUM.fields_by_name['port']._options = None
_GREENPLUM.fields_by_name['override_database']._options = None
_GREENPLUM._options = None
_COCKROACH.fields_by_name['id']._options = None
_COCKROACH.fields_by_name['name']._options = None
_COCKROACH.fields_by_name['healthy']._options = None
_COCKROACH.fields_by_name['tags']._options = None
_COCKROACH.fields_by_name['secret_store_id']._options = None
_COCKROACH.fields_by_name['egress_filter']._options = None
_COCKROACH.fields_by_name['hostname']._options = None
_COCKROACH.fields_by_name['username']._options = None
_COCKROACH.fields_by_name['password']._options = None
_COCKROACH.fields_by_name['database']._options = None
_COCKROACH.fields_by_name['port_override']._options = None
_COCKROACH.fields_by_name['port']._options = None
_COCKROACH.fields_by_name['override_database']._options = None
_COCKROACH._options = None
_REDSHIFT.fields_by_name['id']._options = None
_REDSHIFT.fields_by_name['name']._options = None
_REDSHIFT.fields_by_name['healthy']._options = None
_REDSHIFT.fields_by_name['tags']._options = None
_REDSHIFT.fields_by_name['secret_store_id']._options = None
_REDSHIFT.fields_by_name['egress_filter']._options = None
_REDSHIFT.fields_by_name['hostname']._options = None
_REDSHIFT.fields_by_name['username']._options = None
_REDSHIFT.fields_by_name['password']._options = None
_REDSHIFT.fields_by_name['database']._options = None
_REDSHIFT.fields_by_name['port_override']._options = None
_REDSHIFT.fields_by_name['port']._options = None
_REDSHIFT.fields_by_name['override_database']._options = None
_REDSHIFT._options = None
_CITUS.fields_by_name['id']._options = None
_CITUS.fields_by_name['name']._options = None
_CITUS.fields_by_name['healthy']._options = None
_CITUS.fields_by_name['tags']._options = None
_CITUS.fields_by_name['secret_store_id']._options = None
_CITUS.fields_by_name['egress_filter']._options = None
_CITUS.fields_by_name['hostname']._options = None
_CITUS.fields_by_name['username']._options = None
_CITUS.fields_by_name['password']._options = None
_CITUS.fields_by_name['database']._options = None
_CITUS.fields_by_name['port_override']._options = None
_CITUS.fields_by_name['port']._options = None
_CITUS.fields_by_name['override_database']._options = None
_CITUS._options = None
_PRESTO.fields_by_name['id']._options = None
_PRESTO.fields_by_name['name']._options = None
_PRESTO.fields_by_name['healthy']._options = None
_PRESTO.fields_by_name['tags']._options = None
_PRESTO.fields_by_name['secret_store_id']._options = None
_PRESTO.fields_by_name['egress_filter']._options = None
_PRESTO.fields_by_name['hostname']._options = None
_PRESTO.fields_by_name['password']._options = None
_PRESTO.fields_by_name['database']._options = None
_PRESTO.fields_by_name['port_override']._options = None
_PRESTO.fields_by_name['port']._options = None
_PRESTO.fields_by_name['username']._options = None
_PRESTO.fields_by_name['tls_required']._options = None
_PRESTO._options = None
_RAWTCP.fields_by_name['id']._options = None
_RAWTCP.fields_by_name['name']._options = None
_RAWTCP.fields_by_name['healthy']._options = None
_RAWTCP.fields_by_name['tags']._options = None
_RAWTCP.fields_by_name['secret_store_id']._options = None
_RAWTCP.fields_by_name['egress_filter']._options = None
_RAWTCP.fields_by_name['hostname']._options = None
_RAWTCP.fields_by_name['port_override']._options = None
_RAWTCP.fields_by_name['port']._options = None
_RAWTCP._options = None
_RDP.fields_by_name['id']._options = None
_RDP.fields_by_name['name']._options = None
_RDP.fields_by_name['healthy']._options = None
_RDP.fields_by_name['tags']._options = None
_RDP.fields_by_name['secret_store_id']._options = None
_RDP.fields_by_name['egress_filter']._options = None
_RDP.fields_by_name['hostname']._options = None
_RDP.fields_by_name['username']._options = None
_RDP.fields_by_name['password']._options = None
_RDP.fields_by_name['port_override']._options = None
_RDP.fields_by_name['port']._options = None
_RDP._options = None
_REDIS.fields_by_name['id']._options = None
_REDIS.fields_by_name['name']._options = None
_REDIS.fields_by_name['healthy']._options = None
_REDIS.fields_by_name['tags']._options = None
_REDIS.fields_by_name['secret_store_id']._options = None
_REDIS.fields_by_name['egress_filter']._options = None
_REDIS.fields_by_name['hostname']._options = None
_REDIS.fields_by_name['port_override']._options = None
_REDIS.fields_by_name['password']._options = None
_REDIS.fields_by_name['port']._options = None
_REDIS._options = None
_ELASTICACHEREDIS.fields_by_name['id']._options = None
_ELASTICACHEREDIS.fields_by_name['name']._options = None
_ELASTICACHEREDIS.fields_by_name['healthy']._options = None
_ELASTICACHEREDIS.fields_by_name['tags']._options = None
_ELASTICACHEREDIS.fields_by_name['secret_store_id']._options = None
_ELASTICACHEREDIS.fields_by_name['egress_filter']._options = None
_ELASTICACHEREDIS.fields_by_name['hostname']._options = None
_ELASTICACHEREDIS.fields_by_name['port_override']._options = None
_ELASTICACHEREDIS.fields_by_name['password']._options = None
_ELASTICACHEREDIS.fields_by_name['port']._options = None
_ELASTICACHEREDIS.fields_by_name['tls_required']._options = None
_ELASTICACHEREDIS._options = None
_SNOWFLAKE.fields_by_name['id']._options = None
_SNOWFLAKE.fields_by_name['name']._options = None
_SNOWFLAKE.fields_by_name['healthy']._options = None
_SNOWFLAKE.fields_by_name['tags']._options = None
_SNOWFLAKE.fields_by_name['secret_store_id']._options = None
_SNOWFLAKE.fields_by_name['egress_filter']._options = None
_SNOWFLAKE.fields_by_name['hostname']._options = None
_SNOWFLAKE.fields_by_name['username']._options = None
_SNOWFLAKE.fields_by_name['password']._options = None
_SNOWFLAKE.fields_by_name['database']._options = None
_SNOWFLAKE.fields_by_name['schema']._options = None
_SNOWFLAKE.fields_by_name['port_override']._options = None
_SNOWFLAKE._options = None
_SQLSERVER.fields_by_name['id']._options = None
_SQLSERVER.fields_by_name['name']._options = None
_SQLSERVER.fields_by_name['healthy']._options = None
_SQLSERVER.fields_by_name['tags']._options = None
_SQLSERVER.fields_by_name['secret_store_id']._options = None
_SQLSERVER.fields_by_name['egress_filter']._options = None
_SQLSERVER.fields_by_name['hostname']._options = None
_SQLSERVER.fields_by_name['username']._options = None
_SQLSERVER.fields_by_name['password']._options = None
_SQLSERVER.fields_by_name['database']._options = None
_SQLSERVER.fields_by_name['port_override']._options = None
_SQLSERVER.fields_by_name['schema']._options = None
_SQLSERVER.fields_by_name['port']._options = None
_SQLSERVER.fields_by_name['override_database']._options = None
_SQLSERVER._options = None
_SSH.fields_by_name['id']._options = None
_SSH.fields_by_name['name']._options = None
_SSH.fields_by_name['healthy']._options = None
_SSH.fields_by_name['tags']._options = None
_SSH.fields_by_name['secret_store_id']._options = None
_SSH.fields_by_name['egress_filter']._options = None
_SSH.fields_by_name['hostname']._options = None
_SSH.fields_by_name['username']._options = None
_SSH.fields_by_name['port']._options = None
_SSH.fields_by_name['public_key']._options = None
_SSH.fields_by_name['port_forwarding']._options = None
_SSH.fields_by_name['allow_deprecated_key_exchanges']._options = None
_SSH._options = None
_SSHCERT.fields_by_name['id']._options = None
_SSHCERT.fields_by_name['name']._options = None
_SSHCERT.fields_by_name['healthy']._options = None
_SSHCERT.fields_by_name['tags']._options = None
_SSHCERT.fields_by_name['secret_store_id']._options = None
_SSHCERT.fields_by_name['egress_filter']._options = None
_SSHCERT.fields_by_name['hostname']._options = None
_SSHCERT.fields_by_name['username']._options = None
_SSHCERT.fields_by_name['port']._options = None
_SSHCERT.fields_by_name['port_forwarding']._options = None
_SSHCERT.fields_by_name['allow_deprecated_key_exchanges']._options = None
_SSHCERT._options = None
_SSHCUSTOMERKEY.fields_by_name['id']._options = None
_SSHCUSTOMERKEY.fields_by_name['name']._options = None
_SSHCUSTOMERKEY.fields_by_name['healthy']._options = None
_SSHCUSTOMERKEY.fields_by_name['tags']._options = None
_SSHCUSTOMERKEY.fields_by_name['secret_store_id']._options = None
_SSHCUSTOMERKEY.fields_by_name['egress_filter']._options = None
_SSHCUSTOMERKEY.fields_by_name['hostname']._options = None
_SSHCUSTOMERKEY.fields_by_name['username']._options = None
_SSHCUSTOMERKEY.fields_by_name['port']._options = None
_SSHCUSTOMERKEY.fields_by_name['private_key']._options = None
_SSHCUSTOMERKEY.fields_by_name['port_forwarding']._options = None
_SSHCUSTOMERKEY.fields_by_name['allow_deprecated_key_exchanges']._options = None
_SSHCUSTOMERKEY._options = None
_SYBASE.fields_by_name['id']._options = None
_SYBASE.fields_by_name['name']._options = None
_SYBASE.fields_by_name['healthy']._options = None
_SYBASE.fields_by_name['tags']._options = None
_SYBASE.fields_by_name['secret_store_id']._options = None
_SYBASE.fields_by_name['egress_filter']._options = None
_SYBASE.fields_by_name['hostname']._options = None
_SYBASE.fields_by_name['username']._options = None
_SYBASE.fields_by_name['port_override']._options = None
_SYBASE.fields_by_name['port']._options = None
_SYBASE.fields_by_name['password']._options = None
_SYBASE._options = None
_SYBASEIQ.fields_by_name['id']._options = None
_SYBASEIQ.fields_by_name['name']._options = None
_SYBASEIQ.fields_by_name['healthy']._options = None
_SYBASEIQ.fields_by_name['tags']._options = None
_SYBASEIQ.fields_by_name['secret_store_id']._options = None
_SYBASEIQ.fields_by_name['egress_filter']._options = None
_SYBASEIQ.fields_by_name['hostname']._options = None
_SYBASEIQ.fields_by_name['username']._options = None
_SYBASEIQ.fields_by_name['port_override']._options = None
_SYBASEIQ.fields_by_name['port']._options = None
_SYBASEIQ.fields_by_name['password']._options = None
_SYBASEIQ._options = None
_TERADATA.fields_by_name['id']._options = None
_TERADATA.fields_by_name['name']._options = None
_TERADATA.fields_by_name['healthy']._options = None
_TERADATA.fields_by_name['tags']._options = None
_TERADATA.fields_by_name['secret_store_id']._options = None
_TERADATA.fields_by_name['egress_filter']._options = None
_TERADATA.fields_by_name['hostname']._options = None
_TERADATA.fields_by_name['username']._options = None
_TERADATA.fields_by_name['password']._options = None
_TERADATA.fields_by_name['port_override']._options = None
_TERADATA.fields_by_name['port']._options = None
_TERADATA._options = None
# @@protoc_insertion_point(module_scope)
| 91.866744
| 198,993
| 0.755305
| 140,413
| 794,188
| 4.11558
| 0.008653
| 0.076874
| 0.057702
| 0.07052
| 0.942504
| 0.927099
| 0.8942
| 0.869728
| 0.859134
| 0.832296
| 0
| 0.243575
| 0.070554
| 794,188
| 8,644
| 198,994
| 91.877372
| 0.539297
| 0.005044
| 0
| 0.679689
| 0
| 0.072162
| 0.421918
| 0.386835
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.013023
| 0.000717
| 0
| 0.000717
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
301c3b0971156e7073ae833b676d9369e047f5c6
| 22,054
|
py
|
Python
|
tmrl/custom/custom_memories.py
|
trackmania-rl/tmrl
|
c1f740740a7d57382a451607fdc66d92ba62ea0c
|
[
"MIT"
] | 56
|
2021-07-06T06:46:48.000Z
|
2022-03-27T12:08:08.000Z
|
tmrl/custom/custom_memories.py
|
trackmania-rl/tmrl
|
c1f740740a7d57382a451607fdc66d92ba62ea0c
|
[
"MIT"
] | 6
|
2021-07-06T18:40:18.000Z
|
2022-03-30T21:18:15.000Z
|
tmrl/custom/custom_memories.py
|
trackmania-rl/tmrl
|
c1f740740a7d57382a451607fdc66d92ba62ea0c
|
[
"MIT"
] | 5
|
2021-08-28T00:49:16.000Z
|
2022-03-29T21:22:30.000Z
|
# third-party imports
import numpy as np
import cv2
# local imports
from tmrl.memory_dataloading import MemoryDataloading, TrajMemoryDataloading
# LOCAL BUFFER COMPRESSION ==============================
def get_local_buffer_sample(prev_act, obs, rew, done, info):
"""
Input:
prev_act: action computed from a previous observation and applied to yield obs in the transition (but not influencing the unaugmented observation in real-time envs)
obs, rew, done, info: outcome of the transition
this function creates the object that will actually be stored in local buffers for networking
this is to compress the sample before sending it over the Internet/local network
buffers of such samples will be given as input to the append() method of the dataloading memory
the user must define both this function and the append() method of the dataloading memory
CAUTION: prev_act is the action that comes BEFORE obs (i.e. prev_obs, prev_act(prev_obs), obs(prev_act))
"""
obs_mod = (obs[0], obs[1][-1]) # speed and most recent image only
rew_mod = np.float32(rew)
done_mod = done
return prev_act, obs_mod, rew_mod, done_mod, info
def get_local_buffer_sample_tm20_imgs(prev_act, obs, rew, done, info):
"""
Sample compressor for MemoryTM2020
Input:
prev_act: action computed from a previous observation and applied to yield obs in the transition
obs, rew, done, info: outcome of the transition
this function creates the object that will actually be stored in local buffers for networking
this is to compress the sample before sending it over the Internet/local network
buffers of such samples will be given as input to the append() method of the dataloading memory
the user must define both this function and the append() method of the dataloading memory
CAUTION: prev_act is the action that comes BEFORE obs (i.e. prev_obs, prev_act(prev_obs), obs(prev_act))
"""
prev_act_mod = prev_act
compressed_img = cv2.imencode('.PNG', np.moveaxis(obs[3][-1], 0, -1))
obs_mod = (obs[0], obs[1], obs[2], compressed_img) # speed, gear, rpm, last image
rew_mod = np.float32(rew)
done_mod = done
info_mod = info
return prev_act_mod, obs_mod, rew_mod, done_mod, info_mod
# FUNCTIONS ====================================================
def last_true_in_list(li):
for i in reversed(range(len(li))):
if li[i]:
return i
return None
def replace_hist_before_done(hist, done_idx_in_hist):
last_idx = len(hist) - 1
assert done_idx_in_hist <= last_idx, f"DEBUG: done_idx_in_hist:{done_idx_in_hist}, last_idx:{last_idx}"
if 0 <= done_idx_in_hist < last_idx:
for i in reversed(range(len(hist))):
if i <= done_idx_in_hist:
hist[i] = hist[i + 1]
# MEMORY DATALOADING ===========================================
class MemoryTMNF(MemoryDataloading):
def __init__(self,
memory_size,
batchsize,
path_loc="",
imgs_obs=4,
act_buf_len=1,
nb_steps=1,
use_dataloader=False,
num_workers=0,
pin_memory=False,
remove_size=100,
obs_preprocessor: callable = None,
sample_preprocessor: callable = None,
crc_debug=False,
device="cpu"):
self.imgs_obs = imgs_obs
self.act_buf_len = act_buf_len
self.min_samples = max(self.imgs_obs, self.act_buf_len)
self.start_imgs_offset = max(0, self.min_samples - self.imgs_obs)
self.start_acts_offset = max(0, self.min_samples - self.act_buf_len)
super().__init__(memory_size=memory_size,
batchsize=batchsize,
path_loc=path_loc,
nb_steps=nb_steps,
use_dataloader=use_dataloader,
num_workers=num_workers,
pin_memory=pin_memory,
remove_size=remove_size,
obs_preprocessor=obs_preprocessor,
sample_preprocessor=sample_preprocessor,
crc_debug=crc_debug,
device=device)
def append_buffer(self, buffer): # TODO
return self
def __len__(self):
if len(self.data) == 0:
return 0
res = len(self.data[0]) - self.min_samples - 1
if res < 0:
return 0
else:
return res
def get_transition(self, item): # TODO
pass
# return last_obs, new_act, rew, new_obs, done
class MemoryTMNFLidar(MemoryTMNF):
def get_transition(self, item):
"""
CAUTION: item is the first index of the 4 images in the images history of the OLD observation
CAUTION: in the buffer, a sample is (act, obs(act)) and NOT (obs, act(obs))
i.e. in a sample, the observation is what step returned after being fed act
therefore, in the RTRL setting, act is appended to obs
So we load 5 images from here...
Don't forget the info dict for CRC debugging
"""
idx_last = item + self.min_samples - 1
idx_now = item + self.min_samples
acts = self.load_acts(item)
last_act_buf = acts[:-1]
new_act_buf = acts[1:]
imgs = self.load_imgs(item)
imgs_last_obs = imgs[:-1]
imgs_new_obs = imgs[1:]
# if a reset transition has influenced the observation, special care must be taken
last_dones = self.data[4][idx_now - self.min_samples:idx_now] # self.min_samples values
last_done_idx = last_true_in_list(last_dones) # last occurrence of True
assert last_done_idx is None or last_dones[last_done_idx], f"DEBUG: last_done_idx:{last_done_idx}"
last_infos = self.data[6][idx_now - self.min_samples:idx_now]
last_ignored_dones = ["__no_done" in i for i in last_infos]
last_ignored_done_idx = last_true_in_list(last_ignored_dones) # last occurrence of True
assert last_ignored_done_idx is None or last_ignored_dones[last_ignored_done_idx] and not last_dones[last_ignored_done_idx], f"DEBUG: last_ignored_done_idx:{last_ignored_done_idx}, last_ignored_dones:{last_ignored_dones}, last_dones:{last_dones}"
if last_ignored_done_idx is not None:
last_done_idx = last_ignored_done_idx # FIXME: might not work in extreme cases where a done is ignored right after another done
if last_done_idx is not None:
replace_hist_before_done(hist=new_act_buf, done_idx_in_hist=last_done_idx - self.start_acts_offset - 1)
replace_hist_before_done(hist=last_act_buf, done_idx_in_hist=last_done_idx - self.start_acts_offset)
replace_hist_before_done(hist=imgs_new_obs, done_idx_in_hist=last_done_idx - self.start_imgs_offset - 1)
replace_hist_before_done(hist=imgs_last_obs, done_idx_in_hist=last_done_idx - self.start_imgs_offset)
last_obs = (self.data[2][idx_last], imgs_last_obs, *last_act_buf)
new_act = self.data[1][idx_now]
rew = np.float32(self.data[5][idx_now])
new_obs = (self.data[2][idx_now], imgs_new_obs, *new_act_buf)
done = self.data[4][idx_now]
info = self.data[6][idx_now]
return last_obs, new_act, rew, new_obs, done, info
def load_imgs(self, item):
res = self.data[3][(item + self.start_imgs_offset):(item + self.start_imgs_offset + self.imgs_obs + 1)]
return np.stack(res)
def load_acts(self, item):
res = self.data[1][(item + self.start_acts_offset):(item + self.start_acts_offset + self.act_buf_len + 1)]
return res
def append_buffer(self, buffer):
"""
buffer is a list of samples ( act, obs, rew, done, info)
don't forget to keep the info dictionary in the sample for CRC debugging
"""
first_data_idx = self.data[0][-1] + 1 if self.__len__() > 0 else 0
d0 = [first_data_idx + i for i, _ in enumerate(buffer.memory)] # indexes
d1 = [b[0] for b in buffer.memory] # actions
d2 = [b[1][0] for b in buffer.memory] # speeds
d3 = [b[1][1] for b in buffer.memory] # lidar
d4 = [b[3] for b in buffer.memory] # dones
d5 = [b[2] for b in buffer.memory] # rewards
d6 = [b[4] for b in buffer.memory] # infos
if self.__len__() > 0:
self.data[0] += d0
self.data[1] += d1
self.data[2] += d2
self.data[3] += d3
self.data[4] += d4
self.data[5] += d5
self.data[6] += d6
else:
self.data.append(d0)
self.data.append(d1)
self.data.append(d2)
self.data.append(d3)
self.data.append(d4)
self.data.append(d5)
self.data.append(d6)
to_trim = self.__len__() - self.memory_size
if to_trim > 0:
self.data[0] = self.data[0][to_trim:]
self.data[1] = self.data[1][to_trim:]
self.data[2] = self.data[2][to_trim:]
self.data[3] = self.data[3][to_trim:]
self.data[4] = self.data[4][to_trim:]
self.data[5] = self.data[5][to_trim:]
self.data[6] = self.data[6][to_trim:]
return self
class TrajMemoryTMNF(TrajMemoryDataloading):
def __init__(self,
memory_size,
batchsize,
path_loc="",
imgs_obs=4,
act_buf_len=1,
traj_len=1,
nb_steps=1,
use_dataloader=False,
num_workers=0,
pin_memory=False,
remove_size=100,
obs_preprocessor: callable = None,
crc_debug=False,
device="cpu"):
self.imgs_obs = imgs_obs
self.act_buf_len = act_buf_len
self.traj_len = traj_len
self.min_samples = max(self.imgs_obs, self.act_buf_len)
self.min_samples += self.traj_len - 1
self.start_imgs_offset = max(0, self.min_samples - self.imgs_obs)
self.start_acts_offset = max(0, self.min_samples - self.act_buf_len)
super().__init__(memory_size=memory_size,
batchsize=batchsize,
path_loc=path_loc,
nb_steps=nb_steps,
use_dataloader=use_dataloader,
num_workers=num_workers,
pin_memory=pin_memory,
remove_size=remove_size,
obs_preprocessor=obs_preprocessor,
crc_debug=crc_debug,
device=device)
def append_buffer(self, buffer): # TODO
return self
def __len__(self):
if len(self.data) == 0:
return 0
res = len(self.data[0]) - self.min_samples - 1
if res < 0:
return 0
else:
return res
def get_trajectory(self, item): # TODO
pass
# return last_obs, new_act, rew, new_obs, done
class TrajMemoryTMNFLidar(TrajMemoryTMNF):
def get_trajectory(self, item):
"""
CAUTION: item is the first index of the 4 images in the images history of the OLD observation
CAUTION: in the buffer, a sample is (act, obs(act)) and NOT (obs, act(obs))
i.e. in a sample, the observation is what step returned after being fed act
therefore, in the RTRL setting, act is appended to obs
So we load 5 images from here...
Don't forget the info dict for CRC debugging
"""
idx_now = item + self.min_samples
all_acts = self.load_acts_traj(item)
# new_act_buf = acts[1:]
all_imgs = self.load_imgs_traj(item)
# rew = np.float32(self.data[5][idx_now])
rew_traj = [np.float32(self.data[5][idx_now + i]) for i in range(self.traj_len)]
# new_act = self.data[1][idx_now]
# new_obs = (self.data[2][idx_now], imgs[1:], *new_act_buf)
augm_obs_traj = [(self.data[2][idx_now + i], all_imgs[1 + i:self.imgs_obs + i + 1], *all_acts[1 + i:self.act_buf_len + i + 1]) for i in range(self.traj_len)]
# done = self.data[4][idx_now]
done_traj = [self.data[4][idx_now + i] for i in range(self.traj_len)]
# info = self.data[6][idx_now]
info_traj = [self.data[6][idx_now + i] for i in range(self.traj_len)]
return augm_obs_traj, rew_traj, done_traj, info_traj
def load_imgs_traj(self, item):
res = self.data[3][(item + self.start_imgs_offset):(item + self.start_imgs_offset + self.imgs_obs + self.traj_len)]
return np.stack(res)
def load_acts_traj(self, item):
res = self.data[1][(item + self.start_acts_offset):(item + self.start_acts_offset + self.act_buf_len + self.traj_len)]
return res
def append_buffer(self, buffer):
"""
buffer is a list of samples ( act, obs, rew, done, info)
don't forget to keep the info dictionary in the sample for CRC debugging
"""
first_data_idx = self.data[0][-1] + 1 if self.__len__() > 0 else 0
d0 = [first_data_idx + i for i, _ in enumerate(buffer.memory)] # indexes
d1 = [b[0] for b in buffer.memory] # actions
d2 = [b[1][0] for b in buffer.memory] # speeds
d3 = [b[1][1] for b in buffer.memory] # lidar
d4 = [b[3] for b in buffer.memory] # dones
d5 = [b[2] for b in buffer.memory] # rewards
d6 = [b[4] for b in buffer.memory] # infos
if self.__len__() > 0:
self.data[0] += d0
self.data[1] += d1
self.data[2] += d2
self.data[3] += d3
self.data[4] += d4
self.data[5] += d5
self.data[6] += d6
else:
self.data.append(d0)
self.data.append(d1)
self.data.append(d2)
self.data.append(d3)
self.data.append(d4)
self.data.append(d5)
self.data.append(d6)
to_trim = self.__len__() - self.memory_size
if to_trim > 0:
self.data[0] = self.data[0][to_trim:]
self.data[1] = self.data[1][to_trim:]
self.data[2] = self.data[2][to_trim:]
self.data[3] = self.data[3][to_trim:]
self.data[4] = self.data[4][to_trim:]
self.data[5] = self.data[5][to_trim:]
self.data[6] = self.data[6][to_trim:]
return self
class MemoryTM2020(MemoryDataloading): # TODO: reset transitions
def __init__(self,
memory_size,
batchsize,
path_loc="",
imgs_obs=4,
act_buf_len=1,
nb_steps=1,
use_dataloader=False,
num_workers=0,
pin_memory=False,
remove_size=100,
obs_preprocessor: callable = None,
sample_preprocessor: callable = None,
crc_debug=False,
device="cpu"):
self.imgs_obs = imgs_obs
self.act_buf_len = act_buf_len
self.min_samples = max(self.imgs_obs, self.act_buf_len)
self.start_imgs_offset = max(0, self.min_samples - self.imgs_obs)
self.start_acts_offset = max(0, self.min_samples - self.act_buf_len)
super().__init__(memory_size=memory_size,
batchsize=batchsize,
path_loc=path_loc,
nb_steps=nb_steps,
use_dataloader=use_dataloader,
num_workers=num_workers,
pin_memory=pin_memory,
remove_size=remove_size,
obs_preprocessor=obs_preprocessor,
sample_preprocessor=sample_preprocessor,
crc_debug=crc_debug,
device=device)
def append_buffer(self, buffer):
"""
buffer is a list of samples (act, obs, rew, done, info)
don't forget to keep the info dictionary in the sample for CRC debugging
"""
first_data_idx = self.data[0][-1] + 1 if self.__len__() > 0 else 0
d0 = [(first_data_idx + i) % self.memory_size for i, _ in enumerate(buffer.memory)] # indexes # FIXME: check that this works
d1 = [b[0] for b in buffer.memory] # actions
d2 = [b[1][0] for b in buffer.memory] # speeds
d3 = [b[1][1] for b in buffer.memory] # gear
d4 = [b[1][2] for b in buffer.memory] # rpm
for bi, di in zip(buffer.memory, d0):
cv2.imwrite(str(self.path / (str(di) + '.png')), cv2.imdecode(np.array(bi[1][3][1]), cv2.IMREAD_UNCHANGED))
d5 = [b[3] for b in buffer.memory] # dones
d6 = [b[2] for b in buffer.memory] # rewards
d7 = [b[4] for b in buffer.memory] # infos
if self.__len__() > 0:
self.data[0] += d0
self.data[1] += d1
self.data[2] += d2
self.data[3] += d3
self.data[4] += d4
self.data[5] += d5
self.data[6] += d6
self.data[7] += d7
else:
self.data.append(d0)
self.data.append(d1)
self.data.append(d2)
self.data.append(d3)
self.data.append(d4)
self.data.append(d5)
self.data.append(d6)
self.data.append(d7)
to_trim = self.__len__() - self.memory_size
if to_trim > 0:
self.data[0] = self.data[0][to_trim:]
self.data[1] = self.data[1][to_trim:]
self.data[2] = self.data[2][to_trim:]
self.data[3] = self.data[3][to_trim:]
self.data[4] = self.data[4][to_trim:]
self.data[5] = self.data[5][to_trim:]
self.data[6] = self.data[6][to_trim:]
self.data[7] = self.data[7][to_trim:]
return self
def __len__(self):
if len(self.data) < self.min_samples + 1:
return 0
res = len(self.data[0]) - self.min_samples - 1
if res < 0:
return 0
else:
return res
def get_transition(self, item):
idx_last = item + self.min_samples - 1
idx_now = item + self.min_samples
imgs = self.load_imgs(item)
acts = self.load_acts(item)
last_act_buf = acts[:-1]
new_act_buf = acts[1:]
last_obs = (self.data[2][idx_last], self.data[3][idx_last], self.data[4][idx_last], imgs[:-1], *last_act_buf)
rew = np.float32(self.data[6][idx_now])
new_act = np.array(self.data[1][idx_now], dtype=np.float32)
new_obs = (self.data[2][idx_now], self.data[3][idx_now], self.data[4][idx_now], imgs[1:], *new_act_buf)
done = self.data[5][idx_now]
info = self.data[7][idx_now]
return last_obs, new_act, rew, new_obs, done, info
def load_imgs(self, item):
res = []
for i in range(item + self.start_imgs_offset, item + self.start_imgs_offset + self.imgs_obs + 1):
img_path = str(self.path / (str(self.data[0][i]) + ".png"))
img = cv2.imread(img_path)
res.append(np.moveaxis(img, -1, 0))
return np.array(res)
def load_acts(self, item):
res = self.data[1][(item + self.start_acts_offset):(item + self.start_acts_offset + self.act_buf_len + 1)]
return res
class MemoryTM2020RAM(MemoryTM2020):
"""
Same as MemoryTM2020 but the full buffer is in RAM to avoid dataloading latencies
"""
def append_buffer(self, buffer):
"""
buffer is a list of samples (act, obs, rew, done, info)
don't forget to keep the info dictionary in the sample for CRC debugging
"""
first_data_idx = self.data[0][-1] + 1 if self.__len__() > 0 else 0
d0 = [(first_data_idx + i) % self.memory_size for i, _ in enumerate(buffer.memory)] # indexes # FIXME: check that this works
d1 = [b[0] for b in buffer.memory] # actions
d2 = [b[1][0] for b in buffer.memory] # speeds
d3 = [b[1][1] for b in buffer.memory] # gear
d4 = [b[1][2] for b in buffer.memory] # rpm
d5 = [b[3] for b in buffer.memory] # dones
d6 = [b[2] for b in buffer.memory] # rewards
d7 = [b[4] for b in buffer.memory] # infos
d8 = [np.moveaxis(cv2.imdecode(np.array(b[1][3][1]), cv2.IMREAD_UNCHANGED), -1, 0) for b in buffer.memory]
if self.__len__() > 0:
self.data[0] += d0
self.data[1] += d1
self.data[2] += d2
self.data[3] += d3
self.data[4] += d4
self.data[5] += d5
self.data[6] += d6
self.data[7] += d7
self.data[8] += d8
else:
self.data.append(d0)
self.data.append(d1)
self.data.append(d2)
self.data.append(d3)
self.data.append(d4)
self.data.append(d5)
self.data.append(d6)
self.data.append(d7)
self.data.append(d8)
to_trim = self.__len__() - self.memory_size
if to_trim > 0:
self.data[0] = self.data[0][to_trim:]
self.data[1] = self.data[1][to_trim:]
self.data[2] = self.data[2][to_trim:]
self.data[3] = self.data[3][to_trim:]
self.data[4] = self.data[4][to_trim:]
self.data[5] = self.data[5][to_trim:]
self.data[6] = self.data[6][to_trim:]
self.data[7] = self.data[7][to_trim:]
self.data[8] = self.data[8][to_trim:]
return self
def load_imgs(self, item):
res = self.data[8][(item + self.start_imgs_offset):(item + self.start_imgs_offset + self.imgs_obs + 1)]
return np.array(res)
| 40.690037
| 254
| 0.573909
| 3,149
| 22,054
| 3.803112
| 0.080661
| 0.112224
| 0.036239
| 0.027054
| 0.852622
| 0.81154
| 0.770875
| 0.725534
| 0.714178
| 0.711423
| 0
| 0.028124
| 0.313186
| 22,054
| 541
| 255
| 40.76525
| 0.762527
| 0.174889
| 0
| 0.782278
| 0
| 0
| 0.013888
| 0.009784
| 0
| 0
| 0
| 0.011091
| 0.007595
| 1
| 0.070886
| false
| 0.005063
| 0.007595
| 0.005063
| 0.167089
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
|
0
| 8
|
303685b952f4810eabc059f4e01070819853b23f
| 20,135
|
py
|
Python
|
Asset/Elements/Deterioration/IBMSSinha2009.py
|
vd1371/GIAMS
|
dd6551f344b8d0377131d4496846eb5d03b6189c
|
[
"MIT"
] | null | null | null |
Asset/Elements/Deterioration/IBMSSinha2009.py
|
vd1371/GIAMS
|
dd6551f344b8d0377131d4496846eb5d03b6189c
|
[
"MIT"
] | null | null | null |
Asset/Elements/Deterioration/IBMSSinha2009.py
|
vd1371/GIAMS
|
dd6551f344b8d0377131d4496846eb5d03b6189c
|
[
"MIT"
] | null | null | null |
import numpy as np
from .BaseDeterioration import *
class Markovian(BaseDeterioration):
def __init__(self):
super().__init__()
"""
This is based on IBMS by Sinha et. al. 2009
To be completed
"""
def update_deterioration(self, observation):
'''Dummy model for updating the deterioration models
It can be used in the SHM anslysis
'''
pass
def predict_condition(self, previous_condition, age = None):
'''To predict the condition rating after one time step given some information'''
if self.element.name == SUPERSTRUCTURE:
if self.asset.road_class == NHS or self.asset.road_class == MAJOR:
if self.element.asset.material == STEEL or self.element.asset.material == TIMBER:
if self.element.age in range(0, 7): probs_list = [0.976, 0.936, 0.890, 0.885, 0.920, 0.930, 0.920, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.951, 0.930, 0.903, 0.900, 0.910, 0.915, 0.910, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.948, 0.930, 0.855, 0.870, 0.880, 0.870, 0.860, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.945, 0.920, 0.830, 0.850, 0.868, 0.870, 0.865, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.940, 0.918, 0.857, 0.852, 0.870, 0.880, 0.870, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.930, 0.912, 0.847, 0.865, 0.874, 0.870, 0.870, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.955, 0.957, 0.905, 0.908, 0.907, 0.892, 0.860, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.950, 0.945, 0.925, 0.933, 0.925, 0.918, 0.850, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.945, 0.956, 0.930, 0.945, 0.945, 0.912, 0.876, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.943, 0.955, 0.928, 0.948, 0.955, 0.920, 0.886, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.940, 0.955, 0.932, 0.960, 0.955, 0.925, 0.886, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.890, 0.912, 0.948, 0.958, 0.959, 0.938, 0.910, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.870, 0.890, 0.943, 0.957, 0.957, 0.948, 0.928, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.870, 0.905, 0.941, 0.957, 0.953, 0.955, 0.925, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.850, 0.850, 0.937, 0.956, 0.954, 0.950, 0.930, 1.0]
else: probs_list = [0.850, 0.850, 0.937, 0.956, 0.954, 0.950, 0.930, 1.0]
else:
if self.element.age in range(0, 7): probs_list = [0.989, 0.97, 0.937, 0.955, 0.92, 0.9, 0.9, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.962, 0.958, 0.925, 0.92, 0.89, 0.88, 0.87, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.966, 0.945, 0.88, 0.88, 0.89, 0.88, 0.87, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.95, 0.93, 0.87, 0.88, 0.87, 0.88, 0.87, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.95, 0.932, 0.872, 0.88, 0.87, 0.88, 0.87, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.952, 0.935, 0.878, 0.888, 0.878, 0.88, 0.87, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.95, 0.93, 0.865, 0.88, 0.868, 0.87, 0.85, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.94, 0.92, 0.855, 0.875, 0.86, 0.866, 0.85, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.94, 0.95, 0.885, 0.915, 0.92, 0.9, 0.876, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.93, 0.95, 0.898, 0.92, 0.93, 0.91, 0.876, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.92, 0.955, 0.912, 0.936, 0.955, 0.923, 0.886, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.89, 0.912, 0.958, 0.968, 0.969, 0.948, 0.92, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.87, 0.87, 0.933, 0.94, 0.937, 0.938, 0.928, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.86, 0.865, 0.931, 0.953, 0.946, 0.945, 0.918, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.85, 0.85, 0.937, 0.96, 0.954, 0.95, 0.93, 1.0]
else: probs_list = [0.85, 0.85, 0.937, 0.96, 0.954, 0.95, 0.93, 1.0]
elif self.asset.road_class == MINOR or self.asset.road_class == LOCAL:
if self.element.asset.material == STEEL or self.element.asset.material == TIMBER:
if self.element.age in range(0, 7): probs_list = [0.987, 0.974, 0.95, 0.955, 0.92, 0.9, 0.9, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.972, 0.945, 0.935, 0.91, 0.91, 0.88, 0.88, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.952, 0.934, 0.865, 0.865, 0.87, 0.86, 0.83, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.943, 0.925, 0.905, 0.915, 0.915, 0.93, 0.92, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.938, 0.89, 0.905, 0.88, 0.865, 0.928, 0.9, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.924, 0.885, 0.912, 0.907, 0.904, 0.898, 0.89, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.925, 0.912, 0.911, 0.913, 0.932, 0.941, 0.931, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.941, 0.937, 0.937, 0.938, 0.945, 0.938, 0.928, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.925, 0.941, 0.945, 0.94, 0.95, 0.935, 0.92, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.89, 0.917, 0.963, 0.965, 0.975, 0.957, 0.923, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.9, 0.912, 0.962, 0.963, 0.972, 0.962, 0.913, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.885, 0.912, 0.957, 0.965, 0.965, 0.955, 0.91, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.875, 0.911, 0.958, 0.966, 0.975, 0.965, 0.932, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.87, 0.9, 0.952, 0.965, 0.969, 0.965, 0.92, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.85, 0.881, 0.95, 0.965, 0.967, 0.965, 0.93, 1.0]
else: probs_list = [0.85, 0.881, 0.95, 0.965, 0.967, 0.965, 0.93, 1.0]
else:
if self.element.age in range(0, 7): probs_list = [0.989, 0.979, 0.96, 0.965, 0.95, 0.92, 0.91, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.986, 0.969, 0.95, 0.94, 0.92, 0.9, 0.89, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.962, 0.942, 0.875, 0.875, 0.88, 0.87, 0.84, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.957, 0.945, 0.933, 0.925, 0.925, 0.94, 0.92, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.954, 0.922, 0.93, 0.905, 0.894, 0.938, 0.9, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.944, 0.92, 0.925, 0.92, 0.91, 0.908, 0.89, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.935, 0.912, 0.915, 0.919, 0.928, 0.936, 0.92, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.938, 0.897, 0.913, 0.91, 0.904, 0.91, 0.88, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.925, 0.933, 0.94, 0.931, 0.928, 0.9, 0.875, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.89, 0.88, 0.941, 0.933, 0.956, 0.935, 0.9, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.9, 0.907, 0.96, 0.963, 0.963, 0.945, 0.9, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.88, 0.91, 0.952, 0.958, 0.961, 0.941, 0.91, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.875, 0.894, 0.954, 0.962, 0.967, 0.958, 0.932, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.87, 0.89, 0.947, 0.96, 0.966, 0.956, 0.92, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.85, 0.881, 0.957, 0.97, 0.968, 0.965, 0.94, 1.0]
else: probs_list = [0.85, 0.881, 0.957, 0.97, 0.968, 0.965, 0.94, 1.0]
elif self.element.name == SUBSTRUCTURE:
if self.element.age in range(0, 7): probs_list = [0.9955, 0.993, 0.989, 0.987, 0.985, 0.98, 0.98, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.976, 0.971, 0.976, 0.983, 0.985, 0.97, 0.97, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.972, 0.935, 0.944, 0.95, 0.935, 0.93, 0.93, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.954, 0.92, 0.923, 0.943, 0.955, 0.94, 0.92, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.954, 0.922, 0.93, 0.925, 0.934, 0.938, 0.9, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.944, 0.92, 0.925, 0.922, 0.924, 0.928, 0.89, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.939, 0.915, 0.918, 0.917, 0.919, 0.92, 0.89, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.929, 0.895, 0.905, 0.907, 0.9, 0.91, 0.88, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.929, 0.936, 0.945, 0.935, 0.935, 0.91, 0.88, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.89, 0.88, 0.94, 0.935, 0.955, 0.91, 0.88, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.9, 0.907, 0.958, 0.96, 0.96, 0.94, 0.9, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.89, 0.912, 0.958, 0.965, 0.966, 0.948, 0.92, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.87, 0.89, 0.95, 0.96, 0.964, 0.955, 0.928, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.87, 0.891, 0.951, 0.963, 0.966, 0.955, 0.928, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.85, 0.88, 0.955, 0.968, 0.968, 0.965, 0.94, 1.0]
else: probs_list = [0.85, 0.88, 0.955, 0.968, 0.968, 0.965, 0.94, 1.0]
elif self.element.name == DECK:
if self.asset.road_class == NHS:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.59, 0.555, 0.64, 0.68, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.63, 0.67, 0.66, 0.64, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
else: probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
elif self.asset.road_class == MAJOR:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.59, 0.555, 0.64, 0.68, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.63, 0.67, 0.66, 0.64, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
else: probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
elif self.asset.road_class == MINOR:
if self.element.age in range(0, 7): probs_list = [0.978, 0.971, 0.858, 0.8, 0.75, 0.7, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.71, 0.68, 0.68, 0.645, 0.68, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.78, 0.755, 0.64, 0.64, 0.63, 0.63, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.7, 0.75, 0.725, 0.78, 0.75, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.9, 0.92, 0.96, 0.96, 0.83, 1.0]
else: probs_list = [0.77, 0.83, 0.9, 0.92, 0.96, 0.96, 0.83, 1.0]
elif self.asset.road_class == LOCAL:
if self.element.age in range(0, 7): probs_list = [0.988, 0.971, 0.878, 0.8, 0.75, 0.77, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.864, 0.85, 0.725, 0.745, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.82, 0.82, 0.68, 0.72, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.8, 0.75, 0.645, 0.65, 0.68, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.86, 0.86, 0.89, 0.9, 0.75, 1.0]
else: probs_list = [0.77, 0.83, 0.86, 0.86, 0.89, 0.9, 0.75, 1.0]
elif self.element.name == ARCH:
if self.element.age in range(0, 7): probs_list = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
elif self.element.age in range (7, 13): probs_list = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.9955, 0.99, 0.98, 0.977, 0.965, 0.96, 0.96, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.982, 0.935, 0.959, 0.96, 0.94, 0.938, 0.945, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.96, 0.932, 0.935, 0.94, 0.93, 0.925, 0.922, 1.0]
elif self.element.age in range (31, 37): probs_list = [0.966, 0.935, 0.93, 0.946, 0.955, 0.94, 0.92, 1.0]
elif self.element.age in range (37, 43): probs_list = [0.952, 0.921, 0.926, 0.92, 0.93, 0.933, 0.895, 1.0]
elif self.element.age in range (43, 49): probs_list = [0.94, 0.918, 0.92, 0.917, 0.92, 0.92, 0.884, 1.0]
elif self.element.age in range (49, 55): probs_list = [0.94, 0.917, 0.915, 0.915, 0.919, 0.92, 0.89, 1.0]
elif self.element.age in range (55, 61): probs_list = [0.927, 0.893, 0.907, 0.913, 0.918, 0.93, 0.9, 1.0]
elif self.element.age in range (61, 67): probs_list = [0.92, 0.915, 0.91, 0.905, 0.91, 0.885, 0.84, 1.0]
elif self.element.age in range (67, 73): probs_list = [0.88, 0.87, 0.939, 0.938, 0.96, 0.911, 0.87, 1.0]
elif self.element.age in range (73, 79): probs_list = [0.898, 0.905, 0.957, 0.955, 0.953, 0.935, 0.89, 1.0]
elif self.element.age in range (79, 85): probs_list = [0.89, 0.91, 0.955, 0.962, 0.963, 0.945, 0.91, 1.0]
elif self.element.age in range (85, 91): probs_list = [0.875, 0.895, 0.953, 0.96, 0.97, 0.963, 0.94, 1.0]
elif self.element.age in range (91, 97): probs_list = [0.877, 0.896, 0.958, 0.967, 0.969, 0.965, 0.945, 1.0]
elif self.element.age in range (97, 103): probs_list = [0.85, 0.88, 0.955, 0.965, 0.965, 0.97, 0.948, 1.0]
elif self.element.age in range (103, 109): probs_list = [0.855, 0.892, 0.965, 0.972, 0.982, 0.983, 0.965, 1.0]
elif self.element.age in range (107, 115): probs_list = [0.858, 0.888, 0.963, 0.973, 0.972, 0.973, 0.96, 1.0]
elif self.element.age in range (113, 121): probs_list = [0.855, 0.886, 0.96, 0.973, 0.972, 0.972, 0.955, 1.0]
else: probs_list = [0.855, 0.886, 0.96, 0.973, 0.972, 0.972, 0.955, 1.0]
elif self.element.name == WEARING:
if self.asset.road_class == NHS:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
else: probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.asset.road_class == MAJOR:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.63, 0.67, 0.66, 0.64, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
else: probs_list = [0.77, 0.83, 0.96, 0.95, 0.95, 0.96, 0.85, 1.0]
elif self.asset.road_class == MINOR:
if self.element.age in range(0, 7): probs_list = [0.978, 0.971, 0.858, 0.8, 0.75, 0.7, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.71, 0.68, 0.68, 0.645, 0.68, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.78, 0.755, 0.64, 0.64, 0.63, 0.63, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.7, 0.75, 0.725, 0.78, 0.75, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.9, 0.92, 0.96, 0.96, 0.83, 1.0]
else: probs_list = [0.77, 0.83, 0.9, 0.92, 0.96, 0.96, 0.83, 1.0]
elif self.asset.road_class == LOCAL:
if self.element.age in range(0, 7): probs_list = [0.988, 0.971, 0.878, 0.8, 0.75, 0.77, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.864, 0.85, 0.725, 0.745, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.82, 0.82, 0.68, 0.72, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.8, 0.75, 0.645, 0.65, 0.68, 1.0]
elif self.element.age in range (25, 31): probs_list = [0.77, 0.83, 0.86, 0.86, 0.89, 0.9, 0.75, 1.0]
else: probs_list = [0.77, 0.83, 0.86, 0.86, 0.89, 0.9, 0.75, 1.0]
elif self.element.name == JOINT:
if self.asset.road_class == NHS:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.59, 0.555, 0.64, 0.68, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.63, 0.67, 0.66, 0.64, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
else: probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
elif self.asset.road_class == MAJOR:
if self.element.age in range(0, 7): probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.59, 0.555, 0.64, 0.68, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.63, 0.67, 0.66, 0.64, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
else: probs_list = [0.65, 0.78, 0.84, 0.838, 0.768, 0.745, 0.68, 1.0]
elif self.asset.road_class == MINOR:
if self.element.age in range(0, 7): probs_list = [0.978, 0.971, 0.858, 0.8, 0.75, 0.7, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.71, 0.68, 0.68, 0.645, 0.68, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.78, 0.755, 0.64, 0.64, 0.63, 0.63, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.7, 0.75, 0.725, 0.78, 0.75, 1.0]
else: probs_list = [0.65, 0.78, 0.7, 0.75, 0.725, 0.78, 0.75, 1.0]
elif self.asset.road_class == LOCAL:
if self.element.age in range(0, 7): probs_list = [0.988, 0.971, 0.878, 0.8, 0.75, 0.77, 0.6, 1.0]
elif self.element.age in range (7, 13): probs_list = [0.864, 0.85, 0.725, 0.745, 0.71, 0.67, 0.55, 1.0]
elif self.element.age in range (13, 19): probs_list = [0.82, 0.82, 0.68, 0.72, 0.684, 0.68, 0.66, 1.0]
elif self.element.age in range (19, 25): probs_list = [0.65, 0.78, 0.8, 0.75, 0.645, 0.65, 0.68, 1.0]
else: probs_list = [0.65, 0.78, 0.8, 0.75, 0.645, 0.65, 0.68, 1.0]
elif self.element.name == PATCHING:
if self.asset.road_class == NHS: probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.asset.road_class == MAJOR: probs_list = [0.913, 0.96, 0.864, 0.82, 0.7, 0.75, 0.6, 1.0]
elif self.asset.road_class == MINOR: probs_list = [0.978, 0.971, 0.858, 0.8, 0.75, 0.7, 0.6, 1.0]
elif self.asset.road_class == LOCAL: probs_list = [0.988, 0.971, 0.878, 0.8, 0.75, 0.77, 0.6, 1.0]
else: probs_list = [0.988, 0.971, 0.878, 0.8, 0.75, 0.77, 0.6, 1.0]
if not 'probs_list' in locals():
print ("\n\n\n\n-----------------------------------------\n")
print (self.element.name, self.asset.road_class, self.element.age)
print ("\n-----------------------------------------\n\n\n\n")
if np.random.rand() > probs_list[min(previous_condition, 7)]:
return previous_condition + 1
else:
return previous_condition
| 79.271654
| 113
| 0.596672
| 4,656
| 20,135
| 2.535009
| 0.056271
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| 0.810557
| 0.800305
| 0.798526
| 0.783953
| 0.781073
| 0
| 0.307641
| 0.185548
| 20,135
| 254
| 114
| 79.271654
| 0.412098
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| 0.005129
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| 1
| 0.013953
| false
| 0.004651
| 0.009302
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| null | 0
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| 1
| 1
| 1
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| 0
| 0
|
0
| 8
|
06853528d4baed9503ee2feba60262d7371c6af1
| 63
|
py
|
Python
|
clib/links/__init__.py
|
Swall0w/clib
|
46f659783d5a0a6ec5994c3c707c1cc8a7934385
|
[
"MIT"
] | 1
|
2017-08-27T00:01:27.000Z
|
2017-08-27T00:01:27.000Z
|
clib/links/__init__.py
|
Swall0w/clib
|
46f659783d5a0a6ec5994c3c707c1cc8a7934385
|
[
"MIT"
] | 49
|
2017-08-20T02:09:26.000Z
|
2017-12-31T11:58:27.000Z
|
clib/links/__init__.py
|
Swall0w/clib
|
46f659783d5a0a6ec5994c3c707c1cc8a7934385
|
[
"MIT"
] | 1
|
2017-12-08T08:31:38.000Z
|
2017-12-08T08:31:38.000Z
|
from clib.links import model
from clib.links import connection
| 21
| 33
| 0.84127
| 10
| 63
| 5.3
| 0.6
| 0.301887
| 0.490566
| 0.716981
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.126984
| 63
| 2
| 34
| 31.5
| 0.963636
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| 1
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| 1
| 0
|
0
| 8
|
06a05b0b0948bf0f2d021a638ac4075d364d4e10
| 130
|
py
|
Python
|
aldryn_django_cms/compat.py
|
vladimir-bezrukov/Aldryn-Django-cms
|
0edac2ee0d14fada9e960afe99750b343ceeb706
|
[
"BSD-3-Clause"
] | 1
|
2021-11-04T17:46:35.000Z
|
2021-11-04T17:46:35.000Z
|
aldryn_django_cms/compat.py
|
vladimir-bezrukov/Aldryn-Django-cms
|
0edac2ee0d14fada9e960afe99750b343ceeb706
|
[
"BSD-3-Clause"
] | null | null | null |
aldryn_django_cms/compat.py
|
vladimir-bezrukov/Aldryn-Django-cms
|
0edac2ee0d14fada9e960afe99750b343ceeb706
|
[
"BSD-3-Clause"
] | null | null | null |
try:
# Django>=2.0
from django.urls import url, include
except ImportError:
from django.conf.urls import url, include
| 21.666667
| 45
| 0.707692
| 19
| 130
| 4.842105
| 0.631579
| 0.217391
| 0.282609
| 0.434783
| 0
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| 0.019417
| 0.207692
| 130
| 5
| 46
| 26
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|
0
| 8
|
ebfdf40adf052d188d15c129af2e10b541f65af4
| 169
|
py
|
Python
|
Django/pizzeria/pizzas/admin.py
|
FR98/quinto-compu
|
ea62c400bb41fdd860b85ba889ce5b6393cb89e2
|
[
"MIT"
] | null | null | null |
Django/pizzeria/pizzas/admin.py
|
FR98/quinto-compu
|
ea62c400bb41fdd860b85ba889ce5b6393cb89e2
|
[
"MIT"
] | null | null | null |
Django/pizzeria/pizzas/admin.py
|
FR98/quinto-compu
|
ea62c400bb41fdd860b85ba889ce5b6393cb89e2
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
# Register your models here.
from pizzas.models import Pizza, Ingrediente
admin.site.register(Ingrediente)
admin.site.register(Pizza)
| 21.125
| 44
| 0.816568
| 23
| 169
| 6
| 0.565217
| 0.231884
| 0.289855
| 0.405797
| 0
| 0
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| 0
| 0
| 0
| 0.106509
| 169
| 8
| 45
| 21.125
| 0.913907
| 0.153846
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| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 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
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
23051bd81edbb5ec2bca55a2e453d98885b0d130
| 84
|
py
|
Python
|
test.py
|
ayushmankumar7/binod
|
4ae18dd3222b4b6649055d16c05e73f49c24e5d2
|
[
"MIT"
] | null | null | null |
test.py
|
ayushmankumar7/binod
|
4ae18dd3222b4b6649055d16c05e73f49c24e5d2
|
[
"MIT"
] | null | null | null |
test.py
|
ayushmankumar7/binod
|
4ae18dd3222b4b6649055d16c05e73f49c24e5d2
|
[
"MIT"
] | 1
|
2020-08-11T05:36:59.000Z
|
2020-08-11T05:36:59.000Z
|
from binod.tharu import hello, what_is_binod
#binod
print(hello())
what_is_binod()
| 14
| 44
| 0.785714
| 14
| 84
| 4.428571
| 0.571429
| 0.290323
| 0.354839
| 0.516129
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 84
| 6
| 45
| 14
| 0.826667
| 0.059524
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
001923558f41bdc63ddbd5bac10b8a7a16db8ab2
| 48
|
py
|
Python
|
widgets/InteractivePlotly/__init__.py
|
flatironinstitute/reactopya_examples
|
9b270c2cf3bab7bb53c3eabbae4adb48621cb8ba
|
[
"Apache-2.0"
] | 1
|
2021-09-23T01:09:11.000Z
|
2021-09-23T01:09:11.000Z
|
widgets/InteractivePlotly/__init__.py
|
flatironinstitute/reactopya_examples
|
9b270c2cf3bab7bb53c3eabbae4adb48621cb8ba
|
[
"Apache-2.0"
] | null | null | null |
widgets/InteractivePlotly/__init__.py
|
flatironinstitute/reactopya_examples
|
9b270c2cf3bab7bb53c3eabbae4adb48621cb8ba
|
[
"Apache-2.0"
] | 1
|
2021-09-23T01:09:12.000Z
|
2021-09-23T01:09:12.000Z
|
from .InteractivePlotly import InteractivePlotly
| 48
| 48
| 0.916667
| 4
| 48
| 11
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 48
| 1
| 48
| 48
| 0.977778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
0037665414957d158973191472ab066dfca84228
| 9,222
|
py
|
Python
|
common/buffers.py
|
quantumiracle/Soft-Actor-Critic-for-Reacher
|
479ffd65bd8a091dee7a720a70c87be5d7e7a001
|
[
"Apache-2.0"
] | null | null | null |
common/buffers.py
|
quantumiracle/Soft-Actor-Critic-for-Reacher
|
479ffd65bd8a091dee7a720a70c87be5d7e7a001
|
[
"Apache-2.0"
] | null | null | null |
common/buffers.py
|
quantumiracle/Soft-Actor-Critic-for-Reacher
|
479ffd65bd8a091dee7a720a70c87be5d7e7a001
|
[
"Apache-2.0"
] | null | null | null |
import math
import random
import numpy as np
import torch
class ReplayBuffer:
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.position = 0
def push(self, state, action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (state, action, reward, next_state, done)
self.position = int((self.position + 1) % self.capacity) # as a ring buffer
def sample(self, batch_size):
batch = random.sample(self.buffer, batch_size)
state, action, reward, next_state, done = map(np.stack,
zip(*batch)) # stack for each element
'''
the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
np.stack((1,2)) => array([1, 2])
'''
return state, action, reward, next_state, done
def __len__(
self): # cannot work in multiprocessing case, len(replay_buffer) is not available in proxy of manager!
return len(self.buffer)
def get_length(self):
return len(self.buffer)
class ReplayBufferPER:
"""
Replay buffer with Prioritized Experience Replay (PER),
TD error as sampling weights. This is a simple version without sumtree.
Reference:
https://github.com/Felhof/DiscreteSAC/blob/main/utilities/ReplayBuffer.py
"""
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.position = 0
self.weights = np.zeros(int(capacity))
self.max_weight = 10**-2
self.delta = 10**-4
def push(self, state, action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (state, action, reward, next_state, done)
self.weights[self.position] = self.max_weight # new sample has max weights
self.position = int((self.position + 1) % self.capacity) # as a ring buffer
def sample(self, batch_size):
set_weights = self.weights[:self.position] + self.delta
probabilities = set_weights / sum(set_weights)
self.indices = np.random.choice(range(self.position), batch_size, p=probabilities, replace=False)
batch = np.array(self.buffer)[list(self.indices)]
state, action, reward, next_state, done = map(np.stack,
zip(*batch)) # stack for each element
'''
the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
np.stack((1,2)) => array([1, 2])
'''
return state, action, reward, next_state, done
def update_weights(self, prediction_errors):
max_error = max(prediction_errors)
self.max_weight = max(self.max_weight, max_error)
self.weights[self.indices] = prediction_errors
def __len__(
self): # this is a stupid func! cannot work in multiprocessing case, len(replay_buffer) is not available in proxy of manager!
return len(self.buffer)
def get_length(self):
return len(self.buffer)
class ReplayBufferLSTM:
"""
Replay buffer for agent with LSTM network additionally using previous action, can be used
if the hidden states are not stored (arbitrary initialization of lstm for training).
And each sample contains the whole episode instead of a single step.
"""
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.position = 0
def push(self, state, action, last_action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (state, action, last_action, reward, next_state, done)
self.position = int((self.position + 1) % self.capacity) # as a ring buffer
def sample(self, batch_size):
batch = random.sample(self.buffer, batch_size)
state, action, last_action, reward, next_state, done = map(np.stack,
zip(*batch)) # stack for each element
'''
the * serves as unpack: sum(a,b) <=> batch=(a,b), sum(*batch) ;
zip: a=[1,2], b=[2,3], zip(a,b) => [(1, 2), (2, 3)] ;
the map serves as mapping the function on each list element: map(square, [2,3]) => [4,9] ;
np.stack((1,2)) => array([1, 2])
'''
return state, action, last_action, reward, next_state, done
def __len__(
self): # cannot work in multiprocessing case, len(replay_buffer) is not available in proxy of manager!
return len(self.buffer)
def get_length(self):
return len(self.buffer)
class ReplayBufferLSTM2:
"""
Replay buffer for agent with LSTM network additionally storing previous action,
initial input hidden state and output hidden state of LSTM.
And each sample contains the whole episode instead of a single step.
'hidden_in' and 'hidden_out' are only the initial hidden state for each episode, for LSTM initialization.
"""
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.position = 0
def push(self, hidden_in, hidden_out, state, action, last_action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (hidden_in, hidden_out, state, action, last_action, reward, next_state, done)
self.position = int((self.position + 1) % self.capacity) # as a ring buffer
def sample(self, batch_size):
s_lst, a_lst, la_lst, r_lst, ns_lst, hi_lst, ci_lst, ho_lst, co_lst, d_lst=[],[],[],[],[],[],[],[],[],[]
batch = random.sample(self.buffer, batch_size)
for sample in batch:
(h_in, c_in), (h_out, c_out), state, action, last_action, reward, next_state, done = sample
s_lst.append(state)
a_lst.append(action)
la_lst.append(last_action)
r_lst.append(reward)
ns_lst.append(next_state)
d_lst.append(done)
hi_lst.append(h_in) # h_in: (1, batch_size=1, hidden_size)
ci_lst.append(c_in)
ho_lst.append(h_out)
co_lst.append(c_out)
hi_lst = torch.cat(hi_lst, dim=-2).detach() # cat along the batch dim
ho_lst = torch.cat(ho_lst, dim=-2).detach()
ci_lst = torch.cat(ci_lst, dim=-2).detach()
co_lst = torch.cat(co_lst, dim=-2).detach()
hidden_in = (hi_lst, ci_lst)
hidden_out = (ho_lst, co_lst)
return hidden_in, hidden_out, s_lst, a_lst, la_lst, r_lst, ns_lst, d_lst
def __len__(
self): # cannot work in multiprocessing case, len(replay_buffer) is not available in proxy of manager!
return len(self.buffer)
def get_length(self):
return len(self.buffer)
class ReplayBufferGRU:
"""
Replay buffer for agent with GRU network additionally storing previous action,
initial input hidden state and output hidden state of GRU.
And each sample contains the whole episode instead of a single step.
'hidden_in' and 'hidden_out' are only the initial hidden state for each episode, for GRU initialization.
"""
def __init__(self, capacity):
self.capacity = capacity
self.buffer = []
self.position = 0
def push(self, hidden_in, hidden_out, state, action, last_action, reward, next_state, done):
if len(self.buffer) < self.capacity:
self.buffer.append(None)
self.buffer[self.position] = (hidden_in, hidden_out, state, action, last_action, reward, next_state, done)
self.position = int((self.position + 1) % self.capacity) # as a ring buffer
def sample(self, batch_size):
s_lst, a_lst, la_lst, r_lst, ns_lst, hi_lst, ho_lst, d_lst=[],[],[],[],[],[],[],[]
batch = random.sample(self.buffer, batch_size)
for sample in batch:
h_in, h_out, state, action, last_action, reward, next_state, done = sample
s_lst.append(state)
a_lst.append(action)
la_lst.append(last_action)
r_lst.append(reward)
ns_lst.append(next_state)
d_lst.append(done)
hi_lst.append(h_in) # h_in: (1, batch_size=1, hidden_size)
ho_lst.append(h_out)
hi_lst = torch.cat(hi_lst, dim=-2).detach() # cat along the batch dim
ho_lst = torch.cat(ho_lst, dim=-2).detach()
return hi_lst, ho_lst, s_lst, a_lst, la_lst, r_lst, ns_lst, d_lst
def __len__(
self): # cannot work in multiprocessing case, len(replay_buffer) is not available in proxy of manager!
return len(self.buffer)
def get_length(self):
return len(self.buffer)
| 41.728507
| 138
| 0.616352
| 1,281
| 9,222
| 4.277127
| 0.127244
| 0.06388
| 0.052564
| 0.068991
| 0.815112
| 0.796678
| 0.796678
| 0.796678
| 0.781529
| 0.781529
| 0
| 0.011096
| 0.267079
| 9,222
| 220
| 139
| 41.918182
| 0.799527
| 0.204294
| 0
| 0.741259
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.181818
| false
| 0
| 0.027972
| 0.06993
| 0.34965
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
cc462d732cecaba30e3492bc238ef0f1280ee276
| 19,440
|
py
|
Python
|
accelbyte_py_sdk/api/platform/wrappers/_campaign.py
|
AccelByte/accelbyte-python-sdk
|
dcd311fad111c59da828278975340fb92e0f26f7
|
[
"MIT"
] | null | null | null |
accelbyte_py_sdk/api/platform/wrappers/_campaign.py
|
AccelByte/accelbyte-python-sdk
|
dcd311fad111c59da828278975340fb92e0f26f7
|
[
"MIT"
] | 1
|
2021-10-13T03:46:58.000Z
|
2021-10-13T03:46:58.000Z
|
accelbyte_py_sdk/api/platform/wrappers/_campaign.py
|
AccelByte/accelbyte-python-sdk
|
dcd311fad111c59da828278975340fb92e0f26f7
|
[
"MIT"
] | null | null | null |
# Copyright (c) 2021 AccelByte Inc. All Rights Reserved.
# This is licensed software from AccelByte Inc, for limitations
# and restrictions contact your company contract manager.
#
# Code generated. DO NOT EDIT!
# template file: justice_py_sdk_codegen/__main__.py
# pylint: disable=duplicate-code
# pylint: disable=line-too-long
# pylint: disable=missing-function-docstring
# pylint: disable=missing-function-docstring
# pylint: disable=missing-module-docstring
# pylint: disable=too-many-arguments
# pylint: disable=too-many-branches
# pylint: disable=too-many-instance-attributes
# pylint: disable=too-many-lines
# pylint: disable=too-many-locals
# pylint: disable=too-many-public-methods
# pylint: disable=too-many-return-statements
# pylint: disable=too-many-statements
# pylint: disable=unused-import
from typing import Any, Dict, List, Optional, Tuple, Union
from ....core import HeaderStr
from ....core import get_namespace as get_services_namespace
from ....core import run_request
from ....core import run_request_async
from ....core import same_doc_as
from ..models import BulkOperationResult
from ..models import CampaignCreate
from ..models import CampaignDynamicInfo
from ..models import CampaignInfo
from ..models import CampaignPagingSlicedResult
from ..models import CampaignUpdate
from ..models import CodeCreate
from ..models import CodeCreateResult
from ..models import CodeInfo
from ..models import CodeInfoPagingSlicedResult
from ..models import ErrorEntity
from ..models import RedeemHistoryPagingSlicedResult
from ..models import RedeemRequest
from ..models import RedeemResult
from ..models import ValidationErrorEntity
from ..operations.campaign import ApplyUserRedemption
from ..operations.campaign import BulkDisableCodes
from ..operations.campaign import BulkEnableCodes
from ..operations.campaign import CreateCampaign
from ..operations.campaign import CreateCodes
from ..operations.campaign import DisableCode
from ..operations.campaign import Download
from ..operations.campaign import EnableCode
from ..operations.campaign import GetCampaign
from ..operations.campaign import GetCampaignDynamic
from ..operations.campaign import GetCode
from ..operations.campaign import QueryCampaigns
from ..operations.campaign import QueryCodes
from ..operations.campaign import QueryRedeemHistory
from ..operations.campaign import UpdateCampaign
from ..models import CampaignCreateRedeemTypeEnum, CampaignCreateStatusEnum, CampaignCreateTypeEnum
from ..models import CampaignInfoRedeemTypeEnum, CampaignInfoStatusEnum, CampaignInfoTypeEnum
from ..models import CampaignUpdateRedeemTypeEnum, CampaignUpdateStatusEnum
from ..models import CodeInfoRedeemTypeEnum, CodeInfoStatusEnum, CodeInfoTypeEnum
@same_doc_as(ApplyUserRedemption)
def apply_user_redemption(user_id: str, body: Optional[RedeemRequest] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = ApplyUserRedemption.create(
user_id=user_id,
body=body,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(ApplyUserRedemption)
async def apply_user_redemption_async(user_id: str, body: Optional[RedeemRequest] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = ApplyUserRedemption.create(
user_id=user_id,
body=body,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(BulkDisableCodes)
def bulk_disable_codes(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = BulkDisableCodes.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(BulkDisableCodes)
async def bulk_disable_codes_async(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = BulkDisableCodes.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(BulkEnableCodes)
def bulk_enable_codes(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = BulkEnableCodes.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(BulkEnableCodes)
async def bulk_enable_codes_async(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = BulkEnableCodes.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(CreateCampaign)
def create_campaign(body: Optional[CampaignCreate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = CreateCampaign.create(
body=body,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(CreateCampaign)
async def create_campaign_async(body: Optional[CampaignCreate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = CreateCampaign.create(
body=body,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(CreateCodes)
def create_codes(campaign_id: str, body: Optional[CodeCreate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = CreateCodes.create(
campaign_id=campaign_id,
body=body,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(CreateCodes)
async def create_codes_async(campaign_id: str, body: Optional[CodeCreate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = CreateCodes.create(
campaign_id=campaign_id,
body=body,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(DisableCode)
def disable_code(code: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = DisableCode.create(
code=code,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(DisableCode)
async def disable_code_async(code: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = DisableCode.create(
code=code,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(Download)
def download(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = Download.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(Download)
async def download_async(campaign_id: str, batch_no: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = Download.create(
campaign_id=campaign_id,
batch_no=batch_no,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(EnableCode)
def enable_code(code: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = EnableCode.create(
code=code,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(EnableCode)
async def enable_code_async(code: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = EnableCode.create(
code=code,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCampaign)
def get_campaign(campaign_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCampaign.create(
campaign_id=campaign_id,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCampaign)
async def get_campaign_async(campaign_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCampaign.create(
campaign_id=campaign_id,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCampaignDynamic)
def get_campaign_dynamic(campaign_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCampaignDynamic.create(
campaign_id=campaign_id,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCampaignDynamic)
async def get_campaign_dynamic_async(campaign_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCampaignDynamic.create(
campaign_id=campaign_id,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCode)
def get_code(code: str, redeemable: Optional[bool] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCode.create(
code=code,
redeemable=redeemable,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(GetCode)
async def get_code_async(code: str, redeemable: Optional[bool] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = GetCode.create(
code=code,
redeemable=redeemable,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryCampaigns)
def query_campaigns(limit: Optional[int] = None, name: Optional[str] = None, offset: Optional[int] = None, tag: Optional[str] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryCampaigns.create(
limit=limit,
name=name,
offset=offset,
tag=tag,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryCampaigns)
async def query_campaigns_async(limit: Optional[int] = None, name: Optional[str] = None, offset: Optional[int] = None, tag: Optional[str] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryCampaigns.create(
limit=limit,
name=name,
offset=offset,
tag=tag,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryCodes)
def query_codes(campaign_id: str, active_only: Optional[bool] = None, batch_no: Optional[int] = None, code: Optional[str] = None, limit: Optional[int] = None, offset: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryCodes.create(
campaign_id=campaign_id,
active_only=active_only,
batch_no=batch_no,
code=code,
limit=limit,
offset=offset,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryCodes)
async def query_codes_async(campaign_id: str, active_only: Optional[bool] = None, batch_no: Optional[int] = None, code: Optional[str] = None, limit: Optional[int] = None, offset: Optional[int] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryCodes.create(
campaign_id=campaign_id,
active_only=active_only,
batch_no=batch_no,
code=code,
limit=limit,
offset=offset,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryRedeemHistory)
def query_redeem_history(campaign_id: str, code: Optional[str] = None, limit: Optional[int] = None, offset: Optional[int] = None, user_id: Optional[str] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryRedeemHistory.create(
campaign_id=campaign_id,
code=code,
limit=limit,
offset=offset,
user_id=user_id,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(QueryRedeemHistory)
async def query_redeem_history_async(campaign_id: str, code: Optional[str] = None, limit: Optional[int] = None, offset: Optional[int] = None, user_id: Optional[str] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = QueryRedeemHistory.create(
campaign_id=campaign_id,
code=code,
limit=limit,
offset=offset,
user_id=user_id,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(UpdateCampaign)
def update_campaign(campaign_id: str, body: Optional[CampaignUpdate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = UpdateCampaign.create(
campaign_id=campaign_id,
body=body,
namespace=namespace,
)
return run_request(request, additional_headers=x_additional_headers, **kwargs)
@same_doc_as(UpdateCampaign)
async def update_campaign_async(campaign_id: str, body: Optional[CampaignUpdate] = None, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs):
if namespace is None:
namespace, error = get_services_namespace()
if error:
return None, error
request = UpdateCampaign.create(
campaign_id=campaign_id,
body=body,
namespace=namespace,
)
return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)
| 39.272727
| 299
| 0.712243
| 2,295
| 19,440
| 5.824401
| 0.067538
| 0.114461
| 0.080796
| 0.053864
| 0.800254
| 0.794344
| 0.793596
| 0.793596
| 0.786564
| 0.786564
| 0
| 0.000255
| 0.19357
| 19,440
| 494
| 300
| 39.352227
| 0.852395
| 0.039403
| 0
| 0.754902
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.036765
| false
| 0
| 0.098039
| 0
| 0.281863
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 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
| 8
|
cca40992cd65e4f7d16ffb379df436d9ab4e47b2
| 4,271
|
py
|
Python
|
3rd Energy Level/3p Orbitals.py
|
aminoj/Interactive-Orbitals-Simulation
|
20e405d6a23028049c05f4a0fd73e51857ba9270
|
[
"Apache-2.0"
] | null | null | null |
3rd Energy Level/3p Orbitals.py
|
aminoj/Interactive-Orbitals-Simulation
|
20e405d6a23028049c05f4a0fd73e51857ba9270
|
[
"Apache-2.0"
] | null | null | null |
3rd Energy Level/3p Orbitals.py
|
aminoj/Interactive-Orbitals-Simulation
|
20e405d6a23028049c05f4a0fd73e51857ba9270
|
[
"Apache-2.0"
] | 1
|
2020-04-16T08:02:27.000Z
|
2020-04-16T08:02:27.000Z
|
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
rstride = 14
cstride = 14
MaxBound = 3
MinBound = -3
u = np.linspace(0, 2*np.pi, 100)
v = np.linspace(0, np.pi, 100)
#-------------------------------------------------------------------------------
#Left-Outer Outer
x2 = np.outer(np.cos(u), np.sin(v))
z2 = np.outer(np.sin(u), np.sin(v))
y2 = -0.4*np.cos(u)-2.05
ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Left-Outer Inner
x3 = np.outer(np.cos(u), np.sin(v))
z3 = np.outer(np.sin(u), np.sin(v))
y3 = abs((x3)**2 + (z3)**2 + 5)**(0.5)-4.1
ax.plot_surface(x3, y3, z3, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Outer
x4 = np.outer(np.cos(u), np.sin(v))
z4 = np.outer(np.sin(u), np.sin(v))
y4 = 0.4*np.cos(u)+2.05
ax.plot_surface(x4, y4, z4, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Inner
x5 = np.outer(np.cos(u), np.sin(v))
z5 = np.outer(np.sin(u), np.sin(v))
y5 = abs((x5)**2 + (z5)**2 - 5)**(0.5)-0.35
ax.plot_surface(x5, y5, z5, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#DIFFERENT DIRECTION
#-------------------------------------------------------------------------------
#Left-Outer Outer
y2 = np.outer(np.cos(u), np.sin(v))
z2 = np.outer(np.sin(u), np.sin(v))
x2 = -0.4*np.cos(u)-2.07
ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Left-Outer Inner
y3 = np.outer(np.cos(u), np.sin(v))
z3 = np.outer(np.sin(u), np.sin(v))
x3 = abs((y3)**2 + (z3)**2 + 5)**(0.5)-4.1
ax.plot_surface(x3, y3, z3, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Outer
y4 = np.outer(np.cos(u), np.sin(v))
z4 = np.outer(np.sin(u), np.sin(v))
x4 = 0.4*np.cos(u)+2.07
ax.plot_surface(x4, y4, z4, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Inner
y5 = np.outer(np.cos(u), np.sin(v))
z5 = np.outer(np.sin(u), np.sin(v))
x5 = abs((y5)**2 + (z5)**2 - 5)**(0.5)-0.35
ax.plot_surface(x5, y5, z5, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#DIFFERENT DIRECTION
#-------------------------------------------------------------------------------
#Left-Outer Outer
y2 = np.outer(np.cos(u), np.sin(v))
x2 = np.outer(np.sin(u), np.sin(v))
z2 = -0.4*np.cos(u)-2.07
ax.plot_surface(x2, y2, z2, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Left-Outer Inner
y3 = np.outer(np.cos(u), np.sin(v))
x3 = np.outer(np.sin(u), np.sin(v))
z3 = abs((x3)**2 + (y3)**2 + 5)**(0.5)-4.1
ax.plot_surface(x3, y3, z3, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Outer
x4 = np.outer(np.cos(u), np.sin(v))
y4 = np.outer(np.sin(u), np.sin(v))
z4 = 0.4*np.cos(u)+2.07
ax.plot_surface(x4, y4, z4, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
#-------------------------------------------------------------------------------
#Right-Outer Inner
x5 = np.outer(np.cos(u), np.sin(v))
y5 = np.outer(np.sin(u), np.sin(v))
z5 = abs((x5)**2 + (y5)**2 - 5)**(0.5)-0.35
ax.plot_surface(x5, y5, z5, rstride = rstride, cstride = cstride, color=(0,1,0.44), linewidth=0)
plt.show()
ax.set_xlim3d(MaxBound, MinBound)
ax.set_ylim3d(MaxBound, MinBound)
ax.set_zlim3d(MaxBound, MinBound)
| 29.867133
| 96
| 0.466635
| 639
| 4,271
| 3.092332
| 0.109546
| 0.091093
| 0.109312
| 0.08502
| 0.824393
| 0.812247
| 0.812247
| 0.812247
| 0.773785
| 0.773785
| 0
| 0.066424
| 0.101147
| 4,271
| 142
| 97
| 30.077465
| 0.448294
| 0.314915
| 0
| 0.444444
| 0
| 0
| 0.00069
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.047619
| 0
| 0.047619
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
aea570081e10541cd114330bc273fb7cf4a774cf
| 154
|
py
|
Python
|
plotting_utils/__init__.py
|
fnatanoy/toxic_comments_classification
|
780939be69b7b7ed6e99a09a5247ca219e7c1fe4
|
[
"MIT"
] | null | null | null |
plotting_utils/__init__.py
|
fnatanoy/toxic_comments_classification
|
780939be69b7b7ed6e99a09a5247ca219e7c1fe4
|
[
"MIT"
] | null | null | null |
plotting_utils/__init__.py
|
fnatanoy/toxic_comments_classification
|
780939be69b7b7ed6e99a09a5247ca219e7c1fe4
|
[
"MIT"
] | null | null | null |
from . import plot_target_dist
from . import plot_tfidf
from . import plot_words_cdf
from . import plot_roc_curve
from . import plot_comment_length_hist
| 22
| 38
| 0.831169
| 25
| 154
| 4.72
| 0.52
| 0.423729
| 0.59322
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136364
| 154
| 6
| 39
| 25.666667
| 0.887218
| 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
| 1
| 0
|
0
| 7
|
aeb5e7afd1539d2b81bc61e3bcb7328ede99ba1a
| 210
|
py
|
Python
|
pythoncz/pages/index/views.py
|
honzajavorek/python.cz-2019
|
67a66cabe0df299a6c63590d4c1eaec08a370ec3
|
[
"MIT"
] | 2
|
2019-03-12T22:19:25.000Z
|
2019-09-14T21:40:18.000Z
|
pythoncz/pages/index/views.py
|
honzajavorek/py
|
67a66cabe0df299a6c63590d4c1eaec08a370ec3
|
[
"MIT"
] | 63
|
2019-01-27T19:18:43.000Z
|
2020-01-30T09:22:45.000Z
|
pythoncz/pages/index/views.py
|
honzajavorek/python.cz-2019
|
67a66cabe0df299a6c63590d4c1eaec08a370ec3
|
[
"MIT"
] | null | null | null |
from flask import render_template
from pythoncz import app
@app.route('/')
def index():
return render_template('index.html')
@app.route('/en/')
def index_en():
return render_template('index.html')
| 15
| 40
| 0.704762
| 29
| 210
| 4.965517
| 0.448276
| 0.291667
| 0.277778
| 0.347222
| 0.402778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147619
| 210
| 13
| 41
| 16.153846
| 0.804469
| 0
| 0
| 0.25
| 0
| 0
| 0.119048
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.25
| 0.25
| 0.75
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
aef7f37618aa0c22b5bc88aac79fd44ff6f4fc5c
| 35,918
|
py
|
Python
|
src/main.py
|
mail-ecnu/OTDyadicFair
|
1259eb6d2e4c99b1b1333048eea4cbf94e4ec785
|
[
"MIT"
] | 1
|
2022-03-22T13:17:05.000Z
|
2022-03-22T13:17:05.000Z
|
src/main.py
|
mail-ecnu/OTDyadicFair
|
1259eb6d2e4c99b1b1333048eea4cbf94e4ec785
|
[
"MIT"
] | null | null | null |
src/main.py
|
mail-ecnu/OTDyadicFair
|
1259eb6d2e4c99b1b1333048eea4cbf94e4ec785
|
[
"MIT"
] | null | null | null |
import matplotlib.pyplot as plt
import ot
from scipy.sparse import issparse
import networkx as nx
from sklearn.manifold import TSNE
from node2vec import Node2Vec
from src.ot_laplace_clean import *
import os
import numpy as np
import importlib
import time
import numba as nb
@nb.njit(fastmath=True,parallel=True)
def dist_arr_2(A, B, res):
for i in nb.prange(A.shape[0]):
for j in range(B.shape[0]):
acc=0
for k in range(A.shape[1]):
acc+=(A[i,k]-B[j,k])**2
res[i,j]=np.sqrt(acc)
return res
def repair_random(g, s, prob):
"""
Repairing of the graph by adding random links between nodes of two different groups
:param g: the graph
:param s: protected attribute
:return: the new graph
"""
x = nx.adjacency_matrix(g)
# s = nx.get_node_attributes(g, 's')
# s_arr = np.fromiter(s.values(), dtype=int)
s_arr = s
# Separate rows adjacency matrix based on the protected attribute
idx_p0 = np.where(s_arr == 0)[0]
idx_p1 = np.where(s_arr == 1)[0]
x_random = np.copy(x.todense())
for i in idx_p0:
for j in idx_p1:
if x[i, j] == 0:
x_random[i, j] = np.random.choice([0, 1], p=[1-prob, prob])
x_random[j, i] = x_random[i, j]
new_g = nx.from_numpy_matrix(x_random)
nx.set_node_attributes(new_g, s, 's')
print(nx.density(g))
print(nx.density(new_g))
return new_g
def total_repair_gwemd(g, metric='euclidean', log=False, name='plot_cost_gamma'):
"""
Repairing of the graph with OT and the emd version
:param g: a graph to repair. The protected attribute is a feature of the node
:param metric: the distance metric for the cost matrix
:param log: if true plot the cost matrix and the transportation plan
:param name: name of the file to save the figures
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
idx_p0 = np.where(s == 0)
x_0 = x[idx_p0]
idx_p1 = np.where(s == 1)
x_1 = x[idx_p1]
# Get the barycenter between adj0 and adj1
n0, d0 = x_0.shape
n1, d1 = x_1.shape
# Compute barycenters using POT library
# Uniform distributions on samples
a = np.ones((n0,)) / n0
b = np.ones((n1,)) / n1
# loss matrix
if metric in otdists:
m = np.asarray(ot.dist(x_0, x_1, metric=metric))
elif metric == 'simrank':
sim = nx.simrank_similarity(g)
m_sim = [[sim[u][v] for v in sorted(sim[u])] for u in sorted(sim)]
m = np.asarray(m_sim)
m = np.asarray(m/m.max())
# Exact transport
gamma = ot.emd(a, b, m)
# Total data repair
pi_0 = n0 / (n0 + n1)
pi_1 = 1 - pi_0
x_0_rep = pi_0 * x_0 + n0 * pi_1 * np.dot(gamma, x_1)
x_1_rep = pi_1 * x_1 + n1 * pi_0 * np.dot(gamma.T, x_0)
new_x = np.zeros(x.shape)
new_x[idx_p0, :] = x_0_rep
new_x[idx_p1, :] = x_1_rep
if log:
plt.imshow(gamma)
plt.colorbar()
plt.show()
plt.savefig('gamma_' + name + '.png')
plt.imshow(m)
plt.colorbar()
plt.show()
plt.savefig('costMatrix_' + name + '.png')
return new_x, s, gamma, m
def total_repair_emd(g, metric='euclidean', log=False, name='plot_cost_gamma'):
"""
Repairing of the graph with OT and the emd version
:param g: a graph to repair. The protected attribute is a feature of the node
:param metric: the distance metric for the cost matrix
:param log: if true plot the cost matrix and the transportation plan
:param name: name of the file to save the figures
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
idx_p0 = np.where(s == 0)
x_0 = x[idx_p0]
idx_p1 = np.where(s == 1)
x_1 = x[idx_p1]
# Get the barycenter between adj0 and adj1
n0, d0 = x_0.shape
n1, d1 = x_1.shape
# Compute barycenters using POT library
# Uniform distributions on samples
a = np.ones((n0,)) / n0
b = np.ones((n1,)) / n1
# loss matrix
if metric in otdists:
m = np.asarray(ot.dist(x_0, x_1, metric=metric))
elif metric == 'simrank':
sim = nx.simrank_similarity(g)
m_sim = [[sim[u][v] for v in sorted(sim[u])] for u in sorted(sim)]
m = np.asarray(m_sim)
m = np.asarray(m/m.max())
# Exact transport
gamma = ot.emd(a, b, m)
# Total data repair
pi_0 = n0 / (n0 + n1)
pi_1 = 1 - pi_0
x_0_rep = pi_0 * x_0 + n0 * pi_1 * np.dot(gamma, x_1)
x_1_rep = pi_1 * x_1 + n1 * pi_0 * np.dot(gamma.T, x_0)
new_x = np.zeros(x.shape)
new_x[idx_p0, :] = x_0_rep
new_x[idx_p1, :] = x_1_rep
if log:
plt.imshow(gamma)
plt.colorbar()
plt.show()
plt.savefig('gamma_' + name + '.png')
plt.imshow(m)
plt.colorbar()
plt.show()
plt.savefig('costMatrix_' + name + '.png')
return new_x, s, gamma, m
def my_reg_total_repair_reg(g, metric='sqeuclidean', reg=0.01, eta=1, log=False):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
idx_p0 = np.where(s == 0)
x_0 = x[idx_p0]
idx_p1 = np.where(s == 1)
x_1 = x[idx_p1]
# Get the barycenter between adj0 and adj1
n0, d0 = x_0.shape
n1, d1 = x_1.shape
# Compute barycenters using POT library
# Uniform distributions on samples
a = np.ones((n0,)) / n0
b = np.ones((n1,)) / n1
# loss matrix
if metric in otdists:
m = np.asarray(ot.dist(x_0, x_1, metric=metric))
elif metric == 'simrank':
sim = nx.simrank_similarity(g)
m_sim = [[sim[u][v] for v in sorted(sim[u])] for u in sorted(sim)]
m = np.asarray(m_sim)
m = np.asarray(m/m.max())
# Transport
# kwargs = {'sim': 'gauss', 'alpha': 0.5}
kwargs = {'sim': 'knn', 'nn': 5, 'alpha': 0.5}
pi_0 = n0 / (n0 + n1)
pi_1 = 1 - pi_0
gamma = myreg_computeTransportSymmetric_fw(m, x_0, x_1, idx_p0, idx_p1, method='laplace', metric=metric,
reg=reg, nbitermax=5000,
solver=None, wparam=1, **kwargs)
# Total data repair
x_0_rep = pi_0 * x_0 + n0 * pi_1 * np.dot(gamma, x_1)
x_1_rep = pi_1 * x_1 + n1 * pi_0 * np.dot(gamma.T, x_0)
new_x = np.zeros(x.shape)
new_x[idx_p0, :] = x_0_rep
new_x[idx_p1, :] = x_1_rep
return new_x, s, gamma, m
def get_keys_from_value(d, val):
return np.array([k for k, v in d.items() if v == val])
def multi_total_repair(g, num_iter=1e4, metric='sqeuclidean', reg=0.01, eta=1, log=False):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
idx_ps = []
for val in set(s.values()):
idx_ps.append(get_keys_from_value(s, val))
# s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
x_s = [np.array(x[idx_p]) for idx_p in idx_ps]
# Get the barycenter between adj0 and adj1
n_s = [myx_s.shape[0] for myx_s in x_s]
d_s = [myx_s.shape[1] for myx_s in x_s]
# Compute barycenters using POT library
# Uniform distributions on samples
weights = [np.ones((myn,))/myn for myn in n_s]
X_init = np.random.normal(0., 1., (x.shape[0], x.shape[1]))
# X_init = np.random.normal(0., 1., (x.shape[0], x.shape[1]))
b = np.ones((X_init.shape[0],))/X_init.shape[0]
start = time.time()
X = ot.lp.free_support_barycenter(x_s, weights, X_init, b, numItermax=num_iter,stopThr=1e-5)
print(f'free sbc lasted {time.time()-start} seconds')
new_xs = []
gammas = []
ms = []
for i in range(len(x_s)):
m = ot.dist(x_s[i], X)
ms.append(m)
gamma = ot.emd(weights[i], b, m)
gammas.append(gamma)
new_xs.append(n_s[i]*np.dot(gamma, X))
new_x = np.zeros(x.shape)
i = 0
for myidx in idx_ps:
new_x[myidx, :] = new_xs[i]
i += 1
return new_x, s, gammas, ms
def multi_sym_total_repair(g, metric='sqeuclidean', reg=1e-9, eta=1, log=False, num_iter=1e6,):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
idx_ps = []
for val in set(s.values()):
idx_ps.append(get_keys_from_value(s, val))
# s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
x_s = [np.array(x[idx_p]) for idx_p in idx_ps]
# Get the barycenter between adj0 and adj1
n_s = [myx_s.shape[0] for myx_s in x_s]
d_s = [myx_s.shape[1] for myx_s in x_s]
# Compute barycenters using POT library
# Uniform distributions on samples
weights = [np.ones((myn,))/myn for myn in n_s]
num_classes = len(x_s)
X_init = np.random.normal(0., 1., (x.shape[0], x.shape[1]))
b = np.ones((X_init.shape[0],))/X_init.shape[0]
# Transport
# kwargs = {'sim': 'gauss', 'alpha': 0.5}
start = time.time()
X = sym_free_support_barycenter(x_s, weights, X_init, numItermax=num_iter, reg=reg)
print(f'bc time lasted {time.time() - start} seconds')
new_xs = []
gammas = []
ms = []
for i in range(len(x_s)):
start = time.time()
m = ot.dist(x_s[i], X)
ms.append(m)
gamma = ot.emd(weights[i], b, m)
gammas.append(gamma)
new_xs.append(n_s[i]*np.dot(gamma, X))
print(f'gamma {i} lasted {time.time()-start} seconds')
new_x = np.zeros(x.shape)
i = 0
for myidx in idx_ps:
new_x[myidx, :] = new_xs[i]
i += 1
return new_x, s, gammas, ms
def total_repair_reg(g, metric='sqeuclidean', reg=0.01, eta=1, log=False):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
s = nx.get_node_attributes(g, 's')
s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
idx_p0 = np.where(s == 0)
x_0 = x[idx_p0]
idx_p1 = np.where(s == 1)
x_1 = x[idx_p1]
# Get the barycenter between adj0 and adj1
n0, d0 = x_0.shape
n1, d1 = x_1.shape
# Compute barycenters using POT library
# Uniform distributions on samples
a = np.ones((n0,)) / n0
b = np.ones((n1,)) / n1
# loss matrix
if metric in otdists:
m = np.asarray(ot.dist(x_0, x_1, metric=metric))
elif metric == 'simrank':
sim = nx.simrank_similarity(g)
m_sim = [[sim[u][v] for v in sorted(sim[u])] for u in sorted(sim)]
m = np.asarray(m_sim)
m = np.asarray(m/m.max())
# Transport
# kwargs = {'sim': 'gauss', 'alpha': 0.5}
kwargs = {'sim': 'knn', 'nn': 5, 'alpha': 0.5}
gamma = compute_transport(x_0, x_1, method='laplace', metric=metric,
weights='unif', reg=reg, nbitermax=5000,
solver=None, wparam=1, **kwargs)
# Total data repair
pi_0 = n0 / (n0 + n1)
pi_1 = 1 - pi_0
x_0_rep = pi_0 * x_0 + n0 * pi_1 * np.dot(gamma, x_1)
x_1_rep = pi_1 * x_1 + n1 * pi_0 * np.dot(gamma.T, x_0)
new_x = np.zeros(x.shape)
new_x[idx_p0, :] = x_0_rep
new_x[idx_p1, :] = x_1_rep
return new_x, s, gamma, m
def visuTSNE(X, protS, k=2, seed=0, plotName='tsne_visu'):
# display TSNE scatter plot
tsne = TSNE(n_components=k, random_state=seed)
np.set_printoptions(suppress=True)
Y = tsne.fit_transform(X)
x_coords = Y[:, 0]
y_coords = Y[:, 1]
fig, ax = plt.subplots()
for g in np.unique(protS):
i = np.where(protS == g)
ax.scatter(x_coords[i], y_coords[i], label=g)
ax.legend
plt.savefig(plotName + '.png')
plt.show()
def emb_node2vec(g, s, dimension=32, walk_length=15, num_walks=100, window=10, save=False):
"""
Compute the node embedding using Node2Vec
:param g: a graph
:param s: protected attribute (vector)
:param dimension: dimension of the embedding
:param walk_length: length of the random walk
:param num_walks: number of walks
:param window: window
:param save: if true save the node2vec model
:return: the embedding matrix and the associate protected attribute
"""
node2vec = Node2Vec(g, dimensions=dimension, walk_length=walk_length, num_walks=num_walks)
model = node2vec.fit(window=window, min_count=1)
idx = list(map(int, model.wv.index_to_key))
emb_x = model.wv.vectors
new_s = s[idx]
if save: model.save('node2vec_model')
return emb_x, new_s, model
def load_graph(g, file_str, name):
"""
This function is required for Verse
"""
g_2 = g.copy()
g_2.name = name
nx.write_edgelist(g, file_str, data=False)
g_2.graph['edgelist'] = file_str
g_2.graph['bcsr'] = './verse_input/' + g_2.name + '.bcsr'
g_2.graph['verse.output'] = './verse_output/' + g_2.name + '.bin'
try:
with open(g_2.graph['bcsr']):
pass
except:
os.system('python ../verse-master/python/convert.py ' + g_2.graph['edgelist'] + ' ' + g_2.graph['bcsr'])
return g_2
def verse(g, file_str, name):
g = load_graph(g, file_str, name)
orders = "../verse-master/src/verse -input " + g.graph['bcsr'] + " -output " + g.graph['verse.output'] + \
" -dim 32" + " -alpha 0.85"
os.system(orders)
verse_embeddings = np.fromfile(g.graph['verse.output'], np.float32).reshape(g.number_of_nodes(), 32)
return verse_embeddings
def read_emb(file_to_read):
# read embedding file where first line is number of nodes, dimension of embedding and next lines are node_id,
# embedding vector
with open(file_to_read, 'r') as f:
number_of_nodes, dimension = f.readline().split()
number_of_nodes = int(number_of_nodes)
dimension = int(dimension)
y = [[0 for i in range(dimension)] for j in range(number_of_nodes)]
for i, line in enumerate(f):
line = line.split()
y[int(line[0])] = [float(line[j]) for j in range(1, dimension + 1)]
return y
def fairwalk(input_edgelist, output_emb_file, dict_file):
# compute node2vec embedding
orders = 'python ./fairwalk/src/main.py' + ' --input ' + input_edgelist + ' --output ' + './fairwalk/emb/' \
+ output_emb_file + ' --sensitive_attr ' + dict_file + ' --dimension 32'
os.system(orders)
# print('DONE!')
embeddings = read_emb('./fairwalk/emb/' + output_emb_file)
return embeddings
def free_support_barycenter_laplace(measures_locations, measures_weights, X_init, reg_type='disp', reg_laplace=1e-1, reg_source=1,
b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None, metric = 'sqeuclidean'):
"""
Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance)
The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms.
This problem is considered in [1] (Algorithm 2). There are two differences with the following codes:
- we do not optimize over the weights
- we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting.
Parameters
----------
measures_locations : list of (k_i,d) numpy.ndarray
The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list)
measures_weights : list of (k_i,) numpy.ndarray
Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure
X_init : (k,d) np.ndarray
Initialization of the support locations (on k atoms) of the barycenter
b : (k,) np.ndarray
Initialization of the weights of the barycenter (non-negatives, sum to 1)
weights : (k,) np.ndarray
Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
numItermax : int, optional
Max number of iterations
stopThr : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
X : (k,d) np.ndarray
Support locations (on k atoms) of the barycenter
References
----------
.. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
.. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
"""
iter_count = 0
N = len(measures_locations)
k = X_init.shape[0]
d = X_init.shape[1]
if b is None:
b = np.ones((k,))/k
if weights is None:
weights = np.ones((N,)) / N
X = X_init
log_dict = {}
displacement_square_norms = []
Ti = []
displacement_square_norm = stopThr + 1.
while (displacement_square_norm > stopThr and iter_count < numItermax):
T_sum = np.zeros((k, d))
for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights,
weights.tolist()):
M_i = ot.dist(X, measure_locations_i, metric=metric)
T_i = ot.da.emd_laplace(xs=X, xt=measure_locations_i, a=b, b=measure_weights_i, M=M_i,
reg=reg_type, eta=reg_laplace, alpha=reg_source, numInnerItermax=200000,
verbose=verbose, stopInnerThr=1e-7)
print('Done solving Laplace')
T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i)
Ti.append(T_i)
displacement_square_norm = np.sum(np.square(T_sum-X))
if log:
displacement_square_norms.append(displacement_square_norm)
X = T_sum
if verbose:
print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
iter_count += 1
if log:
log_dict['displacement_square_norms'] = displacement_square_norms
log_dict['T'] = Ti[-N:]
return X, log_dict
else:
return X
def sym_free_support_barycenter(measures_locations, measures_weights, X_init, reg_type='disp', reg_laplace=1e-1, reg_source=1,reg=1e-9,
b=None, weights=None, numItermax=100, stopThr=1e-5, verbose=False, log=None, metric = 'sqeuclidean'):
"""
Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance)
The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms.
This problem is considered in [1] (Algorithm 2). There are two differences with the following codes:
- we do not optimize over the weights
- we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting.
Parameters
----------
measures_locations : list of (k_i,d) numpy.ndarray
The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list)
measures_weights : list of (k_i,) numpy.ndarray
Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure
X_init : (k,d) np.ndarray
Initialization of the support locations (on k atoms) of the barycenter
b : (k,) np.ndarray
Initialization of the weights of the barycenter (non-negatives, sum to 1)
weights : (k,) np.ndarray
Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
numItermax : int, optional
Max number of iterations
stopThr : float, optionalkkk
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
X : (k,d) np.ndarray
Support locations (on k atoms) of the barycenter
References
----------
.. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
.. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
"""
iter_count = 0
N = len(measures_locations)
k = X_init.shape[0]
d = X_init.shape[1]
if b is None:
b = np.ones((k,))/k
if weights is None:
weights = np.ones((N,)) / N
X = X_init
log_dict = {}
displacement_square_norms = []
Ti = []
displacement_square_norm = stopThr + 1.
while (displacement_square_norm > stopThr and iter_count < numItermax):
T_sum = np.zeros((k, d))
for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights,
weights.tolist()):
M_i = ot.dist(X, measure_locations_i, metric=metric)
T_i = multi_computeTransportSymmetric_fw(M_i, X, measure_locations_i, weight_i,method='laplace', metric=metric,
reg=reg,
solver=None, wparam=1)
print('Done solving Laplace')
T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i)
Ti.append(T_i)
displacement_square_norm = np.sum(np.square(T_sum-X))
if log:
displacement_square_norms.append(displacement_square_norm)
X = T_sum
if verbose:
print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
iter_count += 1
if log:
log_dict['displacement_square_norms'] = displacement_square_norms
log_dict['T'] = Ti[-N:]
return X, log_dict
else:
return X
def multi_node_sym_total_repair(g, metric='sqeuclidean', reg=1e-9, eta=1, log=False, num_iter=1e6,reg2=1, reg1=1):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
x2 = nx.get_node_attributes(g, 'feat')
s = nx.get_node_attributes(g, 's')
idx_ps = []
myx2 = []
for val in set(s.values()):
idx_ps.append(get_keys_from_value(s, val))
for tmp in x2.keys():
myx2.append(x2[tmp])
myx2 = np.array(myx2)
x2 = myx2
# s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
x_s = [np.array(x[idx_p]) for idx_p in idx_ps]
x2s = [np.array(x2[idx_p]) for idx_p in idx_ps]
# Get the barycenter between adj0 and adj1
n_s = [myx_s.shape[0] for myx_s in x_s]
d_s = [myx_s.shape[1] for myx_s in x_s]
# Compute barycenters using POT library
# Uniform distributions on samples
weights = [np.ones((myn,))/myn for myn in n_s]
num_classes = len(x_s)
X_init = np.random.normal(0., 1., (x.shape[0], x.shape[1]+x2.shape[1]))
b = np.ones((X_init.shape[0],))/X_init.shape[0]
# Transport
# kwargs = {'sim': 'gauss', 'alpha': 0.5}
start = time.time()
X = sym_node_free_support_barycenter(x_s, x2s, weights, X_init, numItermax=num_iter, reg=reg, reg1=reg1, reg2=reg2, verbose=True)
print(f'bc time lasted {time.time() - start} seconds')
new_xs = []
gammas = []
ms = []
d1, d2 = x_s[0].shape[1], x2s[0].shape[1]
for i in range(len(x_s)):
start = time.time()
myx = np.concatenate([reg1*x_s[i], reg2*x2s[i]], axis=-1)
myres = np.empty((X.shape[0],myx.shape[0]))
X[:d1] = reg1*X[:d1]
X[d1:] = reg2*X[d1:]
m = dist_arr_2(X, myx, myres)
# m = reg1*ot.utils.dist(X[:,:d1],x_s[i] ) + reg2*ot.utils.dist(X[:,d1:], x2s[i])
ms.append(m)
gamma = ot.emd(b, weights[i], m).T
gammas.append(gamma)
new_xs.append(n_s[i]*np.dot(gamma, X))
print(f'gamma {i} lasted {time.time()-start} seconds')
new_x = np.zeros(X_init.shape)
i = 0
for myidx in idx_ps:
new_x[myidx, :] = new_xs[i]
i += 1
my_nodes = {}
for i in range(new_x[:,d1:].shape[0]):
my_nodes[i] = new_x[i, d1:]
return new_x[:,:d1], new_x[:,d1:], s, gammas, ms
def sym_node_free_support_barycenter(measures_locations,measures_locations2, measures_weights, X_init, reg_type='disp', reg_laplace=1e-1, reg_source=1,reg=1e-9, reg1=1, reg2=1,
b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None, metric = 'sqeuclidean'):
"""
Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance)
The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms.
This problem is considered in [1] (Algorithm 2). There are two differences with the following codes:
- we do not optimize over the weights
- we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting.
Parameters
----------
measures_locations : list of (k_i,d) numpy.ndarray
The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list)
measures_weights : list of (k_i,) numpy.ndarray
Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure
X_init : (k,d) np.ndarray
Initialization of the support locations (on k atoms) of the barycenter
b : (k,) np.ndarray
Initialization of the weights of the barycenter (non-negatives, sum to 1)
weights : (k,) np.ndarray
Initialization of the coefficients of the barycenter (non-negatives, sum to 1)
numItermax : int, optional
Max number of iterations
stopThr : float, optionalkkk
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
X : (k,d) np.ndarray
Support locations (on k atoms) of the barycenter
References
----------
.. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014.
.. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762.
"""
iter_count = 0
N = len(measures_locations)
k = X_init.shape[0]
d = X_init.shape[1]
d1, d2 = measures_locations[0].shape[1], measures_locations2[0].shape[1]
if b is None:
b = np.ones((k,))/k
if weights is None:
weights = np.ones((N,)) / N
X = X_init
log_dict = {}
displacement_square_norms = []
Ti = []
displacement_square_norm = stopThr + 1.
while (displacement_square_norm > stopThr and iter_count < numItermax):
T_sum = np.zeros((k, d))
import time
for (measure_locations_i, measure_locations2_i, measure_weights_i, weight_i) in zip(measures_locations, measures_locations2, measures_weights, weights.tolist()):
start = time.time()
T_i = multi_node_computeTransportSymmetric_fw( X, measure_locations_i, measure_locations2_i, weight_i,method='laplace',
reg=reg, reg1=reg1, reg2=reg2,
solver=None, wparam=1, nbitermax=100)
print(f'fw calac lasted {time.time() - start} seconds')
start = time.time()
T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, np.concatenate([np.array(measure_locations_i), np.array(measure_locations2_i)], axis=-1))
Ti.append(T_i)
print(f'update calc lasted {time.time()-start}')
displacement_square_norm = np.sum(np.square(T_sum-X))
if log:
displacement_square_norms.append(displacement_square_norm)
X = T_sum
if verbose:
print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm)
iter_count += 1
if log:
log_dict['displacement_square_norms'] = displacement_square_norms
log_dict['T'] = Ti[-N:]
return X, log_dict
else:
return X
def multi_node_total_repair(g, metric='sqeuclidean', reg=1e-9, eta=1, log=False, num_iter=1e6,reg2=1, reg1=1):
"""
Repairing of the graph with OT and the sinkhorn version
:param g: a graph to repair. The protected attribute is the node attribute
:param metric: the distance metric for the cost matrix
:param reg : entropic regularisation term
:param case: the new graph is by nature a weighed one
:return: the repaired graph, the transportation plan, the cost matrix
"""
x = nx.adjacency_matrix(g)
x2 = nx.get_node_attributes(g, 'feat')
s = nx.get_node_attributes(g, 's')
idx_ps = []
myx2 = []
for val in set(s.values()):
idx_ps.append(get_keys_from_value(s, val))
for tmp in x2.keys():
myx2.append(x2[tmp])
myx2 = np.array(myx2)
x2 = myx2
# s = np.fromiter(s.values(), dtype=int)
otdists = ['cosine', 'dice', 'euclidean', 'hamming', 'jaccard',
'mahalanobis', 'matching', 'seuclidean','sqeuclidean', ]
if issparse(x):
x = x.todense()
# Separate rows adjacency matrix based on the protected attribute
x_s = [np.array(x[idx_p]) for idx_p in idx_ps]
x2s = [np.array(x2[idx_p]) for idx_p in idx_ps]
# Get the barycenter between adj0 and adj1
n_s = [myx_s.shape[0] for myx_s in x_s]
d_s = [myx_s.shape[1] for myx_s in x_s]
# Compute barycenters using POT library
# Uniform distributions on samples
weights = [np.ones((myn,))/myn for myn in n_s]
num_classes = len(x_s)
X_init = np.random.normal(0., 1., (x.shape[0], x.shape[1]+x2.shape[1]))
b = np.ones((X_init.shape[0],))/X_init.shape[0]
xs = []
for i in range(len(x_s)):
xs.append(np.concatenate([reg1*x_s[i], reg2*x2s[i]], axis=-1))
# import pdb; pdb.set_trace()
# Transport
# kwargs = {'sim': 'gauss', 'alpha': 0.5}
start = time.time()
X = ot.lp.free_support_barycenter(xs, weights, X_init, b, numItermax=num_iter, verbose=True)
# X = sym_node_free_support_barycenter(x_s, x2s, weights, X_init, numItermax=num_iter, reg=reg, reg1=reg1, reg2=reg2, verbose=True)
print(f'bc time lasted {time.time() - start} seconds')
new_xs = []
gammas = []
ms = []
d1, d2 = x_s[0].shape[1], x2s[0].shape[1]
for i in range(len(x_s)):
start = time.time()
m = ot.utils.dist(X, xs[i])
# m = reg1*ot.utils.dist(X[:,:d1],x_s[i] ) + reg2*ot.utils.dist(X[:,d1:], x2s[i])
ms.append(m)
gamma = ot.emd(b, weights[i], m).T
gammas.append(gamma)
new_xs.append(n_s[i]*np.dot(gamma, X))
print(f'gamma {i} lasted {time.time()-start} seconds')
new_x = np.zeros(X_init.shape)
i = 0
for myidx in idx_ps:
new_x[myidx, :] = new_xs[i]
i += 1
my_nodes = {}
for i in range(new_x[:,d1:].shape[0]):
my_nodes[i] = new_x[i, d1:]
return new_x[:,:d1], new_x[:,d1:], s, gammas, ms
| 35.457058
| 219
| 0.623448
| 5,462
| 35,918
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| 0.255554
| 35,918
| 1,012
| 220
| 35.492095
| 0.785444
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| 0.001845
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| 0
|
0
| 7
|
4e27e5b3e38ee565b709546d5db476d4fefe3da2
| 147
|
py
|
Python
|
neukivy/tools/colorconvertor.py
|
sagarhm/NeuKivy
|
2f116e332c0b49ad7d1ed3210fff0085375fd899
|
[
"MIT"
] | 31
|
2021-06-22T17:52:06.000Z
|
2022-02-27T07:22:02.000Z
|
neukivy/tools/colorconvertor.py
|
kengoon/NeuKivy
|
1ad64b0dc32cef870d727034fa2313045f3ce4de
|
[
"MIT"
] | 2
|
2021-06-25T02:02:51.000Z
|
2021-06-25T12:16:29.000Z
|
neukivy/tools/colorconvertor.py
|
kengoon/NeuKivy
|
1ad64b0dc32cef870d727034fa2313045f3ce4de
|
[
"MIT"
] | 6
|
2021-06-22T17:52:09.000Z
|
2022-01-27T08:37:46.000Z
|
def rgb_2_dec(color):
return [channel / 255 for channel in color]
def dec_2_rgb(color):
return [int(channel * 255) for channel in color]
| 21
| 52
| 0.70068
| 25
| 147
| 3.96
| 0.44
| 0.222222
| 0.262626
| 0.40404
| 0.545455
| 0.545455
| 0
| 0
| 0
| 0
| 0
| 0.068376
| 0.204082
| 147
| 6
| 53
| 24.5
| 0.777778
| 0
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| 0
| 0
| 0
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| 0.5
| false
| 0
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| 0.5
| 1
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| 1
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| 0
| null | 1
| 1
| 1
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| null | 0
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|
0
| 8
|
9de8ff1e9c51a71b004e71720e8c5a2c0e596131
| 186
|
py
|
Python
|
infinityMusicBot/function/__init__.py
|
INFINITY00800/infinitymusicbot
|
84c878103a1098147d505ad57f47af4233fa6bb8
|
[
"BSD-3-Clause"
] | 3
|
2021-07-22T09:33:07.000Z
|
2021-08-31T11:40:30.000Z
|
infinityMusicBot/function/__init__.py
|
INFINITY00800/infinitymusicbot
|
84c878103a1098147d505ad57f47af4233fa6bb8
|
[
"BSD-3-Clause"
] | null | null | null |
infinityMusicBot/function/__init__.py
|
INFINITY00800/infinitymusicbot
|
84c878103a1098147d505ad57f47af4233fa6bb8
|
[
"BSD-3-Clause"
] | null | null | null |
from infinityMusicBot.function.admins import admins
from infinityMusicBot.function.admins import get
from infinityMusicBot.function.admins import set
__all__ = ["set", "get", "admins"]
| 31
| 51
| 0.811828
| 22
| 186
| 6.681818
| 0.363636
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| 0.571429
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| 5
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| 37.2
| 0.875
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|
0
| 8
|
9dea918e4901e3e16b673d9593937485bd096f15
| 93
|
py
|
Python
|
Intro/53 - validTime.py
|
lucasalme1da/codesignal
|
faff1ae635d04a33a1b59e6f751d266fabca5e71
|
[
"MIT"
] | 2
|
2020-04-15T00:15:03.000Z
|
2021-02-17T18:43:08.000Z
|
Intro/53 - validTime.py
|
lucasalme1da/codesignal
|
faff1ae635d04a33a1b59e6f751d266fabca5e71
|
[
"MIT"
] | null | null | null |
Intro/53 - validTime.py
|
lucasalme1da/codesignal
|
faff1ae635d04a33a1b59e6f751d266fabca5e71
|
[
"MIT"
] | null | null | null |
def validTime(time):
return bool(re.search("^([0-1][0-9]|[2][0-3])[:][0-5][0-9]$", time))
| 46.5
| 72
| 0.526882
| 19
| 93
| 2.578947
| 0.684211
| 0.081633
| 0
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| 0.129412
| 0.086022
| 93
| 2
| 72
| 46.5
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| 0.382979
| 0.382979
| 0
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|
0
| 7
|
9dfc28b6da7ec376014068bdde0b0d9efa587713
| 68,650
|
py
|
Python
|
scripts/sampleOutputs/bkup/cmp_bwavesgccmcfleslie3d/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
scripts/sampleOutputs/bkup/cmp_bwavesgccmcfleslie3d/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
scripts/sampleOutputs/bkup/cmp_bwavesgccmcfleslie3d/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0629239,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.252112,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.336141,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.296357,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.513183,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.294325,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.10387,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.241402,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 5.9483,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0635042,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0107432,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.101394,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0794524,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.164898,
'Execution Unit/Register Files/Runtime Dynamic': 0.0901956,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.262247,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.662859,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 2.51441,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00179321,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00179321,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.0015665,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000608946,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00114134,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00629426,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0170281,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0763796,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.8584,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.228105,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.25942,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 7.31631,
'Instruction Fetch Unit/Runtime Dynamic': 0.587226,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0915311,
'L2/Runtime Dynamic': 0.0150848,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 3.95548,
'Load Store Unit/Data Cache/Runtime Dynamic': 1.32862,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0879453,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0879453,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 4.37246,
'Load Store Unit/Runtime Dynamic': 1.85028,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.216858,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.433716,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0769636,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0782287,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.302078,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0377193,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.593631,
'Memory Management Unit/Runtime Dynamic': 0.115948,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 22.8839,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.221552,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.0178201,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.150343,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.389716,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 5.47267,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0256729,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.222853,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.136569,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.121324,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.195692,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0987785,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.415794,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.117822,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.29516,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0258008,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00508888,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0464949,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0376354,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0722956,
'Execution Unit/Register Files/Runtime Dynamic': 0.0427243,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.104371,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.267482,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 1.35423,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000923722,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000923722,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000830075,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.00033529,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000540636,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00321815,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00794498,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0361799,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.30135,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.102641,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.122883,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.63156,
'Instruction Fetch Unit/Runtime Dynamic': 0.272867,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0408925,
'L2/Runtime Dynamic': 0.00660539,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.55293,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.641921,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0425694,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0425695,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.75395,
'Load Store Unit/Runtime Dynamic': 0.894429,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.104969,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.209938,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0372538,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0378178,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.14309,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0169755,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.363194,
'Memory Management Unit/Runtime Dynamic': 0.0547933,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
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'Renaming Unit/Area': 0.303608,
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'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
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'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
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'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
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'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.95049,
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'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.104149,
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'Instruction Fetch Unit/Peak Dynamic': 4.26367,
'Instruction Fetch Unit/Runtime Dynamic': 0.231521,
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'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0406839,
'L2/Runtime Dynamic': 0.0105108,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Data Cache/Peak Dynamic': 2.34483,
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'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0358372,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0358372,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.51407,
'Load Store Unit/Runtime Dynamic': 0.761795,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0883684,
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'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
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'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
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'Renaming Unit/Free List/Area': 0.0340654,
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'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00137544,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00330943,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0209788,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.33443,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0510985,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0712535,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 3.61771,
'Instruction Fetch Unit/Runtime Dynamic': 0.148016,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0376557,
'L2/Runtime Dynamic': 0.00912655,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.04047,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.400555,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0259902,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0259901,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.1632,
'Load Store Unit/Runtime Dynamic': 0.55472,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0640873,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.128174,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0227448,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0233026,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.0829701,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.00839972,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.27815,
'Memory Management Unit/Runtime Dynamic': 0.0317023,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 13.7518,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0282976,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00351835,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0360453,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0678613,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 1.85113,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 6.686699690072352,
'Runtime Dynamic': 6.686699690072352,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.333746,
'Runtime Dynamic': 0.0848118,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 67.5486,
'Peak Power': 100.661,
'Runtime Dynamic': 12.4989,
'Subthreshold Leakage': 31.5774,
'Subthreshold Leakage with power gating': 13.9484,
'Total Cores/Area': 128.669,
'Total Cores/Gate Leakage': 1.4798,
'Total Cores/Peak Dynamic': 67.2149,
'Total Cores/Runtime Dynamic': 12.4141,
'Total Cores/Subthreshold Leakage': 24.7074,
'Total Cores/Subthreshold Leakage with power gating': 10.2429,
'Total L3s/Area': 61.9075,
'Total L3s/Gate Leakage': 0.0484137,
'Total L3s/Peak Dynamic': 0.333746,
'Total L3s/Runtime Dynamic': 0.0848118,
'Total L3s/Subthreshold Leakage': 6.80085,
'Total L3s/Subthreshold Leakage with power gating': 3.32364,
'Total Leakage': 33.1122,
'Total NoCs/Area': 1.33155,
'Total NoCs/Gate Leakage': 0.00662954,
'Total NoCs/Peak Dynamic': 0.0,
'Total NoCs/Runtime Dynamic': 0.0,
'Total NoCs/Subthreshold Leakage': 0.0691322,
'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}
| 75.109409
| 124
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| 68,650
| 914
| 125
| 75.109409
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0
| 7
|
d1fa71cf63af345b575cfa645762e14ae6773e2b
| 4,571
|
py
|
Python
|
tests/tests_unit/test_lexer.py
|
vegarsti/toydb
|
4d94afd010aa53e1ba859ff74309e8aa5b091dc6
|
[
"Apache-2.0"
] | 1
|
2020-05-27T17:11:58.000Z
|
2020-05-27T17:11:58.000Z
|
tests/tests_unit/test_lexer.py
|
vegarsti/toydb
|
4d94afd010aa53e1ba859ff74309e8aa5b091dc6
|
[
"Apache-2.0"
] | 11
|
2020-05-25T12:58:47.000Z
|
2020-06-27T20:02:08.000Z
|
tests/tests_unit/test_lexer.py
|
vegarsti/toydb
|
4d94afd010aa53e1ba859ff74309e8aa5b091dc6
|
[
"Apache-2.0"
] | null | null | null |
from vgdb.lexer import Lexer
from vgdb.sql_token import Token, TokenType
class TestLexer:
def test_lexer(self):
program = (
"SeLeCT WhErE INsERT inTo vaLues 'abc' 'a b' 1, 2, 3 1234 = ('a', 'b', 1) < > <= LIKE "
">= a_column_name another ( ) FROM b != CREATE TABLE TEXT INT * and DESC or limit ORDER BY OFFSET; A;"
)
lexer = Lexer(program)
assert lexer.next_token() == Token(token_type=TokenType.SELECT, literal="select")
assert lexer.next_token() == Token(token_type=TokenType.WHERE, literal="where")
assert lexer.next_token() == Token(token_type=TokenType.INSERT, literal="insert")
assert lexer.next_token() == Token(token_type=TokenType.INTO, literal="into")
assert lexer.next_token() == Token(token_type=TokenType.VALUES, literal="values")
assert lexer.next_token() == Token(token_type=TokenType.STRING, literal="abc")
assert lexer.next_token() == Token(token_type=TokenType.STRING, literal="a b")
assert lexer.next_token() == Token(token_type=TokenType.INT, literal=1)
assert lexer.next_token() == Token(token_type=TokenType.COMMA, literal=",")
assert lexer.next_token() == Token(token_type=TokenType.INT, literal=2)
assert lexer.next_token() == Token(token_type=TokenType.COMMA, literal=",")
assert lexer.next_token() == Token(token_type=TokenType.INT, literal=3)
assert lexer.next_token() == Token(token_type=TokenType.INT, literal=1234)
assert lexer.next_token() == Token(token_type=TokenType.EQUALS, literal="=")
assert lexer.next_token() == Token(token_type=TokenType.LPAREN, literal="(")
assert lexer.next_token() == Token(token_type=TokenType.STRING, literal="a")
assert lexer.next_token() == Token(token_type=TokenType.COMMA, literal=",")
assert lexer.next_token() == Token(token_type=TokenType.STRING, literal="b")
assert lexer.next_token() == Token(token_type=TokenType.COMMA, literal=",")
assert lexer.next_token() == Token(token_type=TokenType.INT, literal=1)
assert lexer.next_token() == Token(token_type=TokenType.RPAREN, literal=")")
assert lexer.next_token() == Token(token_type=TokenType.LT, literal="<")
assert lexer.next_token() == Token(token_type=TokenType.GT, literal=">")
assert lexer.next_token() == Token(token_type=TokenType.LTEQ, literal="<=")
assert lexer.next_token() == Token(token_type=TokenType.LIKE, literal="like")
assert lexer.next_token() == Token(token_type=TokenType.GTEQ, literal=">=")
assert lexer.next_token() == Token(token_type=TokenType.IDENTIFIER, literal="a_column_name")
assert lexer.next_token() == Token(token_type=TokenType.IDENTIFIER, literal="another")
assert lexer.next_token() == Token(token_type=TokenType.LPAREN, literal="(")
assert lexer.next_token() == Token(token_type=TokenType.RPAREN, literal=")")
assert lexer.next_token() == Token(token_type=TokenType.FROM, literal="from")
assert lexer.next_token() == Token(token_type=TokenType.IDENTIFIER, literal="b")
assert lexer.next_token() == Token(token_type=TokenType.NOT_EQUALS, literal="!=")
assert lexer.next_token() == Token(token_type=TokenType.CREATE, literal="create")
assert lexer.next_token() == Token(token_type=TokenType.TABLE, literal="table")
assert lexer.next_token() == Token(token_type=TokenType.TEXT_TYPE, literal="text")
assert lexer.next_token() == Token(token_type=TokenType.INT_TYPE, literal="int")
assert lexer.next_token() == Token(token_type=TokenType.STAR, literal="*")
assert lexer.next_token() == Token(token_type=TokenType.AND, literal="and")
assert lexer.next_token() == Token(token_type=TokenType.DESC, literal="desc")
assert lexer.next_token() == Token(token_type=TokenType.OR, literal="or")
assert lexer.next_token() == Token(token_type=TokenType.LIMIT, literal="limit")
assert lexer.next_token() == Token(token_type=TokenType.ORDER, literal="order")
assert lexer.next_token() == Token(token_type=TokenType.BY, literal="by")
assert lexer.next_token() == Token(token_type=TokenType.OFFSET, literal="offset")
assert lexer.next_token() == Token(token_type=TokenType.SEMICOLON, literal=";")
assert lexer.next_token() == Token(token_type=TokenType.IDENTIFIER, literal="a")
assert lexer.next_token() == Token(token_type=TokenType.SEMICOLON, literal=";")
assert lexer.next_token() is None
| 74.934426
| 114
| 0.686064
| 579
| 4,571
| 5.233161
| 0.1019
| 0.316832
| 0.242574
| 0.323432
| 0.811551
| 0.811551
| 0.811551
| 0.811551
| 0.584488
| 0.50297
| 0
| 0.004191
| 0.164734
| 4,571
| 60
| 115
| 76.183333
| 0.789419
| 0
| 0
| 0.206897
| 0
| 0.034483
| 0.068913
| 0
| 0
| 0
| 0
| 0
| 0.844828
| 1
| 0.017241
| false
| 0
| 0.034483
| 0
| 0.068966
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| null | 0
| 0
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| 1
| 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ae2a2def200e556f47579b04cd25cf89bd2a8527
| 11,590
|
py
|
Python
|
src/review_1_cercor/4_non_linear.py
|
MezerLab/age-pred-r1
|
5e72fa3ede6306f779a262ded4e2c10d932df038
|
[
"Apache-2.0"
] | null | null | null |
src/review_1_cercor/4_non_linear.py
|
MezerLab/age-pred-r1
|
5e72fa3ede6306f779a262ded4e2c10d932df038
|
[
"Apache-2.0"
] | null | null | null |
src/review_1_cercor/4_non_linear.py
|
MezerLab/age-pred-r1
|
5e72fa3ede6306f779a262ded4e2c10d932df038
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 11 09:31:43 2019
@author: asier.erramuzpe
"""
import numpy as np
from src.data.make_dataset import (load_cort,
load_dataset,
load_cort_areas,
)
from sklearn.decomposition import PCA
from sklearn import linear_model
from sklearn.model_selection import (GridSearchCV,
train_test_split,
)
from sklearn import metrics
from scipy.stats import ttest_ind
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.kernel_ridge import KernelRidge
from sklearn.svm import SVR
areas = load_cort_areas()
AGE = 25
NUM_AREAS = 148
areas_notdeg2_idx = [ 0, 1, 4, 5, 11, 12, 13, 17, 20, 21, 22, 23, 30, 31, 33,
34, 36, 38, 41, 42, 46, 47, 49, 61, 62, 63, 69, 71, 74,
75, 79, 94, 95, 96, 97, 104, 105, 108, 115, 116, 120, 136, 137,
143]
areas_notdeg2 = np.zeros(NUM_AREAS)
areas_notdeg2[areas_notdeg2_idx] = 1
"""
R1 - SVR
"""
data_t1, y_t1, subs_t1 = load_cort(measure = 'r1', cort='midgray')
data_t1 = zscore(data_t1)
NUM_REPS = 1000
X = data_t1.T
youngsters = np.where(y_t1<=AGE)
y_old = np.delete(y_t1, youngsters)
data_old = np.delete(X, youngsters, axis=0)
data_old = np.delete(data_old, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_old, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
adults = np.where(y_t1>AGE)
y_young = np.delete(y_t1, adults)
data_young = np.delete(X, adults, axis=0)
data_young = np.delete(data_young, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_young, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
NUM_PCS = 30 # pca_idx
pca = PCA(n_components=NUM_PCS)
lm = SVR(kernel='linear') # linear, poly, rbf,
error_old = np.zeros(NUM_REPS)
error_old_lim = np.zeros(NUM_REPS)
error_old_tot = np.empty((data_old.shape[0], NUM_REPS))
error_old_tot[:] = np.nan
indices = np.arange(data_old.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_old, idx1, idx2 = train_test_split(data_old, y_old, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_old[idx] = metrics.mean_absolute_error(y_test_old, y_pred)
error_old_lim[idx] = np.mean(y_test_old - y_pred)
error_old_tot[idx2,idx] = y_pred
mean_pred_old = np.nanmean(error_old_tot, axis=1)
np.mean(error_old)
# we want 27 features without reading
NUM_PCS = 27 # pca_idx
pca = PCA(n_components=NUM_PCS)
error_young = np.zeros(NUM_REPS)
error_young_lim = np.zeros(NUM_REPS)
error_young_tot = np.empty((data_young.shape[0], NUM_REPS))
error_young_tot[:] = np.nan
indices = np.arange(data_young.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_young, idx1, idx2 = train_test_split(data_young, y_young, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_young[idx] = metrics.mean_absolute_error(y_test_young, y_pred)
error_young_lim[idx] = np.mean(y_test_young - y_pred)
error_young_tot[idx2,idx] = y_pred
mean_pred_young = np.nanmean(error_young_tot, axis=1)
np.mean(error_young)
plt.scatter(mean_pred_old, y_old)
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.scatter(mean_pred_young, y_young, c='g')
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.xlim(0,100)
plt.title("Age prediction PCA R1")
plt.savefig('./reports/figures/rebuttal/non_linear/SVR_linear.eps', format='eps')
plt.close()
error_young_pca_r1_svr = mean_pred_young - y_young
error_old_pca_r1_svr = mean_pred_old - y_old
np.mean(error_young), np.mean(error_old)
#(3.9609219591446774, 9.877936926806628)
"""
CT - SVR
"""
data_ct, y_ct, subs_ct = load_cort(measure = 't1', cort='volume')
data_ct = zscore(data_ct)
NUM_REPS = 1000
NUM_PCS = 38
pca = PCA(n_components=NUM_PCS)
X = data_ct.T
youngsters = np.where(y_ct<=AGE)
y_old = np.delete(y_ct, youngsters)
data_old = np.delete(X, youngsters, axis=0)
data_old = np.delete(data_old, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_old, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
adults = np.where(y_ct>AGE)
y_young = np.delete(y_ct, adults)
data_young = np.delete(X, adults, axis=0)
data_young = np.delete(data_young, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_young, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
lm = SVR(kernel='linear') # linear, poly, rbf,
error_old = np.zeros(NUM_REPS)
error_old_tot = np.empty((data_old.shape[0], NUM_REPS))
error_old_tot[:] = np.nan
error_old_lim = np.zeros(NUM_REPS)
indices = np.arange(data_old.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_old, idx1, idx2 = train_test_split(data_old, y_old, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_old[idx] = metrics.mean_absolute_error(y_test_old, y_pred)
error_old_lim[idx] = np.mean(y_test_old - y_pred)
error_old_tot[idx2,idx] = y_pred
mean_pred_old = np.nanmean(error_old_tot, axis=1)
np.mean(error_old)
NUM_PCS = 43
pca = PCA(n_components=NUM_PCS)
error_young = np.zeros(NUM_REPS)
error_young_tot = np.empty((data_young.shape[0], NUM_REPS))
error_young_tot[:] = np.nan
error_young_lim = np.zeros(NUM_REPS)
indices = np.arange(data_young.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_young, idx1, idx2 = train_test_split(data_young, y_young, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_young[idx] = metrics.mean_absolute_error(y_test_young, y_pred)
error_young_lim[idx] = np.mean(y_test_young - y_pred)
error_young_tot[idx2,idx] = y_pred
mean_pred_young = np.nanmean(error_young_tot, axis=1)
np.mean(error_young)
plt.scatter(mean_pred_old, y_old)
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.scatter(mean_pred_young, y_young, c='g')
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.title("Age prediction PCA CT")
plt.savefig('./reports/figures/rebuttal/non_linear/SVR_linear_ct.eps', format='eps')
error_young_pca_ct_svr = mean_pred_young - y_young
error_old_pca_ct_svr = mean_pred_old - y_old
np.mean(error_young), np.mean(error_old)
#(3.3729686235279828, 10.009140310737035)
""""
statistics
""""
from scipy.stats import ttest_ind
# linear vs SVR r1
t, p = ttest_ind(np.abs(error_young_pca_r1), np.abs(error_young_pca_r1_svr))
sns.boxplot(data=[np.abs(error_young_pca_r1) , np.abs(error_young_pca_r1_svr)], color='g')
plt.savefig('./reports/figures/rebuttal/non_linear/linear_vs_svr_r1_young_' + str(p) + '.svg', format='svg')
plt.close()
# linear vs SVR r1
t, p = ttest_ind(np.abs(error_old_pca_r1), np.abs(error_old_pca_r1_svr))
sns.boxplot(data=[np.abs(error_old_pca_r1) , np.abs(error_old_pca_r1_svr)], color='b')
plt.savefig('./reports/figures/rebuttal/non_linear/linear_vs_svr_r1_old_' + str(p) + '.svg', format='svg')
plt.close()
# linear vs SVR ct
t, p = ttest_ind(np.abs(error_young_pca_ct_svr), np.abs(error_young_pca_ct))
sns.boxplot(data=[np.abs(error_young_pca_ct_svr) , np.abs(error_young_pca_ct)], color='g')
plt.savefig('./reports/figures/rebuttal/non_linear/linear_vs_svr_ct_young_' + str(p) + '.svg', format='svg')
plt.close()
# linear vs SVR ct
t, p = ttest_ind(np.abs(error_old_pca_ct_svr), np.abs(error_old_pca_ct))
sns.boxplot(data=[np.abs(error_old_pca_ct_svr) , np.abs(error_old_pca_ct)], color='b')
plt.savefig('./reports/figures/rebuttal/non_linear/linear_vs_svr_ct_old_' + str(p) + '.svg', format='svg')
plt.close()
"""
R1 - KernelRidgeReg no groups
"""
data_t1, y_t1, subs_t1 = load_cort(measure = 'r1', cort='midgray')
data_t1 = zscore(data_t1)
NUM_REPS = 1000
X = data_t1.T
y_old = y_t1
data_old = X
data_old = np.delete(data_old, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_old, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
NUM_PCS = 47 # pca_idx
pca = PCA(n_components=NUM_PCS)
lm = KernelRidge(kernel='poly', degree=2) # linear, poly, rbf,
error_old = np.zeros(NUM_REPS)
error_old_lim = np.zeros(NUM_REPS)
error_old_tot = np.empty((data_old.shape[0], NUM_REPS))
error_old_tot[:] = np.nan
indices = np.arange(data_old.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_old, idx1, idx2 = train_test_split(data_old, y_old, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_old[idx] = metrics.mean_absolute_error(y_test_old, y_pred)
error_old_lim[idx] = np.mean(y_test_old - y_pred)
error_old_tot[idx2,idx] = y_pred
mean_pred_old = np.nanmean(error_old_tot, axis=1)
np.mean(error_old)
#9.786671218705714
plt.scatter(mean_pred_old, y_old)
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.savefig('./reports/figures/rebuttal/non_linear/KR_non_linear_all.eps', format='eps')
plt.close()
error_old_pca_r1_kr = mean_pred_old - y_old
"""
CT - KernelRidgeReg no groups
"""
data_ct, y_ct, subs_ct = load_cort(measure = 't1', cort='volume')
data_ct = zscore(data_ct)
NUM_REPS = 1000
X = data_ct.T
y_old = y_ct
data_old = X
data_old = np.delete(data_old, np.where(areas_notdeg2==1), axis=1)
#df_pca, X_pca, pca_evr, df_corr, df_n, df_na, df_nabv, df_rank = pca_full_report(X=data_old, features_=np.delete(areas, np.where(areas_notdeg2==1)), save_plot=False, fig_dpi=50)
NUM_PCS = 49 # pca_idx
pca = PCA(n_components=NUM_PCS)
lm = KernelRidge(kernel='poly', degree=2) # linear, poly, rbf,
error_old = np.zeros(NUM_REPS)
error_old_lim = np.zeros(NUM_REPS)
error_old_tot = np.empty((data_old.shape[0], NUM_REPS))
error_old_tot[:] = np.nan
indices = np.arange(data_old.shape[0])
for idx in range(NUM_REPS):
X_train, X_test, y_train, y_test_old, idx1, idx2 = train_test_split(data_old, y_old, indices)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
lm.fit(X_train_pca, y_train)
y_pred = lm.predict(X_test_pca)
error_old[idx] = metrics.mean_absolute_error(y_test_old, y_pred)
error_old_lim[idx] = np.mean(y_test_old - y_pred)
error_old_tot[idx2,idx] = y_pred
mean_pred_old = np.nanmean(error_old_tot, axis=1)
np.mean(error_old)
#9.118353306745935
plt.scatter(mean_pred_old, y_old)
plt.plot(range(15,100),range(15,100))
plt.xlabel("predicted age")
plt.ylabel("real age")
plt.savefig('./reports/figures/rebuttal/non_linear/KR_non_linear_all_ct.eps', format='eps')
plt.close()
error_old_pca_ct_kr = mean_pred_old - y_old
| 34.909639
| 180
| 0.725798
| 2,108
| 11,590
| 3.662239
| 0.102467
| 0.051813
| 0.02487
| 0.029534
| 0.885881
| 0.872927
| 0.843135
| 0.840415
| 0.824093
| 0.790803
| 0
| 0.043209
| 0.135375
| 11,590
| 331
| 181
| 35.015106
| 0.727173
| 0.123727
| 0
| 0.719149
| 0
| 0.034043
| 0.074089
| 0.047111
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.051064
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| null | 0
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|
0
| 8
|
886bd94bc2d8f964b5be8e24a79e08b36288f101
| 12,443
|
py
|
Python
|
forms/spravka.py
|
Operator2024/garageTUSUR
|
10c5ae5824fbbd433ff4062f992340c7bd740fc1
|
[
"MIT"
] | null | null | null |
forms/spravka.py
|
Operator2024/garageTUSUR
|
10c5ae5824fbbd433ff4062f992340c7bd740fc1
|
[
"MIT"
] | null | null | null |
forms/spravka.py
|
Operator2024/garageTUSUR
|
10c5ae5824fbbd433ff4062f992340c7bd740fc1
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
################################################################################
## Form generated from reading UI file 'spravka.ui'
##
## Created by: Qt User Interface Compiler version 5.15.1
##
## WARNING! All changes made in this file will be lost when recompiling UI file!
################################################################################
from PySide2.QtCore import QSize, QLocale, QMetaObject, QCoreApplication
from PySide2.QtGui import Qt, QFont, QIcon
from PySide2.QtWidgets import QGridLayout, QPushButton, QTextEdit, QTabWidget, QWidget
class Ui_Form(object):
def setupUi(self, Form):
if not Form.objectName():
Form.setObjectName(u"Form")
Form.setWindowModality(Qt.ApplicationModal)
Form.resize(538, 596)
Form.setMinimumSize(QSize(538, 596))
Form.setMaximumSize(QSize(538, 596))
font = QFont()
font.setFamily(u"Comic Sans MS")
font.setPointSize(10)
Form.setFont(font)
icon = QIcon()
icon.addFile(u":/newPrefix/images/python.ico", QSize(), QIcon.Normal, QIcon.Off)
Form.setWindowIcon(icon)
Form.setLocale(QLocale(QLocale.Russian, QLocale.Russia))
self.gridLayout = QGridLayout(Form)
self.gridLayout.setSpacing(0)
self.gridLayout.setObjectName(u"gridLayout")
self.gridLayout.setContentsMargins(0, 0, 0, 0)
self.tabWidget = QTabWidget(Form)
self.tabWidget.setObjectName(u"tabWidget")
self.tab = QWidget()
self.tab.setObjectName(u"tab")
self.gridLayout_2 = QGridLayout(self.tab)
self.gridLayout_2.setObjectName(u"gridLayout_2")
self.textEdit = QTextEdit(self.tab)
self.textEdit.setObjectName(u"textEdit")
self.gridLayout_2.addWidget(self.textEdit, 0, 0, 1, 1)
self.pushButton = QPushButton(self.tab)
self.pushButton.setObjectName(u"pushButton")
self.gridLayout_2.addWidget(self.pushButton, 1, 0, 1, 1)
self.tabWidget.addTab(self.tab, "")
self.tab_2 = QWidget()
self.tab_2.setObjectName(u"tab_2")
self.gridLayout_3 = QGridLayout(self.tab_2)
self.gridLayout_3.setObjectName(u"gridLayout_3")
self.textEdit_2 = QTextEdit(self.tab_2)
self.textEdit_2.setObjectName(u"textEdit_2")
self.gridLayout_3.addWidget(self.textEdit_2, 0, 0, 1, 1)
self.pushButton_2 = QPushButton(self.tab_2)
self.pushButton_2.setObjectName(u"pushButton_2")
self.gridLayout_3.addWidget(self.pushButton_2, 1, 0, 1, 1)
self.tabWidget.addTab(self.tab_2, "")
self.gridLayout.addWidget(self.tabWidget, 0, 0, 1, 1)
self.retranslateUi(Form)
self.tabWidget.setCurrentIndex(0)
QMetaObject.connectSlotsByName(Form)
# setupUi
def retranslateUi(self, Form):
Form.setWindowTitle(QCoreApplication.translate("Form", u"\u0421\u043f\u0440\u0430\u0432\u043a\u0438", None))
self.textEdit.setHtml(QCoreApplication.translate("Form", u"<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:'Comic Sans MS'; font-size:10pt; font-weight:400; font-style:normal;\">\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041e\u041e\u041e "\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u0420\u043e\u0433\u0430 \u0438 \u041a\u043e\u043f\u044b\u0442\u0430"</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0418\u041d\u041d: 12397854690</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text"
"-indent:0px;\">\u0420/c: 494949492339494 \u0432 \u0411\u0430\u043d\u043a\u0435 "\u0410\u041e \u0410\u043b\u044c\u0444\u0430 \u0431\u0430\u043d\u043a"</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041a/\u0441: 88006594930404; \u0411\u0418\u041a: 23030203023</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u042e\u0440. \u0430\u0434\u0440\u0435\u0441: \u0433.\u041f\u0443\u0448\u043a\u0438\u043d, \u0443\u043b\u0438\u0446\u0430 \u041f\u0443\u0448\u043a\u0438\u043d\u0430, \u0434. 11 \u043e\u0444\u0438\u0441 800</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0424\u0430\u043a\u0442. \u0430\u0434\u0440\u0435\u0441: \u0441\u043e\u0432\u043f\u0430\u0434\u0430\u0435\u0442 \u0441 \u042e\u0440. \u0430\u0434\u0440\u0435\u0441\u043e\u043c.</p>\n"
"<p style=\" margi"
"n-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0422\u0435\u043b\u0435\u0444\u043e\u043d: +7 345 7773515</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041e\u0442 11/11/1111 \u0433.</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-weight:600;\">\u0421\u041f\u0420\u0410\u0412\u041a\u0410</span></p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left"
":0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041d\u0430\u0441\u0442\u043e\u044f\u0449\u044f\u044f \u0441\u043f\u0440\u0430\u0432\u043a\u0430 \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u0434\u043e\u0445\u043e\u0434\u044b \u0444\u0438\u0440\u043c\u044b \u0432 \u043f\u0435\u0440\u0438\u043e\u0434 \u0441 00/00/0000 \u043f\u043e 11/11/1111 \u0432 \u0440\u0430\u0437\u043c\u0435\u0440\u0435 {\u0441\u0443\u043c\u043c\u0430} \u0440\u0443\u0431\u043b\u0435\u0439</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0413\u0435\u043d\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0434\u0438\u0440\u0435\u043a\u0442\u043e"
"\u0440 ______________________/ </p></body></html>", None))
self.pushButton.setText(QCoreApplication.translate("Form", u"\u0421\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0432 PDF \u0444\u043e\u0440\u043c\u0430\u0442\u0435", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab), QCoreApplication.translate("Form", u"\u0414\u043e\u0445\u043e\u0434\u044b \u0437\u0430 \u0433\u043e\u0434", None))
self.textEdit_2.setHtml(QCoreApplication.translate("Form", u"<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
"<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
"p, li { white-space: pre-wrap; }\n"
"</style></head><body style=\" font-family:'Comic Sans MS'; font-size:10pt; font-weight:400; font-style:normal;\">\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041e\u041e\u041e "\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f \u0420\u043e\u0433\u0430 \u0438 \u041a\u043e\u043f\u044b\u0442\u0430"</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0418\u041d\u041d: 12397854690</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text"
"-indent:0px;\">\u0420/c: 494949492339494 \u0432 \u0411\u0430\u043d\u043a\u0435 "\u0410\u041e \u0410\u043b\u044c\u0444\u0430 \u0431\u0430\u043d\u043a"</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041a/\u0441: 88006594930404; \u0411\u0418\u041a: 23030203023</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u042e\u0440. \u0430\u0434\u0440\u0435\u0441: \u0433.\u041f\u0443\u0448\u043a\u0438\u043d, \u0443\u043b\u0438\u0446\u0430 \u041f\u0443\u0448\u043a\u0438\u043d\u0430, \u0434. 11 \u043e\u0444\u0438\u0441 800</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0424\u0430\u043a\u0442. \u0430\u0434\u0440\u0435\u0441: \u0441\u043e\u0432\u043f\u0430\u0434\u0430\u0435\u0442 \u0441 \u042e\u0440. \u0430\u0434\u0440\u0435\u0441\u043e\u043c.</p>\n"
"<p style=\" margi"
"n-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0422\u0435\u043b\u0435\u0444\u043e\u043d: +7 345 7773515</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041e\u0442 11/11/1111 \u0433.</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-weight:600;\">\u0421\u041f\u0420\u0410\u0412\u041a\u0410</span></p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left"
":0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u041d\u0430\u0441\u0442\u043e\u044f\u0449\u044f\u044f \u0441\u043f\u0440\u0430\u0432\u043a\u0430 \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u0434\u043e\u0445\u043e\u0434\u044b \u0444\u0438\u0440\u043c\u044b \u0432 \u043f\u0435\u0440\u0438\u043e\u0434 \u0441 00/00/0000 \u043f\u043e 11/11/1111 \u0432 \u0440\u0430\u0437\u043c\u0435\u0440\u0435 {\u0441\u0443\u043c\u043c\u0430} \u0440\u0443\u0431\u043b\u0435\u0439</p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"> </p>\n"
"<p style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\">\u0413\u0435\u043d\u0435\u0440\u0430\u043b\u044c\u043d\u044b\u0439 \u0434\u0438\u0440\u0435\u043a\u0442\u043e"
"\u0440 ______________________/ </p></body></html>", None))
self.pushButton_2.setText(QCoreApplication.translate("Form", u"\u0421\u043e\u0445\u0440\u0430\u043d\u0438\u0442\u044c \u0432 PDF \u0444\u043e\u0440\u043c\u0430\u0442\u0435", None))
self.tabWidget.setTabText(self.tabWidget.indexOf(self.tab_2), QCoreApplication.translate("Form", u"\u0414\u043e\u0445\u043e\u0434\u044b \u0437\u0430 \u043f\u043e\u043b\u0433\u043e\u0434\u0430", None))
# retranslateUi
| 90.166667
| 590
| 0.690911
| 1,857
| 12,443
| 4.590199
| 0.115778
| 0.070389
| 0.051854
| 0.075786
| 0.808893
| 0.787658
| 0.776631
| 0.776631
| 0.776631
| 0.769592
| 0
| 0.210408
| 0.105843
| 12,443
| 137
| 591
| 90.824818
| 0.555725
| 0.018002
| 0
| 0.448598
| 1
| 0.168224
| 0.370392
| 0.20284
| 0
| 0
| 0
| 0
| 0
| 1
| 0.018692
| false
| 0
| 0.028037
| 0
| 0.056075
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 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
| 8
|
88794211f95c55a66c7e8141a44d050fbb189614
| 308
|
py
|
Python
|
Python/ex17.py
|
Davi-s-Brain/projetos_simples
|
7a14f9bba5d662c9fac26e8d7dbec93277b50a67
|
[
"MIT"
] | null | null | null |
Python/ex17.py
|
Davi-s-Brain/projetos_simples
|
7a14f9bba5d662c9fac26e8d7dbec93277b50a67
|
[
"MIT"
] | null | null | null |
Python/ex17.py
|
Davi-s-Brain/projetos_simples
|
7a14f9bba5d662c9fac26e8d7dbec93277b50a67
|
[
"MIT"
] | null | null | null |
#imprime um tabuleiro 4 X 3
print("```")
def asterisco():
return(" #--#--#--#")
def linhas_verticais():
return(" | | | |")
print(asterisco())
print(linhas_verticais())
print(asterisco())
print(linhas_verticais())
print(asterisco())
print(linhas_verticais())
print(asterisco())
print("```")
| 16.210526
| 28
| 0.626623
| 32
| 308
| 5.90625
| 0.375
| 0.31746
| 0.402116
| 0.396825
| 0.640212
| 0.640212
| 0.640212
| 0.640212
| 0.640212
| 0.640212
| 0
| 0.007576
| 0.142857
| 308
| 18
| 29
| 17.111111
| 0.708333
| 0.084416
| 0
| 0.692308
| 0
| 0
| 0.107143
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.153846
| true
| 0
| 0
| 0.153846
| 0.153846
| 0.692308
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
|
0
| 7
|
88a54d7363b6d8765d77061dbf4299fbca3ef5d1
| 2,053
|
py
|
Python
|
gomoku/tests/test_board.py
|
xiaoCarl/gomoku
|
bc004379eae7b3e72eeb26a8f210c53a0e61e8ec
|
[
"Apache-2.0"
] | 1
|
2020-01-21T08:32:46.000Z
|
2020-01-21T08:32:46.000Z
|
gomoku/tests/test_board.py
|
JiaoyangXu/gomoku
|
d3543ee460788b28ce4a578f94bacb4defe68c26
|
[
"Apache-2.0"
] | null | null | null |
gomoku/tests/test_board.py
|
JiaoyangXu/gomoku
|
d3543ee460788b28ce4a578f94bacb4defe68c26
|
[
"Apache-2.0"
] | null | null | null |
import pytest
from gomoku import board
def test_is_won_1():
chess = board.Gomoku(board_size=6)
chess.status=[0,0,0,1,0,0,
0,0,0,1,0,0,
0,0,0,1,0,0,
0,0,0,1,0,0,
0,0,0,1,0,0,
0,0,0,0,0,0]
chess.last_action = 3
chess.moved_player = 1
assert chess.is_won()==True
def test_is_won_2():
chess = board.Gomoku(board_size=6)
chess.moved_player = 2
chess.last_action = 10
chess.status=[0,0,0,0,0,0,
0,0,0,0,2,0,
0,0,0,0,2,0,
0,0,0,0,2,0,
0,0,0,0,2,0,
0,0,0,0,2,0]
assert chess.is_won()==True
def test_is_won_3():
chess = board.Gomoku(board_size=6)
chess.last_action = 7
chess.moved_player = 1
chess.status=[0,0,0,0,0,0,
0,1,0,0,0,0,
0,0,1,0,0,0,
0,0,0,1,0,0,
0,0,0,0,1,0,
0,0,0,0,0,1]
assert chess.is_won()==True
def test_is_won_4():
chess = board.Gomoku(board_size=6)
chess.last_action = 11
chess.moved_player = 1
chess.status=[0,0,0,0,0,0,
0,0,0,0,0,1,
0,0,0,0,1,0,
0,0,0,1,0,0,
0,0,1,0,0,0,
0,1,0,0,0,0]
assert chess.is_won()==True
def test_is_won_5():
chess = board.Gomoku(board_size=6)
chess.last_action = 11
chess.moved_player = 1
chess.status=[0,0,0,0,0,0,
0,0,0,0,0,1,
0,0,0,0,1,0,
0,0,0,1,0,0,
0,0,2,0,0,0,
0,1,0,0,0,0]
assert chess.is_won()==False
def test_is_won_6():
chess = board.Gomoku(board_size=6)
chess.last_action = 11
chess.moved_player = 1
chess.status=[0,0,0,0,0,0,
0,0,0,0,0,1,
0,0,0,0,1,0,
0,0,0,0,0,0,
0,1,1,1,1,1,
0,1,0,0,0,0]
assert chess.is_won()==True
| 23.597701
| 38
| 0.445202
| 378
| 2,053
| 2.306878
| 0.07672
| 0.341743
| 0.402523
| 0.389908
| 0.84633
| 0.83945
| 0.83945
| 0.768349
| 0.768349
| 0.719037
| 0
| 0.1952
| 0.391135
| 2,053
| 86
| 39
| 23.872093
| 0.5024
| 0
| 0
| 0.691176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088235
| 1
| 0.088235
| false
| 0
| 0.029412
| 0
| 0.117647
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 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
| 9
|
31f759bb682c22c176ffa669ddb83514f9e893ce
| 256
|
py
|
Python
|
MagicTelecomAPILib/Http/__init__.py
|
MagicTelecom/mt_python_api
|
b014760809cb1d0cab48a3376cbaface0c4bef66
|
[
"MIT"
] | null | null | null |
MagicTelecomAPILib/Http/__init__.py
|
MagicTelecom/mt_python_api
|
b014760809cb1d0cab48a3376cbaface0c4bef66
|
[
"MIT"
] | null | null | null |
MagicTelecomAPILib/Http/__init__.py
|
MagicTelecom/mt_python_api
|
b014760809cb1d0cab48a3376cbaface0c4bef66
|
[
"MIT"
] | null | null | null |
from MagicTelecomAPILib.Http.HttpMethodEnum import *
from MagicTelecomAPILib.Http.HttpRequest import *
from MagicTelecomAPILib.Http.HttpResponse import *
from MagicTelecomAPILib.Http.HttpClient import *
from MagicTelecomAPILib.Http.RequestsClient import *
| 42.666667
| 52
| 0.863281
| 25
| 256
| 8.84
| 0.36
| 0.497738
| 0.588235
| 0.579186
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.078125
| 256
| 5
| 53
| 51.2
| 0.936441
| 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
| 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
| 8
|
ee26b3d70f92b65e696477f75ba939bb692f8f08
| 17,570
|
py
|
Python
|
routes/professor/rubics.py
|
fvergaracl/gonsa2_1
|
0cb705ca7ba717f0316f4764bbec9bbcb08fcc34
|
[
"MIT"
] | 1
|
2020-07-19T03:17:12.000Z
|
2020-07-19T03:17:12.000Z
|
routes/professor/rubics.py
|
fvergaracl/gonsa2_1
|
0cb705ca7ba717f0316f4764bbec9bbcb08fcc34
|
[
"MIT"
] | 1
|
2021-06-02T00:49:57.000Z
|
2021-06-02T00:49:57.000Z
|
routes/professor/rubics.py
|
fvergaracl/gonsa3
|
0cb705ca7ba717f0316f4764bbec9bbcb08fcc34
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from controllers.controller_rubics import *
from controllers.general_db_functions import *
from controllers.controller_challenges import iam_challenge_owner_by_id
from routes.apidoc import *
apiprub = Namespace('professor/rubic', description="Endpoints utilizados solo por profesores en rubic") # pylint: disable=invalid-name
@apiprub.route('/create', methods=['POST'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
403: 'Forbidden',
404: 'Token not found',
500: 'Internal server error',
503: 'Service Unavailable'
}
)
class Professor_rubic_create(Resource):
test_fields = apins.model('Creación de Rúbrica', {
'title': fields.String(required=True, description='Título o alias de la rúbrica',example='prueba 1'),
'description': fields.String(required=True, description='Descripción de la rúbrica', example='Evaluación de 1-7 en cuanto al aprendizaje'),
'datajson': fields.String(required=True, description='Título o alias de la rubrica', example='[{"id":1,"text":"Evaluación general del desafío","description":"Descripción de rúbrica","min":1,"max":7,"percentage":100}]'),
'id_challenge': fields.String(required=True, description='Id del desafío', example='1')
})
@apiprub.expect(test_fields)
@token_required
def post(self):
"""Metodo para crear rúbrica y enlazarla con un desafío"""
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
data = get_info_token()
title_ = str(request.get_json()['title'].encode('utf-8').strip().decode("utf-8"))
description_ = str(request.get_json()['description'].encode('utf-8').strip().decode("utf-8"))
datajson_ = str(request.get_json()['datajson'].encode('utf-8').strip().decode("utf-8"))
id_challenge_ = str(request.get_json()['id_challenge'].encode('utf-8').strip().decode("utf-8"))
Payload = {"title": title_, "description":description_, "datajson":datajson_, "id_challenge":id_challenge_ }
insert_request_log("/professor/rubic/create" , "POST", str(Payload), ip_, data['email'])
if get_permission_by_user('Create_rubic', data['rol']) == 'True':
if not iam_challenge_owner_by_id(id_challenge_, data['email']):
response_str = {'message': 'Acción no permitida en el desafío', 'code': 403}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
if this_challenge_have_rubic(id_challenge_):
response_str = {'message': 'Este desafío ya tiene rúbica', 'code': 403}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
response = new_rubic(title_, description_, datajson_, id_challenge_)
if response >= 1:
message = 'Rúbrica creada'
code = 200
else:
message = 'Algo sucedió en la base de datos'
code = 503
response_str = {'message': message, 'code': code, 'id_class': response}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
else:
message = 'Acción no permitida'
code = 403
response_str = {'message': message, 'code': code}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/create", 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
@apiprub.route('/edit/<id_rubic>', methods=['PUT'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
400: 'Bad Request',
403: 'Forbidden',
404: 'Token not found',
500: 'Internal server error',
503: 'Service Unavailable'
}
)
class Profesor_edit_a_rubic(Resource):
test_fields = apins.model('Edición de Rúbrica', {
'title': fields.String(required=True, description='Título o alias de la rúbrica', example='prueba 1'),
'description': fields.String(required=True, description='Descripción de la rúbrica',
example='Evaluación de 1-7 en cuanto al aprendizaje'),
'datajson': fields.String(required=True, description='Título o alias de la rubrica',
example='[{"id":1,"text":"Evaluación general del desafío","description":"Descripción de rúbrica","min":1,"max":7,"percentage":100}]'),
'id_challenge': fields.String(required=True, description='Id del desafío', example='1')
})
@apiprub.expect(test_fields)
@token_required
def put(self, id_rubic):
"""Metodo para editar rúbicas
Para desvincular una rúbica de un desafío, solo asigna <strong>-1</strong> a la variable <strong>id_challenge """
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
# EDITAR-ACA
#CHECKEAR SI EL DESAFIO YA FUE INICIADO
title_ = str(request.get_json()['title'].encode('utf-8').strip().decode("utf-8"))
description_ = str(request.get_json()['description'].encode('utf-8').strip().decode("utf-8"))
datajson_ = str(request.get_json()['datajson'].encode('utf-8').strip().decode("utf-8"))
id_challenge_ = str(request.get_json()['id_challenge'].encode('utf-8').strip().decode("utf-8"))
Payload = {"title": title_, "description": description_, "datajson": datajson_,
"id_challenge": id_challenge_}
insert_request_log("/professor/rubic/edit/"+str(id_rubic), "PUT", str(Payload), ip_, data['email'])
if get_permission_by_user('Update_rubic', data['rol']) == 'True':
if not iam_challenge_owner_by_id(id_challenge_, data['email']):
response_str = {'message': 'Acción no permitida en el desafío', 'code': 403}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
response = edit_rubic(title_, description_, datajson_, id_challenge_, id_rubic)
if response >= 1:
message = 'ok'
code = 200
else:
message = 'Algo sucedió en la base de datos'
code = 503
response_str = {'message': message, 'code': code, 'id_class': response}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
else:
message = 'Acción no permitida'
code = 403
response_str = {'message': message, 'code': code}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/edit/" + str(id_rubic), 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
@apiprub.route('/', methods=['GET'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
400: 'Bad Request',
403: 'Not Authorized',
404: 'Token not found',
500: 'Internal server error'
}
)
class Profesor_get_rubic(Resource):
@token_required
def get(self):
"""
Método para obtener todas las rúbricas creadas por el profesor
<strong>Se obtiene un arreglo de los siguientes datos:</strong>
-[0] Id de rúbrica\n
-[1] Titúlo de rúbrica\n
-[2] Descripción de rúbrica\n
-[3] Fecha de creación\n
-[4] Fecha de modificación\n
-[5] data\n
-[6] Id de desafío\n
"""
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
insert_request_log("/professor/rubic/", "GET", "", ip_, data['email'])
my_rubics = get_all_my_rubics(data['email'])
response_str = {'message': "Todas tus rubicas", 'data': my_rubics, 'code': 200}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/", 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
# TEMPLATE
@apiprub.route('/template', methods=['GET'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
400: 'Bad Request',
403: 'Not Authorized',
404: 'Token not found',
500: 'Internal server error'
}
)
class Profesor_get_rubic_template(Resource):
@token_required
def get(self):
"""
Método para obtener todas las rúbricas creadas por el profesor
<strong>Se obtiene un arreglo de los siguientes datos:</strong>
-[0] Id de rúbrica\n
-[1] Titúlo de rúbrica\n
-[2] Descripción de rúbrica\n
-[3] Fecha de creación\n
-[4] Fecha de modificación\n
-[5] Data\n
-[6] Status\n
-[7] Creador\n
"""
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
if not get_permission_by_user('Read_rubic', data['rol']) == 'True':
response_str = {'message': "Acción no permitida", 'code': 403}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
insert_request_log("/professor/rubic/", "GET", "", ip_, data['email'])
all_template_rubics = get_all_template_rubic_show()
response_str = {'message': "Todas los templates de rúbricas", 'data': all_template_rubics, 'code': 200}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/template", 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
@apiprub.route('/template/create', methods=['POST'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
403: 'Forbidden',
404: 'Token not found',
500: 'Internal server error',
503: 'Service Unavailable'
}
)
class Professor_template_rubic_create(Resource):
test_fields = apins.model('Creación de template de Rúbrica', {
'title': fields.String(required=True, description='Título o alias de la rúbrica',example='prueba 1'),
'description': fields.String(required=True, description='Descripción de la rúbrica', example='Evaluación de 1-7 en cuanto al aprendizaje'),
'datajson': fields.String(required=True, description='Título o alias de la rubrica', example='[{"id":1,"text":"Evaluación general del desafío","description":"Descripción de rúbrica","min":1,"max":7,"percentage":100}]'),
})
@apiprub.expect(test_fields)
@token_required
def post(self):
"""Metodo para crear template de rúbrica"""
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
title_ = str(request.get_json()['title'].encode('utf-8').strip().decode("utf-8"))
description_ = str(request.get_json()['description'].encode('utf-8').strip().decode("utf-8"))
datajson_ = str(request.get_json()['datajson'].encode('utf-8').strip().decode("utf-8"))
Payload = {"title": title_, "description": description_, "datajson": datajson_}
insert_request_log("/professor/rubic/template/create", "POST", str(Payload), ip_, data['email'])
if get_permission_by_user('Create_rubic', data['rol']) == 'True':
response = new_template_rubic(title_, description_, datajson_, data['email'])
if response >= 1:
message = 'Template de rúbica creada'
code = 200
else:
message = 'Algo sucedió en la base de datos'
code = 503
response_str = {'message': message, 'code': code, 'id_template_rubica': response}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
else:
message = 'Acción no permitida'
code = 403
response_str = {'message': message, 'code': code}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/create", 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
@apiprub.route('/template/edit/<id_rubic>', methods=['PUT'])
@apiprub.doc(
security='apikey',
responses={
200: 'Success',
400: 'Bad Request',
403: 'Forbidden',
404: 'Token not found',
500: 'Internal server error',
503: 'Service Unavailable'
}
)
class Profesor_edit_a_template_rubic(Resource):
test_fields = apins.model('Edición de Rúbrica', {
'title': fields.String(required=True, description='Título o alias de la rúbrica', example='prueba 1'),
'description': fields.String(required=True, description='Descripción de la rúbrica',
example='Evaluación de 1-7 en cuanto al aprendizaje'),
'datajson': fields.String(required=True, description='Título o alias de la rubrica',
example='[{"id":1,"text":"Evaluación general del desafío","description":"Descripción de rúbrica","min":1,"max":7,"percentage":100}]'),
'status': fields.String(required=True, description='Variable para mostrar o eliminar desafio tipo <strong>["show","hide"]</strong>', example='1')
})
@apiprub.expect(test_fields)
@token_required
def put(self, id_rubic):
"""Metodo para editar template de rúbicas
Para eliminar una rúbrica , solo se debe asignar <strong>hide</strong> en el atributo <strong>status</strong>"""
data = get_info_token()
ip_ = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
try:
title_ = str(request.get_json()['title'].encode('utf-8').strip().decode("utf-8"))
description_ = str(request.get_json()['description'].encode('utf-8').strip().decode("utf-8"))
datajson_ = str(request.get_json()['datajson'].encode('utf-8').strip().decode("utf-8"))
status_ = str(request.get_json()['status'].encode('utf-8').strip().decode("utf-8"))
Payload = {"title": title_, "description": description_, "datajson": datajson_, "status": status_}
insert_request_log("/professor/rubic/template/edit/" + str(id_rubic), "PUT", str(Payload), ip_, data['email'])
if get_permission_by_user('Update_rubic', data['rol']) == 'True':
if not this_is_my_template_rubic(id_rubic, data['email']):
response_str = {'message': 'Acción no permitida en el desafío', 'code': 403}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
response = edit_template_rubic(title_, description_, datajson_, status_, id_rubic)
if response >= 1:
message = 'Template de rúbrica editado exitosamente'
code = 200
else:
message = 'Algo sucedió en la base de datos'
code = 503
response_str = {'message': message, 'code': code}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
else:
message = 'Acción no permitida'
code = 403
response_str = {'message': message, 'code': code}
insert_general_log(str(response_str), ip_, data['email'])
return jsonify(response_str)
except Exception as e:
message = 'Se ha producido un error en nuestros servidores'
code = 500
response_str = str({'error': "/professor/rubic/template/edit/" + str(id_rubic), 'detalle': str(e)})
insert_general_log(response_str, ip_, data['email'])
return jsonify({'message': message, 'code': code})
| 49.215686
| 227
| 0.599431
| 2,034
| 17,570
| 4.996559
| 0.111603
| 0.061694
| 0.029224
| 0.035127
| 0.887435
| 0.869625
| 0.844337
| 0.842173
| 0.842173
| 0.824068
| 0
| 0.020186
| 0.264087
| 17,570
| 356
| 228
| 49.353933
| 0.765816
| 0.061355
| 0
| 0.785953
| 0
| 0.013378
| 0.251389
| 0.043292
| 0
| 0
| 0
| 0.011236
| 0
| 1
| 0.020067
| false
| 0
| 0.013378
| 0
| 0.137124
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
ee2f495057c17816a65b138f16892ac8f00d5a0d
| 147
|
py
|
Python
|
deepwave/base/__init__.py
|
vkazei/deepwave
|
032bb06328673f4f824fbca20f09ba7bb277c8d1
|
[
"MIT"
] | 73
|
2018-07-16T13:57:09.000Z
|
2022-03-24T04:08:27.000Z
|
deepwave/base/__init__.py
|
vkazei/deepwave
|
032bb06328673f4f824fbca20f09ba7bb277c8d1
|
[
"MIT"
] | 41
|
2018-07-14T15:44:13.000Z
|
2022-03-25T09:35:08.000Z
|
deepwave/base/__init__.py
|
vkazei/deepwave
|
032bb06328673f4f824fbca20f09ba7bb277c8d1
|
[
"MIT"
] | 20
|
2018-12-02T14:42:59.000Z
|
2022-03-21T15:52:52.000Z
|
"""Base classes for Deepwave."""
import deepwave.base.model
import deepwave.base.propagator
import deepwave.base.pad
import deepwave.base.extract
| 21
| 32
| 0.809524
| 20
| 147
| 5.95
| 0.45
| 0.470588
| 0.605042
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088435
| 147
| 6
| 33
| 24.5
| 0.88806
| 0.176871
| 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
| 1
| 0
|
0
| 7
|
ee3dbda84dbd69d1f7d03f60f3788d8109810602
| 6,683
|
py
|
Python
|
loldib/getratings/models/NA/na_taliyah/na_taliyah_top.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_taliyah/na_taliyah_top.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_taliyah/na_taliyah_top.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Taliyah_Top_Aatrox(Ratings):
pass
class NA_Taliyah_Top_Ahri(Ratings):
pass
class NA_Taliyah_Top_Akali(Ratings):
pass
class NA_Taliyah_Top_Alistar(Ratings):
pass
class NA_Taliyah_Top_Amumu(Ratings):
pass
class NA_Taliyah_Top_Anivia(Ratings):
pass
class NA_Taliyah_Top_Annie(Ratings):
pass
class NA_Taliyah_Top_Ashe(Ratings):
pass
class NA_Taliyah_Top_AurelionSol(Ratings):
pass
class NA_Taliyah_Top_Azir(Ratings):
pass
class NA_Taliyah_Top_Bard(Ratings):
pass
class NA_Taliyah_Top_Blitzcrank(Ratings):
pass
class NA_Taliyah_Top_Brand(Ratings):
pass
class NA_Taliyah_Top_Braum(Ratings):
pass
class NA_Taliyah_Top_Caitlyn(Ratings):
pass
class NA_Taliyah_Top_Camille(Ratings):
pass
class NA_Taliyah_Top_Cassiopeia(Ratings):
pass
class NA_Taliyah_Top_Chogath(Ratings):
pass
class NA_Taliyah_Top_Corki(Ratings):
pass
class NA_Taliyah_Top_Darius(Ratings):
pass
class NA_Taliyah_Top_Diana(Ratings):
pass
class NA_Taliyah_Top_Draven(Ratings):
pass
class NA_Taliyah_Top_DrMundo(Ratings):
pass
class NA_Taliyah_Top_Ekko(Ratings):
pass
class NA_Taliyah_Top_Elise(Ratings):
pass
class NA_Taliyah_Top_Evelynn(Ratings):
pass
class NA_Taliyah_Top_Ezreal(Ratings):
pass
class NA_Taliyah_Top_Fiddlesticks(Ratings):
pass
class NA_Taliyah_Top_Fiora(Ratings):
pass
class NA_Taliyah_Top_Fizz(Ratings):
pass
class NA_Taliyah_Top_Galio(Ratings):
pass
class NA_Taliyah_Top_Gangplank(Ratings):
pass
class NA_Taliyah_Top_Garen(Ratings):
pass
class NA_Taliyah_Top_Gnar(Ratings):
pass
class NA_Taliyah_Top_Gragas(Ratings):
pass
class NA_Taliyah_Top_Graves(Ratings):
pass
class NA_Taliyah_Top_Hecarim(Ratings):
pass
class NA_Taliyah_Top_Heimerdinger(Ratings):
pass
class NA_Taliyah_Top_Illaoi(Ratings):
pass
class NA_Taliyah_Top_Irelia(Ratings):
pass
class NA_Taliyah_Top_Ivern(Ratings):
pass
class NA_Taliyah_Top_Janna(Ratings):
pass
class NA_Taliyah_Top_JarvanIV(Ratings):
pass
class NA_Taliyah_Top_Jax(Ratings):
pass
class NA_Taliyah_Top_Jayce(Ratings):
pass
class NA_Taliyah_Top_Jhin(Ratings):
pass
class NA_Taliyah_Top_Jinx(Ratings):
pass
class NA_Taliyah_Top_Kalista(Ratings):
pass
class NA_Taliyah_Top_Karma(Ratings):
pass
class NA_Taliyah_Top_Karthus(Ratings):
pass
class NA_Taliyah_Top_Kassadin(Ratings):
pass
class NA_Taliyah_Top_Katarina(Ratings):
pass
class NA_Taliyah_Top_Kayle(Ratings):
pass
class NA_Taliyah_Top_Kayn(Ratings):
pass
class NA_Taliyah_Top_Kennen(Ratings):
pass
class NA_Taliyah_Top_Khazix(Ratings):
pass
class NA_Taliyah_Top_Kindred(Ratings):
pass
class NA_Taliyah_Top_Kled(Ratings):
pass
class NA_Taliyah_Top_KogMaw(Ratings):
pass
class NA_Taliyah_Top_Leblanc(Ratings):
pass
class NA_Taliyah_Top_LeeSin(Ratings):
pass
class NA_Taliyah_Top_Leona(Ratings):
pass
class NA_Taliyah_Top_Lissandra(Ratings):
pass
class NA_Taliyah_Top_Lucian(Ratings):
pass
class NA_Taliyah_Top_Lulu(Ratings):
pass
class NA_Taliyah_Top_Lux(Ratings):
pass
class NA_Taliyah_Top_Malphite(Ratings):
pass
class NA_Taliyah_Top_Malzahar(Ratings):
pass
class NA_Taliyah_Top_Maokai(Ratings):
pass
class NA_Taliyah_Top_MasterYi(Ratings):
pass
class NA_Taliyah_Top_MissFortune(Ratings):
pass
class NA_Taliyah_Top_MonkeyKing(Ratings):
pass
class NA_Taliyah_Top_Mordekaiser(Ratings):
pass
class NA_Taliyah_Top_Morgana(Ratings):
pass
class NA_Taliyah_Top_Nami(Ratings):
pass
class NA_Taliyah_Top_Nasus(Ratings):
pass
class NA_Taliyah_Top_Nautilus(Ratings):
pass
class NA_Taliyah_Top_Nidalee(Ratings):
pass
class NA_Taliyah_Top_Nocturne(Ratings):
pass
class NA_Taliyah_Top_Nunu(Ratings):
pass
class NA_Taliyah_Top_Olaf(Ratings):
pass
class NA_Taliyah_Top_Orianna(Ratings):
pass
class NA_Taliyah_Top_Ornn(Ratings):
pass
class NA_Taliyah_Top_Pantheon(Ratings):
pass
class NA_Taliyah_Top_Poppy(Ratings):
pass
class NA_Taliyah_Top_Quinn(Ratings):
pass
class NA_Taliyah_Top_Rakan(Ratings):
pass
class NA_Taliyah_Top_Rammus(Ratings):
pass
class NA_Taliyah_Top_RekSai(Ratings):
pass
class NA_Taliyah_Top_Renekton(Ratings):
pass
class NA_Taliyah_Top_Rengar(Ratings):
pass
class NA_Taliyah_Top_Riven(Ratings):
pass
class NA_Taliyah_Top_Rumble(Ratings):
pass
class NA_Taliyah_Top_Ryze(Ratings):
pass
class NA_Taliyah_Top_Sejuani(Ratings):
pass
class NA_Taliyah_Top_Shaco(Ratings):
pass
class NA_Taliyah_Top_Shen(Ratings):
pass
class NA_Taliyah_Top_Shyvana(Ratings):
pass
class NA_Taliyah_Top_Singed(Ratings):
pass
class NA_Taliyah_Top_Sion(Ratings):
pass
class NA_Taliyah_Top_Sivir(Ratings):
pass
class NA_Taliyah_Top_Skarner(Ratings):
pass
class NA_Taliyah_Top_Sona(Ratings):
pass
class NA_Taliyah_Top_Soraka(Ratings):
pass
class NA_Taliyah_Top_Swain(Ratings):
pass
class NA_Taliyah_Top_Syndra(Ratings):
pass
class NA_Taliyah_Top_TahmKench(Ratings):
pass
class NA_Taliyah_Top_Taliyah(Ratings):
pass
class NA_Taliyah_Top_Talon(Ratings):
pass
class NA_Taliyah_Top_Taric(Ratings):
pass
class NA_Taliyah_Top_Teemo(Ratings):
pass
class NA_Taliyah_Top_Thresh(Ratings):
pass
class NA_Taliyah_Top_Tristana(Ratings):
pass
class NA_Taliyah_Top_Trundle(Ratings):
pass
class NA_Taliyah_Top_Tryndamere(Ratings):
pass
class NA_Taliyah_Top_TwistedFate(Ratings):
pass
class NA_Taliyah_Top_Twitch(Ratings):
pass
class NA_Taliyah_Top_Udyr(Ratings):
pass
class NA_Taliyah_Top_Urgot(Ratings):
pass
class NA_Taliyah_Top_Varus(Ratings):
pass
class NA_Taliyah_Top_Vayne(Ratings):
pass
class NA_Taliyah_Top_Veigar(Ratings):
pass
class NA_Taliyah_Top_Velkoz(Ratings):
pass
class NA_Taliyah_Top_Vi(Ratings):
pass
class NA_Taliyah_Top_Viktor(Ratings):
pass
class NA_Taliyah_Top_Vladimir(Ratings):
pass
class NA_Taliyah_Top_Volibear(Ratings):
pass
class NA_Taliyah_Top_Warwick(Ratings):
pass
class NA_Taliyah_Top_Xayah(Ratings):
pass
class NA_Taliyah_Top_Xerath(Ratings):
pass
class NA_Taliyah_Top_XinZhao(Ratings):
pass
class NA_Taliyah_Top_Yasuo(Ratings):
pass
class NA_Taliyah_Top_Yorick(Ratings):
pass
class NA_Taliyah_Top_Zac(Ratings):
pass
class NA_Taliyah_Top_Zed(Ratings):
pass
class NA_Taliyah_Top_Ziggs(Ratings):
pass
class NA_Taliyah_Top_Zilean(Ratings):
pass
class NA_Taliyah_Top_Zyra(Ratings):
pass
| 16.026379
| 46
| 0.77151
| 972
| 6,683
| 4.878601
| 0.151235
| 0.203712
| 0.407423
| 0.494728
| 0.808941
| 0.808941
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166243
| 6,683
| 416
| 47
| 16.064904
| 0.851041
| 0
| 0
| 0.498195
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.498195
| 0.00361
| 0
| 0.501805
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 8
|
c98c391f6d55d8f2612150b3d46f145f04159d9b
| 112
|
py
|
Python
|
second_strike_bot/utils.py
|
amshin98/2nd-strike-bot
|
d08bd79435d968416265d263c58f155d2b8f5f21
|
[
"Apache-2.0"
] | null | null | null |
second_strike_bot/utils.py
|
amshin98/2nd-strike-bot
|
d08bd79435d968416265d263c58f155d2b8f5f21
|
[
"Apache-2.0"
] | 11
|
2021-02-05T10:28:55.000Z
|
2021-02-09T05:04:59.000Z
|
second_strike_bot/utils.py
|
amshin98/2nd-strike-bot
|
d08bd79435d968416265d263c58f155d2b8f5f21
|
[
"Apache-2.0"
] | 1
|
2021-02-22T21:22:20.000Z
|
2021-02-22T21:22:20.000Z
|
from asyncio import TimeoutError
def is_reaction_emoji(reaction, emoji):
return reaction.emoji == emoji
| 22.4
| 40
| 0.767857
| 14
| 112
| 6
| 0.642857
| 0.464286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169643
| 112
| 5
| 41
| 22.4
| 0.903226
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
c9bc276590c4d59f221162c7a3546b11ac4d1017
| 238,519
|
py
|
Python
|
analysis/utils/analysis_tools.py
|
chrelli/3DDD_social_mouse_tracker
|
291d2ed90029628dd65db0ce3e8972b721159a15
|
[
"Apache-2.0"
] | 1
|
2022-02-10T07:26:09.000Z
|
2022-02-10T07:26:09.000Z
|
analysis/utils/analysis_tools.py
|
chrelli/3DDD_social_mouse_tracker
|
291d2ed90029628dd65db0ce3e8972b721159a15
|
[
"Apache-2.0"
] | 1
|
2022-02-11T06:55:29.000Z
|
2022-02-12T22:26:44.000Z
|
analysis/utils/analysis_tools.py
|
chrelli/3DDD_social_mouse_tracker
|
291d2ed90029628dd65db0ce3e8972b721159a15
|
[
"Apache-2.0"
] | null | null | null |
# IDEA: Add neck to the posture map?
from IPython import get_ipython
# QT for movable plots
# %load_ext autoreload
# %autoreload 2
import time, os, sys, shutil
# from utils.fitting_utils import *
# for math and plotting
import pandas as pd
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
# %matplotlib notebook
# %matplotlib inline
# %matplotlib widget
# %matplotlib qt
from itertools import compress # for list selection with logical
from tqdm import tqdm
from multiprocessing import Process
# ALLSO JIT STUFF
from numba import jit, njit
# and pytorch
import torch
import sys, os, pickle
# import cv2
from colour import Color
import h5py
from tqdm import tqdm, tqdm_notebook
import glob
import itertools
# setup for pretty plotting
import matplotlib
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 13})
# SET UP MATPLOTLIB
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex=False)
matplotlib.rcParams.update({'font.size': 13})
# AHHH, Ok: https://stackoverflow.com/questions/11367736/matplotlib-consistent-font-using-latex
matplotlib.rcParams['mathtext.fontset'] = 'custom'
matplotlib.rcParams['mathtext.rm'] = 'Liberation Sans'
matplotlib.rcParams['mathtext.it'] = 'Liberation Sans:italic'
matplotlib.rcParams['mathtext.bf'] = 'Liberation Sans:bold'
matplotlib.rcParams['mathtext.fallback_to_cm'] = False
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
def adjust_spines(ax, spines):
for loc, spine in ax.spines.items():
if loc in spines:
spine.set_position(('outward', 10)) # outward by 10 points
spine.set_smart_bounds(True)
else:
spine.set_color('none') # don't draw spine
# turn off ticks where there is no spine
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
# no yaxis ticks
ax.yaxis.set_ticks([])
if 'bottom' in spines:
ax.xaxis.set_ticks_position('bottom')
else:
# no xaxis ticks
ax.xaxis.set_ticks([])
def adjust_spines2(ax, spines):
for loc, spine in ax.spines.items():
if loc in spines:
spine.set_position(('outward', 10)) # outward by 10 points
# spine.set_smart_bounds(True)
else:
spine.set_color('none') # don't draw spine
# turn off ticks where there is no spine
if 'left' in spines:
ax.yaxis.set_ticks_position('left')
else:
# no yaxis ticks
ax.yaxis.set_ticks([])
if 'bottom' in spines:
ax.xaxis.set_ticks_position('bottom')
else:
# no xaxis ticks
ax.xaxis.set_ticks([])
# %debug
# MAKE a function that takes a Frame of raw data and plots a particle
# Maybe make this an object, even though I hate those...
from utils.cuda_tracking_utils_weights_for_figures import *
# from utils.cuda_tracking_utils import unpack_from_jagged, cheap4d
import matplotlib.animation as animation
# def particles_to_body_supports_cuda(part,implant = False):
# if implant:
# beta = part[:,0]
# gamma = part[:,1]
# s = part[:,2]
# #todo naming here is off
# psi = part[:,3]
# theta = part[:,4]
# phi = part[:,5]
# t_body = part[:,6:9]
# else:
# beta = part[:,0]
# gamma = part[:,1]
# s = part[:,2]
# #todo naming here is off
# theta = part[:,3]
# phi = part[:,4]
# t_body = part[:,5:8]
# # calculate vectors holding the hip values!
# # the values are n_particles long
# a_hip = a_hip_0 + a_hip_delta * s
# b_hip = b_hip_0 + b_hip_delta * (1.-s)
# d_hip = .75 * a_hip
# # we need to do cos an sin on the angles!
# # and other places too over and over, let's just keep them in mem?
# one = torch.ones_like(s)
# zero = torch.zeros_like(s)
# # this is the rotation matrix of the body - it does not need
# R_body = make_xyz_rotation(zero,beta,gamma,one,zero)
# ### COORDS IN MOUSE BODY FRAME ###
# # c_hip is zero
# # c_mid is the hinge point
# c_mid = torch.stack([d_hip,zero,zero],dim=1)
# # the nose-pointing vector, make more
# cos_theta = torch.cos(theta)
# sin_theta = torch.sin(theta)
# cos_phi = torch.cos(phi)
# sin_phi = torch.sin(phi)
# # a unit vector pointing to the nose
# nose_pointer = torch.stack([cos_theta, sin_theta*cos_phi, sin_theta*sin_phi], dim=1)
# # a unit vector along the x-axis
# x_pointer = torch.stack([one, zero,zero], dim=1)
# # use the nose-pointing vector to calculate the nose rotation matrix
# R_head = rotation_matrix_vec2vec(x_pointer,nose_pointer)
# c_nose = c_mid + torch.einsum('aij,aj->ai',[R_head, d_nose * x_pointer ])
# # for the implant, we allow rotation about x also (maybe limit this to realistic implant-up-scenarios?)
# if implant:
# cos_psi = torch.cos(psi)
# sin_psi = torch.sin(psi)
# c_impl = c_mid + torch.einsum('aij,aj->ai',[R_head, torch.stack([ x_impl*one, sin_psi*z_impl,cos_psi*z_impl ],dim=1) ])
# c_impl = torch.einsum('aij,aj->ai',[R_body,c_impl]) + t_body
# c_impl = torch.unsqueeze(c_impl,1).transpose(-2,-1)
# else:
# c_impl = c_mid *2.
# # and these are the rest of the anchor points
# c_ass = torch.stack([-a_hip,zero,zero],dim=1)
# c_tip = c_mid + torch.einsum('aij,aj->ai',[R_head, (d_nose+a_nose) * x_pointer ])
# # c_hip = torch.stack([zero, zero, zero], dim=1)
# ### CONVERT FROM BODY FRAME TO WORLD FRAME ###
# # todo maybe pack these and batch in some way?
# c_hip = t_body
# c_nose = torch.einsum('aij,aj->ai',[R_body,c_nose]) + t_body
# c_ass = torch.einsum('aij,aj->ai',[R_body,c_ass]) + t_body
# c_tip = torch.einsum('aij,aj->ai',[R_body,c_tip]) + t_body
# c_mid = torch.einsum('aij,aj->ai',[R_body,c_mid]) + t_body
# # unsqueeze for auto broadcasting
# c_hip = torch.unsqueeze(c_hip,1).transpose(-2,-1)
# c_nose = torch.unsqueeze(c_nose,1).transpose(-2,-1)
# c_ass = torch.unsqueeze(c_ass,1).transpose(-2,-1)
# c_tip = torch.unsqueeze(c_tip,1).transpose(-2,-1)
# c_mid = torch.unsqueeze(c_mid,1).transpose(-2,-1)
# # Make the matrices for the ellipsoids, nose is always the same
# R_nose = torch.einsum('aij,ajk->aik',[R_body,R_head])
# body_support = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
# return body_support
def particles_to_body_supports_cuda(part,implant = False):
if implant:
beta = part[:,0]
gamma = part[:,1]
s = part[:,2]
#todo naming here is off
psi = part[:,3]
theta = part[:,4]
phi = part[:,5]
t_body = part[:,6:9]
else:
beta = part[:,0]
gamma = part[:,1]
s = part[:,2]
#todo naming here is off
theta = part[:,3]
phi = part[:,4]
t_body = part[:,5:8]
# calculate vectors holding the hip values!
# the values are n_particles long
a_hip = a_hip_0 + a_hip_delta * s
b_hip = b_hip_0 + b_hip_delta * (1.-s)
d_hip = .75 * a_hip
# we need to do cos an sin on the angles!
# and other places too over and over, let's just keep them in mem?
one = torch.ones_like(s)
zero = torch.zeros_like(s)
# this is the rotation matrix of the body - it does not need
R_body = make_xyz_rotation(zero,beta,gamma,one,zero)
### COORDS IN MOUSE BODY FRAME ###
# c_hip is zero
# c_mid is the hinge point
c_mid = torch.stack([d_hip,zero,zero],dim=1)
# OLD style of unpacking
# # the nose-pointing vector, make more
# cos_theta = torch.cos(theta)
# sin_theta = torch.sin(theta)
# cos_phi = torch.cos(phi)
# sin_phi = torch.sin(phi)
# # a unit vector pointing to the nose
# nose_pointer = torch.stack([cos_theta, sin_theta*cos_phi, sin_theta*sin_phi], dim=1)
# # a unit vector along the x-axis
# x_pointer = torch.stack([one, zero,zero], dim=1)
# # use the nose-pointing vector to calculate the nose rotation matrix
# R_head = rotation_matrix_vec2vec(x_pointer,nose_pointer)
# c_nose = c_mid + torch.einsum('aij,aj->ai',[R_head, d_nose * x_pointer ])
# New style of unpacking the head!
R_head = make_xyz_rotation(zero,theta,phi,one,zero)
x_pointer = torch.stack([one, zero,zero], dim=1)
c_nose = c_mid + torch.einsum('aij,aj->ai',[R_head, d_nose * x_pointer ])
# for the implant, we allow rotation about x also (maybe limit this to realistic implant-up-scenarios?)
if implant:
cos_psi = torch.cos(psi)
sin_psi = torch.sin(psi)
c_impl = c_mid + torch.einsum('aij,aj->ai',[R_head, torch.stack([ x_impl*one, sin_psi*z_impl,cos_psi*z_impl ],dim=1) ])
c_impl = torch.einsum('aij,aj->ai',[R_body,c_impl]) + t_body
c_impl = torch.unsqueeze(c_impl,1).transpose(-2,-1)
else:
c_impl = c_mid *2.
# and these are the rest of the anchor points
c_ass = torch.stack([-a_hip,zero,zero],dim=1)
c_tip = c_mid + torch.einsum('aij,aj->ai',[R_head, (d_nose+a_nose) * x_pointer ])
# c_hip = torch.stack([zero, zero, zero], dim=1)
### CONVERT FROM BODY FRAME TO WORLD FRAME ###
# todo maybe pack these and batch in some way?
c_hip = t_body
c_nose = torch.einsum('aij,aj->ai',[R_body,c_nose]) + t_body
c_ass = torch.einsum('aij,aj->ai',[R_body,c_ass]) + t_body
c_tip = torch.einsum('aij,aj->ai',[R_body,c_tip]) + t_body
c_mid = torch.einsum('aij,aj->ai',[R_body,c_mid]) + t_body
# unsqueeze for auto broadcasting
c_hip = torch.unsqueeze(c_hip,1).transpose(-2,-1)
c_nose = torch.unsqueeze(c_nose,1).transpose(-2,-1)
c_ass = torch.unsqueeze(c_ass,1).transpose(-2,-1)
c_tip = torch.unsqueeze(c_tip,1).transpose(-2,-1)
c_mid = torch.unsqueeze(c_mid,1).transpose(-2,-1)
# Make the matrices for the ellipsoids, nose is always the same
R_nose = torch.einsum('aij,ajk->aik',[R_body,R_head])
body_support = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
return body_support
class PlotMachine(object):
def __init__(self,tracked_behavior,jagged_lines,what_to_plot = 'guess'):
self.tracked_behavior = tracked_behavior
self.jagged_lines = jagged_lines
# useful functions, get the raw data in plottable format
# pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(jagged_lines[start_frame])
# useful functions, get the fitted data in plottable format
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 13})
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
# unpack
self.guessing_holder = tracked_behavior['guessing_holder']
self.tracking_holder = tracked_behavior['tracking_holder']
self.start_frame = tracked_behavior['start_frame']
self.track_or_guess = what_to_plot
self.n_frames = self.tracking_holder.shape[1]
self.v_ed = None
self.v_ed_reject = None
def kernel_smoothing(self):
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
kernel = easy_kernel(3)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def make_3d_axis(self):
# 3D plot of the
fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(111, projection='3d')
# add to self for use later
self.fig = fig
self.ax = ax
def add_raw_data(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# add to axis 3D plot of Sphere
self.h_pc = self.ax.scatter(X, Y, Z, zdir='z', s=10, c='k', alpha = .05,rasterized=False)
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
# loop over the types of body, and make emptyscatter plots
self.h_kp_list = []
for body in body_indices:
h_kp = self.ax.scatter([],[],[], zdir='z', s=100, c=body_colors[body],rasterized=False)
self.h_kp_list.append(h_kp)
# THEN set the 3d values to be what the shoud be
for body in body_indices:
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# self.h_kp_list = []
# for i,body in enumerate(ikeyp):
# h_kp = self.ax.scatter(keyp[i,0], keyp[i,1], keyp[i,2], zdir='z', s=100, c=body_colors[body],rasterized=False)
# self.h_kp_list.append(h_kp)
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
def plot_skeleton(self,body_support,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# add the body points
for p in [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]:
if (len(p.shape) >= 3):
h_bp = self.ax.scatter(p[:,0,0],p[:,1,0],p[:,2,0],zdir='z', s=50, alpha = 1 , c=color,rasterized=False)
self.h_bp_list[body_idx].append(h_bp)
# and the lines between body parts
for p,q in zip([c_nose,c_nose,c_mid,c_impl,c_impl],[c_mid,c_tip,c_ass,c_nose,c_tip]):
if (len(p.shape) >= 3) and (len(q.shape) >= 3):
for ii in range(p.shape[0]):
h_skel = self.ax.plot([p[ii,0,0],q[ii,0,0]],[p[ii,1,0],q[ii,1,0]],[p[ii,2,0],q[ii,2,0]],c=color,lw = 4)
self.h_skel_list[body_idx].append(h_skel)
def add_wireframe_to_axis(self,ax,R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip',this_color='k',this_alpha=.4):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
h_hip = ax.plot_wireframe(x, y, z, color=this_color,alpha = this_alpha)
return h_hip
def plot_ellipsoids(self,part,body_support,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# this is not so elegant, hm hm
s = part[:,2]
a_hip = a_hip_0 + a_hip_delta * s
b_hip = b_hip_0 + b_hip_delta * (1.-s)
d_hip = .75 * a_hip
for RR,cc,style in zip([R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']):
if (len(cc.shape) >= 3):
h_hip = self.add_wireframe_to_axis(self.ax,RR[0,...],
cc[0,...],
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip.cpu().numpy(),
b_hip.cpu().numpy(),
r_impl.cpu().numpy(),
style=style,this_color=color)
self.h_hip_list[body_idx].append(h_hip)
def add_fit(self,frame,plot_ellipsoids = True):
# get the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
# store
self.body_support_0 = body_support_0
self.body_support_1 = body_support_1
# and plot!
self.h_skel_list = [[],[]]
self.h_bp_list = [[],[]]
self.plot_skeleton(body_support_0,color = 'k',body_idx = 0)
self.plot_skeleton(body_support_1,color = 'peru',body_idx = 1)
self.h_hip_list = [[],[]]
if plot_ellipsoids:
self.plot_ellipsoids(part,body_support_0,color = 'k',body_idx = 0)
self.plot_ellipsoids(part,body_support_1,color = 'peru',body_idx = 1)
def add_trace(self,frame,trace_length=90,trace_clip = None,decay_factor=.9, type_list = ['tip']):
# get the particle, convert to torch tensor, calculate body supports
print(frame)
i_frame = frame-self.start_frame
print(i_frame)
# make a holder for the lines
self.h_trace_list = [[None]*5,[None]*5]
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
print("i_trace_start is {} and i_frame is {}".format(i_trace_start,i_frame))
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,(i_trace_start):(i_frame)]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
if part.shape[1] == 0:
# just skip if there is no trace
print(part.shape)
return
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
# type_list = ['ass','hip','nose','tip']
# self.unpack_trace(body_support_0,what_type=type_list,color='aqua')
# self.unpack_trace(body_support_1,what_type=type_list,color='fuchsia')
self.unpack_trace(body_support_0,body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(body_support_1,body_idx = 1,what_type=type_list,color='peru')
def finish_3d_axis(self,view_style = 'top', zoom = False, dump = False):
# finish the labeling, plot adjustments, dump and show
ax = self.ax
# ax.set_xlabel('$x$ (mm)')
# ax.set_ylabel('\n$y$ (mm)')
# zlabel = ax.set_zlabel('\n$z$ (mm)')
if self.max_range is not None:
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
if view_style == 'top':
az = -30
el = 90
if view_style == 'side':
az = -15
el = 9
if view_style == 'mix':
az = 150
el = 50
if view_style == 'ex':
az = -14
el = 46
if view_style == 'ex':
az = -46
el = 23
if zoom:
def mean_point(body_support,impl = False):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
if impl==True:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip,c_impl])
else:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip])
mean_0 = np.mean(points_0,0)
return mean_0
mean_0 = mean_point(self.body_support_0,impl= True)
mean_1 = mean_point(self.body_support_1, impl = False)
overall_mean = np.hstack([mean_0,mean_1])
mu_zoom = np.mean(overall_mean,1)
scaling = 2
d_zoom = ( mean_0.ravel() - mean_1.ravel() )*scaling
# print(d_zoom)
# print(mu_zoom)
# self.ax.scatter(mu_zoom[0],mu_zoom[1],mu_zoom[2],zdir='z', s=500, alpha = 1 , c='pink',rasterized=False)
pp = np.vstack([mu_zoom-d_zoom,mu_zoom,mu_zoom+d_zoom])
# print(pp)
# self.ax.plot(pp[:,0],pp[:,1],pp[:,2],zdir='z',alpha = 1,lw=10 , c='pink',rasterized=False)
X,Y,Z = pp[:,0],pp[:,1],pp[:,2]
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
ax.view_init(elev=el, azim=az)
def unpack_trace(self,body_support,body_idx = 0,what_type=['hip'],color='k'):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[:,0],dat[:,1],dat[:,2]
h_trace = self.ax.plot(X,Y,Z,lw=2,c=color,alpha = .65)
self.h_trace_list[body_idx][ii_c_list[type_list == ttt][0]] = h_trace
print(what_type)
def dump_to_disk(self,tag = ""):
print("saving w/tag: "+tag)
plt.tight_layout()
plt.savefig('figure_raw_pics/figure_5/fitting_cartoon/Plotter'+tag+'.pdf',transparent=True)
def add_frame_number(self,frame):
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.74, 0.07, "Frame:" ,ha='left',fontsize = 20, transform=self.ax.transAxes)
self.h_frame_number = self.ax.text2D(0.95, 0.07, str(frame),ha='right', fontsize = 20, transform=self.ax.transAxes)
pass
def add_frame_time(self,frame,start_frame):
cam_fps = 60 #Hz
t_now = (frame-start_frame)/cam_fps
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.71, 0.03, "Time:" ,ha='left', fontsize = 20, transform=self.ax.transAxes)
self.h_frame_time = self.ax.text2D(0.95, 0.03, "{:0.2f} s".format(t_now) ,ha='right',fontsize = 20, transform=self.ax.transAxes)
pass
def add_ed(self,frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c='r',lw = 4)
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
ccc = 'blueviolet'
self.h_ed_reject = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c=ccc,lw = 4)
pass
def make(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track' #hk
self.add_fit(frame)
self.track_or_guess = 'track'
self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel1(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = True)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel2(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = False)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel3(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
# self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = False)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def video(self,frame_list=30*60+np.arange(100)*100, savepath = None,view_override = None):
# MAKE STARTING PLOT
self.trace_length = 960
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
self.track_or_guess = 'track'
self.add_fit(frame_list[0])
# self.track_or_guess = 'guess'
self.add_trace(frame_list[0],trace_length=self.trace_length,trace_clip = self.trace_clip ,type_list = self.type_list)
self.track_or_guess = 'track'
if self.v_ed is not None:
self.add_ed(frame_list[0],case='ed')
self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_frame_number(frame):
# self.h_frame_number.set_text("Frame: {}".format(frame))
self.h_frame_number.set_text(str(frame))
def update_frame_time(frame,start_frame = frame_list[0]):
cam_fps = 60. #Hz
t_now = (frame-start_frame)/cam_fps
self.h_frame_time.set_text("{:0.2f} s".format(t_now))
def update_skeleton(body_support,body_idx):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# add the body points
for j,p in enumerate([c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]):
if (len(p.shape) >= 3):
# update the location of the body point!
self.h_bp_list[body_idx][j]._offsets3d = (p[:,0,0],p[:,1,0],p[:,2,0])
# and the lines between body parts
for j, (p,q) in enumerate( zip([c_nose,c_nose,c_mid,c_impl,c_impl],[c_mid,c_tip,c_ass,c_nose,c_tip]) ):
if (len(p.shape) >= 3) and (len(q.shape) >= 3):
for ii in range(p.shape[0]):
# lines are an extra level deep for some stupid matplotlib reason
self.h_skel_list[body_idx][j][0].set_xdata([p[ii,0,0],q[ii,0,0]])
self.h_skel_list[body_idx][j][0].set_ydata([p[ii,1,0],q[ii,1,0]])
self.h_skel_list[body_idx][j][0].set_3d_properties([p[ii,2,0],q[ii,2,0]])
def update_wireframe_lines(h_hip,X,Y,Z):
# h_hip is the handle to the lines3dcollection
# much of the code is taken from the source of the marplotlib wireframe plotting
X, Y, Z = np.broadcast_arrays(X, Y, Z)
rows, cols = Z.shape
rstride = 1
cstride = 1
# We want two sets of lines, one running along the "rows" of
# Z and another set of lines running along the "columns" of Z.
# This transpose will make it easy to obtain the columns.
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
if rstride:
rii = list(range(0, rows, rstride))
# Add the last index only if needed
if rows > 0 and rii[-1] != (rows - 1):
rii += [rows-1]
else:
rii = []
if cstride:
cii = list(range(0, cols, cstride))
# Add the last index only if needed
if cols > 0 and cii[-1] != (cols - 1):
cii += [cols-1]
else:
cii = []
xlines = [X[i] for i in rii]
ylines = [Y[i] for i in rii]
zlines = [Z[i] for i in rii]
txlines = [tX[i] for i in cii]
tylines = [tY[i] for i in cii]
tzlines = [tZ[i] for i in cii]
lines = ([list(zip(xl, yl, zl))
for xl, yl, zl in zip(xlines, ylines, zlines)]
+ [list(zip(xl, yl, zl))
for xl, yl, zl in zip(txlines, tylines, tzlines)])
h_hip.set_segments(lines)
def calculate_wireframe_points(R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip'):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
return x,y,z
def update_ellipsoids(part,body_support,body_idx):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# this is not so elegant, hm hm
s = part[:,2]
a_hip = a_hip_0 + a_hip_delta * s
b_hip = b_hip_0 + b_hip_delta * (1.-s)
d_hip = .75 * a_hip
for jj,(RR,cc,style) in enumerate( zip([R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']) ):
if (len(cc.shape) >= 3):
X,Y,Z = calculate_wireframe_points(RR[0,...],
cc[0,...],
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip.cpu().numpy(),
b_hip.cpu().numpy(),
r_impl.cpu().numpy(),
style)
h_hip = self.h_hip_list[body_idx][jj]
update_wireframe_lines(h_hip,X,Y,Z)
def update_trace_3dlines(body_support,body_idx=0,what_type=['hip']):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[:,0],dat[:,1],dat[:,2]
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_xdata(X)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_ydata(Y)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_3d_properties(Z)
def calculate_new_trace(frame,trace_length=None,trace_clip = None,decay_factor=.9, type_list = None):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,(i_trace_start):(i_frame)]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
# overwrite HACK for now
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
if part.shape[1] == 0:
# just skip if there is no trace
return
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
update_trace_3dlines(body_support_0,body_idx=0,what_type = type_list)
update_trace_3dlines(body_support_1,body_idx=1,what_type = type_list)
if self.v_ed is not None:
def update_ed(frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
self.h_ed_reject[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed_reject[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed_reject[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
pass
def update_pc(frame):
# get new raw data!
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# update
self.h_pc._offsets3d = (X,Y,Z)
# THEN set the 3d values to be what the shoud be
for body in range(4):
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# update the fit as well!
# get the particle, convert to torch tensor, calculate body supports
self.track_or_guess = 'track'
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
update_skeleton(body_support_0,body_idx=0)
update_skeleton(body_support_1,body_idx=1)
update_ellipsoids(part,body_support_0,body_idx=0)
update_ellipsoids(part,body_support_1,body_idx=1)
calculate_new_trace(frame,trace_length=self.trace_length,trace_clip = self.trace_clip,decay_factor=.9, type_list = self.type_list)
update_frame_number(frame)
update_frame_time(frame)
if self.v_ed is not None:
update_ed(frame,case='ed')
update_ed(frame,case='ed_reject')
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, frame_list[1:], interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
ani.save(savepath,writer='imagemagick',fps=fps)
plt.rcParams['animation.html'] = 'html5'
ani
def rotation(self,frame_list=30*60+np.arange(100)*100, savepath = None,view_override = None):
# MAKE STARTING PLOT
self.trace_length = 960
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
self.track_or_guess = 'track'
self.add_fit(frame_list[0])
# self.track_or_guess = 'guess'
self.add_trace(frame_list[0],trace_length=self.trace_length,trace_clip = self.trace_clip ,type_list = self.type_list)
self.track_or_guess = 'track'
if self.v_ed is not None:
self.add_ed(frame_list[0],case='ed')
self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_pc(i):
# get new raw data!
offset = el*np.sin(i*2*np.pi / 360)
plt.gca().view_init(elev=el+offset, azim=az+i)
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, np.linspace(0,360,40), interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~_rotation'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD_rotation'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
ani.save(savepath,writer='imagemagick',fps=fps)
plt.rcParams['animation.html'] = 'html5'
ani
def plot_residuals(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# and get the corresponding fit: the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
# body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
# body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
pos = torch.from_numpy(pos).float().cuda()
# CALCULATE RESIDUAL
dist0,_,self.body_support_0 = particles_to_distance_cuda(part[:,:9],pos[:,:],implant = True)
dist1,_,self.body_support_1 = particles_to_distance_cuda(part[:,9:],pos[:,:])
r = torch.min(dist0[0,:],dist1[0,:])
r = r.cpu().numpy().squeeze()
# Figure out the weighing stuff
w2 = pos_weights**2
rw=np.clip(r,0,.03)
# or weigted mean?
# rm = np.mean(rw)
# correct
rm = np.sum(w2*rw)/np.sum(w2)
# wrong old way:
# rm = np.mean(w2*rw)/np.median(w2)
plt.figure()
plt.plot(r,'.k',label='residual')
plt.plot(rw,'.r',label='cut')
plt.plot(w2,'.g',label='w2')
plt.legend()
plt.show()
# plt.figure()
# plt.hist(w2,100)
# plt.show()
plt.figure(figsize =(10,10))
# plt.scatter(X,Y,c = 1-rw/np.max(rw))
plt.scatter(X,Y,c = w2/np.max(w2))
plt.axis('square')
plt.title("clipped: {}, weighted: {}".format(np.mean(rw),rm))
plt.show()
pass
# tips https://stackoverflow.com/questions/41602588/matplotlib-3d-scatter-animations
# add raw data to plot
# add particle
def kernel_smoothing(self,width = 3):
kernel = easy_kernel(width)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
class PlotMachine_noimpl(object):
def __init__(self,tracked_behavior,jagged_lines,what_to_plot = 'guess', has_implant = False):
self.tracked_behavior = tracked_behavior
self.jagged_lines = jagged_lines
# useful functions, get the raw data in plottable format
# pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(jagged_lines[start_frame])
# useful functions, get the fitted data in plottable format
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 13})
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
# unpack
self.guessing_holder = tracked_behavior['guessing_holder']
self.tracking_holder = tracked_behavior['tracking_holder']
self.start_frame = tracked_behavior['start_frame']
self.has_implant = has_implant
self.track_or_guess = what_to_plot
self.n_frames = self.tracking_holder.shape[1]
self.v_ed = None
self.v_ed_reject = None
def kernel_smoothing(self):
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
kernel = easy_kernel(3)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def make_3d_axis(self):
# 3D plot of the
fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(111, projection='3d')
# add to self for use later
self.fig = fig
self.ax = ax
def add_raw_data(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# add to axis 3D plot of Sphere
self.h_pc = self.ax.scatter(X, Y, Z, zdir='z', s=10, c='k', alpha = .05,rasterized=False)
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
# loop over the types of body, and make emptyscatter plots
self.h_kp_list = []
for body in body_indices:
h_kp = self.ax.scatter([],[],[], zdir='z', s=100, c=body_colors[body],rasterized=False)
self.h_kp_list.append(h_kp)
# THEN set the 3d values to be what the shoud be
for body in body_indices:
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# self.h_kp_list = []
# for i,body in enumerate(ikeyp):
# h_kp = self.ax.scatter(keyp[i,0], keyp[i,1], keyp[i,2], zdir='z', s=100, c=body_colors[body],rasterized=False)
# self.h_kp_list.append(h_kp)
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
def plot_skeleton(self,body_support,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# add the body points
for p in [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]:
if (len(p.shape) >= 3):
h_bp = self.ax.scatter(p[:,0,0],p[:,1,0],p[:,2,0],zdir='z', s=50, alpha = 1 , c=color,rasterized=False)
self.h_bp_list[body_idx].append(h_bp)
# and the lines between body parts
for p,q in zip([c_nose,c_nose,c_mid,c_impl,c_impl],[c_mid,c_tip,c_ass,c_nose,c_tip]):
if (len(p.shape) >= 3) and (len(q.shape) >= 3):
for ii in range(p.shape[0]):
h_skel = self.ax.plot([p[ii,0,0],q[ii,0,0]],[p[ii,1,0],q[ii,1,0]],[p[ii,2,0],q[ii,2,0]],c=color,lw = 4)
self.h_skel_list[body_idx].append(h_skel)
def add_wireframe_to_axis(self,ax,R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip',this_color='k',this_alpha=.4):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
h_hip = ax.plot_wireframe(x, y, z, color=this_color,alpha = this_alpha)
return h_hip
def plot_ellipsoids(self,part,body_support,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# this is not so elegant, hm hm
s = part[:,2]
a_hip = a_hip_0 + a_hip_delta * s
b_hip = b_hip_0 + b_hip_delta * (1.-s)
d_hip = .75 * a_hip
for RR,cc,style in zip([R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']):
if (len(cc.shape) >= 3):
h_hip = self.add_wireframe_to_axis(self.ax,RR[0,...],
cc[0,...],
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip.cpu().numpy(),
b_hip.cpu().numpy(),
r_impl.cpu().numpy(),
style=style,this_color=color)
self.h_hip_list[body_idx].append(h_hip)
def add_fit(self,frame,plot_ellipsoids = True):
# get the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
if self.has_implant:
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
else:
body_support_0 = particles_to_body_supports_cuda(part[:,:8],implant = False)
body_support_1 = particles_to_body_supports_cuda(part[:,8:],implant = False)
# store
self.body_support_0 = body_support_0
self.body_support_1 = body_support_1
# and plot!
self.h_skel_list = [[],[]]
self.h_bp_list = [[],[]]
self.plot_skeleton(body_support_0,color = 'k',body_idx = 0)
self.plot_skeleton(body_support_1,color = 'peru',body_idx = 1)
self.h_hip_list = [[],[]]
if plot_ellipsoids:
self.plot_ellipsoids(part,body_support_0,color = 'k',body_idx = 0)
self.plot_ellipsoids(part,body_support_1,color = 'peru',body_idx = 1)
def add_trace(self,frame,trace_length=90,trace_clip = None,decay_factor=.9, type_list = ['tip']):
# get the particle, convert to torch tensor, calculate body supports
print(frame)
i_frame = frame-self.start_frame
print(i_frame)
# make a holder for the lines
self.h_trace_list = [[None]*5,[None]*5]
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
print("i_trace_start is {} and i_frame is {}".format(i_trace_start,i_frame))
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,(i_trace_start):(i_frame)]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
if part.shape[1] == 0:
# just skip if there is no trace
print(part.shape)
return
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
if self.has_implant:
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
else:
body_support_0 = particles_to_body_supports_cuda(part[:,:8],implant = False)
body_support_1 = particles_to_body_supports_cuda(part[:,8:],implant = False)
# type_list = ['ass','hip','nose','tip']
# self.unpack_trace(body_support_0,what_type=type_list,color='aqua')
# self.unpack_trace(body_support_1,what_type=type_list,color='fuchsia')
self.unpack_trace(body_support_0,body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(body_support_1,body_idx = 1,what_type=type_list,color='peru')
def finish_3d_axis(self,view_style = 'top', zoom = False, dump = False):
# finish the labeling, plot adjustments, dump and show
ax = self.ax
# ax.set_xlabel('$x$ (mm)')
# ax.set_ylabel('\n$y$ (mm)')
# zlabel = ax.set_zlabel('\n$z$ (mm)')
if self.max_range is not None:
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
if view_style == 'top':
az = -30
el = 90
if view_style == 'side':
az = -15
el = 9
if view_style == 'mix':
az = 150
el = 50
if view_style == 'ex':
az = -14
el = 46
if view_style == 'ex':
az = -46
el = 23
if zoom:
def mean_point(body_support,impl = False):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
if impl==True:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip,c_impl])
else:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip])
mean_0 = np.mean(points_0,0)
return mean_0
mean_0 = mean_point(self.body_support_0,impl= True)
mean_1 = mean_point(self.body_support_1, impl = False)
overall_mean = np.hstack([mean_0,mean_1])
mu_zoom = np.mean(overall_mean,1)
scaling = 2
d_zoom = ( mean_0.ravel() - mean_1.ravel() )*scaling
# print(d_zoom)
# print(mu_zoom)
# self.ax.scatter(mu_zoom[0],mu_zoom[1],mu_zoom[2],zdir='z', s=500, alpha = 1 , c='pink',rasterized=False)
pp = np.vstack([mu_zoom-d_zoom,mu_zoom,mu_zoom+d_zoom])
# print(pp)
# self.ax.plot(pp[:,0],pp[:,1],pp[:,2],zdir='z',alpha = 1,lw=10 , c='pink',rasterized=False)
X,Y,Z = pp[:,0],pp[:,1],pp[:,2]
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
ax.view_init(elev=el, azim=az)
def unpack_trace(self,body_support,body_idx = 0,what_type=['hip'],color='k'):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[:,0],dat[:,1],dat[:,2]
h_trace = self.ax.plot(X,Y,Z,lw=2,c=color,alpha = .65)
self.h_trace_list[body_idx][ii_c_list[type_list == ttt][0]] = h_trace
print(what_type)
def dump_to_disk(self,tag = ""):
print("saving w/tag: "+tag)
plt.tight_layout()
plt.savefig('figure_raw_pics/figure_5/fitting_cartoon/Plotter'+tag+'.pdf',transparent=True)
def add_frame_number(self,frame):
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.74, 0.07, "Frame:" ,ha='left',fontsize = 20, transform=self.ax.transAxes)
self.h_frame_number = self.ax.text2D(0.95, 0.07, str(frame),ha='right', fontsize = 20, transform=self.ax.transAxes)
pass
def add_frame_time(self,frame,start_frame):
cam_fps = 60 #Hz
t_now = (frame-start_frame)/cam_fps
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.71, 0.03, "Time:" ,ha='left', fontsize = 20, transform=self.ax.transAxes)
self.h_frame_time = self.ax.text2D(0.95, 0.03, "{:0.2f} s".format(t_now) ,ha='right',fontsize = 20, transform=self.ax.transAxes)
pass
def add_ed(self,frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c='r',lw = 4)
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
ccc = 'blueviolet'
self.h_ed_reject = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c=ccc,lw = 4)
pass
def make(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track' #hk
self.add_fit(frame)
self.track_or_guess = 'track'
self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel1(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = True)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel2(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = False)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def make_skel3(self,frame=None,tag = "",savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
self.make_3d_axis()
if frame is not None:
# self.add_raw_data(frame)
self.track_or_guess = 'track'
self.add_fit(frame,plot_ellipsoids = False)
self.track_or_guess = 'track'
# self.add_trace(frame,trace_length=960,trace_clip = 30*60-960 ,type_list = ['nose'])
if self.v_ed is not None:
self.add_ed(frame,case='ed')
self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
if len(tag) > 0:
self.dump_to_disk(tag = tag)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def video(self,frame_list=30*60+np.arange(100)*100, savepath = None,view_override = None):
# MAKE STARTING PLOT
self.trace_length = 960
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
self.track_or_guess = 'track'
self.add_fit(frame_list[0])
# self.track_or_guess = 'guess'
self.add_trace(frame_list[0],trace_length=self.trace_length,trace_clip = self.trace_clip ,type_list = self.type_list)
self.track_or_guess = 'track'
if self.v_ed is not None:
self.add_ed(frame_list[0],case='ed')
self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_frame_number(frame):
# self.h_frame_number.set_text("Frame: {}".format(frame))
self.h_frame_number.set_text(str(frame))
def update_frame_time(frame,start_frame = frame_list[0]):
cam_fps = 60. #Hz
t_now = (frame-start_frame)/cam_fps
self.h_frame_time.set_text("{:0.2f} s".format(t_now))
def update_skeleton(body_support,body_idx):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# add the body points
for j,p in enumerate([c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]):
if (len(p.shape) >= 3):
# update the location of the body point!
self.h_bp_list[body_idx][j]._offsets3d = (p[:,0,0],p[:,1,0],p[:,2,0])
# and the lines between body parts
for j, (p,q) in enumerate( zip([c_nose,c_nose,c_mid,c_impl,c_impl],[c_mid,c_tip,c_ass,c_nose,c_tip]) ):
if (len(p.shape) >= 3) and (len(q.shape) >= 3):
for ii in range(p.shape[0]):
# lines are an extra level deep for some stupid matplotlib reason
self.h_skel_list[body_idx][j][0].set_xdata([p[ii,0,0],q[ii,0,0]])
self.h_skel_list[body_idx][j][0].set_ydata([p[ii,1,0],q[ii,1,0]])
self.h_skel_list[body_idx][j][0].set_3d_properties([p[ii,2,0],q[ii,2,0]])
def update_wireframe_lines(h_hip,X,Y,Z):
# h_hip is the handle to the lines3dcollection
# much of the code is taken from the source of the marplotlib wireframe plotting
X, Y, Z = np.broadcast_arrays(X, Y, Z)
rows, cols = Z.shape
rstride = 1
cstride = 1
# We want two sets of lines, one running along the "rows" of
# Z and another set of lines running along the "columns" of Z.
# This transpose will make it easy to obtain the columns.
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
if rstride:
rii = list(range(0, rows, rstride))
# Add the last index only if needed
if rows > 0 and rii[-1] != (rows - 1):
rii += [rows-1]
else:
rii = []
if cstride:
cii = list(range(0, cols, cstride))
# Add the last index only if needed
if cols > 0 and cii[-1] != (cols - 1):
cii += [cols-1]
else:
cii = []
xlines = [X[i] for i in rii]
ylines = [Y[i] for i in rii]
zlines = [Z[i] for i in rii]
txlines = [tX[i] for i in cii]
tylines = [tY[i] for i in cii]
tzlines = [tZ[i] for i in cii]
lines = ([list(zip(xl, yl, zl))
for xl, yl, zl in zip(xlines, ylines, zlines)]
+ [list(zip(xl, yl, zl))
for xl, yl, zl in zip(txlines, tylines, tzlines)])
h_hip.set_segments(lines)
def calculate_wireframe_points(R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip'):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
return x,y,z
def update_ellipsoids(part,body_support,body_idx):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
# this is not so elegant, hm hm
s = part[:,2]
a_hip = a_hip_0 + a_hip_delta * s
b_hip = b_hip_0 + b_hip_delta * (1.-s)
d_hip = .75 * a_hip
for jj,(RR,cc,style) in enumerate( zip([R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']) ):
if (len(cc.shape) >= 3):
X,Y,Z = calculate_wireframe_points(RR[0,...],
cc[0,...],
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip.cpu().numpy(),
b_hip.cpu().numpy(),
r_impl.cpu().numpy(),
style)
h_hip = self.h_hip_list[body_idx][jj]
update_wireframe_lines(h_hip,X,Y,Z)
def update_trace_3dlines(body_support,body_idx=0,what_type=['hip']):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[:,0],dat[:,1],dat[:,2]
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_xdata(X)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_ydata(Y)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_3d_properties(Z)
def calculate_new_trace(frame,trace_length=None,trace_clip = None,decay_factor=.9, type_list = None):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,(i_trace_start):(i_frame)]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
# overwrite HACK for now
part = self.guessing_holder[:-1,(i_trace_start):(i_frame)]
if part.shape[1] == 0:
# just skip if there is no trace
return
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
update_trace_3dlines(body_support_0,body_idx=0,what_type = type_list)
update_trace_3dlines(body_support_1,body_idx=1,what_type = type_list)
if self.v_ed is not None:
def update_ed(frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
self.h_ed_reject[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed_reject[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed_reject[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
pass
def update_pc(frame):
# get new raw data!
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# update
self.h_pc._offsets3d = (X,Y,Z)
# THEN set the 3d values to be what the shoud be
for body in range(4):
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# update the fit as well!
# get the particle, convert to torch tensor, calculate body supports
self.track_or_guess = 'track'
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
update_skeleton(body_support_0,body_idx=0)
update_skeleton(body_support_1,body_idx=1)
update_ellipsoids(part,body_support_0,body_idx=0)
update_ellipsoids(part,body_support_1,body_idx=1)
calculate_new_trace(frame,trace_length=self.trace_length,trace_clip = self.trace_clip,decay_factor=.9, type_list = self.type_list)
update_frame_number(frame)
update_frame_time(frame)
if self.v_ed is not None:
update_ed(frame,case='ed')
update_ed(frame,case='ed_reject')
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, frame_list[1:], interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
ani.save(savepath,writer='imagemagick',fps=fps)
plt.rcParams['animation.html'] = 'html5'
ani
def rotation(self,frame_list=30*60+np.arange(100)*100, savepath = None,view_override = None):
# MAKE STARTING PLOT
self.trace_length = 960
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
self.track_or_guess = 'track'
self.add_fit(frame_list[0])
# self.track_or_guess = 'guess'
self.add_trace(frame_list[0],trace_length=self.trace_length,trace_clip = self.trace_clip ,type_list = self.type_list)
self.track_or_guess = 'track'
if self.v_ed is not None:
self.add_ed(frame_list[0],case='ed')
self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_pc(i):
# get new raw data!
offset = el*np.sin(i*2*np.pi / 360)
plt.gca().view_init(elev=el+offset, azim=az+i)
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, np.linspace(0,360,40), interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~_rotation'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD_rotation'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
ani.save(savepath,writer='imagemagick',fps=fps)
plt.rcParams['animation.html'] = 'html5'
ani
def plot_residuals(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# and get the corresponding fit: the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
# body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
# body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
pos = torch.from_numpy(pos).float().cuda()
# CALCULATE RESIDUAL
dist0,_,self.body_support_0 = particles_to_distance_cuda(part[:,:9],pos[:,:],implant = True)
dist1,_,self.body_support_1 = particles_to_distance_cuda(part[:,9:],pos[:,:])
r = torch.min(dist0[0,:],dist1[0,:])
r = r.cpu().numpy().squeeze()
# Figure out the weighing stuff
w2 = pos_weights**2
rw=np.clip(r,0,.03)
# or weigted mean?
# rm = np.mean(rw)
# correct
rm = np.sum(w2*rw)/np.sum(w2)
# wrong old way:
# rm = np.mean(w2*rw)/np.median(w2)
plt.figure()
plt.plot(r,'.k',label='residual')
plt.plot(rw,'.r',label='cut')
plt.plot(w2,'.g',label='w2')
plt.legend()
plt.show()
# plt.figure()
# plt.hist(w2,100)
# plt.show()
plt.figure(figsize =(10,10))
# plt.scatter(X,Y,c = 1-rw/np.max(rw))
plt.scatter(X,Y,c = w2/np.max(w2))
plt.axis('square')
plt.title("clipped: {}, weighted: {}".format(np.mean(rw),rm))
plt.show()
pass
# tips https://stackoverflow.com/questions/41602588/matplotlib-3d-scatter-animations
# add raw data to plot
# add particle
def kernel_smoothing(self,width = 3):
kernel = easy_kernel(width)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
from filterpy.common import kinematic_kf,Q_discrete_white_noise
from filterpy.kalman import FixedLagSmoother
def kalman1_3D(tr,sigma_process,sigma_measure,dt = 1/60):
# make first order kinematic kalman filter
cv = kinematic_kf(dim=3, order=1, dt = dt)
cv.R = np.eye(3) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0,2,4]:
cv.Q[i:(i+2),i:(i+2)] = Q_discrete_white_noise(dim=2, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0,0],0,tr[0,1],0,tr[0,2] ,0 ]]).T
cv.update(tr[i,:])
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i,:][:,np.newaxis])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
tr_filtered = kalman_estimate[[0,2,4],:].T
return tr_filtered
def fls1_3d(tr,sigma_process,sigma_measure,dt = 1/60,N_lag = 16):
# make first order kinematic kalman filter
cv = kinematic_kf(dim=3, order=1, dt = dt)
cv.R = np.eye(3) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0,2,4]:
cv.Q[i:(i+2),i:(i+2)] = Q_discrete_white_noise(dim=2, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0,0],0,tr[0,1],0,tr[0,2] ,0 ]]).T
# also make an FLS smoother
fls = FixedLagSmoother(dim_x=6, dim_z=3, N=N_lag)
fls.x = np.copy(cv.x)
fls.F = np.copy(cv.F)
fls.H = np.copy(cv.H)
fls.P = np.copy(cv.P)
fls.R = np.copy(cv.R)
fls.Q = np.copy(cv.Q)
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i,:][:,np.newaxis])
fls.smooth(tr[i,:][:,np.newaxis])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
fls_estimate = np.hstack(fls.xSmooth)
tr_filtered = kalman_estimate[[0,2,4],:].T
tr_smoothed = fls_estimate[[0,2,4],:].T
return tr_smoothed
def fls2_3d(tr,sigma_process,sigma_measure,dt = 1/60,N_lag = 16):
# make second order kinematic kalman filter
cv = kinematic_kf(dim=3, order=2, dt = dt)
cv.R = np.eye(3) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0,3,6]:
cv.Q[i:(i+3),i:(i+3)] = Q_discrete_white_noise(dim=3, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0,0],0,0,tr[0,1],0,0,tr[0,2],0 ,0 ]]).T
# also make an FLS smoother
fls = FixedLagSmoother(dim_x=9, dim_z=3, N=N_lag)
fls.x = np.copy(cv.x)
fls.F = np.copy(cv.F)
fls.H = np.copy(cv.H)
fls.P = np.copy(cv.P)
fls.R = np.copy(cv.R)
fls.Q = np.copy(cv.Q)
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i,:][:,np.newaxis])
fls.smooth(tr[i,:][:,np.newaxis])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
fls_estimate = np.hstack(fls.xSmooth)
tr_filtered = kalman_estimate[[0,3,6],:].T
tr_smoothed = fls_estimate[[0,3,6],:].T
return tr_smoothed
def kalman_1D(tr,sigma_process,sigma_measure,dt = 1/60):
# make first order kinematic kalman filter
cv = kinematic_kf(dim=1, order=1, dt = dt)
cv.R = np.eye(1) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0]:
cv.Q[i:(i+2),i:(i+2)] = Q_discrete_white_noise(dim=2, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0],0 ]]).T
cv.update(tr[i])
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
tr_filtered = kalman_estimate[[0],:].T
return tr_filtered
def kalman2_3D(tr,sigma_process,sigma_measure,dt = 1/60):
# make second order kinematic kalman filter
cv = kinematic_kf(dim=3, order=2, dt = dt)
cv.R = np.eye(3) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0,3,6]:
cv.Q[i:(i+3),i:(i+3)] = Q_discrete_white_noise(dim=3, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0,0],0,0,tr[0,1],0,0,tr[0,2],0 ,0 ]]).T
cv.update(tr[i,:])
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i,:][:,np.newaxis])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
tr_filtered = kalman_estimate[[0,3,6],:].T
return tr_filtered
def fls2_1d(tr,sigma_process,sigma_measure,dt = 1/60,N_lag = 16):
# make second order kinematic kalman filter
cv = kinematic_kf(dim=1, order=2, dt = dt)
cv.R = np.eye(1) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0]:
cv.Q[i:(i+2),i:(i+2)] = Q_discrete_white_noise(dim=2, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0],0,0]]).T
# also make an FLS smoother
fls = FixedLagSmoother(dim_x=3, dim_z=1, N=N_lag)
fls.x = np.copy(cv.x)
fls.F = np.copy(cv.F)
fls.H = np.copy(cv.H)
fls.P = np.copy(cv.P)
fls.R = np.copy(cv.R)
fls.Q = np.copy(cv.Q)
# print(cv)
# print(fls)
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i])
fls.smooth(tr[i])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
fls_estimate = np.hstack(fls.xSmooth)
tr_filtered = kalman_estimate[[0],:].T
tr_smoothed = fls_estimate[[0],:].T
return tr_smoothed
def fls1_1d(tr,sigma_process,sigma_measure,dt = 1/60,N_lag = 16):
# make second order kinematic kalman filter
cv = kinematic_kf(dim=1, order=1, dt = dt)
cv.R = np.eye(1) * sigma_measure**2
G = np.array([[0.5 * dt**2, dt]], np.float32).T
Q0 = np.matmul(G, G.T) * sigma_process**2
for i in [0]:
cv.Q[i:(i+2),i:(i+2)] = Q_discrete_white_noise(dim=2, dt=dt, var=sigma_process**2)
# cv.Q[i:(i+2),i:(i+2)] = Q0
cv.P = np.ones((cv.dim_x,cv.dim_x))*0.001 +.0001
kalman_estimate = []
# initialize
cv.x = np.array([[ tr[0],0]]).T
# also make an FLS smoother
fls = FixedLagSmoother(dim_x=2, dim_z=1, N=N_lag)
fls.x = np.copy(cv.x)
fls.F = np.copy(cv.F)
fls.H = np.copy(cv.H)
fls.P = np.copy(cv.P)
fls.R = np.copy(cv.R)
fls.Q = np.copy(cv.Q)
fls.x = np.array([[ .9,0]]).T
fls.P = np.array([[2.73445008e-04 ,2.49619926e-05],[2.49619926e-05, 4.56088374e-06]])
for i in tqdm(range(tr.shape[0])):
cv.predict()
cv.update(tr[i])
fls.smooth(tr[i])
kalman_estimate.append(cv.x)
kalman_estimate = np.hstack(kalman_estimate)
fls_estimate = np.hstack(fls.xSmooth)
tr_filtered = kalman_estimate[[0],:].T
tr_smoothed = fls_estimate[[0],:].T
return tr_smoothed
def smooth_body_support(body_support,s):
# unpack to 3D coordinates
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy().squeeze() for i in body_support]
type_list = np.array(['hip','tail','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
# run function for 3D kalman filtering (body coordinates)
sigma_process = .01
sigma_measure = .015
c_hip = fls2_3d(c_hip,sigma_process,sigma_measure,dt = 1/60)
c_ass = fls2_3d(c_ass,sigma_process,sigma_measure,dt = 1/60)
c_mid = fls2_3d(c_mid,sigma_process,sigma_measure,dt = 1/60)
c_nose = fls2_3d(c_nose,sigma_process,sigma_measure,dt = 1/60)
c_tip = fls2_3d(c_tip,sigma_process,sigma_measure,dt = 1/60)
sigma_process = .01
sigma_measure = .02
c_impl = fls2_3d(c_impl,sigma_process,sigma_measure,dt = 1/60)
# run function for 1D kalman filtering (s)
sigma_process = .05
sigma_measure = .3
s = fls1_1d(s,sigma_process,sigma_measure,dt = 1/60)
return [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose],s
# calculate rotation matrices from the smoothed skeleton points, to be used for smooth video playback
def rotation_matrix_vec2vec_numpy(f,t):
# from this paper, ffrom math stacj
# but made batch-able for pytorch
# https://math.stackexchange.com/questions/180418/calculate-rotation-matrix-to-align-vector-a-to-vector-b-in-3d/476311#476311
#rotate vector f onto vector t
# import numpy as np
# v = np.cross(f, t)
# u = v/np.linalg.norm(v)
# c = np.dot(f, t)
# h = (1 - c)/(1 - c**2)
# vx, vy, vz = v
# rot =[[c + h*vx**2, h*vx*vy - vz, h*vx*vz + vy],
# [h*vx*vy+vz, c+h*vy**2, h*vy*vz-vx],
# [h*vx*vz - vy, h*vy*vz + vx, c+h*vz**2]]
# good disussion about smoothing rotation matrices later: https://www.cvl.isy.liu.se/education/graduate/geometry2010/lectures/Lecture7b.pdf
# rotate f onto t
# very fast, but slightly numerically unstable, so we add epsilon!
epsilon = 1e-6
# f = x_pointer
# t = nose_pointer
# cross product
v = np.cross(f,t)
u = v/(np.linalg.norm(v,axis=1)[:,np.newaxis] + epsilon)
# dot product
c = np.einsum('i,ai->a', f,t)
# the factor h
h = (1 - c)/(1 - c**2 + epsilon)
vx, vy, vz = v[:,0],v[:,1],v[:,2]
R = np.stack([np.stack([c + h*vx**2, h*vx*vy - vz, h*vx*vz + vy], axis=1),
np.stack([h*vx*vy+vz, c+h*vy**2, h*vy*vz-vx], axis=1),
np.stack([h*vx*vz - vy, h*vy*vz + vx, c+h*vz**2], axis=1)], axis=1)
return R
# use quarterneons to smooth the body ellipsoid rotations
# convert the rotation matrices to quarternions
from pyquaternion import Quaternion
def unpack_axis_angels(R):
# calculate the axis-angle representation
# https://stackoverflow.com/questions/15022630/how-to-calculate-the-angle-from-rotation-matrix
angle_x = np.arctan2(R[:,2,1],R[:,2,2])
angle_y = np.arctan2(-R[:,2,0],np.sqrt(R[:,2,1]**2 + R[:,2,2]**2 ) )
angle_z = np.arctan2(R[:,1,0],R[:,0,0])
return np.stack((angle_x,angle_y,angle_z),axis = 1)
def averageQuaternions(Q):
# from https://github.com/christophhagen/averaging-quaternions/blob/master/averageQuaternions.py
# Number of quaternions to average
M = Q.shape[0]
A = np.zeros(shape=(4,4))
for i in range(0,M):
q = Q[i,:]
# multiply q with its transposed version q' and add A
A = np.outer(q,q) + A
# scale
A = (1.0/M)*A
# compute eigenvalues and -vectors
eigenValues, eigenVectors = np.linalg.eig(A)
# Sort by largest eigenvalue
eigenVectors = eigenVectors[:,eigenValues.argsort()[::-1]]
# return the real part of the largest eigenvector (has only real part)
return np.real(eigenVectors[:,0].ravel())
def quaternion_smoothing(R):
angles_kalman = unpack_axis_angels(R)
q_x = [ Quaternion(axis = (1.,0.,0.),radians = r) for r in angles_kalman[:,0] ]
q_y = [ Quaternion(axis = (0.,1.,0.),radians = r) for r in angles_kalman[:,1] ]
q_z = [ Quaternion(axis = (0.,0.,1.),radians = r) for r in angles_kalman[:,2] ]
# now, smooth the rotations
q_all = []
for i in range(len(q_x)):
q_all.append(q_x[i]*q_y[i]*q_z[i])
# convert to a matrix w qith w x y z
Q = np.stack([q.elements for q in q_all],axis = 0)
# try a running average first!
Q_run_av = np.copy(Q)
w_length = 9 # must be uneven
h_length = int(np.floor(w_length/2))
for i in tqdm(np.arange(np.floor(w_length/2),Q.shape[0]-np.ceil(w_length/2))):
i = int(i)
Q_run_av[i,:] = averageQuaternions(Q[(i-h_length):(i+h_length+1),:])
# convert back to rotation matrices, to check that converion is fine
R_q_list = [q.rotation_matrix for q in q_all]
R_q = np.stack(R_q_list,axis = 0)
R_q_smooth_list = [Quaternion(Q_run_av[i,:]).rotation_matrix for i in range(Q_run_av.shape[0])]
R_q_smooth = np.stack(R_q_smooth_list,axis = 0)
return R_q,R_q_smooth
def smooth_rotation_matrices(body_support_0_smooth):
# first calculate body vectors from the smoothed skeleton points
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support_0_smooth
#todo, maybe average across the both noisy estimates here, prob won't gain much though..
v_nose = c_nose - c_mid
v_hip = c_mid - c_hip
v_nose = v_nose/np.linalg.norm(v_nose,axis=1)[:,np.newaxis]
v_hip = v_hip/np.linalg.norm(v_hip,axis=1)[:,np.newaxis]
# To calculate R_nose, we ask how we have to rotate a vector along x, so that it points along the hip or nose
f = np.array([1,0,0])
t = v_nose
R_nose_smooth = rotation_matrix_vec2vec_numpy(f,t)
t = v_hip
R_body_smooth = rotation_matrix_vec2vec_numpy(f,t)
return [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body_smooth,R_head,R_nose_smooth]
def smooth_rotation_matrices_quaternion(body_support_0_smooth):
# first calculate body vectors from the smoothed skeleton points
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support_0_smooth
#todo, maybe average across the both noisy estimates here, prob won't gain much though..
v_nose = c_nose - c_mid
v_hip = c_mid - c_hip
v_nose = v_nose/np.linalg.norm(v_nose,axis=1)[:,np.newaxis]
v_hip = v_hip/np.linalg.norm(v_hip,axis=1)[:,np.newaxis]
# To calculate R_nose, we ask how we have to rotate a vector along x, so that it points along the hip or nose
f = np.array([1,0,0])
t = v_nose
R_nose_smooth = rotation_matrix_vec2vec_numpy(f,t)
t = v_hip
R_body_smooth = rotation_matrix_vec2vec_numpy(f,t)
R_q,R_body_smooth = quaternion_smoothing(R_body_smooth)
R_q,R_nose_smooth = quaternion_smoothing(R_nose_smooth)
return [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body_smooth,R_head,R_nose_smooth]
class VideoPlotMachine(object):
def __init__(self,tracked_behavior,jagged_lines,what_to_plot = 'guess'):
self.tracked_behavior = tracked_behavior
self.jagged_lines = jagged_lines
# useful functions, get the raw data in plottable format
# pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(jagged_lines[start_frame])
# useful functions, get the fitted data in plottable format
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 13})
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
# unpack
self.guessing_holder = tracked_behavior['guessing_holder']
self.tracking_holder = tracked_behavior['tracking_holder']
self.start_frame = tracked_behavior['start_frame']
self.track_or_guess = what_to_plot
self.n_frames = self.tracking_holder.shape[1]
self.v_ed = None
self.v_ed_reject = None
def better_smoothing(self):
# get the raw tracking data!
part = self.tracking_holder
# unpack all the 3D coordinates!
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
# and the spine length
s_0 = part[:,2].cpu().numpy()
s_1 = part[:,2+9].cpu().numpy()
# and smooth the data
# from utils.analysis_tools import smooth_body_support
body_support_0_smooth,s_0_smooth = smooth_body_support(body_support_0,s_0)
body_support_1_smooth,s_1_smooth = smooth_body_support(body_support_1,s_1)
# add the smoothed coordinates as numpy arrays
self.body_support_0_raw = [i.cpu().numpy().squeeze() for i in body_support_0]
self.body_support_0_smooth = body_support_0_smooth
self.s_0_raw = s_0
self.s_0_smooth = s_0_smooth
self.body_support_1_raw = [i.cpu().numpy().squeeze() for i in body_support_1]
self.body_support_1_smooth = body_support_1_smooth
self.s_1_raw = s_1
self.s_1_smooth = s_1_smooth
# also smooth the body ellipsoid rotations
self.body_support_0_smooth = smooth_rotation_matrices(body_support_0_smooth)
self.body_support_1_smooth = smooth_rotation_matrices(body_support_1_smooth)
# self.body_support_0_smooth = smooth_rotation_matrices_quaternion(body_support_0_smooth)
# self.body_support_1_smooth = smooth_rotation_matrices_quaternion(body_support_1_smooth)
def kernel_smoothing(self):
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
kernel = easy_kernel(3)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def make_3d_axis(self):
# 3D plot of the
fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(111, projection='3d')
# add to self for use later
self.fig = fig
self.ax = ax
def add_raw_data(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# add to axis 3D plot of Sphere
self.h_pc = self.ax.scatter(X, Y, Z, zdir='z', s=10, c='k', alpha = .05,rasterized=False)
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
# loop over the types of body, and make emptyscatter plots
self.h_kp_list = []
for body in body_indices:
h_kp = self.ax.scatter([],[],[], zdir='z', s=100, c=body_colors[body],rasterized=False)
self.h_kp_list.append(h_kp)
# THEN set the 3d values to be what the shoud be
for body in body_indices:
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# self.h_kp_list = []
# for i,body in enumerate(ikeyp):
# h_kp = self.ax.scatter(keyp[i,0], keyp[i,1], keyp[i,2], zdir='z', s=100, c=body_colors[body],rasterized=False)
# self.h_kp_list.append(h_kp)
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
def plot_skeleton(self,body_support,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
#print("c_hip is {}".format(c_hip))
if body_idx == 0 :
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]
p_line = [c_nose,c_nose,c_mid,c_impl,c_impl]
q_line = [c_mid,c_tip,c_ass,c_nose,c_tip]
elif body_idx == 1:
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip]
p_line = [c_nose,c_nose,c_mid]
q_line = [c_mid,c_tip,c_ass]
# add the body points
for p in p_skel:
h_bp = self.ax.scatter(p[0],p[1],p[2],zdir='z', s=50, alpha = 1 , c=color,rasterized=False)
self.h_bp_list[body_idx].append(h_bp)
# and the lines between body parts
for p,q in zip(p_line,q_line):
h_skel = self.ax.plot([p[0],q[0]],[p[1],q[1]],[p[2],q[2]],c=color,lw = 4)
self.h_skel_list[body_idx].append(h_skel)
def add_wireframe_to_axis(self,ax,R_body,c_hip, a_nose,b_nose,a_hip,b_hip,r_impl,style='hip',this_color='k',this_alpha=.4):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip[:,np.newaxis]
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
h_hip = ax.plot_wireframe(x, y, z, color=this_color,alpha = this_alpha)
return h_hip
def plot_ellipsoids(self,body_support,s,color = 'k',body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
# this is not so elegant, hm hm
a_hip = a_hip_0.cpu().numpy() + a_hip_delta.cpu().numpy() * s
b_hip = b_hip_0.cpu().numpy() + b_hip_delta.cpu().numpy() * (1.-s)
d_hip = .75 * a_hip
if body_idx == 0:
RRs,ccs,styles = [R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']
if body_idx == 1:
RRs,ccs,styles = [R_body,R_nose],[c_hip,c_nose],['hip','nose']
for RR,cc,style in zip(RRs,ccs,styles):
h_hip = self.add_wireframe_to_axis(self.ax,RR,
cc,
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip,
b_hip,
r_impl.cpu().numpy(),
style=style,this_color=color)
self.h_hip_list[body_idx].append(h_hip)
def add_skel_fit(self,frame,fit='raw',plot_ellipsoids = True):
# frame index
i_frame = frame-self.start_frame
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
# and plot!
self.h_skel_list = [[],[]]
self.h_bp_list = [[],[]]
self.plot_skeleton(body_support_0,color = 'k',body_idx = 0)
self.plot_skeleton(body_support_1,color = 'peru',body_idx = 1)
def add_ellip_fit(self,frame,fit='raw',plot_ellipsoids = True):
# frame index
i_frame = frame-self.start_frame
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
self.h_hip_list = [[],[]]
self.plot_ellipsoids(body_support_0,s_0,color = 'k',body_idx = 0)
self.plot_ellipsoids(body_support_1,s_1,color = 'peru',body_idx = 1)
def add_trace(self,frame,trace,trace_length=90,trace_clip = None,decay_factor=.9, type_list = ['tip']):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
# make a holder for the lines
self.h_trace_list = [[None]*5,[None]*5]
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
#print("i_trace_start is {} and i_frame is {}".format(i_trace_start,i_frame))
trace_indices = np.arange(i_trace_start,i_frame)
if trace == 'raw':
self.unpack_trace(self.body_support_0_raw,trace_indices, body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(self.body_support_1_raw,trace_indices, body_idx = 1,what_type=type_list,color='peru')
if trace == 'smooth':
self.unpack_trace(self.body_support_0_smooth,trace_indices, body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(self.body_support_1_smooth,trace_indices, body_idx = 1,what_type=type_list,color='peru')
def finish_3d_axis(self,view_style = 'top', zoom = False, dump = False):
# finish the labeling, plot adjustments, dump and show
ax = self.ax
# ax.set_xlabel('$x$ (mm)')
# ax.set_ylabel('\n$y$ (mm)')
# zlabel = ax.set_zlabel('\n$z$ (mm)')
if self.max_range is not None:
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
if view_style == 'top':
az = -30
el = 90
if view_style == 'side':
az = -15
el = 9
if view_style == 'mix':
az = 150
el = 50
if view_style == 'ex':
az = -14
el = 46
if view_style == 'ex':
az = -46
el = 23
if zoom:
def mean_point(body_support,impl = False):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
if impl==True:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip,c_impl])
else:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip])
mean_0 = np.mean(points_0,0)
return mean_0
mean_0 = mean_point(self.body_support_0,impl= True)
mean_1 = mean_point(self.body_support_1, impl = False)
overall_mean = np.hstack([mean_0,mean_1])
mu_zoom = np.mean(overall_mean,1)
scaling = 2
d_zoom = ( mean_0.ravel() - mean_1.ravel() )*scaling
# print(d_zoom)
# print(mu_zoom)
# self.ax.scatter(mu_zoom[0],mu_zoom[1],mu_zoom[2],zdir='z', s=500, alpha = 1 , c='pink',rasterized=False)
pp = np.vstack([mu_zoom-d_zoom,mu_zoom,mu_zoom+d_zoom])
# print(pp)
# self.ax.plot(pp[:,0],pp[:,1],pp[:,2],zdir='z',alpha = 1,lw=10 , c='pink',rasterized=False)
X,Y,Z = pp[:,0],pp[:,1],pp[:,2]
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
ax.view_init(elev=el, azim=az)
def unpack_trace(self,body_support,trace_indices,body_idx = 0,what_type=['hip'],color='k'):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[trace_indices,0],dat[trace_indices,1],dat[trace_indices,2]
h_trace = self.ax.plot(X,Y,Z,lw=2,c=color,alpha = .65)
self.h_trace_list[body_idx][ii_c_list[type_list == ttt][0]] = h_trace
def dump_to_disk(self,tag = ""):
print("saving w/tag: "+tag)
plt.tight_layout()
plt.savefig('figure_raw_pics/figure_5/fitting_cartoon/Plotter'+tag+'.pdf',transparent=True)
def add_frame_number(self,frame):
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.74, 0.07, "Frame:" ,ha='left',fontsize = 20, transform=self.ax.transAxes)
self.h_frame_number = self.ax.text2D(0.95, 0.07, str(frame),ha='right', fontsize = 20, transform=self.ax.transAxes)
pass
def add_frame_time(self,frame,start_frame):
cam_fps = 60 #Hz
t_now = (frame-start_frame)/cam_fps
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.71, 0.03, "Time:" ,ha='left', fontsize = 20, transform=self.ax.transAxes)
self.h_frame_time = self.ax.text2D(0.95, 0.03, "{:0.2f} s".format(t_now) ,ha='right',fontsize = 20, transform=self.ax.transAxes)
pass
def add_ed(self,frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c='r',lw = 4)
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
ccc = 'blueviolet'
self.h_ed_reject = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c=ccc,lw = 4)
pass
def make_me(self,frame=None,cloud=True,trace='raw',skel='raw',ellip='raw',trace_clip=30*60-960 ,savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
# trace is raw or smooth or None
self.make_3d_axis()
if frame is not None:
if cloud:
self.add_raw_data(frame)
#self.track_or_guess = 'track' #hk
self.add_skel_fit(frame,fit=skel)
self.add_ellip_fit(frame,fit=ellip)
#self.track_or_guess = 'track'
self.add_trace(frame,trace=trace,trace_length=10*60,trace_clip = trace_clip ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame,case='ed')
# self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def video_me(self,frame_list=30*60+np.arange(100)*100,cloud=True,trace='raw',skel='raw',ellip='raw',savepath = None,view_override = None,fps=10,dpi=100,time_offset=0):
self.trace_length = 10*60
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
# add the fitted values
self.add_skel_fit(frame_list[0],fit=skel)
self.add_ellip_fit(frame_list[0],fit=ellip)
self.add_trace(frame_list[0],trace=trace,trace_length=10*60,trace_clip = 30*60-960 ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame_list[0],case='ed')
# self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
# tighten and add frame # and time
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_frame_number(frame):
# self.h_frame_number.set_text("Frame: {}".format(frame))
self.h_frame_number.set_text(str(frame))
def update_frame_time(frame,start_frame = frame_list[0],time_offset=0):
cam_fps = 60. #Hz
t_now = (frame-start_frame)/cam_fps - time_offset
self.h_frame_time.set_text("{:0.2f} s".format(t_now))
def update_skeleton(body_support,body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
if body_idx == 0 :
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]
p_line = [c_nose,c_nose,c_mid,c_impl,c_impl]
q_line = [c_mid,c_tip,c_ass,c_nose,c_tip]
elif body_idx == 1:
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip]
p_line = [c_nose,c_nose,c_mid]
q_line = [c_mid,c_tip,c_ass]
# update the body points
for j,p in enumerate(p_skel):
self.h_bp_list[body_idx][j]._offsets3d = ([p[0]],[p[1]],[p[2]])
# update the lines between body parts
for j,(p,q) in enumerate(zip(p_line,q_line)):
# lines are an extra level deep for some stupid matplotlib reason
self.h_skel_list[body_idx][j][0].set_xdata([p[0],q[0]])
self.h_skel_list[body_idx][j][0].set_ydata([p[1],q[1]])
self.h_skel_list[body_idx][j][0].set_3d_properties([p[2],q[2]])
def update_skel_fit(frame,fit=skel):
# get the data out frame index
i_frame = frame-self.start_frame
# speed up this list nonsense
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
update_skeleton(body_support_0,body_idx = 0)
update_skeleton(body_support_1,body_idx = 1)
def update_wireframe_lines(h_hip,X,Y,Z):
# h_hip is the handle to the lines3dcollection
# much of the code is taken from the source of the marplotlib wireframe plotting
X, Y, Z = np.broadcast_arrays(X, Y, Z)
rows, cols = Z.shape
rstride = 1
cstride = 1
# We want two sets of lines, one running along the "rows" of
# Z and another set of lines running along the "columns" of Z.
# This transpose will make it easy to obtain the columns.
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
if rstride:
rii = list(range(0, rows, rstride))
# Add the last index only if needed
if rows > 0 and rii[-1] != (rows - 1):
rii += [rows-1]
else:
rii = []
if cstride:
cii = list(range(0, cols, cstride))
# Add the last index only if needed
if cols > 0 and cii[-1] != (cols - 1):
cii += [cols-1]
else:
cii = []
xlines = [X[i] for i in rii]
ylines = [Y[i] for i in rii]
zlines = [Z[i] for i in rii]
txlines = [tX[i] for i in cii]
tylines = [tY[i] for i in cii]
tzlines = [tZ[i] for i in cii]
lines = ([list(zip(xl, yl, zl))
for xl, yl, zl in zip(xlines, ylines, zlines)]
+ [list(zip(xl, yl, zl))
for xl, yl, zl in zip(txlines, tylines, tzlines)])
h_hip.set_segments(lines)
def calculate_wireframe_points(R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip'):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip[:,np.newaxis]
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
return x,y,z
def update_ellipsoids(body_support,s,body_idx = 0):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
# this is not so elegant, hm hm
a_hip = a_hip_0.cpu().numpy() + a_hip_delta.cpu().numpy() * s
b_hip = b_hip_0.cpu().numpy() + b_hip_delta.cpu().numpy() * (1.-s)
d_hip = .75 * a_hip
if body_idx == 0:
RRs,ccs,styles = [R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']
if body_idx == 1:
RRs,ccs,styles = [R_body,R_nose],[c_hip,c_nose],['hip','nose']
for jj, (RR,cc,style) in enumerate(zip(RRs,ccs,styles)):
X,Y,Z = calculate_wireframe_points(RR,
cc,
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip,
b_hip,
r_impl.cpu().numpy(),
style=style)
h_hip = self.h_hip_list[body_idx][jj]
update_wireframe_lines(h_hip,X,Y,Z)
def update_ellip_fit(frame,fit = ellip):
# get the data out frame index
i_frame = frame-self.start_frame
# speed up this list nonsense
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
update_ellipsoids(body_support_0,s_0,body_idx = 0)
update_ellipsoids(body_support_1,s_1,body_idx = 1)
def update_trace_3dlines(body_support,trace_indices,body_idx=0,what_type=['hip']):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[trace_indices,0],dat[trace_indices,1],dat[trace_indices,2]
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_xdata(X)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_ydata(Y)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_3d_properties(Z)
def update_trace_fit(frame,trace='raw',trace_length=None,trace_clip = None,decay_factor=.9, type_list = None):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
# these are the indices to plot
trace_indices = np.arange(i_trace_start,i_frame)
if trace =='raw':
body_support_0 = self.body_support_0_raw
body_support_1 = self.body_support_1_raw
elif trace =='smooth':
body_support_0 = self.body_support_0_smooth
body_support_1 = self.body_support_1_smooth
else:
return
if len(trace_indices)== 0:
# just skip if there is no trace
return
update_trace_3dlines(body_support_0,trace_indices,body_idx=0,what_type = type_list)
update_trace_3dlines(body_support_1,trace_indices,body_idx=1,what_type = type_list)
if self.v_ed is not None:
def update_ed(frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
self.h_ed_reject[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed_reject[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed_reject[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
pass
def update_pc(frame):
# get new raw data!
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# update the pointcloud
self.h_pc._offsets3d = (X,Y,Z)
# and update the keypoints
for body in range(4):
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# update the counters
update_frame_number(frame)
update_frame_time(frame,time_offset=time_offset)
# update the fit
update_skel_fit(frame)
update_ellip_fit(frame)
update_trace_fit(frame,trace=trace,trace_length=10*60,trace_clip = 30*60-960 ,type_list = ['nose'])
# # update the fit as well!
# if self.v_ed is not None:
# update_ed(frame,case='ed')
# update_ed(frame,case='ed_reject')
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, frame_list[1:], interval=10)
if savepath is None:
fn = '<"(, ,)~~'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
if savepath[-1] == 'f':
ani.save(savepath,writer='imagemagick',fps=fps,dpi = dpi)
else:
ani.save(savepath,writer='ffmpeg',fps=fps,dpi=dpi)
plt.rcParams['animation.html'] = 'html5'
ani
def rotation(self,frame_list=30*60+np.arange(100)*100,cloud=True,trace='raw',skel='raw',ellip='raw',savepath = None,view_override = None,fps=10,dpi=100):
self.trace_length = 10*60
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
# add the fitted values
self.add_skel_fit(frame_list[0],fit=skel)
self.add_ellip_fit(frame_list[0],fit=ellip)
self.add_trace(frame_list[0],trace=trace,trace_length=10*60,trace_clip = 30*60-960 ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame_list[0],case='ed')
# self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
# tighten and add frame # and time
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_pc(i):
# here, the update rule is simply to change the view
offset = el*np.sin(i*2*np.pi / 360)
plt.gca().view_init(elev=el+offset, azim=az+i)
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, np.linspace(0,360,80), interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~_rotation'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD_rotation'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
if savepath[-1] == 'f':
ani.save(savepath,writer='imagemagick',fps=fps,dpi=dpi)
else:
ani.save(savepath,writer='ffmpeg',fps=fps,dpi=dpi)
plt.rcParams['animation.html'] = 'html5'
ani
def plot_residuals(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# and get the corresponding fit: the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
# body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
# body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
pos = torch.from_numpy(pos).float().cuda()
# CALCULATE RESIDUAL
dist0,_,self.body_support_0 = particles_to_distance_cuda(part[:,:9],pos[:,:],implant = True)
dist1,_,self.body_support_1 = particles_to_distance_cuda(part[:,9:],pos[:,:])
r = torch.min(dist0[0,:],dist1[0,:])
r = r.cpu().numpy().squeeze()
# Figure out the weighing stuff
w2 = pos_weights**2
rw=np.clip(r,0,.03)
# or weigted mean?
# rm = np.mean(rw)
# correct
rm = np.sum(w2*rw)/np.sum(w2)
# wrong old way:
# rm = np.mean(w2*rw)/np.median(w2)
plt.figure()
plt.plot(r,'.k',label='residual')
plt.plot(rw,'.r',label='cut')
plt.plot(w2,'.g',label='w2')
plt.legend()
plt.show()
# plt.figure()
# plt.hist(w2,100)
# plt.show()
plt.figure(figsize =(10,10))
# plt.scatter(X,Y,c = 1-rw/np.max(rw))
plt.scatter(X,Y,c = w2/np.max(w2))
plt.axis('square')
plt.title("clipped: {}, weighted: {}".format(np.mean(rw),rm))
plt.show()
pass
# tips https://stackoverflow.com/questions/41602588/matplotlib-3d-scatter-animations
# add raw data to plot
# add particle
def kernel_smoothing(self,width = 3):
kernel = easy_kernel(width)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
class VideoPlotMachine_noimpl(object):
def __init__(self,tracked_behavior,jagged_lines,what_to_plot = 'guess', has_implant = False):
self.tracked_behavior = tracked_behavior
self.jagged_lines = jagged_lines
# useful functions, get the raw data in plottable format
# pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(jagged_lines[start_frame])
# useful functions, get the fitted data in plottable format
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex='false')
matplotlib.rcParams.update({'font.size': 13})
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
# unpack
self.guessing_holder = tracked_behavior['guessing_holder']
self.tracking_holder = tracked_behavior['tracking_holder']
self.start_frame = tracked_behavior['start_frame']
self.has_implant = has_implant
self.track_or_guess = what_to_plot
self.n_frames = self.tracking_holder.shape[1]
self.v_ed = None
self.v_ed_reject = None
def better_smoothing(self):
# get the raw tracking data!
part = self.tracking_holder
# unpack all the 3D coordinates!
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
# and the spine length
s_0 = part[:,2].cpu().numpy()
s_1 = part[:,2+9].cpu().numpy()
# and smooth the data
# from utils.analysis_tools import smooth_body_support
body_support_0_smooth,s_0_smooth = smooth_body_support(body_support_0,s_0)
body_support_1_smooth,s_1_smooth = smooth_body_support(body_support_1,s_1)
# add the smoothed coordinates as numpy arrays
self.body_support_0_raw = [i.cpu().numpy().squeeze() for i in body_support_0]
self.body_support_0_smooth = body_support_0_smooth
self.s_0_raw = s_0
self.s_0_smooth = s_0_smooth
self.body_support_1_raw = [i.cpu().numpy().squeeze() for i in body_support_1]
self.body_support_1_smooth = body_support_1_smooth
self.s_1_raw = s_1
self.s_1_smooth = s_1_smooth
# also smooth the body ellipsoid rotations
self.body_support_0_smooth = smooth_rotation_matrices(body_support_0_smooth)
self.body_support_1_smooth = smooth_rotation_matrices(body_support_1_smooth)
# self.body_support_0_smooth = smooth_rotation_matrices_quaternion(body_support_0_smooth)
# self.body_support_1_smooth = smooth_rotation_matrices_quaternion(body_support_1_smooth)
def kernel_smoothing(self):
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
kernel = easy_kernel(3)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def make_3d_axis(self):
# 3D plot of the
fig = plt.figure(figsize = (10,10))
ax = fig.add_subplot(111, projection='3d')
# add to self for use later
self.fig = fig
self.ax = ax
def add_raw_data(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# add to axis 3D plot of Sphere
self.h_pc = self.ax.scatter(X, Y, Z, zdir='z', s=10, c='k', alpha = .05,rasterized=False)
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
# loop over the types of body, and make emptyscatter plots
self.h_kp_list = []
for body in body_indices:
h_kp = self.ax.scatter([],[],[], zdir='z', s=100, c=body_colors[body],rasterized=False)
self.h_kp_list.append(h_kp)
# THEN set the 3d values to be what the shoud be
for body in body_indices:
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# self.h_kp_list = []
# for i,body in enumerate(ikeyp):
# h_kp = self.ax.scatter(keyp[i,0], keyp[i,1], keyp[i,2], zdir='z', s=100, c=body_colors[body],rasterized=False)
# self.h_kp_list.append(h_kp)
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
def plot_skeleton(self,body_support,color = 'k',body_idx = 0,has_implant = False):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
#print("c_hip is {}".format(c_hip))
if has_implant:
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]
p_line = [c_nose,c_nose,c_mid,c_impl,c_impl]
q_line = [c_mid,c_tip,c_ass,c_nose,c_tip]
else:
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip]
p_line = [c_nose,c_nose,c_mid]
q_line = [c_mid,c_tip,c_ass]
# add the body points
for p in p_skel:
h_bp = self.ax.scatter(p[0],p[1],p[2],zdir='z', s=50, alpha = 1 , c=color,rasterized=False)
self.h_bp_list[body_idx].append(h_bp)
# and the lines between body parts
for p,q in zip(p_line,q_line):
h_skel = self.ax.plot([p[0],q[0]],[p[1],q[1]],[p[2],q[2]],c=color,lw = 4)
self.h_skel_list[body_idx].append(h_skel)
def add_wireframe_to_axis(self,ax,R_body,c_hip, a_nose,b_nose,a_hip,b_hip,r_impl,style='hip',this_color='k',this_alpha=.4):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
this_color = 'grey'
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip[:,np.newaxis]
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
h_hip = ax.plot_wireframe(x, y, z, color=this_color,alpha = this_alpha)
return h_hip
def plot_ellipsoids(self,body_support,s,color = 'k',body_idx = 0,has_implant=False):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
# this is not so elegant, hm hm
a_hip = a_hip_0.cpu().numpy() + a_hip_delta.cpu().numpy() * s
b_hip = b_hip_0.cpu().numpy() + b_hip_delta.cpu().numpy() * (1.-s)
d_hip = .75 * a_hip
if has_implant:
RRs,ccs,styles = [R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']
else:
RRs,ccs,styles = [R_body,R_nose],[c_hip,c_nose],['hip','nose']
for RR,cc,style in zip(RRs,ccs,styles):
h_hip = self.add_wireframe_to_axis(self.ax,RR,
cc,
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip,
b_hip,
r_impl.cpu().numpy(),
style=style,this_color=color)
self.h_hip_list[body_idx].append(h_hip)
def add_skel_fit(self,frame,fit='raw',plot_ellipsoids = True):
# frame index
i_frame = frame-self.start_frame
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
# and plot!
self.h_skel_list = [[],[]]
self.h_bp_list = [[],[]]
self.plot_skeleton(body_support_0,color = 'k',body_idx = 0,has_implant = self.has_implant)
self.plot_skeleton(body_support_1,color = 'peru',body_idx = 1,has_implant = False)
def add_ellip_fit(self,frame,fit='raw',plot_ellipsoids = True):
# frame index
i_frame = frame-self.start_frame
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
self.h_hip_list = [[],[]]
self.plot_ellipsoids(body_support_0,s_0,color = 'k',body_idx = 0,has_implant=self.has_implant)
self.plot_ellipsoids(body_support_1,s_1,color = 'peru',body_idx = 1,has_implant=False)
def add_trace(self,frame,trace,trace_length=90,trace_clip = None,decay_factor=.9, type_list = ['tip']):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
# make a holder for the lines
self.h_trace_list = [[None]*5,[None]*5]
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
#print("i_trace_start is {} and i_frame is {}".format(i_trace_start,i_frame))
trace_indices = np.arange(i_trace_start,i_frame)
if trace == 'raw':
self.unpack_trace(self.body_support_0_raw,trace_indices, body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(self.body_support_1_raw,trace_indices, body_idx = 1,what_type=type_list,color='peru')
if trace == 'smooth':
self.unpack_trace(self.body_support_0_smooth,trace_indices, body_idx = 0,what_type=type_list,color='black')
self.unpack_trace(self.body_support_1_smooth,trace_indices, body_idx = 1,what_type=type_list,color='peru')
def finish_3d_axis(self,view_style = 'top', zoom = False, dump = False):
# finish the labeling, plot adjustments, dump and show
ax = self.ax
# ax.set_xlabel('$x$ (mm)')
# ax.set_ylabel('\n$y$ (mm)')
# zlabel = ax.set_zlabel('\n$z$ (mm)')
if self.max_range is not None:
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
if view_style == 'top':
az = -30
el = 90
if view_style == 'side':
az = -15
el = 9
if view_style == 'mix':
az = 150
el = 50
if view_style == 'ex':
az = -14
el = 46
if view_style == 'ex':
az = -46
el = 23
if zoom:
def mean_point(body_support,impl = False):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support]
if impl==True:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip,c_impl])
else:
points_0 = np.concatenate([c_hip,c_ass,c_mid,c_nose,c_tip])
mean_0 = np.mean(points_0,0)
return mean_0
mean_0 = mean_point(self.body_support_0,impl= True)
mean_1 = mean_point(self.body_support_1, impl = False)
overall_mean = np.hstack([mean_0,mean_1])
mu_zoom = np.mean(overall_mean,1)
scaling = 2
d_zoom = ( mean_0.ravel() - mean_1.ravel() )*scaling
# print(d_zoom)
# print(mu_zoom)
# self.ax.scatter(mu_zoom[0],mu_zoom[1],mu_zoom[2],zdir='z', s=500, alpha = 1 , c='pink',rasterized=False)
pp = np.vstack([mu_zoom-d_zoom,mu_zoom,mu_zoom+d_zoom])
# print(pp)
# self.ax.plot(pp[:,0],pp[:,1],pp[:,2],zdir='z',alpha = 1,lw=10 , c='pink',rasterized=False)
X,Y,Z = pp[:,0],pp[:,1],pp[:,2]
# for axis adjustment
self.max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
self.mid_x = (X.max()+X.min()) * 0.5
self.mid_y = (Y.max()+Y.min()) * 0.5
self.mid_z = (Z.max()+Z.min()) * 0.5
ax.set_xlim(self.mid_x - self.max_range, self.mid_x + self.max_range)
ax.set_ylim(self.mid_y - self.max_range, self.mid_y + self.max_range)
ax.set_zlim(0, 2*self.max_range)
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
ax.view_init(elev=el, azim=az)
def unpack_trace(self,body_support,trace_indices,body_idx = 0,what_type=['hip'],color='k'):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[trace_indices,0],dat[trace_indices,1],dat[trace_indices,2]
h_trace = self.ax.plot(X,Y,Z,lw=2,c=color,alpha = .65)
self.h_trace_list[body_idx][ii_c_list[type_list == ttt][0]] = h_trace
def dump_to_disk(self,tag = ""):
print("saving w/tag: "+tag)
plt.tight_layout()
plt.savefig('figure_raw_pics/figure_5/fitting_cartoon/Plotter'+tag+'.pdf',transparent=True)
def add_frame_number(self,frame):
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.74, 0.07, "Frame:" ,ha='left',fontsize = 20, transform=self.ax.transAxes)
self.h_frame_number = self.ax.text2D(0.95, 0.07, str(frame),ha='right', fontsize = 20, transform=self.ax.transAxes)
pass
def add_frame_time(self,frame,start_frame):
cam_fps = 60 #Hz
t_now = (frame-start_frame)/cam_fps
# self.ax.text2D(0.05, 0.95, "Frame {}".format(frame), transform=self.ax.transAxes)
self.ax.text2D(0.71, 0.03, "Time:" ,ha='left', fontsize = 20, transform=self.ax.transAxes)
self.h_frame_time = self.ax.text2D(0.95, 0.03, "{:0.2f} s".format(t_now) ,ha='right',fontsize = 20, transform=self.ax.transAxes)
pass
def add_ed(self,frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c='r',lw = 4)
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
ccc = 'blueviolet'
self.h_ed_reject = self.ax.plot([c_nose[0],c_nose[0]+v_ed[0]],[c_nose[1],c_nose[1]+v_ed[1]],[c_nose[2],c_nose[2]+v_ed[2]],c=ccc,lw = 4)
pass
def make_me(self,frame=None,cloud=True,trace='raw',skel='raw',ellip='raw',trace_clip=10*60 ,savepath=None,view_override = None):
# takes a frame number and plots it, with optional tail
# trace is raw or smooth or None
self.make_3d_axis()
self.trace_length = 10*30
self.trace_clip = 10*30
if frame is not None:
if cloud:
self.add_raw_data(frame)
#self.track_or_guess = 'track' #hk
self.add_skel_fit(frame,fit=skel)
self.add_ellip_fit(frame,fit=ellip)
#self.track_or_guess = 'track'
self.add_trace(frame,trace=trace,trace_length=10*60,trace_clip = trace_clip ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame,case='ed')
# self.add_ed(frame,case='ed_reject')
self.finish_3d_axis(view_style='ex')
self.add_frame_number(frame)
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
if savepath is not None:
plt.savefig(savepath,transparent=True)
plt.show()
def video_me(self,frame_list=30*60+np.arange(100)*100,cloud=True,trace='raw',skel='raw',ellip='raw',savepath = None,view_override = None,fps=10,dpi=100,time_offset=0):
self.trace_length = 10*30
self.trace_clip = frame_list[0]
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
# add the fitted values
self.add_skel_fit(frame_list[0],fit=skel)
self.add_ellip_fit(frame_list[0],fit=ellip)
self.add_trace(frame_list[0],trace=trace,trace_length=self.trace_length,trace_clip=self.trace_clip ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame_list[0],case='ed')
# self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
# tighten and add frame # and time
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_frame_number(frame):
# self.h_frame_number.set_text("Frame: {}".format(frame))
self.h_frame_number.set_text(str(frame))
def update_frame_time(frame,start_frame = frame_list[0],time_offset=0):
cam_fps = 60. #Hz
t_now = (frame-start_frame)/cam_fps - time_offset
self.h_frame_time.set_text("{:0.2f} s".format(t_now))
def update_skeleton(body_support,body_idx = 0, has_implant = False):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
if has_implant :
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip,c_impl]
p_line = [c_nose,c_nose,c_mid,c_impl,c_impl]
q_line = [c_mid,c_tip,c_ass,c_nose,c_tip]
else:
p_skel = [c_hip,c_mid,c_nose,c_ass,c_tip]
p_line = [c_nose,c_nose,c_mid]
q_line = [c_mid,c_tip,c_ass]
# update the body points
for j,p in enumerate(p_skel):
self.h_bp_list[body_idx][j]._offsets3d = ([p[0]],[p[1]],[p[2]])
# update the lines between body parts
for j,(p,q) in enumerate(zip(p_line,q_line)):
# lines are an extra level deep for some stupid matplotlib reason
self.h_skel_list[body_idx][j][0].set_xdata([p[0],q[0]])
self.h_skel_list[body_idx][j][0].set_ydata([p[1],q[1]])
self.h_skel_list[body_idx][j][0].set_3d_properties([p[2],q[2]])
def update_skel_fit(frame,fit=skel):
# get the data out frame index
i_frame = frame-self.start_frame
# speed up this list nonsense
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
update_skeleton(body_support_0,body_idx = 0, has_implant = self.has_implant)
update_skeleton(body_support_1,body_idx = 1, has_implant = False)
def update_wireframe_lines(h_hip,X,Y,Z):
# h_hip is the handle to the lines3dcollection
# much of the code is taken from the source of the marplotlib wireframe plotting
X, Y, Z = np.broadcast_arrays(X, Y, Z)
rows, cols = Z.shape
rstride = 1
cstride = 1
# We want two sets of lines, one running along the "rows" of
# Z and another set of lines running along the "columns" of Z.
# This transpose will make it easy to obtain the columns.
tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z)
if rstride:
rii = list(range(0, rows, rstride))
# Add the last index only if needed
if rows > 0 and rii[-1] != (rows - 1):
rii += [rows-1]
else:
rii = []
if cstride:
cii = list(range(0, cols, cstride))
# Add the last index only if needed
if cols > 0 and cii[-1] != (cols - 1):
cii += [cols-1]
else:
cii = []
xlines = [X[i] for i in rii]
ylines = [Y[i] for i in rii]
zlines = [Z[i] for i in rii]
txlines = [tX[i] for i in cii]
tylines = [tY[i] for i in cii]
tzlines = [tZ[i] for i in cii]
lines = ([list(zip(xl, yl, zl))
for xl, yl, zl in zip(xlines, ylines, zlines)]
+ [list(zip(xl, yl, zl))
for xl, yl, zl in zip(txlines, tylines, tzlines)])
h_hip.set_segments(lines)
def calculate_wireframe_points(R_body,c_hip,a_nose,b_nose,a_hip,b_hip,r_impl,style='hip'):
# FIRST PLOT THE ELLIPSE, which is the hip
# generate points on a sphere
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
# get the mesh, by using the equation of an ellipsoid
if style == 'hip':
x=np.cos(u)*a_hip
y=np.sin(u)*np.sin(v)*b_hip
z=np.sin(u)*np.cos(v)*b_hip
if style == 'nose':
x=np.cos(u)*a_nose
y=np.sin(u)*np.sin(v)*b_nose
z=np.sin(u)*np.cos(v)*b_nose
if style == 'impl':
x=np.cos(u)*r_impl
y=np.sin(u)*np.sin(v)*r_impl
z=np.sin(u)*np.cos(v)*r_impl
# pack to matrix of positions
posi = np.vstack((x.ravel(),y.ravel(),z.ravel()))
# apply the rotatation and unpack
# posi_rotated = ((R_body @ (posi.T + c_hip).T ).T + t_body).T
# REMEBRE BODY SUPPORTS ARE [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose]
posi_rotated = np.einsum('ij,ja->ia',R_body,posi) + c_hip[:,np.newaxis]
x = posi_rotated[0,:]
y = posi_rotated[1,:]
z = posi_rotated[2,:]
# reshape for wireframe
x = np.reshape(x, (u.shape) )
y = np.reshape(y, (u.shape) )
z = np.reshape(z, (u.shape) )
return x,y,z
def update_ellipsoids(body_support,s,body_idx = 0, has_implant = False):
# unpack
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
# this is not so elegant, hm hm
a_hip = a_hip_0.cpu().numpy() + a_hip_delta.cpu().numpy() * s
b_hip = b_hip_0.cpu().numpy() + b_hip_delta.cpu().numpy() * (1.-s)
d_hip = .75 * a_hip
if has_implant:
RRs,ccs,styles = [R_body,R_nose,R_nose],[c_hip,c_nose,c_impl],['hip','nose','impl']
else:
RRs,ccs,styles = [R_body,R_nose],[c_hip,c_nose],['hip','nose']
for jj, (RR,cc,style) in enumerate(zip(RRs,ccs,styles)):
X,Y,Z = calculate_wireframe_points(RR,
cc,
a_nose.cpu().numpy(),
b_nose.cpu().numpy(),
a_hip,
b_hip,
r_impl.cpu().numpy(),
style=style)
h_hip = self.h_hip_list[body_idx][jj]
update_wireframe_lines(h_hip,X,Y,Z)
def update_ellip_fit(frame,fit = ellip):
# get the data out frame index
i_frame = frame-self.start_frame
# speed up this list nonsense
if fit =='raw':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_raw]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_raw]
s_0 = self.s_0_raw[i_frame]
s_1 = self.s_1_raw[i_frame]
elif fit =='smooth':
body_support_0 = [ d[i_frame,...] for d in self.body_support_0_smooth]
body_support_1 = [ d[i_frame,...] for d in self.body_support_1_smooth]
s_0 = self.s_0_smooth[i_frame]
s_1 = self.s_1_smooth[i_frame]
else:
return
update_ellipsoids(body_support_0,s_0,body_idx = 0,has_implant = self.has_implant)
update_ellipsoids(body_support_1,s_1,body_idx = 1,has_implant = False)
def update_trace_3dlines(body_support,trace_indices,body_idx=0,what_type=['hip']):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = body_support
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
ii_c_list = np.arange(len(type_list))
# TODO make the decay work!
for ttt in what_type:
# this is also not so elegant
selecta = np.arange(len(type_list))[type_list == ttt]
dat = c_list[selecta[0]].squeeze()
X,Y,Z = dat[trace_indices,0],dat[trace_indices,1],dat[trace_indices,2]
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_xdata(X)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_ydata(Y)
self.h_trace_list[body_idx][ii_c_list[type_list == what_type][0]][0].set_3d_properties(Z)
def update_trace_fit(frame,trace='raw',trace_length=None,trace_clip = None,decay_factor=.9, type_list = None):
# get the particle, convert to torch tensor, calculate body supports
i_frame = frame-self.start_frame
if trace_clip is not None:
i_clip = trace_clip-self.start_frame
i_trace_start = np.max([i_clip, i_frame-trace_length])
else:
i_trace_start = np.max([0, i_frame-trace_length])
# these are the indices to plot
trace_indices = np.arange(i_trace_start,i_frame)
if trace =='raw':
body_support_0 = self.body_support_0_raw
body_support_1 = self.body_support_1_raw
elif trace =='smooth':
body_support_0 = self.body_support_0_smooth
body_support_1 = self.body_support_1_smooth
else:
return
if len(trace_indices)== 0:
# just skip if there is no trace
return
update_trace_3dlines(body_support_0,trace_indices,body_idx=0,what_type = type_list)
update_trace_3dlines(body_support_1,trace_indices,body_idx=1,what_type = type_list)
if self.v_ed is not None:
def update_ed(frame,case='ed'):
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy() for i in body_support_1]
c_nose = np.squeeze(c_mid)
if case == 'ed':
v_ed = self.v_ed[frame-self.start_frame,:]
self.h_ed[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
if case == 'ed_reject':
v_ed = self.v_ed_reject[frame-self.start_frame,:]
self.h_ed_reject[0].set_xdata([c_nose[0],c_nose[0]+v_ed[0]])
self.h_ed_reject[0].set_ydata([c_nose[1],c_nose[1]+v_ed[1]])
self.h_ed_reject[0].set_3d_properties([c_nose[2],c_nose[2]+v_ed[2]])
pass
def update_pc(frame):
# get new raw data!
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# update the pointcloud
self.h_pc._offsets3d = (X,Y,Z)
# and update the keypoints
for body in range(4):
self.h_kp_list[body]._offsets3d = (keyp[ikeyp==body,0], keyp[ikeyp==body,1], keyp[ikeyp==body,2])
# update the counters
update_frame_number(frame)
update_frame_time(frame,time_offset=time_offset)
# update the fit
update_skel_fit(frame)
update_ellip_fit(frame)
update_trace_fit(frame,trace=trace,trace_length=10*60,trace_clip = 30*60-960 ,type_list = ['nose'])
# # update the fit as well!
# if self.v_ed is not None:
# update_ed(frame,case='ed')
# update_ed(frame,case='ed_reject')
# trick for updating line3d collection
# https://mail.python.org/pipermail/matplotlib-users/2015-October/000066.html
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, frame_list[1:], interval=10)
if savepath is None:
fn = '<"(, ,)~~'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
if savepath[-1] == 'f':
ani.save(savepath,writer='imagemagick',fps=fps,dpi = dpi)
else:
ani.save(savepath,writer='ffmpeg',fps=fps,dpi=dpi)
plt.rcParams['animation.html'] = 'html5'
ani
def rotation(self,frame_list=30*60+np.arange(100)*100,cloud=True,trace='raw',skel='raw',ellip='raw',savepath = None,view_override = None,fps=10,dpi=100):
self.trace_length = 10*60
self.trace_clip = 30*60-960
self.type_list = ['nose']
self.make_3d_axis()
self.add_raw_data(frame_list[0])
# add the fitted values
self.add_skel_fit(frame_list[0],fit=skel)
self.add_ellip_fit(frame_list[0],fit=ellip)
self.add_trace(frame_list[0],trace=trace,trace_length=10*60,trace_clip = 30*60-960 ,type_list = ['nose'])
# if self.v_ed is not None:
# self.add_ed(frame_list[0],case='ed')
# self.add_ed(frame_list[0],case='ed_reject')
self.finish_3d_axis(view_style='ex')
# override the view!
if view_override is not None:
el, az = view_override
self.ax.view_init(elev=el, azim=az)
# tighten and add frame # and time
plt.tight_layout()
self.add_frame_number(frame_list[0])
self.add_frame_time(frame_list[0],frame_list[0])
# LOTS OF FUNCTIONS FOR ANIMATION
def update_pc(i):
# here, the update rule is simply to change the view
offset = el*np.sin(i*2*np.pi / 360)
plt.gca().view_init(elev=el+offset, azim=az+i)
# for frame in frame_list[1:]:
ani = animation.FuncAnimation(self.fig, update_pc, np.linspace(0,360,80), interval=10)
fps = 10
if savepath is None:
fn = '<"(, ,)~~_rotation'
if self.v_ed is not None:
fn = '<"(, ,)~~__HEAD_rotation'
savepath = fn+'.gif'
# ani.save(fn+'.mp4',writer='ffmpeg',fps=fps)
if savepath[-1] == 'f':
ani.save(savepath,writer='imagemagick',fps=fps,dpi=dpi)
else:
ani.save(savepath,writer='ffmpeg',fps=fps,dpi=dpi)
plt.rcParams['animation.html'] = 'html5'
ani
def plot_residuals(self,frame):
# unpack the raw data in a plottable format
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# and get the corresponding fit: the particle, convert to torch tensor, calculate body supports
if self.track_or_guess == 'track':
part = self.tracking_holder[:-1,frame-self.start_frame]
if self.track_or_guess == 'guess':
part = self.guessing_holder[:-1,frame-self.start_frame]
part = torch.from_numpy(part).float().unsqueeze(0).cuda()
# body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
# body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
pos = torch.from_numpy(pos).float().cuda()
# CALCULATE RESIDUAL
dist0,_,self.body_support_0 = particles_to_distance_cuda(part[:,:9],pos[:,:],implant = True)
dist1,_,self.body_support_1 = particles_to_distance_cuda(part[:,9:],pos[:,:])
r = torch.min(dist0[0,:],dist1[0,:])
r = r.cpu().numpy().squeeze()
# Figure out the weighing stuff
w2 = pos_weights**2
rw=np.clip(r,0,.03)
# or weigted mean?
# rm = np.mean(rw)
# correct
rm = np.sum(w2*rw)/np.sum(w2)
# wrong old way:
# rm = np.mean(w2*rw)/np.median(w2)
plt.figure()
plt.plot(r,'.k',label='residual')
plt.plot(rw,'.r',label='cut')
plt.plot(w2,'.g',label='w2')
plt.legend()
plt.show()
# plt.figure()
# plt.hist(w2,100)
# plt.show()
plt.figure(figsize =(10,10))
# plt.scatter(X,Y,c = 1-rw/np.max(rw))
plt.scatter(X,Y,c = w2/np.max(w2))
plt.axis('square')
plt.title("clipped: {}, weighted: {}".format(np.mean(rw),rm))
plt.show()
pass
# tips https://stackoverflow.com/questions/41602588/matplotlib-3d-scatter-animations
# add raw data to plot
# add particle
def kernel_smoothing(self,width = 3):
kernel = easy_kernel(width)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
class TrackingWrangler(object):
def __init__(self,tracked_behavior,jagged_lines,what_to_plot = 'guess'):
self.tracked_behavior = tracked_behavior
self.jagged_lines = jagged_lines
# useful functions, get the raw data in plottable format
# pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(jagged_lines[start_frame])
# useful functions, get the fitted data in plottable format
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Liberation Sans"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
matplotlib.rc('font', family='sans-serif')
matplotlib.rc('text', usetex=False)
matplotlib.rcParams.update({'font.size': 13})
# AHHH, Ok: https://stackoverflow.com/questions/11367736/matplotlib-consistent-font-using-latex
matplotlib.rcParams['mathtext.fontset'] = 'custom'
matplotlib.rcParams['mathtext.rm'] = 'Liberation Sans'
matplotlib.rcParams['mathtext.it'] = 'Liberation Sans:italic'
matplotlib.rcParams['mathtext.bf'] = 'Liberation Sans:bold'
matplotlib.rcParams['mathtext.fallback_to_cm'] = False
from palettable.cmocean.sequential import Algae_6
cmpl = Algae_6.mpl_colors
self.cmpl = cmpl
# unpack
self.guessing_holder = tracked_behavior['guessing_holder']
self.tracking_holder = tracked_behavior['tracking_holder']
self.start_frame = tracked_behavior['start_frame']
self.track_or_guess = what_to_plot
self.n_frames = self.tracking_holder.shape[1]
self.scale_bar = [1,1,.1,1,1,1,.01,.01,.01,1,1,.1,1,1,.1,.1,.1]
self.units = ["rad","rad","a.u.",'rad','rad','rad',"m","m","m","rad","rad","a.u.",'rad','rad',"m","m","m"]
self.latex_vars = ['β', 'γ', 's', 'ψ', 'θ', 'φ', 'x', 'y', 'z', 'β', 'γ', 's', 'θ', 'φ', 'x', 'y', 'z']
def better_smoothing(self):
# get the raw tracking data!
part = self.tracking_holder
# unpack all the 3D coordinates!
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
# and the spine length
s_0 = part[:,2].cpu().numpy()
s_1 = part[:,2+9].cpu().numpy()
# and smooth the data
# from utils.analysis_tools import smooth_body_support
body_support_0_smooth,s_0_smooth = smooth_body_support(body_support_0,s_0)
body_support_1_smooth,s_1_smooth = smooth_body_support(body_support_1,s_1)
# add the smoothed coordinates as numpy arrays
self.body_support_0_raw = [i.cpu().numpy().squeeze() for i in body_support_0]
self.body_support_0_smooth = body_support_0_smooth
self.s_0_raw = s_0
self.s_0_smooth = s_0_smooth
self.body_support_1_raw = [i.cpu().numpy().squeeze() for i in body_support_1]
self.body_support_1_smooth = body_support_1_smooth
self.s_1_raw = s_1
self.s_1_smooth = s_1_smooth
# also smooth the body ellipsoid rotations
self.body_support_0_smooth = smooth_rotation_matrices(body_support_0_smooth)
self.body_support_1_smooth = smooth_rotation_matrices(body_support_1_smooth)
# self.body_support_0_smooth = smooth_rotation_matrices_quaternion(body_support_0_smooth)
# self.body_support_1_smooth = smooth_rotation_matrices_quaternion(body_support_1_smooth)
def kernel_smoothing(self):
kernel = easy_kernel(3)
for i in range(17):
self.tracking_holder[i,:] = np.convolve(self.tracking_holder[i,:],kernel,'same')
self.guessing_holder[i,:] = np.convolve(self.guessing_holder[i,:],kernel,'same')
def kernel_smoothing_points(self):
kernel = easy_kernel(3)
for b_i in range(2):
for i in range(5):
dat = self.body_points[b_i][i]
for j in range(3):
dat[:,j] = np.convolve(dat[:,j],kernel,'same')
self.body_points[b_i][i] = dat
def unpack_all_body_support(self):
part = self.tracking_holder[:-1,:]
part = torch.from_numpy(part).float().cuda()
part = torch.transpose(part,0,1)
body_support_0 = particles_to_body_supports_cuda(part[:,:9],implant = True)
body_support_1 = particles_to_body_supports_cuda(part[:,9:],implant = False)
self.all_support_0 = body_support_0
self.all_support_1 = body_support_1
def plot_all_tracking(self,do_wrapping = False,savepath=None):
sc=2
plt.figure(figsize = (18,8) )
n_vars = len(self.tracked_behavior['var'])
start_frame = 0
frame_window = n_frames
scale_bar = [1,1,.1,1,1,1,.01,.01,.01,1,1,.1,1,1,.1,.1,.1]
units = ["rad","rad","a.u.",'rad','rad','rad',"m","m","m","rad","rad","a.u.",'rad','rad',"m","m","m"]
for i in range(n_vars):
plt.subplot(n_vars,1,1+i)
dat0 = self.tracked_behavior['tracking_holder'][i,start_frame:(start_frame+frame_window)]
dat1 = self.tracked_behavior['guessing_holder'][i,start_frame:(start_frame+frame_window)]
# wrap:
def wrap_angles(phases):
phases = (phases + np.pi) % (2 * np.pi) - np.pi
return phases
if units[i] == 'rad' and do_wrapping:
dat0 = wrap_angles(dat0)
dat1 = wrap_angles(dat1)
plt.plot(dat0,c=self.cmpl[1])
plt.plot(dat1,c=self.cmpl[3])
ax = plt.gca()
if i < (n_vars-1):
adjust_spines(ax,[])
else:
adjust_spines(ax,['bottom'])
# adjust_spines(ax,['bottom','left'])
plt.yticks([])
plt.ylabel(self.tracked_behavior['var'][i])
plt.xlim([0,frame_window+10])
ax = plt.gca()
ylim=ax.get_ylim()
xlim=ax.get_xlim()
plt.plot( -2+np.array([1,1])*xlim[1],ylim[0]+np.array([0,1])*scale_bar[i],'-k' )
plt.text(xlim[1],ylim[0]," "+str(scale_bar[i])+' '+units[i] )
if i < 9:
ax.set_facecolor('k')
ax.patch.set_alpha(0.13)
else:
ax.set_facecolor('peru')
ax.patch.set_alpha(0.2)
plt.xlabel('Frames')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0)
plt.tight_layout()
# plt.savefig('figure_raw_pics/figure_5/fitting_cartoon/tracked_full.pdf',transparent=True)
if savepath is not None:
plt.savefig(savepath,transparent=False)
plt.show()
def plot_body_supports(self,savepath=None):
# import matplotlib
# matplotlib.rcParams['mathtext.fontset'] = 'custom'
# matplotlib.rcParams['mathtext.rm'] = 'Liberation Sans'
# matplotlib.rcParams['mathtext.it'] = 'Liberation Sans:italic'
# matplotlib.rcParams['mathtext.bf'] = 'Liberation Sans:bold'
# matplotlib.rcParams['mathtext.fontset'] = 'dejavusans'
# matplotlib.rcParams['font.family'] = 'sans'
# matplotlib.rc('text', usetex=True)
# matplotlib.rc('text', usetex=True)
# from matplotlib import rc
# rc('font',**{'family':'sans-serif','sans-serif':['Liberation Sans']})
## for Palatino and other serif fonts use:
#rc('font',**{'family':'serif','serif':['Palatino']})
# rc('text', usetex=True)
matplotlib.rcParams['text.latex.preamble'] = [
r'\usepackage{siunitx}', # i need upright \micro symbols, but you need...
r'\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts
r'\usepackage{helvet}', # set the normal font here
r'\usepackage{sansmath}', # load up the sansmath so that math -> helvet
r'\sansmath' # <- tricky! -- gotta actually tell tex to use!
]
self.unpack_all_body_support()
subplot_counter = 0
fig = plt.figure(figsize = (20,8) )
start_frame = 0
frame_window = self.n_frames
n_subplots_total = 6 + 5 #(6 w implant, 5 w/o implant)
for body_idx,body_support in enumerate([self.all_support_0,self.all_support_1]):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy().squeeze() for i in body_support]
type_list = np.array(['hip','tail','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
if body_idx == 1:
# don't have the implant for the last one
c_list = c_list[:-1]
for body_part,c in enumerate(c_list):
subplot_counter += 1
plt.subplot(n_subplots_total,1,subplot_counter)
for i in range(3):
plt.plot(c[:,i],c=self.cmpl[i+1])
plt.ylabel("$c_{"+type_list[body_part]+'}$\n[m]')
plt.xlim([0,frame_window+10])
ax = plt.gca()
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['left'].set_bounds(-.1,.1)
plt.yticks([-.1,.1])
if subplot_counter < (n_subplots_total):
pass
# adjust_spines(ax,['left'])
plt.gca().spines['bottom'].set_visible(False)
plt.xticks([])
else:
pass
# adjust_spines(ax,['left','bottom'])
if body_idx == 0:
ax.set_facecolor('k')
ax.patch.set_alpha(0.13)
else:
ax.set_facecolor('peru')
ax.patch.set_alpha(0.2)
plt.xticks(fontname = "Liberation Sans") # This argument will change the font.
plt.yticks(fontname = "Liberation Sans") # This argument will change the font.
plt.xlabel('Frames',fontname = "Liberation Sans")
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0)
# annoyoing hack!
# plt.tight_layout()
# matplotlib.rc('text', usetex=False)
if savepath is not None:
plt.savefig(savepath,transparent=False)
def unpack_body_points(self):
self.unpack_all_body_support()
self.body_points = []
for body_idx,body_support in enumerate([self.all_support_0,self.all_support_1]):
c_hip,c_ass,c_mid,c_nose,c_tip,c_impl,R_body,R_head,R_nose = [i.cpu().numpy().squeeze() for i in body_support]
type_list = np.array(['hip','ass','mid','nose','tip','impl'])
c_list = [c_hip,c_ass,c_mid,c_nose,c_tip,c_impl]
self.body_points.append(c_list)
self.type_list = np.array(['hip','ass','mid','nose','tip','impl'])
def calculate_social_distances(self,event='n2n',hidden = False):
def return_part(ttt = 'ass'):
return int(np.nonzero(self.type_list == ttt)[0])
self.v_nose2nose = self.body_points[0][ return_part('tip') ] - self.body_points[1][ return_part('tip') ]
self.d_nose2nose = np.linalg.norm(self.v_nose2nose,axis=1)
self.v_nose02ass = self.body_points[0][ return_part('tip') ] - self.body_points[1][ return_part('ass') ]
self.d_nose02ass = np.linalg.norm(self.v_nose02ass,axis=1)
self.v_nose12ass = self.body_points[1][ return_part('tip') ] - self.body_points[0][ return_part('ass') ]
self.d_nose12ass = np.linalg.norm(self.v_nose12ass,axis=1)
labels = ["nose_0 <-> nose_1","nose_0 --> ass_1","nose_1 --> ass_0"]
d_list = [self.d_nose2nose,self.d_nose02ass,self.d_nose12ass]
n_subplots = 5
cutoff = .03
cutoff2 = .005
plt.figure(figsize=(18,8))
for i in range(3):
plt.subplot(n_subplots,1,1+i)
plt.plot(d_list[i])
plt.axhline(cutoff,c='g')
plt.axhline(cutoff2,c='r')
plt.ylabel(labels[i]+" [m]")
# plt.xlim([30*60,30*60+4000])
plt.xlim(0,self.n_frames)
if event == 'n2n':
logic_raw = (self.d_nose2nose < .01) * (self.d_nose02ass > .06) * (self.d_nose12ass > .06)
# logic_raw = (self.d_nose2nose > .06) * (self.d_nose02ass > -1) * (self.d_nose12ass < .02)
# logic_raw = (self.d_nose2nose > .06) * (self.d_nose12ass > -1) * (self.d_nose02ass < .02)
from scipy import ndimage
logic_close=ndimage.binary_closing(logic_raw,structure = np.ones(60))
# frames is where it's true and the previous one is false
logic_frames = np.argwhere(logic_close[:-1]*(~logic_close[1:]) ) + 1
plt.subplot(n_subplots,1,4)
plt.plot(logic_raw,c='r')
plt.plot(logic_close,c='k')
for fr in logic_frames:
plt.axvline(fr,c='g')
plt.xlim(0,self.n_frames)
for i in range(n_subplots):
plt.subplot(n_subplots,1,1+i)
# plt.xlim([30*60,30*60+4000])
# plt.xlim([25000,30000])
# save for analysis, the is an index, not the actual frames.
#TODO standardize this somehow?
self.logic_frames = logic_frames.squeeze()
if hidden:
plt.close()
else:
plt.show()
# plt.ylim([0,0.05])
# nose2ass =
# nose2neck =
# hip2hip =
# pass
def calculate_2d_running(self,plot=True,zoom=True,savepath=None):
self.fwd_vel = [None,None]
self.left_vel = [None,None]
self.up_vel = [None,None]
self.fwd = [None,None]
self.left = [None,None]
self.up = [None,None]
guessing_holder = self.guessing_holder
for body_idx in range(2):
c_hip = self.body_points[body_idx][0]
c_ass = self.body_points[body_idx][1]
# todo, do this w/o splitting
x_fit = c_hip[:,0]
y_fit = c_hip[:,1]
z_fit = c_hip[:,2]
dx_fit = np.hstack((np.diff(x_fit),0))
dy_fit = np.hstack((np.diff(y_fit),0))
dz_fit = np.hstack((np.diff(z_fit),0))
# convolve with .5 s box
kernel = np.ones(30)/30
dx_fit = np.convolve(dx_fit,kernel,'same')
dy_fit = np.convolve(dy_fit,kernel,'same')
dz_fit = np.convolve(dz_fit,kernel,'same')
# calculate the xy-speed
v_xy = np.vstack((dx_fit,dy_fit)).T
# calculate the vetor from the ass to the hip
# as a kind of body-direction vector
dir_body = c_hip-c_ass
# take only 2D xy-component and normalize the vector!
dir_body_2d = dir_body[:,:2]/np.sqrt(dir_body[:,0]**2+dir_body[:,1]**2)[:,np.newaxis]
# make a vector, 90deg rotation, so pointing LEFT!!!, rotate by hand
dir_left_2d = np.einsum('ij,aj -> ai', np.array([[0,-1],[1,0]]),dir_body_2d )
# use einsum for a faster dot product
fwd_vel = np.einsum('ij,ij->i',v_xy,dir_body_2d)
left_vel = np.einsum('ij,ij->i',v_xy,dir_left_2d)
up_vel = dz_fit
# smooth again
kernel_length = 30
kernel = np.ones(kernel_length )/kernel_length
# kernel = 1.
fps = 60
conversion_factor = fps #from m/frame to m/s
fwd = np.convolve(fwd_vel,kernel,'same') * conversion_factor
left = np.convolve(left_vel,kernel,'same') * conversion_factor
up = np.convolve(up_vel,kernel,'same') * conversion_factor
# save the calculations
self.fwd_vel[body_idx] = fwd_vel
self.left_vel[body_idx] = left_vel
self.up_vel[body_idx] = up_vel
self.fwd[body_idx] = fwd
self.left[body_idx] = left
self.up[body_idx] = up
if plot:
self.plot_2d_running(body_idx=body_idx,zoom= zoom,savepath=savepath)
def plot_2d_running(self,body_idx = 0,zoom = False,savepath=None):
c_hip = self.body_points[body_idx][0]
c_ass = self.body_points[body_idx][1]
# todo, do this w/o splitting
x_fit = c_hip[:,0]
y_fit = c_hip[:,1]
z_fit = c_hip[:,2]
dx_fit = np.hstack((np.diff(x_fit),0))
dy_fit = np.hstack((np.diff(y_fit),0))
dz_fit = np.hstack((np.diff(z_fit),0))
# convolve with .5 s box
kernel = np.ones(30)/30
dx_fit = np.convolve(dx_fit,kernel,'same')
dy_fit = np.convolve(dy_fit,kernel,'same')
dz_fit = np.convolve(dz_fit,kernel,'same')
axes = []
plt.figure(figsize=(18,8))
plt.subplot(4,1,1)
axes.append(plt.gca())
al =1
plt.plot(x_fit,label='x',c=self.cmpl[1],alpha=al)
plt.plot(y_fit,label='y',c=self.cmpl[2],alpha=al)
plt.plot(z_fit,label='z',c=self.cmpl[3],alpha=al)
plt.legend(loc = 'upper right')
plt.ylabel('Position [m]')
plt.subplot(4,1,2)
axes.append(plt.gca())
al =1
plt.plot(dx_fit,label='dx',c=self.cmpl[1],alpha =al)
plt.plot(dy_fit,label='dy',c=self.cmpl[2],alpha=al)
plt.plot(dz_fit,label='dz',c=self.cmpl[3],alpha=al)
plt.legend(loc = 'upper right')
plt.ylabel('ΔPosition\n[m/s]')
plt.subplot(4,1,3)
axes.append(plt.gca())
plt.plot(self.fwd[body_idx],label="fwd",c=self.cmpl[1])
plt.axhline(0,c='k',ls='--')
# plt.legend(loc = 'upper right')
plt.ylabel('Forward speed\n[m/s]')
plt.subplot(4,1,4)
axes.append(plt.gca())
plt.plot(self.left[body_idx],label="left",c=self.cmpl[1])
plt.axhline(0,c='k',ls='--')
# plt.legend(loc = 'upper right')
plt.ylabel('Left speed\n[m/s]')
# fix some stuff
for i in [2,3]:
#plt.subplot(4,1,1+i)
ax = axes[i]
ax.set_ylim(np.array([-1,1]) *60* 3e-3)
if zoom:
for i in range(4):
#plt.subplot(4,1,1+i)
ax = axes[i]
ll = 10000
offset = 0
ax.set_xlim(np.array([0,ll])+offset)
else:
for i in range(4):
# plt.subplot(4,1,1+i)
# ax = plt.gca()
ax = axes[i]
ll = len(x_fit)
ax.set_xlim(np.array([0,ll]))
for i in range(4):
# plt.subplot(4,1,1+i)
# ax = plt.gca()
ax = axes[i]
if i<3:
adjust_spines(ax,'left')
else:
adjust_spines(ax,['left','bottom'])
plt.xlabel('Frame')
if savepath is not None:
plt.savefig(savepath[:-4]+'_'+str(body_idx)+'.pdf',transparent=False)
plt.show()
def plot_spatial_running(self,body_idx = 0,savepath=None):
c_hip = self.body_points[body_idx][0]
# todo, do this w/o splitting
x_fit = c_hip[:,0]
y_fit = c_hip[:,1]
z_fit = c_hip[:,2]
# convolve with .5 s box
kernel = np.ones(15)/15
x_sm = np.convolve(x_fit,kernel,'same')
y_sm = np.convolve(y_fit,kernel,'same')
z_sm = np.convolve(z_fit,kernel,'same')
plt.figure(figsize = (6,6) )
for idx in np.array_split(np.arange(len(x_sm)), 1000):
plt.plot(x_sm[idx],y_sm[idx],c= self.cmpl[2],alpha =.4)
plt.axis('equal')
plt.axis('off')
if savepath is not None:
plt.savefig(savepath[:-4]+'_'+str(body_idx)+'.pdf',transparent=True)
plt.show()
fig = plt.figure(figsize = (6,6))
from mpl_toolkits.mplot3d import Axes3D
ax = fig.gca(projection='3d')
for idx in np.array_split(np.arange(len(x_sm)), 1000):
plt.plot(x_sm[idx],y_sm[idx],z_sm[idx],c= self.cmpl[2],alpha =.4)
ax.set_xlim(-.10,.20)
ax.set_ylim(-.30,0.)
ax.set_zlim(0,.3)
plt.axis('off')
if savepath is not None:
plt.savefig(savepath[:-4]+'_'+str(body_idx)+'_3D.pdf',transparent=True)
plt.show()
def calculate_2d_running_slow(self,plot=True,zoom=True,savepath=None,hidden=False):
# todo, so slow and stupid, easy to speed up
guessing_holder = self.guessing_holder
self.fwd_vel = [None,None]
self.left_vel = [None,None]
self.fwd = [None,None]
self.left = [None,None]
def rotate_body_model(alpha_body,beta_body,gamma_body):
"""
TODO Very basic, could prob improve A LOOOOT
Returns R_body, to rotate and transform the mouse body model
alpha,beta,gamma is rotation around x,y,z axis respectively
https://en.wikipedia.org/wiki/Rotation_matrix#Basic_rotations
"""
R_alpha = np.empty((3,3))
R_alpha[0,:] = [1.,0.,0.]
R_alpha[1,:] = [0.,np.cos(alpha_body),-np.sin(alpha_body)]
R_alpha[2,:] = [0.,np.sin(alpha_body),np.cos(alpha_body)]
R_beta = np.empty((3,3))
R_beta[0,:] = [np.cos(beta_body),0.,np.sin(beta_body)]
R_beta[1,:] = [0.,1.,0.]
R_beta[2,:] = [-np.sin(beta_body),0.,np.cos(beta_body)]
R_gamma = np.empty((3,3))
R_gamma[0,:] = [np.cos(gamma_body),-np.sin(gamma_body),0.]
R_gamma[1,:] = [np.sin(gamma_body),np.cos(gamma_body),0.]
R_gamma[2,:] = [0.,0.,1.]
return R_alpha@R_beta@R_gamma
for body_idx in range(2):
if body_idx == 0:
beta_fit = guessing_holder[0,:]
gamma_fit = guessing_holder[1,:]
x_fit = guessing_holder[6,:]
y_fit = guessing_holder[7,:]
z_fit = guessing_holder[8,:]
elif body_idx == 1:
beta_fit = guessing_holder[9,:]
gamma_fit = guessing_holder[10,:]
x_fit = guessing_holder[14,:]
y_fit = guessing_holder[15,:]
z_fit = guessing_holder[16,:]
dx_fit = np.hstack((np.diff(x_fit),0))
dy_fit = np.hstack((np.diff(y_fit),0))
dz_fit = np.hstack((np.diff(z_fit),0))
# convolve with .5 s box
kernel = np.ones(30)/30
dx_fit = np.convolve(dx_fit,kernel,'same')
dy_fit = np.convolve(dy_fit,kernel,'same')
dz_fit = np.convolve(dz_fit,kernel,'same')
# calculate the xy-speed
v_xy = np.vstack((dx_fit,dy_fit))
fwd_vel = []
left_vel = []
#todo vecotrize this, already in the pytorch lib
for i in tqdm( range(v_xy.shape[1]-2) ):
v_step = v_xy[:,i]
dir_body = rotate_body_model(0,beta_fit[i],gamma_fit[i])@[1.,0.,0.]
dir_body = dir_body[:2]/np.sqrt(dir_body[0]**2+dir_body[1]**2)
fwd_vel.append(np.dot(v_step,dir_body))
# fwd_vel.append(v_step @ dir_body)
# ALONG Y so actually LEFT
dir_left = rotate_body_model(0,beta_fit[i],gamma_fit[i])@[0.,1.,0.]
dir_left = dir_right[:2]/np.sqrt(dir_left[0]**2+dir_left[1]**2)
left_vel.append(np.dot(v_step,dir_left))
# right_vel.append(v_step @ dir_right)
# stack the speeds
fwd_vel = np.hstack(fwd_vel)
left_vel = np.hstack(left_vel)
#%%
kernel = np.ones(30)/30
# kernel = 1.
fwd = np.convolve(fwd_vel,kernel,'same')
left = np.convolve(left_vel,kernel,'same')
# save the calculations
self.fwd_vel[body_idx] = fwd_vel
self.left_vel[body_idx] = left_vel
self.fwd[body_idx] = fwd
self.left[body_idx] = left
if plot:
plt.figure(figsize=(12,12))
plt.subplot(6,1,1)
plt.plot(x_fit,label='x')
plt.plot(y_fit,label='y')
plt.plot(z_fit,label='z')
plt.legend()
plt.subplot(6,1,2)
plt.plot(dx_fit,label='dx')
plt.plot(dy_fit,label='dy')
plt.plot(dz_fit,label='dz')
plt.legend()
plt.subplot(6,1,3)
plt.plot(fwd,label="fwd")
plt.axhline(0)
plt.legend()
plt.subplot(6,1,4)
plt.plot(left,label="left")
plt.axhline(0)
plt.legend()
if zoom:
for i in range(4):
plt.subplot(6,1,1+i)
ll = 10000
offset = 0
plt.xlim(np.array([0,ll])+offset)
plt.show()
pass
def concatenate_runs_calc_four_states(self):
pass
def calculate_3d_running(self):
pass
def calculate_head_angle(self):
# of the non-implated guy
pass
def calculate_nose2nose(self):
pass
def show_nose2nose_examples(self):
pass
def show_running_examples(self):
pass
def calculate_implant_distance(self,savepath=None):
type_list = np.array(['hip','tail','mid','nose','tip','impl'])
c_nose_0 = self.body_points[0][3]
c_nose_1 = self.body_points[1][3]
c_impl_0 = self.body_points[0][5]
# get out the 3d positions of all the implant keypoints
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
n_frames = self.tracking_holder.shape[1]
# unpack all the keypoints
self.keyp_implant_holder = []
self.keyp_frame_holder = []
self.d_c_nose_0_holder = []
self.d_c_nose_1_holder = []
self.d_c_impl_0_holder = []
for frame in tqdm(range(n_frames)):
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# only if there are some keypoints
if (np.sum(ikeyp == 0) > 0)*((frame-self.start_frame) > 0 ) :
keyp_implant = keyp[ikeyp == 0]
keyp_frame = frame * np.ones(np.sum(ikeyp == 0) )
self.keyp_implant_holder.append(keyp_implant)
self.keyp_frame_holder.append(keyp_frame)
# gete the nose coordinate of the FIT, remember the offset
d_c_nose_0 = np.linalg.norm( keyp_implant- c_nose_0[frame-self.start_frame,:], axis = 1 )
d_c_nose_1 = np.linalg.norm( keyp_implant- c_nose_1[frame-self.start_frame,:], axis = 1 )
d_c_impl_0 = np.linalg.norm( keyp_implant- c_impl_0[frame-self.start_frame,:], axis = 1 )
self.d_c_nose_0_holder.append(d_c_nose_0)
self.d_c_nose_1_holder.append(d_c_nose_1)
self.d_c_impl_0_holder.append(d_c_impl_0)
self.keyp_frame_holder = np.hstack(self.keyp_frame_holder)
self.d_c_nose_0_holder = np.hstack(self.d_c_nose_0_holder)
self.d_c_nose_1_holder = np.hstack(self.d_c_nose_1_holder)
self.d_c_impl_0_holder = np.hstack(self.d_c_impl_0_holder)
# get c_nose of both mice
# plot the implant-to-nose distance
self.plot_implant_distance(savepath=savepath)
# plot the nose-to-nose distance
def plot_implant_distance(self,savepath=None):
plt.figure(figsize = (18,8) )
n_subplots = 4
plt.subplot(n_subplots,1,1)
c_nose_0 = self.body_points[0][3]
c_nose_1 = self.body_points[1][3]
head_distance = np.linalg.norm(c_nose_0 - c_nose_1,axis =1)
plt.plot(head_distance,c = self.cmpl[2])
plt.ylabel("Head-to-head \n distamce [m]")
plt.xlim([0,c_nose_0.shape[0]])
plt.subplot(n_subplots,1,2)
a = 1
mz = .3
cc = 'DodgerBlue'
# cc = self.cmpl[2]
plt.plot(self.keyp_frame_holder,self.d_c_nose_0_holder,'.',c = cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse0\n $c_{nose}$-to-keyp$_{impl}$ \n distamce [m]")
plt.ylim([0,.3])
plt.subplot(n_subplots,1,3)
plt.plot(self.keyp_frame_holder,self.d_c_nose_1_holder,'.',c =cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse1\n $c_{nose}$-to-keyp$_{impl}$ \n distamce [m]")
plt.ylim([0,.3])
plt.subplot(n_subplots,1,4)
plt.plot(self.keyp_frame_holder,self.d_c_impl_0_holder,'.',c = cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse1\n $c_{impl}$-to-keyp$_{impl}$ \n distamce [m]")
plt.ylim([0,.3])
plt.xlabel('Frame')
for subplot_counter in range(4):
plt.subplot(n_subplots,1,1+subplot_counter)
ax = plt.gca()
if subplot_counter < (n_subplots-1):
adjust_spines(ax,['left'])
else:
adjust_spines(ax,['left','bottom'])
if savepath is not None:
plt.savefig(savepath,transparent=False)
plt.show()
def calculate_ear_distance(self,savepath=None,zoom=False):
type_list = np.array(['hip','tail','mid','nose','tip','impl'])
c_nose_0 = self.body_points[0][3]
c_nose_1 = self.body_points[1][3]
c_tip_0 = self.body_points[0][4]
c_tip_0 = self.body_points[0][4]
c_tip_1 = self.body_points[1][4]
# the direction of the nose
v_nose_0 = c_tip_0-c_nose_0
v_nose_1 = c_tip_1-c_nose_1
# get out the 3d positions of all the implant keypoints
body_colors = ['dodgerblue','red','lime','orange']
body_indices = [0,1,2,3]
n_frames = self.tracking_holder.shape[1]
# unpack all the keypoints
self.keyp_ear_holder = []
self.keyp_ear_rotated_holder = []
self.keyp_frame_holder = []
self.d_c_ear_0_holder = []
self.d_c_ear_1_holder = []
self.angle_c_ear_0_holder = []
self.angle_c_ear_1_holder = []
self.rot_holder = []
for frame in tqdm(range(n_frames)):
pos, pos_weights, keyp, pkeyp, ikeyp = unpack_from_jagged(self.jagged_lines[frame])
X, Y, Z = pos[:,0],pos[:,1],pos[:,2]
# only if there are some keypoints
if (np.sum(ikeyp == 1) > 0)*((frame-self.start_frame) > 0 ) :
keyp_ear = keyp[ikeyp == 1]
keyp_frame = frame * np.ones(np.sum(ikeyp == 1) )
self.keyp_ear_holder.append(keyp_ear)
self.keyp_frame_holder.append(keyp_frame)
# gete the nose coordinate of the FIT, remember the offset
d_c_nose_0 = np.linalg.norm( keyp_ear - c_nose_0[frame-self.start_frame,:], axis = 1 )
d_c_nose_1 = np.linalg.norm( keyp_ear - c_nose_1[frame-self.start_frame,:], axis = 1 )
self.d_c_ear_0_holder.append(d_c_nose_0)
self.d_c_ear_1_holder.append(d_c_nose_1)
# also calculate the angle with the nose direction vector
# the vecors from the nose to the ear keypoints
v_nose2ears_0 = keyp_ear - c_nose_0[frame-self.start_frame,:]
v_nose2ears_1 = keyp_ear - c_nose_1[frame-self.start_frame,:]
# now get the projection onto a plane which has the nose vector as the normal
plane_point = c_nose_1[frame-self.start_frame,:]
plane_normal = v_nose_1[frame-self.start_frame,:]
v_up = np.array([0,0,1]) # pointing to z
# now, we rotate
def rotate_f_onto_t(f,t):
# make unit vectors
f = f/np.linalg.norm(f)
t = t/np.linalg.norm(t)
# trick from here: https://math.stackexchange.com/questions/180418/calculate-rotation-matrix-to-align-vector-a-to-vector-b-in-3d
# from this paper: http://cs.brown.edu/research/pubs/pdfs/1999/Moller-1999-EBA.pdf
# to rotate vector f onto vector t (around f x t!), we can do:
v = np.cross(f, t)
u = v/np.linalg.norm(v)
c = np.dot(f, t)
h = (1 - c)/(1 - c**2)
vx, vy, vz = v
rot = np.array([[c + h*vx**2, h*vx*vy - vz, h*vx*vz + vy],
[h*vx*vy+vz, c+h*vy**2, h*vy*vz-vx],
[h*vx*vz - vy, h*vy*vz + vx, c+h*vz**2]])
return rot
# now, find a vector, which would rotate the nose direction vector
# into the xy plane.
rotA = rotate_f_onto_t(plane_normal,plane_normal*np.array([1,1,0]))
# and now, we rotate in the xy plane to point in x-direction
rotB = rotate_f_onto_t(plane_normal*np.array([1,1,0]),np.array([1,0,0]))
# Now, we also rotate the keypoints by the same vector
rot = rotB @ rotA
# let's rotate the ear points into the same space
keyp_ear_rotated = np.einsum('ij,ai->aj',rot,keyp_ear)
self.keyp_ear_rotated_holder.append(keyp_ear_rotated)
# and save their coordinates
self.rot_holder.append(rot)
# stack and concatenate, actually ear, so rename??
self.keyp_ear_holder = np.concatenate(self.keyp_ear_holder,axis=0)
self.keyp_ear_rotated_holder = np.concatenate(self.keyp_ear_rotated_holder,axis=0)
self.keyp_frame_holder = np.hstack(self.keyp_frame_holder)
self.d_c_ear_0_holder = np.hstack(self.d_c_ear_0_holder)
self.d_c_ear_1_holder = np.hstack(self.d_c_ear_1_holder)
print('pik')
# get c_nose of both mice
# plot the implant-to-nose distance
self.plot_ear_distance(savepath=savepath,zoom=zoom)
def plot_ear_distance(self,savepath=None,zoom=False,xlim = None):
# wrangling the ear keypoints, 2nd version
plt.figure(figsize = (14,16) )
n_subplots = 8
plt.subplot(n_subplots,1,1)
c_nose_0 = self.body_points[0][3]
c_nose_1 = self.body_points[1][3]
c_mid_0 = self.body_points[0][2]
c_mid_1 = self.body_points[1][2]
c_tip_0 = self.body_points[0][4]
c_tip_1 = self.body_points[1][4]
# the direction of the nose
v_nose_0 = c_tip_0-c_nose_0
v_nose_1 = c_tip_1-c_nose_1
# the vecors from the nose to the ear keypoints
cutoff = .025
cutoff = .03
close_to_0 = (self.d_c_ear_0_holder < cutoff )*(self.d_c_ear_1_holder > cutoff )
close_to_1 = (self.d_c_ear_1_holder < cutoff )*(self.d_c_ear_0_holder > cutoff )
# INSTEAD,just assign to the closest one?
cutoff = .03
close_to_0 = (self.d_c_ear_0_holder < self.d_c_ear_1_holder) * (self.d_c_ear_0_holder < cutoff )
close_to_1 = (~close_to_0) * (self.d_c_ear_1_holder < cutoff )
plt.subplot(n_subplots,1,1)
a = 1
mz = .3
cc = 'Red'
# cc = self.cmpl[2]
plt.plot(self.keyp_frame_holder,self.d_c_ear_0_holder,'.',c = cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse0\n $c_{nose}$-to-keyp$_{ear}$ \n distamce [m]")
plt.ylim([0,.3])
# plt.ylim([0,.025])
plt.subplot(n_subplots,1,2)
plt.plot(self.keyp_frame_holder,self.d_c_ear_1_holder,'.',c =cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse1\n $c_{nose}$-to-keyp$_{ear}$ \n distamce [m]")
plt.ylim([0,.3])
# plt.ylim([0,.025])
plt.subplot(n_subplots,1,3)
# cc = self.cmpl[2]
plt.plot(self.keyp_frame_holder[close_to_0],self.d_c_ear_0_holder[close_to_0],'.',c = cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse0\n $c_{nose}$-to-keyp$_{ear}$ \n distamce [m]")
plt.ylim([0,.3])
# plt.ylim([0,.025])
plt.subplot(n_subplots,1,4)
plt.plot(self.keyp_frame_holder[close_to_1],self.d_c_ear_1_holder[close_to_1],'.',c =cc,alpha = a,markersize = mz)
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse1\n $c_{nose}$-to-keyp$_{ear}$ \n distamce [m]")
plt.ylim([0,.3])
# plt.ylim([0,.1])
plt.subplot(n_subplots,1,5)
# plot the y
# smoothing, dirty fast version
def easy_kernel(kernel_width = 30):
from scipy import stats
kernel = stats.norm.pdf(np.arange(-3*kernel_width,3*kernel_width+1),scale=kernel_width)
kernel = kernel/np.sum(kernel)
return kernel
dat = self.keyp_ear_holder
ears_x = dat[:,0]
ears_y = dat[:,1]
ears_z = dat[:,2]
kernel = easy_kernel(1)
ears_x_smooth = np.convolve(ears_x[close_to_1],kernel,'same')
ears_y_smooth = np.convolve(ears_y[close_to_1],kernel,'same')
ears_z_smooth = np.convolve(ears_z[close_to_1],kernel,'same')
plt.plot(self.keyp_frame_holder[close_to_1],ears_x[close_to_1],'.',c =self.cmpl[1],alpha = a,markersize = 6*mz,label='y')
plt.plot(self.keyp_frame_holder[close_to_1],ears_y[close_to_1],'.',c =self.cmpl[2],alpha = a,markersize = 6*mz,label='y')
plt.plot(self.keyp_frame_holder[close_to_1],ears_z[close_to_1],'.',c =self.cmpl[3],alpha = a,markersize = 6*mz,label='z')
# plt.plot(self.keyp_frame_holder[close_to_1],ears_x_smooth,'.',c =cmpl[-1],alpha = a,markersize = mz,label='y smooth')
# plt.plot(self.keyp_frame_holder[close_to_1],ears_y_smooth,'.',c =cmpl[-1],alpha = a,markersize = mz,label='z smooth')
# plt.plot(self.keyp_frame_holder[close_to_1],ears_z_smooth,'.',c =cmpl[-1],alpha = a,markersize = mz,label='z smooth')
# plt.legend()
plt.xlim([0,c_nose_0.shape[0]])
plt.ylabel("Mouse1 Ear\ncoordinates [m]")
frames_tracked = np.arange(self.start_frame,self.start_frame+self.n_frames)
plt.subplot(n_subplots,1,7)
c_nose_1_smooth = np.zeros((self.n_frames,3))
for i in range(3):
plt.plot(frames_tracked,c_nose_1[:,i],'.',c =self.cmpl[1+i],markersize=10*mz)
kernel = easy_kernel(3)
c_nose_1_smooth[:,i] = np.convolve(c_nose_1[:,i],kernel,'same')
plt.plot(frames_tracked, c_nose_1_smooth[:,i] ,'-k')
c_tip_1_smooth = np.zeros((self.n_frames,3))
for i in range(3):
kernel = easy_kernel(3)
c_tip_1_smooth[:,i] = np.convolve(c_tip_1[:,i],kernel,'same')
c_mid_1_smooth = np.zeros((self.n_frames,3))
for i in range(3):
kernel = easy_kernel(3)
c_mid_1_smooth[:,i] = np.convolve(c_mid_1[:,i],kernel,'same')
# use pandas to group the vectors!
df = pd.DataFrame(np.vstack([self.keyp_frame_holder[close_to_1], ears_x_smooth,ears_y_smooth,ears_z_smooth]).T, columns = ['frame', 'x','y','z'])
grouped_data = df.groupby(['frame']).mean()
# these are all the frames, where we have an estimate
grouped_frame = np.array(grouped_data.index)
# now, interpolate
from scipy import interpolate
c_ears = np.zeros((self.n_frames,3))
for i,name in enumerate(['x','y','z']):
f_interp = interpolate.interp1d(grouped_frame, grouped_data[name],bounds_error = False,fill_value=0,assume_sorted=False)
print(name)
c_ears[:,i] = f_interp(frames_tracked)
plt.ylabel("Mouse1 Nose\ncoordinates [m]")
c_ears_smooth = np.zeros((self.n_frames,3))
for i in range(3):
kernel = easy_kernel(3)
c_ears_smooth[:,i] = np.convolve(c_ears[:,i],kernel,'same')
# ear direction vector!
v_ed = c_ears-c_mid_1_smooth
v_ed_smooth = np.zeros((self.n_frames,3))
for i in range(3):
kernel = easy_kernel(10)
v_ed_smooth[:,i] = np.convolve(v_ed[:,i],kernel,'same')
v_ed = v_ed/np.linalg.norm(v_ed,axis =1)[:,np.newaxis]
v_ed_smooth = v_ed_smooth/np.linalg.norm(v_ed_smooth,axis =1)[:,np.newaxis]
# also calculate the orthogonal vector! aka vector rejection
v_nose_1 = c_tip_1_smooth-c_nose_1_smooth
v_nose_norm = v_nose_1/np.linalg.norm(v_nose_1,axis =1)[:,np.newaxis]
# projection is row wise
dotp = np.einsum('ai,ai->a',v_ed,v_nose_norm)
v_project = dotp[:,np.newaxis]*v_nose_norm
v_reject = v_ed - v_project
# ear direction
v_ed_reject = v_reject/np.linalg.norm(v_reject,axis =1)[:,np.newaxis]
v_ed_reject_smooth = np.zeros((self.n_frames,3))
for i in range(3):
kernel = easy_kernel(10)
v_ed_reject_smooth[:,i] = np.convolve(v_ed_reject[:,i],kernel,'same')
plt.subplot(n_subplots,1,8)
for i in [2]:#range(3):
aa = .05
if zoom:
aa = 1.
plt.plot(frames_tracked,v_ed[:,i],'.',c='r',alpha = .1)
plt.plot(frames_tracked,v_ed_smooth[:,i],'-',c='r',alpha = 1.)
plt.plot(frames_tracked,v_ed_reject[:,i],'.',c='blueviolet',alpha =.1)
plt.plot(frames_tracked,v_ed_reject_smooth[:,i],'-',c='blueviolet',alpha =1.)
plt.ylim([-1,1])
plt.ylabel("Mouse1 Ear\ndirection vector\nz-component")
plt.xlabel("Frame")
plt.subplot(n_subplots,1,6)
for i in range(3):
plt.plot(frames_tracked,c_ears[:,i],'-',c=self.cmpl[1+i])
plt.ylabel("Mouse1 Ear\ncoordinates\nsmooth & interp [m]")
for subplot_counter in range(n_subplots):
plt.subplot(n_subplots,1,1+subplot_counter)
ax = plt.gca()
if subplot_counter < (n_subplots-1):
adjust_spines(ax,['left'])
else:
adjust_spines(ax,['left','bottom'])
# plt.xlim(np.array([3000,4000])+1000+2000)
# # good example!
# plt.xlim(np.array([3000,4000])+1000+2000)
# good example zoomed
if zoom:
plt.xlim(np.array([6400,7000+400]))
else:
plt.xlim([frames_tracked[0],frames_tracked[-1]])
plt.xlim([0,len(frames_tracked)])
# plt.xlim([frames_tracked[0],frames_tracked[-1]])
if xlim is not None:
plt.xlim(xlim)
# savepath = 'figure_raw_pics/figure_5_S/Ear-vector-calculation.png'
if savepath is not None:
plt.savefig(savepath,transparent=False)
plt.show()
# add to the plotter
self.v_ed = .06*v_ed
self.v_ed_reject = .06*v_ed_reject
# add to the plotter
self.v_ed_smooth = .06*v_ed_smooth
self.v_ed_reject_smooth = .06*v_ed_reject_smooth
# add to self for use later !
self.c_ears_1 = c_ears
self.c_ears_smooth_1 = c_ears_smooth
def make_figure5(self):
# plot XYZ for both mice
# plot the running speed for both mice
# plot the z speed for both mice
# Fit arma model for running and rearing
# calculate the social distances
# Plot the 'Ethogram' for Running
# Plot the 'Ethogram' for Z-running (rearing)
# Plot the social ethogram (n2n,n2ass,n2ass)
# follwing is nose-ass, while running, for a while, for example. Mounting would be center above centers
# Select a few good examples for identified behaviors
pass
from matplotlib.patches import Ellipse
def plot_cov_ellipse(cov, pos, nstd=2, ax=None, **kwargs):
# from here: https://stackoverflow.com/questions/12301071/multidimensional-confidence-intervals
"""
Plots an `nstd` sigma error ellipse based on the specified covariance
matrix (`cov`). Additional keyword arguments are passed on to the
ellipse patch artist.
Parameters
----------
cov : The 2x2 covariance matrix to base the ellipse on
pos : The location of the center of the ellipse. Expects a 2-element
sequence of [x0, y0].
nstd : The radius of the ellipse in numbers of standard deviations.
Defaults to 2 standard deviations.
ax : The axis that the ellipse will be plotted on. Defaults to the
current axis.
Additional keyword arguments are pass on to the ellipse patch.
Returns
-------
A matplotlib ellipse artist
"""
def eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:,order]
if ax is None:
ax = plt.gca()
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
# Width and height are "full" widths, not radius
width, height = 2 * nstd * np.sqrt(vals)
ellip = Ellipse(xy=pos, width=width, height=height, angle=theta, **kwargs)
ax.add_artist(ellip)
return ellip
def plot_t_ellipse(nu, cov, pos, fraction = .5, ax=None, **kwargs):
# from here: https://stackoverflow.com/questions/12301071/multidimensional-confidence-intervals
"""
Plots an `nstd` sigma error ellipse based on the specified covariance
matrix (`cov`). Additional keyword arguments are passed on to the
ellipse patch artist.
Parameters
----------
cov : The 2x2 covariance matrix to base the ellipse on
pos : The location of the center of the ellipse. Expects a 2-element
sequence of [x0, y0].
nstd : The radius of the ellipse in numbers of standard deviations.
Defaults to 2 standard deviations.
ax : The axis that the ellipse will be plotted on. Defaults to the
current axis.
Additional keyword arguments are pass on to the ellipse patch.
Returns
-------
A matplotlib ellipse artist
"""
def eigsorted(cov):
vals, vecs = np.linalg.eigh(cov)
order = vals.argsort()[::-1]
return vals[order], vecs[:,order]
if ax is None:
ax = plt.gca()
vals, vecs = eigsorted(cov)
theta = np.degrees(np.arctan2(*vecs[:,0][::-1]))
# Width and height are "full" widths, not radius
nstd = 1
# This gives the width and height, of 1 standard deviation
# of a Gaussian, with the same covariance
width, height = 2 * nstd * np.sqrt(vals)
#
ellip = Ellipse(xy=pos, width=width, height=height, angle=theta, **kwargs)
ax.add_artist(ellip)
return ellip
from matplotlib.colors import LinearSegmentedColormap
def gradient_cmap(colors, nsteps=256, bounds=None):
# from https://github.com/slinderman/ssm/
# Make a colormap that interpolates between a set of colors
ncolors = len(colors)
# assert colors.shape[1] == 3
if bounds is None:
bounds = np.linspace(0,1,ncolors)
reds = []
greens = []
blues = []
alphas = []
for b,c in zip(bounds, colors):
reds.append((b, c[0], c[0]))
greens.append((b, c[1], c[1]))
blues.append((b, c[2], c[2]))
alphas.append((b, c[3], c[3]) if len(c) == 4 else (b, 1., 1.))
cdict = {'red': tuple(reds),
'green': tuple(greens),
'blue': tuple(blues),
'alpha': tuple(alphas)}
cmap = LinearSegmentedColormap('grad_colormap', cdict, nsteps)
return cmap
def plot_2dhist_all(y_data):
# Plot the raw data (whitened!
import matplotlib
#profile with a lower number of states
# y_data = y_data[80000:85000,:]
bins = (np.linspace(-4,6,10*4),np.linspace(-10,10,6*4))
plt.figure(figsize = (4,2.7))
plt.hist2d(y_data[:,0],y_data[:,1],bins = bins,cmap = 'Greys',norm=matplotlib.colors.LogNorm())
plt.xlim([-4,6])
plt.ylim([-10,10])
# plt.title('all data')
plt.xlabel('Fwd speed [z]')
plt.ylabel('Left speed [z]')
plt.tight_layout()
ax = plt.gca()
# adjust_spines2(ax,['Bottom','Left'])
ax.set_yticks([-8,0,8])
ax.set_xticks([-4,0,6])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['left'].set_bounds(-8,8)
plt.savefig('figs/all_histo.png',transparent = True,dpi=600)
plt.show()
#profile with a lower number of states
def fit_kmeans(data,hidden_dim,colors,show_plots = True):
data_dim = data.shape[1]
np.random.seed(1987)
# for initializing, we use k-means, should we use the most likely state as first?
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=hidden_dim, init='k-means++', max_iter=300, n_init=10, random_state=0)
pred_y = kmeans.fit_predict(data.numpy())
mu_prior = torch.tensor(kmeans.cluster_centers_,dtype=torch.float).squeeze()
std_prior = torch.zeros(hidden_dim,data_dim)
for i in range(hidden_dim):
for j in range(data_dim):
std_prior[i,j] = torch.std(data[pred_y == i,j])
print("prior mu: {} and std: {}".format(mu_prior,std_prior))
if show_plots:
plt.figure(figsize = (4,2.7))
for i in range(hidden_dim):
dat = data[pred_y==i]
plt.scatter(dat[::25,0].numpy(),dat[::25,1].numpy(),
c=colors[i],s=3,alpha = .3)
# plt.plot(mu_prior[i,0],mu_prior[i,1],'o', c='k')
plt.plot(mu_prior[i,0] * np.ones(2),mu_prior[i,1]+np.array([-1,1])*std_prior[i,1].numpy(),'-', c='k')
plt.plot(mu_prior[i,0]+np.array([-1,1])*std_prior[i,0].numpy(),mu_prior[i,1]*np.ones(2),'-', c='k')
plt.xlabel('Fwd speed [z]')
plt.ylabel('Left speed [z]')
plt.tight_layout()
ax = plt.gca()
# adjust_spines(ax,['Bottom','Left'])
plt.xlim([-4,6])
plt.ylim([-10,10])
ax.set_yticks([-8,0,8])
ax.set_xticks([-4,0,6])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['left'].set_bounds(-8,8)
plt.savefig( 'figs/kmeans.png',transparent = True,dpi=600)
plt.show()
return mu_prior,std_prior
def plot_latent_ellisoids(mus,Sigmas,data_np,colors,savepath = None):
plt.figure(figsize=(4,2.7))
ax = plt.gca()
plt.hist2d(data_np[:,0],data_np[:,1],40, cmap='Greys',norm=matplotlib.colors.LogNorm())
plt.xlabel('Fwd speed [z]')
plt.ylabel('Left speed [z]')
hidden_dim = mus.shape[0]
for state in range(hidden_dim):
mean = mus[state]
cov = Sigmas[state]
plt.plot(mean[0],mean[1],'o',c=colors[state])
for std in range(4):
ellip = plot_cov_ellipse(cov, mean, nstd=std, ax=ax,lw=2)
ellip.set_edgecolor(colors[state])
ellip.set_facecolor('None')
plt.xlim([-4,6])
plt.ylim([-10,10])
ax.set_yticks([-8,0,8])
ax.set_xticks([-4,0,6])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['left'].set_bounds(-8,8)
if savepath is not None:
plt.tight_layout()
ax = plt.gca()
# adjust_spines(ax,['Bottom','Left'])
plt.xlim([-4,6])
plt.ylim([-10,10])
plt.savefig(savepath,transparent=True,dpi=600)
plt.show()
def plot_map_estimate(map_esti_holder,colors,data_np,i=-1,savepath=None):
# unpack
transition,locs,scales,lkj = map_esti_holder[i]
hidden_dim,data_dim = locs.shape
# Calculate the covariance
L_om = np.zeros([hidden_dim,data_dim,data_dim])
Sigma = np.zeros([hidden_dim,data_dim,data_dim])
# print(scales)
# print(lkj)
for k in range(hidden_dim):
# make a matrix of the scales
s_matrix = np.diag( np.sqrt(scales[k,...]) )
lkj_matrix=lkj[k,...]
L_om[k,...] = s_matrix @ (lkj_matrix @ s_matrix)
# mirror the lower to the upper!
X = L_om[k,...]
Sigma[k,...] = X + X.T - np.diag(np.diag(X))
plot_latent_ellisoids(locs,Sigma,data_np,colors,savepath=savepath)
from scipy.stats import norm
def plot_map_estimate_z(map_esti_holder,colors,data_np,i=-1,savepath=None):
# unpack
transition,locs,scales = map_esti_holder[i]
hidden_dim = locs.shape[0]
plt.figure(figsize=(3.5,2.4))
# plt.hist(data_np,100,color = 'lightgrey')
color_offset = 5
for state in range(hidden_dim):
xxx= np.linspace(-6,6,1000)
yyy = norm.pdf(xxx,loc = locs[state],scale = scales[state])
plt.fill_between(xxx,yyy,color = colors[state+color_offset],alpha = .5)
for state in range(hidden_dim):
xxx= np.linspace(-6,6,1000)
yyy = norm.pdf(xxx,loc = locs[state],scale = scales[state])
plt.plot(xxx,yyy,color = colors[state+color_offset],lw = 2)
plt.xlim([-6,6])
plt.xlabel('Up speed [z]')
plt.yticks([])
ax = plt.gca()
# adjust_spines(ax,['Bottom','Left'])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['bottom'].set_bounds(-5,5)
plt.gca().spines['left'].set_bounds(-0,1.4)
plt.ylabel('Density')
plt.yticks([0,1.4])
plt.xticks([-5,0,5])
plt.gca().set_yticklabels(' ')
plt.tight_layout()
if savepath is not None:
plt.savefig(savepath,transparent=True,dpi=600)
plt.show()
def plot_losses(losses,savepath = None,z=False):
if z:
plt.figure(figsize=(3,2))
else:
plt.figure(figsize=(4,2.3))
plt.plot(np.log(losses),':ok',markersize = 4)
plt.xlabel('Iterations')
plt.ylabel('log(Loss)')
# pyplot.yscale('log')
ax = plt.gca()
# adjust_spines(ax,['Bottom','Left'])
buf = 10
ax.set_xlim([-buf,len(losses)+buf])
plt.xticks([0,100])
# ax.set_xticklabels([])
ybuf=20
plt.ylim(np.log([losses[-1]-ybuf,losses[0]+ybuf]))
plt.yticks(np.log([losses[-1]-ybuf,losses[0]+ybuf]))
ax.set_yticklabels([])
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
# plt.gca().spines['left'].set_bounds(-8,8)
plt.gca().spines['bottom'].set_bounds(0,len(losses))
ax.xaxis.labelpad = -10
# plt.tight_layout()
if savepath is not None:
plt.savefig(savepath,
transparent = True,dpi=600)
plt.show()
| 40.940439
| 171
| 0.561917
| 35,191
| 238,519
| 3.587451
| 0.033787
| 0.031803
| 0.013783
| 0.00606
| 0.893795
| 0.876598
| 0.8644
| 0.852962
| 0.844732
| 0.838442
| 0
| 0.028967
| 0.303972
| 238,519
| 5,826
| 172
| 40.940439
| 0.731478
| 0.173676
| 0
| 0.817308
| 0
| 0.001923
| 0.031604
| 0.00239
| 0
| 0
| 0
| 0.000515
| 0
| 1
| 0.056044
| false
| 0.008242
| 0.017033
| 0.000275
| 0.090659
| 0.00467
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
c9cc0e143e3a6603523a47e9045482d191e87d89
| 38,000
|
py
|
Python
|
sdk/python/pulumi_oci/objectstorage/objectstorage_preauthrequest.py
|
EladGabay/pulumi-oci
|
6841e27d4a1a7e15c672306b769912efbfd3ba99
|
[
"ECL-2.0",
"Apache-2.0"
] | 5
|
2021-08-17T11:14:46.000Z
|
2021-12-31T02:07:03.000Z
|
sdk/python/pulumi_oci/objectstorage/objectstorage_preauthrequest.py
|
pulumi-oci/pulumi-oci
|
6841e27d4a1a7e15c672306b769912efbfd3ba99
|
[
"ECL-2.0",
"Apache-2.0"
] | 1
|
2021-09-06T11:21:29.000Z
|
2021-09-06T11:21:29.000Z
|
sdk/python/pulumi_oci/objectstorage/objectstorage_preauthrequest.py
|
pulumi-oci/pulumi-oci
|
6841e27d4a1a7e15c672306b769912efbfd3ba99
|
[
"ECL-2.0",
"Apache-2.0"
] | 2
|
2021-08-24T23:31:30.000Z
|
2022-01-02T19:26:54.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['ObjectstoragePreauthrequestArgs', 'ObjectstoragePreauthrequest']
@pulumi.input_type
class ObjectstoragePreauthrequestArgs:
def __init__(__self__, *,
access_type: pulumi.Input[str],
bucket: pulumi.Input[str],
namespace: pulumi.Input[str],
time_expires: pulumi.Input[str],
bucket_listing_action: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
object: Optional[pulumi.Input[str]] = None,
object_name: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a ObjectstoragePreauthrequest resource.
:param pulumi.Input[str] access_type: The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
:param pulumi.Input[str] bucket: The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
:param pulumi.Input[str] namespace: The Object Storage namespace used for the request.
:param pulumi.Input[str] time_expires: The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
:param pulumi.Input[str] bucket_listing_action: Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
:param pulumi.Input[str] name: A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
:param pulumi.Input[str] object: Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] object_name: The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
pulumi.set(__self__, "access_type", access_type)
pulumi.set(__self__, "bucket", bucket)
pulumi.set(__self__, "namespace", namespace)
pulumi.set(__self__, "time_expires", time_expires)
if bucket_listing_action is not None:
pulumi.set(__self__, "bucket_listing_action", bucket_listing_action)
if name is not None:
pulumi.set(__self__, "name", name)
if object is not None:
warnings.warn("""The 'object' field has been deprecated. Please use 'object_name' instead.""", DeprecationWarning)
pulumi.log.warn("""object is deprecated: The 'object' field has been deprecated. Please use 'object_name' instead.""")
if object is not None:
pulumi.set(__self__, "object", object)
if object_name is not None:
pulumi.set(__self__, "object_name", object_name)
@property
@pulumi.getter(name="accessType")
def access_type(self) -> pulumi.Input[str]:
"""
The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
"""
return pulumi.get(self, "access_type")
@access_type.setter
def access_type(self, value: pulumi.Input[str]):
pulumi.set(self, "access_type", value)
@property
@pulumi.getter
def bucket(self) -> pulumi.Input[str]:
"""
The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
"""
return pulumi.get(self, "bucket")
@bucket.setter
def bucket(self, value: pulumi.Input[str]):
pulumi.set(self, "bucket", value)
@property
@pulumi.getter
def namespace(self) -> pulumi.Input[str]:
"""
The Object Storage namespace used for the request.
"""
return pulumi.get(self, "namespace")
@namespace.setter
def namespace(self, value: pulumi.Input[str]):
pulumi.set(self, "namespace", value)
@property
@pulumi.getter(name="timeExpires")
def time_expires(self) -> pulumi.Input[str]:
"""
The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
return pulumi.get(self, "time_expires")
@time_expires.setter
def time_expires(self, value: pulumi.Input[str]):
pulumi.set(self, "time_expires", value)
@property
@pulumi.getter(name="bucketListingAction")
def bucket_listing_action(self) -> Optional[pulumi.Input[str]]:
"""
Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
"""
return pulumi.get(self, "bucket_listing_action")
@bucket_listing_action.setter
def bucket_listing_action(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "bucket_listing_action", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def object(self) -> Optional[pulumi.Input[str]]:
"""
Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object")
@object.setter
def object(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "object", value)
@property
@pulumi.getter(name="objectName")
def object_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object_name")
@object_name.setter
def object_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "object_name", value)
@pulumi.input_type
class _ObjectstoragePreauthrequestState:
def __init__(__self__, *,
access_type: Optional[pulumi.Input[str]] = None,
access_uri: Optional[pulumi.Input[str]] = None,
bucket: Optional[pulumi.Input[str]] = None,
bucket_listing_action: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
object: Optional[pulumi.Input[str]] = None,
object_name: Optional[pulumi.Input[str]] = None,
par_id: Optional[pulumi.Input[str]] = None,
time_created: Optional[pulumi.Input[str]] = None,
time_expires: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering ObjectstoragePreauthrequest resources.
:param pulumi.Input[str] access_type: The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
:param pulumi.Input[str] access_uri: The URI to embed in the URL `https://objectstorage.${var.region}.oraclecloud.com{var.access_uri}` when using the pre-authenticated request.
:param pulumi.Input[str] bucket: The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
:param pulumi.Input[str] bucket_listing_action: Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
:param pulumi.Input[str] name: A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
:param pulumi.Input[str] namespace: The Object Storage namespace used for the request.
:param pulumi.Input[str] object: Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] object_name: The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] par_id: The unique identifier for the pre-authenticated request. This can be used to manage operations against the pre-authenticated request, such as GET or DELETE.
:param pulumi.Input[str] time_created: The date when the pre-authenticated request was created as per specification [RFC 3339](https://tools.ietf.org/html/rfc3339).
:param pulumi.Input[str] time_expires: The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
if access_type is not None:
pulumi.set(__self__, "access_type", access_type)
if access_uri is not None:
pulumi.set(__self__, "access_uri", access_uri)
if bucket is not None:
pulumi.set(__self__, "bucket", bucket)
if bucket_listing_action is not None:
pulumi.set(__self__, "bucket_listing_action", bucket_listing_action)
if name is not None:
pulumi.set(__self__, "name", name)
if namespace is not None:
pulumi.set(__self__, "namespace", namespace)
if object is not None:
warnings.warn("""The 'object' field has been deprecated. Please use 'object_name' instead.""", DeprecationWarning)
pulumi.log.warn("""object is deprecated: The 'object' field has been deprecated. Please use 'object_name' instead.""")
if object is not None:
pulumi.set(__self__, "object", object)
if object_name is not None:
pulumi.set(__self__, "object_name", object_name)
if par_id is not None:
pulumi.set(__self__, "par_id", par_id)
if time_created is not None:
pulumi.set(__self__, "time_created", time_created)
if time_expires is not None:
pulumi.set(__self__, "time_expires", time_expires)
@property
@pulumi.getter(name="accessType")
def access_type(self) -> Optional[pulumi.Input[str]]:
"""
The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
"""
return pulumi.get(self, "access_type")
@access_type.setter
def access_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "access_type", value)
@property
@pulumi.getter(name="accessUri")
def access_uri(self) -> Optional[pulumi.Input[str]]:
"""
The URI to embed in the URL `https://objectstorage.${var.region}.oraclecloud.com{var.access_uri}` when using the pre-authenticated request.
"""
return pulumi.get(self, "access_uri")
@access_uri.setter
def access_uri(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "access_uri", value)
@property
@pulumi.getter
def bucket(self) -> Optional[pulumi.Input[str]]:
"""
The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
"""
return pulumi.get(self, "bucket")
@bucket.setter
def bucket(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "bucket", value)
@property
@pulumi.getter(name="bucketListingAction")
def bucket_listing_action(self) -> Optional[pulumi.Input[str]]:
"""
Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
"""
return pulumi.get(self, "bucket_listing_action")
@bucket_listing_action.setter
def bucket_listing_action(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "bucket_listing_action", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def namespace(self) -> Optional[pulumi.Input[str]]:
"""
The Object Storage namespace used for the request.
"""
return pulumi.get(self, "namespace")
@namespace.setter
def namespace(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "namespace", value)
@property
@pulumi.getter
def object(self) -> Optional[pulumi.Input[str]]:
"""
Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object")
@object.setter
def object(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "object", value)
@property
@pulumi.getter(name="objectName")
def object_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object_name")
@object_name.setter
def object_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "object_name", value)
@property
@pulumi.getter(name="parId")
def par_id(self) -> Optional[pulumi.Input[str]]:
"""
The unique identifier for the pre-authenticated request. This can be used to manage operations against the pre-authenticated request, such as GET or DELETE.
"""
return pulumi.get(self, "par_id")
@par_id.setter
def par_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "par_id", value)
@property
@pulumi.getter(name="timeCreated")
def time_created(self) -> Optional[pulumi.Input[str]]:
"""
The date when the pre-authenticated request was created as per specification [RFC 3339](https://tools.ietf.org/html/rfc3339).
"""
return pulumi.get(self, "time_created")
@time_created.setter
def time_created(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "time_created", value)
@property
@pulumi.getter(name="timeExpires")
def time_expires(self) -> Optional[pulumi.Input[str]]:
"""
The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
return pulumi.get(self, "time_expires")
@time_expires.setter
def time_expires(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "time_expires", value)
class ObjectstoragePreauthrequest(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
access_type: Optional[pulumi.Input[str]] = None,
bucket: Optional[pulumi.Input[str]] = None,
bucket_listing_action: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
object: Optional[pulumi.Input[str]] = None,
object_name: Optional[pulumi.Input[str]] = None,
time_expires: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
This resource provides the Preauthenticated Request resource in Oracle Cloud Infrastructure Object Storage service.
Creates a pre-authenticated request specific to the bucket.
## Example Usage
```python
import pulumi
import pulumi_oci as oci
test_preauthenticated_request = oci.objectstorage.ObjectstoragePreauthrequest("testPreauthenticatedRequest",
access_type=var["preauthenticated_request_access_type"],
bucket=var["preauthenticated_request_bucket"],
namespace=var["preauthenticated_request_namespace"],
time_expires=var["preauthenticated_request_time_expires"],
bucket_listing_action=var["preauthenticated_request_bucket_listing_action"],
object=var["preauthenticated_request_object"])
```
## Import
PreauthenticatedRequests can be imported using the `id`, e.g.
```sh
$ pulumi import oci:objectstorage/objectstoragePreauthrequest:ObjectstoragePreauthrequest test_preauthenticated_request "n/{namespaceName}/b/{bucketName}/p/{parId}"
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] access_type: The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
:param pulumi.Input[str] bucket: The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
:param pulumi.Input[str] bucket_listing_action: Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
:param pulumi.Input[str] name: A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
:param pulumi.Input[str] namespace: The Object Storage namespace used for the request.
:param pulumi.Input[str] object: Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] object_name: The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] time_expires: The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ObjectstoragePreauthrequestArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
This resource provides the Preauthenticated Request resource in Oracle Cloud Infrastructure Object Storage service.
Creates a pre-authenticated request specific to the bucket.
## Example Usage
```python
import pulumi
import pulumi_oci as oci
test_preauthenticated_request = oci.objectstorage.ObjectstoragePreauthrequest("testPreauthenticatedRequest",
access_type=var["preauthenticated_request_access_type"],
bucket=var["preauthenticated_request_bucket"],
namespace=var["preauthenticated_request_namespace"],
time_expires=var["preauthenticated_request_time_expires"],
bucket_listing_action=var["preauthenticated_request_bucket_listing_action"],
object=var["preauthenticated_request_object"])
```
## Import
PreauthenticatedRequests can be imported using the `id`, e.g.
```sh
$ pulumi import oci:objectstorage/objectstoragePreauthrequest:ObjectstoragePreauthrequest test_preauthenticated_request "n/{namespaceName}/b/{bucketName}/p/{parId}"
```
:param str resource_name: The name of the resource.
:param ObjectstoragePreauthrequestArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ObjectstoragePreauthrequestArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
access_type: Optional[pulumi.Input[str]] = None,
bucket: Optional[pulumi.Input[str]] = None,
bucket_listing_action: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
object: Optional[pulumi.Input[str]] = None,
object_name: Optional[pulumi.Input[str]] = None,
time_expires: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ObjectstoragePreauthrequestArgs.__new__(ObjectstoragePreauthrequestArgs)
if access_type is None and not opts.urn:
raise TypeError("Missing required property 'access_type'")
__props__.__dict__["access_type"] = access_type
if bucket is None and not opts.urn:
raise TypeError("Missing required property 'bucket'")
__props__.__dict__["bucket"] = bucket
__props__.__dict__["bucket_listing_action"] = bucket_listing_action
__props__.__dict__["name"] = name
if namespace is None and not opts.urn:
raise TypeError("Missing required property 'namespace'")
__props__.__dict__["namespace"] = namespace
if object is not None and not opts.urn:
warnings.warn("""The 'object' field has been deprecated. Please use 'object_name' instead.""", DeprecationWarning)
pulumi.log.warn("""object is deprecated: The 'object' field has been deprecated. Please use 'object_name' instead.""")
__props__.__dict__["object"] = object
__props__.__dict__["object_name"] = object_name
if time_expires is None and not opts.urn:
raise TypeError("Missing required property 'time_expires'")
__props__.__dict__["time_expires"] = time_expires
__props__.__dict__["access_uri"] = None
__props__.__dict__["par_id"] = None
__props__.__dict__["time_created"] = None
super(ObjectstoragePreauthrequest, __self__).__init__(
'oci:objectstorage/objectstoragePreauthrequest:ObjectstoragePreauthrequest',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
access_type: Optional[pulumi.Input[str]] = None,
access_uri: Optional[pulumi.Input[str]] = None,
bucket: Optional[pulumi.Input[str]] = None,
bucket_listing_action: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
object: Optional[pulumi.Input[str]] = None,
object_name: Optional[pulumi.Input[str]] = None,
par_id: Optional[pulumi.Input[str]] = None,
time_created: Optional[pulumi.Input[str]] = None,
time_expires: Optional[pulumi.Input[str]] = None) -> 'ObjectstoragePreauthrequest':
"""
Get an existing ObjectstoragePreauthrequest resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] access_type: The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
:param pulumi.Input[str] access_uri: The URI to embed in the URL `https://objectstorage.${var.region}.oraclecloud.com{var.access_uri}` when using the pre-authenticated request.
:param pulumi.Input[str] bucket: The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
:param pulumi.Input[str] bucket_listing_action: Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
:param pulumi.Input[str] name: A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
:param pulumi.Input[str] namespace: The Object Storage namespace used for the request.
:param pulumi.Input[str] object: Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] object_name: The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
:param pulumi.Input[str] par_id: The unique identifier for the pre-authenticated request. This can be used to manage operations against the pre-authenticated request, such as GET or DELETE.
:param pulumi.Input[str] time_created: The date when the pre-authenticated request was created as per specification [RFC 3339](https://tools.ietf.org/html/rfc3339).
:param pulumi.Input[str] time_expires: The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ObjectstoragePreauthrequestState.__new__(_ObjectstoragePreauthrequestState)
__props__.__dict__["access_type"] = access_type
__props__.__dict__["access_uri"] = access_uri
__props__.__dict__["bucket"] = bucket
__props__.__dict__["bucket_listing_action"] = bucket_listing_action
__props__.__dict__["name"] = name
__props__.__dict__["namespace"] = namespace
__props__.__dict__["object"] = object
__props__.__dict__["object_name"] = object_name
__props__.__dict__["par_id"] = par_id
__props__.__dict__["time_created"] = time_created
__props__.__dict__["time_expires"] = time_expires
return ObjectstoragePreauthrequest(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="accessType")
def access_type(self) -> pulumi.Output[str]:
"""
The operation that can be performed on this resource. Allowed Values: `ObjectRead`, `ObjectWrite`, `ObjectReadWrite`, `AnyObjectReadWrite` or `AnyObjectRead`
"""
return pulumi.get(self, "access_type")
@property
@pulumi.getter(name="accessUri")
def access_uri(self) -> pulumi.Output[str]:
"""
The URI to embed in the URL `https://objectstorage.${var.region}.oraclecloud.com{var.access_uri}` when using the pre-authenticated request.
"""
return pulumi.get(self, "access_uri")
@property
@pulumi.getter
def bucket(self) -> pulumi.Output[str]:
"""
The name of the bucket. Avoid entering confidential information. Example: `my-new-bucket1`
"""
return pulumi.get(self, "bucket")
@property
@pulumi.getter(name="bucketListingAction")
def bucket_listing_action(self) -> pulumi.Output[str]:
"""
Specifies whether a list operation is allowed on a PAR with accessType "AnyObjectRead" or "AnyObjectReadWrite". Deny: Prevents the user from performing a list operation. ListObjects: Authorizes the user to perform a list operation.
"""
return pulumi.get(self, "bucket_listing_action")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
A user-specified name for the pre-authenticated request. Names can be helpful in managing pre-authenticated requests. Avoid entering confidential information.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def namespace(self) -> pulumi.Output[str]:
"""
The Object Storage namespace used for the request.
"""
return pulumi.get(self, "namespace")
@property
@pulumi.getter
def object(self) -> pulumi.Output[str]:
"""
Deprecated. Instead use `object_name`. Requests that include both `object` and `object_name` will be rejected. (Optional) The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object")
@property
@pulumi.getter(name="objectName")
def object_name(self) -> pulumi.Output[str]:
"""
The name of the object that is being granted access to by the pre-authenticated request. Avoid entering confidential information. The object name can be null and if so, the pre-authenticated request grants access to the entire bucket if the access type allows that. The object name can be a prefix as well, in that case pre-authenticated request grants access to all the objects within the bucket starting with that prefix provided that we have the correct access type.
"""
return pulumi.get(self, "object_name")
@property
@pulumi.getter(name="parId")
def par_id(self) -> pulumi.Output[str]:
"""
The unique identifier for the pre-authenticated request. This can be used to manage operations against the pre-authenticated request, such as GET or DELETE.
"""
return pulumi.get(self, "par_id")
@property
@pulumi.getter(name="timeCreated")
def time_created(self) -> pulumi.Output[str]:
"""
The date when the pre-authenticated request was created as per specification [RFC 3339](https://tools.ietf.org/html/rfc3339).
"""
return pulumi.get(self, "time_created")
@property
@pulumi.getter(name="timeExpires")
def time_expires(self) -> pulumi.Output[str]:
"""
The expiration date for the pre-authenticated request as per [RFC 3339](https://tools.ietf.org/html/rfc3339). After this date the pre-authenticated request will no longer be valid.
"""
return pulumi.get(self, "time_expires")
| 60.897436
| 632
| 0.696421
| 4,828
| 38,000
| 5.340721
| 0.054267
| 0.053752
| 0.067326
| 0.061431
| 0.912352
| 0.893931
| 0.881404
| 0.861431
| 0.858367
| 0.839674
| 0
| 0.003225
| 0.216579
| 38,000
| 623
| 633
| 60.995185
| 0.862916
| 0.516237
| 0
| 0.688385
| 1
| 0
| 0.121644
| 0.02052
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15864
| false
| 0.002833
| 0.014164
| 0
| 0.269122
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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|
0
| 7
|
c9da0694b316919a529d896689363396357aff59
| 74,888
|
py
|
Python
|
gewittergefahr/deep_learning/cnn_setup.py
|
dopplerchase/GewitterGefahr
|
4415b08dd64f37eba5b1b9e8cc5aa9af24f96593
|
[
"MIT"
] | 26
|
2018-10-04T01:07:35.000Z
|
2022-01-29T08:49:32.000Z
|
gewittergefahr/deep_learning/cnn_setup.py
|
liuximarcus/GewitterGefahr
|
d819874d616f98a25187bfd3091073a2e6d5279e
|
[
"MIT"
] | 4
|
2017-12-25T02:01:08.000Z
|
2018-12-19T01:54:21.000Z
|
gewittergefahr/deep_learning/cnn_setup.py
|
liuximarcus/GewitterGefahr
|
d819874d616f98a25187bfd3091073a2e6d5279e
|
[
"MIT"
] | 11
|
2017-12-10T23:05:29.000Z
|
2022-01-29T08:49:33.000Z
|
"""CNN setup (building the architecture and compiling)."""
import numpy
import keras.layers
from gewittergefahr.gg_utils import error_checking
from gewittergefahr.deep_learning import architecture_utils
from gewittergefahr.deep_learning import keras_metrics
NUM_ROWS_KEY = 'num_grid_rows'
NUM_COLUMNS_KEY = 'num_grid_columns'
NUM_DIMENSIONS_KEY = 'num_dimensions'
NUM_CLASSES_KEY = 'num_classes'
NUM_CONV_LAYER_SETS_KEY = 'num_conv_layer_sets'
NUM_CONV_LAYERS_PER_SET_KEY = 'num_conv_layers_per_set'
FIRST_NUM_FILTERS_KEY = 'first_num_filters'
CONV_LAYER_DROPOUT_KEY = 'conv_layer_dropout_fraction'
NUM_DENSE_LAYERS_KEY = 'num_dense_layers'
DENSE_LAYER_DROPOUT_KEY = 'dense_layer_dropout_fraction'
NUM_CHANNELS_KEY = 'num_channels'
NUM_FIELDS_KEY = 'num_fields'
NUM_HEIGHTS_KEY = 'num_heights'
L1_WEIGHT_KEY = 'l1_weight'
L2_WEIGHT_KEY = 'l2_weight'
ACTIVATION_FUNCTION_KEY = 'activation_function_string'
ALPHA_FOR_ELU_KEY = 'alpha_for_elu'
ALPHA_FOR_RELU_KEY = 'alpha_for_relu'
USE_BATCH_NORM_KEY = 'use_batch_normalization'
NUM_CONV_KERNEL_ROWS = 3
NUM_CONV_KERNEL_COLUMNS = 3
NUM_CONV_KERNEL_HEIGHTS = 3
DEFAULT_NUM_CONV_LAYER_SETS = 2
DEFAULT_NUM_CONV_LAYERS_PER_SET = 2
DEFAULT_CONV_LAYER_DROPOUT_FRACTION = None
DEFAULT_NUM_DENSE_LAYERS = 3
DEFAULT_DENSE_LAYER_DROPOUT_FRACTION = 0.5
DEFAULT_L1_WEIGHT = 0.
DEFAULT_L2_WEIGHT = 0.001
DEFAULT_USE_BATCH_NORM_FLAG = True
DEFAULT_ACTIVATION_FUNCTION_STRING = architecture_utils.RELU_FUNCTION_STRING
VALID_NUMBERS_OF_SOUNDING_HEIGHTS = numpy.array([49], dtype=int)
DEFAULT_METRIC_FUNCTION_LIST = [
keras_metrics.accuracy, keras_metrics.binary_accuracy,
keras_metrics.binary_csi, keras_metrics.binary_frequency_bias,
keras_metrics.binary_pod, keras_metrics.binary_pofd,
keras_metrics.binary_peirce_score, keras_metrics.binary_success_ratio,
keras_metrics.binary_focn
]
def _create_sounding_layers(
num_fields, num_heights, num_conv_layer_sets,
num_conv_layers_per_set, padding_type_string, l1_weight, l2_weight,
pooling_type_string, activation_function_string, alpha_for_elu,
alpha_for_relu, use_batch_normalization, dropout_fraction,
do_separable_conv, first_num_filters=None,
first_num_spatial_filters=None, num_non_spatial_filters=None):
"""Creates convolution and pooling layers to process sounding data.
:param num_fields: See doc for `check_sounding_options`.
:param num_heights: Same.
:param num_conv_layer_sets: See doc for `check_radar_options`.
:param num_conv_layers_per_set: Same.
:param padding_type_string: Padding type (see doc for
`architecture_utils._check_convolution_options`).
:param l1_weight: See doc for `check_radar_options`.
:param l2_weight: Same.
:param pooling_type_string: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param dropout_fraction: Same.
:param do_separable_conv: See doc for `check_sounding_options`.
:param first_num_filters: Same.
:param first_num_spatial_filters: Same.
:param num_non_spatial_filters: Same.
:return: input_layer_object: Input layer (instance of `keras.layers.Input`),
used to feed soundings into the CNN.
:return: flattening_layer_object: Flattening layer (instance of
`keras.layers.Flatten`), which outputs scalar features.
:return: num_scalar_features: Number of scalar features output by
flattening layer.
"""
first_num_filters, num_non_spatial_filters = check_sounding_options(
num_fields=num_fields, num_heights=num_heights,
do_separable_conv=do_separable_conv,
first_num_filters=first_num_filters,
first_num_spatial_filters=first_num_spatial_filters,
num_non_spatial_filters=num_non_spatial_filters)
input_layer_object = keras.layers.Input(
shape=(num_heights, num_fields)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
current_layer_object = None
current_num_filters = None
current_num_spatial_filters = None
for i in range(num_conv_layer_sets):
if current_layer_object is None:
if do_separable_conv:
current_num_spatial_filters = first_num_spatial_filters + 0
else:
current_num_filters = first_num_filters + 0
else:
if do_separable_conv:
current_num_spatial_filters *= 2
else:
current_num_filters *= 2
for _ in range(num_conv_layers_per_set):
if current_layer_object is None:
this_input_layer_object = input_layer_object
# if do_separable_conv:
# current_num_spatial_filters = first_num_spatial_filters + 0
# else:
# current_num_filters = first_num_filters + 0
else:
this_input_layer_object = current_layer_object
# if do_separable_conv:
# current_num_spatial_filters *= 2
# else:
# current_num_filters *= 2
if do_separable_conv:
current_layer_object = (
architecture_utils.get_1d_separable_conv_layer(
num_kernel_rows=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1,
num_spatial_filters=current_num_spatial_filters,
num_non_spatial_filters=num_non_spatial_filters,
padding_type_string=architecture_utils.YES_PADDING_STRING,
weight_regularizer=regularizer_object
)(this_input_layer_object)
)
else:
current_layer_object = architecture_utils.get_1d_conv_layer(
num_kernel_rows=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_filters=current_num_filters,
padding_type_string=architecture_utils.YES_PADDING_STRING,
weight_regularizer=regularizer_object
)(this_input_layer_object)
current_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(current_layer_object)
if dropout_fraction is not None:
current_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction
)(current_layer_object)
if use_batch_normalization:
current_layer_object = (
architecture_utils.get_batch_norm_layer()(
current_layer_object)
)
# TODO(thunderhoser): This condition is a HACK.
current_num_heights = current_layer_object.shape[1]
if not (current_num_heights < 8 and i == num_conv_layer_sets - 1):
current_layer_object = architecture_utils.get_1d_pooling_layer(
num_rows_in_window=2, num_rows_per_stride=2,
pooling_type_string=pooling_type_string
)(current_layer_object)
these_dimensions = numpy.array(
current_layer_object.get_shape().as_list()[1:], dtype=int)
num_scalar_features = numpy.prod(these_dimensions)
current_layer_object = architecture_utils.get_flattening_layer()(
current_layer_object)
return input_layer_object, current_layer_object, num_scalar_features
def _get_output_layer_and_loss(num_classes):
"""Creates output layer and loss function.
:param num_classes: Number of classes for target variable.
:return: dense_layer_object: Last dense layer (instance of
`keras.layers.Dense` with no activation).
:return: activation_layer_object: Last activation layer (to go after the
dense layer).
:return: loss_function: Loss function (instance of
`keras.losses.binary_crossentropy` or
`keras.losses.categorical_crossentropy`).
"""
if num_classes == 2:
num_output_units = 1
loss_function = keras.losses.binary_crossentropy
activation_function_string = architecture_utils.SIGMOID_FUNCTION_STRING
else:
num_output_units = num_classes
loss_function = keras.losses.categorical_crossentropy
activation_function_string = architecture_utils.SOFTMAX_FUNCTION_STRING
dense_layer_object = architecture_utils.get_dense_layer(
num_output_units=num_output_units)
activation_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string)
return dense_layer_object, activation_layer_object, loss_function
def _create_dense_layers(
flattening_layer_object, num_scalar_features, num_classes,
num_dense_layers, dropout_fraction, activation_function_string,
alpha_for_elu, alpha_for_relu, use_batch_normalization):
"""Creates dense layers (to go between flattening layer and output).
:param flattening_layer_object: Input to first dense layer (instance of
`keras.layers.Flatten`).
:param num_scalar_features: Number of features output by flattening layer.
:param num_classes: Number of classes for target variable.
:param num_dense_layers: Desired number of dense layers.
:param dropout_fraction: Dropout fraction for each dense layer.
:param activation_function_string: See doc for `check_radar_options`.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:return: output_layer_object: Output layer for CNN.
"""
_, num_outputs_by_dense_layer = (
architecture_utils.get_dense_layer_dimensions(
num_input_units=num_scalar_features, num_classes=num_classes,
num_dense_layers=num_dense_layers)
)
layer_object = None
for k in range(num_dense_layers - 1):
if layer_object is None:
this_input_layer_object = flattening_layer_object
else:
this_input_layer_object = layer_object
layer_object = architecture_utils.get_dense_layer(
num_output_units=num_outputs_by_dense_layer[k]
)(this_input_layer_object)
layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(layer_object)
if dropout_fraction is not None:
layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=dropout_fraction
)(layer_object)
if use_batch_normalization:
layer_object = (
architecture_utils.get_batch_norm_layer()(layer_object)
)
dense_layer_object, activation_layer_object = _get_output_layer_and_loss(
num_classes
)[:2]
if num_dense_layers == 1:
layer_object = dense_layer_object(flattening_layer_object)
else:
layer_object = dense_layer_object(layer_object)
if dropout_fraction is not None and num_dense_layers == 1:
layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=dropout_fraction
)(layer_object)
return activation_layer_object(layer_object)
def check_radar_options(
num_grid_rows, num_grid_columns, num_dimensions, num_classes,
num_conv_layer_sets, num_conv_layers_per_set,
conv_layer_dropout_fraction, num_dense_layers,
dense_layer_dropout_fraction, l1_weight, l2_weight,
activation_function_string, alpha_for_elu, alpha_for_relu,
use_batch_normalization, do_separable_conv, first_num_filters=None,
first_num_spatial_filters=None, num_non_spatial_filters=None,
num_channels=None, num_fields=None, num_heights=None):
"""Checks options for part of CNN that handles radar data.
:param num_grid_rows: Number of rows in each grid.
:param num_grid_columns: Number of columns in each grid.
:param num_dimensions: Number of dimensions in each grid.
:param num_classes: Number of classes for target variable.
:param num_conv_layer_sets: Number of sets of convolution layers
(uninterrupted by pooling).
:param num_conv_layers_per_set: Number of convolution layers in each set.
:param conv_layer_dropout_fraction: Dropout fraction for convolution layers.
This may be None.
:param num_dense_layers: Number of dense (fully connected) layers at the
end.
:param dense_layer_dropout_fraction: Dropout fraction for dense layers.
This may be None. Also, dropout will *not* be used for the last dense
layer.
:param l1_weight: L1 regularization weight (used for each conv or dense
layer).
:param l2_weight: L2 regularization weight (used for each conv or dense
layer).
:param activation_function_string: Activation function (used for each conv
or dense layer, except the last dense layer). See doc for
`architecture_utils.get_activation_layer`.
:param alpha_for_elu: See doc for
`architecture_utils.get_activation_layer`.
:param alpha_for_relu: Same.
:param use_batch_normalization: Boolean flag. If True, will use batch
normalization after each conv or dense layer.
:param do_separable_conv: Boolean flag. If True, will do depthwise-
separable convolution. If False, will do traditional convolution.
:param first_num_filters: [used only if `do_separable_conv = False`]
Number of filters produced by first convolution layer. The number of
filters in each successive conv layer will double.
:param first_num_spatial_filters: [used only if `do_separable_conv = True`]
Number of spatial filters (applied independently to each input channel)
produced by first convolution layer. Will double with each successive
conv layer.
:param num_non_spatial_filters: [used only if `do_separable_conv = True`]
Number of non-spatial filters (applied independently to each spatial
position) produced by each conv layer.
:param num_channels: [used only if num_dimensions = 2]
Number of channels (field/height pairs).
:param num_fields: [used only if num_dimensions = 3]
Number of fields.
:param num_heights: [used only if num_dimensions = 3]
Number of heights.
:return: first_num_filters: Same as input, except value may have been
replaced with default.
:return: num_non_spatial_filters: Same.
"""
error_checking.assert_is_integer(num_grid_rows)
error_checking.assert_is_geq(num_grid_rows, 8)
error_checking.assert_is_integer(num_grid_columns)
error_checking.assert_is_geq(num_grid_columns, 8)
error_checking.assert_is_integer(num_dimensions)
error_checking.assert_is_geq(num_dimensions, 2)
error_checking.assert_is_leq(num_dimensions, 3)
error_checking.assert_is_integer(num_classes)
error_checking.assert_is_geq(num_classes, 2)
if num_dimensions == 2:
error_checking.assert_is_integer(num_channels)
error_checking.assert_is_geq(num_channels, 1)
else:
error_checking.assert_is_integer(num_fields)
error_checking.assert_is_geq(num_fields, 1)
error_checking.assert_is_integer(num_heights)
error_checking.assert_is_geq(num_heights, 4)
error_checking.assert_is_integer(num_conv_layer_sets)
error_checking.assert_is_geq(num_conv_layer_sets, 1)
error_checking.assert_is_integer(num_conv_layers_per_set)
error_checking.assert_is_geq(num_conv_layers_per_set, 1)
if conv_layer_dropout_fraction is not None:
error_checking.assert_is_greater(conv_layer_dropout_fraction, 0.)
error_checking.assert_is_less_than(conv_layer_dropout_fraction, 1.)
error_checking.assert_is_integer(num_dense_layers)
error_checking.assert_is_geq(num_dense_layers, 1)
if dense_layer_dropout_fraction is not None:
error_checking.assert_is_greater(dense_layer_dropout_fraction, 0.)
error_checking.assert_is_less_than(dense_layer_dropout_fraction, 1.)
error_checking.assert_is_geq(l1_weight, 0.)
error_checking.assert_is_geq(l2_weight, 0.)
architecture_utils.check_activation_function(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu)
error_checking.assert_is_boolean(use_batch_normalization)
error_checking.assert_is_boolean(do_separable_conv)
if not do_separable_conv:
if first_num_filters is None:
if num_dimensions == 2:
first_num_filters = 8 * num_channels
else:
first_num_filters = 8 * num_fields
error_checking.assert_is_integer(first_num_filters)
error_checking.assert_is_geq(first_num_filters, 4)
return first_num_filters, None
if num_non_spatial_filters is None:
if num_dimensions == 2:
num_non_spatial_filters = num_channels + 0
else:
num_non_spatial_filters = num_fields + 0
error_checking.assert_is_integer(first_num_spatial_filters)
error_checking.assert_is_greater(first_num_spatial_filters, 0)
error_checking.assert_is_integer(num_non_spatial_filters)
error_checking.assert_is_greater(num_non_spatial_filters, 0)
error_checking.assert_is_geq(
first_num_spatial_filters * num_non_spatial_filters, 4)
return None, num_non_spatial_filters
def check_sounding_options(
num_fields, num_heights, do_separable_conv, first_num_filters=None,
first_num_spatial_filters=None, num_non_spatial_filters=None):
"""Checks options for part of CNN that handles sounding data.
:param num_fields: Number of sounding fields (variables).
:param num_heights: Number of sounding heights.
:param do_separable_conv: See doc for `check_radar_options`.
:param first_num_filters: Same.
:param first_num_spatial_filters: Same.
:param num_non_spatial_filters: Same.
:return: first_num_filters: Same as input, except value may have been
replaced with default.
:return: num_non_spatial_filters: Same.
"""
error_checking.assert_is_integer(num_fields)
error_checking.assert_is_geq(num_fields, 1)
error_checking.assert_is_integer(num_heights)
if num_heights not in VALID_NUMBERS_OF_SOUNDING_HEIGHTS:
error_string = (
'\n{0:s}\nValid numbers of sounding heights (listed above) do not '
'include {1:d}.'
).format(str(VALID_NUMBERS_OF_SOUNDING_HEIGHTS), num_heights)
raise ValueError(error_string)
error_checking.assert_is_boolean(do_separable_conv)
if not do_separable_conv:
if first_num_filters is None:
first_num_filters = 8 * num_fields
error_checking.assert_is_integer(first_num_filters)
error_checking.assert_is_geq(first_num_filters, 4)
return first_num_filters, None
if num_non_spatial_filters is None:
num_non_spatial_filters = num_fields + 0
error_checking.assert_is_integer(first_num_spatial_filters)
error_checking.assert_is_greater(first_num_spatial_filters, 0)
error_checking.assert_is_integer(num_non_spatial_filters)
error_checking.assert_is_greater(num_non_spatial_filters, 0)
error_checking.assert_is_geq(
first_num_spatial_filters * num_non_spatial_filters, 4)
return None, num_non_spatial_filters
def create_2d_cnn(
num_radar_rows, num_radar_columns, num_radar_channels, num_classes,
num_sounding_fields, num_sounding_heights, use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST,
first_num_radar_filters=None, first_num_sounding_filters=None):
"""Creates CNN to take in 2-D radar data (and possibly soundings).
This CNN does *not* use depthwise-separable convolution.
:param num_radar_rows: See doc for `check_radar_options`.
:param num_radar_columns: Same.
:param num_radar_channels: Same.
:param num_classes: Same.
:param num_sounding_fields: See doc for `check_sounding_options`.
:param num_sounding_heights: Same.
:param use_padding: Boolean flag. If True, will use edge-padding so that
convolution does not change image size. If False, will not use edge-
padding.
:param pooling_type_string: Pooling type (see doc for
`architecture_utils._check_pooling_options`).
:param num_conv_layer_sets: See doc for `check_radar_options`.
:param num_conv_layers_per_set: Same.
:param conv_layer_dropout_fraction: Same.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param first_num_radar_filters: Same.
:param first_num_sounding_filters: See doc for `check_sounding_options`.
:param list_of_metric_functions: List of Keras metrics. Will be used to
report performance during training.
:return: cnn_model_object: Untrained instance of `keras.models.Model`.
"""
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
error_checking.assert_is_integer(num_sounding_fields)
error_checking.assert_is_integer(num_sounding_heights)
include_soundings = num_sounding_fields > 0 or num_sounding_heights > 0
first_num_radar_filters, _ = check_radar_options(
num_grid_rows=num_radar_rows, num_grid_columns=num_radar_columns,
num_dimensions=2, num_classes=num_classes,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
num_channels=num_radar_channels, do_separable_conv=False,
first_num_filters=first_num_radar_filters)
radar_input_layer_object = keras.layers.Input(
shape=(num_radar_rows, num_radar_columns, num_radar_channels)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
radar_layer_object = None
current_num_filters = None
for _ in range(num_conv_layer_sets):
if current_num_filters is None:
current_num_filters = first_num_radar_filters + 0
else:
current_num_filters *= 2
for _ in range(num_conv_layers_per_set):
if radar_layer_object is None:
this_input_layer_object = radar_input_layer_object
# current_num_filters = first_num_radar_filters + 0
else:
this_input_layer_object = radar_layer_object
# current_num_filters *= 2
radar_layer_object = architecture_utils.get_2d_conv_layer(
num_filters=current_num_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_rows_per_stride=1, num_columns_per_stride=1,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
radar_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(radar_layer_object)
if conv_layer_dropout_fraction is not None:
radar_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(radar_layer_object)
if use_batch_normalization:
radar_layer_object = (
architecture_utils.get_batch_norm_layer()(
radar_layer_object)
)
radar_layer_object = architecture_utils.get_2d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_rows_per_stride=2, num_columns_per_stride=2,
pooling_type_string=pooling_type_string
)(radar_layer_object)
these_dimensions = numpy.array(
radar_layer_object.get_shape().as_list()[1:], dtype=int)
num_radar_features = numpy.prod(these_dimensions)
radar_layer_object = architecture_utils.get_flattening_layer()(
radar_layer_object)
if include_soundings:
(sounding_input_layer_object, sounding_layer_object,
num_sounding_features
) = _create_sounding_layers(
num_fields=num_sounding_fields, num_heights=num_sounding_heights,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction,
do_separable_conv=False,
first_num_filters=first_num_sounding_filters)
layer_object = keras.layers.concatenate(
[radar_layer_object, sounding_layer_object]
)
else:
layer_object = radar_layer_object
num_sounding_features = 0
layer_object = _create_dense_layers(
flattening_layer_object=layer_object,
num_scalar_features=num_radar_features + num_sounding_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
if include_soundings:
model_object = keras.models.Model(
inputs=[radar_input_layer_object, sounding_input_layer_object],
outputs=layer_object)
else:
model_object = keras.models.Model(
inputs=radar_input_layer_object, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
def create_cnn_soundings_only(
num_fields, num_heights, first_num_filters, num_classes,
use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST):
"""Creates CNN that convolves over soundings only.
:param num_fields: Number of sounding fields.
:param num_heights: Number of sounding heights.
:param first_num_filters: Number of filters produced by first conv layer.
Will double with each successive conv layer.
:param num_classes: See doc for `create_2d_cnn`.
:param use_padding: Same.
:param pooling_type_string: Same.
:param num_conv_layer_sets: Same.
:param num_conv_layers_per_set: Same.
:param conv_layer_dropout_fraction: Same.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param list_of_metric_functions: Same.
:return: cnn_model_object: Untrained instance of `keras.models.Model`.
"""
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
check_radar_options(
num_grid_rows=32, num_grid_columns=32, num_dimensions=2,
num_channels=32, first_num_filters=32, num_classes=num_classes,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
do_separable_conv=False)
check_sounding_options(
num_fields=num_fields, num_heights=num_heights,
first_num_filters=first_num_filters, do_separable_conv=False)
input_layer_object, layer_object, num_features = _create_sounding_layers(
num_fields=num_fields, num_heights=num_heights,
first_num_filters=first_num_filters,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction, do_separable_conv=False)
layer_object = _create_dense_layers(
flattening_layer_object=layer_object, num_scalar_features=num_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
model_object = keras.models.Model(
inputs=input_layer_object, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
def create_separable_2d_cnn(
num_radar_rows, num_radar_columns, num_radar_channels, num_classes,
num_sounding_fields, num_sounding_heights, use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST,
first_num_spatial_radar_filters=None,
num_non_spatial_radar_filters=None,
first_num_spatial_sounding_filters=None,
num_non_spatial_sounding_filters=None):
"""Creates CNN to take in 2-D radar data (and possibly soundings).
This CNN does depthwise-separable convolution.
:param num_radar_rows: See doc for `check_radar_options`.
:param num_radar_columns: Same.
:param num_radar_channels: Same.
:param num_classes: Same.
:param num_sounding_fields: See doc for `check_sounding_options`.
:param num_sounding_heights: Same.
:param use_padding: See doc for `create_2d_cnn`.
:param pooling_type_string: Pooling type (see doc for
`architecture_utils._check_pooling_options`).
:param num_conv_layer_sets: See doc for `check_radar_options`.
:param num_conv_layers_per_set: Same.
:param conv_layer_dropout_fraction: Same.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param first_num_spatial_radar_filters: Same.
:param num_non_spatial_radar_filters: Same.
:param first_num_spatial_sounding_filters: See doc for
`check_sounding_options`.
:param num_non_spatial_sounding_filters: Same.
:param list_of_metric_functions: List of Keras metrics. Will be used to
report performance during training.
:return: cnn_model_object: Untrained instance of `keras.models.Model`.
"""
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
error_checking.assert_is_integer(num_sounding_fields)
error_checking.assert_is_integer(num_sounding_heights)
include_soundings = num_sounding_fields > 0 or num_sounding_heights > 0
_, num_non_spatial_radar_filters = check_radar_options(
num_grid_rows=num_radar_rows, num_grid_columns=num_radar_columns,
num_dimensions=2, num_classes=num_classes,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
num_channels=num_radar_channels, do_separable_conv=True,
first_num_spatial_filters=first_num_spatial_radar_filters,
num_non_spatial_filters=num_non_spatial_radar_filters)
radar_input_layer_object = keras.layers.Input(
shape=(num_radar_rows, num_radar_columns, num_radar_channels)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
radar_layer_object = None
current_num_spatial_filters = None
for _ in range(num_conv_layer_sets):
for _ in range(num_conv_layers_per_set):
if radar_layer_object is None:
this_input_layer_object = radar_input_layer_object
current_num_spatial_filters = (
first_num_spatial_radar_filters + 0)
else:
this_input_layer_object = radar_layer_object
current_num_spatial_filters *= 2
radar_layer_object = architecture_utils.get_2d_separable_conv_layer(
num_spatial_filters=current_num_spatial_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_rows_per_stride=1, num_columns_per_stride=1,
num_non_spatial_filters=num_non_spatial_radar_filters,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
radar_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(radar_layer_object)
if conv_layer_dropout_fraction is not None:
radar_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(radar_layer_object)
if use_batch_normalization:
radar_layer_object = (
architecture_utils.get_batch_norm_layer()(
radar_layer_object)
)
radar_layer_object = architecture_utils.get_2d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_rows_per_stride=2, num_columns_per_stride=2,
pooling_type_string=pooling_type_string
)(radar_layer_object)
these_dimensions = numpy.array(
radar_layer_object.get_shape().as_list()[1:], dtype=int)
num_radar_features = numpy.prod(these_dimensions)
radar_layer_object = architecture_utils.get_flattening_layer()(
radar_layer_object)
if include_soundings:
(sounding_input_layer_object, sounding_layer_object,
num_sounding_features
) = _create_sounding_layers(
num_fields=num_sounding_fields, num_heights=num_sounding_heights,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction,
do_separable_conv=True,
first_num_spatial_filters=first_num_spatial_sounding_filters,
num_non_spatial_filters=num_non_spatial_sounding_filters)
layer_object = keras.layers.concatenate(
[radar_layer_object, sounding_layer_object]
)
else:
layer_object = radar_layer_object
num_sounding_features = 0
layer_object = _create_dense_layers(
flattening_layer_object=layer_object,
num_scalar_features=num_radar_features + num_sounding_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
if include_soundings:
model_object = keras.models.Model(
inputs=[radar_input_layer_object, sounding_input_layer_object],
outputs=layer_object)
else:
model_object = keras.models.Model(
inputs=radar_input_layer_object, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
def create_2d3d_cnn(
num_refl_rows, num_refl_columns, num_refl_heights, num_az_shear_fields,
num_classes, num_sounding_fields, num_sounding_heights,
use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
first_num_refl_filters=None, first_num_shear_filters=None,
first_num_sounding_filters=None,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST):
"""Creates CNN to take in 2-D and 3-D radar data (and possibly soundings).
In this case, 3-D radar fields contain reflectivity and 2-D fields contain
azimuthal shear.
:param num_refl_rows: Number of rows in reflectivity grid.
:param num_refl_columns: Number of columns in reflectivity grid.
:param num_refl_heights: Number of heights in reflectivity grid.
:param num_az_shear_fields: Number of azimuthal-shear fields.
See doc for `check_radar_options`.
:param num_sounding_fields: See doc for `check_sounding_options`.
:param num_sounding_heights: Same.
:param use_padding: See doc for `create_2d_cnn`.
:param pooling_type_string: Pooling type (see doc for
`architecture_utils._check_pooling_options`).
:param num_conv_layer_sets: See doc for `check_radar_options`.
:param num_conv_layers_per_set: Same.
:param first_num_refl_filters: Number of filters created by first
convolution layer for reflectivity data. This will double with
ach successive conv layer.
:param first_num_shear_filters: Same but for azimuthal shear.
:param first_num_sounding_filters: Same but for sounding.
:param conv_layer_dropout_fraction: See doc for `check_radar_options`.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param list_of_metric_functions: List of Keras metrics. Will be used to
report performance during training.
:return: cnn_model_object: Untrained instance of `keras.models.Model`.
"""
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
error_checking.assert_is_integer(num_sounding_fields)
error_checking.assert_is_integer(num_sounding_heights)
include_soundings = num_sounding_fields > 0 or num_sounding_heights > 0
first_num_refl_filters, _ = check_radar_options(
num_grid_rows=num_refl_rows, num_grid_columns=num_refl_columns,
num_heights=num_refl_heights, num_fields=1, num_dimensions=3,
num_classes=num_classes, num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
do_separable_conv=False, first_num_filters=first_num_refl_filters)
first_num_shear_filters, _ = check_radar_options(
num_grid_rows=2 * num_refl_rows, num_grid_columns=2 * num_refl_columns,
num_channels=num_az_shear_fields, num_dimensions=2,
num_classes=num_classes, num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
do_separable_conv=False, first_num_filters=first_num_shear_filters)
shear_input_layer_object = keras.layers.Input(
shape=(2 * num_refl_rows, 2 * num_refl_columns, num_az_shear_fields)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
shear_layer_object = None
for _ in range(num_conv_layers_per_set):
if shear_layer_object is None:
this_input_layer_object = shear_input_layer_object
else:
this_input_layer_object = shear_layer_object
shear_layer_object = architecture_utils.get_2d_conv_layer(
num_filters=first_num_shear_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_rows_per_stride=1, num_columns_per_stride=1,
padding_type_string=architecture_utils.YES_PADDING_STRING,
weight_regularizer=regularizer_object
)(this_input_layer_object)
shear_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(shear_layer_object)
if conv_layer_dropout_fraction is not None:
shear_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(shear_layer_object)
if use_batch_normalization:
shear_layer_object = architecture_utils.get_batch_norm_layer()(
shear_layer_object
)
shear_layer_object = architecture_utils.get_2d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_rows_per_stride=2, num_columns_per_stride=2,
pooling_type_string=pooling_type_string
)(shear_layer_object)
refl_input_layer_object = keras.layers.Input(
shape=(num_refl_rows, num_refl_columns, num_refl_heights, 1)
)
refl_layer_object = keras.layers.Reshape(
target_shape=(num_refl_rows, num_refl_columns, num_refl_heights)
)(refl_input_layer_object)
radar_layer_object = keras.layers.concatenate(
[refl_layer_object, shear_layer_object], axis=-1
)
current_num_filters = None
for _ in range(num_conv_layer_sets):
if current_num_filters is None:
current_num_filters = (
first_num_shear_filters + first_num_refl_filters
)
else:
current_num_filters *= 2
for _ in range(num_conv_layers_per_set):
this_input_layer_object = radar_layer_object
radar_layer_object = architecture_utils.get_2d_conv_layer(
num_filters=current_num_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_rows_per_stride=1, num_columns_per_stride=1,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
radar_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(radar_layer_object)
if conv_layer_dropout_fraction is not None:
radar_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(radar_layer_object)
if use_batch_normalization:
radar_layer_object = (
architecture_utils.get_batch_norm_layer()(
radar_layer_object)
)
radar_layer_object = architecture_utils.get_2d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_rows_per_stride=2, num_columns_per_stride=2,
pooling_type_string=pooling_type_string
)(radar_layer_object)
these_dimensions = numpy.array(
radar_layer_object.get_shape().as_list()[1:], dtype=int
)
num_radar_features = numpy.prod(these_dimensions)
radar_layer_object = architecture_utils.get_flattening_layer()(
radar_layer_object)
if include_soundings:
(sounding_input_layer_object, sounding_layer_object,
num_sounding_features
) = _create_sounding_layers(
num_fields=num_sounding_fields, num_heights=num_sounding_heights,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction,
do_separable_conv=False,
first_num_filters=first_num_sounding_filters)
layer_object = keras.layers.concatenate(
[radar_layer_object, sounding_layer_object]
)
else:
layer_object = radar_layer_object
num_sounding_features = 0
layer_object = _create_dense_layers(
flattening_layer_object=layer_object,
num_scalar_features=num_radar_features + num_sounding_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
input_layer_objects = [refl_input_layer_object, shear_input_layer_object]
if include_soundings:
input_layer_objects.append(sounding_input_layer_object)
model_object = keras.models.Model(
inputs=input_layer_objects, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
def create_3d_cnn(
num_radar_rows, num_radar_columns, num_radar_heights, num_radar_fields,
num_classes, num_sounding_fields, num_sounding_heights,
use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
first_num_radar_filters=None, first_num_sounding_filters=None,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST):
"""Creates CNN to take in 3-D radar data (and possibly soundings).
:param num_radar_rows: See doc for `check_radar_options`.
:param num_radar_columns: Same.
:param num_radar_heights: Same.
:param num_radar_fields: Same.
:param num_classes: Same.
:param num_sounding_fields: See doc for `check_sounding_options`.
:param num_sounding_heights: Same.
:param use_padding: See doc for `create_2d_cnn`.
:param pooling_type_string: Pooling type (see doc for
`architecture_utils._check_pooling_options`).
:param num_conv_layer_sets: See doc for `check_radar_options`.
:param num_conv_layers_per_set: Same.
:param first_num_radar_filters: Same.
:param first_num_sounding_filters: See doc for `check_sounding_options`.
:param conv_layer_dropout_fraction: See doc for `check_radar_options`.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param list_of_metric_functions: List of Keras metrics. Will be used to
report performance during training.
:return: cnn_model_object: Untrained instance of `keras.models.Model`.
"""
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
error_checking.assert_is_integer(num_sounding_fields)
error_checking.assert_is_integer(num_sounding_heights)
include_soundings = num_sounding_fields > 0 or num_sounding_heights > 0
first_num_radar_filters, _ = check_radar_options(
num_grid_rows=num_radar_rows, num_grid_columns=num_radar_columns,
num_dimensions=3, num_classes=num_classes,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
num_heights=num_radar_heights, num_fields=num_radar_fields,
do_separable_conv=False, first_num_filters=first_num_radar_filters)
radar_input_layer_object = keras.layers.Input(
shape=(num_radar_rows, num_radar_columns, num_radar_heights,
num_radar_fields)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
radar_layer_object = None
current_num_filters = None
for i in range(num_conv_layer_sets):
if current_num_filters is None:
current_num_filters = first_num_radar_filters + 0
else:
current_num_filters *= 2
for _ in range(num_conv_layers_per_set):
if radar_layer_object is None:
this_input_layer_object = radar_input_layer_object
else:
this_input_layer_object = radar_layer_object
radar_layer_object = architecture_utils.get_3d_conv_layer(
num_filters=current_num_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_kernel_heights=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_columns_per_stride=1,
num_heights_per_stride=1,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
radar_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(radar_layer_object)
if conv_layer_dropout_fraction is not None:
radar_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(radar_layer_object)
if use_batch_normalization:
radar_layer_object = (
architecture_utils.get_batch_norm_layer()(
radar_layer_object)
)
radar_layer_object = architecture_utils.get_3d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_heights_in_window=2, num_rows_per_stride=2,
num_columns_per_stride=2, num_heights_per_stride=2,
pooling_type_string=pooling_type_string
)(radar_layer_object)
these_dimensions = numpy.array(
radar_layer_object.get_shape().as_list()[1:], dtype=int)
num_radar_features = numpy.prod(these_dimensions)
radar_layer_object = architecture_utils.get_flattening_layer()(
radar_layer_object)
if include_soundings:
(sounding_input_layer_object, sounding_layer_object,
num_sounding_features
) = _create_sounding_layers(
num_fields=num_sounding_fields, num_heights=num_sounding_heights,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction,
do_separable_conv=False,
first_num_filters=first_num_sounding_filters)
layer_object = keras.layers.concatenate(
[radar_layer_object, sounding_layer_object]
)
else:
layer_object = radar_layer_object
num_sounding_features = 0
layer_object = _create_dense_layers(
flattening_layer_object=layer_object,
num_scalar_features=num_radar_features + num_sounding_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
if include_soundings:
model_object = keras.models.Model(
inputs=[radar_input_layer_object, sounding_input_layer_object],
outputs=layer_object)
else:
model_object = keras.models.Model(
inputs=radar_input_layer_object, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
def _create_sounding_layers_resnet(
num_fields, num_heights, num_conv_layer_sets,
num_conv_layers_per_set, padding_type_string, l1_weight, l2_weight,
pooling_type_string, activation_function_string, alpha_for_elu,
alpha_for_relu, use_batch_normalization, dropout_fraction,
first_num_filters):
"""Creates conv and pooling layers, with residual blocks, for sounding data.
:param num_fields: See doc for `_create_sounding_layers`.
:param num_heights: Same.
:param num_conv_layer_sets: Same.
:param num_conv_layers_per_set: Same.
:param padding_type_string: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param pooling_type_string: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param dropout_fraction: Same.
:param first_num_filters: Same.
:return: input_layer_object: Same.
:return: flattening_layer_object: Same.
:return: num_scalar_features: Same.
"""
# TODO(thunderhoser): This is my first attempt at res-nets. I don't know
# what the best architecture is, especially vis-a-vis where activation and
# batch-norm layers should go.
first_num_filters, _ = check_sounding_options(
num_fields=num_fields, num_heights=num_heights, do_separable_conv=False,
first_num_filters=first_num_filters)
input_layer_object = keras.layers.Input(
shape=(num_heights, num_fields)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
current_layer_object = None
current_num_filters = None
for _ in range(num_conv_layer_sets):
if current_layer_object is None:
this_input_layer_object = input_layer_object
current_num_filters = first_num_filters + 0
else:
this_input_layer_object = current_layer_object
current_num_filters *= 2
current_layer_object = architecture_utils.get_1d_conv_layer(
num_kernel_rows=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_filters=current_num_filters,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
current_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(current_layer_object)
if dropout_fraction is not None:
current_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction
)(current_layer_object)
if use_batch_normalization:
current_layer_object = architecture_utils.get_batch_norm_layer()(
current_layer_object)
new_layer_object = None
for _ in range(num_conv_layers_per_set):
if new_layer_object is None:
this_input_layer_object = current_layer_object
else:
this_input_layer_object = new_layer_object
new_layer_object = architecture_utils.get_1d_conv_layer(
num_kernel_rows=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_filters=current_num_filters,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
new_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(new_layer_object)
if dropout_fraction is not None:
new_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction
)(new_layer_object)
if use_batch_normalization:
new_layer_object = (
architecture_utils.get_batch_norm_layer()(new_layer_object)
)
current_layer_object = keras.layers.add(
[current_layer_object, new_layer_object]
)
current_layer_object = architecture_utils.get_1d_pooling_layer(
num_rows_in_window=2, num_rows_per_stride=2,
pooling_type_string=pooling_type_string
)(current_layer_object)
these_dimensions = numpy.array(
current_layer_object.get_shape().as_list()[1:], dtype=int)
num_scalar_features = numpy.prod(these_dimensions)
current_layer_object = architecture_utils.get_flattening_layer()(
current_layer_object)
return input_layer_object, current_layer_object, num_scalar_features
def create_3d_resnet(
num_radar_rows, num_radar_columns, num_radar_heights, num_radar_fields,
num_classes, num_sounding_fields, num_sounding_heights,
use_padding=False,
pooling_type_string=architecture_utils.MAX_POOLING_STRING,
num_conv_layer_sets=DEFAULT_NUM_CONV_LAYER_SETS,
num_conv_layers_per_set=DEFAULT_NUM_CONV_LAYERS_PER_SET,
first_num_radar_filters=None, first_num_sounding_filters=None,
conv_layer_dropout_fraction=DEFAULT_CONV_LAYER_DROPOUT_FRACTION,
num_dense_layers=DEFAULT_NUM_DENSE_LAYERS,
dense_layer_dropout_fraction=DEFAULT_DENSE_LAYER_DROPOUT_FRACTION,
l1_weight=DEFAULT_L1_WEIGHT, l2_weight=DEFAULT_L2_WEIGHT,
activation_function_string=DEFAULT_ACTIVATION_FUNCTION_STRING,
alpha_for_elu=architecture_utils.DEFAULT_ALPHA_FOR_ELU,
alpha_for_relu=architecture_utils.DEFAULT_ALPHA_FOR_RELU,
use_batch_normalization=DEFAULT_USE_BATCH_NORM_FLAG,
list_of_metric_functions=DEFAULT_METRIC_FUNCTION_LIST):
"""Creates res-net to take in 3-D radar data (and possibly soundings).
:param num_radar_rows: See doc for `create_3d_cnn`.
:param num_radar_columns: Same.
:param num_radar_heights: Same.
:param num_radar_fields: Same.
:param num_classes: Same.
:param num_sounding_fields: Same.
:param num_sounding_heights: Same.
:param use_padding: Same.
:param pooling_type_string: Same.
:param num_conv_layer_sets: Same.
:param num_conv_layers_per_set: Same.
:param first_num_radar_filters: Same.
:param first_num_sounding_filters: Same.
:param conv_layer_dropout_fraction: Same.
:param num_dense_layers: Same.
:param dense_layer_dropout_fraction: Same.
:param l1_weight: Same.
:param l2_weight: Same.
:param activation_function_string: Same.
:param alpha_for_elu: Same.
:param alpha_for_relu: Same.
:param use_batch_normalization: Same.
:param list_of_metric_functions: Same.
:return: resnet_model_object: Untrained instance of `keras.models.Model`.
"""
# TODO(thunderhoser): This is my first attempt at res-nets. I don't know
# what the best architecture is, especially vis-a-vis where activation and
# batch-norm layers should go.
error_checking.assert_is_boolean(use_padding)
padding_type_string = (
architecture_utils.YES_PADDING_STRING if use_padding
else architecture_utils.NO_PADDING_STRING
)
error_checking.assert_is_integer(num_sounding_fields)
error_checking.assert_is_integer(num_sounding_heights)
include_soundings = num_sounding_fields > 0 or num_sounding_heights > 0
first_num_radar_filters, _ = check_radar_options(
num_grid_rows=num_radar_rows, num_grid_columns=num_radar_columns,
num_dimensions=3, num_classes=num_classes,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
conv_layer_dropout_fraction=conv_layer_dropout_fraction,
num_dense_layers=num_dense_layers,
dense_layer_dropout_fraction=dense_layer_dropout_fraction,
l1_weight=l1_weight, l2_weight=l2_weight,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
num_heights=num_radar_heights, num_fields=num_radar_fields,
do_separable_conv=False, first_num_filters=first_num_radar_filters)
radar_input_layer_object = keras.layers.Input(
shape=(num_radar_rows, num_radar_columns, num_radar_heights,
num_radar_fields)
)
regularizer_object = architecture_utils.get_weight_regularizer(
l1_weight=l1_weight, l2_weight=l2_weight)
radar_layer_object = None
current_num_filters = None
for i in range(num_conv_layer_sets):
if current_num_filters is None:
current_num_filters = first_num_radar_filters + 0
this_input_layer_object = radar_input_layer_object
else:
current_num_filters *= 2
this_input_layer_object = radar_layer_object
radar_layer_object = architecture_utils.get_3d_conv_layer(
num_filters=current_num_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_kernel_heights=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_columns_per_stride=1,
num_heights_per_stride=1, padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
radar_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(radar_layer_object)
if conv_layer_dropout_fraction is not None:
radar_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(radar_layer_object)
if use_batch_normalization:
radar_layer_object = architecture_utils.get_batch_norm_layer()(
radar_layer_object)
new_layer_object = None
for _ in range(num_conv_layers_per_set):
if new_layer_object is None:
this_input_layer_object = radar_layer_object
else:
this_input_layer_object = new_layer_object
new_layer_object = architecture_utils.get_3d_conv_layer(
num_filters=current_num_filters,
num_kernel_rows=NUM_CONV_KERNEL_ROWS,
num_kernel_columns=NUM_CONV_KERNEL_COLUMNS,
num_kernel_heights=NUM_CONV_KERNEL_HEIGHTS,
num_rows_per_stride=1, num_columns_per_stride=1,
num_heights_per_stride=1,
padding_type_string=padding_type_string,
weight_regularizer=regularizer_object
)(this_input_layer_object)
new_layer_object = architecture_utils.get_activation_layer(
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu
)(new_layer_object)
if conv_layer_dropout_fraction is not None:
new_layer_object = architecture_utils.get_dropout_layer(
dropout_fraction=conv_layer_dropout_fraction
)(new_layer_object)
if use_batch_normalization:
new_layer_object = (
architecture_utils.get_batch_norm_layer()(new_layer_object)
)
radar_layer_object = keras.layers.add(
[radar_layer_object, new_layer_object]
)
if i == num_conv_layer_sets - 1:
this_height_factor = 1
else:
this_height_factor = 2
radar_layer_object = architecture_utils.get_3d_pooling_layer(
num_rows_in_window=2, num_columns_in_window=2,
num_heights_in_window=this_height_factor, num_rows_per_stride=2,
num_columns_per_stride=2, num_heights_per_stride=this_height_factor,
pooling_type_string=pooling_type_string
)(radar_layer_object)
these_dimensions = numpy.array(
radar_layer_object.get_shape().as_list()[1:], dtype=int)
num_radar_features = numpy.prod(these_dimensions)
radar_layer_object = architecture_utils.get_flattening_layer()(
radar_layer_object)
if include_soundings:
(sounding_input_layer_object, sounding_layer_object,
num_sounding_features
) = _create_sounding_layers_resnet(
num_fields=num_sounding_fields, num_heights=num_sounding_heights,
num_conv_layer_sets=num_conv_layer_sets,
num_conv_layers_per_set=num_conv_layers_per_set,
padding_type_string=padding_type_string, l1_weight=l1_weight,
l2_weight=l2_weight, pooling_type_string=pooling_type_string,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization,
dropout_fraction=conv_layer_dropout_fraction,
first_num_filters=first_num_sounding_filters)
layer_object = keras.layers.concatenate(
[radar_layer_object, sounding_layer_object]
)
else:
layer_object = radar_layer_object
num_sounding_features = 0
layer_object = _create_dense_layers(
flattening_layer_object=layer_object,
num_scalar_features=num_radar_features + num_sounding_features,
num_classes=num_classes, num_dense_layers=num_dense_layers,
dropout_fraction=dense_layer_dropout_fraction,
activation_function_string=activation_function_string,
alpha_for_elu=alpha_for_elu, alpha_for_relu=alpha_for_relu,
use_batch_normalization=use_batch_normalization)
if include_soundings:
model_object = keras.models.Model(
inputs=[radar_input_layer_object, sounding_input_layer_object],
outputs=layer_object)
else:
model_object = keras.models.Model(
inputs=radar_input_layer_object, outputs=layer_object)
loss_function = _get_output_layer_and_loss(num_classes)[-1]
model_object.compile(
loss=loss_function, optimizer=keras.optimizers.Adam(),
metrics=list_of_metric_functions)
model_object.summary()
return model_object
| 43.488966
| 82
| 0.733642
| 9,702
| 74,888
| 5.130179
| 0.029582
| 0.0705
| 0.046612
| 0.023467
| 0.917827
| 0.884174
| 0.847327
| 0.822333
| 0.801318
| 0.787917
| 0
| 0.006184
| 0.211863
| 74,888
| 1,721
| 83
| 43.514236
| 0.837112
| 0.185824
| 0
| 0.763731
| 0
| 0
| 0.006529
| 0.002126
| 0
| 0
| 0
| 0.001743
| 0.054926
| 1
| 0.010462
| false
| 0
| 0.004359
| 0
| 0.027027
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
c9fd1db7fb8b288acda80e092c55bcca05f6951d
| 41
|
py
|
Python
|
packages/python/accern_xyme/v3/__init__.py
|
Accern/accern-xyme
|
887536144539eb93a798830f312aaebf09c4afc9
|
[
"MIT"
] | 3
|
2019-12-09T20:59:44.000Z
|
2021-07-12T11:32:59.000Z
|
packages/python/accern_xyme/v3/__init__.py
|
Accern/accern-xyme
|
887536144539eb93a798830f312aaebf09c4afc9
|
[
"MIT"
] | 13
|
2020-01-10T23:26:31.000Z
|
2022-02-11T19:16:50.000Z
|
packages/python/accern_xyme/v3/__init__.py
|
Accern/accern-xyme
|
887536144539eb93a798830f312aaebf09c4afc9
|
[
"MIT"
] | 1
|
2020-12-09T15:21:52.000Z
|
2020-12-09T15:21:52.000Z
|
from accern_xyme.v3.accern_xyme import *
| 20.5
| 40
| 0.829268
| 7
| 41
| 4.571429
| 0.714286
| 0.625
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.027027
| 0.097561
| 41
| 1
| 41
| 41
| 0.837838
| 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
| 1
| 0
|
0
| 7
|
4eb4a9c58a239fd61418c54c8a2231539e00f100
| 186
|
py
|
Python
|
test.py
|
boqchen/sr_transfomer
|
b783dae7fac4f069dad96a18b9399dcfc5c8df7f
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
test.py
|
boqchen/sr_transfomer
|
b783dae7fac4f069dad96a18b9399dcfc5c8df7f
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
test.py
|
boqchen/sr_transfomer
|
b783dae7fac4f069dad96a18b9399dcfc5c8df7f
|
[
"Apache-2.0",
"MIT"
] | null | null | null |
# test cuda
import torch
print(torch.cuda.is_available()) #True: cuda is available
#test cuDNN
from torch.backends import cudnn
print(cudnn.is_available()) #True: cuDNN is available
| 26.571429
| 58
| 0.768817
| 28
| 186
| 5.035714
| 0.392857
| 0.312057
| 0.212766
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134409
| 186
| 6
| 59
| 31
| 0.875776
| 0.354839
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 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
| 1
|
0
| 7
|
4ebcd0a0fe7aa919cf0312964fa168dba74b6ee1
| 31,700
|
py
|
Python
|
examples/core/trainers.py
|
zyh1999/pytorch-quantum
|
c00bd564a99001fee2fd6b30e5e34562ab981e28
|
[
"MIT"
] | 98
|
2021-07-23T07:11:32.000Z
|
2021-12-19T14:04:58.000Z
|
examples/core/trainers.py
|
zyh1999/pytorch-quantum
|
c00bd564a99001fee2fd6b30e5e34562ab981e28
|
[
"MIT"
] | 2
|
2021-02-11T19:01:48.000Z
|
2021-04-04T20:29:57.000Z
|
examples/core/trainers.py
|
zyh1999/pytorch-quantum
|
c00bd564a99001fee2fd6b30e5e34562ab981e28
|
[
"MIT"
] | 12
|
2021-07-23T07:10:47.000Z
|
2021-12-16T23:44:44.000Z
|
import torch.nn as nn
import torchquantum as tq
import torch.nn.utils.prune
from typing import Any, Callable, Dict
from torchpack.train import Trainer
from torchpack.utils.typing import Optimizer, Scheduler
from torchpack.utils.config import configs
from torchquantum.utils import (get_unitary_loss, legalize_unitary,
build_module_op_list)
from torchquantum.super_utils import ArchSampler
from torchquantum.prune_utils import (PhaseL1UnstructuredPruningMethod,
ThresholdScheduler)
from torchpack.utils.logging import logger
from torchpack.callbacks.writers import TFEventWriter
from examples.core.tools.quantize import PACTActivationQuantizer
__all__ = ['QTrainer', 'LayerRegressionTrainer', 'SuperQTrainer',
'PruningTrainer', 'QNoiseAwareTrainer']
class LayerRegressionTrainer(Trainer):
def __init__(self, *, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: Scheduler) -> None:
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
def _before_epoch(self) -> None:
self.model.train()
def _run_step(self, feed_dict: Dict[str, Any]) -> Dict[str, Any]:
if configs.run.device == 'gpu':
inputs = feed_dict['input'].cuda(non_blocking=True)
targets = feed_dict['output'].cuda(non_blocking=True)
else:
inputs = feed_dict['input']
targets = feed_dict['output']
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
if loss.requires_grad:
self.summary.add_scalar('loss', loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'outputs': outputs, 'targets': targets}
def _after_epoch(self) -> None:
self.model.eval()
self.scheduler.step()
def _state_dict(self) -> Dict[str, Any]:
state_dict = dict()
state_dict['model'] = self.model.state_dict()
state_dict['optimizer'] = self.optimizer.state_dict()
state_dict['scheduler'] = self.scheduler.state_dict()
return state_dict
def _load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self.model.load_state_dict(state_dict['model'])
self.optimizer.load_state_dict(state_dict['optimizer'])
self.scheduler.load_state_dict(state_dict['scheduler'])
class QTrainer(Trainer):
def __init__(self, *, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: Scheduler) -> None:
self.model = model
self.legalized_model = None
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.solution = None
self.score = None
def _before_epoch(self) -> None:
self.model.train()
def run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
output_dict = self._run_step(feed_dict, legalize=legalize)
return output_dict
def _run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
if configs.run.device == 'gpu':
inputs = feed_dict[configs.dataset.input_name].cuda(
non_blocking=True)
targets = feed_dict[configs.dataset.target_name].cuda(
non_blocking=True)
else:
inputs = feed_dict[configs.dataset.input_name]
targets = feed_dict[configs.dataset.target_name]
if legalize:
outputs = self.legalized_model(inputs)
else:
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
nll_loss = loss.item()
unitary_loss = 0
if configs.regularization.unitary_loss:
unitary_loss = get_unitary_loss(self.model)
if configs.regularization.unitary_loss_lambda_trainable:
loss += self.model.unitary_loss_lambda[0] * unitary_loss
else:
loss += configs.regularization.unitary_loss_lambda * \
unitary_loss
if loss.requires_grad:
for k, group in enumerate(self.optimizer.param_groups):
self.summary.add_scalar(f'lr/lr_group{k}', group['lr'])
self.summary.add_scalar('loss', loss.item())
self.summary.add_scalar('nll_loss', nll_loss)
if getattr(self.model, 'sample_arch', None) is not None:
for writer in self.summary.writers:
if isinstance(writer, TFEventWriter):
writer.writer.add_text(
'sample_arch', str(self.model.sample_arch),
self.global_step)
if configs.regularization.unitary_loss:
if configs.regularization.unitary_loss_lambda_trainable:
self.summary.add_scalar(
'u_loss_lambda',
self.model.unitary_loss_lambda.item())
else:
self.summary.add_scalar(
'u_loss_lambda',
configs.regularization.unitary_loss_lambda)
self.summary.add_scalar('u_loss', unitary_loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'outputs': outputs, 'targets': targets}
def _after_epoch(self) -> None:
self.model.eval()
self.scheduler.step()
if configs.legalization.legalize:
if self.epoch_num % configs.legalization.epoch_interval == 0:
legalize_unitary(self.model)
def _after_step(self, output_dict) -> None:
if configs.legalization.legalize:
if self.global_step % configs.legalization.step_interval == 0:
legalize_unitary(self.model)
def _state_dict(self) -> Dict[str, Any]:
state_dict = dict()
# need to store model arch because of randomness of random layers
state_dict['model_arch'] = self.model
state_dict['model'] = self.model.state_dict()
state_dict['optimizer'] = self.optimizer.state_dict()
state_dict['scheduler'] = self.scheduler.state_dict()
if getattr(self.model, 'sample_arch', None) is not None:
state_dict['sample_arch'] = self.model.sample_arch
if self.solution is not None:
state_dict['solution'] = self.solution
state_dict['score'] = self.score
if getattr(self.model, 'encoder', None) is not None:
if getattr(self.model.encoder, 'func_list', None) is not None:
state_dict['encoder_func_list'] = self.model.encoder.func_list
if getattr(self.model, 'q_layer', None) is not None:
state_dict['q_layer_op_list'] = build_module_op_list(
self.model.q_layer)
if getattr(self.model, 'measure', None) is not None:
if getattr(self.model.measure,
'v_c_reg_mapping', None) is not None:
state_dict['v_c_reg_mapping'] = \
self.model.measure.v_c_reg_mapping
if getattr(self.model, 'nodes', None) is not None:
state_dict['encoder_func_list'] = [
node.encoder.func_list for node in self.model.nodes]
if configs.model.transpile_before_run:
# only save op_list if transpile before run
state_dict['q_layer_op_list'] = [
build_module_op_list(node.q_layer) for node in
self.model.nodes]
state_dict['v_c_reg_mapping'] = [
node.measure.v_c_reg_mapping for node in self.model.nodes]
for attr in ['v_c_reg_mapping', 'encoder_func_list',
'q_layer_op_list']:
if state_dict.get(attr, None) is None:
logger.warning(f"No {attr} found, will not save it.")
return state_dict
def _load_state_dict(self, state_dict: Dict[str, Any]) -> None:
# self.model.load_state_dict(state_dict['model'])
self.optimizer.load_state_dict(state_dict['optimizer'])
self.scheduler.load_state_dict(state_dict['scheduler'])
class SuperQTrainer(Trainer):
def __init__(self, *, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: Scheduler) -> None:
self.model = model
self.legalized_model = None
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.sample_arch = None
self.arch_sampler = ArchSampler(
model=model,
strategy=configs.model.sampler.strategy.dict(),
n_layers_per_block=configs.model.arch.n_layers_per_block
)
def _before_epoch(self) -> None:
self.model.train()
self.arch_sampler.set_total_steps(self.steps_per_epoch *
self.num_epochs)
def _before_step(self, feed_dict: Dict[str, Any]) -> None:
self.sample_arch = self.arch_sampler.get_uniform_sample_arch()
self.model.set_sample_arch(self.sample_arch)
def run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
output_dict = self._run_step(feed_dict, legalize=legalize)
return output_dict
def _run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
if configs.run.device == 'gpu':
inputs = feed_dict[configs.dataset.input_name].cuda(
non_blocking=True)
targets = feed_dict[configs.dataset.target_name].cuda(
non_blocking=True)
else:
inputs = feed_dict[configs.dataset.input_name]
targets = feed_dict[configs.dataset.target_name]
if legalize:
outputs = self.legalized_model(inputs)
else:
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
# nll_loss = loss.item()
unitary_loss = 0
if configs.regularization.unitary_loss:
unitary_loss = get_unitary_loss(self.model)
if configs.regularization.unitary_loss_lambda_trainable:
loss += self.model.unitary_loss_lambda[0] * unitary_loss
else:
loss += configs.regularization.unitary_loss_lambda * \
unitary_loss
if loss.requires_grad:
# during training
for k, group in enumerate(self.optimizer.param_groups):
self.summary.add_scalar(f'lr/lr_group{k}', group['lr'])
self.summary.add_scalar('loss', loss.item())
# self.summary.add_scalar('nll_loss', nll_loss)
self.summary.add_scalar('sta',
self.arch_sampler.current_stage)
self.summary.add_scalar('chk',
self.arch_sampler.current_chunk)
self.summary.add_scalar('n_ops',
self.arch_sampler.sample_n_ops)
if getattr(self.model, 'sample_arch', None) is not None:
for writer in self.summary.writers:
if isinstance(writer, TFEventWriter):
writer.writer.add_text(
'sample_arch', str(self.model.sample_arch),
self.global_step)
if configs.regularization.unitary_loss:
if configs.regularization.unitary_loss_lambda_trainable:
self.summary.add_scalar(
'u_loss_lambda',
self.model.unitary_loss_lambda.item())
else:
self.summary.add_scalar(
'u_loss_lambda',
configs.regularization.unitary_loss_lambda)
self.summary.add_scalar('u_loss', unitary_loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'outputs': outputs, 'targets': targets}
def _after_epoch(self) -> None:
self.model.eval()
self.scheduler.step()
if configs.legalization.legalize:
if self.epoch_num % configs.legalization.epoch_interval == 0:
legalize_unitary(self.model)
def _after_step(self, output_dict) -> None:
if configs.legalization.legalize:
if self.global_step % configs.legalization.step_interval == 0:
legalize_unitary(self.model)
def _state_dict(self) -> Dict[str, Any]:
state_dict = dict()
# need to store model arch because of randomness of random layers
state_dict['model_arch'] = self.model
state_dict['model'] = self.model.state_dict()
state_dict['optimizer'] = self.optimizer.state_dict()
state_dict['scheduler'] = self.scheduler.state_dict()
if getattr(self.model, 'sample_arch', None) is not None:
state_dict['sample_arch'] = self.model.sample_arch
try:
state_dict['v_c_reg_mapping'] = self.model.measure.v_c_reg_mapping
except AttributeError:
logger.warning(f"No v_c_reg_mapping found, will not save it.")
return state_dict
def _load_state_dict(self, state_dict: Dict[str, Any]) -> None:
# self.model.load_state_dict(state_dict['model'], strict=False)
self.optimizer.load_state_dict(state_dict['optimizer'])
self.scheduler.load_state_dict(state_dict['scheduler'])
class PruningTrainer(Trainer):
"""
Perform pruning-aware training
"""
def __init__(self, *, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: Scheduler) -> None:
self.model = model
self.legalized_model = None
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.solution = None
self.score = None
self._parameters_to_prune = None
self._target_pruning_amount = None
self._init_pruning_amount = None
self.prune_amount_scheduler = None
self.prune_amount = None
self.init_pruning()
@staticmethod
def extract_prunable_parameters(model: nn.Module) -> list:
_parameters_to_prune = [
(module, "params")
for _, module in model.named_modules() if isinstance(module,
tq.Operator)
and module.params is not None]
return _parameters_to_prune
def init_pruning(self) -> None:
"""
Initialize pruning procedure
"""
self._parameters_to_prune = self.extract_prunable_parameters(
self.model)
self._target_pruning_amount = configs.prune.target_pruning_amount
self._init_pruning_amount = configs.prune.init_pruning_amount
self.prune_amount_scheduler = ThresholdScheduler(
configs.prune.start_epoch, configs.prune.end_epoch,
self._init_pruning_amount,
self._target_pruning_amount)
self.prune_amount = self._init_pruning_amount
def _remove_pruning(self):
for module, name in self._parameters_to_prune:
nn.utils.prune.remove(module, name)
def _prune_model(self, prune_amount) -> None:
"""
Perform global threshold/percentage pruning on the quantum model.
This function just performs pruning re-parametrization, i.e.,
record weight_orig and generate weight_mask
"""
# first clear current pruning container, since we do not want cascaded
# pruning methods
# remove operation will make pruning permanent
if self.epoch_num > 1:
self._remove_pruning()
# perform global phase pruning based on the given pruning amount
nn.utils.prune.global_unstructured(
self._parameters_to_prune,
pruning_method=PhaseL1UnstructuredPruningMethod,
amount=prune_amount,
)
self.summary.add_scalar('prune_amount', prune_amount)
def _before_epoch(self) -> None:
self.model.train()
def run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
output_dict = self._run_step(feed_dict, legalize=legalize)
return output_dict
def _run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
if configs.run.device == 'gpu':
inputs = feed_dict[configs.dataset.input_name].cuda(
non_blocking=True)
targets = feed_dict[configs.dataset.target_name].cuda(
non_blocking=True)
else:
inputs = feed_dict[configs.dataset.input_name]
targets = feed_dict[configs.dataset.target_name]
if legalize:
outputs = self.legalized_model(inputs)
else:
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
nll_loss = loss.item()
unitary_loss = 0
if configs.regularization.unitary_loss:
unitary_loss = get_unitary_loss(self.model)
if configs.regularization.unitary_loss_lambda_trainable:
loss += self.model.unitary_loss_lambda[0] * unitary_loss
else:
loss += configs.regularization.unitary_loss_lambda * \
unitary_loss
if loss.requires_grad:
for k, group in enumerate(self.optimizer.param_groups):
self.summary.add_scalar(f'lr/lr_group{k}', group['lr'])
self.summary.add_scalar('loss', loss.item())
self.summary.add_scalar('nll_loss', nll_loss)
if getattr(self.model, 'sample_arch', None) is not None:
for writer in self.summary.writers:
if isinstance(writer, TFEventWriter):
writer.writer.add_text(
'sample_arch', str(self.model.sample_arch),
self.global_step)
if configs.regularization.unitary_loss:
if configs.regularization.unitary_loss_lambda_trainable:
self.summary.add_scalar(
'u_loss_lambda',
self.model.unitary_loss_lambda.item())
else:
self.summary.add_scalar(
'u_loss_lambda',
configs.regularization.unitary_loss_lambda)
self.summary.add_scalar('u_loss', unitary_loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'outputs': outputs, 'targets': targets}
def _after_epoch(self) -> None:
self.model.eval()
self.scheduler.step()
if configs.legalization.legalize:
if self.epoch_num % configs.legalization.epoch_interval == 0:
legalize_unitary(self.model)
# update pruning amount using the scheduler
self.prune_amount = self.prune_amount_scheduler.step()
# prune the model
self._prune_model(self.prune_amount)
# commit pruned parameters after training
if self.epoch_num == self.num_epochs:
self._remove_pruning()
def _after_step(self, output_dict) -> None:
if configs.legalization.legalize:
if self.global_step % configs.legalization.step_interval == 0:
legalize_unitary(self.model)
def _state_dict(self) -> Dict[str, Any]:
state_dict = dict()
# need to store model arch because of randomness of random layers
state_dict['model_arch'] = self.model
state_dict['model'] = self.model.state_dict()
state_dict['optimizer'] = self.optimizer.state_dict()
state_dict['scheduler'] = self.scheduler.state_dict()
if getattr(self.model, 'sample_arch', None) is not None:
state_dict['sample_arch'] = self.model.sample_arch
try:
state_dict['q_layer_op_list'] = build_module_op_list(
self.model.q_layer)
state_dict['encoder_func_list'] = self.model.encoder.func_list
except AttributeError:
logger.warning(f"No q_layer_op_list or encoder_func_list found, "
f"will not save them")
if self.solution is not None:
state_dict['solution'] = self.solution
state_dict['score'] = self.score
try:
state_dict['v_c_reg_mapping'] = self.model.measure.v_c_reg_mapping
except AttributeError:
logger.warning(f"No v_c_reg_mapping found, will not save it.")
return state_dict
def _load_state_dict(self, state_dict: Dict[str, Any]) -> None:
# self.model.load_state_dict(state_dict['model'])
self.optimizer.load_state_dict(state_dict['optimizer'])
self.scheduler.load_state_dict(state_dict['scheduler'])
class QNoiseAwareTrainer(Trainer):
def __init__(self, *, model: nn.Module, criterion: Callable,
optimizer: Optimizer, scheduler: Scheduler) -> None:
self.model = model
self.legalized_model = None
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.solution = None
self.score = None
self.quantizers = []
self.init_act_quant()
def init_act_quant(self):
device = torch.device('cuda') if configs.run.device == 'gpu' else \
torch.device('cpu')
if configs.trainer.act_quant:
quantizers = []
assert getattr(self.model, 'nodes', None) is not None
# single Q node
for k, node in enumerate(self.model.nodes):
# optionally skip the quantization of last layer output
if configs.trainer.act_quant_skip_last_node and k == len(
self.model.nodes) - 1:
continue
quantizer = PACTActivationQuantizer(
module=node,
precision=getattr(configs.trainer, 'act_quant_bit', None),
level=getattr(configs.trainer, 'act_quant_level', None),
alpha=1.0,
backprop_alpha=False,
quant_ratio=configs.trainer.act_quant_ratio,
device=device,
lower_bound=configs.trainer.act_quant_lower_bound,
upper_bound=configs.trainer.act_quant_upper_bound,
)
quantizers.append(quantizer)
self.quantizers = quantizers
def register_act_quant_hook(self):
for quantizer in self.quantizers:
quantizer.register_hook()
def remove_act_quant_hook(self):
for quantizer in self.quantizers:
quantizer.remove_hook()
def _before_epoch(self) -> None:
self.model.train()
# adjust the noise magnitude
if self.model.noise_model_tq is not None:
self.model.noise_model_tq.mode = 'train'
self.model.noise_model_tq.adjust_noise(self.epoch_num)
if getattr(self.model, 'nodes', None) is not None:
for node in self.model.nodes:
if node.noise_model_tq is not None:
node.noise_model_tq.mode = 'train'
node.noise_model_tq.adjust_noise(self.epoch_num)
self.register_act_quant_hook()
def run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
output_dict = self._run_step(feed_dict, legalize=legalize)
return output_dict
def _run_step(self, feed_dict: Dict[str, Any], legalize=False) -> Dict[
str, Any]:
if configs.run.device == 'gpu':
inputs = feed_dict[configs.dataset.input_name].cuda(
non_blocking=True)
targets = feed_dict[configs.dataset.target_name].cuda(
non_blocking=True)
else:
inputs = feed_dict[configs.dataset.input_name]
targets = feed_dict[configs.dataset.target_name]
if legalize:
outputs = self.legalized_model(inputs)
else:
outputs = self.model(inputs)
loss = self.criterion(outputs, targets)
nll_loss = loss.item()
unitary_loss = 0
if configs.regularization.unitary_loss:
unitary_loss = get_unitary_loss(self.model)
if configs.regularization.unitary_loss_lambda_trainable:
loss += self.model.unitary_loss_lambda[0] * unitary_loss
else:
loss += configs.regularization.unitary_loss_lambda * \
unitary_loss
if configs.trainer.act_quant and configs.trainer.act_quant_loss:
mse_all = self.model.mse_all
act_quant_loss = sum(mse_all)
loss += configs.trainer.act_quant_loss_lambda * act_quant_loss
if loss.requires_grad:
for k, group in enumerate(self.optimizer.param_groups):
self.summary.add_scalar(f'lr/lr_group{k}', group['lr'])
self.summary.add_scalar('loss', loss.item())
self.summary.add_scalar('nll_loss', nll_loss)
if configs.trainer.act_quant and configs.trainer.act_quant_loss:
self.summary.add_scalar('qloss', act_quant_loss.item())
if self.model.noise_model_tq is not None:
if self.model.noise_model_tq.noise_total_prob is not None:
noise_total_prob = \
self.model.noise_model_tq.noise_total_prob
if isinstance(noise_total_prob, list):
noise_total_prob = noise_total_prob[0]
else:
noise_total_prob = -1
self.summary.add_scalar('noip', noise_total_prob)
if getattr(self.model, 'nodes', None) is not None:
noise_total_prob = -1 if self.model.nodes[
0].noise_model_tq.noise_total_prob is None else \
self.model.nodes[0].noise_model_tq.noise_total_prob
if isinstance(noise_total_prob, list):
noise_total_prob = noise_total_prob[0]
self.summary.add_scalar('noip', noise_total_prob)
if getattr(self.model, 'sample_arch', None) is not None:
for writer in self.summary.writers:
if isinstance(writer, TFEventWriter):
writer.writer.add_text(
'sample_arch', str(self.model.sample_arch),
self.global_step)
if configs.regularization.unitary_loss:
if configs.regularization.unitary_loss_lambda_trainable:
self.summary.add_scalar(
'u_loss_lambda',
self.model.unitary_loss_lambda.item())
else:
self.summary.add_scalar(
'u_loss_lambda',
configs.regularization.unitary_loss_lambda)
self.summary.add_scalar('u_loss', unitary_loss.item())
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return {'outputs': outputs, 'targets': targets}
def _after_epoch(self) -> None:
self.model.eval()
self.scheduler.step()
if configs.legalization.legalize:
if self.epoch_num % configs.legalization.epoch_interval == 0:
legalize_unitary(self.model)
# set to eval mode, will not add noise
if self.model.noise_model_tq is not None:
self.model.noise_model_tq.mode = 'eval'
if getattr(self.model, 'nodes', None) is not None:
for node in self.model.nodes:
if node.noise_model_tq is not None:
node.noise_model_tq.mode = 'eval'
def _after_step(self, output_dict) -> None:
if configs.legalization.legalize:
if self.global_step % configs.legalization.step_interval == 0:
legalize_unitary(self.model)
def _state_dict(self) -> Dict[str, Any]:
# remove hook otherwise the 'model' cannot be pickled
self.remove_act_quant_hook()
state_dict = dict()
# need to store model arch because of randomness of random layers
state_dict['model_arch'] = self.model
state_dict['model'] = self.model.state_dict()
state_dict['optimizer'] = self.optimizer.state_dict()
state_dict['scheduler'] = self.scheduler.state_dict()
if getattr(self.model, 'sample_arch', None) is not None:
state_dict['sample_arch'] = self.model.sample_arch
if self.solution is not None:
state_dict['solution'] = self.solution
state_dict['score'] = self.score
if getattr(self.model, 'encoder', None) is not None:
if getattr(self.model.encoder, 'func_list', None) is not None:
state_dict['encoder_func_list'] = self.model.encoder.func_list
if getattr(self.model, 'q_layer', None) is not None:
state_dict['q_layer_op_list'] = build_module_op_list(
self.model.q_layer)
if getattr(self.model, 'measure', None) is not None:
if getattr(self.model.measure,
'v_c_reg_mapping', None) is not None:
state_dict['v_c_reg_mapping'] = \
self.model.measure.v_c_reg_mapping
if getattr(self.model, 'noise_model_tq', None) is not None:
state_dict['noise_model_tq'] = self.model.noise_model_tq
if configs.trainer.act_quant:
state_dict['act_quant'] = {
'act_quant_bit': self.quantizers[0].precision,
'act_quant_level': self.quantizers[0].level,
'act_quant_ratio': self.quantizers[0].quant_ratio,
'act_quant_lower_bound': self.quantizers[
0].lower_bound,
'act_quant_upper_bound': self.quantizers[
0].upper_bound,
}
if getattr(self.model, 'nodes', None) is not None:
state_dict['encoder_func_list'] = [
node.encoder.func_list for node in self.model.nodes]
if configs.model.transpile_before_run:
# only save op_list if transpile before run
state_dict['q_layer_op_list'] = [
build_module_op_list(node.q_layer) for node in
self.model.nodes]
state_dict['v_c_reg_mapping'] = [
node.measure.v_c_reg_mapping for node in self.model.nodes]
# one node has one own noise model
state_dict['noise_model_tq'] = [
node.noise_model_tq for node in self.model.nodes]
for attr in ['v_c_reg_mapping', 'encoder_func_list',
'q_layer_op_list', 'noise_model_tq']:
if state_dict.get(attr, None) is None:
logger.warning(f"No {attr} found, will not save it.")
return state_dict
def _load_state_dict(self, state_dict: Dict[str, Any]) -> None:
# self.model.load_state_dict(state_dict['model'])
self.optimizer.load_state_dict(state_dict['optimizer'])
self.scheduler.load_state_dict(state_dict['scheduler'])
| 41.986755
| 78
| 0.602114
| 3,678
| 31,700
| 4.93366
| 0.073953
| 0.062989
| 0.017359
| 0.035269
| 0.803538
| 0.770087
| 0.764411
| 0.760884
| 0.745398
| 0.736306
| 0
| 0.001498
| 0.30489
| 31,700
| 754
| 79
| 42.04244
| 0.82201
| 0.043249
| 0
| 0.759016
| 0
| 0
| 0.060015
| 0.002117
| 0
| 0
| 0
| 0
| 0.001639
| 1
| 0.07541
| false
| 0
| 0.021311
| 0
| 0.129508
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| null | 0
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|
0
| 7
|
0944981506e12d30b31ca708ae76656e7e0bf01b
| 124
|
py
|
Python
|
infos/modules/passport/__init__.py
|
IzayoiRin/izyNews
|
cea8ba19226b3c41e0a21bbdb33b8a4bb9cc97e3
|
[
"MIT"
] | null | null | null |
infos/modules/passport/__init__.py
|
IzayoiRin/izyNews
|
cea8ba19226b3c41e0a21bbdb33b8a4bb9cc97e3
|
[
"MIT"
] | null | null | null |
infos/modules/passport/__init__.py
|
IzayoiRin/izyNews
|
cea8ba19226b3c41e0a21bbdb33b8a4bb9cc97e3
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
passportBlp = Blueprint("passportBlp", __name__, url_prefix="/passport")
from .views import *
| 20.666667
| 72
| 0.774194
| 14
| 124
| 6.5
| 0.714286
| 0.43956
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120968
| 124
| 5
| 73
| 24.8
| 0.834862
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.333333
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
|
0
| 7
|
1185bb4f3f2503976dc774d24210512bc5992931
| 121
|
py
|
Python
|
core/recc/chrono/datetime.py
|
bogonets/answer
|
57f892a9841980bcbc35fa1e27521b34cd94bc25
|
[
"MIT"
] | 3
|
2021-06-20T02:24:10.000Z
|
2022-01-26T23:55:33.000Z
|
core/recc/chrono/datetime.py
|
bogonets/answer
|
57f892a9841980bcbc35fa1e27521b34cd94bc25
|
[
"MIT"
] | null | null | null |
core/recc/chrono/datetime.py
|
bogonets/answer
|
57f892a9841980bcbc35fa1e27521b34cd94bc25
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from datetime import datetime
def today() -> datetime:
return datetime.now().astimezone()
| 15.125
| 38
| 0.652893
| 14
| 121
| 5.642857
| 0.785714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010101
| 0.181818
| 121
| 7
| 39
| 17.285714
| 0.787879
| 0.173554
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
eed433610ff1ff8aaeb7a404d6faf29d1f704609
| 1,374
|
py
|
Python
|
Python/Problem0008.py
|
agileshaw/Project-Euler
|
e0e70e0ae940c3fb648606b96ded6ee3c6e6e534
|
[
"MIT"
] | null | null | null |
Python/Problem0008.py
|
agileshaw/Project-Euler
|
e0e70e0ae940c3fb648606b96ded6ee3c6e6e534
|
[
"MIT"
] | null | null | null |
Python/Problem0008.py
|
agileshaw/Project-Euler
|
e0e70e0ae940c3fb648606b96ded6ee3c6e6e534
|
[
"MIT"
] | null | null | null |
# Problem0008 - Largest product in a series
#
# https://github.com/agileshaw/Project-Euler
def product(sub):
tmp = 1
for i in sub:
tmp *= int(i)
return tmp
if __name__ == "__main__":
num = "7316717653133062491922511967442657474235534919493496983520312774506326239578318016984801869478851843858615607891129494954595017379583319528532088055111254069874715852386305071569329096329522744304355766896648950445244523161731856403098711121722383113622298934233803081353362766142828064444866452387493035890729629049156044077239071381051585930796086670172427121883998797908792274921901699720888093776657273330010533678812202354218097512545405947522435258490771167055601360483958644670632441572215539753697817977846174064955149290862569321978468622482839722413756570560574902614079729686524145351004748216637048440319989000889524345065854122758866688116427171479924442928230863465674813919123162824586178664583591245665294765456828489128831426076900422421902267105562632111110937054421750694165896040807198403850962455444362981230987879927244284909188845801561660979191338754992005240636899125607176060588611646710940507754100225698315520005593572972571636269561882670428252483600823257530420752963450"
length = len(num)
result = max(product(num[i : i + 13]) for i in range(length - 12))
print("The largest product in the series is: %d" % result)
| 85.875
| 1,012
| 0.893741
| 57
| 1,374
| 21.403509
| 0.631579
| 0.022951
| 0.02623
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.789515
| 0.069869
| 1,374
| 15
| 1,013
| 91.6
| 0.165102
| 0.061135
| 0
| 0
| 0
| 0
| 0.81493
| 0.777605
| 0
| 1
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.2
| 0.1
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e10892543fddc8596e78eec7e95e4ae21f8e40c3
| 27,275
|
py
|
Python
|
pipeline_sdk/api/pipeline/pipeline_client.py
|
easyopsapis/easyops-api-python
|
adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0
|
[
"Apache-2.0"
] | 5
|
2019-07-31T04:11:05.000Z
|
2021-01-07T03:23:20.000Z
|
pipeline_sdk/api/pipeline/pipeline_client.py
|
easyopsapis/easyops-api-python
|
adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0
|
[
"Apache-2.0"
] | null | null | null |
pipeline_sdk/api/pipeline/pipeline_client.py
|
easyopsapis/easyops-api-python
|
adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
import os
import sys
import pipeline_sdk.api.pipeline.create_pb2
import pipeline_sdk.model.pipeline.pipeline_pb2
import pipeline_sdk.api.pipeline.create_trigger_pb2
import pipeline_sdk.model.pipeline.trigger_pb2
import pipeline_sdk.api.pipeline.delete_pb2
import google.protobuf.empty_pb2
import pipeline_sdk.api.pipeline.delete_trigger_pb2
import pipeline_sdk.api.pipeline.delete_triggers_pb2
import pipeline_sdk.api.pipeline.execute_pb2
import pipeline_sdk.api.pipeline.get_pb2
import pipeline_sdk.api.pipeline.get_pipeline_v2_pb2
import pipeline_sdk.api.pipeline.get_trigger_pb2
import pipeline_sdk.api.pipeline.get_trigger_detail_pb2
import pipeline_sdk.api.pipeline.handle_hook_pb2
import pipeline_sdk.api.pipeline.list_pb2
import pipeline_sdk.api.pipeline.list_trigger_pb2
import pipeline_sdk.api.pipeline.list_v2_pb2
import pipeline_sdk.api.pipeline.update_pb2
import pipeline_sdk.api.pipeline.update_trigger_pb2
import pipeline_sdk.utils.http_util
import google.protobuf.json_format
class PipelineClient(object):
def __init__(self, server_ip="", server_port=0, service_name="", host=""):
"""
初始化client
:param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由
:param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由
:param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和service_name同时设置，server_ip优先级更高
:param host: 指定sdk请求服务的host名称, 如cmdb.easyops-only.com
"""
if server_ip == "" and server_port != 0 or server_ip != "" and server_port == 0:
raise Exception("server_ip和server_port必须同时指定")
self._server_ip = server_ip
self._server_port = server_port
self._service_name = service_name
self._host = host
def create(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.create_pb2.CreateRequest, int, str, int) -> pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
创建流水线
:param request: create请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.Create"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines".format(
project_id=request.project_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="POST",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def create_trigger(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.create_trigger_pb2.CreateTriggerRequest, int, str, int) -> pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
创建流水线钩子
:param request: create_trigger请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.CreateTrigger"
uri = "/api/pipeline/v1/triggers"
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="POST",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.trigger_pb2.Trigger()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def delete_pipeline(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.delete_pb2.DeletePipelineRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
删除流水线
:param request: delete_pipeline请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.DeletePipeline"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines/{pipeline_id}".format(
project_id=request.project_id,
pipeline_id=request.pipeline_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="DELETE",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def delete_trigger(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.delete_trigger_pb2.DeleteTriggerRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
删除流水线钩子
:param request: delete_trigger请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.DeleteTrigger"
uri = "/api/pipeline/v1/triggers/{id}".format(
id=request.id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="DELETE",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def delete_triggers(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.delete_triggers_pb2.DeleteTriggersRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
删除流水线所有钩子
:param request: delete_triggers请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.DeleteTriggers"
uri = "/api/pipeline/v1/triggers"
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="DELETE",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def execute(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.execute_pb2.ExecuteRequest, int, str, int) -> pipeline_sdk.api.pipeline.execute_pb2.ExecuteResponse
"""
手动触发执行流水线
:param request: execute请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.execute_pb2.ExecuteResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.Execute"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines/{pipeline_id}/execute".format(
project_id=request.project_id,
pipeline_id=request.pipeline_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="POST",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.execute_pb2.ExecuteResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def get(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.get_pb2.GetRequest, int, str, int) -> pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
获取流水线详情
:param request: get请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.Get"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines/{pipeline_id}".format(
project_id=request.project_id,
pipeline_id=request.pipeline_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def get_v_2(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.get_pipeline_v2_pb2.GetV2Request, int, str, int) -> pipeline_sdk.api.pipeline.get_pipeline_v2_pb2.GetV2Response
"""
获取流水线详情
:param request: get_v_2请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.get_pipeline_v2_pb2.GetV2Response
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.GetV2"
uri = "/api/pipeline/v2/projects/{project_id}/pipelines/{pipeline_id}".format(
project_id=request.project_id,
pipeline_id=request.pipeline_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.get_pipeline_v2_pb2.GetV2Response()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def get_trigger(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.get_trigger_pb2.GetTriggerRequest, int, str, int) -> pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
获取钩子详情
:param request: get_trigger请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.GetTrigger"
uri = "/api/pipeline/v1/triggers/{id}".format(
id=request.id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.trigger_pb2.Trigger()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def get_trigger_detail(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.get_trigger_detail_pb2.GetTriggerDetailRequest, int, str, int) -> pipeline_sdk.api.pipeline.get_trigger_detail_pb2.GetTriggerDetailResponse
"""
获取钩子相关信息
:param request: get_trigger_detail请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.get_trigger_detail_pb2.GetTriggerDetailResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.GetTriggerDetail"
uri = "/api/pipeline/v1/triggers/{id}/detail".format(
id=request.id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.get_trigger_detail_pb2.GetTriggerDetailResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def handle_hook(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.handle_hook_pb2.HandleHookRequest, int, str, int) -> pipeline_sdk.api.pipeline.handle_hook_pb2.HandleHookResponse
"""
webhook触发执行流水线
:param request: handle_hook请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.handle_hook_pb2.HandleHookResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.HandleHook"
uri = "/api/pipeline/v1/webhook/{provider}/hook/{token}".format(
provider=request.provider,
token=request.token,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="POST",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.handle_hook_pb2.HandleHookResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def list(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.list_pb2.ListRequest, int, str, int) -> pipeline_sdk.api.pipeline.list_pb2.ListResponse
"""
流水线列表
:param request: list请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.list_pb2.ListResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.List"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines".format(
project_id=request.project_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.list_pb2.ListResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def list_trigger(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.list_trigger_pb2.ListTriggerRequest, int, str, int) -> pipeline_sdk.api.pipeline.list_trigger_pb2.ListTriggerResponse
"""
钩子列表
:param request: list_trigger请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.list_trigger_pb2.ListTriggerResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.ListTrigger"
uri = "/api/pipeline/v1/triggers"
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.list_trigger_pb2.ListTriggerResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def list_v_2(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.list_v2_pb2.ListV2Request, int, str, int) -> pipeline_sdk.api.pipeline.list_v2_pb2.ListV2Response
"""
流水线列表
:param request: list_v_2请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.api.pipeline.list_v2_pb2.ListV2Response
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.ListV2"
uri = "/api/pipeline/v2/projects/{project_id}/pipelines".format(
project_id=request.project_id,
)
requestParam = request
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.api.pipeline.list_v2_pb2.ListV2Response()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def update(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.update_pb2.UpdateRequest, int, str, int) -> pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
修改流水线
:param request: update请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.Update"
uri = "/api/pipeline/v1/projects/{project_id}/pipelines/{pipeline_id}".format(
project_id=request.project_id,
pipeline_id=request.pipeline_id,
)
requestParam = request.pipeline
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="PUT",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.pipeline_pb2.Pipeline()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def update_trigger(self, request, org, user, timeout=10):
# type: (pipeline_sdk.api.pipeline.update_trigger_pb2.UpdateTriggerRequest, int, str, int) -> pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
更新流水线钩子
:param request: update_trigger请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: pipeline_sdk.model.pipeline.trigger_pb2.Trigger
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.pipeline.pipeline.UpdateTrigger"
uri = "/api/pipeline/v1/triggers/{id}".format(
id=request.id,
)
requestParam = request.trigger
rsp_obj = pipeline_sdk.utils.http_util.do_api_request(
method="PUT",
src_name="logic.pipeline_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = pipeline_sdk.model.pipeline.trigger_pb2.Trigger()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
| 37.672652
| 182
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0
| 7
|
e1146fd86604176a8073bf40b6435a0fca5870bf
| 87,031
|
py
|
Python
|
packages/contracts/tests/accounts.py
|
EthereumGeeks/dev
|
daf2d0fb3418cac564461d03c1a9fed4fdec3589
|
[
"CC-BY-4.0",
"MIT"
] | 133
|
2021-04-06T09:59:40.000Z
|
2022-03-29T17:53:12.000Z
|
packages/contracts/tests/accounts.py
|
EthereumGeeks/dev
|
daf2d0fb3418cac564461d03c1a9fed4fdec3589
|
[
"CC-BY-4.0",
"MIT"
] | 228
|
2021-04-05T14:19:34.000Z
|
2022-03-31T08:01:38.000Z
|
packages/contracts/tests/accounts.py
|
sparklingGibbon/dev
|
daf2d0fb3418cac564461d03c1a9fed4fdec3589
|
[
"CC-BY-4.0",
"MIT"
] | 169
|
2021-04-06T09:55:59.000Z
|
2022-03-31T08:20:06.000Z
|
def import_accounts(accounts):
accounts.add("0xbbfbee4961061d506ffbb11dfea64eba16355cbf1d9c29613126ba7fec0aed5d")
accounts.add("0x804365e293b9fab9bd11bddd39082396d56d30779efbb3ffb0a6089027902c4a")
accounts.add("0x1f52464c2fb44e9b7e0808f2c5fe56d87b73eb3bca0e72c66f9f74d7c6c9a81f")
accounts.add("0x905e216d8acdabbd095f11162327c5e6e80cc59a51283732cd4fe1299b33b7a6")
accounts.add("0xe21bbdc4c57125bec3e05467423dfc3da8754d862140550fc7b3d2833ad1bdeb")
accounts.add("0xb591fb79dd7065964210e7e527c87f97523da07ef8d16794f09750d5eef959b5")
accounts.add("0xfe613f76efbfd03a16624ed8d96777966770f353e83d6f7611c11fdfcdfa48d1")
accounts.add("0x52f94fdeaaf7c8551bda5924f2b52ff438125b9b5170c04ea2e268bd945ff155")
accounts.add("0xa26ebb1df46424945009db72c7a7ba034027450784b93f34000169b35fd3adaa")
accounts.add("0x3ff6c8dfd3ab60a14f2a2d4650387f71fe736b519d990073e650092faaa621fa")
accounts.add("0x99f34926ebf872c6689937d4d61cd35d50d3cbd9d6af255741785697c9f55cca")
accounts.add("0xb880cc5df5a9a04246e3de63c2ab16f79f22f98afab0a48ad2c8dcb392d97a32")
accounts.add("0xdfd40f887637d6cadfac1ed48e3cc96c02c32f3c7bad852ad42d6c5876776bd3")
accounts.add("0xf437253c0564b932c4ae87e906c4fb4b4358c3f110677455a4d3ee2c75597099")
accounts.add("0x3b9b37426f774566e3f75f7bfb43300b4a5bf25867656fe7f54124e8d171f4e1")
accounts.add("0x011de45c128617877e9ba772bb01575700a69da2794787838941a3856f8e3bae")
accounts.add("0x609f2a48fd2a66768c93069d25e148590b8d9f86a59cbefa815a4cd2934ff0b7")
accounts.add("0xaec60ce6011fa81651c71224fb41c59f6da3689b78580de6673c1cecc3ec8413")
accounts.add("0x1a0d45599090ca4bd43cf39201af9dcab14fb1b798ec380b15b2ed645792a6f3")
accounts.add("0x0115ab9a3f02d963f7001ba7e958496acd0280ff200e69fbccb44bb8ffea4f96")
accounts.add("0x19cb1796832575b3b2ba6aca2f509031f22fefaa986bb9a6e1cac30f63380454")
accounts.add("0x45d71b142293d8d608f0454f7e1d8a7b86670eb7831b2c9963fd3e0b631687df")
accounts.add("0xb8cf7163686d1fea0349fdc1e415d73db12dae51d31ff5cc9db95295060da153")
accounts.add("0x19a507e9d23c477388e9cd37cd1c0c7754c50a7e344fc2d81de94544227bc359")
accounts.add("0x3c223047eb639a54bdee2ed6885c80b432015c3f330abf418cd8f97997114a4f")
accounts.add("0x2eea31ec178a86e89ba10e03574fa14a051a5aa6b6d5975067348f0e7b4e16ce")
accounts.add("0xc51f14435db60762c3bb884ffffee8a5598d4d5c7888bdcef9454e7e3d3cf5cb")
accounts.add("0xe4a761acc064ccb672a77ba058f372d66bf3ed342eab6435d6bbdf901c61fdc3")
accounts.add("0x116d030d5c2a45387accab7fd7dc8d6cef58b036128a96742a58d708f1ea98da")
accounts.add("0xc9e37aac41ee3671a16947a62ea72135bdebda81557e6957b2c50518bbc430e9")
accounts.add("0x5c9bab38a3ba60c6a85dbae3bde7aff5ab4de1a229b658edbd58a2e8a34220b8")
accounts.add("0x77a0704df008979c305ad71c26c74e0009b7ff9fb93ee342c693d1a1fd84fc2d")
accounts.add("0xd0d72f0d0c61060895daf84af0be755605af372200ef5cf14f26aa342d8bbc49")
accounts.add("0x16e486f6ea89e5d7ea997fd641b6c8f6887a00c466ed574143d2c8913f3615dc")
accounts.add("0x700b4c2742586292300dcf0ffd39bdfc2f3e4a1054ec95c7f570c9feca7dc297")
accounts.add("0x17f3e3be69c0ecc3d54caa8cd6a23702ac1924cea90ea366ec7a3b725fbe8568")
accounts.add("0x2d7a1ae9bf76b7b46a4abcbe43bbfd33ae16cf738f124e5b398cd1c558750f96")
accounts.add("0x7e1675d3750a9e3a3684647c5958b503b8bc05fbb1d9cddb937778bc5c33315f")
accounts.add("0x57a0ca858a961ac260b870d05f4857bb45ffaf0189f7b651c5f2ecb0df455f94")
accounts.add("0x73535c82c647a3b225075c29c6cb40165584ba55e46b4f85a8817bcaf28ceadf")
accounts.add("0x1139579c50f280d9c74c82908158fc40d80f3ec892209dcb892af4a88104b796")
accounts.add("0x7cd19de27184d81888f7047a793d6ddd69dc418aa7153e9d81136662e0169fe1")
accounts.add("0xb5e62e6c33170e1ef8901ecf58bc57e3147a94bd7edf3acf3288f755c4f88e33")
accounts.add("0x8e5efe655f7059b0b1b0f890b3432185fb3d656d9ae97310943ec5482af20185")
accounts.add("0xb838e9f4bbafae3f8d045202a9caf696cb445404769b564b5f3d5ecdefa2eac8")
accounts.add("0x5833dddcc9c4c27a7956b488e650d8e6e1904a00be6b2d66e4ddd7083af1cb03")
accounts.add("0x5c2ee938f804df553bb58fac019560fe746046b2e97cb2b45e4a50e3b6017b17")
accounts.add("0xfe67c1d5ad13ff2c035708b8d5ae2443591d53905eac44ebf14768aed59e01f9")
accounts.add("0x44498a9aecb7a2c6f5347c92525563124484e58cae3bec4379f47fb12e62542b")
accounts.add("0xeeee56dfa5c3d7ae660a464520d77269f827f84b8035c8e60e94914dbb74fad2")
accounts.add("0xead0d78b315a9caf5a9a7aa2ae5e1d13fde04633ab04b51c7da322d35316ca0c")
accounts.add("0x4e2140ef599fc0e09bf532484c48e7482ddc17fbd0d414c26a56e56778574a04")
accounts.add("0x39356289457156aba38ec522cf08cd5c96c8f5170a842531688f024af131bd10")
accounts.add("0x198a814925d4230d10e71ee9967e43e56370a06ee0547bfbb2636b6f80e47a8c")
accounts.add("0xfe7d588816912210fab6d05cec3c60fa343b13d61efbde4b79ea6af78bb7a1a0")
accounts.add("0x728d60b7c1b53cd769a8e3a6d36696a28ab8047f39f4e4ab08bc647f5e13955b")
accounts.add("0xb0f01dc444b30881cd2ccf79b91c118d1771fc78a8ae8d1f72e355a1b7cfc599")
accounts.add("0x24a2c167e299f066c125320a9bcce1559cd9171073bda188d5ad35f075e194a9")
accounts.add("0x76ded0389bb45c60891925338f37ecdfc821fe711360c3c082cc6602cce0f933")
accounts.add("0x39b51a62842e27c802cf10247a9390350a1f5da704662939975b43c2d4c377d9")
accounts.add("0x2cc04170f3cb8af8991f1c8258dd1d55ecb03455522aab63ed0ce532d502b121")
accounts.add("0x68d803b6ad2d6618e1599095cbab3720dbdf63631d0db5d50a7ad38d7260ca65")
accounts.add("0x1328b37636e043b6bc42223760962e6be1ccd14a3a87a51706796215d3824722")
accounts.add("0xc21dd3370a9b5fc4274ca98c9645da2870323dd1200492b79d038a328f39e49b")
accounts.add("0xf0c0cb3f968fcb629978065a23a761bc70ed228da5c338a63046f6d745344616")
accounts.add("0x651d704bf49b59dfbcdce297b06fed2e39f00d91b5873d51e3534671b4894659")
accounts.add("0x634cf9beec67e44b781534820afdcb35f6b5946f586be6166b9e637d6e933a4f")
accounts.add("0xeac537272b4337e43299af63174753395a325a7a13054876833beef0eb657108")
accounts.add("0xde25439a3b033d900633d467936ebf6f9effa6b7439043fc1938e44b2700ef35")
accounts.add("0x580da4f4a2d7fdaccd4e3bf1a0e57b5ea6b69744b7d46bf34778b382969d783e")
accounts.add("0x009d944531c7ed7b5f9e16bea1319ee19e3504d4652b9b9e94626757381b0857")
accounts.add("0x519309ce53224a5ce5a1aefa6ea26ed8dc2e6f0a215d69d1e6f6d976d1b3ee52")
accounts.add("0x5d2c6aa42db8bc093beb7241d3b53a0b59560247bb25f4fdfbdfdb2a40566c19")
accounts.add("0x5afb55bd870d1a4dc7bcc603f21cb25e8bae3e79d45b05bee9d56180433c1a64")
accounts.add("0xdefd6f37a156dfeef13ba804be36fa0a6b64bf7584d1c98815138ef08ff67c0a")
accounts.add("0x4c179698c9e87ab041860e279ac0194502a2ca33abf59f8faf03d544cd128c9c")
accounts.add("0x77c8d569f4bf41cba86874812bc66f7d1c1d712e8042189ec924cdfb2af7a01a")
accounts.add("0x8205ecc37e4a1018f02b31063ebb34db541f02c599d8c4c9d781b77cddfddb5b")
accounts.add("0x83cc9c6be4d611bd30cf702dabb6129abd46d46b8e6bc2749c6fd79e9964105c")
accounts.add("0xbea5802d821c344b915bc0b27083b923469adb0ce65b0502d95fde74b37dd6a8")
accounts.add("0xcd93f6550159f551cfb44eda9441b550bd940727f969f065a0f96cadd3c966d3")
accounts.add("0x0c344289a7bfa9709069f7334241b62e8ea65c2da8329836909cc8b4da5ca462")
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0
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01487efee229b7725bbb24fbc560b130de90f8c5
| 119
|
py
|
Python
|
dashboard/rulesets/scheduler/app/env.py
|
airspot-dev/krules
|
0e402feef51c6189a163a62912480cfac0c438bb
|
[
"Apache-2.0"
] | 10
|
2021-02-26T13:00:22.000Z
|
2022-03-31T11:38:28.000Z
|
dashboard/rulesets/scheduler/app/env.py
|
airspot-dev/krules
|
0e402feef51c6189a163a62912480cfac0c438bb
|
[
"Apache-2.0"
] | 1
|
2021-08-03T10:21:12.000Z
|
2021-08-03T10:21:12.000Z
|
dashboard/rulesets/scheduler/app/env.py
|
airspot-dev/krules
|
0e402feef51c6189a163a62912480cfac0c438bb
|
[
"Apache-2.0"
] | null | null | null |
from django.apps import apps
from django.conf import settings
def init():
apps.populate(settings.INSTALLED_APPS)
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0
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3a38128c890e3b85ce63d22fb0b9e2626aa2911a
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|
py
|
Python
|
loldib/getratings/models/NA/na_karthus/na_karthus_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_karthus/na_karthus_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_karthus/na_karthus_mid.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Karthus_Mid_Aatrox(Ratings):
pass
class NA_Karthus_Mid_Ahri(Ratings):
pass
class NA_Karthus_Mid_Akali(Ratings):
pass
class NA_Karthus_Mid_Alistar(Ratings):
pass
class NA_Karthus_Mid_Amumu(Ratings):
pass
class NA_Karthus_Mid_Anivia(Ratings):
pass
class NA_Karthus_Mid_Annie(Ratings):
pass
class NA_Karthus_Mid_Ashe(Ratings):
pass
class NA_Karthus_Mid_AurelionSol(Ratings):
pass
class NA_Karthus_Mid_Azir(Ratings):
pass
class NA_Karthus_Mid_Bard(Ratings):
pass
class NA_Karthus_Mid_Blitzcrank(Ratings):
pass
class NA_Karthus_Mid_Brand(Ratings):
pass
class NA_Karthus_Mid_Braum(Ratings):
pass
class NA_Karthus_Mid_Caitlyn(Ratings):
pass
class NA_Karthus_Mid_Camille(Ratings):
pass
class NA_Karthus_Mid_Cassiopeia(Ratings):
pass
class NA_Karthus_Mid_Chogath(Ratings):
pass
class NA_Karthus_Mid_Corki(Ratings):
pass
class NA_Karthus_Mid_Darius(Ratings):
pass
class NA_Karthus_Mid_Diana(Ratings):
pass
class NA_Karthus_Mid_Draven(Ratings):
pass
class NA_Karthus_Mid_DrMundo(Ratings):
pass
class NA_Karthus_Mid_Ekko(Ratings):
pass
class NA_Karthus_Mid_Elise(Ratings):
pass
class NA_Karthus_Mid_Evelynn(Ratings):
pass
class NA_Karthus_Mid_Ezreal(Ratings):
pass
class NA_Karthus_Mid_Fiddlesticks(Ratings):
pass
class NA_Karthus_Mid_Fiora(Ratings):
pass
class NA_Karthus_Mid_Fizz(Ratings):
pass
class NA_Karthus_Mid_Galio(Ratings):
pass
class NA_Karthus_Mid_Gangplank(Ratings):
pass
class NA_Karthus_Mid_Garen(Ratings):
pass
class NA_Karthus_Mid_Gnar(Ratings):
pass
class NA_Karthus_Mid_Gragas(Ratings):
pass
class NA_Karthus_Mid_Graves(Ratings):
pass
class NA_Karthus_Mid_Hecarim(Ratings):
pass
class NA_Karthus_Mid_Heimerdinger(Ratings):
pass
class NA_Karthus_Mid_Illaoi(Ratings):
pass
class NA_Karthus_Mid_Irelia(Ratings):
pass
class NA_Karthus_Mid_Ivern(Ratings):
pass
class NA_Karthus_Mid_Janna(Ratings):
pass
class NA_Karthus_Mid_JarvanIV(Ratings):
pass
class NA_Karthus_Mid_Jax(Ratings):
pass
class NA_Karthus_Mid_Jayce(Ratings):
pass
class NA_Karthus_Mid_Jhin(Ratings):
pass
class NA_Karthus_Mid_Jinx(Ratings):
pass
class NA_Karthus_Mid_Kalista(Ratings):
pass
class NA_Karthus_Mid_Karma(Ratings):
pass
class NA_Karthus_Mid_Karthus(Ratings):
pass
class NA_Karthus_Mid_Kassadin(Ratings):
pass
class NA_Karthus_Mid_Katarina(Ratings):
pass
class NA_Karthus_Mid_Kayle(Ratings):
pass
class NA_Karthus_Mid_Kayn(Ratings):
pass
class NA_Karthus_Mid_Kennen(Ratings):
pass
class NA_Karthus_Mid_Khazix(Ratings):
pass
class NA_Karthus_Mid_Kindred(Ratings):
pass
class NA_Karthus_Mid_Kled(Ratings):
pass
class NA_Karthus_Mid_KogMaw(Ratings):
pass
class NA_Karthus_Mid_Leblanc(Ratings):
pass
class NA_Karthus_Mid_LeeSin(Ratings):
pass
class NA_Karthus_Mid_Leona(Ratings):
pass
class NA_Karthus_Mid_Lissandra(Ratings):
pass
class NA_Karthus_Mid_Lucian(Ratings):
pass
class NA_Karthus_Mid_Lulu(Ratings):
pass
class NA_Karthus_Mid_Lux(Ratings):
pass
class NA_Karthus_Mid_Malphite(Ratings):
pass
class NA_Karthus_Mid_Malzahar(Ratings):
pass
class NA_Karthus_Mid_Maokai(Ratings):
pass
class NA_Karthus_Mid_MasterYi(Ratings):
pass
class NA_Karthus_Mid_MissFortune(Ratings):
pass
class NA_Karthus_Mid_MonkeyKing(Ratings):
pass
class NA_Karthus_Mid_Mordekaiser(Ratings):
pass
class NA_Karthus_Mid_Morgana(Ratings):
pass
class NA_Karthus_Mid_Nami(Ratings):
pass
class NA_Karthus_Mid_Nasus(Ratings):
pass
class NA_Karthus_Mid_Nautilus(Ratings):
pass
class NA_Karthus_Mid_Nidalee(Ratings):
pass
class NA_Karthus_Mid_Nocturne(Ratings):
pass
class NA_Karthus_Mid_Nunu(Ratings):
pass
class NA_Karthus_Mid_Olaf(Ratings):
pass
class NA_Karthus_Mid_Orianna(Ratings):
pass
class NA_Karthus_Mid_Ornn(Ratings):
pass
class NA_Karthus_Mid_Pantheon(Ratings):
pass
class NA_Karthus_Mid_Poppy(Ratings):
pass
class NA_Karthus_Mid_Quinn(Ratings):
pass
class NA_Karthus_Mid_Rakan(Ratings):
pass
class NA_Karthus_Mid_Rammus(Ratings):
pass
class NA_Karthus_Mid_RekSai(Ratings):
pass
class NA_Karthus_Mid_Renekton(Ratings):
pass
class NA_Karthus_Mid_Rengar(Ratings):
pass
class NA_Karthus_Mid_Riven(Ratings):
pass
class NA_Karthus_Mid_Rumble(Ratings):
pass
class NA_Karthus_Mid_Ryze(Ratings):
pass
class NA_Karthus_Mid_Sejuani(Ratings):
pass
class NA_Karthus_Mid_Shaco(Ratings):
pass
class NA_Karthus_Mid_Shen(Ratings):
pass
class NA_Karthus_Mid_Shyvana(Ratings):
pass
class NA_Karthus_Mid_Singed(Ratings):
pass
class NA_Karthus_Mid_Sion(Ratings):
pass
class NA_Karthus_Mid_Sivir(Ratings):
pass
class NA_Karthus_Mid_Skarner(Ratings):
pass
class NA_Karthus_Mid_Sona(Ratings):
pass
class NA_Karthus_Mid_Soraka(Ratings):
pass
class NA_Karthus_Mid_Swain(Ratings):
pass
class NA_Karthus_Mid_Syndra(Ratings):
pass
class NA_Karthus_Mid_TahmKench(Ratings):
pass
class NA_Karthus_Mid_Taliyah(Ratings):
pass
class NA_Karthus_Mid_Talon(Ratings):
pass
class NA_Karthus_Mid_Taric(Ratings):
pass
class NA_Karthus_Mid_Teemo(Ratings):
pass
class NA_Karthus_Mid_Thresh(Ratings):
pass
class NA_Karthus_Mid_Tristana(Ratings):
pass
class NA_Karthus_Mid_Trundle(Ratings):
pass
class NA_Karthus_Mid_Tryndamere(Ratings):
pass
class NA_Karthus_Mid_TwistedFate(Ratings):
pass
class NA_Karthus_Mid_Twitch(Ratings):
pass
class NA_Karthus_Mid_Udyr(Ratings):
pass
class NA_Karthus_Mid_Urgot(Ratings):
pass
class NA_Karthus_Mid_Varus(Ratings):
pass
class NA_Karthus_Mid_Vayne(Ratings):
pass
class NA_Karthus_Mid_Veigar(Ratings):
pass
class NA_Karthus_Mid_Velkoz(Ratings):
pass
class NA_Karthus_Mid_Vi(Ratings):
pass
class NA_Karthus_Mid_Viktor(Ratings):
pass
class NA_Karthus_Mid_Vladimir(Ratings):
pass
class NA_Karthus_Mid_Volibear(Ratings):
pass
class NA_Karthus_Mid_Warwick(Ratings):
pass
class NA_Karthus_Mid_Xayah(Ratings):
pass
class NA_Karthus_Mid_Xerath(Ratings):
pass
class NA_Karthus_Mid_XinZhao(Ratings):
pass
class NA_Karthus_Mid_Yasuo(Ratings):
pass
class NA_Karthus_Mid_Yorick(Ratings):
pass
class NA_Karthus_Mid_Zac(Ratings):
pass
class NA_Karthus_Mid_Zed(Ratings):
pass
class NA_Karthus_Mid_Ziggs(Ratings):
pass
class NA_Karthus_Mid_Zilean(Ratings):
pass
class NA_Karthus_Mid_Zyra(Ratings):
pass
| 16.026379
| 46
| 0.77151
| 972
| 6,683
| 4.878601
| 0.151235
| 0.203712
| 0.407423
| 0.494728
| 0.808941
| 0.808941
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166243
| 6,683
| 416
| 47
| 16.064904
| 0.851041
| 0
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| 0.498195
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| 0
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| 1
| 0
| true
| 0.498195
| 0.00361
| 0
| 0.501805
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 8
|
3a8205860303ff79756d4b800c176c20d61bfb79
| 3,704
|
py
|
Python
|
tests/test1.py
|
vlrusu/ads124s08
|
887e451dbfa387f4f9a2082a914682c55915d1fd
|
[
"MIT"
] | null | null | null |
tests/test1.py
|
vlrusu/ads124s08
|
887e451dbfa387f4f9a2082a914682c55915d1fd
|
[
"MIT"
] | null | null | null |
tests/test1.py
|
vlrusu/ads124s08
|
887e451dbfa387f4f9a2082a914682c55915d1fd
|
[
"MIT"
] | null | null | null |
# from pathlib import Path
import sys
from time import sleep
# path_root = Path(__file__).parents[2]
# sys.path.append(str(path_root))
#print(sys.path)
from ads124s08.ads124s08 import ads124s08
a = ads124s08.ads124s08(0,0)
a.open()
a._command([ads124s08.ADS124S08_CMD_NOP,ads124s08.ADS124S08_CMD_WAKEUP])
a._command([ads124s08.ADS124S08_CMD_NOP,ads124s08.ADS124S08_CMD_RESET])
#set registers disables PGA, internal oscillator, single shot, low latency (default) 20SPS (default) internal 2.5V ref
a._writereg(ads124s08.ADS124S08_REG_PGA,[0x08,0x34,0x0A])
#sys 8 samples(default) disable CRC at the end and enable STATUS in preamble (4 words in DATAREAD)
a._writereg(ads124s08.ADS124S08_REG_SYS,[0x91]) #was 0x11 here
#AIN0 + and AINCOM -
#a._writereg(ads124s08.ADS124S08_REG_INPMUX,[0xC])
print("ID=", hex(a._readDeviceID()))
print("STATUS=",hex(a._readStatus()))
print("GAIN=",hex(a._readGain()))
print("INPMUX=",hex(a._readIMUX()))
print("REFERENCE=",hex(a._readInternalRef()))
sleep(0.3)
a._command([ads124s08.ADS124S08_CMD_START])
sleep(0.3)
v=a._readData()
print ("DATA="," ".join(hex(x) for x in v))
voltage = v[0]<<16 | v[1] <<8 | v[2]
print ("DATAVOLTAGE=",hex(voltage))
#MCP3426_FS = 2^24
MCP3426_REFV = 2.5
if voltage > 0x7FFFFF:
# print("here",float(voltage-0x800000),2**23,float(voltage-0x800000)/float(2**23))
voltage = -MCP3426_REFV + float(voltage - 0x800000)*MCP3426_REFV/float(2**23)
else:
print("done")
voltage = float(voltage)*MCP3426_REFV/(2**23)
print ("VOLTAGE=",voltage)
a._command([ads124s08.ADS124S08_CMD_STOP])
#set IDAC to 500uA
a._writereg(ads124s08.ADS124S08_REG_IDACMAG,[0x7])
#set IDAC1 to AIN0
channel = int(sys.argv[1])
reg = (0xF << 4) | channel
a._writereg(ads124s08.ADS124S08_REG_IDACMUX,[reg])
#set IMUXP+ to AIN0 and IMUXP- to AINCOM
reg = (channel << 4) | 0xC
a._writereg(ads124s08.ADS124S08_REG_INPMUX,[reg])
#sys 8 samples(default) disable CRC at the end and enable STATUS in preamble (4 words in DATAREAD)
a._writereg(ads124s08.ADS124S08_REG_SYS,[0x11]) #was 0x11 here
print("ID=", hex(a._readDeviceID()))
print("STATUS=",hex(a._readStatus()))
print("GAIN=",hex(a._readGain()))
print("INPMUX=",hex(a._readIMUX()))
print("REFERENCE=",hex(a._readInternalRef()))
a._command([ads124s08.ADS124S08_CMD_START])
sleep(0.3)
v=a._readData()
print ("DATA="," ".join(hex(x) for x in v))
voltage = v[0]<<16 | v[1] <<8 | v[2]
print ("DATAVOLTAGE=",hex(voltage))
#MCP3426_FS = 2^24
MCP3426_REFV = 2.5
if voltage > 0x7FFFFF:
# print("here",float(voltage-0x800000),2**23,float(voltage-0x800000)/float(2**23))
voltage = -MCP3426_REFV + float(voltage - 0x800000)*MCP3426_REFV/float(2**23)
else:
print("done")
voltage = float(voltage)*MCP3426_REFV/(2**23)
print ("VOLTAGE=",voltage)
a._command([ads124s08.ADS124S08_CMD_STOP])
for i in range(0,11):
reg = (i << 4) | 0xC
a._writereg(ads124s08.ADS124S08_REG_INPMUX,[reg])
#sys 8 samples(default) disable CRC at the end and enable STATUS in preamble (4 words in DATAREAD)
a._writereg(ads124s08.ADS124S08_REG_SYS,[0x11]) #was 0x11 here
a._command([ads124s08.ADS124S08_CMD_START])
sleep(0.3)
v=a._readData()
# print ("DATA="," ".join(hex(x) for x in v))
voltage = v[0]<<16 | v[1] <<8 | v[2]
# print ("DATAVOLTAGE=",hex(voltage))
#MCP3426_FS = 2^24
MCP3426_REFV = 2.5
if voltage > 0x7FFFFF:
# print("here",float(voltage-0x800000),2**23,float(voltage-0x800000)/float(2**23))
voltage = -MCP3426_REFV + float(voltage - 0x800000)*MCP3426_REFV/float(2**23)
else:
voltage = float(voltage)*MCP3426_REFV/(2**23)
print ("channel=",i,"VOLTAGE=",voltage)
a._command([ads124s08.ADS124S08_CMD_STOP])
| 29.396825
| 118
| 0.703564
| 561
| 3,704
| 4.486631
| 0.203209
| 0.150179
| 0.083433
| 0.096544
| 0.810886
| 0.775129
| 0.760826
| 0.760826
| 0.727056
| 0.684942
| 0
| 0.148423
| 0.12689
| 3,704
| 125
| 119
| 29.632
| 0.62987
| 0.294006
| 0
| 0.757143
| 0
| 0
| 0.054054
| 0
| 0
| 0
| 0.032432
| 0
| 0
| 1
| 0
| false
| 0
| 0.042857
| 0
| 0.042857
| 0.271429
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
3ae3ddc339a9b4039d66c5d5d40202d921797a93
| 5,601
|
py
|
Python
|
src/cmcandy/Python_language_Answers/offer2_076.py
|
ch98road/leetcode
|
a9b4be54a169b30f6711809b892dd1f79f2a17e7
|
[
"MIT"
] | null | null | null |
src/cmcandy/Python_language_Answers/offer2_076.py
|
ch98road/leetcode
|
a9b4be54a169b30f6711809b892dd1f79f2a17e7
|
[
"MIT"
] | null | null | null |
src/cmcandy/Python_language_Answers/offer2_076.py
|
ch98road/leetcode
|
a9b4be54a169b30f6711809b892dd1f79f2a17e7
|
[
"MIT"
] | 1
|
2020-11-26T03:01:12.000Z
|
2020-11-26T03:01:12.000Z
|
# from typing import List
# class Solution:
# def findKthLargest(self, nums: List[int], k: int) -> int:
# # 撸一个大根堆
# self.nums = nums
# self.length = len(nums)
# self.k = k
# # 构造一个堆
# self.buildPile()
# for index in range(k - 1):
# # 顶点最大值弹出
# self.nums[self.length - 1], self.nums[0] = self.nums[0], self.nums[self.length - 1]
# self.length -= 1
# self.modifyTree(0, self.length)
# return self.nums[0]
# def buildPile(self):
# index = self.length - 1
# tmp = 0
# if index % 2 == 1:
# if self.nums[index] > self.nums[index // 2]:
# # 交换
# self.nums[index], self.nums[index // 2] = self.nums[index // 2], self.nums[index]
# index -= 1
# while index > 0:
# # 比较得两自问题的较大者
# if self.nums[index] > self.nums[index - 1]:
# tmp = index
# else:
# tmp = index - 1
# # 如果底部较大，上浮
# if self.nums[tmp] > self.nums[index // 2 - 1]:
# self.nums[tmp], self.nums[index // 2 - 1] = self.nums[index // 2 - 1], self.nums[tmp]
# self.modifyTree(tmp)
# index -= 2
# def modifyTree(self, root=0, length=None):
# if length is None:
# length = self.length
# # 假设下方子树都已满足大根堆，然后只有顶端不满足
# if root * 2 + 1 >= length:
# # 下方没有子节点了，直接返回
# return
# elif root * 2 + 2 >= length:
# # 下方右侧没有
# if self.nums[root * 2 + 1] > self.nums[root]:
# self.nums[root], self.nums[root * 2 + 1] = self.nums[root * 2 + 1], self.nums[root]
# else:
# if self.nums[root * 2 + 1] > self.nums[root * 2 + 2]:
# tmp = root * 2 + 1
# else:
# tmp = root * 2 + 2
# if self.nums[tmp] > self.nums[root]:
# self.nums[root], self.nums[tmp] = self.nums[tmp], self.nums[root]
# self.modifyTree(tmp, length)
# # 调整完毕，返回
# return
# if __name__ == '__main__':
# s = Solution()
# # s.nums = [3,2,1,5,6,4]
# # s.findKthLargest([3, 2, 1, 5, 6, 4], 3)
# s.findKthLargest([2, 1], 2)
# # s.buildPile()
# pass
import sys
from typing import List
class Solution:
def findKthLargest(self, nums: List[int], k: int, length: int) -> int:
# 撸一个大根堆
self.nums = nums
self.length = length
self.k = k
# 构造一个堆
self.buildPile()
for index in range(k - 1):
# 顶点最大值弹出
self.nums[self.length -
1], self.nums[0] = self.nums[0], self.nums[self.length -
1]
self.length -= 1
self.modifyTree(0, self.length)
return self.nums[0]
def buildPile(self):
index = self.length - 1
tmp = 0
if index % 2 == 1:
if self.nums[index] > self.nums[index // 2]:
# 交换
self.nums[index], self.nums[index //
2] = self.nums[index //
2], self.nums[index]
index -= 1
while index > 0:
# 比较得两自问题的较大者
if self.nums[index] > self.nums[index - 1]:
tmp = index
else:
tmp = index - 1
# 如果底部较大，上浮
if self.nums[tmp] > self.nums[index // 2 - 1]:
self.nums[tmp], self.nums[index // 2 -
1] = self.nums[index // 2 -
1], self.nums[tmp]
self.modifyTree(tmp)
index -= 2
def modifyTree(self, root=0, length=None):
if length is None:
length = self.length
# 假设下方子树都已满足大根堆，然后只有顶端不满足
if root * 2 + 1 >= length:
# 下方没有子节点了，直接返回
return
elif root * 2 + 2 >= length:
# 下方右侧没有
if self.nums[root * 2 + 1] > self.nums[root]:
self.nums[root], self.nums[root * 2 +
1] = self.nums[root * 2 +
1], self.nums[root]
else:
if self.nums[root * 2 + 1] > self.nums[root * 2 + 2]:
tmp = root * 2 + 1
else:
tmp = root * 2 + 2
if self.nums[tmp] > self.nums[root]:
self.nums[root], self.nums[tmp] = self.nums[tmp], self.nums[
root]
self.modifyTree(tmp, length)
# 调整完毕，返回
return
s = Solution()
# for line in sys.stdin:
# # a = list(map(int, line.split()))
# a = line.split()
# a[0], a[1] = int(a[0]), int(a[1])
# # b = list(map(int, sys.stdin.readline().split()))
# b = sys.stdin.readline().split()
# for index in range(a[0]):
# b[index] = int(b[index])
# print(s.findKthLargest(b, a[1], a[0]))
for line in sys.stdin:
a = list(map(int, line.rstrip().split()))
b = list(map(int, sys.stdin.readline().rstrip().split()))
# print(s.findKthLargest(b, a[1], a[0]))
b.sort()
print(b[a[0]])
tmp = 0
for line in sys.stdin:
if tmp == 0:
a = list(map(int, line.split()))
tmp = 1
else:
b = list(map(int, line.split()))
print(s.findKthLargest(b, a[1], a[0]))
tmp = 0
| 33.538922
| 103
| 0.434744
| 669
| 5,601
| 3.627803
| 0.09716
| 0.230738
| 0.117841
| 0.057684
| 0.899464
| 0.893696
| 0.871034
| 0.848785
| 0.811702
| 0.78698
| 0
| 0.037689
| 0.422067
| 5,601
| 167
| 104
| 33.538922
| 0.712079
| 0.474558
| 0
| 0.157143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.042857
| false
| 0
| 0.028571
| 0
| 0.128571
| 0.028571
| 0
| 0
| 0
| null | 1
| 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
| 7
|
c90abb6f0a17b5e65257f676a8a3383b65efb625
| 207
|
py
|
Python
|
codewars/7kyu/amrlotfy77/Recursive reverse string/test_bench.py
|
ictcubeMENA/Training_one
|
dff6bee96ba42babe4888e5cf9a9448a6fd93fc3
|
[
"MIT"
] | null | null | null |
codewars/7kyu/amrlotfy77/Recursive reverse string/test_bench.py
|
ictcubeMENA/Training_one
|
dff6bee96ba42babe4888e5cf9a9448a6fd93fc3
|
[
"MIT"
] | 2
|
2019-01-22T10:53:42.000Z
|
2019-01-31T08:02:48.000Z
|
codewars/7kyu/amrlotfy77/Recursive reverse string/test_bench.py
|
ictcubeMENA/Training_one
|
dff6bee96ba42babe4888e5cf9a9448a6fd93fc3
|
[
"MIT"
] | 13
|
2019-01-22T10:37:42.000Z
|
2019-01-25T13:30:43.000Z
|
from main import reverse, reverse1
def test1(benchmark):
assert benchmark(reverse, "hello world") == "dlrow olleh"
def test(benchmark):
assert benchmark(reverse1, "hello world") == "dlrow olleh"
| 20.7
| 62
| 0.710145
| 25
| 207
| 5.88
| 0.56
| 0.204082
| 0.326531
| 0.272109
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017442
| 0.169082
| 207
| 9
| 63
| 23
| 0.837209
| 0
| 0
| 0
| 0
| 0
| 0.21256
| 0
| 0
| 0
| 0
| 0
| 0.4
| 1
| 0.4
| false
| 0
| 0.2
| 0
| 0.6
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
c92b0055a69d5451b2688fd59b316577d6a95878
| 3,022
|
py
|
Python
|
Whois.py
|
dicky077/Termux-Whois
|
c9033b9af4bafec13414b710cb753b3de4b4c7e2
|
[
"Apache-2.0"
] | null | null | null |
Whois.py
|
dicky077/Termux-Whois
|
c9033b9af4bafec13414b710cb753b3de4b4c7e2
|
[
"Apache-2.0"
] | null | null | null |
Whois.py
|
dicky077/Termux-Whois
|
c9033b9af4bafec13414b710cb753b3de4b4c7e2
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python2
#Author Tool By Dicky077
import marshal
exec(marshal.loads('c\x00\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00@\x00\x00\x00su\x00\x00\x00d\x00\x00d\x01\x00l\x00\x00Z\x00\x00d\x00\x00d\x01\x00l\x01\x00Z\x01\x00y\x10\x00d\x00\x00d\x01\x00l\x02\x00Z\x02\x00Wn\x19\x00\x01\x01\x01d\x02\x00GHe\x01\x00j\x03\x00d\x03\x00\x83\x01\x00\x01n\x01\x00Xe\x00\x00j\x04\x00d\x04\x00\x83\x01\x00\x01d\x05\x00\x84\x00\x00Z\x05\x00d\x06\x00\x84\x00\x00Z\x06\x00e\x05\x00\x83\x00\x00\x01e\x06\x00\x83\x00\x00\x01d\x01\x00S(\x07\x00\x00\x00i\xff\xff\xff\xffNsF\x00\x00\x00Module whois is not installed, run command (pip2 install python-whois)i\x00\x00\x00\x00t\x05\x00\x00\x00clearc\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00C\x00\x00\x00s\t\x00\x00\x00d\x01\x00GHd\x00\x00S(\x02\x00\x00\x00NsQ\x01\x00\x00\x1b[1;96m\n __ _\n _ __/ /_ ____ (_)____\n | | /| / / __ \\/ __ \\/ / ___/\n | |/ |/ / / / / /_/ / (__ )\n |__/|__/_/ /_/\\____/_/____/\n \x1b[1;92mWhois \x1b[1;91mCode By \x1b[1;93mDicky077\x1b[1;92m\n _______________________________\n |_______________________________|\n\n \x1b[1;93mWhois Client For Termux\n(\x00\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00t\x00\x00\x00\x00t\x06\x00\x00\x00banner\r\x00\x00\x00s\x02\x00\x00\x00\x00\x0cc\x00\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00C\x00\x00\x00s\xb6\x00\x00\x00y\x10\x00t\x00\x00d\x01\x00\x83\x01\x00}\x00\x00Wn@\x00\x04t\x01\x00k\n\x00r0\x00\x01\x01\x01t\x02\x00j\x03\x00d\x02\x00\x83\x01\x00\x01n#\x00\x04t\x04\x00k\n\x00rR\x00\x01\x01\x01d\x03\x00GHt\x02\x00j\x03\x00d\x02\x00\x83\x01\x00\x01n\x01\x00Xt\x05\x00t\x06\x00j\x06\x00|\x00\x00\x83\x01\x00\x83\x01\x00}\x01\x00d\x04\x00j\x07\x00|\x01\x00\x83\x01\x00}\x02\x00|\x02\x00j\x08\x00d\x05\x00d\x04\x00\x83\x02\x00j\x08\x00d\x06\x00d\x04\x00\x83\x02\x00j\x08\x00d\x07\x00d\x04\x00\x83\x02\x00j\x08\x00d\x08\x00d\x04\x00\x83\x02\x00}\x03\x00|\x03\x00GHd\x00\x00S(\t\x00\x00\x00Ns\x12\x00\x00\x00\x1b[1;92mURL >\x1b[00m i\x00\x00\x00\x00s\x01\x00\x00\x00\nR\x01\x00\x00\x00t\x01\x00\x00\x00{t\x01\x00\x00\x00}t\x01\x00\x00\x00,t\x01\x00\x00\x00"(\t\x00\x00\x00t\t\x00\x00\x00raw_inputt\x11\x00\x00\x00KeyboardInterruptt\x03\x00\x00\x00syst\x04\x00\x00\x00exitt\x08\x00\x00\x00EOFErrort\x03\x00\x00\x00strt\x05\x00\x00\x00whoist\x04\x00\x00\x00joint\x07\x00\x00\x00replace(\x04\x00\x00\x00t\x03\x00\x00\x00urlt\r\x00\x00\x00dirty_responet\x03\x00\x00\x00gett\r\x00\x00\x00clean_respone(\x00\x00\x00\x00(\x00\x00\x00\x00R\x01\x00\x00\x00R\x12\x00\x00\x00\x1b\x00\x00\x00s\x16\x00\x00\x00\x00\x01\x03\x01\x10\x01\r\x01\x10\x01\r\x01\x05\x01\x11\x01\x15\x01\x0f\x016\x01(\x07\x00\x00\x00t\x02\x00\x00\x00osR\t\x00\x00\x00R\r\x00\x00\x00R\n\x00\x00\x00t\x06\x00\x00\x00systemR\x02\x00\x00\x00R\x12\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00R\x01\x00\x00\x00t\x08\x00\x00\x00<module>\x03\x00\x00\x00s\x16\x00\x00\x00\x0c\x01\x0c\x01\x03\x01\x10\x01\x03\x01\x05\x01\x11\x02\r\x02\t\x0e\t\r\x07\x01'))
#done
| 431.714286
| 2,951
| 0.73362
| 625
| 3,022
| 3.3776
| 0.1856
| 0.37802
| 0.27712
| 0.238749
| 0.392705
| 0.28991
| 0.215064
| 0.180483
| 0.119375
| 0.119375
| 0
| 0.377111
| 0.04004
| 3,022
| 6
| 2,952
| 503.666667
| 0.350569
| 0.015884
| 0
| 0
| 0
| 0.5
| 0.985527
| 0.880848
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
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| 0
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| 0
| 1
| 1
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| 1
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| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 13
|
a352faa213d0c378e4725867be7c9f827601691e
| 1,189
|
py
|
Python
|
laspec/snstat.py
|
hypergravity/laspec
|
e27a3d858f41d2abff7a03007709f775521bc37b
|
[
"MIT"
] | 9
|
2019-08-15T06:42:48.000Z
|
2021-11-23T01:58:31.000Z
|
laspec/snstat.py
|
hypergravity/laspec
|
e27a3d858f41d2abff7a03007709f775521bc37b
|
[
"MIT"
] | null | null | null |
laspec/snstat.py
|
hypergravity/laspec
|
e27a3d858f41d2abff7a03007709f775521bc37b
|
[
"MIT"
] | 7
|
2019-12-27T08:19:38.000Z
|
2021-11-23T10:16:36.000Z
|
import numpy as np
"""
{'p_gauss_1': array([ 0.07349721, -0.60647022, 1.97806105, -3.22994084, 2.72007585,
-0.92812989]),
'p_gauss_2': array([ 0.08434813, -0.69694429, 2.28047526, -3.73821453, 3.15612997,
-0.99158461]),
'p_q': array([ 0.05712779, -0.47050592, 1.56888875, -2.75649024, 2.71900493,
-0.28637106]),
'p_std': array([ 0.08344418, -0.68630504, 2.21084202, -3.50529186, 2.77854093,
0.08576048])}
"""
def eval_xi_1(n):
""" convert to MAD """
p = np.array([0.07349721, -0.60647022, 1.97806105, -3.22994084, 2.72007585, -0.92812989])
return 10. ** np.polyval(p, np.log10(n))
def eval_xi_2(n):
""" convert to MAD """
p = np.array([0.08434813, -0.69694429, 2.28047526, -3.73821453, 3.15612997, -0.99158461])
return 10. ** np.polyval(p, np.log10(n))
def eval_zeta_q(n):
""" convert to std """
p = np.array([0.05712779, -0.47050592, 1.56888875, -2.75649024, 2.71900493, -0.28637106])
return np.polyval(p, np.log10(n))
def eval_zeta_std(n):
""" convert to std """
p = np.array([0.08344418, -0.68630504, 2.21084202, -3.50529186, 2.77854093, 0.08576048])
return np.polyval(p, np.log10(n))
| 32.135135
| 93
| 0.614802
| 188
| 1,189
| 3.81383
| 0.260638
| 0.066946
| 0.055788
| 0.050209
| 0.927476
| 0.927476
| 0.927476
| 0.885635
| 0.796374
| 0.750349
| 0
| 0.459959
| 0.180824
| 1,189
| 36
| 94
| 33.027778
| 0.276181
| 0.052145
| 0
| 0.307692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.307692
| false
| 0
| 0.076923
| 0
| 0.692308
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
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| 1
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| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 10
|
a39c97f2a42b7c8a52584deeb27bcacb1bb81939
| 201
|
py
|
Python
|
plotly/graph_objs/surface/contours/__init__.py
|
gnestor/plotly.py
|
a8ae062795ddbf9867b8578fe6d9e244948c15ff
|
[
"MIT"
] | 12
|
2020-04-18T18:10:22.000Z
|
2021-12-06T10:11:15.000Z
|
plotly/graph_objs/surface/contours/__init__.py
|
gnestor/plotly.py
|
a8ae062795ddbf9867b8578fe6d9e244948c15ff
|
[
"MIT"
] | 27
|
2020-04-28T21:23:12.000Z
|
2021-06-25T15:36:38.000Z
|
plotly/graph_objs/surface/contours/__init__.py
|
gnestor/plotly.py
|
a8ae062795ddbf9867b8578fe6d9e244948c15ff
|
[
"MIT"
] | 6
|
2020-04-18T23:07:08.000Z
|
2021-11-18T07:53:06.000Z
|
from ._z import Z
from plotly.graph_objs.surface.contours import z
from ._y import Y
from plotly.graph_objs.surface.contours import y
from ._x import X
from plotly.graph_objs.surface.contours import x
| 28.714286
| 48
| 0.820896
| 36
| 201
| 4.416667
| 0.277778
| 0.188679
| 0.283019
| 0.358491
| 0.754717
| 0.754717
| 0.754717
| 0
| 0
| 0
| 0
| 0
| 0.119403
| 201
| 6
| 49
| 33.5
| 0.898305
| 0
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| true
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| 1
| 0
| 1
| 0
|
0
| 8
|
a39dba685565c047e6e53f19e1a9501f46193c4e
| 15,757
|
py
|
Python
|
aoc2019/day08/2.py
|
shoreofwonder/adventofcode
|
15fd2f761533a48b456e510b0a59f7cbc64e8e91
|
[
"MIT"
] | null | null | null |
aoc2019/day08/2.py
|
shoreofwonder/adventofcode
|
15fd2f761533a48b456e510b0a59f7cbc64e8e91
|
[
"MIT"
] | null | null | null |
aoc2019/day08/2.py
|
shoreofwonder/adventofcode
|
15fd2f761533a48b456e510b0a59f7cbc64e8e91
|
[
"MIT"
] | null | null | null |
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1"
def scan(img):
ncols = 25
nrows = 6
ptr = 0
layers = []
while ptr < len(img):
layer = []
for row in range(nrows):
layer.append(img[ptr:ptr+ncols])
ptr += ncols
layers.append(layer)
return layers
def count(char, s):
return len([c for c in s if c == char])
layers = scan(img)
result = list('2'*(25*6))
for layer in layers:
s = ''.join(layer)
for i in range(len(s)):
print(len(s), len(result))
if s[i] != '2' and result[i] == '2':
result[i] = s[i]
result = ''.join(result)
result = result.replace('2', ' ')
result = result.replace('1', 'X')
result = result.replace('0', ' ')
result_img = scan(result)[0]
print('\n'.join(result_img))
| 437.694444
| 15,008
| 0.977407
| 113
| 15,757
| 136.274336
| 0.318584
| 0.003117
| 0.003702
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.966152
| 0.013772
| 15,757
| 36
| 15,009
| 437.694444
| 0.024775
| 0
| 0
| 0
| 0
| 0
| 0.952596
| 0.951897
| 0
| 1
| 0
| 0
| 0
| 1
| 0.068966
| false
| 0
| 0
| 0.034483
| 0.137931
| 0.068966
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
a39e1f6fba6fe6f3e96d76ef992613d931d2d3c1
| 8,597
|
py
|
Python
|
pizza_cutter/slice_utils/tests/test_symmetrize.py
|
beckermr/pizza-cutter
|
04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6
|
[
"BSD-3-Clause"
] | null | null | null |
pizza_cutter/slice_utils/tests/test_symmetrize.py
|
beckermr/pizza-cutter
|
04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6
|
[
"BSD-3-Clause"
] | 194
|
2018-10-24T23:40:47.000Z
|
2021-11-17T16:02:35.000Z
|
pizza_cutter/slice_utils/tests/test_symmetrize.py
|
beckermr/pizza-cutter
|
04eefd2d4b2a63975fe809c60b5c8e7e3fcf26c6
|
[
"BSD-3-Clause"
] | null | null | null |
import numpy as np
from ..symmetrize import (
symmetrize_bmask,
symmetrize_weight)
def test_symmetrize_weight():
weight = np.ones((5, 5))
weight[:, 0] = 0
symmetrize_weight(weight=weight)
assert np.all(weight[:, 0] == 0)
assert np.all(weight[-1, :] == 0)
def test_symmetrize_weight_no_sym_mask():
weight = np.ones((5, 5))
weight[:, 0] = 0
mask = np.ones_like(weight).astype(bool)
mask[3:5, 0] = False
mask = ~mask
symmetrize_weight(weight=weight, no_sym_mask=mask)
assert np.all(weight[:, 0] == 0)
assert np.all(weight[-1, :3] == 0)
assert np.all(weight[-1, 3:] == 1)
def test_symmetrize_weight_angle():
weight = np.ones((64, 64))
weight[:, 0] = 0
weight_orig = weight.copy()
weights = []
for angle in [45, 90, 135, 180, 225, 270, 315]:
weight_r = weight_orig.copy()
symmetrize_weight(weight=weight_r, angle=angle)
weights.append(weight_r)
msk = weight_r == 0
weight[msk] = 0
assert np.array_equal(weight, np.rot90(weight))
assert np.array_equal(weight, np.rot90(np.rot90(weight)))
assert np.array_equal(weight, np.rot90(np.rot90(np.rot90(weight))))
if False:
import matplotlib.pyplot as plt
fig, axs = plt.subplots(nrows=3, ncols=3)
axs = axs.ravel()
weights.append(weight_orig)
weights.append(weight)
for i, bm in enumerate(weights):
ax = axs[i]
ax.pcolormesh(bm)
ax.set_aspect(1)
import pdb
pdb.set_trace()
def test_symmetrize_weight_angle_no_sym_mask():
weight = np.ones((64, 64))
weight[:, 0] = 0
weight_orig = weight.copy()
mask = np.ones_like(weight).astype(bool)
mask[55:, 0] = False
mask = ~mask
for angle in [90, 180, 270]:
weight_r = weight_orig.copy()
symmetrize_weight(weight=weight_r, angle=angle, no_sym_mask=mask)
assert np.all(weight_r[:, 0] == 0)
if angle == 90:
assert np.all(weight_r[-1, :55] == 0)
assert np.all(weight_r[-1, 55:] == 1)
elif angle == 180:
assert np.all(weight_r[-55:, -1] == 0)
assert np.all(weight_r[:-55, -1] == 1)
elif angle == 270:
assert np.all(weight_r[0, -55:] == 0)
# this test overalps with index [0, 0] that was weight zero start with
assert np.all(weight_r[0, 1:-55] == 1)
else:
assert False, "missed an assert!"
def test_symmetrize_bmask():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
symmetrize_bmask(bmask=bmask)
assert np.array_equal(
bmask,
[[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 3, 1]])
def test_symmetrize_bmask_sym_flags():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask[0, -1] = 2**9
symflags = (2**0) | (2**1)
symmetrize_bmask(bmask=bmask, sym_flags=symflags)
assert np.array_equal(
bmask,
[[1, 0, 2, 2**9],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 3, 1]])
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask[0, -1] = 2**9
symmetrize_bmask(bmask=bmask)
assert np.array_equal(
bmask,
[[1 | 2**9, 0, 2, 2**9],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 3, 1]])
def test_symmetrize_bmask_no_sym_mask():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
mask = np.ones_like(bmask).astype(bool)
mask[2:4, 0] = False
mask = ~mask
symmetrize_bmask(bmask=bmask, no_sym_mask=mask)
assert np.array_equal(
bmask,
[[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 2, 0]])
def test_symmetrize_bmask_no_sym_mask_sym_flags():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask[0, -1] = 2**9
symflags = (2**0) | (2**1)
mask = np.ones_like(bmask).astype(bool)
mask[2:4, 0] = False
mask = ~mask
symmetrize_bmask(bmask=bmask, no_sym_mask=mask, sym_flags=symflags)
assert np.array_equal(
bmask,
[[1, 0, 2, 2**9],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 2, 0]])
def test_symmetrize_bmask_angle():
bmask = np.zeros((64, 64), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask_orig = bmask.copy()
bmasks = []
for angle in [45, 90, 135, 180, 225, 270, 315]:
bmask_r = bmask_orig.copy()
symmetrize_bmask(bmask=bmask_r, angle=angle)
bmasks.append(bmask_r)
bmask |= bmask_r
assert np.array_equal(bmask, np.rot90(bmask))
assert np.array_equal(bmask, np.rot90(np.rot90(bmask)))
assert np.array_equal(bmask, np.rot90(np.rot90(np.rot90(bmask))))
if False:
import matplotlib.pyplot as plt
fig, axs = plt.subplots(nrows=3, ncols=3)
axs = axs.ravel()
bmasks.append(bmask_orig)
bmasks.append(bmask)
for i, bm in enumerate(bmasks):
ax = axs[i]
ax.pcolormesh(bm)
ax.set_aspect(1)
import pdb
pdb.set_trace()
def test_symmetrize_bmask_angle_no_sym_mask_sym_flags():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask[0, -1] = 2**9
symflags = (2**0) | (2**1)
mask = np.ones_like(bmask).astype(bool)
mask[2:4, 0] = False
mask = ~mask
for angle in [90, 180, 270]:
bmask_r = bmask.copy()
symmetrize_bmask(
bmask=bmask_r,
angle=angle,
no_sym_mask=mask,
sym_flags=symflags,
)
if angle == 90:
assert np.array_equal(
bmask_r,
[[1, 0, 2, 2**9],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 2, 0]])
elif angle == 180:
assert np.array_equal(
bmask_r,
[[1, 2, 2, 2**9],
[1, 2, 2, 0],
[1, 2, 2, 1],
[1, 2, 2, 1]])
elif angle == 270:
assert np.array_equal(
bmask_r,
[[1, 0, 3, 1 | 2**9],
[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0]])
else:
assert False, "missed an assert!"
def test_symmetrize_bmask_angle_no_sym_mask():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
mask = np.ones_like(bmask).astype(bool)
mask[2:4, 0] = False
mask = ~mask
for angle in [90, 180, 270]:
bmask_r = bmask.copy()
symmetrize_bmask(bmask=bmask_r, angle=angle, no_sym_mask=mask)
if angle == 90:
assert np.array_equal(
bmask_r,
[[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 2, 0]])
elif angle == 180:
assert np.array_equal(
bmask_r,
[[1, 2, 2, 0],
[1, 2, 2, 0],
[1, 2, 2, 1],
[1, 2, 2, 1]])
elif angle == 270:
assert np.array_equal(
bmask_r,
[[1, 0, 3, 1],
[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0]])
else:
assert False, "missed an assert!"
def test_symmetrize_bmask_angle_sym_flags():
bmask = np.zeros((4, 4), dtype=np.int32)
bmask[:, 0] = 1
bmask[:, -2] = 2
bmask[0, -1] = 2**9
symflags = (2**0) | (2**1)
for angle in [90, 180, 270]:
bmask_r = bmask.copy()
symmetrize_bmask(
bmask=bmask_r,
angle=angle,
sym_flags=symflags,
)
if angle == 90:
assert np.array_equal(
bmask_r,
[[1, 0, 2, 2**9],
[3, 2, 2, 2],
[1, 0, 2, 0],
[1, 1, 3, 1]])
elif angle == 180:
assert np.array_equal(
bmask_r,
[[1, 2, 2, 1 | 2**9],
[1, 2, 2, 1],
[1, 2, 2, 1],
[1, 2, 2, 1]])
elif angle == 270:
assert np.array_equal(
bmask_r,
[[1, 1, 3, 1 | 2**9],
[1, 0, 2, 0],
[3, 2, 2, 2],
[1, 0, 2, 0]])
else:
assert False, "missed an assert!"
| 27.119874
| 82
| 0.480284
| 1,216
| 8,597
| 3.266447
| 0.069901
| 0.024673
| 0.015861
| 0.090634
| 0.880916
| 0.853474
| 0.823766
| 0.785247
| 0.732628
| 0.732628
| 0
| 0.102484
| 0.363266
| 8,597
| 316
| 83
| 27.205696
| 0.623128
| 0.00791
| 0
| 0.742537
| 0
| 0
| 0.007975
| 0
| 0
| 0
| 0
| 0
| 0.134328
| 1
| 0.044776
| false
| 0
| 0.022388
| 0
| 0.067164
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
6eb700bf20e9068d5e62d816b2f2810beeee94e4
| 7,539
|
py
|
Python
|
padre/tests/ansible_utils/test_playbookrun.py
|
krislindgren/padre
|
56e3342a953fdc472adc11ce301acabf6c595760
|
[
"MIT"
] | null | null | null |
padre/tests/ansible_utils/test_playbookrun.py
|
krislindgren/padre
|
56e3342a953fdc472adc11ce301acabf6c595760
|
[
"MIT"
] | null | null | null |
padre/tests/ansible_utils/test_playbookrun.py
|
krislindgren/padre
|
56e3342a953fdc472adc11ce301acabf6c595760
|
[
"MIT"
] | null | null | null |
import mock
from testtools import TestCase
from padre.ansible_utils import PlaybookRun
FAKE_ANSIBLE_PATH = "ansible-playbook"
FAKE_ANSIBLE_PLAY = "fake.yaml"
class PlaybookRunTest(TestCase):
def test_playbookrun_init(self):
PlaybookRun()
@mock.patch("padre.utils.find_executable")
def test_playbookrun_set_get_extras_init(self, mock_find):
tmp = PlaybookRun(extra_vars={'a': 'b'})
self.assertEqual("b", tmp.get_extra_var("a"))
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--extra-vars', 'a=b', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_set_get_extras(self, mock_find):
tmp = PlaybookRun()
tmp.set_extra_var("a", "b")
self.assertEqual("b", tmp.get_extra_var("a"))
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--extra-vars', 'a=b', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_inventory(self, mock_find):
tmp = PlaybookRun()
tmp.inventory = "blah"
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--inventory-file',
'blah', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_inventory_init(self, mock_find):
tmp = PlaybookRun(inventory='blah')
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--inventory-file',
'blah', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_start_at_init(self, mock_find):
tmp = PlaybookRun(start_at='blah')
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--start-at-task',
'blah', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_start_at(self, mock_find):
tmp = PlaybookRun()
tmp.start_at = "blah"
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--start-at-task',
'blah', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_diff(self, mock_find):
tmp = PlaybookRun()
tmp.diff = True
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--diff', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_diff_init(self, mock_find):
tmp = PlaybookRun(diff=True)
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--diff', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_verbosity_init(self, mock_find):
tmp = PlaybookRun(verbosity=1)
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '-v', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_user_init(self, mock_find):
tmp = PlaybookRun(remote_user='bob')
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--user', 'bob', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_user(self, mock_find):
tmp = PlaybookRun()
tmp.remote_user = "bob"
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--user', 'bob', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_check(self, mock_find):
tmp = PlaybookRun()
tmp.check = True
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--check', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_check_init(self, mock_find):
tmp = PlaybookRun(check=True)
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--check', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_limit_init(self, mock_find):
tmp = PlaybookRun(limit='bbb')
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--limit', "bbb", FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_limit(self, mock_find):
tmp = PlaybookRun()
tmp.limit = "bbb"
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--limit', "bbb", FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_step_init(self, mock_find):
tmp = PlaybookRun(step=True)
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--step', FAKE_ANSIBLE_PLAY],
cmd)
@mock.patch("padre.utils.find_executable")
def test_playbookrun_step(self, mock_find):
tmp = PlaybookRun()
tmp.step = True
mock_find.return_value = FAKE_ANSIBLE_PATH
cmd = tmp.form_command(FAKE_ANSIBLE_PLAY)
mock_find.assert_called_with(FAKE_ANSIBLE_PATH)
self.assertEqual(
[FAKE_ANSIBLE_PATH, '--step', FAKE_ANSIBLE_PLAY],
cmd)
| 38.269036
| 74
| 0.666401
| 932
| 7,539
| 4.990343
| 0.063305
| 0.205762
| 0.167706
| 0.069447
| 0.949903
| 0.949903
| 0.841969
| 0.841969
| 0.841969
| 0.841969
| 0
| 0.000173
| 0.234912
| 7,539
| 196
| 75
| 38.464286
| 0.806172
| 0
| 0
| 0.754286
| 0
| 0
| 0.093646
| 0.060883
| 0
| 0
| 0
| 0
| 0.205714
| 1
| 0.102857
| false
| 0
| 0.017143
| 0
| 0.125714
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 1
| 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
| 8
|
42df37c053a14c857a20e4e38a612e971f18b36c
| 1,206
|
py
|
Python
|
hw_asr/model/quartznet_configs.py
|
ivan-gorin/asr_project_template
|
6a9f908d7f287ac2a66d5740fa6c73133825845a
|
[
"MIT"
] | null | null | null |
hw_asr/model/quartznet_configs.py
|
ivan-gorin/asr_project_template
|
6a9f908d7f287ac2a66d5740fa6c73133825845a
|
[
"MIT"
] | null | null | null |
hw_asr/model/quartznet_configs.py
|
ivan-gorin/asr_project_template
|
6a9f908d7f287ac2a66d5740fa6c73133825845a
|
[
"MIT"
] | null | null | null |
configs = {
"5x5":
[
{'out_channels': 256, 'repeat': 1, 'kernel_size': 33, 'stride': 2, 'dilation': 1, 'dropout': 0.2, 'residual': False, 'time_separable': True},
{'out_channels': 256, 'repeat': 5, 'kernel_size': 33, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': True, 'time_separable': True},
{'out_channels': 256, 'repeat': 5, 'kernel_size': 39, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': True, 'time_separable': True},
{'out_channels': 512, 'repeat': 5, 'kernel_size': 51, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': True, 'time_separable': True},
{'out_channels': 512, 'repeat': 5, 'kernel_size': 63, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': True, 'time_separable': True},
{'out_channels': 512, 'repeat': 5, 'kernel_size': 75, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': True, 'time_separable': True},
{'out_channels': 512, 'repeat': 1, 'kernel_size': 87, 'stride': 1, 'dilation': 2, 'dropout': 0.2, 'residual': False, 'time_separable': True},
{'out_channels': 1024, 'repeat': 1, 'kernel_size': 1, 'stride': 1, 'dilation': 1, 'dropout': 0.2, 'residual': False, 'time_separable': False}
]
}
| 60.3
| 145
| 0.608624
| 162
| 1,206
| 4.382716
| 0.17284
| 0.123944
| 0.101408
| 0.191549
| 0.83662
| 0.83662
| 0.83662
| 0.83662
| 0.826761
| 0.752113
| 0
| 0.080313
| 0.1534
| 1,206
| 20
| 146
| 60.3
| 0.615083
| 0
| 0
| 0
| 0
| 0
| 0.479702
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 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
| 7
|
42e7bd6c73a83513ef617f68099e27c48eee675c
| 3,800
|
py
|
Python
|
ddb_ngsflow/coverage/sambamba.py
|
dgaston/ddbio-ngsflow
|
b5a0f65b92f4de5706ae22b1c710c080761a4634
|
[
"BSD-2-Clause"
] | 1
|
2016-03-24T18:03:13.000Z
|
2016-03-24T18:03:13.000Z
|
ddb_ngsflow/coverage/sambamba.py
|
dgaston/ddbio-ngsflow
|
b5a0f65b92f4de5706ae22b1c710c080761a4634
|
[
"BSD-2-Clause"
] | null | null | null |
ddb_ngsflow/coverage/sambamba.py
|
dgaston/ddbio-ngsflow
|
b5a0f65b92f4de5706ae22b1c710c080761a4634
|
[
"BSD-2-Clause"
] | null | null | null |
"""
.. module:: utilities
:platform: Unix, OSX
:synopsis: A module of methods for various sambamba functions. This is a general catch all module for methoods
not better implemented elsewhere.
.. moduleauthor:: Daniel Gaston <daniel.gaston@dal.ca>
"""
import sys
import csv
from collections import defaultdict
from ddb_ngsflow import pipeline
def sambamba_region_coverage(job, config, name, samples, input_bam):
"""Run SamBambam to calculate the coverage of targeted regions
:param config: The configuration dictionary.
:type config: dict.
:param name: sample/library name.
:type name: str.
:param input_bam: The input_bam file name to process.
:type samples: dict
:param samples: The samples configuration dictionary
:type input_bam: str.
:returns: str -- The output BED file name.
"""
output = "{}.sambamba_regioncoverage.bed".format(name)
logfile = "{}.sambamba_coverage.log".format(name)
command = ["{}".format(config['sambamba']['bin']),
"depth region",
"-L",
"{}".format(samples[name]['regions']),
"-t",
"{}".format(config['sambamba']['num_cores']),
"-T",
"{}".format(config['coverage_threshold']),
"-T",
"{}".format(config['coverage_threshold2']),
"{}".format(input_bam),
">",
"{}".format(output)]
job.fileStore.logToMaster("SamBamba Coverage Command: {}\n".format(command))
pipeline.run_and_log_command(" ".join(command), logfile)
return output
def sambamba_base_coverage(job, config, name, samples, input_bam):
"""Run SamBambam to calculate the coverage of targeted regions
:param config: The configuration dictionary.
:type config: dict.
:param name: sample/library name.
:type name: str.
:param input_bam: The input_bam file name to process.
:type samples: dict
:param samples: The samples configuration dictionary
:type input_bam: str.
:returns: str -- The output BED file name.
"""
output = "{}.sambamba_base_coverage.bed".format(name)
logfile = "{}.sambamba_coverage.log".format(name)
command = ["{}".format(config['sambamba']['bin']),
"depth base",
"-L",
"{}".format(samples[name]['regions']),
"-z",
"-t",
"{}".format(config['sambamba']['num_cores']),
"{}".format(input_bam),
">",
"{}".format(output)]
job.fileStore.logToMaster("SamBamba Coverage Command: {}\n".format(command))
pipeline.run_and_log_command(" ".join(command), logfile)
return output
def sambamba_total_base_coverage(job, config, name, samples, input_bam):
"""Run SamBambam to calculate the coverage of targeted regions
:param config: The configuration dictionary.
:type config: dict.
:param name: sample/library name.
:type name: str.
:param input_bam: The input_bam file name to process.
:type samples: dict
:param samples: The samples configuration dictionary
:type input_bam: str.
:returns: str -- The output BED file name.
"""
output = "{}.sambamba_total_base_coverage.bed".format(name)
logfile = "{}.sambamba_coverage.log".format(name)
command = ["{}".format(config['sambamba']['bin']),
"depth base",
"-z",
"-t",
"{}".format(config['sambamba']['num_cores']),
"{}".format(input_bam),
">",
"{}".format(output)]
job.fileStore.logToMaster("SamBamba Coverage Command: {}\n".format(command))
pipeline.run_and_log_command(" ".join(command), logfile)
return output
| 33.043478
| 113
| 0.605263
| 415
| 3,800
| 5.431325
| 0.212048
| 0.053239
| 0.071872
| 0.02795
| 0.833185
| 0.811003
| 0.798137
| 0.798137
| 0.798137
| 0.798137
| 0
| 0.000353
| 0.254211
| 3,800
| 114
| 114
| 33.333333
| 0.794989
| 0.357632
| 0
| 0.754717
| 0
| 0
| 0.210113
| 0.072363
| 0
| 0
| 0
| 0
| 0
| 1
| 0.056604
| false
| 0
| 0.075472
| 0
| 0.188679
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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0
| 7
|
6e27ee15c4bfc9e3e7d7cdc79ef5bd8fac56fa22
| 21,267
|
py
|
Python
|
modelFood/NlabelCell.py
|
IBM/mpnn
|
218697020de8ecb48b908cc1ed58c4633de398a1
|
[
"Apache-2.0"
] | 1
|
2020-08-01T15:52:17.000Z
|
2020-08-01T15:52:17.000Z
|
modelFood/NlabelCell.py
|
IBM/mpnn
|
218697020de8ecb48b908cc1ed58c4633de398a1
|
[
"Apache-2.0"
] | 6
|
2019-12-16T21:59:48.000Z
|
2022-02-10T00:13:21.000Z
|
modelFood/NlabelCell.py
|
IBM/mpnn
|
218697020de8ecb48b908cc1ed58c4633de398a1
|
[
"Apache-2.0"
] | 6
|
2019-11-02T05:48:42.000Z
|
2021-09-03T03:38:27.000Z
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.rnn.python.ops import core_rnn_cell
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops.rnn_cell_impl import LSTMStateTuple
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import nest
from .config import Config
_Linear = core_rnn_cell._Linear
class NLabelCellWrapper(rnn_cell_impl.RNNCell):
def __init__(self, cell, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4k=None):
super(NLabelCellWrapper, self).__init__(_reuse=reuse)
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._state_is_tuple = state_is_tuple
self._cell = cell
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 =emb_M3
self.emb_M4k = emb_M4k
self.config = Config()
@property
def state_size(self):
size = (self._cell.state_size, self.config.use_K_histroy*self.config.label_emb_size)
if self._state_is_tuple:
#print("state return size",size)
return size
else:
return sum(list(size))
@property
def output_size(self):
return self.config.label_emb_size
def call(self, inputs, state):
"""Long short-term memory cell with attention (LSTMA)."""
if self._state_is_tuple:
state, hisTrack= state
#print("inputs",inputs)
#
# print("state",state)
cell_output, new_state = self._cell(inputs,state)
#print("cell_out",cell_output)
#print("new_state",new_state)
c_prev,m_prev = new_state
m_prev=tf.expand_dims(m_prev,axis=1)
#print("c_prev",c_prev)
#print("m_prev", m_prev)
label_emb= tf.nn.relu(tf.matmul(cell_output,self.emb_M3))
label_emb = tf.expand_dims(label_emb,axis=1)
#print(label_emb)
hisTrack=tf.reshape(hisTrack,shape=[-1,self.config.use_K_histroy,self.config.label_emb_size])
new_hisTrack = tf.slice(hisTrack, [0,1, 0], [-1,self.config.use_K_histroy-1, self.config.label_emb_size])
#print("new_hisTrack", new_hisTrack)
#print("label_emb", label_emb)
concat_hisTrack=tf.concat([new_hisTrack,label_emb],axis=1)
#print("concat_hisTrack_tmp",concat_hisTrack)
concat_all= tf.concat([concat_hisTrack,m_prev],axis=1)
concat_all_flatten=tf.reshape(concat_all,shape=[-1,(self.config.use_K_histroy+1)*self.config.label_emb_size])
concat_hisTrack_flatten=tf.reshape(concat_hisTrack,shape=[-1,self.config.use_K_histroy*self.config.label_emb_size])
m =tf.nn.relu(tf.matmul(concat_all_flatten,self.emb_M4k))
new_state_tuple= LSTMStateTuple(cell_output,m)
new_send_state=(new_state_tuple,concat_hisTrack_flatten)
#print("new_send_state",new_send_state)
#print("cell_output",cell_output)
return cell_output, new_send_state
class NLabelCellWrapperAfterAtt(rnn_cell_impl.RNNCell):
def __init__(self, cell, attn_length, attn_size=None, attn_vec_size=None,
input_size=None, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4k=None):
super(NLabelCellWrapperAfterAtt, self).__init__(_reuse=reuse)
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if attn_length <= 0:
raise ValueError("attn_length should be greater than zero, got %s"
% str(attn_length))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if attn_size is None:
attn_size = cell.output_size
if attn_vec_size is None:
attn_vec_size = attn_size
self._state_is_tuple = state_is_tuple
self._cell = cell
self._attn_vec_size = attn_vec_size
self._input_size = input_size
self._attn_size = attn_size
self._attn_length = attn_length
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 =emb_M3
self.emb_M4k = emb_M4k
self.config = Config()
@property
def state_size(self):
size = (self._cell.state_size, self.config.use_K_histroy*self.config.label_emb_size)
if self._state_is_tuple:
#print("state return size",size)
return size
else:
return sum(list(size))
@property
def output_size(self):
return self.config.label_emb_size
def call(self, inputs, state):
"""Long short-term memory cell with attention (LSTMA)."""
if self._state_is_tuple:
state, hisTrack= state
states ,attns, attn_states=state
#print("inputs",inputs)
#print("state",state)
cell_output, new_state = self._cell(inputs,state)
#print("cell_out",cell_output)
#print("new_state",new_state)
new_state, _ ,_ =new_state
c_prev,m_prev = new_state
m_prev=tf.expand_dims(m_prev,axis=1)
#print("c_prev",c_prev)
#print("m_prev", m_prev)
label_emb= tf.nn.relu(tf.matmul(cell_output,self.emb_M3))
label_emb = tf.expand_dims(label_emb,axis=1)
#print(label_emb)
hisTrack=tf.reshape(hisTrack,shape=[-1,self.config.use_K_histroy,self.config.label_emb_size])
new_hisTrack = tf.slice(hisTrack, [0,1, 0], [-1,self.config.use_K_histroy-1, self.config.label_emb_size])
#print("new_hisTrack", new_hisTrack)
#print("label_emb", label_emb)
concat_hisTrack=tf.concat([new_hisTrack,label_emb],axis=1)
#print("concat_hisTrack_tmp",concat_hisTrack)
concat_all= tf.concat([concat_hisTrack,m_prev],axis=1)
concat_all_flatten=tf.reshape(concat_all,shape=[-1,(self.config.use_K_histroy+1)*self.config.label_emb_size])
concat_hisTrack_flatten=tf.reshape(concat_hisTrack,shape=[-1,self.config.use_K_histroy*self.config.label_emb_size])
m =tf.nn.relu(tf.matmul(concat_all_flatten,self.emb_M4k))
new_state_tuple= (LSTMStateTuple(cell_output,m),attns,attn_states)
new_send_state=(new_state_tuple,concat_hisTrack_flatten)
#print("new_send_state",new_send_state)
#print("cell_output",cell_output)
return cell_output, new_send_state
class LabelCellWrapperAfterAtt(rnn_cell_impl.RNNCell):
def __init__(self, cell, attn_length, attn_size=None, attn_vec_size=None,
input_size=None, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4=None):
super(LabelCellWrapperAfterAtt, self).__init__(_reuse=reuse)
# if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access
# raise TypeError("The parameter cell is not RNNCell.")
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if attn_length <= 0:
raise ValueError("attn_length should be greater than zero, got %s"
% str(attn_length))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if attn_size is None:
attn_size = cell.output_size
if attn_vec_size is None:
attn_vec_size = attn_size
self._state_is_tuple = state_is_tuple
self._cell = cell
self._input_size = input_size
self._attn_size = attn_size
self._attn_length = attn_length
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 =emb_M3
self.emb_M4 = emb_M4
self.config = Config()
@property
def state_size(self):
size = (self._cell.state_size)
if self._state_is_tuple:
#print("state return size",size)
return size
else:
return sum(list(size))
@property
def output_size(self):
return self.config.label_emb_size
def call(self, inputs, state):
"""Long short-term memory cell with attention (LSTMA)."""
if self._state_is_tuple:
states ,attns, attn_states=state
#print("inputs",inputs)
#print("state",state)
cell_output, new_state = self._cell(inputs,state)
#print("cell_out",cell_output)
#print("new_state",new_state)
new_state, _ ,_ =new_state
c_prev,m_prev = new_state
m_prev=tf.expand_dims(m_prev,axis=1)
#print("c_prev",c_prev)
#print("m_prev", m_prev)
label_emb= tf.nn.relu(tf.matmul(cell_output,self.emb_M3))
label_emb = tf.expand_dims(label_emb,axis=1)
#print(label_emb)
concat_all= tf.concat([label_emb,m_prev],axis=1)
concat_all_flatten=tf.reshape(concat_all,shape=[-1,2*self.config.label_emb_size])
m =tf.nn.relu(tf.matmul(concat_all_flatten,self.emb_M4))
new_state_tuple= (LSTMStateTuple(cell_output,m),attns,attn_states)
new_send_state=new_state_tuple
#print("new_send_state",new_send_state)
#print("cell_output",cell_output)
return cell_output, new_send_state
class LabelCellWrapper(rnn_cell_impl.RNNCell):
def __init__(self, cell, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4=None):
super(LabelCellWrapper, self).__init__(_reuse=reuse)
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._state_is_tuple = state_is_tuple
self._cell = cell
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 =emb_M3
self.emb_M4 = emb_M4
self.config = Config()
@property
def state_size(self):
size = (self._cell.state_size)
if self._state_is_tuple:
#print("state return size",size)
return size
else:
return sum(list(size))
@property
def output_size(self):
return self.config.label_emb_size
#before attention
def call(self, inputs, state):
if self._state_is_tuple:
state= state
#print("inputs",inputs)
#print("state",state)
cell_output, new_state = self._cell(inputs, state)
#print("cell_out",cell_output)
#print("new_state",new_state)
c_prev,m_prev = new_state
m_prev=tf.expand_dims(m_prev,axis=1)
#print("c_prev",c_prev)
#print("m_prev", m_prev)
label_emb= tf.nn.relu(tf.matmul(cell_output,self.emb_M3))
label_emb = tf.expand_dims(label_emb,axis=1)
#print(label_emb)
concat_all= tf.concat([label_emb,m_prev],axis=1)
concat_all_flatten=tf.reshape(concat_all,shape=[-1,2*self.config.label_emb_size])
m =tf.nn.relu(tf.matmul(concat_all_flatten,self.emb_M4))
new_state_tuple= LSTMStateTuple(cell_output,m)
new_send_state=new_state_tuple
#print("new_send_state",new_send_state)
#print("cell_output",cell_output)
return cell_output, new_send_state
class NLabelAttentionCellWrapper(rnn_cell_impl.RNNCell):
def __init__(self, cell, attn_length, attn_size=None, attn_vec_size=None,
input_size=None, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4k=None):
super(NLabelAttentionCellWrapper, self).__init__(_reuse=reuse)
#print("build attentionCellWrapper")
if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access
raise TypeError("The parameter cell is not RNNCell.")
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if attn_length <= 0:
raise ValueError("attn_length should be greater than zero, got %s"
% str(attn_length))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
if attn_size is None:
attn_size = cell.output_size
if attn_vec_size is None:
attn_vec_size = attn_size
self._state_is_tuple = state_is_tuple
self._cell = cell
self._attn_vec_size = attn_vec_size
self._input_size = input_size
self._attn_size = attn_size
self._attn_length = attn_length
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 = emb_M3
self.emb_M4k = emb_M4k
self.config = Config()
@property
def state_size(self):
size = (self._cell.state_size, self._attn_size,
self._attn_size * self._attn_length,self.config.use_K_histroy*self.config.label_emb_size)
if self._state_is_tuple:
return size
else:
return sum(list(size))
@property
def output_size(self):
return self._attn_size
def call(self, inputs, state):
"""Long short-term memory cell with attention (LSTMA)."""
if self._state_is_tuple:
state, attns, attn_states,histotry = state
else:
states = state
state = array_ops.slice(states, [0, 0], [-1, self._cell.state_size])
attns = array_ops.slice(
states, [0, self._cell.state_size], [-1, self._attn_size])
attn_states = array_ops.slice(
states, [0, self._cell.state_size + self._attn_size],
[-1, self._attn_size * self._attn_length])
attn_states = array_ops.reshape(attn_states,
[-1, self._attn_length, self._attn_size])
input_size = self._input_size
if input_size is None:
input_size = inputs.get_shape().as_list()[1]
if self._linear1 is None:
self._linear1 = _Linear([inputs, attns], input_size, True)
inputs = self._linear1([inputs, attns])
cell_output, new_state = self._cell(inputs, state)
#print("new state",new_state)
if self._state_is_tuple:
new_state_cat = array_ops.concat(nest.flatten(new_state), 1)
else:
new_state_cat = new_state
new_attns, new_attn_states = self._attention(new_state_cat, attn_states)
with vs.variable_scope("attn_output_projection"):
if self._linear2 is None:
self._linear2 = _Linear([cell_output, new_attns], self._attn_size, True)
output = self._linear2([cell_output, new_attns])
#print("output",output)
new_attn_states = array_ops.concat(
[new_attn_states, array_ops.expand_dims(output, 1)], 1)
new_attn_states = array_ops.reshape(
new_attn_states, [-1, self._attn_length * self._attn_size])
c_new, h_new = new_state
#print("c_new", c_new)
#print("h_new", h_new)
label_emb = tf.nn.relu(tf.matmul(output, self.emb_M3))
# label_emb = tf.expand_dims(label_emb, axis=1)
#print("label emb",label_emb)
#print("new stat", new_state)
pre_history = histotry
pre_history= tf.reshape(pre_history, shape=[-1, self.config.use_K_histroy, self.config.label_emb_size])
#print("pre_history",pre_history)
new_history = tf.slice(pre_history, [0, 1, 0], [-1, self.config.use_K_histroy - 1, self.config.label_emb_size])
#print("new_history", new_history)
# print("label_emb", label_emb)
concat_his = tf.concat([new_history, tf.expand_dims(label_emb,axis=1)], axis=1)
#print("concat_his_tmp", concat_his)
concat_all = tf.concat([concat_his, tf.expand_dims(c_new,axis=1)], axis=1)
#print("c_new",c_new)
concat_all_flatten = tf.reshape(concat_all,
shape=[-1, (self.config.use_K_histroy + 1) * self.config.label_emb_size])
concat_his_flatten = tf.reshape(concat_his,
shape=[-1, self.config.use_K_histroy * self.config.label_emb_size])
c = tf.nn.relu(tf.matmul(concat_all_flatten, self.emb_M4k))
new_state= LSTMStateTuple(c, h_new)
new_wrapper_state = (new_state, new_attns, new_attn_states, concat_his_flatten)
return output, new_wrapper_state
def _attention(self, query, attn_states):
conv2d = nn_ops.conv2d
reduce_sum = math_ops.reduce_sum
softmax = nn_ops.softmax
tanh = math_ops.tanh
with vs.variable_scope("attention"):
k = vs.get_variable(
"attn_w", [1, 1, self._attn_size, self._attn_vec_size])
v = vs.get_variable("attn_v", [self._attn_vec_size])
hidden = array_ops.reshape(attn_states,
[-1, self._attn_length, 1, self._attn_size])
hidden_features = conv2d(hidden, k, [1, 1, 1, 1], "SAME")
if self._linear3 is None:
self._linear3 = _Linear(query, self._attn_vec_size, True)
y = self._linear3(query)
y = array_ops.reshape(y, [-1, 1, 1, self._attn_vec_size])
s = reduce_sum(v * tanh(hidden_features + y), [2, 3])
a = softmax(s)
d = reduce_sum(
array_ops.reshape(a, [-1, self._attn_length, 1, 1]) * hidden, [1, 2])
new_attns = array_ops.reshape(d, [-1, self._attn_size])
new_attn_states = array_ops.slice(attn_states, [0, 1, 0], [-1, -1, -1])
return new_attns, new_attn_states
class NLabelNoAttentionCellWrapper(rnn_cell_impl.RNNCell):
def __init__(self, cell,
input_size=None, state_is_tuple=True, reuse=None,emb_M3=None,emb_M4k=None):
super(NLabelNoAttentionCellWrapper, self).__init__(_reuse=reuse)
#
if not rnn_cell_impl._like_rnncell(cell): # pylint: disable=protected-access
raise TypeError("The parameter cell is not RNNCell.")
if nest.is_sequence(cell.state_size) and not state_is_tuple:
raise ValueError("Cell returns tuple of states, but the flag "
"state_is_tuple is not set. State size is: %s"
% str(cell.state_size))
if not state_is_tuple:
logging.warn(
"%s: Using a concatenated state is slower and will soon be "
"deprecated. Use state_is_tuple=True.", self)
self._state_is_tuple = state_is_tuple
self._cell = cell
self._input_size = input_size
self._reuse = reuse
self._linear1 = None
self._linear2 = None
self._linear3 = None
self.emb_M3 = emb_M3
self.emb_M4k = emb_M4k
self.config = Config()
self._output_size= cell.output_size
@property
def state_size(self):
size = (self._cell.state_size,self.config.use_K_histroy*self.config.label_emb_size)
if self._state_is_tuple:
return size
else:
return sum(list(size))
@property
def output_size(self):
return self._output_size
def call(self, inputs, state):
"""Long short-term memory cell with attention (LSTMA)."""
if self._state_is_tuple:
state,histotry = state
cell_output, new_state = self._cell(inputs, state)
#print("new state",new_state)
output = cell_output
#print("output",output)
c_new, h_new = new_state
# print("c_new", c_new)
# print("h_new", h_new)
label_emb = tf.nn.relu(tf.matmul(output, self.emb_M3))
# label_emb = tf.expand_dims(label_emb, axis=1)
#print("label emb",label_emb)
#print("new stat", new_state)
pre_history = histotry
pre_history= tf.reshape(pre_history, shape=[-1, self.config.use_K_histroy, self.config.label_emb_size])
#print("pre_history",pre_history)
new_history = tf.slice(pre_history, [0, 1, 0], [-1, self.config.use_K_histroy - 1, self.config.label_emb_size])
#print("new_history", new_history)
# print("label_emb", label_emb)
concat_his = tf.concat([new_history, tf.expand_dims(label_emb,axis=1)], axis=1)
#print("concat_his_tmp", concat_his)
concat_all = tf.concat([concat_his, tf.expand_dims(c_new,axis=1)], axis=1)
#print("c_new",c_new)
concat_all_flatten = tf.reshape(concat_all,
shape=[-1, (self.config.use_K_histroy + 1) * self.config.label_emb_size])
concat_his_flatten = tf.reshape(concat_his,
shape=[-1, self.config.use_K_histroy * self.config.label_emb_size])
c = tf.nn.relu(tf.matmul(concat_all_flatten, self.emb_M4k))
new_state= LSTMStateTuple(c, h_new)
new_wrapper_state = (new_state, concat_his_flatten)
return output, new_wrapper_state
| 30.381429
| 121
| 0.675272
| 3,111
| 21,267
| 4.282224
| 0.052716
| 0.039634
| 0.049542
| 0.03513
| 0.864285
| 0.844918
| 0.828404
| 0.82315
| 0.814217
| 0.798979
| 0
| 0.010973
| 0.21578
| 21,267
| 699
| 122
| 30.424893
| 0.787804
| 0.115719
| 0
| 0.770202
| 0
| 0
| 0.072089
| 0.001177
| 0
| 0
| 0
| 0
| 0
| 1
| 0.063131
| false
| 0
| 0.035354
| 0.015152
| 0.176768
| 0.002525
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
6e4b23d92cb6113249a0f545d0e9b63b60ebfab5
| 99
|
py
|
Python
|
Utils/Device.py
|
ThisGame42/Deep-Learning-Models-4-Muscle-Bones-Segmentation
|
1f34f0c5870b57994d652c0d77600ec7f25ec4a9
|
[
"Apache-2.0"
] | 2
|
2021-03-15T10:22:55.000Z
|
2021-06-03T15:44:01.000Z
|
Utils/Device.py
|
ThisGame42/Deep-Learning-Models-4-Muscle-Bones-Segmentation
|
1f34f0c5870b57994d652c0d77600ec7f25ec4a9
|
[
"Apache-2.0"
] | null | null | null |
Utils/Device.py
|
ThisGame42/Deep-Learning-Models-4-Muscle-Bones-Segmentation
|
1f34f0c5870b57994d652c0d77600ec7f25ec4a9
|
[
"Apache-2.0"
] | 1
|
2022-02-02T03:52:32.000Z
|
2022-02-02T03:52:32.000Z
|
import torch
def get_device() -> str:
return "cuda:0" if torch.cuda.is_available() else "cpu"
| 19.8
| 59
| 0.686869
| 16
| 99
| 4.125
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012195
| 0.171717
| 99
| 5
| 59
| 19.8
| 0.792683
| 0
| 0
| 0
| 0
| 0
| 0.09
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
958dd7533ce403932354647c54a9a2c1ad666eac
| 153
|
py
|
Python
|
tests/test_dummy.py
|
ashesh705/master-of-coin
|
8ce253cd1c73005856c896a155ef25804d95d02f
|
[
"MIT"
] | null | null | null |
tests/test_dummy.py
|
ashesh705/master-of-coin
|
8ce253cd1c73005856c896a155ef25804d95d02f
|
[
"MIT"
] | null | null | null |
tests/test_dummy.py
|
ashesh705/master-of-coin
|
8ce253cd1c73005856c896a155ef25804d95d02f
|
[
"MIT"
] | null | null | null |
""" Dummy test case to initialize repo"""
from src.dummy import dummy
def test_dummy() -> None:
"""Dummy test case"""
assert dummy() is True
| 15.3
| 41
| 0.647059
| 22
| 153
| 4.454545
| 0.636364
| 0.183673
| 0.265306
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.222222
| 153
| 9
| 42
| 17
| 0.823529
| 0.326797
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
95ade7f9c768b0d9277c6076c5f8b44bb69f709c
| 18
|
py
|
Python
|
code.py
|
ljh15952/sample_60202241
|
6483916b5e0aecdce0423f1c9830f2440cb6d314
|
[
"MIT"
] | null | null | null |
code.py
|
ljh15952/sample_60202241
|
6483916b5e0aecdce0423f1c9830f2440cb6d314
|
[
"MIT"
] | null | null | null |
code.py
|
ljh15952/sample_60202241
|
6483916b5e0aecdce0423f1c9830f2440cb6d314
|
[
"MIT"
] | null | null | null |
print("60202241")
| 9
| 17
| 0.722222
| 2
| 18
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.470588
| 0.055556
| 18
| 1
| 18
| 18
| 0.294118
| 0
| 0
| 0
| 0
| 0
| 0.444444
| 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
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
25164334e3576c9088fc1e5604c419b898e24770
| 462
|
py
|
Python
|
parser/team04/Interpreter/Instruction/union.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 35
|
2020-12-07T03:11:43.000Z
|
2021-04-15T17:38:16.000Z
|
parser/team04/Interpreter/Instruction/union.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 47
|
2020-12-09T01:29:09.000Z
|
2021-01-13T05:37:50.000Z
|
parser/team04/Interpreter/Instruction/union.py
|
webdev188/tytus
|
847071edb17b218f51bb969d335a8ec093d13f94
|
[
"MIT"
] | 556
|
2020-12-07T03:13:31.000Z
|
2021-06-17T17:41:10.000Z
|
from Interpreter.Instruction.instruction import Instruction
class union_all(Instruction):
def __init__(self, left, right):
self.left = left
self.right = right
def execute(self, env):
print("Se ejecutó la instrucción 'UNION ALL'")
class union(Instruction):
def __init__(self, left, right):
self.left = left
self.right = right
def execute(self, env):
print("Se ejecutó la instrucción 'UNION'")
| 23.1
| 59
| 0.651515
| 56
| 462
| 5.214286
| 0.339286
| 0.109589
| 0.123288
| 0.150685
| 0.726027
| 0.726027
| 0.726027
| 0.726027
| 0.726027
| 0.726027
| 0
| 0
| 0.248918
| 462
| 19
| 60
| 24.315789
| 0.841499
| 0
| 0
| 0.615385
| 0
| 0
| 0.151515
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.307692
| false
| 0
| 0.076923
| 0
| 0.538462
| 0.153846
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
252f0d9b04f21118a0ab7411f527bccdb9a1d793
| 4,713
|
py
|
Python
|
tests/unit_tests/time_series/test_time_series_from_nexus.py
|
poc11/resqpy
|
5dfbfb924f8ee9b2712fb8e38bff96ee8ee9d8e2
|
[
"MIT"
] | 35
|
2021-05-24T17:43:55.000Z
|
2022-03-15T15:47:59.000Z
|
tests/unit_tests/time_series/test_time_series_from_nexus.py
|
poc11/resqpy
|
5dfbfb924f8ee9b2712fb8e38bff96ee8ee9d8e2
|
[
"MIT"
] | 355
|
2021-05-24T16:28:16.000Z
|
2022-03-14T14:43:40.000Z
|
tests/unit_tests/time_series/test_time_series_from_nexus.py
|
poc11/resqpy
|
5dfbfb924f8ee9b2712fb8e38bff96ee8ee9d8e2
|
[
"MIT"
] | 12
|
2021-06-02T09:50:46.000Z
|
2022-03-02T20:29:56.000Z
|
import datetime
from resqpy.time_series._from_nexus_summary import _process_summary_entries, time_series_from_nexus_summary
def test_process_summary_entries_none():
# arrange
summary_entries = []
# act
result = _process_summary_entries(summary_entries)
# assert
assert result is None
# (time step no., cumulative time (days), date (dd/mm/yyyy))
def test_process_summary_entries_first():
# arrange
summary_entries = [(0, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 1
assert result.timestamps[0] == '2010-05-24T00:00:00Z'
def test_process_summary_entries_delta():
# arrange
summary_entries = [(1, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 2
assert result.timestamps[1] == '2010-05-24T00:00:00Z'
assert result.timestamps[0] == '2010-05-23T00:00:00Z'
def test_process_summary_entries_out_of_sequence():
# arrange
summary_entries = [(0, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date()),
(0, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 1
assert result.timestamps[0] == '2010-05-24T00:00:00Z'
def test_process_summary_entries_out_of_sequence_v2():
# arrange
summary_entries = [(1, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date()),
(3, 4, datetime.datetime.strptime('28052010', '%d%m%Y').date()),
(2, 2, datetime.datetime.strptime('26052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 4
assert result.timestamps[0] == '2010-05-23T00:00:00Z'
assert result.timestamps[1] == '2010-05-24T00:00:00Z'
assert result.timestamps[2] == '2010-05-25T00:00:00Z'
assert result.timestamps[3] == '2010-05-27T00:00:00Z'
def test_process_summary_entries_in_sequence():
# arrange
summary_entries = [(1, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date()),
(3, 4, datetime.datetime.strptime('28052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 4
assert result.timestamps[0] == '2010-05-23T00:00:00Z'
assert result.timestamps[1] == '2010-05-24T00:00:00Z'
assert result.timestamps[2] == '2010-05-24T00:00:00Z'
assert result.timestamps[3] == '2010-05-27T00:00:00Z'
def test_process_summary_entries_incorrect_days():
# arrange
summary_entries = [(1, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date()),
(2, 0, datetime.datetime.strptime('28052010', '%d%m%Y').date())]
# act
result = _process_summary_entries(summary_entries)
# assert
assert len(result.timestamps) == 3
assert result.timestamps[0] == '2010-05-23T00:00:00Z'
assert result.timestamps[1] == '2010-05-24T00:00:00Z'
assert result.timestamps[2] == '2010-05-24T00:00:00Z'
def test_time_series_from_nexus_summary_is_not_file(mocker):
# arrange
is_file_mock = mocker.MagicMock(return_value = False)
mocker.patch('os.path.isfile', is_file_mock)
# log_warning_mock = mocker.MagicMock()
# logging_mock = mocker.MagicMock()
# logging_mock.getlogger.warning = log_warning_mock
# mocker.patch(logging, logging_mock)
# act
result = time_series_from_nexus_summary('test')
# assert
assert result is None
# log_warning_mock.assert_called_once_with('Summary file not found: ' + 'test')
def test_time_series_from_nexus_summary_if_no_file():
# arrange
# act
result = time_series_from_nexus_summary('')
# assert
assert result is None
# def test_time_series_from_nexus_summary_open_file(mocker):
# # arrange
# is_file_mock = mocker.MagicMock(return_value=True)
# mocker.patch('os.path.isfile', is_file_mock)
# summary_entries = [(1, 1, datetime.datetime.strptime('24052010', '%d%m%Y').date()),
# (2, 0, datetime.datetime.strptime('28052010', '%d%m%Y').date())]
# open_file_mock = mocker.MagicMock(return_value=summary_entries)
# mocker.patch(resqpy.time_series.time_series_from_nexus_summary.open_file, open_file_mock)
# # act
# result = time_series_from_nexus_summary('test')
# # assert
# assert result is None
# open_file_mock.assert_called_once_with('test')
| 36.820313
| 107
| 0.677912
| 624
| 4,713
| 4.870192
| 0.126603
| 0.14281
| 0.103653
| 0.029944
| 0.873972
| 0.792037
| 0.780849
| 0.724251
| 0.702534
| 0.680158
| 0
| 0.09371
| 0.180352
| 4,713
| 127
| 108
| 37.110236
| 0.692985
| 0.23955
| 0
| 0.534483
| 0
| 0
| 0.13356
| 0
| 0
| 0
| 0
| 0
| 0.413793
| 1
| 0.155172
| false
| 0
| 0.034483
| 0
| 0.189655
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c26a6ca559700effeb053c13c1f0f9a7cbce6b39
| 5,979
|
py
|
Python
|
pydeep/sklearn_wrap.py
|
dsakagi/pydeep
|
e4d14da74d6a6d007a9cd627b5dd81c9f1bfaa72
|
[
"BSD-2-Clause-FreeBSD"
] | 2
|
2017-09-22T20:28:32.000Z
|
2020-04-01T23:55:49.000Z
|
pydeep/sklearn_wrap.py
|
dsakagi/pydeep
|
e4d14da74d6a6d007a9cd627b5dd81c9f1bfaa72
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
pydeep/sklearn_wrap.py
|
dsakagi/pydeep
|
e4d14da74d6a6d007a9cd627b5dd81c9f1bfaa72
|
[
"BSD-2-Clause-FreeBSD"
] | null | null | null |
'''Provide a sklearn-style API for a regressor, classifier, and transformer
'''
from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
import neural
class ShallowDropoutRegressor(BaseEstimator, RegressorMixin):
'''A single-hidden layer dropout neural network.
Parameters
----------
n_hidden : int
Number of units in the hidden layer
momentum : float
The momentum in SGD training (constant in this implementation)
learning_rate : float
The learning rate
encoding_layer : {'RectifiedLinear', 'Linear', 'Logistic'}
The activation function of the first encoding layer
epochs : int
The number of training epochs to carry out
batchsize : int
The number of samples to put into each batch. A small number
can affect adversely by overfitting the first few examples if
the learning rate is high; a large number can affect adversely
by requiring too much memory
'''
def __init__(self, n_hidden=100, momentum=0.1, learning_rate=0.1,
encoding_layer='Logistic', epochs=100, batchsize=100):
self.ntp = neural.NetTrainParams()
self.ntp.eta = neural.scalar_schedule.ConstantSchedule(learning_rate)
self.ntp.mu = neural.scalar_schedule.ConstantSchedule(momentum)
self.ntp.batchsize = batchsize
self.ntp.maxepoch = epochs
self.net = None
self.n_hidden = n_hidden
self.encoding_layer = encoding_layer
def fit(X, Y):
'''Fit the data'''
if len(X) != len(Y):
raise ValueError('Predictors (size %d) and response (size %d) do not match length' % (len(X), len(Y)))
if self.net is not None:
pass
#Make sure that new data matches
else:
indims = X.shape[1]
outdims = Y.shape[1]
arch = [indims, self.n_hidden, outdims]
lts = [encoding_layer, 'LinearZero']
regs = [neural.NetReg() for lt in lts]
for reg in regs:
reg.dropout = True
reg.drop_rate = 0.5
reg.max_constraint = True
reg.max_unit_weight = 25
self.net = neural.Net(arch, lts, regs)
neural.train_sgd(self.net, X, Y, self.ntp)
return self
def predict(X):
'''Make some predictions'''
if self.net is None:
raise ValueError('This neural network has not yet been trained')
if X.shape[1] != self.n_hidden:
raise ValueError('Number of columns in row does not match this network\'s input')
return self.net.predict(X)
def fit_predict(X,Y):
return self.fit(X,Y).predict(X)
def transform(X):
if self.net is None:
raise ValueError('This neural network has not yet been trained')
return self.net.predict(X)
def fit_transform(X,Y):
return self.fit(X,Y).transform(X)
class ShallowDropoutClassifier(BaseEstimator, ClassifierMixin):
'''A single-hidden layer dropout neural network.
Although this touts itself as a classifier, it is actually just
a logistic prediction function (i.e. sigmoid) so it will perhaps
not behave as you expect if you are trying to get it to predict
multi-class
Parameters
----------
n_hidden : int
Number of units in the hidden layer
momentum : float
The momentum in SGD training (constant in this implementation)
learning_rate : float
The learning rate
encoding_layer : {'RectifiedLinear', 'Linear', 'Logistic'}
The activation function of the first encoding layer
epochs : int
The number of training epochs to carry out
batchsize : int
The number of samples to put into each batch. A small number
can affect adversely by overfitting the first few examples if
the learning rate is high; a large number can affect adversely
by requiring too much memory
'''
def __init__(self, n_hidden=100, momentum=0.1, learning_rate=0.1,
encoding_layer='Logistic', epochs=100, batchsize=100):
self.ntp = neural.NetTrainParams()
self.ntp.eta = neural.scalar_schedule.ConstantSchedule(learning_rate)
self.ntp.mu = neural.scalar_schedule.ConstantSchedule(momentum)
self.ntp.batchsize = batchsize
self.ntp.maxepoch = epochs
self.net = None
self.n_hidden = n_hidden
self.encoding_layer = encoding_layer
def fit(X, Y):
'''Fit the data'''
if len(X) != len(Y):
raise ValueError('Predictors (size %d) and response (size %d) do not match length' % (len(X), len(Y)))
if self.net is not None:
pass
#Make sure that new data matches
else:
indims = X.shape[1]
outdims = Y.shape[1]
arch = [indims, self.n_hidden, outdims]
lts = [encoding_layer, 'Logistic']
regs = [neural.NetReg() for lt in lts]
for reg in regs:
reg.dropout = True
reg.drop_rate = 0.5
reg.max_constraint = True
reg.max_unit_weight = 25
self.net = neural.Net(arch, lts, regs)
neural.train_sgd(self.net, X, Y, self.ntp)
return self
def predict(X):
'''Make some predictions'''
if self.net is None:
raise ValueError('This neural network has not yet been trained')
if X.shape[1] != self.n_hidden:
raise ValueError('Number of columns in row does not match this network\'s input')
return self.net.predict(X)
def fit_predict(X,Y):
return self.fit(X,Y).predict(X)
def transform(X):
if self.net is None:
raise ValueError('This neural network has not yet been trained')
return self.net.predict(X)
def fit_transform(X,Y):
return self.fit(X,Y).transform(X)
| 33.402235
| 114
| 0.617495
| 788
| 5,979
| 4.616751
| 0.211929
| 0.030786
| 0.024189
| 0.018142
| 0.882353
| 0.882353
| 0.882353
| 0.861462
| 0.861462
| 0.861462
| 0
| 0.009506
| 0.296203
| 5,979
| 178
| 115
| 33.589888
| 0.855038
| 0.294865
| 0
| 0.933333
| 0
| 0.011111
| 0.110169
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0.022222
| 0.022222
| 0.044444
| 0.288889
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
6c25f464c42cd280b0272b8ea5695653cf560ec9
| 2,093
|
py
|
Python
|
gas_fuzz/fuzzing_rules/numeric_rules.py
|
danno-s/gas-fuzzer
|
1c8ef5bdd3315e5a4ebc9af42f78140ddbb69db2
|
[
"MIT"
] | 3
|
2020-10-28T06:00:47.000Z
|
2021-03-03T23:04:12.000Z
|
gas_fuzz/fuzzing_rules/numeric_rules.py
|
danno-s/gas-fuzzer
|
1c8ef5bdd3315e5a4ebc9af42f78140ddbb69db2
|
[
"MIT"
] | null | null | null |
gas_fuzz/fuzzing_rules/numeric_rules.py
|
danno-s/gas-fuzzer
|
1c8ef5bdd3315e5a4ebc9af42f78140ddbb69db2
|
[
"MIT"
] | 1
|
2020-10-28T09:28:17.000Z
|
2020-10-28T09:28:17.000Z
|
from .base import BaseFuzzerRule
from type_fuzzing import (
UIntFuzzer,
IntFuzzer,
FixedFuzzer,
UFixedFuzzer
)
import logging
class GreaterThan(BaseFuzzerRule):
def __init__(self, rules, **kwargs):
super().__init__(rules, **kwargs)
self.min = rules["min"]
def applicable_to(self, fuzzer):
return type(fuzzer) in [UIntFuzzer, IntFuzzer, FixedFuzzer, UFixedFuzzer]
def apply_to(self, fuzzer):
fuzzer.greater_than(self.min)
def __str__(self):
return f"Greater Than ({self.min})"
class GreaterThanEqual(BaseFuzzerRule):
def __init__(self, rules, **kwargs):
super().__init__(rules, **kwargs)
self.min = rules["min"]
def applicable_to(self, fuzzer):
return type(fuzzer) in [UIntFuzzer, IntFuzzer, FixedFuzzer, UFixedFuzzer]
def apply_to(self, fuzzer):
fuzzer.greater_than_equal(self.min)
def __str__(self):
return f"Greater Than Equal ({self.min})"
class LessThan(BaseFuzzerRule):
def __init__(self, rules, **kwargs):
super().__init__(rules, **kwargs)
self.max = rules["max"]
def applicable_to(self, fuzzer):
return type(fuzzer) in [UIntFuzzer, IntFuzzer, FixedFuzzer, UFixedFuzzer]
def apply_to(self, fuzzer):
fuzzer.less_than(self.max)
def __str__(self):
return f"Less Than ({self.max})"
class LessThanEqual(BaseFuzzerRule):
def __init__(self, rules, **kwargs):
super().__init__(rules, **kwargs)
self.max = rules["max"]
def applicable_to(self, fuzzer):
return type(fuzzer) in [UIntFuzzer, IntFuzzer, FixedFuzzer, UFixedFuzzer]
def apply_to(self, fuzzer):
fuzzer.less_than_equal(self.max)
def __str__(self):
return f"Less Than Equal({self.max})"
class Limits(BaseFuzzerRule):
def __init__(self, rules, **kwargs):
super().__init__(rules, **kwargs)
self.min = rules["min"]
self.max = rules["max"]
def __str__(self):
return f"Limits ({self.min} => {self.max})"
| 27.539474
| 81
| 0.634974
| 240
| 2,093
| 5.225
| 0.154167
| 0.087719
| 0.076555
| 0.167464
| 0.826954
| 0.790271
| 0.774322
| 0.774322
| 0.774322
| 0.667464
| 0
| 0
| 0.239369
| 2,093
| 75
| 82
| 27.906667
| 0.787688
| 0
| 0
| 0.6
| 0
| 0
| 0.074534
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.327273
| false
| 0
| 0.054545
| 0.163636
| 0.636364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 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
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 10
|
6c3359c48bc496850212952a650bc87514d45978
| 1,923
|
py
|
Python
|
tracking/__init__.py
|
koravel/orders_generator
|
1a73f8cd7a363a44b9aa442f1e32bea816083644
|
[
"Apache-2.0"
] | null | null | null |
tracking/__init__.py
|
koravel/orders_generator
|
1a73f8cd7a363a44b9aa442f1e32bea816083644
|
[
"Apache-2.0"
] | null | null | null |
tracking/__init__.py
|
koravel/orders_generator
|
1a73f8cd7a363a44b9aa442f1e32bea816083644
|
[
"Apache-2.0"
] | null | null | null |
class ReportDataKeys:
red = "red"
green = "green"
blue = "blue"
amount = "amount"
avg = "avg"
sum = "sum"
min = "min"
max = "max"
gen_time = "gen_time"
rabbit_consumed = "rabbit_consumed"
mysql_red = "mysql_red"
mysql_green = "mysql_green"
mysql_blue = "mysql_blue"
mysql_total = "mysql_total"
select_report_data_query = "select(select count(order_id) from order_records as w where (select count(order_id) " \
"from order_records as q where q.order_id=w.order_id group by order_id)=1 and " \
"status in(3,4,5)) as red1,(select count(order_id) from order_records as w " \
"where (select count(order_id) from order_records as q where q.order_id=w.order_id group " \
"by order_id)=2 and (select count(order_id) from order_records as r where " \
"r.order_id=w.order_id and status=1)=0) as red2,(select count(order_id) " \
"from order_records as w where (select count(order_id) from order_records as q " \
"where q.order_id=w.order_id group by order_id)=1 and status=1) as blue1," \
"(select count(order_id) from order_records as w where (select count(order_id) " \
"from order_records as q where q.order_id=w.order_id group by order_id)=2 and " \
"(select count(order_id) from order_records as r where r.order_id=w.order_id and " \
"status in(3,4,5))=0) as blue2,(select count(order_id) from order_records as w " \
"where (select count(order_id) from order_records as q where q.order_id=w.order_id " \
"group by order_id)=3) as green,(select count(order_id) from order_records) as total;"
| 62.032258
| 120
| 0.573583
| 275
| 1,923
| 3.792727
| 0.163636
| 0.214765
| 0.199425
| 0.224353
| 0.745925
| 0.745925
| 0.736337
| 0.736337
| 0.701822
| 0.701822
| 0
| 0.014798
| 0.332293
| 1,923
| 30
| 121
| 64.1
| 0.797508
| 0
| 0
| 0
| 0
| 0.137931
| 0.628632
| 0.077655
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0.517241
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 10
|
6c4766c2a81b6fa434ce83769a4d107067da9163
| 46
|
py
|
Python
|
oleda/__init__.py
|
ultralab-by/oleda
|
40573864268d99dddf95d1b2b8daeccf8b440b9a
|
[
"Apache-2.0"
] | null | null | null |
oleda/__init__.py
|
ultralab-by/oleda
|
40573864268d99dddf95d1b2b8daeccf8b440b9a
|
[
"Apache-2.0"
] | null | null | null |
oleda/__init__.py
|
ultralab-by/oleda
|
40573864268d99dddf95d1b2b8daeccf8b440b9a
|
[
"Apache-2.0"
] | null | null | null |
from .eda import *
from .eda_anova import *
| 9.2
| 24
| 0.695652
| 7
| 46
| 4.428571
| 0.571429
| 0.451613
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.217391
| 46
| 4
| 25
| 11.5
| 0.861111
| 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
| 1
| 0
|
0
| 7
|
dd04e47d61b6966385d9dc8a6624042ded7ece8b
| 160
|
py
|
Python
|
ananse/commands/__init__.py
|
simonvh/ANANSE
|
ad494da23896ebef87624421d012c4d189cb9a43
|
[
"MIT"
] | null | null | null |
ananse/commands/__init__.py
|
simonvh/ANANSE
|
ad494da23896ebef87624421d012c4d189cb9a43
|
[
"MIT"
] | null | null | null |
ananse/commands/__init__.py
|
simonvh/ANANSE
|
ad494da23896ebef87624421d012c4d189cb9a43
|
[
"MIT"
] | 1
|
2022-03-02T20:45:15.000Z
|
2022-03-02T20:45:15.000Z
|
from ananse.commands.binding import binding # noqa
from ananse.commands.influence import influence # noqa
from ananse.commands.network import network # noqa
| 40
| 55
| 0.8125
| 21
| 160
| 6.190476
| 0.380952
| 0.230769
| 0.415385
| 0.338462
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.13125
| 160
| 3
| 56
| 53.333333
| 0.935252
| 0.0875
| 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
| 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
| 8
|
06c4b31a7cfb1bb882b32ec93dd267ede849700a
| 2,103
|
py
|
Python
|
tests.py
|
piethrower/DiceAverage
|
66b6cd80c924d639ad22cfdde634be8910fd6dc6
|
[
"MIT"
] | null | null | null |
tests.py
|
piethrower/DiceAverage
|
66b6cd80c924d639ad22cfdde634be8910fd6dc6
|
[
"MIT"
] | null | null | null |
tests.py
|
piethrower/DiceAverage
|
66b6cd80c924d639ad22cfdde634be8910fd6dc6
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python3
import unittest
import math
import Dice_Average as dice
from Dice_Average import Dist
class TestDiceRoller(unittest.TestCase):
def test_math(self):
self.assertEqual(dice.parse_math("-1"), Dist(-1))
self.assertEqual(dice.parse_math("-10"), Dist(-10))
self.assertEqual(dice.parse_math("-1030"), Dist(-1030))
self.assertEqual(dice.parse_math("1+1"), Dist(2))
self.assertEqual(dice.parse_math("1+10"), Dist(11))
self.assertEqual(dice.parse_math("10+20+30"), Dist(60))
self.assertEqual(dice.parse_math("1-1"), Dist(0))
self.assertEqual(dice.parse_math("1-10"), Dist(-9))
self.assertEqual(dice.parse_math("10-20-30"), Dist(-40))
self.assertEqual(dice.parse_math("1*1"), Dist(1))
self.assertEqual(dice.parse_math("1*10"), Dist(10))
self.assertEqual(dice.parse_math("10*20*30"), Dist(10 * 20 * 30))
self.assertEqual(dice.parse_math("1/1"), Dist(1))
self.assertEqual(dice.parse_math("1/10"), Dist(0.1))
self.assertEqual(dice.parse_math("10/20/30"), Dist(10 / 20 / 30))
self.assertEqual(dice.parse_math("2%2"), Dist(0))
self.assertEqual(dice.parse_math("1%2"), Dist(1))
self.assertEqual(dice.parse_math("11%10"), Dist(1))
self.assertEqual(dice.parse_math("1030%30"), Dist(10))
self.assertEqual(dice.parse_math("-2%2"), Dist(0))
self.assertEqual(dice.parse_math("-1%2"), Dist(1))
self.assertEqual(dice.parse_math("-11%10"), Dist(9))
self.assertEqual(dice.parse_math("-1030%30"), Dist(20))
self.assertEqual(dice.parse_math("1^1"), Dist(1))
self.assertEqual(dice.parse_math("2^3"), Dist(8))
self.assertEqual(dice.parse_math("10^0"), Dist(1))
self.assertEqual(dice.parse_math("2^-2"), Dist(0.25))
self.assertEqual(dice.parse_math("2*3-60*0.2"), Dist(-6))
self.assertEqual(dice.parse_math("2^3-6/-6"), Dist(9))
self.assertEqual(dice.parse_math("20%15/5+2"), Dist(3))
if __name__ == '__main__':
# Main module
unittest.main(buffer=True)
| 39.679245
| 73
| 0.634807
| 318
| 2,103
| 4.069182
| 0.132075
| 0.347759
| 0.440495
| 0.556414
| 0.826121
| 0.826121
| 0.778207
| 0.702473
| 0.474498
| 0.388717
| 0
| 0.090493
| 0.169757
| 2,103
| 52
| 74
| 40.442308
| 0.65063
| 0.01379
| 0
| 0
| 0
| 0
| 0.076737
| 0
| 0
| 0
| 0
| 0
| 0.789474
| 1
| 0.026316
| false
| 0
| 0.105263
| 0
| 0.157895
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
661659a63806cff88f96ed5e59353e05064c2fed
| 63
|
py
|
Python
|
g2pg/__init__.py
|
jesska-f/g2pg
|
f03519e0ab7940ebb05ddb295813c44eac5d5ab0
|
[
"MIT"
] | 3
|
2020-07-20T06:13:02.000Z
|
2021-11-18T19:53:10.000Z
|
g2pg/__init__.py
|
jesska-f/g2pg
|
f03519e0ab7940ebb05ddb295813c44eac5d5ab0
|
[
"MIT"
] | null | null | null |
g2pg/__init__.py
|
jesska-f/g2pg
|
f03519e0ab7940ebb05ddb295813c44eac5d5ab0
|
[
"MIT"
] | null | null | null |
from .g2pg import get_df_from_gsheet
from .g2pg import df_to_db
| 31.5
| 36
| 0.857143
| 13
| 63
| 3.769231
| 0.615385
| 0.326531
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.035714
| 0.111111
| 63
| 2
| 37
| 31.5
| 0.839286
| 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
| 1
| 0
|
0
| 7
|
b0d21955a62c170ef326bc4f56c720d840de41ea
| 125
|
py
|
Python
|
databind.core/src/test/test_star_imports.py
|
NiklasRosenstein/databind
|
d3811283fc023d234845ac9946328681b5e531bd
|
[
"MIT"
] | 2
|
2020-11-25T14:31:10.000Z
|
2021-08-17T14:33:53.000Z
|
databind.core/src/test/test_star_imports.py
|
NiklasRosenstein/databind
|
d3811283fc023d234845ac9946328681b5e531bd
|
[
"MIT"
] | 17
|
2020-09-05T04:51:43.000Z
|
2022-02-24T22:59:24.000Z
|
databind.core/src/test/test_star_imports.py
|
NiklasRosenstein/databind
|
d3811283fc023d234845ac9946328681b5e531bd
|
[
"MIT"
] | null | null | null |
from databind.core.annotations import *
from databind.core.dataclasses import *
from databind.core import * # type: ignore
| 25
| 43
| 0.784
| 16
| 125
| 6.125
| 0.5
| 0.367347
| 0.489796
| 0.44898
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136
| 125
| 4
| 44
| 31.25
| 0.907407
| 0.096
| 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
| 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
| 8
|
7c038e5d2aecfcd6d222a6db11441c9f50680304
| 50
|
py
|
Python
|
tests/integration/conftest.py
|
tomchuk/meetup_20160428
|
9879e42c7535c6af9bee10697c6fdb046b63473d
|
[
"MIT"
] | null | null | null |
tests/integration/conftest.py
|
tomchuk/meetup_20160428
|
9879e42c7535c6af9bee10697c6fdb046b63473d
|
[
"MIT"
] | null | null | null |
tests/integration/conftest.py
|
tomchuk/meetup_20160428
|
9879e42c7535c6af9bee10697c6fdb046b63473d
|
[
"MIT"
] | null | null | null |
from .fixtures import *
from ..fixtures import *
| 12.5
| 24
| 0.72
| 6
| 50
| 6
| 0.5
| 0.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.18
| 50
| 3
| 25
| 16.666667
| 0.878049
| 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
| 1
| 0
|
0
| 7
|
9fe9c4ca8bf1e4a470fb9e75f86ca4123cbacf67
| 6,932
|
py
|
Python
|
libraries/botframework-connector/tests/test_auth.py
|
matthewshim-ms/botbuilder-python
|
f3dbce50de01a43afdcf3efdbdca6e0d5c96dc98
|
[
"MIT"
] | null | null | null |
libraries/botframework-connector/tests/test_auth.py
|
matthewshim-ms/botbuilder-python
|
f3dbce50de01a43afdcf3efdbdca6e0d5c96dc98
|
[
"MIT"
] | null | null | null |
libraries/botframework-connector/tests/test_auth.py
|
matthewshim-ms/botbuilder-python
|
f3dbce50de01a43afdcf3efdbdca6e0d5c96dc98
|
[
"MIT"
] | null | null | null |
import pytest
from botbuilder.schema import Activity
from botframework.connector.auth import JwtTokenValidation
from botframework.connector.auth import SimpleCredentialProvider
from botframework.connector.auth import EmulatorValidation
from botframework.connector.auth import ChannelValidation
import asyncio
class TestAuth():
EmulatorValidation.TO_BOT_FROM_EMULATOR_TOKEN_VALIDATION_PARAMETERS.ignore_expiration = True
ChannelValidation.TO_BOT_FROM_CHANNEL_TOKEN_VALIDATION_PARAMETERS.ignore_expiration = True
@pytest.mark.asyncio
async def test_connector_auth_header_correct_app_id_and_service_url_should_validate(self):
activity = Activity(service_url = 'https://webchat.botframework.com/')
header = 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsImtpZCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSIsIng1dCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSJ9.eyJzZXJ2aWNldXJsIjoiaHR0cHM6Ly93ZWJjaGF0LmJvdGZyYW1ld29yay5jb20vIiwiaXNzIjoiaHR0cHM6Ly9hcGkuYm90ZnJhbWV3b3JrLmNvbSIsImF1ZCI6IjM5NjE5YTU5LTVhMGMtNGY5Yi04N2M1LTgxNmM2NDhmZjM1NyIsImV4cCI6MTUxNjczNzUyMCwibmJmIjoxNTE2NzM2OTIwfQ.TBgpxbDS-gx1wm7ldvl7To-igfskccNhp-rU1mxUMtGaDjnsU--usH4OXZfzRsZqMlnXWXug_Hgd_qOr5RH8wVlnXnMWewoZTSGZrfp8GOd7jHF13Gz3F1GCl8akc3jeK0Ppc8R_uInpuUKa0SopY0lwpDclCmvDlz4PN6yahHkt_666k-9UGmRt0DDkxuYjbuYG8EDZxyyAhr7J6sFh3yE2UGRpJjRDB4wXWqv08Cp0Gn9PAW2NxOyN8irFzZH5_YZqE3DXDAYZ_IOLpygXQR0O-bFIhLDVxSz6uCeTBRjh8GU7XJ_yNiRDoaby7Rd2IfRrSnvMkBRsB8MsWN8oXg'
credentials = SimpleCredentialProvider('39619a59-5a0c-4f9b-87c5-816c648ff357', '')
try:
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
except:
pytest.fail("Unexpected error")
@pytest.mark.asyncio
async def test_connector_auth_header_with_different_bot_app_id_should_not_validate(self):
activity = Activity(service_url = 'https://webchat.botframework.com/')
header = 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsImtpZCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSIsIng1dCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSJ9.eyJzZXJ2aWNldXJsIjoiaHR0cHM6Ly93ZWJjaGF0LmJvdGZyYW1ld29yay5jb20vIiwiaXNzIjoiaHR0cHM6Ly9hcGkuYm90ZnJhbWV3b3JrLmNvbSIsImF1ZCI6IjM5NjE5YTU5LTVhMGMtNGY5Yi04N2M1LTgxNmM2NDhmZjM1NyIsImV4cCI6MTUxNjczNzUyMCwibmJmIjoxNTE2NzM2OTIwfQ.TBgpxbDS-gx1wm7ldvl7To-igfskccNhp-rU1mxUMtGaDjnsU--usH4OXZfzRsZqMlnXWXug_Hgd_qOr5RH8wVlnXnMWewoZTSGZrfp8GOd7jHF13Gz3F1GCl8akc3jeK0Ppc8R_uInpuUKa0SopY0lwpDclCmvDlz4PN6yahHkt_666k-9UGmRt0DDkxuYjbuYG8EDZxyyAhr7J6sFh3yE2UGRpJjRDB4wXWqv08Cp0Gn9PAW2NxOyN8irFzZH5_YZqE3DXDAYZ_IOLpygXQR0O-bFIhLDVxSz6uCeTBRjh8GU7XJ_yNiRDoaby7Rd2IfRrSnvMkBRsB8MsWN8oXg'
credentials = SimpleCredentialProvider('00000000-0000-0000-0000-000000000000', '')
with pytest.raises(Exception):
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
@pytest.mark.asyncio
async def test_connector_auth_header_and_no_credential_should_not_validate(self):
activity = Activity(service_url = 'https://webchat.botframework.com/')
header = 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsImtpZCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSIsIng1dCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSJ9.eyJzZXJ2aWNldXJsIjoiaHR0cHM6Ly93ZWJjaGF0LmJvdGZyYW1ld29yay5jb20vIiwiaXNzIjoiaHR0cHM6Ly9hcGkuYm90ZnJhbWV3b3JrLmNvbSIsImF1ZCI6IjM5NjE5YTU5LTVhMGMtNGY5Yi04N2M1LTgxNmM2NDhmZjM1NyIsImV4cCI6MTUxNjczNzUyMCwibmJmIjoxNTE2NzM2OTIwfQ.TBgpxbDS-gx1wm7ldvl7To-igfskccNhp-rU1mxUMtGaDjnsU--usH4OXZfzRsZqMlnXWXug_Hgd_qOr5RH8wVlnXnMWewoZTSGZrfp8GOd7jHF13Gz3F1GCl8akc3jeK0Ppc8R_uInpuUKa0SopY0lwpDclCmvDlz4PN6yahHkt_666k-9UGmRt0DDkxuYjbuYG8EDZxyyAhr7J6sFh3yE2UGRpJjRDB4wXWqv08Cp0Gn9PAW2NxOyN8irFzZH5_YZqE3DXDAYZ_IOLpygXQR0O-bFIhLDVxSz6uCeTBRjh8GU7XJ_yNiRDoaby7Rd2IfRrSnvMkBRsB8MsWN8oXg'
credentials = SimpleCredentialProvider('', '')
with pytest.raises(Exception):
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
@pytest.mark.asyncio
async def test_emulator_msa_header_correct_app_id_and_service_url_should_validate(self):
activity = Activity(service_url = '')
header = 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsIng1dCI6IlNTUWRoSTFjS3ZoUUVEU0p4RTJnR1lzNDBRMCIsImtpZCI6IlNTUWRoSTFjS3ZoUUVEU0p4RTJnR1lzNDBRMCJ9.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.B0t4sSsqIQ3IT2rfpZXqAdAGJSr3aihwk-jJd8as2pAoeQVcQNir_Anvvnjbo5MsB0DCyWFa9xnEmBRiTW_Ww97Z9bZhnCXq4D4vN8dmgEMV_Aci1tI4agy3coCX4fBRc76SHjqJ_ucl850aqR3d_0sfl0TPoDclE4jWssX2YTNzUAMEgisbYe9xv8FfK7AUR8ABS1teTfnWGVYyVFgC7vptSjw-de8sgz7pv8vVtLEKBrrb1FBSzHbbnZ-cQaLLHeIM4agamXf4w45o7_1uHorrp1Tg5oPrsbiayC-dt4lpC9smU5agpyUWCorKZI0Fp3aryG4519cYuLyXuUVh0A'
credentials = SimpleCredentialProvider('39619a59-5a0c-4f9b-87c5-816c648ff357', '')
try:
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
except:
pytest.fail("Unexpected error")
@pytest.mark.asyncio
async def test_emulator_msa_header_and_no_credential_should_not_validate(self):
activity = Activity(service_url = '')
header = 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsImtpZCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSIsIng1dCI6IkdDeEFyWG9OOFNxbzdQd2VBNy16NjVkZW5KUSJ9.eyJzZXJ2aWNldXJsIjoiaHR0cHM6Ly93ZWJjaGF0LmJvdGZyYW1ld29yay5jb20vIiwiaXNzIjoiaHR0cHM6Ly9hcGkuYm90ZnJhbWV3b3JrLmNvbSIsImF1ZCI6IjM5NjE5YTU5LTVhMGMtNGY5Yi04N2M1LTgxNmM2NDhmZjM1NyIsImV4cCI6MTUxNjczNzUyMCwibmJmIjoxNTE2NzM2OTIwfQ.TBgpxbDS-gx1wm7ldvl7To-igfskccNhp-rU1mxUMtGaDjnsU--usH4OXZfzRsZqMlnXWXug_Hgd_qOr5RH8wVlnXnMWewoZTSGZrfp8GOd7jHF13Gz3F1GCl8akc3jeK0Ppc8R_uInpuUKa0SopY0lwpDclCmvDlz4PN6yahHkt_666k-9UGmRt0DDkxuYjbuYG8EDZxyyAhr7J6sFh3yE2UGRpJjRDB4wXWqv08Cp0Gn9PAW2NxOyN8irFzZH5_YZqE3DXDAYZ_IOLpygXQR0O-bFIhLDVxSz6uCeTBRjh8GU7XJ_yNiRDoaby7Rd2IfRrSnvMkBRsB8MsWN8oXg'
credentials = SimpleCredentialProvider('00000000-0000-0000-0000-000000000000', '')
with pytest.raises(Exception):
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
@pytest.mark.asyncio
async def test_empty_header_and_no_credential_should_validate(self):
activity = Activity(service_url = '')
header = ''
credentials = SimpleCredentialProvider('', '')
try:
await JwtTokenValidation.assert_valid_activity(activity, header, credentials)
except:
pytest.fail("Unexpected error")
| 101.941176
| 1,073
| 0.874639
| 391
| 6,932
| 15.189258
| 0.253197
| 0.032329
| 0.017175
| 0.022226
| 0.778919
| 0.75181
| 0.736656
| 0.736656
| 0.726217
| 0.710052
| 0
| 0.10302
| 0.082804
| 6,932
| 67
| 1,074
| 103.462687
| 0.831079
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| 0.689655
| 0
| 0
| 0.600837
| 0.574582
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| 0.103448
| 1
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| false
| 0
| 0.12069
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| 0.137931
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| 1
| null | 0
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| 1
| 1
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| 1
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|
0
| 10
|
b02154541bf581fe0d5bfe479547cee8038c941c
| 72
|
py
|
Python
|
py_pdf_term/stylings/__init__.py
|
kumachan-mis/pdf-slides-term
|
cf3319e4de723bd9424d23141803342d3c649103
|
[
"MIT"
] | 1
|
2021-01-08T16:05:30.000Z
|
2021-01-08T16:05:30.000Z
|
py_pdf_term/stylings/__init__.py
|
kumachan-mis/py-slides-term
|
1e9337b97ae8968950489e728fc7aeeeb7eb1f4b
|
[
"MIT"
] | 21
|
2021-01-03T13:50:59.000Z
|
2021-06-17T00:27:49.000Z
|
py_pdf_term/stylings/__init__.py
|
kumachan-mis/pdf-slides-term
|
cf3319e4de723bd9424d23141803342d3c649103
|
[
"MIT"
] | null | null | null |
from ._stylings import * # NoQA
from ._stylings import __all__ # NoQA
| 24
| 38
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0
| 7
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c6b8b952fede09ddc3cb92a1323157f85b85b20a
| 13,858
|
py
|
Python
|
tests/integration/test_github_fas_verification.py
|
majamassarini/packit-service
|
12baf67799412c8fa56e2a821cd9d584e2437141
|
[
"MIT"
] | 20
|
2019-05-24T12:33:05.000Z
|
2020-07-28T06:03:57.000Z
|
tests/integration/test_github_fas_verification.py
|
majamassarini/packit-service
|
12baf67799412c8fa56e2a821cd9d584e2437141
|
[
"MIT"
] | 735
|
2019-05-15T11:52:36.000Z
|
2020-08-02T23:21:44.000Z
|
tests/integration/test_github_fas_verification.py
|
majamassarini/packit-service
|
12baf67799412c8fa56e2a821cd9d584e2437141
|
[
"MIT"
] | 28
|
2019-05-16T13:32:03.000Z
|
2020-07-29T10:23:54.000Z
|
# Copyright Contributors to the Packit project.
# SPDX-License-Identifier: MIT
import json
import pytest
from celery.canvas import Signature
from flexmock import flexmock
from ogr.services.github import GithubProject
from packit.config import Deployment
from packit_service.config import ServiceConfig, PackageConfigGetter
from packit_service.constants import SANDCASTLE_WORK_DIR
from packit_service.models import (
AllowlistStatus,
AllowlistModel,
GithubInstallationModel,
)
from packit_service.worker.allowlist import Allowlist
from packit_service.worker.jobs import SteveJobs
from packit_service.worker.monitoring import Pushgateway
from packit_service.worker.tasks import run_github_fas_verification_handler
from tests.spellbook import DATA_DIR, first_dict_value, get_parameters_from_results
def issue_comment_event():
return json.loads(
(DATA_DIR / "webhooks" / "github" / "issue_comment_verify_fas.json").read_text()
)
def test_verification_successful():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").once()
flexmock(Pushgateway).should_receive("push").once().and_return()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue_comment = flexmock()
issue = flexmock(
author="packit-as-a-service[bot]",
title="User example-user needs to be approved.",
close=lambda: None,
get_comment=lambda issue_id: issue_comment,
)
flexmock(issue_comment).should_receive("add_reaction").once()
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
flexmock(Allowlist).should_receive("is_approved").and_return(False)
flexmock(Allowlist).should_receive(
"is_github_username_from_fas_account_matching"
).with_args(fas_account="my-fas-account", sender_login="phracek").and_return(True)
flexmock(AllowlistModel).should_receive("add_namespace").with_args(
"github.com/example-user",
AllowlistStatus.approved_automatically.value,
"my-fas-account",
).once()
flexmock(GithubInstallationModel).should_receive("get_by_account_login").with_args(
"example-user"
).and_return(flexmock(sender_login="phracek"))
msg = (
"Namespace `github.com/example-user` approved successfully "
"using FAS account `my-fas-account`!"
)
flexmock(issue).should_receive("comment").with_args(msg).once()
results = run_github_fas_verification_handler(
package_config=package_config,
event=event_dict,
job_config=job_config,
)
assert first_dict_value(results["job"])["success"]
def test_verification_not_successful():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").once()
flexmock(Pushgateway).should_receive("push").once().and_return()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue_comment = flexmock()
issue = flexmock(
author="packit-as-a-service[bot]",
title="User example-user needs to be approved.",
close=lambda: None,
get_comment=lambda issue_id: issue_comment,
)
flexmock(issue_comment).should_receive("add_reaction").once()
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
flexmock(Allowlist).should_receive("is_approved").and_return(False)
flexmock(Allowlist).should_receive(
"is_github_username_from_fas_account_matching"
).with_args(fas_account="my-fas-account", sender_login="phracek").and_return(False)
flexmock(AllowlistModel).should_receive("add_namespace").never()
flexmock(GithubInstallationModel).should_receive("get_by_account_login").with_args(
"example-user"
).and_return(flexmock(sender_login="phracek"))
msg = (
"We were not able to find a match between the GitHub Username field in the FAS account "
"`my-fas-account` and GitHub user `phracek`. Please, check that you have set "
"[the field]"
"(https://accounts.fedoraproject.org/user/my-fas-account/settings/profile/#github) "
"correctly and try again or contact [us](https://packit.dev/#contact)."
)
flexmock(issue).should_receive("comment").with_args(msg).once()
results = run_github_fas_verification_handler(
package_config=package_config,
event=event_dict,
job_config=job_config,
)
assert first_dict_value(results["job"])["success"]
@pytest.mark.parametrize(
"comment",
(["/packit verify-fas more names", "/packit verify-fas"]),
)
def test_verification_incorrect_format(comment):
event_issue_comment = issue_comment_event()
event_issue_comment["comment"]["body"] = comment
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").once()
flexmock(Pushgateway).should_receive("push").once().and_return()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue_comment = flexmock()
issue = flexmock(
author="packit-as-a-service[bot]",
title="User example-user needs to be approved.",
close=lambda: None,
get_comment=lambda issue_id: issue_comment,
)
flexmock(issue_comment).should_receive("add_reaction").once()
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(event_issue_comment)
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
flexmock(Allowlist).should_receive(
"is_github_username_from_fas_account_matching"
).never()
flexmock(AllowlistModel).should_receive("add_namespace").never()
flexmock(GithubInstallationModel).should_receive("get_by_account_login").with_args(
"example-user"
).and_return(flexmock(sender_login="phracek"))
msg = (
"Incorrect format of the Packit verification comment command. The expected format: "
"`/packit verify-fas my-fas-account`"
)
flexmock(issue).should_receive("comment").with_args(msg).once()
results = run_github_fas_verification_handler(
package_config=package_config,
event=event_dict,
job_config=job_config,
)
assert not first_dict_value(results["job"])["success"]
def test_verification_already_approved():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").once()
flexmock(Pushgateway).should_receive("push").once().and_return()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue_comment = flexmock()
issue = flexmock(
author="packit-as-a-service[bot]",
title="User example-user needs to be approved.",
close=lambda: None,
get_comment=lambda issue_id: issue_comment,
)
flexmock(issue_comment).should_receive("add_reaction").once()
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
flexmock(Allowlist).should_receive("is_approved").and_return(True)
flexmock(AllowlistModel).should_receive("add_namespace").never()
flexmock(GithubInstallationModel).should_receive("get_by_account_login").with_args(
"example-user"
).and_return(flexmock(sender_login="phracek"))
msg = "Namespace `github.com/example-user` was already approved."
flexmock(issue).should_receive("comment").with_args(msg).once()
results = run_github_fas_verification_handler(
package_config=package_config,
event=event_dict,
job_config=job_config,
)
assert first_dict_value(results["job"])["success"]
def test_verification_wrong_repository():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").never()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue = flexmock(
author="not-packit",
title="User example-user needs to be approved.",
close=lambda: None,
)
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
def test_verification_wrong_issue():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").never()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue = flexmock(
author="not-packit",
title="User example-user needs to be approved.",
close=lambda: None,
)
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
def test_verification_not_original_triggerer():
config = ServiceConfig()
config.command_handler_work_dir = SANDCASTLE_WORK_DIR
config.deployment = Deployment.prod
flexmock(ServiceConfig).should_receive("get_service_config").and_return(config)
flexmock(Signature).should_receive("apply_async").once()
flexmock(Pushgateway).should_receive("push").once().and_return()
flexmock(GithubProject).should_receive("is_private").and_return(False)
flexmock(GithubProject).should_receive("get_latest_release").and_return(None)
flexmock(PackageConfigGetter).should_receive(
"get_package_config_from_repo"
).and_return(None)
issue_comment = flexmock()
issue = flexmock(
author="packit-as-a-service[bot]",
title="User example-user needs to be approved.",
close=lambda: None,
get_comment=lambda issue_id: issue_comment,
)
flexmock(issue_comment).should_receive("add_reaction").once()
flexmock(GithubProject).should_receive("get_issue").and_return(issue)
processing_results = SteveJobs().process_message(issue_comment_event())
event_dict, job, job_config, package_config = get_parameters_from_results(
processing_results
)
assert json.dumps(event_dict)
flexmock(Allowlist).should_receive("is_approved").and_return(True)
flexmock(AllowlistModel).should_receive("add_namespace").never()
flexmock(GithubInstallationModel).should_receive("get_by_account_login").with_args(
"example-user"
).and_return(flexmock(sender_login="somebody-else"))
msg = (
"Packit verification comment command not created by the person who "
"installed the application."
)
flexmock(issue).should_receive("comment").with_args(msg).once()
results = run_github_fas_verification_handler(
package_config=package_config,
event=event_dict,
job_config=job_config,
)
assert first_dict_value(results["job"])["success"]
| 38.176309
| 96
| 0.739861
| 1,627
| 13,858
| 5.979717
| 0.106945
| 0.09888
| 0.054271
| 0.073389
| 0.839552
| 0.833076
| 0.833076
| 0.833076
| 0.833076
| 0.822695
| 0
| 0
| 0.151321
| 13,858
| 362
| 97
| 38.281768
| 0.827226
| 0.00534
| 0
| 0.72449
| 0
| 0.003401
| 0.184892
| 0.03991
| 0
| 0
| 0
| 0
| 0.040816
| 1
| 0.027211
| false
| 0
| 0.047619
| 0.003401
| 0.078231
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c6bd865c4ba420ef91d14b34bf34f8be1e78039a
| 117
|
py
|
Python
|
openselfsup/models/heads/__init__.py
|
bin20192019/OpenSelfSup
|
6891da7612b9ddfbd06beb7ffad3592513d190bb
|
[
"Apache-2.0"
] | 2
|
2020-06-23T14:28:24.000Z
|
2020-06-23T14:28:31.000Z
|
openselfsup/models/heads/__init__.py
|
Yipeng-Sun/OpenSelfSup
|
55af4e9ba1934e1e1c18f208481db97ed507db76
|
[
"Apache-2.0"
] | null | null | null |
openselfsup/models/heads/__init__.py
|
Yipeng-Sun/OpenSelfSup
|
55af4e9ba1934e1e1c18f208481db97ed507db76
|
[
"Apache-2.0"
] | null | null | null |
from .contrastive_head import ContrastiveHead
from .cls_head import ClsHead
from .multi_cls_head import MultiClsHead
| 29.25
| 45
| 0.871795
| 16
| 117
| 6.125
| 0.5625
| 0.306122
| 0.265306
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.102564
| 117
| 3
| 46
| 39
| 0.933333
| 0
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| 0
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| true
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| 0
| null | 1
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| 0
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| null | 0
| 0
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| 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
05e367d85b637e36ad8aa48a6bc26f15125362af
| 15,919
|
py
|
Python
|
tests.py
|
MadryLab/glm_saga
|
4016cfe8f529bb67ebf1ff40135cf9e0181a77e8
|
[
"MIT"
] | 10
|
2021-05-12T17:40:27.000Z
|
2022-02-22T00:30:57.000Z
|
tests.py
|
MadryLab/glm_saga
|
4016cfe8f529bb67ebf1ff40135cf9e0181a77e8
|
[
"MIT"
] | null | null | null |
tests.py
|
MadryLab/glm_saga
|
4016cfe8f529bb67ebf1ff40135cf9e0181a77e8
|
[
"MIT"
] | 2
|
2021-07-21T19:40:36.000Z
|
2022-02-22T01:15:39.000Z
|
from glm_saga.elasticnet import *
from sklearn.linear_model import LogisticRegression, ElasticNet
from sklearn.datasets import make_classification, make_regression
from sklearn.metrics import r2_score
def toy_example(verbose=False, tol=1e-5):
NITERS = 2000
NUM_CLASSES=3
ALPHA = 0.5
STEP_SIZE = 1
DEVICE = 'cpu'
# dataset parameter
NUM_FEATURES = 10
X, y = make_classification(n_features=NUM_FEATURES, random_state=0, n_classes=NUM_CLASSES,
n_informative=3)
# Standardize input
X = (X-X.mean(0))/X.std(0)
# calculate maximum lam, use half of maximum for testing purposes
LAMBDA = maximum_reg(ch.from_numpy(X).float(),ch.from_numpy(y).long(), group=False) / max(0.001, ALPHA)
LAMBDA = LAMBDA / 2
# Convert to sklearn parameters
C = 1/(LAMBDA*X.shape[0])
regr = LogisticRegression(random_state=0, penalty='elasticnet',
multi_class='multinomial',
l1_ratio=ALPHA, C=C,
solver='saga', max_iter=NITERS, tol=1e-6)
regr.fit(X, y)
if verbose:
print(regr.coef_)
print(regr.intercept_)
print(regr.predict(np.zeros((1,10))))
X,y = ch.from_numpy(X).float(), ch.from_numpy(y).long()
linear = nn.Linear(X.size(1),NUM_CLASSES)
weight = linear.weight
bias = linear.bias
# Print sklearn loss
weight.data = ch.from_numpy(regr.coef_).float()
bias.data = ch.from_numpy(regr.intercept_).float()
loss_sklearn = elastic_loss(linear, X, y, LAMBDA, ALPHA).item()
print(f"sklearn loss: {loss_sklearn}")
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_sklearn = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
# note: sklearn doesn't do group elasticnet
train(linear, X, y, STEP_SIZE, NITERS, LAMBDA, ALPHA, group=False)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,NUM_FEATURES)).max(1)[1])
loss_pg = elastic_loss(linear, X, y, LAMBDA, ALPHA).item()
print(f"proximal gradient loss: {loss_pg}")
assert abs(loss_pg - loss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_pg = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(nnz_pg - nnz_sklearn) < tol
dataset = IndexedTensorDataset(X,y)
loader = DataLoader(dataset, batch_size=20, shuffle=True)
LAMBDA_loader = maximum_reg_loader(loader, group=False) / max(0.001, ALPHA)
print("lam check", LAMBDA, LAMBDA_loader/2)
assert abs(LAMBDA - LAMBDA_loader/2) < tol
for p in [weight,bias]:
p.data.zero_()
p.grad.zero_()
train_spg(linear, loader, 0.1*STEP_SIZE, 2*NITERS, LAMBDA, ALPHA, group=False, verbose=None)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,NUM_FEATURES)).max(1)[1])
loss_spg = elastic_loss(linear, X, y, LAMBDA, ALPHA).item()
print(f"spg loss: {loss_spg}")
assert abs(loss_spg - loss_sklearn) < tol
for p in [weight,bias]:
p.data.zero_()
p.grad.zero_()
train_saga(linear, loader, STEP_SIZE, NITERS, LAMBDA, ALPHA, group=False, verbose=None)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,NUM_FEATURES)).max(1)[1])
loss_saga = elastic_loss(linear, X, y, LAMBDA, ALPHA).item()
print(f"saga loss: {loss_saga}")
assert abs(loss_saga - loss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_saga = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(nnz_saga - nnz_sklearn) < tol
def toy_dataloader_example(verbose=False, tol=1e-5):
NITERS = 2000
NUM_CLASSES=3
ALPHA = 0.5
STEP_SIZE = 1
DEVICE = 'cpu'
# dataset parameter
NUM_FEATURES = 10
X, y = make_classification(n_features=NUM_FEATURES, random_state=0, n_classes=NUM_CLASSES,
n_informative=3)
X,y = ch.from_numpy(X).float(), ch.from_numpy(y).long()
import torch.nn as nn
model = nn.Sequential(
nn.Linear(NUM_FEATURES, 10),
nn.ReLU(),
nn.Linear(10,5)
)
ds = IndexedTensorDataset(X,y)
loader = DataLoader(ds, batch_size=2, shuffle=True)
preprocess = NormalizedRepresentation(loader, model)
# Standardize input
with ch.no_grad():
Z = model(X)
Z = (Z - Z.mean(0))/Z.std(0)
# calculate maximum lam
LAMBDA = maximum_reg(Z,y, group=False) / max(0.001, ALPHA)
# for testing purposes use half of maximum lambda
LAMBDA = LAMBDA / 2
# Convert to sklearn parameters
C = 1/(LAMBDA*X.shape[0])
regr = LogisticRegression(random_state=0, penalty='elasticnet',
multi_class='multinomial',
l1_ratio=ALPHA, C=C,
solver='saga', max_iter=NITERS, tol=1e-6)
regr.fit(Z.numpy(), y.numpy())
if verbose:
print(regr.coef_)
print(regr.intercept_)
print(regr.predict(np.zeros((1,5))))
linear = nn.Linear(Z.size(1),NUM_CLASSES)
weight = linear.weight
bias = linear.bias
# Print sklearn loss
weight.data = ch.from_numpy(regr.coef_).float()
bias.data = ch.from_numpy(regr.intercept_).float()
loss_sklearn = elastic_loss(linear, Z, y, LAMBDA, ALPHA).item()
print(f"sklearn loss: {loss_sklearn}")
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_sklearn = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
# sklearn doesn't do group elasticnet
train(linear, Z, y, STEP_SIZE, NITERS, LAMBDA, ALPHA, group=False)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
loss_pg = elastic_loss(linear, Z, y, LAMBDA, ALPHA).item()
print(f"proximal gradient loss: {loss_pg}")
assert abs(loss_pg - loss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_pg = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(nnz_pg - nnz_sklearn) < tol
dataset = IndexedTensorDataset(X,y)
loader = DataLoader(dataset, batch_size=20, shuffle=False)
LAMBDA_loader = maximum_reg_loader(loader, group=False, preprocess=preprocess) / max(0.001, ALPHA)
print("lam check", LAMBDA, LAMBDA_loader / 2)
assert abs(LAMBDA - LAMBDA_loader/2) < tol
for p in [weight,bias]:
p.data.zero_()
p.grad.zero_()
train_spg(linear, loader, 0.1*STEP_SIZE, 2*NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=None, preprocess=preprocess)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
loss_spg = elastic_loss(linear, Z, y, LAMBDA, ALPHA).item()
print(f"spg loss: {loss_spg}")
assert abs(loss_spg - loss_sklearn) < tol
for p in [weight,bias]:
p.data.zero_()
p.grad.zero_()
alt_loader = add_index_to_dataloader(DataLoader(TensorDataset(X,y), batch_size=20, shuffle=True))
train_saga(linear, alt_loader, STEP_SIZE, NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=None, preprocess=preprocess)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
loss_saga = elastic_loss(linear, Z, y, LAMBDA, ALPHA).item()
print(f"saga loss: {loss_saga}")
assert abs(loss_saga - loss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_saga = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(nnz_saga - nnz_sklearn) < tol
for p in [weight,bias]:
p.data.zero_()
p.grad.zero_()
print("==> testing with sample weights")
# sample_weight = np.arange(Z.size(0))/Z.size(0)
sample_weight = np.ones((Z.size(0),))
sample_weight[::2] = 0.5
regr.fit(Z.numpy(), y.numpy(), sample_weight = sample_weight)
if verbose:
print(regr.coef_)
print(regr.intercept_)
print(regr.predict(np.zeros((1,5))))
linear = nn.Linear(Z.size(1),NUM_CLASSES)
weight = linear.weight
bias = linear.bias
# Print sklearn loss
weight.data = ch.from_numpy(regr.coef_).float()
bias.data = ch.from_numpy(regr.intercept_).float()
sample_weight_pth = ch.from_numpy(sample_weight).float()
wloss_sklearn = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='multinomial', sample_weight=sample_weight_pth).item()
print(f"sklearn weighted loss: {wloss_sklearn}")
for p in [weight,bias]:
p.data.zero_()
weighted_loader = add_index_to_dataloader(DataLoader(TensorDataset(X,y), batch_size=20, shuffle=True), sample_weight=sample_weight_pth)
train_saga(linear, weighted_loader, STEP_SIZE, NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=None, preprocess=preprocess, family='multinomial', tol=1e-7, lookbehind=100)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
wloss_saga = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='multinomial', sample_weight=sample_weight_pth).item()
print(f"saga loss: {wloss_saga}")
assert abs(wloss_saga - wloss_sklearn) < tol
for p in [weight,bias]:
p.data.zero_()
alt_weighted_loader = DataLoader(IndexedTensorDataset(X,y,sample_weight_pth), batch_size=20, shuffle=True)
train_saga(linear, alt_weighted_loader, STEP_SIZE, NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=None, preprocess=preprocess, family='multinomial', tol=1e-7, lookbehind=100)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
wloss_saga = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='multinomial', sample_weight=sample_weight_pth).item()
print(f"alt saga loss: {wloss_saga}")
assert abs(wloss_saga - wloss_sklearn) < tol
def toy_regression_example(verbose=False, tol=1e-2):
# sklearn needs a lot of iterations to converge
NITERS = 100000
NUM_TARGETS=3
ALPHA = 0.9
STEP_SIZE = 0.1
DEVICE = 'cpu'
# dataset parameter
NUM_FEATURES = 10
X, y = make_regression(n_features=NUM_FEATURES, random_state=0, n_targets=NUM_TARGETS,
n_informative=2)
X,y = ch.from_numpy(X).float(), ch.from_numpy(y).float()
import torch.nn as nn
model = nn.Sequential(
nn.Linear(NUM_FEATURES, 10),
nn.ReLU(),
nn.Linear(10,5)
)
ds = IndexedTensorDataset(X,y)
loader = DataLoader(ds, batch_size=2, shuffle=True)
preprocess = NormalizedRepresentation(loader, model)
# Standardize input
with ch.no_grad():
Z = model(X)
Z = (Z - Z.mean(0))/Z.std(0)
# calculate maximum lam
LAMBDA = maximum_reg(Z,y, group=False, family='gaussian') / max(0.001, ALPHA)
# for testing purposes use half of maximum lambda
LAMBDA = LAMBDA / 2
# Convert to sklearn parameters
C = 1/(LAMBDA*X.shape[0])
regr = ElasticNet(alpha=LAMBDA, l1_ratio=ALPHA, tol=1e-8, max_iter=NITERS, selection='random')
regr.fit(Z.numpy(), y.numpy())
if verbose:
print(regr.coef_)
print(regr.intercept_)
print(regr.predict(np.zeros((1,5))))
linear = nn.Linear(Z.size(1),NUM_TARGETS)
weight = linear.weight
bias = linear.bias
# Print sklearn loss
weight.data = ch.from_numpy(regr.coef_).float()
bias.data = ch.from_numpy(regr.intercept_).float()
loss_sklearn = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='gaussian').item()
print(f"sklearn loss: {loss_sklearn}")
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_sklearn = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
for p in [weight,bias]:
p.data.zero_()
dataset = IndexedTensorDataset(X,y)
loader = DataLoader(dataset, batch_size=20, shuffle=True)
LAMBDA_loader = maximum_reg_loader(loader, group=False, family='gaussian', preprocess=preprocess) / max(0.001, ALPHA)
print("lam check", LAMBDA, LAMBDA_loader/2)
assert abs(LAMBDA - LAMBDA_loader/2) < tol
alt_loader = add_index_to_dataloader(DataLoader(TensorDataset(X,y), batch_size=20, shuffle=True))
train_saga(linear, alt_loader, STEP_SIZE, NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=NITERS//10, preprocess=preprocess, family='gaussian', tol=1e-9, lookbehind=100)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
loss_saga = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='gaussian').item()
print(f"saga loss: {loss_saga}")
assert abs(loss_saga - loss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
nnz_saga = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(nnz_saga - nnz_sklearn) < tol
print("==> testing with sample weights")
# sample_weight = np.arange(Z.size(0))/Z.size(0)
sample_weight = np.ones((Z.size(0),))
sample_weight[::2] = 0.5
# Elasticnet in sklearn does a thing where sample weights
# are automatically rescaled to sum to n_samples. See:
# https://github.com/scikit-learn/scikit-learn/blob/15a949460/sklearn/linear_model/_coordinate_descent.py#L791
#
# To make it consistent, we do the same thing here so that
# SAGA is seeing the same weights
sample_weight *= Z.size(0)/(sample_weight.sum())
regr.fit(Z.numpy(), y.numpy(), sample_weight = sample_weight)
if verbose:
print(regr.coef_)
print(regr.intercept_)
print(regr.predict(np.zeros((1,5))))
linear = nn.Linear(Z.size(1),NUM_TARGETS)
weight = linear.weight
bias = linear.bias
# Print sklearn loss
weight.data = ch.from_numpy(regr.coef_).float()
bias.data = ch.from_numpy(regr.intercept_).float()
sample_weight_pth = ch.from_numpy(sample_weight).float()
wloss_sklearn = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='gaussian', sample_weight=sample_weight_pth).item()
print(f"sklearn weighted loss: {wloss_sklearn}")
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
wnnz_sklearn = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
for p in [weight,bias]:
p.data.zero_()
weighted_loader = add_index_to_dataloader(DataLoader(TensorDataset(X,y), batch_size=20, shuffle=True), sample_weight=sample_weight_pth)
train_saga(linear, weighted_loader, STEP_SIZE, NITERS, ch.ones(1)*LAMBDA, ALPHA, group=False, verbose=NITERS//10, preprocess=preprocess, family='gaussian', tol=1e-7, lookbehind=100)
if verbose:
print(weight)
print(bias)
print(linear(ch.zeros(1,5)).max(1)[1])
wloss_saga = elastic_loss(linear, Z, y, LAMBDA, ALPHA, family='gaussian', sample_weight=sample_weight_pth).item()
print(f"saga loss: {wloss_saga}")
assert abs(wloss_saga - wloss_sklearn) < tol
nnz = (weight.abs() > 1e-5).sum().item()
total = weight.numel()
wnnz_saga = nnz/total
print(f"weight nnz {nnz}/{total} ({nnz/total:.4f})")
assert abs(wnnz_saga - wnnz_sklearn) < tol
if __name__ == "__main__":
print("==> toy example tests")
toy_example()
print("==> dataloader tests")
toy_dataloader_example()
print("==> regression tests")
toy_regression_example()
| 36.595402
| 187
| 0.629122
| 2,205
| 15,919
| 4.391837
| 0.089796
| 0.038414
| 0.022718
| 0.020446
| 0.894465
| 0.887753
| 0.885481
| 0.884655
| 0.866791
| 0.86266
| 0
| 0.02234
| 0.232364
| 15,919
| 435
| 188
| 36.595402
| 0.770131
| 0.064326
| 0
| 0.801242
| 0
| 0
| 0.080873
| 0
| 0
| 0
| 0
| 0
| 0.059006
| 1
| 0.009317
| false
| 0
| 0.018634
| 0
| 0.02795
| 0.242236
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
afc942e3ddf09f5cb7f879468ac3abb0da2aa06a
| 231
|
py
|
Python
|
Chapter 01/Chap01_Example1.50.py
|
bpbpublications/Programming-Techniques-using-Python
|
49b785f37e95a3aad1d36cef51e219ac56e5e9f0
|
[
"MIT"
] | null | null | null |
Chapter 01/Chap01_Example1.50.py
|
bpbpublications/Programming-Techniques-using-Python
|
49b785f37e95a3aad1d36cef51e219ac56e5e9f0
|
[
"MIT"
] | null | null | null |
Chapter 01/Chap01_Example1.50.py
|
bpbpublications/Programming-Techniques-using-Python
|
49b785f37e95a3aad1d36cef51e219ac56e5e9f0
|
[
"MIT"
] | null | null | null |
print("\U0001F600")
print("\U0001F618")
print("\U0001F600")
print("\U0001F618")
print("\U0001F600")
print("\U0001F618")
print("\U0001F602")
print("\U0001F60D")
print("\U0001F642")
print("\U0001F923")
print("\U0001F609")
| 19.25
| 20
| 0.666667
| 22
| 231
| 7
| 0.363636
| 0.272727
| 0.37013
| 0.545455
| 0.577922
| 0.577922
| 0.577922
| 0.577922
| 0.577922
| 0
| 0
| 0.363636
| 0.095238
| 231
| 11
| 21
| 21
| 0.373206
| 0
| 0
| 0.545455
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 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
| 1
|
0
| 8
|
bba79ca419b21c39d817f520f91de3439c0e1cf6
| 7,855
|
py
|
Python
|
tab_automl/automl/datasets.py
|
VishnuBhaarath/Tabular-AutoML
|
9f6259c8764bbc409244af49661757700b1c5b0c
|
[
"MIT"
] | 18
|
2022-01-17T06:43:25.000Z
|
2022-02-09T01:36:24.000Z
|
tab_automl/automl/datasets.py
|
VishnuBhaarath/Tabular-AutoML
|
9f6259c8764bbc409244af49661757700b1c5b0c
|
[
"MIT"
] | 23
|
2022-01-17T07:13:46.000Z
|
2022-02-13T11:03:33.000Z
|
tab_automl/automl/datasets.py
|
VishnuBhaarath/Tabular-AutoML
|
9f6259c8764bbc409244af49661757700b1c5b0c
|
[
"MIT"
] | 11
|
2022-01-27T14:58:37.000Z
|
2022-02-02T06:06:25.000Z
|
"""
This file holds all codes for custom and defined datasets.
"""
import sqlite3
import pandas as pd
class ClassificationDataset:
"""
Dataset Class for Classification.
Currently supports single feature target only.
"""
def __init__(self, path):
# reading the dataset from source
if path.endswith(".txt"):
self.data = pd.read_table(path, delimiter='\s')
elif path.endswith(".json"):
self.data = pd.read_json(path)
elif path.endswith(".xlsx"):
self.data = pd.read_excel(path)
elif path.endswith(".sqlite"):
table_name = input("table name :")
db = sqlite3.connect(path)
self.data = pd.read_sql_query(f'Select * from {table_name}', db)
elif path.endswith(".csv"):
self.data = pd.read_csv(path)
else:
print("File type not supported")
# storing the columns overview
self.columns = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
self.labels = None
print(f"Populated the dataframe with data records...")
def prepare_x_and_y(self,
feature_set_columns,
target_column):
"""
Function to segregate X and y from dataset
Args:
feature_set_columns: (List) Columns that'll be present on X.
target_column: (Any) Column that'll be present on Y.
Returns:
x: (pandas.DataFrame) Feature set / Affecting features.
y: (pandas) Target set / dependent feature.
"""
x = self.data[feature_set_columns]
y = self.data[[target_column]]
self.labels = pd.Series(y.all()).unique()
print(f"X feature set and target feature has been split...")
return x, y
class RegressionDataset:
"""
Dataset Class for Regression.
Currently supports single feature target only.
"""
def __init__(self, path):
# reading the dataset from source
if path.endswith(".txt"):
self.data = pd.read_table(path, delimiter='\s')
elif path.endswith(".json"):
self.data = pd.read_json(path)
elif path.endswith(".xlsx"):
self.data = pd.read_excel(path)
elif path.endswith(".sqlite"):
table_name = input("table name :")
db = sqlite3.connect(path)
self.data = pd.read_sql_query(f'Select * from {table_name}', db)
elif path.endswith(".csv"):
self.data = pd.read_csv(path)
else:
print("File type not supported")
# storing the columns overview
self.columns = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
print(f"Populated the dataframe with data records...")
def prepare_x_and_y(self,
feature_set_columns,
target_column):
"""
Function to segregate X and y from dataset
Args:
feature_set_columns: (List) Columns that'll be present on X.
target_column: (Any) Column that'll be present on Y.
Returns:
2 pandas.DataFrame object i.e. X and Y
"""
x = self.data[feature_set_columns]
y = self.data[[target_column]]
print(f"X feature set and target feature has been split...")
return x, y
class ClusteringDataset:
"""
Dataset Class for Clustering.
Currently supports single feature target only.
"""
def __init__(self, path):
# reading the dataset from source
if path.endswith(".txt"):
self.data = pd.read_table(path, delimiter='\s')
elif path.endswith(".json"):
self.data = pd.read_json(path)
elif path.endswith(".xlsx"):
self.data = pd.read_excel(path)
elif path.endswith(".sqlite"):
table_name = input("table name :")
db = sqlite3.connect(path)
self.data = pd.read_sql_query(f'Select * from {table_name}', db)
elif path.endswith(".csv"):
self.data = pd.read_csv(path)
else:
print("File type not supported")
# storing the columns overview
self.columns = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
self.labels = None
print(f"Populated the dataframe with data records...")
class Iris:
"""
Iris Dataset
============
This class will be an example for
other classification datasets or for
custom datasets prepared on comma
separated value format to be precise.
"""
def __init__(self):
# reading the dataset from source
self.data = pd.read_csv("tab_automl/datasets/Iris.csv")
# storing the columns overview
self.columns = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
self.labels = None
print(f"Populated the dataframe with data records...")
def prepare_x_and_y(self,
feature_set_columns=['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'],
target_column="Species"):
"""
Function to segregate X and y from dataset.
Args:
feature_set_columns: (List) Columns that'll be present on X.
target_column: (Any) Column that'll be present on Y.
Returns:
2 pandas.DataFrame object i.e. X and Y
"""
x = self.data[feature_set_columns]
y = self.data[[target_column]]
self.labels = pd.Series(y.all()).unique()
print(f"X feature set and target feature has been split...")
return x, y
class Wine:
"""
Wine Dataset
============
This class will be an example for
other regression datasets or for
custom datasets prepared on comma
separated value format to be precise.
"""
def __init__(self):
# reading the dataset from source
self.data = pd.read_csv("tab_automl/datasets/wine.csv")
# storing the columns overview
self.columns_dtypes = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
print(f"Populated the dataframe with data records...")
def prepare_x_and_y(self,
feature_set_columns=['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'],
target_column='quality'):
"""
Function to segregate X and y from dataset.
Args:
feature_set_columns: (List) Columns that'll be present on X.
target_column: (Any) Column that'll be present on Y.
Returns:
2 pandas.DataFrame object i.e. X and Y
"""
x = self.data[feature_set_columns]
y = self.data[[target_column]]
print(f"X feature set and target feature has been split...")
return x, y
class Credit_Card_Customer_Data:
"""
Credit_Card_Customer_Data Dataset
============
This class will be an example for
other clustering datasets or for
custom datasets prepared on comma
separated value format to be precise.
"""
def __init__(self):
# reading the dataset from source
self.data = pd.read_csv("tab_automl/datasets/Credit_Card_Customer_Data.csv")
# storing the columns overview
self.columns = pd.Series([str(self.data[feature].dtype) for feature in self.data.columns])
self.labels = None
print(f"Populated the dataframe with data records...")
"""
Defining x
"""
x = self.data
print(f"X feature set has been set...")
return x
| 30.803922
| 216
| 0.59478
| 980
| 7,855
| 4.653061
| 0.152041
| 0.068421
| 0.039474
| 0.055263
| 0.85307
| 0.849342
| 0.849342
| 0.840789
| 0.840789
| 0.815132
| 0
| 0.001271
| 0.298791
| 7,855
| 254
| 217
| 30.925197
| 0.826616
| 0.273456
| 0
| 0.824074
| 0
| 0
| 0.201106
| 0.020015
| 0
| 0
| 0
| 0
| 0
| 1
| 0.092593
| false
| 0
| 0.018519
| 0
| 0.212963
| 0.12963
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
bbbccff187ba87731260e7a93056c20bf270c0d9
| 14,842
|
py
|
Python
|
swagger_client/api/wars_api.py
|
rseichter/bootini-star
|
a80258f01a05e4df38748b8cb47dfadabd42c20d
|
[
"MIT"
] | null | null | null |
swagger_client/api/wars_api.py
|
rseichter/bootini-star
|
a80258f01a05e4df38748b8cb47dfadabd42c20d
|
[
"MIT"
] | null | null | null |
swagger_client/api/wars_api.py
|
rseichter/bootini-star
|
a80258f01a05e4df38748b8cb47dfadabd42c20d
|
[
"MIT"
] | null | null | null |
# coding: utf-8
"""
EVE Swagger Interface
An OpenAPI for EVE Online # noqa: E501
OpenAPI spec version: 0.8.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from swagger_client.api_client import ApiClient
class WarsApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def get_wars(self, **kwargs): # noqa: E501
"""List wars # noqa: E501
Return a list of wars --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars(async=True)
>>> result = thread.get()
:param async bool
:param str datasource: The server name you would like data from
:param int max_war_id: Only return wars with ID smaller than this.
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: list[int]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_wars_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_wars_with_http_info(**kwargs) # noqa: E501
return data
def get_wars_with_http_info(self, **kwargs): # noqa: E501
"""List wars # noqa: E501
Return a list of wars --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars_with_http_info(async=True)
>>> result = thread.get()
:param async bool
:param str datasource: The server name you would like data from
:param int max_war_id: Only return wars with ID smaller than this.
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: list[int]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['datasource', 'max_war_id', 'user_agent', 'x_user_agent'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_wars" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'datasource' in params:
query_params.append(('datasource', params['datasource'])) # noqa: E501
if 'max_war_id' in params:
query_params.append(('max_war_id', params['max_war_id'])) # noqa: E501
if 'user_agent' in params:
query_params.append(('user_agent', params['user_agent'])) # noqa: E501
header_params = {}
if 'x_user_agent' in params:
header_params['X-User-Agent'] = params['x_user_agent'] # noqa: E501
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 = [] # noqa: E501
return self.api_client.call_api(
'/v1/wars/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[int]', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_wars_war_id(self, war_id, **kwargs): # noqa: E501
"""Get war information # noqa: E501
Return details about a war --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars_war_id(war_id, async=True)
>>> result = thread.get()
:param async bool
:param int war_id: ID for a war (required)
:param str datasource: The server name you would like data from
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: GetWarsWarIdOk
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_wars_war_id_with_http_info(war_id, **kwargs) # noqa: E501
else:
(data) = self.get_wars_war_id_with_http_info(war_id, **kwargs) # noqa: E501
return data
def get_wars_war_id_with_http_info(self, war_id, **kwargs): # noqa: E501
"""Get war information # noqa: E501
Return details about a war --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars_war_id_with_http_info(war_id, async=True)
>>> result = thread.get()
:param async bool
:param int war_id: ID for a war (required)
:param str datasource: The server name you would like data from
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: GetWarsWarIdOk
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['war_id', 'datasource', 'user_agent', 'x_user_agent'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_wars_war_id" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'war_id' is set
if ('war_id' not in params or
params['war_id'] is None):
raise ValueError("Missing the required parameter `war_id` when calling `get_wars_war_id`") # noqa: E501
if 'war_id' in params and params['war_id'] < 1: # noqa: E501
raise ValueError("Invalid value for parameter `war_id` when calling `get_wars_war_id`, must be a value greater than or equal to `1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'war_id' in params:
path_params['war_id'] = params['war_id'] # noqa: E501
query_params = []
if 'datasource' in params:
query_params.append(('datasource', params['datasource'])) # noqa: E501
if 'user_agent' in params:
query_params.append(('user_agent', params['user_agent'])) # noqa: E501
header_params = {}
if 'x_user_agent' in params:
header_params['X-User-Agent'] = params['x_user_agent'] # noqa: E501
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 = [] # noqa: E501
return self.api_client.call_api(
'/v1/wars/{war_id}/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='GetWarsWarIdOk', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def get_wars_war_id_killmails(self, war_id, **kwargs): # noqa: E501
"""List kills for a war # noqa: E501
Return a list of kills related to a war --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars_war_id_killmails(war_id, async=True)
>>> result = thread.get()
:param async bool
:param int war_id: A valid war ID (required)
:param str datasource: The server name you would like data from
:param int page: Which page of results to return
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: list[GetWarsWarIdKillmails200Ok]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async'):
return self.get_wars_war_id_killmails_with_http_info(war_id, **kwargs) # noqa: E501
else:
(data) = self.get_wars_war_id_killmails_with_http_info(war_id, **kwargs) # noqa: E501
return data
def get_wars_war_id_killmails_with_http_info(self, war_id, **kwargs): # noqa: E501
"""List kills for a war # noqa: E501
Return a list of kills related to a war --- This route is cached for up to 3600 seconds # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async=True
>>> thread = api.get_wars_war_id_killmails_with_http_info(war_id, async=True)
>>> result = thread.get()
:param async bool
:param int war_id: A valid war ID (required)
:param str datasource: The server name you would like data from
:param int page: Which page of results to return
:param str user_agent: Client identifier, takes precedence over headers
:param str x_user_agent: Client identifier, takes precedence over User-Agent
:return: list[GetWarsWarIdKillmails200Ok]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['war_id', 'datasource', 'page', 'user_agent', 'x_user_agent'] # noqa: E501
all_params.append('async')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_wars_war_id_killmails" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'war_id' is set
if ('war_id' not in params or
params['war_id'] is None):
raise ValueError("Missing the required parameter `war_id` when calling `get_wars_war_id_killmails`") # noqa: E501
if 'war_id' in params and params['war_id'] < 1: # noqa: E501
raise ValueError("Invalid value for parameter `war_id` when calling `get_wars_war_id_killmails`, must be a value greater than or equal to `1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'war_id' in params:
path_params['war_id'] = params['war_id'] # noqa: E501
query_params = []
if 'datasource' in params:
query_params.append(('datasource', params['datasource'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'user_agent' in params:
query_params.append(('user_agent', params['user_agent'])) # noqa: E501
header_params = {}
if 'x_user_agent' in params:
header_params['X-User-Agent'] = params['x_user_agent'] # noqa: E501
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 = [] # noqa: E501
return self.api_client.call_api(
'/v1/wars/{war_id}/killmails/', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[GetWarsWarIdKillmails200Ok]', # noqa: E501
auth_settings=auth_settings,
async=params.get('async'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 41.227778
| 169
| 0.616696
| 1,861
| 14,842
| 4.692101
| 0.100484
| 0.037792
| 0.020614
| 0.024737
| 0.935066
| 0.924989
| 0.923958
| 0.911819
| 0.907581
| 0.897618
| 0
| 0.020686
| 0.293222
| 14,842
| 359
| 170
| 41.342618
| 0.811725
| 0.051543
| 0
| 0.763441
| 1
| 0.010753
| 0.209459
| 0.039863
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.021505
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
bbcab7ee9f1cba453213ea346f9dbc4599d13db4
| 2,891
|
py
|
Python
|
python/se3-legacy/tests/test_firefox.py
|
saucelabs-training/platform-config-tests
|
11dfab8f9be2fe118ed0b0fa4adebb75a5f1a64c
|
[
"MIT"
] | 1
|
2021-11-17T22:29:42.000Z
|
2021-11-17T22:29:42.000Z
|
python/se3-legacy/tests/test_firefox.py
|
saucelabs-training/platform-config-tests
|
11dfab8f9be2fe118ed0b0fa4adebb75a5f1a64c
|
[
"MIT"
] | null | null | null |
python/se3-legacy/tests/test_firefox.py
|
saucelabs-training/platform-config-tests
|
11dfab8f9be2fe118ed0b0fa4adebb75a5f1a64c
|
[
"MIT"
] | 1
|
2021-11-17T22:29:35.000Z
|
2021-11-17T22:29:35.000Z
|
def test_latest_win10(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 10'
caps['version'] = 'latest'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_late_win10(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 10'
caps['version'] = '91'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_early_win10(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 10'
caps['version'] = '4'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_latest_win7(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 7'
caps['version'] = 'latest'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_late_win7(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 7'
caps['version'] = '91'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_early_win7(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Windows 7'
caps['version'] = '4'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_late_linux(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Linux'
caps['version'] = '45'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_early_linux(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'Linux'
caps['version'] = '4'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_late_yosemite(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'OS X 10.10'
caps['version'] = '47'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_early_yosemite(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'OS X 10.10'
caps['version'] = '32'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_latest_big_sur(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'macOS 11.00'
caps['version'] = 'latest'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_late_big_sur(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'macOS 11.00'
caps['version'] = '91'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
def test_early_big_sur(helpers):
caps = {}
caps['browserName'] = 'firefox'
caps['platform'] = 'macOS 11.00'
caps['version'] = '60'
driver = helpers.start_driver(caps)
helpers.validate_google(driver)
| 24.709402
| 39
| 0.643376
| 333
| 2,891
| 5.42042
| 0.105105
| 0.050416
| 0.108033
| 0.187258
| 0.988366
| 0.988366
| 0.988366
| 0.988366
| 0.988366
| 0.957895
| 0
| 0.02384
| 0.202006
| 2,891
| 116
| 40
| 24.922414
| 0.758561
| 0
| 0
| 0.813187
| 0
| 0
| 0.202006
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0
| 0
| 0.142857
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 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
| 8
|
bbf86f67ee392fd9401af3bb943b23f563fabfda
| 6,032
|
py
|
Python
|
tests/features/ml_test_evaluation_steps.py
|
osroca/bigmler
|
8e4ccaea8fa5329674c7e6909381a9688a8bb24b
|
[
"Apache-2.0"
] | 1
|
2021-08-30T20:18:57.000Z
|
2021-08-30T20:18:57.000Z
|
tests/features/ml_test_evaluation_steps.py
|
osroca/bigmler
|
8e4ccaea8fa5329674c7e6909381a9688a8bb24b
|
[
"Apache-2.0"
] | null | null | null |
tests/features/ml_test_evaluation_steps.py
|
osroca/bigmler
|
8e4ccaea8fa5329674c7e6909381a9688a8bb24b
|
[
"Apache-2.0"
] | 1
|
2021-08-30T20:19:05.000Z
|
2021-08-30T20:19:05.000Z
|
import os
import time
import csv
import json
from lettuce import step, world
from subprocess import check_call, CalledProcessError
from bigmler.checkpoint import file_number_of_lines
from common_steps import check_debug
@step(r'I create BigML multi-label resources tagged as "(.*)" with "(.*)" label separator and "(\d*)" labels uploading train "(.*)" file with "(.*)" field separator to evaluate and log evaluation in "(.*)"')
def i_create_all_ml_evaluations(step, tag=None, label_separator=None, number_of_labels=None, data=None, training_separator=None, output=None):
if tag is None or label_separator is None or training_separator is None or number_of_labels is None or data is None or output is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
world.number_of_models = int(number_of_labels)
try:
command = "bigmler --multi-label --train " + data + " --label-separator \"" + label_separator + "\" --training-separator \"" + training_separator + "\" --evaluate --store --output " + output + " --tag " + tag + " --max-batch-models 1"
retcode = check_call(command, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
@step(r'I create BigML multi-label resources tagged as "(.*)" with "(.*)" label separator and "(\d*)" labels uploading train "(.*)" file with "(.*)" field separator to evaluate and log in "(.*)"')
def i_create_all_ml_resources_for_evaluation(step, tag=None, label_separator=None, number_of_labels=None, data=None, training_separator=None, output=None):
if tag is None or label_separator is None or training_separator is None or number_of_labels is None or data is None or output is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
world.number_of_models = int(number_of_labels)
try:
command = "bigmler --multi-label --train " + data + " --label-separator \"" + label_separator + "\" --training-separator \"" + training_separator + "\" --store --output " + output + " --tag " + tag + " --max-batch-models 1"
retcode = check_call(command, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
@step(r'I check that the evaluation is ready')
def i_check_evaluation_ready(step):
evaluation_file = "%s%sevaluation.json" % (world.directory, os.sep)
try:
evaluation_file = open(evaluation_file, "r")
evaluation = json.loads(evaluation_file.readline().strip())
evaluation_file.close()
assert True
except:
assert False
@step(r'I create BigML multi-label\sresources using source to evaluate and log evaluation in "(.*)"')
def i_create_ml_evaluations_from_source(step, output=None):
if output is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
try:
retcode = check_call("bigmler --multi-label --source " + world.source['resource'] + " --evaluate --store --output " + output, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
@step(r'I create BigML multi-label\sresources using dataset to evaluate and log evaluation in "(.*)"')
def i_create_ml_evaluations_from_dataset(step, output=None):
if output is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
try:
command = ("bigmler --multi-label --dataset " +
world.dataset['resource'] + " --evaluate --store --output "
+ output)
command = check_debug(command)
retcode = check_call(command, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
@step(r'I create BigML multi-label\sresources using models in file "(.*)" to evaluate and log evaluation in "(.*)"')
def i_create_ml_evaluations_from_models(step, models_file=None, output=None):
if output is None or models_file is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
try:
command = ("bigmler --multi-label --models " + models_file +
" --dataset " + world.dataset['resource'] +
" --evaluate --store --output " + output)
command = check_debug(command)
retcode = check_call(command, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
@step(r'I create BigML multi-label\sresources using models tagged as "(.*)" to evaluate and log evaluation in "(.*)"')
def i_create_ml_evaluations_from_tagged_models(step, tag=None, output=None):
if output is None or tag is None:
assert False
world.directory = os.path.dirname(output)
world.folders.append(world.directory)
try:
command = ("bigmler --multi-label --model-tag " + tag + " --dataset " +
world.dataset['resource'] + " --evaluate --store --output "
+ output)
command = check_debug(command)
retcode = check_call(command, shell=True)
if retcode < 0:
assert False
else:
world.output = output
assert True
except (OSError, CalledProcessError, IOError) as exc:
assert False, str(exc)
| 44.681481
| 242
| 0.64821
| 750
| 6,032
| 5.1
| 0.132
| 0.054641
| 0.025098
| 0.018824
| 0.835556
| 0.826928
| 0.821176
| 0.821176
| 0.80549
| 0.788758
| 0
| 0.001747
| 0.240882
| 6,032
| 134
| 243
| 45.014925
| 0.833588
| 0
| 0
| 0.701613
| 0
| 0.016129
| 0.244198
| 0.014589
| 0
| 0
| 0
| 0
| 0.209677
| 1
| 0.056452
| false
| 0
| 0.064516
| 0
| 0.120968
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 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
| 7
|
a522f016c3d6eb7a597d21a32f0b1e26c2a61934
| 191
|
py
|
Python
|
profiles_api/admin.py
|
Glezarts/profiles-rest-api
|
1e55568e79d905eaf2d5a75f56dea207dccd7666
|
[
"MIT"
] | null | null | null |
profiles_api/admin.py
|
Glezarts/profiles-rest-api
|
1e55568e79d905eaf2d5a75f56dea207dccd7666
|
[
"MIT"
] | null | null | null |
profiles_api/admin.py
|
Glezarts/profiles-rest-api
|
1e55568e79d905eaf2d5a75f56dea207dccd7666
|
[
"MIT"
] | null | null | null |
import imp
from statistics import mode
from django.contrib import admin
from profiles_api import models
admin.site.register(models.UserProfile)
admin.site.register(models.ProfileFeedItem)
| 19.1
| 43
| 0.842932
| 26
| 191
| 6.153846
| 0.576923
| 0.1125
| 0.2125
| 0.2875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.099476
| 191
| 9
| 44
| 21.222222
| 0.930233
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 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
| 7
|
a572b49453c41766b332bd82dbebc89799f20c3f
| 43
|
py
|
Python
|
sales/application/query.py
|
smart-protection/fastapi-pycones-upr
|
16ce2545978c4ddcf884a8c691b63ee60f1f0d67
|
[
"Apache-2.0"
] | 6
|
2021-08-16T18:33:31.000Z
|
2022-01-09T04:19:34.000Z
|
sales/application/query.py
|
smart-protection/fastapi-pycones-upr
|
16ce2545978c4ddcf884a8c691b63ee60f1f0d67
|
[
"Apache-2.0"
] | null | null | null |
sales/application/query.py
|
smart-protection/fastapi-pycones-upr
|
16ce2545978c4ddcf884a8c691b63ee60f1f0d67
|
[
"Apache-2.0"
] | 1
|
2021-09-23T03:30:17.000Z
|
2021-09-23T03:30:17.000Z
|
import abc
class Query(abc.ABC):
pass
| 8.6
| 21
| 0.674419
| 7
| 43
| 4.142857
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.232558
| 43
| 5
| 22
| 8.6
| 0.878788
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 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
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
a5ba1165fea2b293c67874e7b7081a87f2b8f149
| 1,210
|
py
|
Python
|
api/app/users/mixins.py
|
danielamassiceti/Baobab
|
354a0cf5ad05a9fb42a0522e7f212f2f5c351390
|
[
"Apache-2.0"
] | null | null | null |
api/app/users/mixins.py
|
danielamassiceti/Baobab
|
354a0cf5ad05a9fb42a0522e7f212f2f5c351390
|
[
"Apache-2.0"
] | null | null | null |
api/app/users/mixins.py
|
danielamassiceti/Baobab
|
354a0cf5ad05a9fb42a0522e7f212f2f5c351390
|
[
"Apache-2.0"
] | null | null | null |
from flask_restful import reqparse
class SignupMixin(object):
req_parser = reqparse.RequestParser()
req_parser.add_argument('email', type=str, required=True)
req_parser.add_argument('firstname', type=str, required=True)
req_parser.add_argument('lastname', type=str, required=True)
req_parser.add_argument('user_title', type=str, required=True)
req_parser.add_argument('nationality_country_id', type=int, required=True)
req_parser.add_argument('residence_country_id', type=int, required=True)
req_parser.add_argument('user_ethnicity', type=str, required=True)
req_parser.add_argument('user_gender', type=str, required=True)
req_parser.add_argument('affiliation', type=str, required=True)
req_parser.add_argument('department', type=str, required=True)
req_parser.add_argument('user_disability', type=str, required=True)
req_parser.add_argument('user_category_id', type=int, required=True)
req_parser.add_argument('password', type=str, required=True)
class AuthenticateMixin(object):
req_parser = reqparse.RequestParser()
req_parser.add_argument('email', type=str, required=True)
req_parser.add_argument('password', type=str, required=True)
| 48.4
| 78
| 0.768595
| 164
| 1,210
| 5.408537
| 0.213415
| 0.172492
| 0.202931
| 0.338219
| 0.815107
| 0.815107
| 0.815107
| 0.810598
| 0.634724
| 0.430665
| 0
| 0
| 0.108264
| 1,210
| 25
| 79
| 48.4
| 0.822057
| 0
| 0
| 0.3
| 0
| 0
| 0.142031
| 0.018167
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.1
| 0.05
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 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
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
a5baeb9213a5c3d132232a126f799ce0265a5a2b
| 14,423
|
py
|
Python
|
Topaz/GeneratedConjureQuadruple.py
|
Rhodolite/Gem.py.UnitTest
|
eaa8b6855bcfbb12f67e7eb146928814543ef9d4
|
[
"MIT"
] | null | null | null |
Topaz/GeneratedConjureQuadruple.py
|
Rhodolite/Gem.py.UnitTest
|
eaa8b6855bcfbb12f67e7eb146928814543ef9d4
|
[
"MIT"
] | null | null | null |
Topaz/GeneratedConjureQuadruple.py
|
Rhodolite/Gem.py.UnitTest
|
eaa8b6855bcfbb12f67e7eb146928814543ef9d4
|
[
"MIT"
] | null | null | null |
#
# Copyright (c) 2017-2018 Joy Diamond. All rights reserved.
#
@gem('Topaz.GeneratedConjureQuadruple')
def gem():
from Gem import create_herd_3, create_herd_4, create_herd_many
map__lookup = Map.get
map__provide = Map.setdefault
map__store = Map.__setitem__
@share
def produce_simplified_conjure_quadruple(
name, Meta, cache,
lookup = absent,
provide = absent,
store = absent,
):
lookup = cache.get
provide = cache.setdefault
store = cache.__setitem__
@rename('simplified_conjure_%s', name)
def simplified_conjure_quadruple(k1, k2, k3, k4):
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k1', 'k2', 'k3', 'k4') k1, k2, k3, k4> 0; 0 0 p q r s; 0 k1 k2 k3 k4>, 0, 0)
p = lookup(k1)
if p is none:
q = Meta(k1, k2, k3, k4)
assert (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3) and (q.k4 is k4)
return provide(k1, q)
if p.k2 is k2:
if p.k3 is k3:
if p.k4 is k4: return p
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
store(k1, create_horde_2(2, p.k4, k4, p, r))
return r
q = Meta(k1, k2, k3, k4)
assert (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3) and (q.k4 is k4)
store(k1, create_horde_2(1, p.k3, k3, p, q))
return q
if not p.is_herd:
q = Meta(k1, k2, k3, k4)
assert (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3) and (q.k4 is k4)
herd = create_herd_2(p.k2, k2, p, q)
store(k1, herd)
return q
if p.skip is 0:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k1', 'k2', 'k3', 'k4') k1, k2, k3, k4> 1; 0 p q r s 0; k1 k2 k3 k4 0>, 0, 0)
q = p.glimpse(k2, absent)
if q.k3 is k3:
if q.k4 is k4: return q
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
p.displace(k2, create_horde_2(1, q.k4, k4, q, r))
return r
if not q.is_herd:
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
if q is absent:
p_ = p.insert(k2, r)
if p is not p_: store(k1, p_)
return r
herd = create_herd_2(q.k3, k3, q, r)
p.displace(k2, herd)
return r
if q.skip is 0:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k1', 'k2', 'k3', 'k4') k1, k2, k3, k4> 2; p q r s 0 0; k2 k3 k4 0 0>, 0, 0)
r = q.glimpse(k3, absent)
if r.k4 is k4: return r
if not r.is_herd:
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
if r is absent:
q_ = q.insert(k3, s)
if q is not q_: p.displace(k2, q_)
return s
herd = create_herd_2(r.k4, k4, r, s)
q.displace(k3, herd)
return s
s = r.glimpse(k4)
if s is not none:
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
r_ = r.insert(k4, s)
if r is not r_: q.displace(k3, r_)
return s
assert q.skip is 1
q_k3 = q.sample().k3
if q_k3 is not k3:
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
p.displace(k2, create_herd_2(q_k3, k3, q.remove_skip(), r))
return r
s = q.glimpse(k4)
if s is not none:
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
q_ = q.insert(k4, s)
if q is not q_:
assert q_.sample().k3 is k3
p.displace(k2, q_)
return s
p_sample = p.sample()
p_k2 = p_sample.k2
if p_k2 is not k2:
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
store(k1, create_herd_2(p_k2, k2, p.remove_skip(), r))
return r
if p.skip is 1:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k1', 'k2', 'k3', 'k4') k1, k2, k3, k4> 2; 0 p r s 0 0; k1 k3 k4 0 0>, 0, k2)
r = p.glimpse(k3, absent)
if r.k4 is k4: return r
if not r.is_herd:
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
if r is absent:
p_ = p.insert(k3, s)
if p is not p_:
assert p_.sample().k2 is k2
store(k1, p_)
return s
herd = create_herd_2(r.k4, k4, r, s)
p.displace(k3, herd)
return s
s = r.glimpse(k4)
if s is not none:
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
r_ = r.insert(k4, s)
if r is not r_: p.displace(k3, r_)
return s
assert p.skip is 2
p_k3 = p_sample.k3
if p_k3 is not k3:
r = Meta(k1, k2, k3, k4)
assert (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3) and (r.k4 is k4)
store(k1, create_horde_2(1, p_k3, k3, p.remove_skip(2), r))
return r
s = p.glimpse(k4)
if s is not none:
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3) and (s.k4 is k4)
p_ = p.insert(k4, s)
if p is not p_:
assert (p_.sample().k2 is k2) and (p_.sample().k3 is k3)
store(k1, p_)
return s
return simplified_conjure_quadruple
@share
def produce_simplified_conjure_quadruple__4123(
name, Meta, cache,
lookup = absent,
provide = absent,
store = absent,
):
lookup = cache.get
provide = cache.setdefault
store = cache.__setitem__
@rename('simplified_conjure_%s', name)
def simplified_conjure_quadruple__4123(k1, k2, k3, k4):
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k4', 'k1', 'k2', 'k3') k1, k2, k3, k4> 0; 0 0 p q r s; 0 k4 k1 k2 k3>, 0, 0)
p = lookup(k4)
if p is none:
q = Meta(k1, k2, k3, k4)
assert (q.k4 is k4) and (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3)
return provide(k4, q)
if p.k1 is k1:
if p.k2 is k2:
if p.k3 is k3: return p
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
store(k4, create_horde_2(2, p.k3, k3, p, r))
return r
q = Meta(k1, k2, k3, k4)
assert (q.k4 is k4) and (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3)
store(k4, create_horde_2(1, p.k2, k2, p, q))
return q
if not p.is_herd:
q = Meta(k1, k2, k3, k4)
assert (q.k4 is k4) and (q.k1 is k1) and (q.k2 is k2) and (q.k3 is k3)
herd = create_herd_2(p.k1, k1, p, q)
store(k4, herd)
return q
if p.skip is 0:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k4', 'k1', 'k2', 'k3') k1, k2, k3, k4> 1; 0 p q r s 0; k4 k1 k2 k3 0>, 0, 0)
q = p.glimpse(k1, absent)
if q.k2 is k2:
if q.k3 is k3: return q
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
p.displace(k1, create_horde_2(1, q.k3, k3, q, r))
return r
if not q.is_herd:
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
if q is absent:
p_ = p.insert(k1, r)
if p is not p_: store(k4, p_)
return r
herd = create_herd_2(q.k2, k2, q, r)
p.displace(k1, herd)
return r
if q.skip is 0:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k4', 'k1', 'k2', 'k3') k1, k2, k3, k4> 2; p q r s 0 0; k1 k2 k3 0 0>, 0, 0)
r = q.glimpse(k2, absent)
if r.k3 is k3: return r
if not r.is_herd:
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
if r is absent:
q_ = q.insert(k2, s)
if q is not q_: p.displace(k1, q_)
return s
herd = create_herd_2(r.k3, k3, r, s)
q.displace(k2, herd)
return s
s = r.glimpse(k3)
if s is not none:
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
r_ = r.insert(k3, s)
if r is not r_: q.displace(k2, r_)
return s
assert q.skip is 1
q_k2 = q.sample().k2
if q_k2 is not k2:
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
p.displace(k1, create_herd_2(q_k2, k2, q.remove_skip(), r))
return r
s = q.glimpse(k3)
if s is not none:
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
q_ = q.insert(k3, s)
if q is not q_:
assert q_.sample().k2 is k2
p.displace(k1, q_)
return s
p_sample = p.sample()
p_k1 = p_sample.k1
if p_k1 is not k1:
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
store(k4, create_herd_2(p_k1, k1, p.remove_skip(), r))
return r
if p.skip is 1:
#create_next(<KeyData <CommonKeyData 7 7 0 4 ('k4', 'k1', 'k2', 'k3') k1, k2, k3, k4> 2; 0 p r s 0 0; k4 k2 k3 0 0>, 0, k1)
r = p.glimpse(k2, absent)
if r.k3 is k3: return r
if not r.is_herd:
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
if r is absent:
p_ = p.insert(k2, s)
if p is not p_:
assert p_.sample().k1 is k1
store(k4, p_)
return s
herd = create_herd_2(r.k3, k3, r, s)
p.displace(k2, herd)
return s
s = r.glimpse(k3)
if s is not none:
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
r_ = r.insert(k3, s)
if r is not r_: p.displace(k2, r_)
return s
assert p.skip is 2
p_k2 = p_sample.k2
if p_k2 is not k2:
r = Meta(k1, k2, k3, k4)
assert (r.k4 is k4) and (r.k1 is k1) and (r.k2 is k2) and (r.k3 is k3)
store(k4, create_horde_2(1, p_k2, k2, p.remove_skip(2), r))
return r
s = p.glimpse(k3)
if s is not none:
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
return s
s = Meta(k1, k2, k3, k4)
assert (s.k4 is k4) and (s.k1 is k1) and (s.k2 is k2) and (s.k3 is k3)
p_ = p.insert(k3, s)
if p is not p_:
assert (p_.sample().k1 is k1) and (p_.sample().k2 is k2)
store(k4, p_)
return s
return simplified_conjure_quadruple__4123
| 38.771505
| 142
| 0.413437
| 2,271
| 14,423
| 2.544694
| 0.029502
| 0.04153
| 0.055027
| 0.063679
| 0.923343
| 0.900156
| 0.870047
| 0.824883
| 0.797024
| 0.76778
| 0
| 0.113132
| 0.481523
| 14,423
| 371
| 143
| 38.876011
| 0.659668
| 0.071552
| 0
| 0.703448
| 1
| 0
| 0.005457
| 0.005457
| 0
| 0
| 0
| 0
| 0.165517
| 1
| 0.017241
| false
| 0
| 0.003448
| 0
| 0.17931
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 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
| 8
|
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