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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d70cab04f6e5b57cc3952aaa12835949ddd7e07e
| 2,168
|
py
|
Python
|
drf_stripe/migrations/0002_alter_feature_description_alter_price_freq_and_more.py
|
joshuakoh1/drf-stripe-subscription
|
a350e1d16338602c59f6d2a7f14a5c1572cf1a10
|
[
"MIT"
] | 33
|
2022-01-13T00:03:52.000Z
|
2022-03-16T09:58:58.000Z
|
drf_stripe/migrations/0002_alter_feature_description_alter_price_freq_and_more.py
|
joshuakoh1/drf-stripe-subscription
|
a350e1d16338602c59f6d2a7f14a5c1572cf1a10
|
[
"MIT"
] | 12
|
2022-02-08T03:56:15.000Z
|
2022-03-30T14:03:42.000Z
|
drf_stripe/migrations/0002_alter_feature_description_alter_price_freq_and_more.py
|
joshuakoh1/drf-stripe-subscription
|
a350e1d16338602c59f6d2a7f14a5c1572cf1a10
|
[
"MIT"
] | 1
|
2022-02-07T22:15:38.000Z
|
2022-02-07T22:15:38.000Z
|
# Generated by Django 4.0.1 on 2022-02-08 08:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('drf_stripe', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='feature',
name='description',
field=models.CharField(blank=True, max_length=256, null=True),
),
migrations.AlterField(
model_name='price',
name='freq',
field=models.CharField(blank=True, max_length=64, null=True),
),
migrations.AlterField(
model_name='price',
name='nickname',
field=models.CharField(blank=True, max_length=256, null=True),
),
migrations.AlterField(
model_name='product',
name='description',
field=models.CharField(blank=True, max_length=1024, null=True),
),
migrations.AlterField(
model_name='product',
name='name',
field=models.CharField(blank=True, max_length=256, null=True),
),
migrations.AlterField(
model_name='subscription',
name='cancel_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscription',
name='ended_at',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscription',
name='period_end',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscription',
name='period_start',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscription',
name='trial_end',
field=models.DateTimeField(blank=True, null=True),
),
migrations.AlterField(
model_name='subscription',
name='trial_start',
field=models.DateTimeField(blank=True, null=True),
),
]
| 31.42029
| 75
| 0.560424
| 203
| 2,168
| 5.866995
| 0.256158
| 0.184719
| 0.230898
| 0.267842
| 0.821998
| 0.821998
| 0.821998
| 0.790092
| 0.641478
| 0.584383
| 0
| 0.023114
| 0.321494
| 2,168
| 68
| 76
| 31.882353
| 0.78654
| 0.020756
| 0
| 0.693548
| 1
| 0
| 0.104668
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.016129
| 0
| 0.064516
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d76c12b27ddb83e0144770c03ae44108a14222bc
| 102
|
py
|
Python
|
nr_pypackage/templates/package_blueprints/examples/simple.py
|
nitred/nr-pypackage
|
426fa4ffca5a69ca4ed6f70cdf9d9d6be76e4e45
|
[
"MIT"
] | 1
|
2019-07-10T07:00:24.000Z
|
2019-07-10T07:00:24.000Z
|
nr_pypackage/templates/package_flask/examples/simple.py
|
nitred/nr-pypackage
|
426fa4ffca5a69ca4ed6f70cdf9d9d6be76e4e45
|
[
"MIT"
] | 9
|
2018-09-27T10:33:58.000Z
|
2018-10-26T12:47:57.000Z
|
nr_pypackage/templates/package_simple/examples/simple.py
|
nitred/nr-pypackage
|
426fa4ffca5a69ca4ed6f70cdf9d9d6be76e4e45
|
[
"MIT"
] | null | null | null |
"""Basic functionality."""
import {{ package_name_safe }}
print({{ package_name_safe }}.__version__)
| 20.4
| 42
| 0.72549
| 11
| 102
| 6
| 0.727273
| 0.333333
| 0.454545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.098039
| 102
| 4
| 43
| 25.5
| 0.717391
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.5
| null | null | 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
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 1
|
0
| 7
|
ad4db0f2e1449cc611cfa7a91b607e18cf535484
| 13,501
|
py
|
Python
|
lxmls/readers/galton.py
|
gomesfernanda/lxmls_lab
|
74b60b9e79aaa2994aee9428b623c04e93807bda
|
[
"MIT"
] | null | null | null |
lxmls/readers/galton.py
|
gomesfernanda/lxmls_lab
|
74b60b9e79aaa2994aee9428b623c04e93807bda
|
[
"MIT"
] | null | null | null |
lxmls/readers/galton.py
|
gomesfernanda/lxmls_lab
|
74b60b9e79aaa2994aee9428b623c04e93807bda
|
[
"MIT"
] | null | null | null |
import numpy as np
_data = np.array([
[70.5, 61.7], [68.5, 61.7], [65.5, 61.7], [64.5, 61.7], [64, 61.7], [67.5, 62.2], [67.5, 62.2], [67.5, 62.2],
[66.5, 62.2], [66.5, 62.2], [66.5, 62.2], [64.5, 62.2], [70.5, 63.2], [69.5, 63.2], [68.5, 63.2], [68.5, 63.2],
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])
def load():
return _data.copy()
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ad4ec9156bc071bd45b7480fec5ee9919669a8d8
| 4,517
|
py
|
Python
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/common/djangoapps/util/tests/test_json_request.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | 3
|
2021-12-15T04:58:18.000Z
|
2022-02-06T12:15:37.000Z
|
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/common/djangoapps/util/tests/test_json_request.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | null | null | null |
Part-03-Understanding-Software-Crafting-Your-Own-Tools/models/edx-platform/common/djangoapps/util/tests/test_json_request.py
|
osoco/better-ways-of-thinking-about-software
|
83e70d23c873509e22362a09a10d3510e10f6992
|
[
"MIT"
] | 1
|
2019-01-02T14:38:50.000Z
|
2019-01-02T14:38:50.000Z
|
"""
Test for JsonResponse and JsonResponseBadRequest util classes.
"""
import json
import unittest
from unittest import mock
from django.http import HttpResponse, HttpResponseBadRequest
from common.djangoapps.util.json_request import JsonResponse, JsonResponseBadRequest
class JsonResponseTestCase(unittest.TestCase):
"""
A set of tests to make sure that JsonResponse Class works correctly.
"""
def test_empty(self):
resp = JsonResponse()
assert isinstance(resp, HttpResponse)
assert resp.content.decode('utf-8') == ''
assert resp.status_code == 204
assert resp['content-type'] == 'application/json'
def test_empty_string(self):
resp = JsonResponse("")
assert isinstance(resp, HttpResponse)
assert resp.content.decode('utf-8') == ''
assert resp.status_code == 204
assert resp['content-type'] == 'application/json'
def test_string(self):
resp = JsonResponse("foo")
assert resp.content.decode('utf-8') == '"foo"'
assert resp.status_code == 200
assert resp['content-type'] == 'application/json'
def test_dict(self):
obj = {"foo": "bar"}
resp = JsonResponse(obj)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 200
assert resp['content-type'] == 'application/json'
def test_set_status_kwarg(self):
obj = {"error": "resource not found"}
resp = JsonResponse(obj, status=404)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 404
assert resp['content-type'] == 'application/json'
def test_set_status_arg(self):
obj = {"error": "resource not found"}
resp = JsonResponse(obj, 404)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 404
assert resp['content-type'] == 'application/json'
def test_encoder(self):
obj = [1, 2, 3]
encoder = object()
with mock.patch.object(json, "dumps", return_value="[1,2,3]") as dumps:
resp = JsonResponse(obj, encoder=encoder)
assert resp.status_code == 200
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
kwargs = dumps.call_args[1]
assert kwargs['cls'] is encoder
class JsonResponseBadRequestTestCase(unittest.TestCase):
"""
A set of tests to make sure that the JsonResponseBadRequest wrapper class
works as intended.
"""
def test_empty(self):
resp = JsonResponseBadRequest()
assert isinstance(resp, HttpResponseBadRequest)
assert resp.content.decode('utf-8') == ''
assert resp.status_code == 400
assert resp['content-type'] == 'application/json'
def test_empty_string(self):
resp = JsonResponseBadRequest("")
assert isinstance(resp, HttpResponse)
assert resp.content.decode('utf-8') == ''
assert resp.status_code == 400
assert resp['content-type'] == 'application/json'
def test_dict(self):
obj = {"foo": "bar"}
resp = JsonResponseBadRequest(obj)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 400
assert resp['content-type'] == 'application/json'
def test_set_status_kwarg(self):
obj = {"error": "resource not found"}
resp = JsonResponseBadRequest(obj, status=404)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 404
assert resp['content-type'] == 'application/json'
def test_set_status_arg(self):
obj = {"error": "resource not found"}
resp = JsonResponseBadRequest(obj, 404)
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
assert resp.status_code == 404
assert resp['content-type'] == 'application/json'
def test_encoder(self):
obj = [1, 2, 3]
encoder = object()
with mock.patch.object(json, "dumps", return_value="[1,2,3]") as dumps:
resp = JsonResponseBadRequest(obj, encoder=encoder)
assert resp.status_code == 400
compare = json.loads(resp.content.decode('utf-8'))
assert obj == compare
kwargs = dumps.call_args[1]
assert kwargs['cls'] is encoder
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0
| 8
|
a8fb4ea6f0d0e91ac88c70ec86e33699f2a119fb
| 68,669
|
py
|
Python
|
scripts/sampleOutputs/bkup/cmp_bwavesGemsFDTDastaromnetpp_reverse/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
scripts/sampleOutputs/bkup/cmp_bwavesGemsFDTDastaromnetpp_reverse/power.py
|
TugberkArkose/MLScheduler
|
e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061
|
[
"Unlicense"
] | null | null | null |
scripts/sampleOutputs/bkup/cmp_bwavesGemsFDTDastaromnetpp_reverse/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,
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'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
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'Subthreshold Leakage with power gating': 0.0259246},
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'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,
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'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
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'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': 0.000900895,
'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': 0.00664302,
'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': 0.0184972,
'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': 0.0602888,
'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': 3.83489,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.18253,
'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': 0.204768,
'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': 6.24237,
'Instruction Fetch Unit/Runtime Dynamic': 0.472727,
'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': 0.0602166,
'L2/Runtime Dynamic': 0.0150679,
'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': 3.21503,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.969406,
'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': 0.0639901,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0639901,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 3.51844,
'Load Store Unit/Runtime Dynamic': 1.34897,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.157789,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.315578,
'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': 0.0559997,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0568862,
'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': 0.238439,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.029976,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.49375,
'Memory Management Unit/Runtime Dynamic': 0.0868623,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 20.2218,
'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': 0.0107502,
'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': 0.0120909,
'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': 0.122899,
'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': 0.14574,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 4.04283,
'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': 0.0122753,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.21233,
'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': 0.0833849,
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'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,
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'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,
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'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': 0.0473876,
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'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
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'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
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'Execution Unit/Integer ALUs/Peak Dynamic': 0.053785,
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'Execution Unit/Peak Dynamic': 4.06507,
'Execution Unit/Register Files/Area': 0.570804,
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'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0157532,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00244132,
'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': 0.0215547,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0180551,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
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'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
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'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
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'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0484793,
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'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 0.974838,
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'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': 0.000419976,
'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': 0.000419976,
'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': 0.000378632,
'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': 0.000153593,
'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,
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'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000259362,
'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': 0.00147795,
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'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': 0.00356819,
'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,
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'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
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'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,
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'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.10404,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0543024,
'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': 0.0589515,
'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': 3.37614,
'Instruction Fetch Unit/Runtime Dynamic': 0.135657,
'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': 0.0367244,
'L2/Runtime Dynamic': 0.0109601,
'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': 1.86798,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.319788,
'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': 0.0204097,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0204098,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 1.96436,
'Load Store Unit/Runtime Dynamic': 0.440852,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0503269,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.100654,
'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,
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'Memory Management Unit/Dtlb/Area': 0.0879726,
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'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0184067,
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'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,
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'Memory Management Unit/Itlb/Runtime Dynamic': 0.00892007,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
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'Memory Management Unit/Peak Dynamic': 0.255436,
'Memory Management Unit/Runtime Dynamic': 0.0273268,
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'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
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'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
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'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
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'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': 0.0289381,
'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': 0.0735075,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 1.66314,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
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'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': 0.0339663,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.229367,
'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': 0.180576,
'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': 0.0876667,
'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,
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'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.141403,
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'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,
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'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0713755,
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'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.300445,
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'Execution Unit/Integer ALUs/Area': 0.47087,
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'Execution Unit/Peak Dynamic': 4.28457,
'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': 0.0341147,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00367713,
'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': 0.0394228,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0271947,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0735375,
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'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': 0.0915462,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.233217,
'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': 1.19928,
'Execution Unit/Subthreshold Leakage': 1.79543,
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'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': 0.000334588,
'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': 0.000334588,
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'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': 0.000303263,
'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': 0.000123873,
'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': 0.000390653,
'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': 0.00136309,
'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': 0.00278505,
'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': 0.0261429,
'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': 1.66291,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0667535,
'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': 0.0887931,
'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': 3.96213,
'Instruction Fetch Unit/Runtime Dynamic': 0.185838,
'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': 0.0436662,
'L2/Runtime Dynamic': 0.00941398,
'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': 2.2744,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.51189,
'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': 0.0335584,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0335584,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.43287,
'Load Store Unit/Runtime Dynamic': 0.710947,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0827493,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.165498,
'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': 0.029368,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.030019,
'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': 0.103394,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0109575,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.309952,
'Memory Management Unit/Runtime Dynamic': 0.0409765,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 14.6227,
'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': 0.0897409,
'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': 0.00504741,
'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': 0.0432531,
'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': 0.138041,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.28449,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328},
{'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': 0.0263682,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.223399,
'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': 0.136561,
'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': 0.124213,
'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': 0.200352,
'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': 0.101131,
'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': 0.425696,
'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,
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'Execution Unit/Integer ALUs/Peak Dynamic': 0.121127,
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'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.30377,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
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'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0257993,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00521007,
'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': 0.0477843,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0385316,
'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': 0.0735836,
'Execution Unit/Register Files/Runtime Dynamic': 0.0437417,
'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': 0.107262,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.281513,
'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': 1.37973,
'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': 0.000796685,
'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': 0.000796685,
'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': 0.00072888,
'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': 0.000301286,
'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': 0.00055351,
'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': 0.00287576,
'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': 0.00638919,
'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': 0.0370414,
'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': 2.35615,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0909398,
'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': 0.125809,
'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': 4.68902,
'Instruction Fetch Unit/Runtime Dynamic': 0.263056,
'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': 0.0337421,
'L2/Runtime Dynamic': 0.00546562,
'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': 2.5463,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.635836,
'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': 0.0423552,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0423552,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.74632,
'Load Store Unit/Runtime Dynamic': 0.887073,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.104441,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.208881,
'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': 0.0370664,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0375713,
'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': 0.146497,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0149135,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.366279,
'Memory Management Unit/Runtime Dynamic': 0.0524848,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 15.7286,
'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': 0.0678661,
'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': 0.00643008,
'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': 0.0628388,
'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': 0.137135,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 2.72494,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 5.743259952002859,
'Runtime Dynamic': 5.743259952002859,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.320448,
'Runtime Dynamic': 0.113467,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 64.1807,
'Peak Power': 97.2929,
'Runtime Dynamic': 10.8289,
'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': 63.8602,
'Total Cores/Runtime Dynamic': 10.7154,
'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': 0.320448,
'Total L3s/Runtime Dynamic': 0.113467,
'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}}
| 75.130197
| 124
| 0.68236
| 8,082
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| 5.791759
| 0.067558
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| 0.938986
| 0.930526
| 0.918157
| 0.886817
| 0.861821
| 0.841163
| 0
| 0.132925
| 0.224133
| 68,669
| 914
| 125
| 75.130197
| 0.745655
| 0
| 0
| 0.642232
| 0
| 0
| 0.656837
| 0.048056
| 0
| 0
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| 1
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| false
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d101f7d76670af1a4c3904e4dbfcc13e52645394
| 215
|
py
|
Python
|
nlpaug/augmenter/char/__init__.py
|
booltime/nlpaug
|
d21e51bacd170dcd3dddfc34a401f0215f91dbf1
|
[
"MIT"
] | 1
|
2021-09-08T09:18:02.000Z
|
2021-09-08T09:18:02.000Z
|
nlpaug/augmenter/char/__init__.py
|
booltime/nlpaug
|
d21e51bacd170dcd3dddfc34a401f0215f91dbf1
|
[
"MIT"
] | null | null | null |
nlpaug/augmenter/char/__init__.py
|
booltime/nlpaug
|
d21e51bacd170dcd3dddfc34a401f0215f91dbf1
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from nlpaug.augmenter.char.char_augmenter import *
from nlpaug.augmenter.char.ocr import *
from nlpaug.augmenter.char.random import *
from nlpaug.augmenter.char.qwerty import *
| 43
| 50
| 0.837209
| 30
| 215
| 5.8
| 0.333333
| 0.229885
| 0.367816
| 0.574713
| 0.666667
| 0
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| 0
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| 0.088372
| 215
| 5
| 51
| 43
| 0.887755
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| true
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
d104873ec9d8fc0792061c23f4e84ce722730c59
| 2,905
|
py
|
Python
|
Python/python-tutorials/algo/ProjectEuler/p8.py
|
zhongyangynag/code-study
|
5410929554107a384a09d899c6fa3d16ed383d2b
|
[
"MIT"
] | null | null | null |
Python/python-tutorials/algo/ProjectEuler/p8.py
|
zhongyangynag/code-study
|
5410929554107a384a09d899c6fa3d16ed383d2b
|
[
"MIT"
] | null | null | null |
Python/python-tutorials/algo/ProjectEuler/p8.py
|
zhongyangynag/code-study
|
5410929554107a384a09d899c6fa3d16ed383d2b
|
[
"MIT"
] | null | null | null |
# Project Euler Problem 8
# Discover the largest product of five consecutive digits
# in the 1000-digit number.
#----------------------Method I-------------------------
def method1():
num="73167176531330624919225119674426574742355349194934\
96983520312774506326239578318016984801869478851843\
85861560789112949495459501737958331952853208805511\
12540698747158523863050715693290963295227443043557\
66896648950445244523161731856403098711121722383113\
62229893423380308135336276614282806444486645238749\
30358907296290491560440772390713810515859307960866\
70172427121883998797908792274921901699720888093776\
65727333001053367881220235421809751254540594752243\
52584907711670556013604839586446706324415722155397\
53697817977846174064955149290862569321978468622482\
83972241375657056057490261407972968652414535100474\
82166370484403199890008895243450658541227588666881\
16427171479924442928230863465674813919123162824586\
17866458359124566529476545682848912883142607690042\
24219022671055626321111109370544217506941658960408\
07198403850962455444362981230987879927244284909188\
84580156166097919133875499200524063689912560717606\
05886116467109405077541002256983155200055935729725\
71636269561882670428252483600823257530420752963450"
result=0
i=0
while i<995:
if num[i]==0:
i=i+5
continue
product=int(num[i])*int(num[i+1])*int(num[i+2])*int(num[i+3])*int(num[i+4])
if product>result:
result=product
i=i+1
print result
#-------------------Method II------------------------
def method2():
seq = '''73167176531330624919225119674426574742355349194934
96983520312774506326239578318016984801869478851843
85861560789112949495459501737958331952853208805511
12540698747158523863050715693290963295227443043557
66896648950445244523161731856403098711121722383113
62229893423380308135336276614282806444486645238749
30358907296290491560440772390713810515859307960866
70172427121883998797908792274921901699720888093776
65727333001053367881220235421809751254540594752243
52584907711670556013604839586446706324415722155397
53697817977846174064955149290862569321978468622482
83972241375657056057490261407972968652414535100474
82166370484403199890008895243450658541227588666881
16427171479924442928230863465674813919123162824586
17866458359124566529476545682848912883142607690042
24219022671055626321111109370544217506941658960408
07198403850962455444362981230987879927244284909188
84580156166097919133875499200524063689912560717606
05886116467109405077541002256983155200055935729725
71636269561882670428252483600823257530420752963450
'''.split('\n')[:-1]
seq = [ int(x) for x in list(''.join(seq))]
products = [ reduce(lambda a,b: a*b, seq[i:i+5]) for i in range(0,len(seq)-4)]
print max(products)
if __name__=='__main__':
import profile # profile the program
profile.run("method1()")
profile.run("method2()")
| 42.720588
| 83
| 0.836833
| 160
| 2,905
| 15.14375
| 0.48125
| 0.009905
| 0.014445
| 0.165085
| 0.825423
| 0.825423
| 0.825423
| 0.825423
| 0.825423
| 0.825423
| 0
| 0.758143
| 0.080551
| 2,905
| 68
| 84
| 42.720588
| 0.149008
| 0.079862
| 0
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| 0
| 0
| 0.392951
| 0.374953
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.016393
| null | null | 0.032787
| 0
| 0
| 1
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
0f3926ab9cf88de805bbe537db66f18b0d2476af
| 18,867
|
py
|
Python
|
sdk/synapse/azure-synapse/azure/synapse/aio/operations_async/_monitoring_operations_async.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 2,728
|
2015-01-09T10:19:32.000Z
|
2022-03-31T14:50:33.000Z
|
sdk/synapse/azure-synapse/azure/synapse/aio/operations_async/_monitoring_operations_async.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 17,773
|
2015-01-05T15:57:17.000Z
|
2022-03-31T23:50:25.000Z
|
sdk/synapse/azure-synapse/azure/synapse/aio/operations_async/_monitoring_operations_async.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 1,916
|
2015-01-19T05:05:41.000Z
|
2022-03-31T19:36:44.000Z
|
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import HttpResponseError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from ... import models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class MonitoringOperations:
"""MonitoringOperations async operations.
You should not instantiate directly this class, but create a Client instance that will create it for you and attach it as attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.synapse.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def get_history_server_data(
self,
workspace_name: str,
pool_name: str,
livy_id: str,
app_id: str,
attempt_id: str,
**kwargs
) -> "models.HistoryServerDataResponse":
"""Get History Server Data for a given workspace, pool, livyid, appid and attemptId.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:param pool_name: The spark pool name.
:type pool_name: str
:param livy_id: The livy id.
:type livy_id: str
:param app_id: The application id.
:type app_id: str
:param attempt_id: The attempt id.
:type attempt_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: HistoryServerDataResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.HistoryServerDataResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.HistoryServerDataResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_history_server_data.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
'poolName': self._serialize.url("pool_name", pool_name, 'str'),
'livyId': self._serialize.url("livy_id", livy_id, 'str'),
'appId': self._serialize.url("app_id", app_id, 'str'),
'attemptId': self._serialize.url("attempt_id", attempt_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('HistoryServerDataResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_history_server_data.metadata = {'url': '/monitoring/workloadTypes/spark/pools/{poolName}/livyIds/{livyId}/applications/{appId}/attemptIds/{attemptId}/historyServerData'}
async def get_spark_job_list(
self,
workspace_name: str,
**kwargs
) -> "models.SparkJobListViewResponse":
"""Get list of spark applications for the workspace.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SparkJobListViewResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.SparkJobListViewResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.SparkJobListViewResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_spark_job_list.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('SparkJobListViewResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_spark_job_list.metadata = {'url': '/monitoring/workloadTypes/spark/Applications'}
async def get_application_details(
self,
workspace_name: str,
pool_name: str,
livy_id: str,
**kwargs
) -> "models.SparkJobListViewResponse":
"""Get one spark application details given the workspace name, pool name and livyid.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:param pool_name: The spark pool name.
:type pool_name: str
:param livy_id: The livy id.
:type livy_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: SparkJobListViewResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.SparkJobListViewResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.SparkJobListViewResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_application_details.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
'poolName': self._serialize.url("pool_name", pool_name, 'str'),
'livyId': self._serialize.url("livy_id", livy_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('SparkJobListViewResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_application_details.metadata = {'url': '/monitoring/workloadTypes/spark/pools/{poolName}/livyIds/{livyId}'}
async def get_history_server_properties(
self,
workspace_name: str,
**kwargs
) -> "models.HistoryServerPropertiesResponse":
"""Get History server properties.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: HistoryServerPropertiesResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.HistoryServerPropertiesResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.HistoryServerPropertiesResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_history_server_properties.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('HistoryServerPropertiesResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_history_server_properties.metadata = {'url': '/monitoring/workloadTypes/spark/historyServerProperties'}
async def get_history_server_diagnostic(
self,
workspace_name: str,
pool_name: str,
livy_id: str,
app_id: str,
attempt_id: str,
**kwargs
) -> "models.HistoryServerDiagnosticResponse":
"""Get History Server Diagnostic Data for a given workspace, pool, livyid, appid and attemptId.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:param pool_name: The spark pool name.
:type pool_name: str
:param livy_id: The livy id.
:type livy_id: str
:param app_id: The application id.
:type app_id: str
:param attempt_id: The attempt id.
:type attempt_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: HistoryServerDiagnosticResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.HistoryServerDiagnosticResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.HistoryServerDiagnosticResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_history_server_diagnostic.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
'poolName': self._serialize.url("pool_name", pool_name, 'str'),
'livyId': self._serialize.url("livy_id", livy_id, 'str'),
'appId': self._serialize.url("app_id", app_id, 'str'),
'attemptId': self._serialize.url("attempt_id", attempt_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('HistoryServerDiagnosticResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_history_server_diagnostic.metadata = {'url': '/monitoring/workloadTypes/spark/pools/{poolName}/livyIds/{livyId}/applications/{appId}/attemptIds/{attemptId}/historyServerDiagnostic'}
async def get_history_server_graph(
self,
workspace_name: str,
pool_name: str,
livy_id: str,
app_id: str,
attempt_id: str,
**kwargs
) -> "models.HistoryServerGraphResponse":
"""Get History Server Graph Data for a given workspace, pool, livyid, appid and attemptId.
:param workspace_name: The name of the workspace to execute operations on.
:type workspace_name: str
:param pool_name: The spark pool name.
:type pool_name: str
:param livy_id: The livy id.
:type livy_id: str
:param app_id: The application id.
:type app_id: str
:param attempt_id: The attempt id.
:type attempt_id: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: HistoryServerGraphResponse or or the result of cls(response)
:rtype: ~azure.synapse.models.HistoryServerGraphResponse or None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls: ClsType["models.HistoryServerGraphResponse"] = kwargs.pop('cls', None)
error_map = kwargs.pop('error_map', {})
api_version = "2019-11-01-preview"
# Construct URL
url = self.get_history_server_graph.metadata['url']
path_format_arguments = {
'workspaceName': self._serialize.url("workspace_name", workspace_name, 'str', skip_quote=True),
'SynapseDnsSuffix': self._serialize.url("self._config.synapse_dns_suffix", self._config.synapse_dns_suffix, 'str', skip_quote=True),
'poolName': self._serialize.url("pool_name", pool_name, 'str'),
'livyId': self._serialize.url("livy_id", livy_id, 'str'),
'appId': self._serialize.url("app_id", app_id, 'str'),
'attemptId': self._serialize.url("attempt_id", attempt_id, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters: Dict[str, Any] = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters: Dict[str, Any] = {}
header_parameters['Accept'] = 'application/json'
# Construct and send request
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 401]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response)
deserialized = None
if response.status_code == 200:
deserialized = self._deserialize('HistoryServerGraphResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_history_server_graph.metadata = {'url': '/monitoring/workloadTypes/spark/pools/{poolName}/livyIds/{livyId}/applications/{appId}/attemptIds/{attemptId}/historyServerGraph'}
| 45.572464
| 189
| 0.668628
| 2,113
| 18,867
| 5.762896
| 0.096545
| 0.03523
| 0.034163
| 0.02168
| 0.831157
| 0.810298
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| 0.796502
| 0.791164
| 0
| 0.007036
| 0.224148
| 18,867
| 413
| 190
| 45.682809
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| 0.777293
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| 0.0131
| 0.176136
| 0.097811
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| 0.004367
| false
| 0
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| null | 0
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| 0
| 0
| 0
|
0
| 7
|
0f4307086ca5c4f37a5372175812bc3442c62fb3
| 137
|
py
|
Python
|
jupyterlab_powerpoint/tests/test_all.py
|
timkpaine/jupyterlab_powerpoint
|
2975982291884d59e6581c55db28b7407a3a2c56
|
[
"Apache-2.0"
] | 19
|
2019-09-06T07:52:42.000Z
|
2022-03-30T21:24:21.000Z
|
jupyterlab_powerpoint/tests/test_all.py
|
timkpaine/jupyterlab_powerpoint
|
2975982291884d59e6581c55db28b7407a3a2c56
|
[
"Apache-2.0"
] | 14
|
2019-10-23T17:27:31.000Z
|
2022-03-27T15:15:41.000Z
|
jupyterlab_powerpoint/tests/test_all.py
|
timkpaine/jupyterlab_powerpoint
|
2975982291884d59e6581c55db28b7407a3a2c56
|
[
"Apache-2.0"
] | 3
|
2019-10-01T02:16:58.000Z
|
2020-11-29T18:13:49.000Z
|
# for Coverage
from jupyterlab_powerpoint import * # noqa: F401, F403
from jupyterlab_powerpoint.extension import * # noqa: F401, F403
| 34.25
| 65
| 0.773723
| 17
| 137
| 6.117647
| 0.588235
| 0.269231
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| 0.346154
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| 0
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| 0
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| 0
| 0.103448
| 0.153285
| 137
| 3
| 66
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| true
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| 1
| 0
|
0
| 8
|
7e33bb78546ed6d470b80fb2d11725bab16ee1d6
| 86,831
|
py
|
Python
|
opensilexClientToolsPython/api/experiments_api.py
|
OpenSILEX/opensilexClientToolsPython
|
41b1e7e707670ecf1b2c06d79bdd9749945788cb
|
[
"RSA-MD"
] | null | null | null |
opensilexClientToolsPython/api/experiments_api.py
|
OpenSILEX/opensilexClientToolsPython
|
41b1e7e707670ecf1b2c06d79bdd9749945788cb
|
[
"RSA-MD"
] | 7
|
2021-05-25T14:06:04.000Z
|
2021-11-05T15:42:14.000Z
|
opensilexClientToolsPython/api/experiments_api.py
|
OpenSILEX/opensilexClientToolsPython
|
41b1e7e707670ecf1b2c06d79bdd9749945788cb
|
[
"RSA-MD"
] | null | null | null |
# coding: utf-8
"""
OpenSilex API
No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501
OpenAPI spec version: INSTANCE-SNAPSHOT
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 opensilexClientToolsPython.api_client import ApiClient
class ExperimentsApi(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 create_experiment(self, **kwargs): # noqa: E501
"""Add an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_experiment(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param ExperimentCreationDTO body: Experiment description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_experiment_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.create_experiment_with_http_info(**kwargs) # noqa: E501
return data
def create_experiment_with_http_info(self, **kwargs): # noqa: E501
"""Add an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_experiment_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param ExperimentCreationDTO body: Experiment description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
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 create_experiment" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 delete_experiment(self, uri, **kwargs): # noqa: E501
"""Delete an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_experiment(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_experiment_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.delete_experiment_with_http_info(uri, **kwargs) # noqa: E501
return data
def delete_experiment_with_http_info(self, uri, **kwargs): # noqa: E501
"""Delete an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_experiment_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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 delete_experiment" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `delete_experiment`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}', 'DELETE',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 export_experiment_data_list(self, uri, **kwargs): # noqa: E501
"""export experiment data # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.export_experiment_data_list(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str start_date: Search by minimal date
:param str end_date: Search by maximal date
:param str timezone: Precise the timezone corresponding to the given dates
:param list[str] scientific_objects: Search by objects
:param list[str] variables: Search by variables
:param float min_confidence: Search by minimal confidence index
:param float max_confidence: Search by maximal confidence index
:param str provenance: Search by provenance uri
:param str metadata: Search by metadata
:param str mode: Format wide or long
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.export_experiment_data_list_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.export_experiment_data_list_with_http_info(uri, **kwargs) # noqa: E501
return data
def export_experiment_data_list_with_http_info(self, uri, **kwargs): # noqa: E501
"""export experiment data # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.export_experiment_data_list_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str start_date: Search by minimal date
:param str end_date: Search by maximal date
:param str timezone: Precise the timezone corresponding to the given dates
:param list[str] scientific_objects: Search by objects
:param list[str] variables: Search by variables
:param float min_confidence: Search by minimal confidence index
:param float max_confidence: Search by maximal confidence index
:param str provenance: Search by provenance uri
:param str metadata: Search by metadata
:param str mode: Format wide or long
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'start_date', 'end_date', 'timezone', 'scientific_objects', 'variables', 'min_confidence', 'max_confidence', 'provenance', 'metadata', 'mode', 'order_by', 'page', 'page_size', ] # noqa: E501
all_params.append('async_req')
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 export_experiment_data_list" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `export_experiment_data_list`") # noqa: E501
if 'min_confidence' in params and params['min_confidence'] > 1: # noqa: E501
raise ValueError("Invalid value for parameter `min_confidence` when calling `export_experiment_data_list`, must be a value less than or equal to `1`") # noqa: E501
if 'min_confidence' in params and params['min_confidence'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `min_confidence` when calling `export_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'max_confidence' in params and params['max_confidence'] > 1: # noqa: E501
raise ValueError("Invalid value for parameter `max_confidence` when calling `export_experiment_data_list`, must be a value less than or equal to `1`") # noqa: E501
if 'max_confidence' in params and params['max_confidence'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `max_confidence` when calling `export_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'page' in params and params['page'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page` when calling `export_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'page_size' in params and params['page_size'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page_size` when calling `export_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'start_date' in params:
query_params.append(('start_date', params['start_date'])) # noqa: E501
if 'end_date' in params:
query_params.append(('end_date', params['end_date'])) # noqa: E501
if 'timezone' in params:
query_params.append(('timezone', params['timezone'])) # noqa: E501
if 'scientific_objects' in params:
query_params.append(('scientific_objects', params['scientific_objects'])) # noqa: E501
collection_formats['scientific_objects'] = 'multi' # noqa: E501
if 'variables' in params:
query_params.append(('variables', params['variables'])) # noqa: E501
collection_formats['variables'] = 'multi' # noqa: E501
if 'min_confidence' in params:
query_params.append(('min_confidence', params['min_confidence'])) # noqa: E501
if 'max_confidence' in params:
query_params.append(('max_confidence', params['max_confidence'])) # noqa: E501
if 'provenance' in params:
query_params.append(('provenance', params['provenance'])) # noqa: E501
if 'metadata' in params:
query_params.append(('metadata', params['metadata'])) # noqa: E501
if 'mode' in params:
query_params.append(('mode', params['mode'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
collection_formats['order_by'] = 'multi' # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['text/plain']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/data/export', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_available_facilities(self, uri, **kwargs): # noqa: E501
"""Get facilities available for an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_facilities(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_available_facilities_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_available_facilities_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_available_facilities_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get facilities available for an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_facilities_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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_available_facilities" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_available_facilities`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/available_facilities', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[InfrastructureFacilityGetDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_available_factors(self, uri, **kwargs): # noqa: E501
"""Get factors with their levels associated to an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_factors(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[FactorDetailsGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_available_factors_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_available_factors_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_available_factors_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get factors with their levels associated to an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_factors_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[FactorDetailsGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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_available_factors" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_available_factors`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/factors', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[FactorDetailsGetDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_available_species(self, uri, **kwargs): # noqa: E501
"""Get species present in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_species(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[SpeciesDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_available_species_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_available_species_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_available_species_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get species present in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_available_species_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[SpeciesDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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_available_species" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_available_species`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/species', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[SpeciesDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_experiment(self, uri, **kwargs): # noqa: E501
"""Get an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_experiment(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ExperimentGetDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_experiment_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_experiment_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_experiment_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_experiment_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: ExperimentGetDTO
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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_experiment" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_experiment`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ExperimentGetDTO', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_experiments_by_ur_is(self, uris, **kwargs): # noqa: E501
"""Get experiments URIs # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_experiments_by_ur_is(uris, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] uris: Experiments URIs (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[ExperimentGetListDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_experiments_by_ur_is_with_http_info(uris, **kwargs) # noqa: E501
else:
(data) = self.get_experiments_by_ur_is_with_http_info(uris, **kwargs) # noqa: E501
return data
def get_experiments_by_ur_is_with_http_info(self, uris, **kwargs): # noqa: E501
"""Get experiments URIs # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_experiments_by_ur_is_with_http_info(uris, async_req=True)
>>> result = thread.get()
:param async_req bool
:param list[str] uris: Experiments URIs (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[ExperimentGetListDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uris', ] # noqa: E501
all_params.append('async_req')
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_experiments_by_ur_is" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uris' is set
if ('uris' not in params or
params['uris'] is None):
raise ValueError("Missing the required parameter `uris` when calling `get_experiments_by_ur_is`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'uris' in params:
query_params.append(('uris', params['uris'])) # noqa: E501
collection_formats['uris'] = 'multi' # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/by_uris', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[ExperimentGetListDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_facilities(self, uri, **kwargs): # noqa: E501
"""Get facilities involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_facilities(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_facilities_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_facilities_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_facilities_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get facilities involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_facilities_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: list[InfrastructureFacilityGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', ] # noqa: E501
all_params.append('async_req')
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_facilities" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_facilities`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/facilities', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[InfrastructureFacilityGetDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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_used_variables1(self, uri, **kwargs): # noqa: E501
"""Get variables involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_used_variables1(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param list[str] scientific_objects: Search by objects uris
:param str accept_language: Request accepted language
:return: list[NamedResourceDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_used_variables1_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.get_used_variables1_with_http_info(uri, **kwargs) # noqa: E501
return data
def get_used_variables1_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get variables involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_used_variables1_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param list[str] scientific_objects: Search by objects uris
:param str accept_language: Request accepted language
:return: list[NamedResourceDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'scientific_objects', ] # noqa: E501
all_params.append('async_req')
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_used_variables1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `get_used_variables1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'scientific_objects' in params:
query_params.append(('scientific_objects', params['scientific_objects'])) # noqa: E501
collection_formats['scientific_objects'] = 'multi' # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/variables', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[NamedResourceDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 import_csv_data1(self, uri, provenance, file, **kwargs): # noqa: E501
"""Import a CSV file for the given experiment URI and scientific object type. # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.import_csv_data1(uri, provenance, file, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str provenance: Provenance URI (required)
:param file file: Data file (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: DataCSVValidationDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.import_csv_data1_with_http_info(uri, provenance, file, **kwargs) # noqa: E501
else:
(data) = self.import_csv_data1_with_http_info(uri, provenance, file, **kwargs) # noqa: E501
return data
def import_csv_data1_with_http_info(self, uri, provenance, file, **kwargs): # noqa: E501
"""Import a CSV file for the given experiment URI and scientific object type. # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.import_csv_data1_with_http_info(uri, provenance, file, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str provenance: Provenance URI (required)
:param file file: Data file (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: DataCSVValidationDTO
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'provenance', 'file', ] # noqa: E501
all_params.append('async_req')
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 import_csv_data1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `import_csv_data1`") # noqa: E501
# verify the required parameter 'provenance' is set
if ('provenance' not in params or
params['provenance'] is None):
raise ValueError("Missing the required parameter `provenance` when calling `import_csv_data1`") # noqa: E501
# verify the required parameter 'file' is set
if ('file' not in params or
params['file'] is None):
raise ValueError("Missing the required parameter `file` when calling `import_csv_data1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'provenance' in params:
query_params.append(('provenance', params['provenance'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
if 'file' in params:
local_var_files['file'] = params['file'] # noqa: E501
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/data/import', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DataCSVValidationDTO', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 search_experiment_data_list(self, uri, **kwargs): # noqa: E501
"""Search data # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiment_data_list(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str start_date: Search by minimal date
:param str end_date: Search by maximal date
:param str timezone: Precise the timezone corresponding to the given dates
:param list[str] scientific_objects: Search by objects
:param list[str] variables: Search by variables
:param float min_confidence: Search by minimal confidence index
:param float max_confidence: Search by maximal confidence index
:param list[str] provenances: Search by provenance uri
:param str metadata: Search by metadata
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[DataGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_experiment_data_list_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.search_experiment_data_list_with_http_info(uri, **kwargs) # noqa: E501
return data
def search_experiment_data_list_with_http_info(self, uri, **kwargs): # noqa: E501
"""Search data # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiment_data_list_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str start_date: Search by minimal date
:param str end_date: Search by maximal date
:param str timezone: Precise the timezone corresponding to the given dates
:param list[str] scientific_objects: Search by objects
:param list[str] variables: Search by variables
:param float min_confidence: Search by minimal confidence index
:param float max_confidence: Search by maximal confidence index
:param list[str] provenances: Search by provenance uri
:param str metadata: Search by metadata
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[DataGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'start_date', 'end_date', 'timezone', 'scientific_objects', 'variables', 'min_confidence', 'max_confidence', 'provenances', 'metadata', 'order_by', 'page', 'page_size', ] # noqa: E501
all_params.append('async_req')
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 search_experiment_data_list" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `search_experiment_data_list`") # noqa: E501
if 'min_confidence' in params and params['min_confidence'] > 1: # noqa: E501
raise ValueError("Invalid value for parameter `min_confidence` when calling `search_experiment_data_list`, must be a value less than or equal to `1`") # noqa: E501
if 'min_confidence' in params and params['min_confidence'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `min_confidence` when calling `search_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'max_confidence' in params and params['max_confidence'] > 1: # noqa: E501
raise ValueError("Invalid value for parameter `max_confidence` when calling `search_experiment_data_list`, must be a value less than or equal to `1`") # noqa: E501
if 'max_confidence' in params and params['max_confidence'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `max_confidence` when calling `search_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'page' in params and params['page'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page` when calling `search_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
if 'page_size' in params and params['page_size'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page_size` when calling `search_experiment_data_list`, must be a value greater than or equal to `0`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'start_date' in params:
query_params.append(('start_date', params['start_date'])) # noqa: E501
if 'end_date' in params:
query_params.append(('end_date', params['end_date'])) # noqa: E501
if 'timezone' in params:
query_params.append(('timezone', params['timezone'])) # noqa: E501
if 'scientific_objects' in params:
query_params.append(('scientific_objects', params['scientific_objects'])) # noqa: E501
collection_formats['scientific_objects'] = 'multi' # noqa: E501
if 'variables' in params:
query_params.append(('variables', params['variables'])) # noqa: E501
collection_formats['variables'] = 'multi' # noqa: E501
if 'min_confidence' in params:
query_params.append(('min_confidence', params['min_confidence'])) # noqa: E501
if 'max_confidence' in params:
query_params.append(('max_confidence', params['max_confidence'])) # noqa: E501
if 'provenances' in params:
query_params.append(('provenances', params['provenances'])) # noqa: E501
collection_formats['provenances'] = 'multi' # noqa: E501
if 'metadata' in params:
query_params.append(('metadata', params['metadata'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
collection_formats['order_by'] = 'multi' # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/data', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[DataGetDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 search_experiment_provenances(self, uri, **kwargs): # noqa: E501
"""Get provenances involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiment_provenances(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str name: Regex pattern for filtering by name
:param str description: Search by description
:param str activity: Search by activity URI
:param str activity_type: Search by activity type
:param str agent: Search by agent URI
:param str agent_type: Search by agent type
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[ProvenanceGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_experiment_provenances_with_http_info(uri, **kwargs) # noqa: E501
else:
(data) = self.search_experiment_provenances_with_http_info(uri, **kwargs) # noqa: E501
return data
def search_experiment_provenances_with_http_info(self, uri, **kwargs): # noqa: E501
"""Get provenances involved in an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiment_provenances_with_http_info(uri, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str authorization: Authentication token (required)
:param str name: Regex pattern for filtering by name
:param str description: Search by description
:param str activity: Search by activity URI
:param str activity_type: Search by activity type
:param str agent: Search by agent URI
:param str agent_type: Search by agent type
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[ProvenanceGetDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'name', 'description', 'activity', 'activity_type', 'agent', 'agent_type', 'order_by', 'page', 'page_size', ] # noqa: E501
all_params.append('async_req')
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 search_experiment_provenances" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `search_experiment_provenances`") # noqa: E501
if 'page' in params and params['page'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page` when calling `search_experiment_provenances`, must be a value greater than or equal to `0`") # noqa: E501
if 'page_size' in params and params['page_size'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page_size` when calling `search_experiment_provenances`, must be a value greater than or equal to `0`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'name' in params:
query_params.append(('name', params['name'])) # noqa: E501
if 'description' in params:
query_params.append(('description', params['description'])) # noqa: E501
if 'activity' in params:
query_params.append(('activity', params['activity'])) # noqa: E501
if 'activity_type' in params:
query_params.append(('activity_type', params['activity_type'])) # noqa: E501
if 'agent' in params:
query_params.append(('agent', params['agent'])) # noqa: E501
if 'agent_type' in params:
query_params.append(('agent_type', params['agent_type'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
collection_formats['order_by'] = 'multi' # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/provenances', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[ProvenanceGetDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 search_experiments(self, **kwargs): # noqa: E501
"""Search experiments # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiments(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str name: Regex pattern for filtering by name
:param int year: Search by year
:param bool is_ended: Search ended(false) or active experiments(true)
:param list[str] species: Search by involved species
:param list[str] factors: Search by studied effect
:param list[str] projects: Search by related project uri
:param bool is_public: Search private(false) or public experiments(true)
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[ExperimentGetListDTO]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.search_experiments_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.search_experiments_with_http_info(**kwargs) # noqa: E501
return data
def search_experiments_with_http_info(self, **kwargs): # noqa: E501
"""Search experiments # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.search_experiments_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param str name: Regex pattern for filtering by name
:param int year: Search by year
:param bool is_ended: Search ended(false) or active experiments(true)
:param list[str] species: Search by involved species
:param list[str] factors: Search by studied effect
:param list[str] projects: Search by related project uri
:param bool is_public: Search private(false) or public experiments(true)
:param list[str] order_by: List of fields to sort as an array of fieldName=asc|desc
:param int page: Page number
:param int page_size: Page size
:param str accept_language: Request accepted language
:return: list[ExperimentGetListDTO]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['name', 'year', 'is_ended', 'species', 'factors', 'projects', 'is_public', 'order_by', 'page', 'page_size', ] # noqa: E501
all_params.append('async_req')
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 search_experiments" % key
)
params[key] = val
del params['kwargs']
if 'page' in params and params['page'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page` when calling `search_experiments`, must be a value greater than or equal to `0`") # noqa: E501
if 'page_size' in params and params['page_size'] < 0: # noqa: E501
raise ValueError("Invalid value for parameter `page_size` when calling `search_experiments`, must be a value greater than or equal to `0`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'name' in params:
query_params.append(('name', params['name'])) # noqa: E501
if 'year' in params:
query_params.append(('year', params['year'])) # noqa: E501
if 'is_ended' in params:
query_params.append(('is_ended', params['is_ended'])) # noqa: E501
if 'species' in params:
query_params.append(('species', params['species'])) # noqa: E501
collection_formats['species'] = 'multi' # noqa: E501
if 'factors' in params:
query_params.append(('factors', params['factors'])) # noqa: E501
collection_formats['factors'] = 'multi' # noqa: E501
if 'projects' in params:
query_params.append(('projects', params['projects'])) # noqa: E501
collection_formats['projects'] = 'multi' # noqa: E501
if 'is_public' in params:
query_params.append(('is_public', params['is_public'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
collection_formats['order_by'] = 'multi' # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'page_size' in params:
query_params.append(('page_size', params['page_size'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # 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
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[ExperimentGetListDTO]', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 update_experiment(self, **kwargs): # noqa: E501
"""Update an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_experiment(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param ExperimentCreationDTO body: Experiment description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_experiment_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.update_experiment_with_http_info(**kwargs) # noqa: E501
return data
def update_experiment_with_http_info(self, **kwargs): # noqa: E501
"""Update an experiment # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_experiment_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param str authorization: Authentication token (required)
:param ExperimentCreationDTO body: Experiment description
:param str accept_language: Request accepted language
:return: ObjectUriResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', ] # noqa: E501
all_params.append('async_req')
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 update_experiment" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['application/json']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='ObjectUriResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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 validate_csv1(self, uri, provenance, file, **kwargs): # noqa: E501
"""Import a CSV file for the given experiment URI and scientific object type. # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.validate_csv1(uri, provenance, file, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str provenance: Provenance URI (required)
:param file file: Data file (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: DataCSVValidationDTO
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.validate_csv1_with_http_info(uri, provenance, file, **kwargs) # noqa: E501
else:
(data) = self.validate_csv1_with_http_info(uri, provenance, file, **kwargs) # noqa: E501
return data
def validate_csv1_with_http_info(self, uri, provenance, file, **kwargs): # noqa: E501
"""Import a CSV file for the given experiment URI and scientific object type. # noqa: E501
# noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.validate_csv1_with_http_info(uri, provenance, file, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str uri: Experiment URI (required)
:param str provenance: Provenance URI (required)
:param file file: Data file (required)
:param str authorization: Authentication token (required)
:param str accept_language: Request accepted language
:return: DataCSVValidationDTO
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['uri', 'provenance', 'file', ] # noqa: E501
all_params.append('async_req')
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 validate_csv1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'uri' is set
if ('uri' not in params or
params['uri'] is None):
raise ValueError("Missing the required parameter `uri` when calling `validate_csv1`") # noqa: E501
# verify the required parameter 'provenance' is set
if ('provenance' not in params or
params['provenance'] is None):
raise ValueError("Missing the required parameter `provenance` when calling `validate_csv1`") # noqa: E501
# verify the required parameter 'file' is set
if ('file' not in params or
params['file'] is None):
raise ValueError("Missing the required parameter `file` when calling `validate_csv1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'uri' in params:
path_params['uri'] = params['uri'] # noqa: E501
query_params = []
if 'provenance' in params:
query_params.append(('provenance', params['provenance'])) # noqa: E501
header_params = {}
#if 'authorization' in params:
# header_params['Authorization'] = params['authorization'] # noqa: E501
#if 'accept_language' in params:
# header_params['Accept-Language'] = params['accept_language'] # noqa: E501
form_params = []
local_var_files = {}
if 'file' in params:
local_var_files['file'] = params['file'] # noqa: E501
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = [] # noqa: E501
return self.api_client.call_api(
'/core/experiments/{uri}/data/import_validation', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DataCSVValidationDTO', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_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)
| 43.655606
| 220
| 0.617855
| 9,921
| 86,831
| 5.198367
| 0.026005
| 0.059256
| 0.013767
| 0.022337
| 0.968705
| 0.960716
| 0.954976
| 0.95104
| 0.947744
| 0.942295
| 0
| 0.019605
| 0.288042
| 86,831
| 1,988
| 221
| 43.677565
| 0.814639
| 0.371054
| 0
| 0.800589
| 1
| 0.015717
| 0.227333
| 0.050472
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032417
| false
| 0
| 0.013752
| 0
| 0.094303
| 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
|
0e7cdd3bc2fd7aefcf90ad086a2bb84c7b61f79c
| 70
|
py
|
Python
|
pd3_plot/__init__.py
|
cemel-jhu/pd3_plot
|
fe47c383670282d2d4f3055909fde90f3372a3cf
|
[
"MIT"
] | 3
|
2020-08-17T17:30:31.000Z
|
2020-09-24T05:20:21.000Z
|
pd3_plot/__init__.py
|
cemel-jhu/pd3_plot
|
fe47c383670282d2d4f3055909fde90f3372a3cf
|
[
"MIT"
] | null | null | null |
pd3_plot/__init__.py
|
cemel-jhu/pd3_plot
|
fe47c383670282d2d4f3055909fde90f3372a3cf
|
[
"MIT"
] | null | null | null |
from pd3_plot.Graph import Graph
from pd3_plot.Plotter import Plotter
| 23.333333
| 36
| 0.857143
| 12
| 70
| 4.833333
| 0.5
| 0.241379
| 0.37931
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.032258
| 0.114286
| 70
| 2
| 37
| 35
| 0.903226
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 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
|
0e7f9d0186b4b0fd25a0c8f7debbeb293f5696f8
| 131
|
py
|
Python
|
src/algoritmia/problems/shortestpaths/general/__init__.py
|
DavidLlorens/algoritmia
|
40ca0a89ea6de9b633fa5f697f0a28cae70816a2
|
[
"MIT"
] | 6
|
2018-09-15T15:09:10.000Z
|
2022-02-27T01:23:11.000Z
|
src/algoritmia/problems/shortestpaths/general/__init__.py
|
JeromeIllgner/algoritmia
|
406afe7206f2411557859bf03480c16db7dcce0d
|
[
"MIT"
] | null | null | null |
src/algoritmia/problems/shortestpaths/general/__init__.py
|
JeromeIllgner/algoritmia
|
406afe7206f2411557859bf03480c16db7dcce0d
|
[
"MIT"
] | 5
|
2018-07-10T20:19:55.000Z
|
2021-03-31T03:32:22.000Z
|
from algoritmia.problems.shortestpaths.general.bellmanford import BellmanFordShortestPathsFinder, PreBellmanFordShortestPathsFinder
| 131
| 131
| 0.931298
| 9
| 131
| 13.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.030534
| 131
| 1
| 131
| 131
| 0.96063
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
0e8a782a89116d657fbc676c188f4bac43ebbb75
| 3,765
|
py
|
Python
|
app/api/log/handlers.py
|
DARKMOONlite/Crafty_Controller_Fabric
|
20d1254e16e16ac47aa2345f86cfafc6ff7b5bd3
|
[
"Apache-2.0"
] | null | null | null |
app/api/log/handlers.py
|
DARKMOONlite/Crafty_Controller_Fabric
|
20d1254e16e16ac47aa2345f86cfafc6ff7b5bd3
|
[
"Apache-2.0"
] | 8
|
2022-02-09T05:38:23.000Z
|
2022-02-09T06:43:59.000Z
|
app/api/log/handlers.py
|
DARKMOONlite/Crafty_Controller_Fabric
|
20d1254e16e16ac47aa2345f86cfafc6ff7b5bd3
|
[
"Apache-2.0"
] | null | null | null |
import logging.config
import os
from app.base.handlers import BaseHandler
from app.classes.helpers import helper
from app.classes.models import check_role_permission
from app.classes.multiserv import multi
logger = logging.getLogger(__name__)
class SearchMCLogs(BaseHandler):
def initialize(self, mcserver):
self.mcserver = mcserver
def post(self):
token = self.get_argument('token')
user = self.authenticate_user(token)
if user is None:
self.access_denied('unknown')
if not check_role_permission(user, 'api_access') and not check_role_permission(user, 'logs'):
self.access_denied(user)
search_string = self.get_argument('query', default=None, strip=True)
server_id = self.get_argument('id')
server = multi.get_server_obj(server_id)
logfile = os.path.join(server.server_path, 'logs', 'latest.log')
data = helper.search_file(logfile, search_string)
line_list = []
if data:
for line in data:
line_list.append({'line_num': line[0], 'message': line[1]})
self.return_response(200, {}, line_list, {})
class GetMCLogs(BaseHandler):
def initialize(self, mcserver):
self.mcserver = mcserver
def get(self):
token = self.get_argument('token')
user = self.authenticate_user(token)
if user is None:
self.access_denied('unknown')
if not check_role_permission(user, 'api_access') and not check_role_permission(user, 'logs'):
self.access_denied(user)
server_id = self.get_argument('id')
server = multi.get_server_obj(server_id)
logfile = os.path.join(server.server_path, 'logs', 'latest.log')
data = helper.search_file(logfile, '')
line_list = []
if data:
for line in data:
line_list.append({'line_num': line[0], 'message': line[1]})
self.return_response(200, {}, line_list, {})
class GetCraftyLogs(BaseHandler):
def get(self):
token = self.get_argument('token')
user = self.authenticate_user(token)
if user is None:
self.access_denied('unknown')
if not check_role_permission(user, 'api_access') and not check_role_permission(user, 'logs'):
self.access_denied(user)
filename = self.get_argument('name')
logfile = os.path.join('logs', filename + '.log')
data = helper.search_file(logfile, '')
line_list = []
if data:
for line in data:
line_list.append({'line_num': line[0], 'message': line[1]})
self.return_response(200, {}, line_list, {})
class SearchCraftyLogs(BaseHandler):
def post(self):
token = self.get_argument('token')
user = self.authenticate_user(token)
if user is None:
self.access_denied('unknown')
if not check_role_permission(user, 'api_access') and not check_role_permission(user, 'logs'):
self.access_denied(user)
filename = self.get_argument('name')
query = self.get_argument('query')
logfile = os.path.join('logs', filename + '.log')
data = helper.search_file(logfile, query)
line_list = []
if data:
for line in data:
line_list.append({'line_num': line[0], 'message': line[1]})
self.return_response(200, {}, line_list, {})
| 31.90678
| 101
| 0.570252
| 425
| 3,765
| 4.854118
| 0.176471
| 0.046534
| 0.07271
| 0.085313
| 0.81968
| 0.81968
| 0.81968
| 0.81968
| 0.81968
| 0.76539
| 0
| 0.007806
| 0.319522
| 3,765
| 117
| 102
| 32.179487
| 0.797424
| 0
| 0
| 0.810127
| 0
| 0
| 0.061089
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.075949
| false
| 0
| 0.075949
| 0
| 0.202532
| 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
|
7ee5572d9b0b7967c40aa85b52f3512bf1d5f30d
| 11,134
|
py
|
Python
|
skyway/event_triggers.py
|
erpcloudsystems/skyway
|
d846897bd48be7d87de17020e7114f3ba7216e47
|
[
"MIT"
] | null | null | null |
skyway/event_triggers.py
|
erpcloudsystems/skyway
|
d846897bd48be7d87de17020e7114f3ba7216e47
|
[
"MIT"
] | null | null | null |
skyway/event_triggers.py
|
erpcloudsystems/skyway
|
d846897bd48be7d87de17020e7114f3ba7216e47
|
[
"MIT"
] | null | null | null |
from __future__ import unicode_literals
import frappe
from frappe import auth
import datetime
import json, ast
################ Quotation
@frappe.whitelist()
def quot_before_insert(doc, method=None):
pass
@frappe.whitelist()
def quot_after_insert(doc, method=None):
pass
@frappe.whitelist()
def quot_onload(doc, method=None):
pass
@frappe.whitelist()
def quot_before_validate(doc, method=None):
pass
@frappe.whitelist()
def quot_validate(doc, method=None):
pass
@frappe.whitelist()
def quot_on_submit(doc, method=None):
pass
@frappe.whitelist()
def quot_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def quot_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def quot_before_save(doc, method=None):
pass
@frappe.whitelist()
def quot_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def quot_on_update(doc, method=None):
pass
################ Sales Order
@frappe.whitelist()
def so_before_insert(doc, method=None):
pass
@frappe.whitelist()
def so_after_insert(doc, method=None):
pass
@frappe.whitelist()
def so_onload(doc, method=None):
pass
@frappe.whitelist()
def so_before_validate(doc, method=None):
pass
@frappe.whitelist()
def so_validate(doc, method=None):
pass
@frappe.whitelist()
def so_on_submit(doc, method=None):
pass
@frappe.whitelist()
def so_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def so_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def so_before_save(doc, method=None):
pass
@frappe.whitelist()
def so_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def so_on_update(doc, method=None):
pass
################ Delivery Note
@frappe.whitelist()
def dn_before_insert(doc, method=None):
pass
@frappe.whitelist()
def dn_after_insert(doc, method=None):
pass
@frappe.whitelist()
def dn_onload(doc, method=None):
pass
@frappe.whitelist()
def dn_before_validate(doc, method=None):
pass
@frappe.whitelist()
def dn_validate(doc, method=None):
pass
@frappe.whitelist()
def dn_on_submit(doc, method=None):
pass
@frappe.whitelist()
def dn_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def dn_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def dn_before_save(doc, method=None):
pass
@frappe.whitelist()
def dn_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def dn_on_update(doc, method=None):
pass
################ Sales Invoice
@frappe.whitelist()
def siv_before_insert(doc, method=None):
pass
@frappe.whitelist()
def siv_after_insert(doc, method=None):
pass
def siv_onload(doc, method=None):
pass
@frappe.whitelist()
def siv_before_validate(doc, method=None):
pass
@frappe.whitelist()
def siv_validate(doc, method=None):
pass
@frappe.whitelist()
def siv_on_submit(doc, method=None):
pass
@frappe.whitelist()
def siv_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def siv_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def siv_before_save(doc, method=None):
pass
@frappe.whitelist()
def siv_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def siv_on_update(doc, method=None):
pass
################ Payment Entry
@frappe.whitelist()
def pe_before_insert(doc, method=None):
pass
@frappe.whitelist()
def pe_after_insert(doc, method=None):
pass
@frappe.whitelist()
def pe_onload(doc, method=None):
pass
@frappe.whitelist()
def pe_before_validate(doc, method=None):
pass
@frappe.whitelist()
def pe_validate(doc, method=None):
pass
@frappe.whitelist()
def pe_on_submit(doc, method=None):
if doc.reference_name:
frappe.db.set_value('Ticket', doc.reference_name, "payment_entry_status", doc.status)
@frappe.whitelist()
def pe_on_cancel(doc, method=None):
if doc.reference_name:
frappe.db.set_value('Ticket', doc.reference_name, "payment_entry", "")
frappe.db.set_value('Ticket', doc.reference_name, "payment_entry_status", "")
@frappe.whitelist()
def pe_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def pe_before_save(doc, method=None):
pass
@frappe.whitelist()
def pe_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def pe_on_update(doc, method=None):
pass
################ Material Request
@frappe.whitelist()
def mr_before_insert(doc, method=None):
pass
@frappe.whitelist()
def mr_after_insert(doc, method=None):
pass
@frappe.whitelist()
def mr_onload(doc, method=None):
pass
@frappe.whitelist()
def mr_before_validate(doc, method=None):
pass
@frappe.whitelist()
def mr_validate(doc, method=None):
pass
@frappe.whitelist()
def mr_on_submit(doc, method=None):
pass
@frappe.whitelist()
def mr_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def mr_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def mr_before_save(doc, method=None):
pass
@frappe.whitelist()
def mr_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def mr_on_update(doc, method=None):
pass
################ Purchase Order
@frappe.whitelist()
def po_before_insert(doc, method=None):
pass
@frappe.whitelist()
def po_after_insert(doc, method=None):
pass
@frappe.whitelist()
def po_onload(doc, method=None):
pass
@frappe.whitelist()
def po_before_validate(doc, method=None):
pass
@frappe.whitelist()
def po_validate(doc, method=None):
pass
@frappe.whitelist()
def po_on_submit(doc, method=None):
pass
@frappe.whitelist()
def po_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def po_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def po_before_save(doc, method=None):
pass
@frappe.whitelist()
def po_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def po_on_update(doc, method=None):
pass
################ Purchase Receipt
@frappe.whitelist()
def pr_before_insert(doc, method=None):
pass
@frappe.whitelist()
def pr_after_insert(doc, method=None):
pass
@frappe.whitelist()
def pr_onload(doc, method=None):
pass
@frappe.whitelist()
def pr_before_validate(doc, method=None):
pass
@frappe.whitelist()
def pr_validate(doc, method=None):
pass
@frappe.whitelist()
def pr_on_submit(doc, method=None):
pass
@frappe.whitelist()
def pr_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def pr_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def pr_before_save(doc, method=None):
pass
@frappe.whitelist()
def pr_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def pr_on_update(doc, method=None):
pass
################ Purchase Invoice
@frappe.whitelist()
def piv_before_insert(doc, method=None):
pass
@frappe.whitelist()
def piv_after_insert(doc, method=None):
pass
@frappe.whitelist()
def piv_onload(doc, method=None):
pass
@frappe.whitelist()
def piv_before_validate(doc, method=None):
pass
@frappe.whitelist()
def piv_validate(doc, method=None):
pass
@frappe.whitelist()
def piv_on_submit(doc, method=None):
pass
@frappe.whitelist()
def piv_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def piv_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def piv_before_save(doc, method=None):
pass
@frappe.whitelist()
def piv_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def piv_on_update(doc, method=None):
pass
################ Employee Advance
@frappe.whitelist()
def emad_before_insert(doc, method=None):
pass
@frappe.whitelist()
def emad_after_insert(doc, method=None):
pass
@frappe.whitelist()
def emad_onload(doc, method=None):
pass
@frappe.whitelist()
def emad_before_validate(doc, method=None):
pass
@frappe.whitelist()
def emad_validate(doc, method=None):
pass
@frappe.whitelist()
def emad_on_submit(doc, method=None):
pass
@frappe.whitelist()
def emad_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def emad_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def emad_before_save(doc, method=None):
pass
@frappe.whitelist()
def emad_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def emad_on_update(doc, method=None):
pass
################ Expense Claim
@frappe.whitelist()
def excl_before_insert(doc, method=None):
pass
@frappe.whitelist()
def excl_after_insert(doc, method=None):
pass
@frappe.whitelist()
def excl_onload(doc, method=None):
pass
@frappe.whitelist()
def excl_before_validate(doc, method=None):
pass
@frappe.whitelist()
def excl_validate(doc, method=None):
pass
@frappe.whitelist()
def excl_on_submit(doc, method=None):
pass
@frappe.whitelist()
def excl_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def excl_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def excl_before_save(doc, method=None):
pass
@frappe.whitelist()
def excl_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def excl_on_update(doc, method=None):
pass
################ Stock Entry
@frappe.whitelist()
def ste_before_insert(doc, method=None):
pass
@frappe.whitelist()
def ste_after_insert(doc, method=None):
pass
@frappe.whitelist()
def ste_onload(doc, method=None):
pass
@frappe.whitelist()
def ste_before_validate(doc, method=None):
pass
@frappe.whitelist()
def ste_validate(doc, method=None):
if doc.ticket:
frappe.db.set_value('Ticket Items', doc.ticket_item, "stock_entry", doc.name)
if doc.docstatus == 0:
frappe.db.set_value('Ticket Items', doc.ticket_item, "stock_entry_status", "Draft")
if doc.docstatus == 1:
frappe.db.set_value('Ticket Items', doc.ticket_item, "stock_entry_status", "Submitted")
@frappe.whitelist()
def ste_on_submit(doc, method=None):
pass
@frappe.whitelist()
def ste_on_cancel(doc, method=None):
if doc.ticket:
frappe.db.set_value('Ticket Items', doc.ticket_item, "stock_entry", "")
frappe.db.set_value('Ticket Items', doc.ticket_item, "stock_entry_status", "")
@frappe.whitelist()
def ste_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def ste_before_save(doc, method=None):
pass
@frappe.whitelist()
def ste_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def ste_on_update(doc, method=None):
pass
################ Blanket Order
@frappe.whitelist()
def blank_before_insert(doc, method=None):
pass
@frappe.whitelist()
def blank_after_insert(doc, method=None):
pass
@frappe.whitelist()
def blank_onload(doc, method=None):
pass
@frappe.whitelist()
def blank_before_validate(doc, method=None):
pass
@frappe.whitelist()
def blank_validate(doc, method=None):
pass
@frappe.whitelist()
def blank_on_submit(doc, method=None):
pass
@frappe.whitelist()
def blank_on_cancel(doc, method=None):
pass
@frappe.whitelist()
def blank_on_update_after_submit(doc, method=None):
pass
@frappe.whitelist()
def blank_before_save(doc, method=None):
pass
@frappe.whitelist()
def blank_before_cancel(doc, method=None):
pass
@frappe.whitelist()
def blank_on_update(doc, method=None):
pass
| 23.44
| 99
| 0.720496
| 1,583
| 11,134
| 4.864182
| 0.045483
| 0.167143
| 0.241429
| 0.306883
| 0.962857
| 0.924026
| 0.892468
| 0.875195
| 0.864286
| 0.298442
| 0
| 0.000207
| 0.134184
| 11,134
| 474
| 100
| 23.489451
| 0.798548
| 0.016706
| 0
| 0.643341
| 0
| 0
| 0.020579
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.322799
| false
| 0.31377
| 0.011287
| 0
| 0.334086
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
7d0c757edba664ad96214cc05e20fb0aa3e78684
| 7,506
|
py
|
Python
|
GenerationController/MCStep.py
|
MagicCubeProject/MagicSim
|
02d7e0bcfabccda759ed8cbe7dd873386452fdb8
|
[
"Apache-2.0"
] | null | null | null |
GenerationController/MCStep.py
|
MagicCubeProject/MagicSim
|
02d7e0bcfabccda759ed8cbe7dd873386452fdb8
|
[
"Apache-2.0"
] | null | null | null |
GenerationController/MCStep.py
|
MagicCubeProject/MagicSim
|
02d7e0bcfabccda759ed8cbe7dd873386452fdb8
|
[
"Apache-2.0"
] | null | null | null |
from Enumerations.facet import Facet
from MagicCube.MagicCube import MagicCube
from GenerationController.MCMoves import MCubeMoves
class MCStep(object):
def __init__(self , magicCubeState = None):
if magicCubeState is None:
self.magicCube = MagicCube()
else:
self.magicCube = MagicCube(magicCubeState)
def move(self,move_type = MCubeMoves.IDENTICAL):
if move_type is MCubeMoves.IDENTICAL:
pass
#Simple Moves
elif move_type is MCubeMoves.FRONT:
self.magicCube.rotate(Facet.FRONT)
elif move_type is MCubeMoves.RIGHT:
self.magicCube.rotate(Facet.RIGHT)
elif move_type is MCubeMoves.DOWN:
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.UP:
self.magicCube.rotate(Facet.UP)
elif move_type is MCubeMoves.LEFT:
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.BACK:
self.magicCube.rotate(Facet.BACK)
#Revers of simple moves
elif move_type is MCubeMoves.FRONT_INVERS:
self.magicCube.rotate(Facet.FRONT,True)
elif move_type is MCubeMoves.RIGHT_INVERS:
self.magicCube.rotate(Facet.RIGHT,True)
elif move_type is MCubeMoves.DOWN_INVERS:
self.magicCube.rotate(Facet.DOWN,True)
elif move_type is MCubeMoves.UP_INVERS:
self.magicCube.rotate(Facet.UP,True)
elif move_type is MCubeMoves.LEFT_INVERS:
self.magicCube.rotate(Facet.LEFT,True)
elif move_type is MCubeMoves.BACK_INVERS:
self.magicCube.rotate(Facet.BACK,True)
#Double Moves
elif move_type is MCubeMoves.FRONT_DOUBLE:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.FRONT)
elif move_type is MCubeMoves.RIGHT_DOUBLE:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.RIGHT)
elif move_type is MCubeMoves.DOWN_DOUBLE:
self.magicCube.rotate(Facet.DOWN)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.UP_DOUBLE:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.UP)
elif move_type is MCubeMoves.LEFT_DOUBLE:
self.magicCube.rotate(Facet.LEFT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.BACK_DOUBLE:
self.magicCube.rotate(Facet.BACK)
self.magicCube.rotate(Facet.BACK)
#Hard Moves
elif move_type is MCubeMoves.FRONT_BACK:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.FRONT_BACK_INVERS:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK,True)
elif move_type is MCubeMoves.FRONT_BACK_DOUBLE:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.RIGHT_LEFT:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.RIGHT_LEFT_INVERS:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT,True)
elif move_type is MCubeMoves.RIGHT_LEFT_DOUBLE:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.UP_DOWN:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.UP_DOWN_INVERS:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN,True)
elif move_type is MCubeMoves.UP_DOWN_DOUBLE:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.FRONT_INVERS_BACK:
self.magicCube.rotate(Facet.FRONT,True)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.FRONT_INVERS_BACK_INVERS:
self.magicCube.rotate(Facet.FRONT,True)
self.magicCube.rotate(Facet.BACK,True)
elif move_type is MCubeMoves.FRONT_INVERS_BACK_DOUBLE:
self.magicCube.rotate(Facet.FRONT,True)
self.magicCube.rotate(Facet.BACK)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.RIGHT_INVERS_LEFT:
self.magicCube.rotate(Facet.RIGHT,True)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.RIGHT_INVERS_LEFT_INVERS:
self.magicCube.rotate(Facet.RIGHT,True)
self.magicCube.rotate(Facet.LEFT,True)
elif move_type is MCubeMoves.RIGHT_INVERS_LEFT_DOUBLE:
self.magicCube.rotate(Facet.RIGHT,True)
self.magicCube.rotate(Facet.LEFT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.UP_INVERS_DOWN:
self.magicCube.rotate(Facet.UP,True)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.UP_INVERS_DOWN_INVERS:
self.magicCube.rotate(Facet.UP,True)
self.magicCube.rotate(Facet.DOWN,True)
elif move_type is MCubeMoves.UP_INVERS_DOWN_DOUBLE:
self.magicCube.rotate(Facet.UP,True)
self.magicCube.rotate(Facet.DOWN)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.FRONT_DOUBLE_BACK:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.FRONT_DOUBLE_BACK_INVERS:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK,True)
elif move_type is MCubeMoves.FRONT_DOUBLE_BACK_DOUBLE:
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.FRONT)
self.magicCube.rotate(Facet.BACK)
self.magicCube.rotate(Facet.BACK)
elif move_type is MCubeMoves.RIGHT_DOUBLE_LEFT:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.RIGHT_DOUBLE_LEFT_INVERS:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT,True)
elif move_type is MCubeMoves.RIGHT_DOUBLE_LEFT_DOUBLE:
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.RIGHT)
self.magicCube.rotate(Facet.LEFT)
self.magicCube.rotate(Facet.LEFT)
elif move_type is MCubeMoves.UP_DOUBLE_DOWN:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN)
elif move_type is MCubeMoves.UP_DOUBLE_DOWN_INVERS:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN,True)
elif move_type is MCubeMoves.UP_DOUBLE_DOWN_DOUBLE:
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.UP)
self.magicCube.rotate(Facet.DOWN)
self.magicCube.rotate(Facet.DOWN)
| 45.768293
| 62
| 0.657474
| 908
| 7,506
| 5.296256
| 0.042952
| 0.26492
| 0.379289
| 0.479102
| 0.929923
| 0.929923
| 0.897484
| 0.855479
| 0.804325
| 0.804325
| 0
| 0
| 0.258993
| 7,506
| 163
| 63
| 46.04908
| 0.864617
| 0.007461
| 0
| 0.627451
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.013072
| false
| 0.006536
| 0.019608
| 0
| 0.039216
| 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
|
7d1baa78cc571d9a4ec5a80d1bf88c6c64ba0ec9
| 164
|
py
|
Python
|
core/drivers/extract/__init__.py
|
cmu-db/cmdbac
|
1f981e6f110728e51ba4ffdb90ff2d4ce091057a
|
[
"Apache-2.0"
] | 31
|
2016-04-07T04:54:29.000Z
|
2021-11-30T02:30:57.000Z
|
core/drivers/extract/__init__.py
|
cmu-db/db-webcrawler
|
1f981e6f110728e51ba4ffdb90ff2d4ce091057a
|
[
"Apache-2.0"
] | 22
|
2015-12-19T14:49:18.000Z
|
2021-09-07T23:48:24.000Z
|
core/drivers/extract/__init__.py
|
cmu-db/dbac
|
1f981e6f110728e51ba4ffdb90ff2d4ce091057a
|
[
"Apache-2.0"
] | 7
|
2016-05-13T01:02:01.000Z
|
2019-10-06T16:52:54.000Z
|
from extract import extract_forms, extract_all_forms, extract_all_forms_with_cookie
from extract import extract_urls, extract_all_urls, extract_all_urls_with_cookie
| 82
| 83
| 0.908537
| 26
| 164
| 5.192308
| 0.307692
| 0.296296
| 0.251852
| 0.355556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.067073
| 164
| 2
| 84
| 82
| 0.882353
| 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
|
7d1e0ba87e99f91e7a9467d10ab32ae55f748a08
| 182
|
py
|
Python
|
rsdf/__init__.py
|
bufferapp/rsdf
|
98b6a6332f9e3f9151274e69b799089678a38173
|
[
"MIT"
] | 12
|
2017-02-10T02:07:04.000Z
|
2021-12-12T09:12:33.000Z
|
rsdf/__init__.py
|
bufferapp/rsdf
|
98b6a6332f9e3f9151274e69b799089678a38173
|
[
"MIT"
] | 8
|
2017-02-10T09:16:52.000Z
|
2021-05-26T14:21:33.000Z
|
rsdf/__init__.py
|
bufferapp/rsdf
|
98b6a6332f9e3f9151274e69b799089678a38173
|
[
"MIT"
] | 4
|
2017-02-10T01:41:53.000Z
|
2021-09-03T04:07:28.000Z
|
from pandas import DataFrame
from .redshift import to_redshift
from .s3 import to_s3
setattr(DataFrame, to_redshift.__name__, to_redshift)
setattr(DataFrame, to_s3.__name__, to_s3)
| 26
| 53
| 0.82967
| 28
| 182
| 4.892857
| 0.321429
| 0.218978
| 0.262774
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.02454
| 0.104396
| 182
| 6
| 54
| 30.333333
| 0.815951
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 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
|
7d2cbdbaa8b018ce6a5b934a28bf845c2d0b6ef3
| 8,148
|
py
|
Python
|
dashboard/views/_brother/_events.py
|
beta-nu-theta-chi/ox-dashboard
|
842d86a381f26159b2c5bad39a95169496832023
|
[
"MIT"
] | null | null | null |
dashboard/views/_brother/_events.py
|
beta-nu-theta-chi/ox-dashboard
|
842d86a381f26159b2c5bad39a95169496832023
|
[
"MIT"
] | 70
|
2016-11-16T18:49:02.000Z
|
2021-04-26T00:47:18.000Z
|
dashboard/views/_brother/_events.py
|
beta-nu-theta-chi/ox-dashboard
|
842d86a381f26159b2c5bad39a95169496832023
|
[
"MIT"
] | null | null | null |
from django.contrib import messages
from django.http import HttpResponseRedirect
from django.shortcuts import render
from django.urls import reverse
from dashboard.forms import ExcuseForm
from dashboard.models import (
ChapterEvent,
ServiceEvent,
PhilanthropyEvent,
RecruitmentEvent,
HealthAndSafetyEvent,
Excuse
)
from dashboard.utils import get_human_readable_model_name
def brother_chapter_event(request, event_id, view):
""" Renders the brother page for chapter event with a excuse form """
if not request.user.is_authenticated: # brother auth check
messages.error(request, "Brother not logged in before viewing brother chapter events")
return HttpResponseRedirect(reverse('dashboard:home'))
event = ChapterEvent.objects.get(pk=event_id)
form = ExcuseForm(request.POST or None)
brother = request.user.brother
# get the excuses the brother has submitted for this event
excuse = Excuse.objects.filter(event=event_id, brother=brother)
if request.method == 'POST':
if form.is_valid():
instance = form.save(commit=False)
instance.brother = brother
instance.event = event
instance.save()
return HttpResponseRedirect(reverse('dashboard:brother'))
context = {
'type': view,
'form': form,
'event': event,
'excuse_exists': excuse.exists(),
'brother': brother,
'attended': event.attendees_brothers.filter(pk=brother.pk).exists(),
'button': 'Submit Excuse',
'event_type': get_human_readable_model_name(event)
}
# if an excuse has been submitted, add the excuse to the context
if excuse.exists():
context.update({ 'excuse': excuse[0], })
elif not excuse.exists():
context.update({ 'button_name': 'excuse' })
return render(request, "events/base-event.html", context)
def brother_service_event(request, event_id, view):
""" Renders the brother page for service event with a excuse form """
if not request.user.is_authenticated: # brother auth check
messages.error(request, "Brother not logged in before viewing brother chapter events")
return HttpResponseRedirect(reverse('dashboard:home'))
brother = request.user.brother
# get the excuses the brother has submitted for this event
excuse = Excuse.objects.filter(event=event_id, brother=brother)
event = ServiceEvent.objects.get(pk=event_id)
form = ExcuseForm(request.POST or None)
if request.method == 'POST':
if 'excuse' in request.POST:
if form.is_valid():
instance = form.save(commit=False)
instance.brother = brother
instance.event = event
instance.save()
return HttpResponseRedirect(reverse('dashboard:brother'))
context = {
'type': view,
'event': event,
'form': form,
'excuse_exists': excuse.exists(),
'attended': event.attendees_brothers.filter(pk=brother.pk).exists(),
'button': 'Submit Excuse',
'event_type': get_human_readable_model_name(event)
}
# if an excuse has been submitted, add the excuse to the context
if excuse.exists():
context.update({ 'excuse': excuse[0], })
elif not excuse.exists():
context.update({ 'button_name': 'excuse' })
return render(request, "events/base-event.html", context)
def brother_philanthropy_event(request, event_id, view):
""" Renders the brother page for service event with a excuse form """
if view is not 'general' and not request.user.is_authenticated: # brother auth check
messages.error(request, "Brother not logged in before viewing brother chapter events")
return HttpResponseRedirect(reverse('dashboard:home'))
brother = request.user.brother
# get the excuses the brother has submitted for this event
excuse = Excuse.objects.filter(event=event_id, brother=brother)
event = PhilanthropyEvent.objects.get(pk=event_id)
form = ExcuseForm(request.POST or None)
if request.method == 'POST':
if 'excuse' in request.POST:
if form.is_valid():
instance = form.save(commit=False)
instance.brother = brother
instance.event = event
instance.save()
return HttpResponseRedirect(reverse('dashboard:brother'))
context = {
'type': view,
'event': event,
'form': form,
'excuse_exists': excuse.exists(),
'attended': event.attendees_brothers.filter(pk=brother.pk).exists(),
'button': 'Submit Excuse',
'event_type': get_human_readable_model_name(event)
}
# if an excuse has been submitted, add the excuse to the context
if excuse.exists():
context.update({ 'excuse': excuse[0], })
elif not excuse.exists():
context.update({ 'button_name': 'excuse' })
return render(request, "events/base-event.html", context)
def brother_recruitment_event(request, event_id, view):
""" Renders the brother page for recruitment event with a excuse form """
if not request.user.is_authenticated: # brother auth check
messages.error(request, "Brother not logged in before viewing brother chapter events")
return HttpResponseRedirect(reverse('dashboard:home'))
brother = request.user.brother
# get the excuses the brother has submitted for this event
excuse = Excuse.objects.filter(event=event_id, brother=brother)
event = RecruitmentEvent.objects.get(pk=event_id)
attendees_pnms = event.attendees_pnms.all()
form = ExcuseForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
instance = form.save(commit=False)
instance.brother = brother
instance.event = event
instance.save()
return HttpResponseRedirect(reverse('dashboard:brother'))
context = {
'form': form,
'type': view,
'attendees_pnms': attendees_pnms,
'event': event,
'excuse_exists': excuse.exists(),
'attended': event.attendees_brothers.filter(pk=brother.pk).exists(),
'button': 'Submit Excuse',
'event_type': get_human_readable_model_name(event)
}
# if an excuse has been submitted, add the excuse to the context
if excuse.exists():
context.update({ 'excuse': excuse[0], })
elif not excuse.exists():
context.update({ 'button_name': 'excuse' })
return render(request, "events/base-event.html", context)
def brother_hs_event(request, event_id, view):
""" Renders the brother page for health and safety event with a excuse form """
if not request.user.is_authenticated: # brother auth check
messages.error(request, "Brother not logged in before viewing brother Health and Safety events")
return HttpResponseRedirect(reverse('dashboard:home'))
brother = request.user.brother
# get the excuses the brother has submitted for this event
excuse = Excuse.objects.filter(event=event_id, brother=brother)
event = HealthAndSafetyEvent.objects.get(pk=event_id)
form = ExcuseForm(request.POST or None)
if request.method == 'POST':
if form.is_valid():
instance = form.save(commit=False)
instance.brother = brother
instance.event = event
instance.save()
return HttpResponseRedirect(reverse('dashboard:brother'))
context = {
'type': view,
'event': event,
'form': form,
'excuse_exists': excuse.exists(),
'brother': brother,
'attended': event.attendees_brothers.filter(pk=brother.pk).exists(),
'button': 'Submit Excuse',
'event_type': get_human_readable_model_name(event)
}
# if an excuse has been submitted, add the excuse to the context
if excuse.exists():
context.update({ 'excuse': excuse[0], })
elif not excuse.exists():
context.update({ 'button_name': 'excuse' })
return render(request, "events/base-event.html", context)
| 36.375
| 104
| 0.658812
| 952
| 8,148
| 5.551471
| 0.103992
| 0.045412
| 0.062441
| 0.07947
| 0.890823
| 0.882498
| 0.878713
| 0.878713
| 0.878713
| 0.878713
| 0
| 0.000802
| 0.234904
| 8,148
| 223
| 105
| 36.538117
| 0.846968
| 0.125675
| 0
| 0.8
| 0
| 0
| 0.150749
| 0.015541
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030303
| false
| 0
| 0.042424
| 0
| 0.163636
| 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
|
ada5e97d642ebd0c440cd9f6c0e7eb8286152aef
| 76,257
|
py
|
Python
|
psdaq/psdaq/pyxpm/surf/xilinx/_Gtpe2Channel.py
|
ZhenghengLi/lcls2
|
94e75c6536954a58c8937595dcac295163aa1cdf
|
[
"BSD-3-Clause-LBNL"
] | 134
|
2017-02-22T18:07:00.000Z
|
2022-03-21T16:12:23.000Z
|
python/surf/xilinx/_Gtpe2Channel.py
|
a-panella/surf
|
b7c116c9f84760bda2c1ea9fa89fddef58dd831d
|
[
"BSD-3-Clause-LBNL"
] | 251
|
2017-04-26T23:42:42.000Z
|
2022-03-03T18:48:43.000Z
|
python/surf/xilinx/_Gtpe2Channel.py
|
a-panella/surf
|
b7c116c9f84760bda2c1ea9fa89fddef58dd831d
|
[
"BSD-3-Clause-LBNL"
] | 38
|
2017-02-21T21:15:03.000Z
|
2022-02-06T00:22:37.000Z
|
#-----------------------------------------------------------------------------
# Description:
# PyRogue Gtpe2Channel
#-----------------------------------------------------------------------------
# This file is part of the 'SLAC Firmware Standard Library'. It is subject to
# the license terms in the LICENSE.txt file found in the top-level directory
# of this distribution and at:
# https://confluence.slac.stanford.edu/display/ppareg/LICENSE.html.
# No part of the 'SLAC Firmware Standard Library', including this file, may be
# copied, modified, propagated, or distributed except according to the terms
# contained in the LICENSE.txt file.
#-----------------------------------------------------------------------------
import pyrogue as pr
class Gtpe2Channel(pr.Device):
def __init__(self, **kwargs):
super().__init__(**kwargs)
##############################
# Variables
##############################
self.add(pr.RemoteVariable(
name = "ACJTAG_RESET",
description = "",
offset = (0x0000<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ACJTAG_DEBUG_MODE",
description = "",
offset = (0x0000<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ACJTAG_MODE",
description = "",
offset = (0x0000<<2),
bitSize = 1,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "UCODEER_CLR",
description = "",
offset = (0x0000<<2),
bitSize = 1,
bitOffset = 1,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUFRESET_TIME",
description = "",
offset = (0x000C<<2),
bitSize = 5,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDRPHRESET_TIME",
description = "",
offset = (0x000D<<2),
bitSize = 5,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDRFREQRESET_TIME",
description = "",
offset = (0x000D<<2),
bitSize = 5,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPMARESET_TIME",
description = "",
offset = (0x000D<<2),
bitSize = 5,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPCSRESET_TIME",
description = "",
offset = (0x000E<<2),
bitSize = 5,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPMRESET_TIME",
description = "",
offset = (0x000E<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXISCANRESET_TIME",
description = "",
offset = (0x000F<<2),
bitSize = 5,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXSYNC_OVRD",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXSYNC_OVRD",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXSYNC_SKIP_DA",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXSYNC_SKIP_DA",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXSYNC_MULTILANE",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXSYNC_MULTILANE",
description = "",
offset = (0x0010<<2),
bitSize = 1,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPCSRESET_TIME",
description = "",
offset = (0x0010<<2),
bitSize = 5,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPMARESET_TIME",
description = "",
offset = (0x0010<<2),
bitSize = 5,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_XCLK_SEL",
description = "",
offset = (0x0011<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_DATA_WIDTH",
description = "",
offset = (0x0011<<2),
bitSize = 3,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_CLK25_DIV",
description = "",
offset = (0x0011<<2),
bitSize = 5,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_CM_SEL",
description = "",
offset = (0x0011<<2),
bitSize = 2,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPRBS_ERR_LOOPBACK",
description = "",
offset = (0x0011<<2),
bitSize = 1,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_BURST_SEQ_LEN",
description = "",
offset = (0x0012<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "OUTREFCLK_SEL_INV",
description = "",
offset = (0x0012<<2),
bitSize = 2,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_BURST_VAL",
description = "",
offset = (0x0012<<2),
bitSize = 3,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXOOB_CFG",
description = "",
offset = (0x0012<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SAS_MIN_COM",
description = "",
offset = (0x0013<<2),
bitSize = 6,
bitOffset = 9,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MIN_BURST",
description = "",
offset = (0x0013<<2),
bitSize = 6,
bitOffset = 3,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_EIDLE_VAL",
description = "",
offset = (0x0013<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MIN_WAKE",
description = "",
offset = (0x0014<<2),
bitSize = 6,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MIN_INIT",
description = "",
offset = (0x0014<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SAS_MAX_COM",
description = "",
offset = (0x0015<<2),
bitSize = 7,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MAX_BURST",
description = "",
offset = (0x0015<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MAX_WAKE",
description = "",
offset = (0x0016<<2),
bitSize = 6,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_MAX_INIT",
description = "",
offset = (0x0016<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXOSCALRESET_TIMEOUT",
description = "",
offset = (0x0017<<2),
bitSize = 5,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXOSCALRESET_TIME",
description = "",
offset = (0x0017<<2),
bitSize = 5,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TRANS_TIME_RATE",
description = "",
offset = (0x0018<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_LOOPBACK_CFG",
description = "",
offset = (0x0019<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_PREDRIVER_MODE",
description = "",
offset = (0x0019<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_EIDLE_DEASSERT_DELAY",
description = "",
offset = (0x0019<<2),
bitSize = 3,
bitOffset = 9,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_EIDLE_ASSERT_DELAY",
description = "",
offset = (0x0019<<2),
bitSize = 3,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_LOOPBACK_DRIVE_HIZ",
description = "",
offset = (0x0019<<2),
bitSize = 1,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_DRIVE_MODE",
description = "",
offset = (0x0019<<2),
bitSize = 5,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PD_TRANS_TIME_TO_P2",
description = "",
offset = (0x001A<<2),
bitSize = 8,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PD_TRANS_TIME_NONE_P2",
description = "",
offset = (0x001A<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PD_TRANS_TIME_FROM_P2",
description = "",
offset = (0x001B<<2),
bitSize = 12,
bitOffset = 1,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PCS_PCIE_EN",
description = "",
offset = (0x001B<<2),
bitSize = 1,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXBUF_RESET_ON_RATE_CHANGE",
description = "",
offset = (0x001C<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXBUF_EN",
description = "",
offset = (0x001C<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXGEARBOX_EN",
description = "",
offset = (0x001C<<2),
bitSize = 1,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "GEARBOX_MODE",
description = "",
offset = (0x001C<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_HOLD_DURING_EIDLE",
description = "",
offset = (0x001E<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_OS_CFG",
description = "",
offset = (0x0024<<2),
bitSize = 13,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_LF_CFG_WRD1",
description = "",
offset = (0x002A<<2),
bitSize = 2,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_HF_CFG",
description = "",
offset = (0x002A<<2),
bitSize = 14,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_LF_CFG_WRD0",
description = "",
offset = (0x002B<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_QUALIFIER_WRD0",
description = "",
offset = (0x002C<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_QUALIFIER_WRD1",
description = "",
offset = (0x002D<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_QUALIFIER_WRD2",
description = "",
offset = (0x002E<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_QUALIFIER_WRD3",
description = "",
offset = (0x002F<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_QUALIFIER_WRD4",
description = "",
offset = (0x0030<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_SDATA_MASK_WRD0",
description = "",
offset = (0x0036<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_SDATA_MASK_WRD1",
description = "",
offset = (0x0037<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_SDATA_MASK_WRD2",
description = "",
offset = (0x0038<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_SDATA_MASK_WRD3",
description = "",
offset = (0x0039<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_SDATA_MASK_WRD4",
description = "",
offset = (0x003A<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_PRESCALE",
description = "",
offset = (0x003B<<2),
bitSize = 5,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_VERT_OFFSET",
description = "",
offset = (0x003B<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_HORZ_OFFSET",
description = "",
offset = (0x003C<<2),
bitSize = 12,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_DISPERR_SEQ_MATCH",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "DEC_PCOMMA_DETECT",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "DEC_MCOMMA_DETECT",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "DEC_VALID_COMMA_ONLY",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_ERRDET_EN",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 9,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_EYE_SCAN_EN",
description = "",
offset = (0x003D<<2),
bitSize = 1,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_CONTROL",
description = "",
offset = (0x003D<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_COMMA_ENABLE",
description = "",
offset = (0x003E<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_MCOMMA_VALUE",
description = "",
offset = (0x003F<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXSLIDE_MODE",
description = "",
offset = (0x0040<<2),
bitSize = 2,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_PCOMMA_VALUE",
description = "",
offset = (0x0040<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_COMMA_WORD",
description = "",
offset = (0x0041<<2),
bitSize = 2,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_SIG_VALID_DLY",
description = "",
offset = (0x0041<<2),
bitSize = 5,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_PCOMMA_DET",
description = "",
offset = (0x0041<<2),
bitSize = 1,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_MCOMMA_DET",
description = "",
offset = (0x0041<<2),
bitSize = 1,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SHOW_REALIGN_COMMA",
description = "",
offset = (0x0041<<2),
bitSize = 1,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ALIGN_COMMA_DOUBLE",
description = "",
offset = (0x0041<<2),
bitSize = 1,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXSLIDE_AUTO_WAIT",
description = "",
offset = (0x0041<<2),
bitSize = 4,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_CORRECT_USE",
description = "",
offset = (0x0044<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_1_ENABLE",
description = "",
offset = (0x0044<<2),
bitSize = 4,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_1_1",
description = "",
offset = (0x0044<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_MAX_LAT",
description = "",
offset = (0x0045<<2),
bitSize = 6,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_1_2",
description = "",
offset = (0x0045<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_MIN_LAT",
description = "",
offset = (0x0046<<2),
bitSize = 6,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_1_3",
description = "",
offset = (0x0046<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_REPEAT_WAIT",
description = "",
offset = (0x0047<<2),
bitSize = 5,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_1_4",
description = "",
offset = (0x0047<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_USE",
description = "",
offset = (0x0048<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_ENABLE",
description = "",
offset = (0x0048<<2),
bitSize = 4,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_1",
description = "",
offset = (0x0048<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_KEEP_IDLE",
description = "",
offset = (0x0049<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_PRECEDENCE",
description = "",
offset = (0x0049<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_LEN",
description = "",
offset = (0x0049<<2),
bitSize = 2,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_2",
description = "",
offset = (0x0049<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_3",
description = "",
offset = (0x004A<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXGEARBOX_EN",
description = "",
offset = (0x004B<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COR_SEQ_2_4",
description = "",
offset = (0x004B<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_1_ENABLE",
description = "",
offset = (0x004C<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_1_1",
description = "",
offset = (0x004C<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_LEN",
description = "",
offset = (0x004D<<2),
bitSize = 2,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_1_2",
description = "",
offset = (0x004D<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_KEEP_ALIGN",
description = "",
offset = (0x004E<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_1_3",
description = "",
offset = (0x004E<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_1_4",
description = "",
offset = (0x004F<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_ENABLE",
description = "",
offset = (0x0050<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_USE",
description = "",
offset = (0x0050<<2),
bitSize = 1,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_1",
description = "",
offset = (0x0050<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "FTS_LANE_DESKEW_CFG",
description = "",
offset = (0x0051<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "FTS_LANE_DESKEW_EN",
description = "",
offset = (0x0051<<2),
bitSize = 1,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_2",
description = "",
offset = (0x0051<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "FTS_DESKEW_SEQ_ENABLE",
description = "",
offset = (0x0052<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CBCC_DATA_SOURCE_SEL",
description = "",
offset = (0x0052<<2),
bitSize = 1,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_3",
description = "",
offset = (0x0052<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_MAX_SKEW",
description = "",
offset = (0x0053<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CHAN_BOND_SEQ_2_4",
description = "",
offset = (0x0053<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXDLY_TAP_CFG",
description = "",
offset = (0x0054<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXDLY_CFG",
description = "",
offset = (0x0055<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPH_MONITOR_SEL",
description = "",
offset = (0x0057<<2),
bitSize = 5,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_DDI_SEL",
description = "",
offset = (0x0057<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_XCLK_SEL",
description = "",
offset = (0x0059<<2),
bitSize = 1,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_EN",
description = "",
offset = (0x0059<<2),
bitSize = 1,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXOOB_CFG",
description = "",
offset = (0x005A<<2),
bitSize = 1,
bitOffset = 9,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "LOOPBACK_CFG",
description = "",
offset = (0x005A<<2),
bitSize = 1,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG5",
description = "",
offset = (0x005D<<2),
bitSize = 3,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG4",
description = "",
offset = (0x005D<<2),
bitSize = 1,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG3",
description = "",
offset = (0x005D<<2),
bitSize = 1,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG2",
description = "",
offset = (0x005D<<2),
bitSize = 2,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG1",
description = "",
offset = (0x005D<<2),
bitSize = 2,
bitOffset = 2,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_CFG0",
description = "",
offset = (0x005D<<2),
bitSize = 2,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "SATA_PLL_CFG",
description = "",
offset = (0x005E<<2),
bitSize = 2,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPHDLY_CFG_WRD0",
description = "",
offset = (0x0060<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPHDLY_CFG_WRD1",
description = "",
offset = (0x0061<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXDLY_CFG",
description = "",
offset = (0x0062<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXDLY_TAP_CFG",
description = "",
offset = (0x0063<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPH_CFG",
description = "",
offset = (0x0064<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPH_MONITOR_SEL",
description = "",
offset = (0x0065<<2),
bitSize = 5,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_BIAS_CFG",
description = "",
offset = (0x0066<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXOOB_CLK_CFG",
description = "",
offset = (0x0068<<2),
bitSize = 1,
bitOffset = 3,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_CLKMUX_EN",
description = "",
offset = (0x0068<<2),
bitSize = 1,
bitOffset = 1,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_CLKMUX_EN",
description = "",
offset = (0x0068<<2),
bitSize = 1,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TERM_RCAL_CFG",
description = "",
offset = (0x0069<<2),
bitSize = 15,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
# "This feature is intended for internal use only." (UG482)
# self.add(pr.RemoteVariable(
# name = "TERM_RCAL_OVRD",
# description = "",
# offset = (0x006A<<2),
# bitSize = 3,
# bitOffset = 13,
# base = pr.UInt,
# mode = "RW",
# ))
self.add(pr.RemoteVariable(
name = "TX_CLK25_DIV",
description = "",
offset = (0x006A<<2),
bitSize = 5,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV5",
description = "",
offset = (0x006B<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV4",
description = "",
offset = (0x006B<<2),
bitSize = 4,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_DATA_WIDTH",
description = "",
offset = (0x006B<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PCS_RSVD_ATTR_WRD0",
description = "",
offset = (0x006F<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PCS_RSVD_ATTR_WRD1",
description = "",
offset = (0x0070<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PCS_RSVD_ATTR_WRD2",
description = "",
offset = (0x0071<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_FULL_1",
description = "",
offset = (0x0075<<2),
bitSize = 7,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_FULL_0",
description = "",
offset = (0x0075<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_FULL_3",
description = "",
offset = (0x0076<<2),
bitSize = 7,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_FULL_2",
description = "",
offset = (0x0076<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_LOW_0",
description = "",
offset = (0x0077<<2),
bitSize = 7,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_FULL_4",
description = "",
offset = (0x0077<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_LOW_2",
description = "",
offset = (0x0078<<2),
bitSize = 7,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_LOW_1",
description = "",
offset = (0x0078<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_LOW_4",
description = "",
offset = (0x0079<<2),
bitSize = 7,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MARGIN_LOW_3",
description = "",
offset = (0x0079<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_DEEMPH1",
description = "",
offset = (0x007A<<2),
bitSize = 6,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_DEEMPH0",
description = "",
offset = (0x007A<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_RXDETECT_REF",
description = "",
offset = (0x007C<<2),
bitSize = 3,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_MAINCURSOR_SEL",
description = "",
offset = (0x007C<<2),
bitSize = 1,
bitOffset = 3,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV3",
description = "",
offset = (0x007C<<2),
bitSize = 2,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV7",
description = "",
offset = (0x007D<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV6",
description = "",
offset = (0x007D<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TX_RXDETECT_CFG",
description = "",
offset = (0x007D<<2),
bitSize = 14,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CLK_COMMON_SWING",
description = "",
offset = (0x007E<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_CM_TRIM",
description = "",
offset = (0x007E<<2),
bitSize = 4,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_CFG1",
description = "",
offset = (0x0081<<2),
bitSize = 1,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_CFG",
description = "",
offset = (0x0081<<2),
bitSize = 4,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV2_WRD0",
description = "",
offset = (0x0082<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV2_WRD1",
description = "",
offset = (0x0083<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "DMONITOR_CFG_WRD0",
description = "",
offset = (0x0086<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "DMONITOR_CFG_WRD1",
description = "",
offset = (0x0087<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_BIAS_STARTUP_DISABLE",
description = "",
offset = (0x0088<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_HF_CFG3",
description = "",
offset = (0x0088<<2),
bitSize = 4,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXOUT_DIV",
description = "",
offset = (0x0088<<2),
bitSize = 3,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXOUT_DIV",
description = "",
offset = (0x0088<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG_WRD0",
description = "",
offset = (0x0089<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG_WRD1",
description = "",
offset = (0x008A<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG_WRD2",
description = "",
offset = (0x008B<<2),
bitSize = 11,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG3",
description = "",
offset = (0x008C<<2),
bitSize = 7,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPI_CFG0",
description = "",
offset = (0x008D<<2),
bitSize = 3,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_CM_CFG",
description = "",
offset = (0x008D<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG5",
description = "",
offset = (0x008D<<2),
bitSize = 2,
bitOffset = 10,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_LF_CFG2",
description = "",
offset = (0x008D<<2),
bitSize = 5,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_HF_CFG2",
description = "",
offset = (0x008D<<2),
bitSize = 5,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_IPCM_CFG",
description = "",
offset = (0x008E<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_INCM_CFG",
description = "",
offset = (0x008E<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG4",
description = "",
offset = (0x008E<<2),
bitSize = 1,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG6",
description = "",
offset = (0x008E<<2),
bitSize = 4,
bitOffset = 9,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_GC_CFG",
description = "",
offset = (0x008E<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_GC_CFG2",
description = "",
offset = (0x008F<<2),
bitSize = 3,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPI_CFG1",
description = "",
offset = (0x008F<<2),
bitSize = 1,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPI_CFG2",
description = "",
offset = (0x008F<<2),
bitSize = 1,
bitOffset = 3,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXLPM_OSINT_CFG",
description = "",
offset = (0x008F<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_CLK_PHASE_SEL",
description = "",
offset = (0x0091<<2),
bitSize = 1,
bitOffset = 15,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "USE_PCS_CLK_PHASE_SEL",
description = "",
offset = (0x0091<<2),
bitSize = 1,
bitOffset = 14,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "CFOK_CFG2",
description = "",
offset = (0x0091<<2),
bitSize = 7,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ADAPT_CFG0_WRD0",
description = "",
offset = (0x0092<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ADAPT_CFG0_WRD1",
description = "",
offset = (0x0093<<2),
bitSize = 4,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_PPM_CFG",
description = "",
offset = (0x0095<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_GREY_SEL",
description = "",
offset = (0x0096<<2),
bitSize = 1,
bitOffset = 5,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_INVSTROBE_SEL",
description = "",
offset = (0x0096<<2),
bitSize = 1,
bitOffset = 4,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_PPMCLK_SEL",
description = "",
offset = (0x0096<<2),
bitSize = 1,
bitOffset = 3,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXPI_SYNFREQ_PPM",
description = "",
offset = (0x0096<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TST_RSV_WRD0",
description = "",
offset = (0x0097<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TST_RSV_WRD1",
description = "",
offset = (0x0098<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV_WRD0",
description = "",
offset = (0x0099<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "PMA_RSV_WRD1",
description = "",
offset = (0x009A<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_BUFFER_CFG",
description = "",
offset = (0x009B<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_THRESH_OVRD",
description = "",
offset = (0x009C<<2),
bitSize = 1,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_RESET_ON_EIDLE",
description = "",
offset = (0x009C<<2),
bitSize = 1,
bitOffset = 6,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_THRESH_UNDFLW",
description = "",
offset = (0x009C<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_EIDLE_HI_CNT",
description = "",
offset = (0x009D<<2),
bitSize = 4,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_EIDLE_LO_CNT",
description = "",
offset = (0x009D<<2),
bitSize = 4,
bitOffset = 8,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_ADDR_MODE",
description = "",
offset = (0x009D<<2),
bitSize = 1,
bitOffset = 7,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_THRESH_OVFLW",
description = "",
offset = (0x009D<<2),
bitSize = 6,
bitOffset = 1,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_DEFER_RESET_BUF_EN",
description = "",
offset = (0x009D<<2),
bitSize = 1,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_RESET_ON_COMMAALIGN",
description = "",
offset = (0x009E<<2),
bitSize = 1,
bitOffset = 2,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_RESET_ON_RATE_CHANGE",
description = "",
offset = (0x009E<<2),
bitSize = 1,
bitOffset = 1,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXBUF_RESET_ON_CB_CHANGE",
description = "",
offset = (0x009E<<2),
bitSize = 1,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "TXDLY_LCFG",
description = "",
offset = (0x009F<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXDLY_LCFG",
description = "",
offset = (0x00A0<<2),
bitSize = 9,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPH_CFG_WRD0",
description = "",
offset = (0x00A1<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPH_CFG_WRD1",
description = "",
offset = (0x00A2<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPHDLY_CFG_WRD0",
description = "",
offset = (0x00A3<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXPHDLY_CFG_WRD1",
description = "",
offset = (0x00A4<<2),
bitSize = 8,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RX_DEBUG_CFG",
description = "",
offset = (0x00A5<<2),
bitSize = 14,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "ES_PMA_CFG",
description = "",
offset = (0x00A6<<2),
bitSize = 10,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_PH_RESET_ON_EIDLE",
description = "",
offset = (0x00A7<<2),
bitSize = 1,
bitOffset = 13,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_FR_RESET_ON_EIDLE",
description = "",
offset = (0x00A7<<2),
bitSize = 1,
bitOffset = 12,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_HOLD_DURING_EIDLE",
description = "",
offset = (0x00A7<<2),
bitSize = 1,
bitOffset = 11,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_LOCK_CFG",
description = "",
offset = (0x00A7<<2),
bitSize = 6,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD0",
description = "",
offset = (0x00A8<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD1",
description = "",
offset = (0x00A9<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD2",
description = "",
offset = (0x00AA<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD3",
description = "",
offset = (0x00AB<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD4",
description = "",
offset = (0x00AC<<2),
bitSize = 16,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
self.add(pr.RemoteVariable(
name = "RXCDR_CFG_WRD5",
description = "",
offset = (0x00AD<<2),
bitSize = 3,
bitOffset = 0,
base = pr.UInt,
mode = "RW",
))
| 29.85787
| 78
| 0.339575
| 5,465
| 76,257
| 4.634584
| 0.0785
| 0.069923
| 0.089901
| 0.229746
| 0.877014
| 0.795641
| 0.785613
| 0.728877
| 0.685684
| 0.680946
| 0
| 0.063488
| 0.552815
| 76,257
| 2,553
| 79
| 29.869565
| 0.679247
| 0.012655
| 0
| 0.856954
| 0
| 0
| 0.056296
| 0.005757
| 0
| 0
| 0.020104
| 0
| 0.00088
| 1
| 0.00044
| false
| 0
| 0.00044
| 0
| 0.00132
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 1
| 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
|
adaabbc49010a558fb98770271c79fd28447a38e
| 12,723
|
py
|
Python
|
tests/test_parser.py
|
MarkKoz/prop-logic
|
f5835f83af9b6ddbbc18013f90cf684d313ecec0
|
[
"MIT"
] | null | null | null |
tests/test_parser.py
|
MarkKoz/prop-logic
|
f5835f83af9b6ddbbc18013f90cf684d313ecec0
|
[
"MIT"
] | null | null | null |
tests/test_parser.py
|
MarkKoz/prop-logic
|
f5835f83af9b6ddbbc18013f90cf684d313ecec0
|
[
"MIT"
] | null | null | null |
import pytest
from prop_logic import lexer
from prop_logic.connectives import Conjunction, Implication, Negation
from prop_logic.nodes import BinaryFormula, UnaryFormula, Variable
from prop_logic.parser import Parser
PARAMS_GROUPED = [
(
"A & B > C",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
(
"(A & B) > C",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
(
"A & (B > C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"(A & B > C)",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
(
"(A & (B > C))",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"((A & B) > C)",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
]
PARAMS_UNGROUPED_NOT = [
(
"~A & B > C",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"A & ~B > C",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"A & B > ~C",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"~A & ~B > C",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"A & ~B > ~C",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
(
"~A & B > ~C",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"~A & ~B > ~C",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
]
PARAMS_GROUPED_NOT = [
(
"(~A & B) > C",
BinaryFormula(
BinaryFormula(UnaryFormula(Negation, Variable("A")), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
(
"(A & ~B) > C",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, UnaryFormula(Negation, Variable("B"))),
Implication,
Variable("C"),
),
),
(
"(A & B) > ~C",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"(~A & ~B) > C",
BinaryFormula(
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
UnaryFormula(Negation, Variable("B")),
),
Implication,
Variable("C"),
),
),
(
"(~A & B) > ~C",
BinaryFormula(
BinaryFormula(UnaryFormula(Negation, Variable("A")), Conjunction, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"(A & ~B) > ~C",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, UnaryFormula(Negation, Variable("B"))),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"(~A & ~B) > ~C",
BinaryFormula(
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
UnaryFormula(Negation, Variable("B")),
),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
# Next
(
"~A & (B > C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"A & (~B > C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"A & (B > ~C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"~A & (~B > C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"~A & (B > ~C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"A & (~B > ~C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
(
"~A & (~B > ~C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
# Next
(
"((~A & B) > C)",
BinaryFormula(
BinaryFormula(UnaryFormula(Negation, Variable("A")), Conjunction, Variable("B")),
Implication,
Variable("C"),
),
),
(
"((A & ~B) > C)",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, UnaryFormula(Negation, Variable("B"))),
Implication,
Variable("C"),
),
),
(
"((A & B) > ~C)",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"((~A & ~B) > C)",
BinaryFormula(
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
UnaryFormula(Negation, Variable("B")),
),
Implication,
Variable("C"),
),
),
(
"((~A & B) > ~C)",
BinaryFormula(
BinaryFormula(UnaryFormula(Negation, Variable("A")), Conjunction, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"((A & ~B) > ~C)",
BinaryFormula(
BinaryFormula(Variable("A"), Conjunction, UnaryFormula(Negation, Variable("B"))),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
(
"((~A & ~B) > ~C)",
BinaryFormula(
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
UnaryFormula(Negation, Variable("B")),
),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
# Next
(
"(~A & (B > C))",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"(A & (~B > C))",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"(A & (B > ~C))",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"(~A & (~B > C))",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"(~A & (B > ~C))",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"(A & (~B > ~C))",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
(
"(~A & (~B > ~C))",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
# Next
(
"(~A & B > C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, Variable("C")),
),
),
(
"(A & ~B > C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"(A & B > ~C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"(~A & ~B > C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(UnaryFormula(Negation, Variable("B")), Implication, Variable("C")),
),
),
(
"(A & ~B > ~C)",
BinaryFormula(
Variable("A"),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
(
"(~A & B > ~C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(Variable("B"), Implication, UnaryFormula(Negation, Variable("C"))),
),
),
(
"(~A & ~B > ~C)",
BinaryFormula(
UnaryFormula(Negation, Variable("A")),
Conjunction,
BinaryFormula(
UnaryFormula(Negation, Variable("B")),
Implication,
UnaryFormula(Negation, Variable("C")),
),
),
),
]
def get_ast(function):
tokens = lexer.lex(function)
parser = Parser(tokens)
return parser.parse()
@pytest.mark.parametrize(["function", "expected"], PARAMS_GROUPED)
def test_parser_grouped(function, expected):
assert get_ast(function) == expected
@pytest.mark.parametrize(["function", "expected"], PARAMS_UNGROUPED_NOT)
def test_parser_ungrouped_not(function, expected):
assert get_ast(function) == expected
@pytest.mark.parametrize(["function", "expected"], PARAMS_GROUPED_NOT)
def test_parser_grouped_not(function, expected):
assert get_ast(function) == expected
| 26.89852
| 93
| 0.462863
| 881
| 12,723
| 6.654938
| 0.040863
| 0.245608
| 0.343851
| 0.130991
| 0.935869
| 0.934846
| 0.934846
| 0.925124
| 0.916596
| 0.916596
| 0
| 0
| 0.384029
| 12,723
| 472
| 94
| 26.955508
| 0.748118
| 0.001493
| 0
| 0.743421
| 0
| 0
| 0.063627
| 0
| 0
| 0
| 0
| 0
| 0.006579
| 1
| 0.008772
| false
| 0
| 0.010965
| 0
| 0.02193
| 0
| 0
| 0
| 0
| null | 1
| 1
| 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
| 9
|
bc243f4cf4d1da23056b540125e58fc950dd853e
| 105
|
py
|
Python
|
autodisc/autodisc/systems/__init__.py
|
flowersteam/automated_discovery_of_lenia_patterns
|
97cc7cde2120fa95225d1e470e00b8aa8c034e97
|
[
"MIT"
] | 10
|
2019-10-05T16:22:11.000Z
|
2021-12-30T14:09:42.000Z
|
autodisc/autodisc/systems/__init__.py
|
flowersteam/automated_discovery_of_lenia_patterns
|
97cc7cde2120fa95225d1e470e00b8aa8c034e97
|
[
"MIT"
] | null | null | null |
autodisc/autodisc/systems/__init__.py
|
flowersteam/automated_discovery_of_lenia_patterns
|
97cc7cde2120fa95225d1e470e00b8aa8c034e97
|
[
"MIT"
] | 2
|
2019-10-14T12:12:38.000Z
|
2020-09-16T11:18:26.000Z
|
import autodisc.systems.lenia
import autodisc.systems.statistics
from autodisc.systems.lenia import Lenia
| 35
| 40
| 0.87619
| 14
| 105
| 6.571429
| 0.428571
| 0.48913
| 0.456522
| 0.565217
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.066667
| 105
| 3
| 40
| 35
| 0.938776
| 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
| 0
| 0
|
0
| 7
|
70e7bfd0b5706cb795e065f65f4c3243482becfb
| 67
|
py
|
Python
|
satchmo/apps/shipping/modules/per/__init__.py
|
funwhilelost/satchmo
|
589a5d797533ea15dfde9af7f36e304092d22a94
|
[
"BSD-3-Clause"
] | 16
|
2015-03-06T14:42:27.000Z
|
2019-12-23T21:37:01.000Z
|
satchmo/apps/shipping/modules/per/__init__.py
|
funwhilelost/satchmo
|
589a5d797533ea15dfde9af7f36e304092d22a94
|
[
"BSD-3-Clause"
] | null | null | null |
satchmo/apps/shipping/modules/per/__init__.py
|
funwhilelost/satchmo
|
589a5d797533ea15dfde9af7f36e304092d22a94
|
[
"BSD-3-Clause"
] | 8
|
2015-01-28T16:02:37.000Z
|
2022-03-03T21:29:40.000Z
|
import shipper
def get_methods():
return [shipper.Shipper()]
| 11.166667
| 30
| 0.701493
| 8
| 67
| 5.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179104
| 67
| 5
| 31
| 13.4
| 0.836364
| 0
| 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
|
cb7b480fed73e2b2692e28e97b7958ad0daac87e
| 293
|
py
|
Python
|
odoo-13.0/addons/l10n_latam_invoice_document/models/__init__.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | null | null | null |
odoo-13.0/addons/l10n_latam_invoice_document/models/__init__.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | null | null | null |
odoo-13.0/addons/l10n_latam_invoice_document/models/__init__.py
|
VaibhavBhujade/Blockchain-ERP-interoperability
|
b5190a037fb6615386f7cbad024d51b0abd4ba03
|
[
"MIT"
] | null | null | null |
# Part of Odoo. See LICENSE file for full copyright and licensing details.
from . import res_company
from . import l10n_latam_document_type
from . import account_journal
from . import account_move
from . import account_move_line
from . import account_chart_template
from . import ir_sequence
| 29.3
| 74
| 0.822526
| 44
| 293
| 5.227273
| 0.636364
| 0.304348
| 0.295652
| 0.182609
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007968
| 0.143345
| 293
| 9
| 75
| 32.555556
| 0.908367
| 0.245734
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
cb877f56c863f25d5c0fb7545491e44672d5bcdb
| 88,224
|
py
|
Python
|
server/imageProcessor/common/constants.py
|
perryjiang/similo
|
48866f5c619efb6a11130f59daa15456f2c1ee1a
|
[
"MIT"
] | null | null | null |
server/imageProcessor/common/constants.py
|
perryjiang/similo
|
48866f5c619efb6a11130f59daa15456f2c1ee1a
|
[
"MIT"
] | 1
|
2021-03-31T18:51:37.000Z
|
2021-03-31T18:51:37.000Z
|
server/imageProcessor/common/constants.py
|
vimmada/similo
|
48866f5c619efb6a11130f59daa15456f2c1ee1a
|
[
"MIT"
] | 1
|
2018-08-01T23:57:33.000Z
|
2018-08-01T23:57:33.000Z
|
image = 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
"""
KEEP_HISTORY = 50
TOKEN_EXP_HRS = 72
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0
| 8
|
cbc0f46cc29f89b79bb91432710420474004747b
| 203
|
py
|
Python
|
admin/views/__init__.py
|
Simple2B/cortex-backend
|
9cf6802b0eff9254875bcbe553517500ccfc9082
|
[
"MIT"
] | 1
|
2021-10-17T13:28:51.000Z
|
2021-10-17T13:28:51.000Z
|
admin/views/__init__.py
|
Simple2B/cortex-backend
|
9cf6802b0eff9254875bcbe553517500ccfc9082
|
[
"MIT"
] | null | null | null |
admin/views/__init__.py
|
Simple2B/cortex-backend
|
9cf6802b0eff9254875bcbe553517500ccfc9082
|
[
"MIT"
] | null | null | null |
# flake8: noqa F401
from .auth import auth_blueprint
from .mail_message import message_blueprint
# from .admin import CortexAdminIndexView
from .admin import CortexAdminIndexView, DoctorAdminModelView
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0
| 7
|
cbe97ac18c75b062feaf9f55e5a5a336124dde57
| 3,779
|
py
|
Python
|
scobra/analysis/Match.py
|
nihalzp/scobra
|
de1faa73fb4d186d9567bfa8e174b3fd6f1833ef
|
[
"MIT"
] | 7
|
2016-03-16T09:03:41.000Z
|
2019-09-20T05:55:02.000Z
|
scobra/analysis/Match.py
|
nihalzp/scobra
|
de1faa73fb4d186d9567bfa8e174b3fd6f1833ef
|
[
"MIT"
] | 11
|
2019-10-03T15:04:58.000Z
|
2020-05-11T17:27:10.000Z
|
scobra/analysis/Match.py
|
nihalzp/scobra
|
de1faa73fb4d186d9567bfa8e174b3fd6f1833ef
|
[
"MIT"
] | 6
|
2016-03-16T09:04:54.000Z
|
2021-07-24T15:03:41.000Z
|
""" Not functional! To be modified """
def MatchFlux(self, md, vd, lo=0, hi=100, samedirec=True, count=50,
display=False, tol=1e-6):
""" pre: md = matched flux dict; vd = vary flux dict / vary met bound dict """
state = self.GetState()
avg = (float(lo)+float(hi))/2
if set(vd.keys()).issubset(self.cnames.values()): # vary flux dict
self.SetSumReacsConstraint(vd,avg)
elif set(vd.keys()).issubset(self.rnames.values()) or set(vd.keys()).issubset(self.smexterns.rnames): # vary met bound dict
metdic = {}
for met in vd:
metdic[met] = (avg*vd[met],avg*vd[met])
self.SetMetBounds(metdic)
else:
print("vary dict error")
self.Solve(False)
reac = md.keys()[0]
val = md[reac]
sol = self.GetSol(IncZeroes=True)[reac]
if set(vd.keys()).issubset(self.cnames.values()): # vary flux dict
self.DelSumReacsConstraint()
elif set(vd.keys()).issubset(self.rnames.values()) or set(vd.keys()).issubset(self.smexterns.rnames): # vary met bound dict
for met in vd:
metdic[met] = (0,0)
self.SetMetBounds(metdic) # set met bounds back to balance
self.SetState(state)
if display:
print("Varying value = " + str(avg))
print(reac + " " + str(sol))
if abs(1-(sol/val)) < tol:
return avg
else:
if samedirec:
if sol > val:
hi = avg
else:
lo = avg
else:
if sol > val:
lo = avg
else:
hi = avg
count = count - 1
if count > 0:
return self.MatchFlux(md, vd, lo, hi, samedirec, count, display, tol)
else:
return None
print("Varying value = " + str(avg))
print(reac + " " + str(sol))
def MatchRatio(self,numdic,domdic,val,vd,lo=0,hi=100,samedirec=True,count=50,display=False,tol=1e-6):
state = self.GetState()
avg = (float(lo)+float(hi))/2
if set(vd.keys()).issubset(self.cnames.values()): # vary flux dict
self.SetSumReacsConstraint(vd,avg,'matchratio')
elif set(vd.keys()).issubset(self.rnames.values()) or set(vd.keys()).issubset(self.smexterns.rnames): # vary met bound dict
metdic = {}
for met in vd:
metdic[met] = (avg*vd[met],avg*vd[met])
self.SetMetBounds(metdic)
else:
print("vary dict error")
self.Solve(False)
num = 0.0
for reac in numdic:
num += self.GetSol(IncZeroes=True)[reac]*numdic[reac]
dom = 0.0
for reac in domdic:
dom += self.GetSol(IncZeroes=True)[reac]*domdic[reac]
sol = num/dom
self.SetState(state)
if set(vd.keys()).issubset(self.cnames.values()): # vary flux dict
self.DelSumReacsConstraint('matchratio')
elif set(vd.keys()).issubset(self.rnames.values()) or set(vd.keys()).issubset(self.smexterns.rnames): # vary met bound dict
for met in vd:
metdic[met] = (0,0)
self.SetMetBounds(metdic) # set met bounds back to balance
if display:
print("Varying value = " + str(avg))
print(reac + " " + str(sol))
if abs(1-(sol/val)) < tol:
self.SetState(state)
return avg
else:
if samedirec:
if sol > val:
hi = avg
else:
lo = avg
else:
if sol > val:
lo = avg
else:
hi = avg
count = count - 1
if count > 0:
return self.MatchRatio(numdic, domdic, val, vd, lo, hi, samedirec, count, display, tol)
else:
self.SetState(state)
print("Varying value = " + str(avg))
print("Ratio =",sol)
| 35.650943
| 127
| 0.547235
| 487
| 3,779
| 4.246407
| 0.158111
| 0.029014
| 0.052224
| 0.098646
| 0.858317
| 0.794004
| 0.780464
| 0.780464
| 0.747582
| 0.729207
| 0
| 0.012727
| 0.31384
| 3,779
| 105
| 128
| 35.990476
| 0.784805
| 0.080974
| 0
| 0.785714
| 0
| 0
| 0.036801
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.020408
| false
| 0
| 0
| 0
| 0.071429
| 0.102041
| 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
|
1db4cd7906c590483b7316a2383124bc989c77fa
| 815
|
py
|
Python
|
testData/inspectionv18/configDuplicate.py
|
alek-sun/pydantic-pycharm-plugin
|
6b07519aadf0ff8b8a644c1f9ede88e09c687c80
|
[
"Apache-2.0",
"MIT"
] | 238
|
2019-08-05T12:46:09.000Z
|
2022-03-25T08:53:25.000Z
|
testData/inspectionv18/configDuplicate.py
|
alek-sun/pydantic-pycharm-plugin
|
6b07519aadf0ff8b8a644c1f9ede88e09c687c80
|
[
"Apache-2.0",
"MIT"
] | 415
|
2019-07-15T17:39:35.000Z
|
2022-03-31T01:18:38.000Z
|
testData/inspectionv18/configDuplicate.py
|
collerek/pydantic-pycharm-plugin
|
7068ece42334cc9fbe927d779d199c86d5139888
|
[
"Apache-2.0",
"MIT"
] | 7
|
2019-08-09T01:03:16.000Z
|
2022-02-08T03:28:19.000Z
|
import abc
from pydantic import BaseModel
class E(BaseModel, <error descr="Specifying config in two places is ambiguous, use either Config attribute or class kwargs">allow_mutation=True</error>):
class <error descr="Specifying config in two places is ambiguous, use either Config attribute or class kwargs">Config</error>:
allow_mutation= True
class F(BaseModel, allow_mutation=True):
pass
class G(BaseModel):
class Config:
allow_mutation=True
class H(BaseModel, metaclass=abc.ABCMeta, <error descr="Specifying config in two places is ambiguous, use either Config attribute or class kwargs">allow_mutation=True</error>):
class <error descr="Specifying config in two places is ambiguous, use either Config attribute or class kwargs">Config</error>:
allow_mutation= True
| 40.75
| 176
| 0.760736
| 114
| 815
| 5.385965
| 0.263158
| 0.127036
| 0.166124
| 0.169381
| 0.732899
| 0.732899
| 0.732899
| 0.732899
| 0.732899
| 0.732899
| 0
| 0
| 0.161963
| 815
| 19
| 177
| 42.894737
| 0.898975
| 0
| 0
| 0.384615
| 0
| 0
| 0.43681
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.076923
| 0.153846
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
1dc9181cbc768d9cae6e1cc76d2607177f4f4105
| 29
|
py
|
Python
|
mpu9150/__init__.py
|
stsdc/mpu9150
|
8f906de2860c9bec3fad7e5394c0417a7fa3de1a
|
[
"MIT"
] | null | null | null |
mpu9150/__init__.py
|
stsdc/mpu9150
|
8f906de2860c9bec3fad7e5394c0417a7fa3de1a
|
[
"MIT"
] | null | null | null |
mpu9150/__init__.py
|
stsdc/mpu9150
|
8f906de2860c9bec3fad7e5394c0417a7fa3de1a
|
[
"MIT"
] | 1
|
2022-02-25T04:07:34.000Z
|
2022-02-25T04:07:34.000Z
|
from .mpu9150 import mpu9150
| 14.5
| 28
| 0.827586
| 4
| 29
| 6
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.32
| 0.137931
| 29
| 1
| 29
| 29
| 0.64
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
3803e0d49200543e32c106b47fad4571f9470dd1
| 98
|
py
|
Python
|
ckanext-dcdj_theme/ckanext/dcdj_theme/tests/test_plugin.py
|
devgateway/dcdj-ckan
|
a0a09b62432860b95287e24f096ef819401f52fc
|
[
"Apache-2.0"
] | null | null | null |
ckanext-dcdj_theme/ckanext/dcdj_theme/tests/test_plugin.py
|
devgateway/dcdj-ckan
|
a0a09b62432860b95287e24f096ef819401f52fc
|
[
"Apache-2.0"
] | null | null | null |
ckanext-dcdj_theme/ckanext/dcdj_theme/tests/test_plugin.py
|
devgateway/dcdj-ckan
|
a0a09b62432860b95287e24f096ef819401f52fc
|
[
"Apache-2.0"
] | null | null | null |
"""Tests for plugin.py."""
import ckanext.dcdj_theme.plugin as plugin
def test_plugin():
pass
| 19.6
| 42
| 0.72449
| 15
| 98
| 4.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 98
| 5
| 43
| 19.6
| 0.821429
| 0.204082
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
38521b09d26270e8b0a994a6e1bd39227d6d3887
| 3,931
|
py
|
Python
|
test.py
|
yoshou/tf-mips
|
35008e5aac6f703b151fa8f894bc96353a216a8d
|
[
"MIT"
] | null | null | null |
test.py
|
yoshou/tf-mips
|
35008e5aac6f703b151fa8f894bc96353a216a8d
|
[
"MIT"
] | null | null | null |
test.py
|
yoshou/tf-mips
|
35008e5aac6f703b151fa8f894bc96353a216a8d
|
[
"MIT"
] | null | null | null |
from unittest import TestCase, main
import numpy as np
from alu_ctrl import *
from alu import *
import tensorflow as tf
class AluTest(TestCase):
def test_add(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 1, b: 2, ctrl: alu_ctrl_add})
self.assertEqual(result_y, 3)
self.assertEqual(result_oflow, False)
self.assertEqual(result_zero, False)
def test_add_zero(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 0, b: 0, ctrl: alu_ctrl_add})
self.assertEqual(result_zero, True)
def test_add_oflow(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 2147483647, b: 1, ctrl: alu_ctrl_add})
self.assertEqual(result_oflow, True)
def test_sub(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 904, b: 0, ctrl: alu_ctrl_sub})
self.assertEqual(result_y, 904)
self.assertEqual(result_oflow, False)
self.assertEqual(result_zero, False)
def test_and(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 52435234, b: 765754, ctrl: alu_ctrl_and})
self.assertEqual(result_y, 52435234 & 765754)
def test_sll(self):
a = tf.placeholder(tf.int32)
b = tf.placeholder(tf.int32)
ctrl = tf.placeholder(tf.int32)
y, oflow, zero = alu(
a, b, ctrl)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, result_oflow, result_zero = sess.run(
[y, oflow, zero], {a: 52435234, b: 4, ctrl: alu_ctrl_sll})
self.assertEqual(result_y, 52435234 << 4)
class SignExtendTest(TestCase):
def test_sign_extend(self):
x = tf.placeholder(tf.int32)
y = sign_extend(x, 16)
with tf.compat.v1.Session() as sess:
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
result_y, = sess.run(
[y], {x: -20000 & ((1 << 16) - 1)})
self.assertEqual(result_y, -20000)
if __name__ == "__main__":
main()
| 28.693431
| 79
| 0.568558
| 517
| 3,931
| 4.148936
| 0.116054
| 0.115152
| 0.132867
| 0.177156
| 0.813986
| 0.769231
| 0.769231
| 0.72028
| 0.72028
| 0.72028
| 0
| 0.050854
| 0.314678
| 3,931
| 136
| 80
| 28.904412
| 0.74536
| 0
| 0
| 0.648936
| 0
| 0
| 0.002035
| 0
| 0
| 0
| 0
| 0
| 0.117021
| 1
| 0.074468
| false
| 0
| 0.053191
| 0
| 0.148936
| 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
|
386220b6370747413f981d4aded722aee42832c7
| 816
|
py
|
Python
|
planner/FD/src/driver/portfolios/seq_opt_fdss_2.py
|
karthikv792/PlanningAssistance
|
5693c844e9067591ea1414ee9586bcd2dfff6f51
|
[
"MIT"
] | 4
|
2019-04-23T10:41:35.000Z
|
2019-10-27T05:14:42.000Z
|
planner/FD/src/driver/portfolios/seq_opt_fdss_2.py
|
karthikv792/PlanningAssistance
|
5693c844e9067591ea1414ee9586bcd2dfff6f51
|
[
"MIT"
] | null | null | null |
planner/FD/src/driver/portfolios/seq_opt_fdss_2.py
|
karthikv792/PlanningAssistance
|
5693c844e9067591ea1414ee9586bcd2dfff6f51
|
[
"MIT"
] | 4
|
2018-01-16T00:00:22.000Z
|
2019-11-01T23:35:01.000Z
|
# -*- coding: utf-8 -*-
OPTIMAL = True
CONFIGS = [
(1, ["--search",
"astar(merge_and_shrink(merge_strategy=merge_linear(variable_order=reverse_level),shrink_strategy=shrink_bisimulation(max_states=infinity,threshold=1,greedy=true),label_reduction=label_reduction(before_shrinking=true,before_merging=false)))"]),
(1, ["--search",
"astar(merge_and_shrink(merge_strategy=merge_linear(variable_order=reverse_level),shrink_strategy=shrink_bisimulation(max_states=200000,greedy=false),label_reduction=label_reduction(before_shrinking=true,before_merging=false)))"]),
(1, ["--search",
"astar(lmcount(lm_merged([lm_rhw(),lm_hm(m=1)]),admissible=true),mpd=true)"]),
(1, ["--search",
"astar(lmcut())"]),
(1, ["--search",
"astar(blind())"]),
]
| 48
| 255
| 0.682598
| 98
| 816
| 5.387755
| 0.418367
| 0.066288
| 0.113636
| 0.064394
| 0.708333
| 0.708333
| 0.708333
| 0.708333
| 0.708333
| 0.708333
| 0
| 0.019802
| 0.133578
| 816
| 16
| 256
| 51
| 0.727016
| 0.025735
| 0
| 0.384615
| 0
| 0.230769
| 0.764187
| 0.678436
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 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
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
38a69f02638ec0793bf331c319737876e8edb0ea
| 230
|
py
|
Python
|
pytwitchplays/__init__.py
|
benjiJanssens/PyTwitchPlays
|
aade33a0264afc9aebad1a9f1501849dd4f07139
|
[
"MIT"
] | null | null | null |
pytwitchplays/__init__.py
|
benjiJanssens/PyTwitchPlays
|
aade33a0264afc9aebad1a9f1501849dd4f07139
|
[
"MIT"
] | null | null | null |
pytwitchplays/__init__.py
|
benjiJanssens/PyTwitchPlays
|
aade33a0264afc9aebad1a9f1501849dd4f07139
|
[
"MIT"
] | null | null | null |
# noinspection PyUnresolvedReferences
from .py_twitch_plays import TwitchPlays
# noinspection PyUnresolvedReferences
from .action import Action
# noinspection PyUnresolvedReferences
from .direct_input_keyboard_scan_codes import *
| 32.857143
| 47
| 0.878261
| 23
| 230
| 8.521739
| 0.608696
| 0.520408
| 0.581633
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.091304
| 230
| 6
| 48
| 38.333333
| 0.937799
| 0.465217
| 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
|
38bd34ccae61a7ac0bca831f60ca7ba35188a0c7
| 44
|
py
|
Python
|
detector/__init__.py
|
nodonoughue/emitter-detection-python
|
ebff19acebcc1edfd941280e05f8ddf2ff20c974
|
[
"MIT"
] | null | null | null |
detector/__init__.py
|
nodonoughue/emitter-detection-python
|
ebff19acebcc1edfd941280e05f8ddf2ff20c974
|
[
"MIT"
] | null | null | null |
detector/__init__.py
|
nodonoughue/emitter-detection-python
|
ebff19acebcc1edfd941280e05f8ddf2ff20c974
|
[
"MIT"
] | null | null | null |
from . import squareLaw
from . import xcorr
| 14.666667
| 23
| 0.772727
| 6
| 44
| 5.666667
| 0.666667
| 0.588235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 44
| 2
| 24
| 22
| 0.944444
| 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
|
38d1a1b1dc9fe148506df0de82096027bf81fe3b
| 241
|
py
|
Python
|
utils.py
|
Raiszo/ppo-4Quadrotor
|
c2a9bfc33510f19507c37aea86375dcdbf89f815
|
[
"MIT"
] | null | null | null |
utils.py
|
Raiszo/ppo-4Quadrotor
|
c2a9bfc33510f19507c37aea86375dcdbf89f815
|
[
"MIT"
] | null | null | null |
utils.py
|
Raiszo/ppo-4Quadrotor
|
c2a9bfc33510f19507c37aea86375dcdbf89f815
|
[
"MIT"
] | 1
|
2018-11-21T11:45:57.000Z
|
2018-11-21T11:45:57.000Z
|
import tensorflow as tf
def get_session():
# tf_config = tf.ConfigProto(
# inter_op_parallelism_threads=1,
# intra_op_parallelism_threads=1
# )
return tf.Session()
# return tf.Session(config=tf_config)
| 20.083333
| 41
| 0.659751
| 30
| 241
| 5
| 0.533333
| 0.106667
| 0.266667
| 0.28
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.01105
| 0.248963
| 241
| 11
| 42
| 21.909091
| 0.81768
| 0.564315
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0.333333
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
2a0e3dbf90e203da408582332dbb014ba38546f2
| 112
|
py
|
Python
|
cryptlex/lexfloatclient/__init__.py
|
cryptlex/lexfloatclient-python
|
404735f2e4a5c16cb35db794d8580ef33cc83018
|
[
"MIT"
] | 1
|
2019-02-15T11:08:51.000Z
|
2019-02-15T11:08:51.000Z
|
cryptlex/lexfloatclient/__init__.py
|
cryptlex/lexfloatclient-python
|
404735f2e4a5c16cb35db794d8580ef33cc83018
|
[
"MIT"
] | null | null | null |
cryptlex/lexfloatclient/__init__.py
|
cryptlex/lexfloatclient-python
|
404735f2e4a5c16cb35db794d8580ef33cc83018
|
[
"MIT"
] | null | null | null |
from cryptlex.lexfloatclient.lexfloatclient import LexFloatClient, LexFloatStatusCodes, LexFloatClientException
| 56
| 111
| 0.910714
| 8
| 112
| 12.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.053571
| 112
| 1
| 112
| 112
| 0.962264
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
2a7cccfd0e537d3d90014467fa5817a57c7b9dba
| 78
|
py
|
Python
|
gym_maze/envs/__init__.py
|
UIA-CAIR/DeepMaze
|
a6e1f01e1712fe5279f4b87f309269830864f8c2
|
[
"Unlicense"
] | 3
|
2021-06-04T08:25:43.000Z
|
2021-12-18T16:32:17.000Z
|
gym_maze/envs/__init__.py
|
UIA-CAIR/DeepMaze
|
a6e1f01e1712fe5279f4b87f309269830864f8c2
|
[
"Unlicense"
] | null | null | null |
gym_maze/envs/__init__.py
|
UIA-CAIR/DeepMaze
|
a6e1f01e1712fe5279f4b87f309269830864f8c2
|
[
"Unlicense"
] | 2
|
2021-06-06T08:42:08.000Z
|
2021-09-01T11:27:58.000Z
|
from gym_maze.envs.maze_env import *
from gym_maze.envs.no_maze_env import *
| 19.5
| 39
| 0.807692
| 15
| 78
| 3.866667
| 0.466667
| 0.241379
| 0.37931
| 0.517241
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 78
| 3
| 40
| 26
| 0.84058
| 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
|
aa63444617c245f8f1faed3bae2f55ded679837c
| 8,197
|
py
|
Python
|
tests/test_story.py
|
gasman/webstories
|
9c58e0dd27b31016d7d56e1b55346330c7c5aab1
|
[
"BSD-3-Clause"
] | 2
|
2021-07-07T17:13:31.000Z
|
2022-01-11T17:34:06.000Z
|
tests/test_story.py
|
gasman/webstories
|
9c58e0dd27b31016d7d56e1b55346330c7c5aab1
|
[
"BSD-3-Clause"
] | 1
|
2021-12-01T11:00:25.000Z
|
2021-12-01T11:22:55.000Z
|
tests/test_story.py
|
torchbox/webstories
|
9c58e0dd27b31016d7d56e1b55346330c7c5aab1
|
[
"BSD-3-Clause"
] | null | null | null |
import unittest
from bs4 import BeautifulSoup
from webstories import Story, StoryPage
class TestStory(unittest.TestCase):
def setUp(self):
self.example_html = """
<!doctype html>
<html ⚡>
<head>
<meta charset="utf-8">
<title>Joy of Pets</title>
<link rel="canonical" href="pets.html">
<meta name="viewport" content="width=device-width,minimum-scale=1,initial-scale=1">
<style amp-boilerplate>body{-webkit-animation:-amp-start 8s steps(1,end) 0s 1 normal both;-moz-animation:-amp-start 8s steps(1,end) 0s 1 normal both;-ms-animation:-amp-start 8s steps(1,end) 0s 1 normal both;animation:-amp-start 8s steps(1,end) 0s 1 normal both}@-webkit-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-moz-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-ms-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-o-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}</style><noscript><style amp-boilerplate>body{-webkit-animation:none;-moz-animation:none;-ms-animation:none;animation:none}</style></noscript>
<script async src="https://cdn.ampproject.org/v0.js"></script>
<script async custom-element="amp-video"
src="https://cdn.ampproject.org/v0/amp-video-0.1.js"></script>
<script async custom-element="amp-story"
src="https://cdn.ampproject.org/v0/amp-story-1.0.js"></script>
<style amp-custom>#page1 {background-color: blue;}</style>
</head>
<body>
<!-- Cover page -->
<amp-story standalone
title="Joy of Pets"
publisher="AMP tutorials"
publisher-logo-src="assets/AMP-Brand-White-Icon.svg"
poster-portrait-src="assets/cover.jpg">
<amp-story-page id="cover">
<amp-story-grid-layer template="fill">
<amp-img src="assets/cover.jpg"
width="720" height="1280"
layout="responsive">
</amp-img>
</amp-story-grid-layer>
<amp-story-grid-layer template="vertical">
<h1>The Joy of Pets</h1>
<p>By AMP Tutorials</p>
</amp-story-grid-layer>
</amp-story-page>
<!-- Page 1 -->
<amp-story-page id="page1">
<amp-story-grid-layer template="vertical">
<h1>Cats</h1>
<amp-img src="assets/cat.jpg"
width="720" height="1280"
layout="responsive">
</amp-img>
<q>Dogs come when they're called. Cats take a message and get back to you. --Mary Bly</q>
</amp-story-grid-layer>
</amp-story-page>
</amp-story>
</body>
</html>
"""
self.expected_clean_html_good = """
<amp-story-page id="page1">
<amp-story-grid-layer template="vertical">
<h1>Cats</h1>
<amp-img height="1280" layout="responsive" src="assets/cat.jpg" width="720">
</amp-img>
<q>Dogs come when they're called. Cats take a message and get back to you. --Mary Bly</q>
</amp-story-grid-layer>
</amp-story-page>
"""
self.example_bad_html = """
<!doctype html>
<html ⚡>
<head>
<meta charset="utf-8">
<title>Joy of Pets</title>
<link rel="canonical" href="pets.html">
<meta name="viewport" content="width=device-width,minimum-scale=1,initial-scale=1">
<style amp-boilerplate>body{-webkit-animation:-amp-start 8s steps(1,end) 0s 1 normal both;-moz-animation:-amp-start 8s steps(1,end) 0s 1 normal both;-ms-animation:-amp-start 8s steps(1,end) 0s 1 normal both;animation:-amp-start 8s steps(1,end) 0s 1 normal both}@-webkit-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-moz-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-ms-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@-o-keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}@keyframes -amp-start{from{visibility:hidden}to{visibility:visible}}</style><noscript><style amp-boilerplate>body{-webkit-animation:none;-moz-animation:none;-ms-animation:none;animation:none}</style></noscript>
<script async src="https://cdn.ampproject.org/v0.js"></script>
<script async custom-element="amp-video"
src="https://cdn.ampproject.org/v0/amp-video-0.1.js"></script>
<script async custom-element="amp-story"
src="https://cdn.ampproject.org/v0/amp-story-1.0.js"></script>
<style amp-custom>
</style>
</head>
<body>
<!-- Cover page -->
<amp-story standalone
title="Joy of Pets"
publisher="AMP tutorials"
publisher-logo-src="assets/AMP-Brand-White-Icon.svg"
poster-portrait-src="assets/cover.jpg">
<amp-story-page id="cover">
<amp-story-grid-layer template="fill" data-coffee="yes" sugar="no">
<script>alert('evil javascript');</script>
<script type="application/json">{"text": "JSON is OK"}</script>
<amp-img src="assets/cover.jpg"
width="720" height="1280"
layout="responsive">
</amp-img>
</amp-story-grid-layer>
<amp-story-grid-layer template="vertical">
<h1>The Joy of Pets</h1>
<p>By AMP Tutorials</p>
<form>
<p>look at me, I'm in a form</p>
</form>
</amp-story-grid-layer>
</amp-story-page>
</amp-story>
</body>
</html>
"""
self.expected_clean_html_bad = """
<amp-story-page id="cover">
<amp-story-grid-layer template="fill" data-coffee="yes">
<script type="application/json">{"text": "JSON is OK"}</script>
<amp-img src="assets/cover.jpg" width="720" height="1280" layout="responsive">
</amp-img>
</amp-story-grid-layer>
<amp-story-grid-layer template="vertical">
<h1>The Joy of Pets</h1>
<p>By AMP Tutorials</p>
<p>look at me, I'm in a form</p>
</amp-story-grid-layer>
</amp-story-page>
"""
def assertHTMLEqual(self, str1, str2):
soup1 = BeautifulSoup(str1.strip(), 'html.parser')
soup2 = BeautifulSoup(str2.strip(), 'html.parser')
self.assertEqual(soup1, soup2)
def test_properties(self):
story = Story(self.example_html)
self.assertEqual(story.title, "Joy of Pets")
self.assertEqual(story.publisher, "AMP tutorials")
self.assertEqual(story.custom_css, "#page1 {background-color: blue;}")
self.assertEqual(story.pages[0].id, "cover")
def test_reject_invalid_story(self):
with self.assertRaises(Story.InvalidStoryException):
Story("<!doctype html><html><head></head><body><p>Not a story</p></body></html>")
def test_clean_html(self):
story = Story(self.example_html)
self.assertHTMLEqual(story.pages[1].get_clean_html(), self.expected_clean_html_good)
story = Story(self.example_bad_html)
self.assertHTMLEqual(story.pages[0].get_clean_html(), self.expected_clean_html_bad)
def test_clean_html_static_method(self):
bad_html = """
<amp-story-page id="cover">
<amp-story-grid-layer template="fill" data-coffee="yes" sugar="no">
<script>alert('evil javascript');</script>
<script type="application/json">{"text": "JSON is OK"}</script>
<amp-img src="assets/cover.jpg"
width="720" height="1280"
layout="responsive">
</amp-img>
</amp-story-grid-layer>
<amp-story-grid-layer template="vertical">
<h1>The Joy of Pets</h1>
<p>By AMP Tutorials</p>
<form>
<p>look at me, I'm in a form</p>
</form>
</amp-story-grid-layer>
</amp-story-page>
"""
self.assertHTMLEqual(StoryPage.clean_html_fragment(bad_html), self.expected_clean_html_bad)
| 45.793296
| 767
| 0.60144
| 1,063
| 8,197
| 4.601129
| 0.163688
| 0.065426
| 0.04907
| 0.069515
| 0.847475
| 0.835616
| 0.819873
| 0.792885
| 0.779391
| 0.771826
| 0
| 0.019683
| 0.237648
| 8,197
| 178
| 768
| 46.050562
| 0.762682
| 0
| 0
| 0.773006
| 0
| 0.092025
| 0.834818
| 0.293156
| 0
| 0
| 0
| 0
| 0.06135
| 1
| 0.03681
| false
| 0
| 0.018405
| 0
| 0.06135
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 1
| 0
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| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 8
|
aa9407ddf2e745978e00a20ffdeda77349085d66
| 1,032,889
|
py
|
Python
|
data/bin/mimishim/emu/powerkatz64.py
|
psmitty7373/koadic
|
e136fe8c6c021c885e8720b22aa6b0c50d371d4c
|
[
"Apache-2.0"
] | 2,255
|
2017-07-26T00:57:17.000Z
|
2021-12-20T06:39:28.000Z
|
data/bin/mimishim/emu/powerkatz64.py
|
psmitty7373/koadic
|
e136fe8c6c021c885e8720b22aa6b0c50d371d4c
|
[
"Apache-2.0"
] | 129
|
2017-07-31T06:56:00.000Z
|
2021-11-17T04:40:34.000Z
|
data/bin/mimishim/emu/powerkatz64.py
|
psmitty7373/koadic
|
e136fe8c6c021c885e8720b22aa6b0c50d371d4c
|
[
"Apache-2.0"
] | 598
|
2017-07-25T20:57:25.000Z
|
2021-12-01T06:24:22.000Z
|
x64 = 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0
| 9
|
aabe06cd3aaf7b1df94349e693eb683a5def05dc
| 21,385
|
py
|
Python
|
ibutsu_client/api/widget_config_api.py
|
ibutsu/ibutsu-client-python
|
8cd34d7fc8f9a2225195ae375a17200b992dde01
|
[
"MIT"
] | 3
|
2020-07-02T14:48:08.000Z
|
2021-11-27T14:06:33.000Z
|
ibutsu_client/api/widget_config_api.py
|
ibutsu/ibutsu-client-python
|
8cd34d7fc8f9a2225195ae375a17200b992dde01
|
[
"MIT"
] | 6
|
2020-07-07T16:13:37.000Z
|
2021-11-10T17:02:59.000Z
|
ibutsu_client/api/widget_config_api.py
|
ibutsu/ibutsu-client-python
|
8cd34d7fc8f9a2225195ae375a17200b992dde01
|
[
"MIT"
] | 5
|
2020-07-02T18:13:03.000Z
|
2021-11-03T09:21:11.000Z
|
"""
Ibutsu API
A system to store and query test results # noqa: E501
The version of the OpenAPI document: 1.13.4
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from ibutsu_client.api_client import ApiClient, Endpoint as _Endpoint
from ibutsu_client.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types
)
from ibutsu_client.model.widget_config import WidgetConfig
from ibutsu_client.model.widget_config_list import WidgetConfigList
class WidgetConfigApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
self.add_widget_config_endpoint = _Endpoint(
settings={
'response_type': (WidgetConfig,),
'auth': [
'jwt'
],
'endpoint_path': '/widget-config',
'operation_id': 'add_widget_config',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'widget_config',
],
'required': [],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'widget_config':
(WidgetConfig,),
},
'attribute_map': {
},
'location_map': {
'widget_config': 'body',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client
)
self.delete_widget_config_endpoint = _Endpoint(
settings={
'response_type': None,
'auth': [
'jwt'
],
'endpoint_path': '/widget-config/{id}',
'operation_id': 'delete_widget_config',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'id',
],
'required': [
'id',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [],
'content_type': [],
},
api_client=api_client
)
self.get_widget_config_endpoint = _Endpoint(
settings={
'response_type': (WidgetConfig,),
'auth': [
'jwt'
],
'endpoint_path': '/widget-config/{id}',
'operation_id': 'get_widget_config',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'id',
],
'required': [
'id',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client
)
self.get_widget_config_list_endpoint = _Endpoint(
settings={
'response_type': (WidgetConfigList,),
'auth': [
'jwt'
],
'endpoint_path': '/widget-config',
'operation_id': 'get_widget_config_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'page',
'page_size',
],
'required': [],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'filter':
([str],),
'page':
(int,),
'page_size':
(int,),
},
'attribute_map': {
'filter': 'filter',
'page': 'page',
'page_size': 'pageSize',
},
'location_map': {
'filter': 'query',
'page': 'query',
'page_size': 'query',
},
'collection_format_map': {
'filter': 'multi',
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client
)
self.update_widget_config_endpoint = _Endpoint(
settings={
'response_type': (WidgetConfig,),
'auth': [
'jwt'
],
'endpoint_path': '/widget-config/{id}',
'operation_id': 'update_widget_config',
'http_method': 'PUT',
'servers': None,
},
params_map={
'all': [
'id',
'widget_config',
],
'required': [
'id',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'id':
(str,),
'widget_config':
(WidgetConfig,),
},
'attribute_map': {
'id': 'id',
},
'location_map': {
'id': 'path',
'widget_config': 'body',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client
)
def add_widget_config(
self,
**kwargs
):
"""Create a widget configuration # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.add_widget_config(async_req=True)
>>> result = thread.get()
Keyword Args:
widget_config (WidgetConfig): Widget configuration. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
WidgetConfig
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.add_widget_config_endpoint.call_with_http_info(**kwargs)
def delete_widget_config(
self,
id,
**kwargs
):
"""Delete a widget configuration # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_widget_config(id, async_req=True)
>>> result = thread.get()
Args:
id (str): ID of widget configuration to delete
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
None
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
return self.delete_widget_config_endpoint.call_with_http_info(**kwargs)
def get_widget_config(
self,
id,
**kwargs
):
"""Get a single widget configuration # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_widget_config(id, async_req=True)
>>> result = thread.get()
Args:
id (str): ID of widget config to return
Keyword Args:
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
WidgetConfig
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
return self.get_widget_config_endpoint.call_with_http_info(**kwargs)
def get_widget_config_list(
self,
**kwargs
):
"""Get the list of widget configurations # noqa: E501
A list of widget configurations # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_widget_config_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter ([str]): Fields to filter by. [optional]
page (int): Set the page of items to return, defaults to 1. [optional]
page_size (int): Set the number of items per page, defaults to 25. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
WidgetConfigList
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
return self.get_widget_config_list_endpoint.call_with_http_info(**kwargs)
def update_widget_config(
self,
id,
**kwargs
):
"""Updates a single widget configuration # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_widget_config(id, async_req=True)
>>> result = thread.get()
Args:
id (str): ID of widget configuration to update
Keyword Args:
widget_config (WidgetConfig): Widget configuration. [optional]
_return_http_data_only (bool): response data without head status
code and headers. Default is True.
_preload_content (bool): if False, the urllib3.HTTPResponse object
will be returned without reading/decoding response data.
Default is True.
_request_timeout (int/float/tuple): timeout setting for this request. If
one number provided, it will be total request timeout. It can also
be a pair (tuple) of (connection, read) timeouts.
Default is None.
_check_input_type (bool): specifies if type checking
should be done one the data sent to the server.
Default is True.
_check_return_type (bool): specifies if type checking
should be done one the data received from the server.
Default is True.
_host_index (int/None): specifies the index of the server
that we want to use.
Default is read from the configuration.
async_req (bool): execute request asynchronously
Returns:
WidgetConfig
If the method is called asynchronously, returns the request
thread.
"""
kwargs['async_req'] = kwargs.get(
'async_req', False
)
kwargs['_return_http_data_only'] = kwargs.get(
'_return_http_data_only', True
)
kwargs['_preload_content'] = kwargs.get(
'_preload_content', True
)
kwargs['_request_timeout'] = kwargs.get(
'_request_timeout', None
)
kwargs['_check_input_type'] = kwargs.get(
'_check_input_type', True
)
kwargs['_check_return_type'] = kwargs.get(
'_check_return_type', True
)
kwargs['_host_index'] = kwargs.get('_host_index')
kwargs['id'] = \
id
return self.update_widget_config_endpoint.call_with_http_info(**kwargs)
| 34.052548
| 89
| 0.481599
| 1,943
| 21,385
| 5.058672
| 0.099331
| 0.050056
| 0.026452
| 0.02747
| 0.879845
| 0.853393
| 0.834876
| 0.826635
| 0.802014
| 0.79296
| 0
| 0.003458
| 0.432079
| 21,385
| 627
| 90
| 34.106858
| 0.805846
| 0.359224
| 0
| 0.636145
| 1
| 0
| 0.213062
| 0.028047
| 0
| 0
| 0
| 0
| 0
| 1
| 0.014458
| false
| 0
| 0.014458
| 0
| 0.043373
| 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
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
aada10781ff8195c7be09bf31333385169db41e4
| 4,464
|
py
|
Python
|
cgatpipelines/tools/pipeline_docs/pipeline_exome/trackers/Filtered.py
|
kevinrue/cgat-flow
|
02b5a1867253c2f6fd6b4f3763e0299115378913
|
[
"MIT"
] | 49
|
2015-04-13T16:49:25.000Z
|
2022-03-29T10:29:14.000Z
|
cgatpipelines/tools/pipeline_docs/pipeline_exome/trackers/Filtered.py
|
kevinrue/cgat-flow
|
02b5a1867253c2f6fd6b4f3763e0299115378913
|
[
"MIT"
] | 252
|
2015-04-08T13:23:34.000Z
|
2019-03-18T21:51:29.000Z
|
cgatpipelines/tools/pipeline_docs/pipeline_exome/trackers/Filtered.py
|
kevinrue/cgat-flow
|
02b5a1867253c2f6fd6b4f3763e0299115378913
|
[
"MIT"
] | 22
|
2015-05-21T00:37:52.000Z
|
2019-09-25T05:04:27.000Z
|
from CGATReport.Tracker import *
from exomeReport import *
class recessives(ExomeTracker):
pattern = "(.*)_recessive_table$"
def __call__(self, track, slice=None):
return self.getAll(
"SELECT r.CHROM, r.POS, r.REF, r.ALT, r.ID, SNPEFF_CODON_CHANGE, SNPEFF_AMINO_ACID_CHANGE, SNPEFF_EXON_ID, SNPEFF_GENE_NAME, SNPEFF_TRANSCRIPT_ID, SNPEFF_EFFECT, SNPEFF_FUNCTIONAL_CLASS, SNPEFF_IMPACT, SNPEFF_GENE_BIOTYPE, EFF, dbNSFP_1000Gp1_AF, dbNSFP_ESP6500_AA_AF, dbNSFP_ESP6500_EA_AF, AC_Adj, AN_Adj, (CAST(AC_Adj AS FLOAT)/AN_Adj) AS ExAC, dbNSFP_29way_logOdds, dbNSFP_GERP___NR, dbNSFP_GERP___RS, dbNSFP_Interpro_domain, dbNSFP_Polyphen2_HVAR_pred, dbNSFP_SIFT_score, CLNDBN, CLNORIGIN, CLNSIG, FILTER, BaseQRankSum, FS, MQ, MQ0, MQRankSum, QD, ReadPosRankSum FROM %(track)s_recessive_table AS r INNER JOIN all_samples_snpeff_table AS s ON r.CHROM = s.CHROM AND r.POS = s.POS WHERE (dbNSFP_1000Gp1_AF is null OR dbNSFP_1000Gp1_AF<0.01) AND (dbNSFP_ESP6500_AA_AF is null OR dbNSFP_ESP6500_AA_AF<0.01) AND (ExAC is null OR ExAC<0.01) AND FILTER='PASS' " % locals())
class dominants(ExomeTracker):
pattern = "(.*)_dominant_table$"
def __call__(self, track, slice=None):
return self.getAll(
"SELECT d.CHROM, d.POS, d.REF, d.ALT, d.ID, SNPEFF_CODON_CHANGE, SNPEFF_AMINO_ACID_CHANGE, SNPEFF_EXON_ID, SNPEFF_GENE_NAME, SNPEFF_TRANSCRIPT_ID, SNPEFF_EFFECT, SNPEFF_FUNCTIONAL_CLASS, SNPEFF_IMPACT, SNPEFF_GENE_BIOTYPE, EFF, dbNSFP_1000Gp1_AF, dbNSFP_ESP6500_AA_AF, dbNSFP_ESP6500_EA_AF, AC_Adj, AN_Adj, (CAST(AC_Adj AS FLOAT)/AN_Adj) AS ExAC, dbNSFP_29way_logOdds, dbNSFP_GERP___NR, dbNSFP_GERP___RS, dbNSFP_Interpro_domain, dbNSFP_Polyphen2_HVAR_pred, dbNSFP_SIFT_score, CLNDBN, CLNORIGIN, CLNSIG, FILTER, BaseQRankSum, FS, MQ, MQ0, MQRankSum, QD, ReadPosRankSum FROM %(track)s_dominant_table AS d INNER JOIN all_samples_snpeff_table AS s ON d.CHROM = s.CHROM AND d.POS = s.POS WHERE (dbNSFP_1000Gp1_AF is null OR dbNSFP_1000Gp1_AF<0.001) AND (dbNSFP_ESP6500_AA_AF is null OR dbNSFP_ESP6500_AA_AF<0.001) AND (ExAC<0.001 OR ExAC is null) AND FILTER='PASS' " % locals())
class deNovos(ExomeTracker):
pattern = "(.*)_filtered_table$"
def __call__(self, track, slice=None):
return self.getAll(
"SELECT f.CHROM, f.POS, f.REF, f.ALT, f.ID, SNPEFF_CODON_CHANGE, SNPEFF_AMINO_ACID_CHANGE, SNPEFF_EXON_ID, SNPEFF_GENE_NAME, SNPEFF_TRANSCRIPT_ID, SNPEFF_EFFECT, SNPEFF_FUNCTIONAL_CLASS, SNPEFF_IMPACT, SNPEFF_GENE_BIOTYPE, EFF, dbNSFP_1000Gp1_AF, dbNSFP_ESP6500_AA_AF, dbNSFP_ESP6500_EA_AF, AC_Adj, AN_Adj, (CAST(AC_Adj AS FLOAT)/AN_Adj) AS ExAC, dbNSFP_29way_logOdds, dbNSFP_GERP___NR, dbNSFP_GERP___RS, dbNSFP_Interpro_domain, dbNSFP_Polyphen2_HVAR_pred, dbNSFP_SIFT_score, CLNDBN, CLNORIGIN, CLNSIG, FILTER, BaseQRankSum, FS, MQ, MQ0, MQRankSum, QD, ReadPosRankSum FROM %(track)s_filtered_table AS f INNER JOIN all_samples_snpeff_table AS s ON f.CHROM = s.CHROM AND f.POS = s.POS WHERE (dbNSFP_1000Gp1_AF is null OR dbNSFP_1000Gp1_AF<0.001) AND (dbNSFP_ESP6500_AA_AF is null OR dbNSFP_ESP6500_AA_AF<0.001) AND (ExAC<0.001 OR ExAC is null) AND FILTER='PASS' " % locals())
class comp_hets(ExomeTracker):
pattern = "(.*)_compound_hets_table$"
def __call__(self, track, slice=None):
return self.getAll(
"SELECT c.rowid, g.CHROM, g.POS, g.REF, g.ALT, g.ID, SNPEFF_CODON_CHANGE, SNPEFF_AMINO_ACID_CHANGE, SNPEFF_EXON_ID, SNPEFF_GENE_NAME, SNPEFF_TRANSCRIPT_ID, SNPEFF_EFFECT, SNPEFF_FUNCTIONAL_CLASS, SNPEFF_IMPACT, SNPEFF_GENE_BIOTYPE, dbNSFP_1000Gp1_AF, dbNSFP_ESP6500_AA_AF, AC_Adj, AN_Adj, (CAST(AC_Adj AS FLOAT)/AN_Adj) AS ExAC, dbNSFP_ESP6500_EA_AF, dbNSFP_29way_logOdds, dbNSFP_GERP___NR, dbNSFP_GERP___RS, dbNSFP_Interpro_domain, dbNSFP_Polyphen2_HVAR_pred, dbNSFP_SIFT_score, g.CLNDBN, g.CLNORIGIN, g.CLNSIG, g.FILTER, s.EFF, g.BaseQRankSum, g.FS, g.MQ, g.MQ0, g.MQRankSum, g.QD, g.ReadPosRankSum, gene, family_genotypes, family_members FROM %(track)s_compound_hets_table AS c INNER JOIN all_samples_snpsift_table AS g ON c.gene = g.SNPEFF_GENE_NAME AND c.qual = g.QUAL AND c.depth = g.DP AND c.ref = g.REF AND c.alt = g.ALT INNER JOIN all_samples_snpeff_table AS s ON g.CHROM = s.CHROM AND g.POS = s.POS AND g.REF = s.REF AND g.ALT = s.ALT WHERE c.rowid IN (SELECT MAX(%(track)s_compound_hets_table.rowid) FROM %(track)s_compound_hets_table GROUP BY chrom, start, end, codon_change) AND (g.FILTER = 'PASS' OR g.FILTER = 'GENE_OF_INTEREST')" % locals())
| 111.6
| 1,169
| 0.779122
| 752
| 4,464
| 4.256649
| 0.158245
| 0.056857
| 0.04686
| 0.053108
| 0.754452
| 0.739769
| 0.722899
| 0.713527
| 0.713527
| 0.669791
| 0
| 0.039612
| 0.123432
| 4,464
| 39
| 1,170
| 114.461538
| 0.778431
| 0
| 0
| 0.363636
| 0
| 0.181818
| 0.852566
| 0.287699
| 0
| 0
| 0
| 0
| 0
| 1
| 0.181818
| false
| 0.181818
| 0.090909
| 0.181818
| 0.818182
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 0
|
0
| 11
|
2aad2f0d726afdb04befa7c4aa492c6536e308ad
| 86
|
py
|
Python
|
days/day032/pip_upgrade_all_venv_packages/test_requirements_purring.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | 2
|
2018-10-28T17:12:37.000Z
|
2018-10-28T17:12:39.000Z
|
days/day032/pip_upgrade_all_venv_packages/test_requirements_purring.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | 3
|
2018-10-28T17:11:04.000Z
|
2018-10-29T22:36:36.000Z
|
days/day032/pip_upgrade_all_venv_packages/test_requirements_purring.py
|
alex-vegan/100daysofcode-with-python-course
|
b6c12316abe18274b7963371b8f0ed2fd549ef07
|
[
"MIT"
] | null | null | null |
from requirements_purring import remove_version
def test_remove_version():
pass
| 14.333333
| 47
| 0.813953
| 11
| 86
| 6
| 0.818182
| 0.393939
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151163
| 86
| 5
| 48
| 17.2
| 0.90411
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 8
|
2dd13aafe18a2fb3d82d905bff4581bf682eb3bb
| 469
|
py
|
Python
|
python/ql/test/query-tests/Security/CWE-295/paramiko_host_key.py
|
vadi2/codeql
|
a806a4f08696d241ab295a286999251b56a6860c
|
[
"MIT"
] | 4,036
|
2020-04-29T00:09:57.000Z
|
2022-03-31T14:16:38.000Z
|
python/ql/test/query-tests/Security/CWE-295/paramiko_host_key.py
|
vadi2/codeql
|
a806a4f08696d241ab295a286999251b56a6860c
|
[
"MIT"
] | 2,970
|
2020-04-28T17:24:18.000Z
|
2022-03-31T22:40:46.000Z
|
python/ql/test/query-tests/Security/CWE-295-MissingHostKeyValidation/paramiko_host_key.py
|
ScriptBox99/github-codeql
|
2ecf0d3264db8fb4904b2056964da469372a235c
|
[
"MIT"
] | 794
|
2020-04-29T00:28:25.000Z
|
2022-03-30T08:21:46.000Z
|
from paramiko.client import AutoAddPolicy, WarningPolicy, RejectPolicy, SSHClient
client = SSHClient()
client.set_missing_host_key_policy(AutoAddPolicy) # bad
client.set_missing_host_key_policy(RejectPolicy) # good
client.set_missing_host_key_policy(WarningPolicy) # bad
# Using instances
client.set_missing_host_key_policy(AutoAddPolicy()) # bad
client.set_missing_host_key_policy(RejectPolicy()) # good
client.set_missing_host_key_policy(WarningPolicy()) # bad
| 33.5
| 81
| 0.840085
| 60
| 469
| 6.166667
| 0.283333
| 0.145946
| 0.259459
| 0.324324
| 0.72973
| 0.72973
| 0.72973
| 0.72973
| 0.72973
| 0.72973
| 0
| 0
| 0.078891
| 469
| 13
| 82
| 36.076923
| 0.856481
| 0.08742
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.125
| 0
| 0.125
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
2dfb93ae43f13386382f4d6a8e6212930c339b9e
| 12,003
|
py
|
Python
|
alexia/english_version/base_plus_other.py
|
steinunnfridriks/ALEXIA
|
3699f9c28215f55be47f0b47c754a5142561a0cf
|
[
"Apache-2.0"
] | null | null | null |
alexia/english_version/base_plus_other.py
|
steinunnfridriks/ALEXIA
|
3699f9c28215f55be47f0b47c754a5142561a0cf
|
[
"Apache-2.0"
] | null | null | null |
alexia/english_version/base_plus_other.py
|
steinunnfridriks/ALEXIA
|
3699f9c28215f55be47f0b47c754a5142561a0cf
|
[
"Apache-2.0"
] | null | null | null |
from xml.etree import ElementTree as ET
from string import punctuation
import glob
from alexia.sql.sql_lookup import SQLDatabase, SQLiteQuery
from alexia.IGC_extractor import IGCWord, IGCExtractor
def lemmabase_wordforms(database, IGC_folder, prop_names):
"""
Iterates through the IGC, outputting a list of lemmas
and their frequencies as well as all wordforms that appear
alongside the lemma in the corpus. Useful for detecting whether
a word only appears in certain context (e.g. fixed expressions)
or whether a certain wordform never appears. Can be modified to
fit the user's need.
"""
dim = SQLDatabase(db_name='databases/dim_lemmas_word_forms.db')
dci = SQLDatabase(db_name='databases/dci.db')
filters = SQLDatabase(db_name='databases/IGC_filters.db') # Predefined stop-word list based on the IGC
pos_to_ignore = ['e', 'c', 'v', 'as', 'to', 'tp', 'ta', 'au'] # The POS tags that should not be displayed in the results
IGC = IGCExtractor(folder=str(IGC_folder))
freqdic = {}
print("""
============================================================
Reading corpus files.
============================================================
""")
for word in IGC.extract(forms=True, lemmas=True, pos=True):
try:
if prop_names==False:
if word.pos.startswith('n') and word.pos.endswith('s'): # Ignore proper names
continue
if word.pos in pos_to_ignore:
continue
if (not all(i.isalpha() or i == '-' for i in word.lemma)): # Ignore if not only letters or letters and hyphen
continue
if len(word.lemma) < 3: # Ignore very short words, likely to be particles
continue
if '-' in [word.lemma[0], word.lemma[1], word.lemma[-1]]: # Ignore words that start with '[anyLetter?]-' or end with '-'
continue
# Ignore unwanted words, such as names, foreign words, stopwords, abbreviations
filter_query = SQLiteQuery(word.lemma, 'filter', 'FILTER_WORD_FORMS', cursor=filters.cursor)
if filter_query.exists:
continue
else:
if database == 'DCI':
query = SQLiteQuery(word.lemma, 'lemma','DCI_ELEMENT', cursor = dci.cursor) # Capitalized words included
query_lower = SQLiteQuery(word.lemma.lower(),'lemma','DCI_ELEMENT', cursor = dci.cursor)
elif database == 'DIM':
query = SQLiteQuery(word.lemma, 'lemma','DIM_ELEMENT', cursor = dim.cursor) # Capitalized words included
query_lower = SQLiteQuery(word.lemma.lower(),'lemma','DIM_ELEMENT', cursor = dim.cursor)
if not query.exists and not query_lower.exists:
if word.lemma in freqdic:
if word.word_form not in freqdic[word.lemma]['wordforms']:
freqdic[word.lemma]['wordforms'].append(word.word_form)
freqdic[word.lemma]['freq'] += 1
else:
freqdic[word.lemma] = {}
freqdic[word.lemma]['freq'] = 1
freqdic[word.lemma]['wordforms'] = [word.word_form]
except IndexError:
continue
except ET.ParseError:
continue
print("""
============================================================
Sorting candidate frequencies.
============================================================
""")
if IGC_folder == "corpora/IGC/":
with open(f'output/{database}/IGC_lemma_plus_wordform.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print("""
============================================================
Output file IGC_lemma_plus_wordform.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
elif IGC_folder == "corpora/IGC/CC_BY/":
with open(f'output/{database}/CC_BY_lemma_plus_wordform.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print("""
============================================================
Output file CC_BY_lemma_plus_wordform.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
elif IGC_folder == "corpora/IGC/TIC/":
with open(f'output/{database}/TIC_lemma_plus_wordform.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print("""
============================================================
Output file TIC_lemma_plus_wordform.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
else:
namefolder = IGC_folder.split("/")[3]
with open(f'output/{database}/'+namefolder+'_lemma_plus_wordform.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print(f"""
============================================================
Output file {namefolder}_lemma_plus_wordform.freq is ready
and can be found in the output/DIM/ directory.
============================================================
""")
def wordformbase_lemmas(IGC_folder, prop_names):
"""
Iterates through the IGC, outputting a list of wordforms
and their frequencies as well as all lemmas that appear
alongside the wordform in the corpus. Useful for detecting
if a wordform exists in multiple word classes and for
detecting errors in lemmatization. Can be modified to
fit the user's need.
"""
dim = SQLDatabase(db_name='databases/dim_lemmas_word_forms.db')
filters = SQLDatabase(db_name='databases/IGC_filters.db') # Predefined stop-word list based on the IGC
pos_to_ignore = ['e', 'c', 'v', 'as', 'to', 'tp', 'ta', 'au'] # The POS tags that should not be displayed in the results
IGC = IGCExtractor(folder=str(IGC_folder))
freqdic = {}
print("""
============================================================
Reading corpus files.
============================================================
""")
for word in IGC.extract(forms=True, lemmas=True, pos=True):
try:
if prop_names==False:
if word.pos.startswith('n') and word.pos.endswith('s'): # Ignore proper names
continue
if word.pos in pos_to_ignore:
continue
if (not all(i.isalpha() or i == '-' for i in word.word_form)): # Ignore if not only letters or letters and hyphen
continue
if len(word.word_form) < 3: # Ignore very short words, likely to be particles
continue
if '-' in [word.word_form[0], word.word_form[1], word.word_form[-1]]: # Ignore words that start with '[anyLetter?]-' or end with '-'
continue
# Ignore unwanted words, such as names, foreign words, stopwords, abbreviations
filter_query = SQLiteQuery(word.word_form, 'filter', 'FILTER_WORD_FORMS', cursor=filters.cursor)
if filter_query.exists:
continue
else:
query = SQLiteQuery(word.word_form, 'word_form','DIM_ELEMENT', cursor = dim.cursor) # Capitalized words included
query_lower = SQLiteQuery(word.word_form.lower(),'word_form','DIM_ELEMENT', cursor = dim.cursor)
if not query.exists and not query_lower.exists:
if word.word_form in freqdic:
if word.lemma not in freqdic[word.word_form]['lemmas']:
freqdic[word.word_form]['lemmas'].append(word.lemma)
freqdic[word.word_form]['freq'] += 1
else:
freqdic[word.word_form] = {}
freqdic[word.word_form]['freq'] = 1
freqdic[word.word_form]['lemmas'] = [word.lemma]
except IndexError:
continue
except ET.ParseError:
continue
print("""
============================================================
Sorting candidate frequencies.
============================================================
""")
if IGC_folder == "corpora/IGC/":
with open('output/DIM/IGC_wordform_plus_lemma.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print(f"""
============================================================
Output file IGC_wordform_plus_lemma.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
elif IGC_folder == "corpora/IGC/CC_BY/":
with open('output/DIM/CC_BY_wordform_plus_lemma.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print(f"""
============================================================
Output file CC_BY_wordform_plus_lemma.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
elif IGC_folder == "corpora/IGC/TIC/":
with open('output/DIM/TIC_wordform_plus_lemma.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print(f"""
============================================================
Output file TIC_wordform_plus_lemma.freq is ready and can be
found in the output/DIM/ directory.
============================================================
""")
else:
namefolder = IGC_folder.split("/")[3]
with open('output/DIM/'+namefolder+'_wordform_plus_lemma.freq', mode='w+') as outputfile:
candidates = {k: v for k, v in sorted(freqdic.items(),
key=lambda item: item[1]['freq'], reverse=True)}
for key, value in candidates.items():
outputfile.write(key+': '+str(value)+ '\n')
print(f"""
============================================================
Output file {namefolder}_wordform_plus_lemma.freq is ready
and can be found in the output/DIM/ directory.
============================================================
""")
| 51.737069
| 144
| 0.507373
| 1,301
| 12,003
| 4.578017
| 0.138355
| 0.030222
| 0.036266
| 0.028207
| 0.855776
| 0.795836
| 0.766286
| 0.753526
| 0.753526
| 0.753526
| 0
| 0.002513
| 0.270599
| 12,003
| 232
| 145
| 51.737069
| 0.677784
| 0.118637
| 0
| 0.735294
| 0
| 0
| 0.337272
| 0.201182
| 0
| 0
| 0
| 0
| 0
| 1
| 0.009804
| false
| 0
| 0.02451
| 0
| 0.034314
| 0.058824
| 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
|
faa85af3506501a62d372b3c080e48669ca9bb6d
| 2,719
|
py
|
Python
|
expressionparser.py
|
kentdlee/GenComp
|
084da463b00557b4e0181c1a8c8d1554b4c7b2fb
|
[
"MIT"
] | 2
|
2020-03-03T03:29:56.000Z
|
2020-06-09T03:13:10.000Z
|
expressionparser.py
|
kentdlee/GenComp
|
084da463b00557b4e0181c1a8c8d1554b4c7b2fb
|
[
"MIT"
] | null | null | null |
expressionparser.py
|
kentdlee/GenComp
|
084da463b00557b4e0181c1a8c8d1554b4c7b2fb
|
[
"MIT"
] | null | null | null |
from expressionbackend import *
from genparser import *
class expressionParser(Parser):
def __init__(self):
super().__init__({0: LR0State(0,frozenset({LR0Item(0,Production(0,6,[7, 5],'E'),0,set()), LR0Item(1,Production(1,7,[7, 3, 8],'Add(E,T)'),0,set()), LR0Item(2,Production(2,7,[8],'T'),0,set()), LR0Item(3,Production(3,8,[8, 4, 9],'Mul(T,F)'),0,set()), LR0Item(4,Production(4,8,[9],'F'),0,set()), LR0Item(5,Production(5,9,[0],'Num(number)'),0,set()), LR0Item(6,Production(6,9,[1, 7, 2],'E'),0,set())}),{8: 1, 9: 2, 7: 5, 0: 4, 1: 3},False), 1: LR0State(1,frozenset({LR0Item(7,Production(2,7,[8],'T'),1,{2, 3, 5}), LR0Item(7,Production(3,8,[8, 4, 9],'Mul(T,F)'),1,set())}),{4: 9},False), 2: LR0State(2,frozenset({LR0Item(7,Production(4,8,[9],'F'),1,{2, 3, 4, 5})}),{},False), 3: LR0State(3,frozenset({LR0Item(1,Production(1,7,[7, 3, 8],'Add(E,T)'),0,set()), LR0Item(7,Production(6,9,[1, 7, 2],'E'),1,set()), LR0Item(2,Production(2,7,[8],'T'),0,set()), LR0Item(3,Production(3,8,[8, 4, 9],'Mul(T,F)'),0,set()), LR0Item(4,Production(4,8,[9],'F'),0,set()), LR0Item(5,Production(5,9,[0],'Num(number)'),0,set()), LR0Item(6,Production(6,9,[1, 7, 2],'E'),0,set())}),{8: 1, 9: 2, 7: 11, 0: 4, 1: 3},False), 4: LR0State(4,frozenset({LR0Item(7,Production(5,9,[0],'Num(number)'),1,{2, 3, 4, 5})}),{},False), 5: LR0State(5,frozenset({LR0Item(7,Production(0,6,[7, 5],'E'),1,set()), LR0Item(7,Production(1,7,[7, 3, 8],'Add(E,T)'),1,set())}),{3: 6, 5: 7},False), 6: LR0State(6,frozenset({LR0Item(1,Production(3,8,[8, 4, 9],'Mul(T,F)'),0,set()), LR0Item(2,Production(4,8,[9],'F'),0,set()), LR0Item(2,Production(1,7,[7, 3, 8],'Add(E,T)'),2,set()), LR0Item(3,Production(5,9,[0],'Num(number)'),0,set()), LR0Item(4,Production(6,9,[1, 7, 2],'E'),0,set())}),{8: 8, 9: 2, 0: 4, 1: 3},False), 7: LR0State(7,frozenset({LR0Item(2,Production(0,6,[7, 5],'E'),2,set())}),{},True), 8: LR0State(8,frozenset({LR0Item(5,Production(3,8,[8, 4, 9],'Mul(T,F)'),1,set()), LR0Item(5,Production(1,7,[7, 3, 8],'Add(E,T)'),3,{2, 3, 5})}),{4: 9},False), 9: LR0State(9,frozenset({LR0Item(1,Production(5,9,[0],'Num(number)'),0,set()), LR0Item(2,Production(6,9,[1, 7, 2],'E'),0,set()), LR0Item(2,Production(3,8,[8, 4, 9],'Mul(T,F)'),2,set())}),{0: 4, 1: 3, 9: 10},False), 10: LR0State(10,frozenset({LR0Item(3,Production(3,8,[8, 4, 9],'Mul(T,F)'),3,{2, 3, 4, 5})}),{},False), 11: LR0State(11,frozenset({LR0Item(7,Production(1,7,[7, 3, 8],'Add(E,T)'),1,set()), LR0Item(7,Production(6,9,[1, 7, 2],'E'),2,set())}),{2: 12, 3: 6},False), 12: LR0State(12,frozenset({LR0Item(2,Production(6,9,[1, 7, 2],'E'),3,{2, 3, 4, 5})}),{},False)},['number', "'('", "')'", "'+'", "'*'", 'endoffile', 'Start', 'E', 'T', 'F'])
def eval(self,expression):
return eval(expression)
| 271.9
| 2,553
| 0.581096
| 543
| 2,719
| 2.895028
| 0.07919
| 0.133588
| 0.111959
| 0.057888
| 0.624682
| 0.581425
| 0.512087
| 0.512087
| 0.496183
| 0.387405
| 0
| 0.145218
| 0.065465
| 2,719
| 9
| 2,554
| 302.111111
| 0.473436
| 0
| 0
| 0
| 0
| 0
| 0.077602
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.285714
| 0.142857
| 0.857143
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
faaa30f1a95e2ca2aedd0a5d9b2246affa9ac2fb
| 6,675
|
py
|
Python
|
tests/uniswap/test_pairs.py
|
forest-friends/vyper-common-contracts
|
c541634e3d0752801ed06b8cf6dc0a7c59555fdd
|
[
"MIT"
] | null | null | null |
tests/uniswap/test_pairs.py
|
forest-friends/vyper-common-contracts
|
c541634e3d0752801ed06b8cf6dc0a7c59555fdd
|
[
"MIT"
] | null | null | null |
tests/uniswap/test_pairs.py
|
forest-friends/vyper-common-contracts
|
c541634e3d0752801ed06b8cf6dc0a7c59555fdd
|
[
"MIT"
] | null | null | null |
def test_add_pair_owner_only(stakable_uniswap_proxy, thomas, morpheus, ownable_exception_tester):
ownable_exception_tester(
stakable_uniswap_proxy.addUniswapPair, thomas, {'from': morpheus})
ownable_exception_tester(
stakable_uniswap_proxy.addUniswapPair, morpheus, {'from': thomas})
def test_remove_pair_owner_only(stakable_uniswap_proxy, thomas, morpheus, ownable_exception_tester):
ownable_exception_tester(
stakable_uniswap_proxy.removeUniswapPair, thomas, {'from': morpheus})
ownable_exception_tester(
stakable_uniswap_proxy.removeUniswapPair, morpheus, {'from': thomas})
def test_add_pairs(stakable_uniswap_proxy, deployer, thomas, morpheus, trinity, exception_tester, ZERO_ADDRESS):
assert stakable_uniswap_proxy.lastPairIndex() == 0
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 0
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(2) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(3) == ZERO_ADDRESS
stakable_uniswap_proxy.addUniswapPair(thomas, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 1
assert stakable_uniswap_proxy.indexByPair(thomas) == 1
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 0
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == thomas
assert stakable_uniswap_proxy.uniswapPairs(2) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(3) == ZERO_ADDRESS
exception_tester("pair is exist", stakable_uniswap_proxy.addUniswapPair,
thomas, {'from': deployer})
exception_tester("", stakable_uniswap_proxy.addUniswapPair,
ZERO_ADDRESS, {'from': deployer})
stakable_uniswap_proxy.removeUniswapPair(thomas, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 0
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 0
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == thomas
assert stakable_uniswap_proxy.uniswapPairs(2) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(3) == ZERO_ADDRESS
stakable_uniswap_proxy.addUniswapPair(trinity, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 1
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 1
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(3) == ZERO_ADDRESS
stakable_uniswap_proxy.addUniswapPair(thomas, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 2
assert stakable_uniswap_proxy.indexByPair(thomas) == 2
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 1
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == thomas
assert stakable_uniswap_proxy.uniswapPairs(3) == ZERO_ADDRESS
stakable_uniswap_proxy.addUniswapPair(morpheus, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 3
assert stakable_uniswap_proxy.indexByPair(thomas) == 2
assert stakable_uniswap_proxy.indexByPair(morpheus) == 3
assert stakable_uniswap_proxy.indexByPair(trinity) == 1
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == thomas
assert stakable_uniswap_proxy.uniswapPairs(3) == morpheus
stakable_uniswap_proxy.removeUniswapPair(thomas, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 2
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 2
assert stakable_uniswap_proxy.indexByPair(trinity) == 1
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == morpheus
assert stakable_uniswap_proxy.uniswapPairs(3) == morpheus
stakable_uniswap_proxy.removeUniswapPair(morpheus, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 1
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 1
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == morpheus
assert stakable_uniswap_proxy.uniswapPairs(3) == morpheus
exception_tester("pair is not exist",
stakable_uniswap_proxy.removeUniswapPair, morpheus, {'from': deployer})
stakable_uniswap_proxy.removeUniswapPair(trinity, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 0
assert stakable_uniswap_proxy.indexByPair(thomas) == 0
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 0
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == trinity
assert stakable_uniswap_proxy.uniswapPairs(2) == morpheus
assert stakable_uniswap_proxy.uniswapPairs(3) == morpheus
stakable_uniswap_proxy.addUniswapPair(thomas, {'from': deployer})
assert stakable_uniswap_proxy.lastPairIndex() == 1
assert stakable_uniswap_proxy.indexByPair(thomas) == 1
assert stakable_uniswap_proxy.indexByPair(morpheus) == 0
assert stakable_uniswap_proxy.indexByPair(trinity) == 0
assert stakable_uniswap_proxy.uniswapPairs(0) == ZERO_ADDRESS
assert stakable_uniswap_proxy.uniswapPairs(1) == thomas
assert stakable_uniswap_proxy.uniswapPairs(2) == morpheus
assert stakable_uniswap_proxy.uniswapPairs(3) == morpheus
| 54.713115
| 112
| 0.778277
| 751
| 6,675
| 6.58988
| 0.045273
| 0.300061
| 0.400081
| 0.420287
| 0.968883
| 0.963629
| 0.936149
| 0.899374
| 0.883007
| 0.858759
| 0
| 0.01385
| 0.134682
| 6,675
| 121
| 113
| 55.165289
| 0.842971
| 0
| 0
| 0.801887
| 0
| 0
| 0.014082
| 0
| 0
| 0
| 0
| 0
| 0.754717
| 1
| 0.028302
| false
| 0
| 0
| 0
| 0.028302
| 0
| 0
| 0
| 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
faae68c1456e95ca30651784d29252a99fcf4da9
| 97,069
|
py
|
Python
|
fudge/covariances/test/test_base.py
|
brown170/fudge
|
4f818b0e0b0de52bc127dd77285b20ce3568c97a
|
[
"BSD-3-Clause"
] | 14
|
2019-08-29T23:46:24.000Z
|
2022-03-21T10:16:25.000Z
|
fudge/covariances/test/test_base.py
|
brown170/fudge
|
4f818b0e0b0de52bc127dd77285b20ce3568c97a
|
[
"BSD-3-Clause"
] | 1
|
2020-08-04T16:14:45.000Z
|
2021-12-01T01:54:34.000Z
|
fudge/covariances/test/test_base.py
|
brown170/fudge
|
4f818b0e0b0de52bc127dd77285b20ce3568c97a
|
[
"BSD-3-Clause"
] | 2
|
2022-03-03T22:41:41.000Z
|
2022-03-03T22:54:43.000Z
|
#! /usr/bin/env python3
# encoding: utf-8
# <<BEGIN-copyright>>
# Copyright 2021, Lawrence Livermore National Security, LLC.
# See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
# <<END-copyright>>
"""
test fudge/covariances/
dbrown, 12/5/2012
"""
import unittest, copy, os
defaultAccuracy = 0.001
from fudge.covariances.covarianceSuite import readXML as CovReadXML
from fudge.reactionSuite import readXML as RxnReadXML
from fudge import covariances
import xData.axes as axesModule
import xData.link as linkModule
import xData.gridded as griddedModule
import xData.values as valuesModule
import xData.xDataArray as arrayModule
import xData.formatVersion as formatVersionModule
TEST_DATA_PATH, this_filename = os.path.split(__file__)
HEvaluation = RxnReadXML( open(TEST_DATA_PATH+os.sep+'n-001_H_001.endf.gnds.xml') )
HCovariance = CovReadXML( open(TEST_DATA_PATH+os.sep+'n-001_H_001.endf.gndsCov.xml'), reactionSuite=HEvaluation )
class TestCaseBase( unittest.TestCase ):
def assertXMLListsEqual(self,x1,x2):
listAssertEqual = getattr( self, 'assertCountEqual', None )
if( listAssertEqual is None ) : listAssertEqual = getattr( self, 'assertItemsEqual' )
x1List = []
for line in x1:
if '\n' in line: x1List += [x.strip() for x in line.split('\n')]
else: x1List.append(line.strip())
x2List = []
for line in x2:
if '\n' in line: x2List += [x.strip() for x in line.split('\n')]
else: x2List.append(line.strip())
return listAssertEqual( x1List, x2List )
class Test_covariance_baseClass( TestCaseBase ):
def setUp(self):
# The COMMARA-2.0 33 group structure
self.__groupBoundaries = [
1.9640E+07, 1.0000E+07, 6.0653E+06, 3.6788E+06, 2.2313E+06, 1.3534E+06,
8.2085E+05, 4.9787E+05, 3.0197E+05, 1.8316E+05, 1.1109E+05, 6.7380E+04,
4.0868E+04, 2.4788E+04, 1.5034E+04, 9.1188E+03, 5.5308E+03, 3.3546E+03,
2.0347E+03, 1.2341E+03, 7.4852E+02, 4.5400E+02, 3.0433E+02, 1.4863E+02,
9.1661E+01, 6.7904E+01, 4.0169E+01, 2.2603E+01, 1.3710E+01, 8.3153E+00,
4.0000E+00, 5.4000E-01, 1.0000E-01, 1e-5]
self.__groupBoundaries.reverse()
self.__groupUnit = 'eV'
def test_toXMLList(self):
self.assertXMLListsEqual( HCovariance.covarianceSections[1].toXMLList(formatVersion=formatVersionModule.version_1_10), '''<section label="n + H1">
<rowData ENDF_MFMT="33,2" href="$reactions#/reactionSuite/reactions/reaction[@label='n + H1']/crossSection/XYs1d[@label='eval']"/>
<covarianceMatrix label="eval" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="eV" style="boundaries">
<values>1e-5 2e-5 5e-5 1e-4 2e-4 5e-4 1e-3 2e-3 5e-3 1e-2 0.0253 5e-2 0.1 0.2 0.5 1 2 5 10 20 50 1e2 2e2 5e2 1e3 2e3 4e3 6e3 8e3 1e4 1.5e4 2e4 4e4 6e4 8e4 1e5 1.5e5 2e5 3e5 4e5 5e5 6e5 7e5 8e5 9e5 1e6 1.2e6 1.4e6 1.6e6 1.8e6 2e6 2.2e6 2.4e6 2.6e6 2.8e6 3e6 3.2e6 3.4e6 3.6e6 3.8e6 4e6 4.2e6 4.4e6 4.6e6 4.8e6 5e6 5.5e6 6e6 6.5e6 7e6 7.5e6 8e6 8.5e6 9e6 9.5e6 1e7 1.05e7 1.1e7 1.15e7 1.2e7 1.25e7 1.3e7 1.35e7 1.4e7 1.45e7 1.5e7 1.55e7 1.6e7 1.65e7 1.7e7 1.75e7 1.8e7 1.85e7 1.9e7 1.95e7 2e7</values></grid>
<grid index="1" label="column_energy_bounds" unit="eV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="95,95" symmetry="lower">
<values>1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.715461e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.71546e-6 1.715461e-6 1.715465e-6 1.71546e-6 1.71546e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715458e-6 1.715462e-6 1.715462e-6 1.715464e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715462e-6 1.715463e-6
1.715467e-6 1.715463e-6 1.715464e-6 1.715462e-6 1.715462e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715466e-6 1.715469e-6 1.715465e-6 1.715475e-6 1.715468e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715472e-6 1.715473e-6 1.715472e-6 1.715473e-6 1.715474e-6 1.715474e-6 1.715479e-6 1.715484e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715488e-6 1.715489e-6 1.715495e-6 1.715488e-6 1.715488e-6 1.715491e-6 1.715493e-6 1.715498e-6 1.715501e-6 1.715514e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715522e-6 1.715517e-6 1.715522e-6 1.715523e-6 1.715524e-6 1.715523e-6 1.715525e-6 1.715534e-6 1.715551e-6 1.715584e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715583e-6 1.715584e-6 1.715583e-6 1.715583e-6 1.715584e-6 1.715587e-6 1.715589e-6 1.715595e-6 1.715611e-6 1.715644e-6 1.715705e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6 1.715747e-6
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4.591887e-7 4.602152e-7 4.621391e-7 4.659831e-7 4.711002e-7 4.762078e-7 4.813054e-7 4.902011e-7 5.028512e-7 5.341547e-7 5.831873e-7 6.308564e-7 6.771316e-7 7.54806e-7 8.588407e-7 1.00126e-6 1.169749e-6 1.318512e-6 1.451543e-6 1.571822e-6 1.681608e-6 1.782635e-6 1.876257e-6 2.005102e-6 2.159247e-6 2.297355e-6 2.422598e-6 2.537207e-6 2.642808e-6 2.740612e-6 2.83154e-6 2.916308e-6 2.995478e-6 3.069499e-6 3.138735e-6 3.203485e-6 3.263996e-6 3.320475e-6 3.373097e-6 3.422013e-6 3.46735e-6 3.509221e-6 3.547725e-6 3.607258e-6 3.675664e-6 3.725305e-6 3.756959e-6 3.771276e-6 3.768826e-6 3.750122e-6 3.71564e-6 3.665832e-6 3.601128e-6 3.521952e-6 3.428714e-6 3.321822e-6 3.201677e-6 3.06868e-6 2.923227e-6 2.765714e-6 2.596533e-6 2.416075e-6 2.224728e-6 2.022876e-6 1.810902e-6 1.589185e-6 1.358097e-6 1.118008e-6 8.692832e-7 6.122806e-7 3.473533e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.90449e-7 -4.904491e-7 -4.904492e-7 -4.904492e-7 -4.904496e-7 -4.904501e-7 -4.904516e-7 -4.904543e-7 -4.904611e-7 -4.90475e-7 -4.90501e-7 -4.905701e-7 -4.907086e-7 -4.909682e-7 -4.916595e-7 -4.930421e-7 -4.956297e-7 -5.007897e-7 -5.076385e-7 -5.14452e-7 -5.212306e-7
-5.330108e-7 -5.496654e-7 -5.904346e-7 -6.53302e-7 -7.135199e-7 -7.713386e-7 -8.673365e-7 -9.944789e-7 -1.166837e-6 -1.369005e-6 -1.546544e-6 -1.704777e-6 -1.847505e-6 -1.977543e-6 -2.097026e-6 -2.207605e-6 -2.359556e-6 -2.540977e-6 -2.703148e-6 -2.84986e-6 -2.983771e-6 -3.106814e-6 -3.220429e-6 -3.325711e-6 -3.423509e-6 -3.514489e-6 -3.599189e-6 -3.67804e-6 -3.751397e-6 -3.819558e-6 -3.88277e-6 -3.941244e-6 -3.995161e-6 -4.044677e-6 -4.089929e-6 -4.131037e-6 -4.193319e-6 -4.261829e-6 -4.307297e-6 -4.330723e-6 -4.332957e-6 -4.314757e-6 -4.276817e-6 -4.219782e-6 -4.14427e-6 -4.050874e-6 -3.94017e-6 -3.812723e-6 -3.669086e-6 -3.509803e-6 -3.335414e-6 -3.146449e-6 -2.943431e-6 -2.726879e-6 -2.4973e-6 -2.255199e-6 -2.00107e-6 -1.7354e-6 -1.458667e-6 -1.17134e-6 -8.738791e-7 -5.667356e-7 -2.503496e-7 7.484793e-8 4.084368e-7 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462924e-6 -1.462925e-6 -1.462925e-6 -1.462928e-6 -1.462931e-6 -1.462939e-6 -1.462958e-6 -1.462996e-6 -1.463069e-6 -1.463262e-6 -1.463648e-6 -1.46437e-6 -1.466298e-6 -1.47015e-6 -1.477363e-6 -1.491754e-6 -1.510871e-6 -1.529907e-6 -1.548862e-6 -1.58184e-6 -1.628539e-6 -1.743198e-6 -1.920787e-6 -2.091611e-6 -2.256145e-6
-2.530188e-6 -2.894323e-6 -3.389377e-6 -3.971517e-6 -4.483558e-6 -4.94037e-6 -5.352708e-6 -5.728591e-6 -6.074117e-6 -6.39402e-6 -6.833811e-6 -7.359212e-6 -7.829191e-6 -8.254672e-6 -8.643331e-6 -9.000747e-6 -9.331077e-6 -9.637484e-6 -9.92242e-6 -1.018781e-5 -1.043521e-5 -1.066586e-5 -1.088078e-5 -1.108083e-5 -1.126674e-5 -1.143909e-5 -1.159842e-5 -1.174516e-5 -1.187972e-5 -1.200244e-5 -1.218959e-5 -1.239845e-5 -1.254142e-5 -1.262132e-5 -1.264053e-5 -1.260118e-5 -1.250519e-5 -1.235436e-5 -1.215041e-5 -1.189497e-5 -1.158965e-5 -1.123601e-5 -1.083558e-5 -1.038989e-5 -9.900427e-6 -9.368679e-6 -8.796112e-6 -8.184171e-6 -7.53429e-6 -6.847886e-6 -6.126353e-6 -5.37107e-6 -4.583391e-6 -3.76465e-6 -2.916157e-6 -2.039198e-6 -1.135033e-6 -2.048958e-7 7.500079e-7 1.728501e-6</values></array></gridded2d></covarianceMatrix></section>'''.split('\n') )
def test_convertAxesToUnits(self):
listAssertEqual = getattr( self, 'assertCountEqual', None )
if( listAssertEqual is None ) : listAssertEqual = getattr( self, 'assertItemsEqual' )
originalUnits=[a.unit for a in HCovariance.covarianceSections[1]['eval'].matrix.axes]
newUnits=["","MeV","MeV"]
# convert to MeV
HCovariance.covarianceSections[1]['eval'].convertAxesToUnits(newUnits)
listAssertEqual( newUnits, [ a.unit for a in HCovariance.covarianceSections[1]['eval'].matrix.axes ] )
# convert back
HCovariance.covarianceSections[1]['eval'].convertAxesToUnits(originalUnits)
listAssertEqual( originalUnits, [ a.unit for a in HCovariance.covarianceSections[1]['eval'].matrix.axes ] )
def test_toCovarianceMatrix(self):
self.assertXMLListsEqual(HCovariance.covarianceSections[1]['eval'].toXMLList(),
HCovariance.covarianceSections[1]['eval'].toCovarianceMatrix().toXMLList())
@unittest.skip("interactive test")
def test_plot(self):
from fudge import covariances
# ...................... example matrix 'a' ......................
axes = axesModule.axes(
labelsUnits={0: ('matrix_elements', 'b**2'), 1: ('column_energy_bounds', 'MeV'),
2: ('row_energy_bounds', 'MeV')})
axes[2] = axesModule.grid(axes[2].label, axes[2].index, axes[2].unit,
style=axesModule.boundariesGridToken,
values=valuesModule.values([1.0000E-07, 1.1109E-01, 1.3534E+00, 1.9640E+01]))
axes[1] = axesModule.grid(axes[1].label, axes[1].index, axes[1].unit,
style=axesModule.linkGridToken,
values=linkModule.link(link=axes[2].values, relative=True))
myMatrix = arrayModule.full((3, 3), [4.0, 1.0, 9.0, 0.0, 0.0, 25.0], symmetry=arrayModule.symmetryLowerToken)
a=covariances.covarianceMatrix.covarianceMatrix('eval', matrix=griddedModule.gridded2d(axes, myMatrix),
type=covariances.tokens.relativeToken)
a.plot()
def test_toCorrelationMatrix(self):
x = axesModule.axes(rank=3)
x[0] = axesModule.axis(index="0", label="matrix_elements", unit="")
x[2] = axesModule.grid(index="2", label="row_energy_bounds", unit="eV", style="boundaries",
values=valuesModule.values([1e-5, 0.1e6, 1.0e6, 2.0e6, 20.0e6]))
x[1] = axesModule.grid(index="1", label="column_energy_bounds", unit="eV", style="link",
values=linkModule.link(x[2].values, relative=True))
a = arrayModule.diagonal(data=valuesModule.values([0.0, 0.25, 0.16, 0.0]), shape=(4, 4))
g = griddedModule.gridded2d(x, a)
m = covariances.covarianceMatrix.covarianceMatrix(label='0', matrix=g, type='absolute')
m.removeExtraZeros()
self.assertXMLListsEqual(m.getCorrelationMatrix().toXMLList(), """<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="eV" style="boundaries">
<values>1e5 1e6 2e6</values></grid>
<grid index="1" label="column_energy_bounds" unit="eV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="2,2" symmetry="lower">
<values>1 0 1</values></array></gridded2d>""".split('\n'))
def test_getRowBounds(self):
self.assertEqual( HCovariance.covarianceSections[1]['eval'].rowBounds(), (1.e-5, 2.e7) )
def test_getColumnBounds(self):
self.assertEqual( HCovariance.covarianceSections[1]['eval'].columnBounds(), (1.e-5, 2.e7) )
def test_toAbsolute_and_toRelative(self):
'''
The constant covariance in is 0.01 everywhere and relative. So, in absolute covariance
it is ::
cov(E,E') = sigma(E)*0.0144*sigma(E')
With a constant cross section of 1.5 b, the covariance should be (1.5 b)^2*0.0144 = 3.24e-2 b^2
'''
import fudge.reactionData.crossSection
XYs1d = fudge.reactionData.crossSection.XYs1d
ptwise = XYs1d( axes=XYs1d.defaultAxes(), data=[ [1e-5,1.5], [20.0e6,1.5] ] )
original = copy.copy(HCovariance.covarianceSections[1]['eval'])
# start as relative, so first call should do nothing
self.assertEqual( HCovariance.covarianceSections[1]['eval'].toRelative().toXMLList(), original.toXMLList() )
# call to absolute better change things
self.assertNotEqual(
HCovariance.covarianceSections[1]['eval'].toAbsolute(ptwise).toXMLList(),
[ x.replace('relative','absolute').replace('eval','toAbsolute') for x in original.toXMLList() ] )
# call to relative better bring us back
self.assertEqual( HCovariance.covarianceSections[1]['eval'].toRelative().toXMLList(), original.toXMLList() )
def test_check(self):
listAssertEqual = getattr( self, 'assertCountEqual', None )
if( listAssertEqual is None ) : listAssertEqual = getattr( self, 'assertItemsEqual' )
listAssertEqual( HCovariance.covarianceSections[1].check( { 'checkUncLimits' : False, 'negativeEigenTolerance' : 1e-8, 'eigenvalueRatioTolerance' : 1e8 } ), [] )
def test_fix(self): pass
def test_group1(self):
'''a covariance on a a coarse group whose group boundaries align with the COMMARA-2.0 group boundaries'''
axes = axesModule.axes(
labelsUnits={0: ('matrix_elements', ''), 1: ('column_energy_bounds', 'MeV'),
2: ('row_energy_bounds', 'MeV')})
axes[2] = axesModule.grid(axes[2].label, axes[2].index, axes[2].unit,
style=axesModule.boundariesGridToken,
values=valuesModule.values([1.0000E-07, 1.1109E-01, 1.3534E+00, 1.9640E+01]))
axes[1] = axesModule.grid(axes[1].label, axes[1].index, axes[1].unit,
style=axesModule.linkGridToken,
values=linkModule.link(link=axes[2].values, relative=True))
myMatrix = arrayModule.full((3, 3), [4.0, 1.0, 9.0, 0.0, 0.0, 25.0],
symmetry=arrayModule.symmetryLowerToken)
a = covariances.covarianceMatrix.covarianceMatrix('eval', matrix=griddedModule.gridded2d(axes, myMatrix),
type=covariances.tokens.relativeToken)
self.assertEqual( list( map( str, a.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [] )
self.assertXMLListsEqual(a.toXMLList(), '''<covarianceMatrix label="eval" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="MeV" style="boundaries">
<values>1e-7 0.11109 1.3534 19.64</values></grid>
<grid index="1" label="column_energy_bounds" unit="MeV" style="link">
<link href="../../grid[@index=\'2\']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="3,3" symmetry="lower">
<values>4 1 9 0 0 25</values></array></gridded2d></covarianceMatrix>'''.split('\n'))
from xData import XYs as XYsModule
testXYsAxes = axesModule.axes(labelsUnits={0: ('crossSection', 'b'), 1: ('energy_in', 'MeV')})
testXYsAxes[0] = axesModule.axis(axes[2].label, axes[2].index, axes[2].unit)
testXYsAxes[1] = axesModule.axis(axes[1].label, axes[1].index, axes[1].unit)
testXYs = XYsModule.XYs1d([[1.0000E-07, 2.0], [1.9640E+01, 2.0]], axes=testXYsAxes)
aAbs = a.toAbsolute(rowData=testXYs)
self.assertXMLListsEqual(aAbs.toXMLList(),
'''<covarianceMatrix label="toAbsolute" type="absolute">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="MeV" style="boundaries">
<values>1e-7 0.11109 1.3534 19.64</values></grid>
<grid index="1" label="column_energy_bounds" unit="MeV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit="MeV**2"/></axes>
<array shape="3,3" symmetry="lower">
<values>16 4 36 0 0 1e2</values></array></gridded2d></covarianceMatrix>'''.split('\n'))
aRel = aAbs.toRelative(rowData=testXYs)
self.assertEqual('\n'.join(aRel.toXMLList()), '\n'.join(a.toXMLList()))
g = a.group(groupBoundaries=(self.__groupBoundaries, self.__groupBoundaries),
groupUnit=(self.__groupUnit, self.__groupUnit))
self.assertEqual(g.matrix.axes[0].unit, '')
self.assertEqual(g.matrix.axes[1].unit, 'eV')
self.assertEqual(g.matrix.axes[2].unit, 'eV')
self.assertEqual( list( map(str, g.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [])
self.assertXMLListsEqual(g.toXMLList(), '''<covarianceMatrix label="eval" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="eV" style="boundaries">
<values>1e-7 0.11109 1.3534 19.64</values></grid>
<grid index="1" label="column_energy_bounds" unit="eV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="33,33" symmetry="lower">
<values>7.70526075264686e-32 5.5516702901548e-16 4 5.5516702901548e-16 4 4 5.5516702901548e-16 4 4 4 5.5516702901548e-16 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4
4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5.5516702901548e-16 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1.3879175725387e-16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9
1.3879175725387e-16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 1.3879175725387e-16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 1.3879175725387e-16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9 9 9 1.3879175725387e-16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 25 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 25 25 25 25</values></array></gridded2d></covarianceMatrix>'''.split('\n'))
if False:
a.matrix.plot()
a.plot(xlog=True, ylog=True)
g.matrix.plot()
g.plot(xlog=True, ylog=True)
def test_group2(self):
'''a covariance on a coarse group whose group boundaries don't quite align with the COMMARA-2.0 group boundaries'''
axes = axesModule.axes(
labelsUnits={0: ('matrix_elements', ''), 1: ('column_energy_bounds', 'MeV'),
2: ('row_energy_bounds', 'MeV')})
axes[2] = axesModule.grid(axes[2].label, axes[2].index, axes[2].unit,
style=axesModule.boundariesGridToken,
values=valuesModule.values([1.0e-5, 0.100, 1.0, 20.0]))
axes[1] = axesModule.grid(axes[1].label, axes[1].index, axes[1].unit,
style=axesModule.linkGridToken,
values=linkModule.link(link=axes[2].values, relative=True))
myMatrix = arrayModule.full((3, 3), [4.0, 1.0, 9.0, 0.0, 0.0, 25.0],
symmetry=arrayModule.symmetryLowerToken)
a = covariances.covarianceMatrix.covarianceMatrix('eval', matrix=griddedModule.gridded2d(axes, myMatrix),
type=covariances.tokens.relativeToken)
self.assertEqual( list( map(str, a.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [])
self.assertXMLListsEqual(a.toXMLList(),
"""<covarianceMatrix label="eval" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="MeV" style="boundaries">
<values>1e-5 0.1 1 20</values></grid>
<grid index="1" label="column_energy_bounds" unit="MeV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="3,3" symmetry="lower">
<values>4 1 9 0 0 25</values></array></gridded2d></covarianceMatrix>""".split('\n'))
g = a.group(groupBoundaries=(self.__groupBoundaries, self.__groupBoundaries),
groupUnit=(self.__groupUnit, self.__groupUnit))
g.convertAxesToUnits(('b**2', 'MeV', 'MeV'))
self.assertEqual(g.matrix.axes[0].unit, 'b**2')
self.assertEqual(g.matrix.axes[1].unit, 'MeV')
self.assertEqual(g.matrix.axes[2].unit, 'MeV')
self.assertEqual( list( map(str, g.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [])
self.assertXMLListsEqual(g.toXMLList(),
'''<covarianceMatrix label="eval" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="MeV" style="boundaries">
<values>1e-11 1e-7 1e-6 2e-5</values></grid>
<grid index="1" label="column_energy_bounds" unit="MeV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit="b**2"/></axes>
<array shape="33,33" symmetry="lower">
<values>0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.89179125483575 0 0 0 0 2.75084805457208 4 0 0 0 0 2.75084805457208 4 4 0 0 0 0 2.75084805457208 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4
4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4
4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.75084805457208 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0 0 0 0 2.22739395496322 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.238846945779 3.18579319060795 0 0 0 0 0.68771201364302 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3.02974147792267 9
0 0 0 0 0.68771201364302 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3.02974147792267 9 9 0 0 0 0 0.68771201364302 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3.02974147792267 9 9 9 0 0 0 0 0.68771201364302 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3.02974147792267 9 9 9 9 0 0 0 0 0.231346553833719 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432 0.336400337996432
1.019206057215 3.02760304196789 3.02760304196789 3.02760304196789 3.02760304196789 12.0275994719183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.5899915500892 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.5899915500892 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.5899915500892 25 25 25 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.5899915500892 25 25 25 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16.5899915500892 25 25 25 25 25</values></array></gridded2d></covarianceMatrix>'''.split('\n'))
if False:
a.matrix.plot()
a.plot(xlog=True, ylog=True)
g.matrix.plot()
g.plot(xlog=True, ylog=True)
def test_group3(self):
'''a covariance on a fine group whose group boundaries don't align with the COMMARA-2.0 group boundaries at all'''
axes = axesModule.axes(
labelsUnits={0: ('matrix_elements', 'b**2'), 1: ('column_energy_bounds', 'MeV'),
2: ('row_energy_bounds', 'MeV')})
axes[2] = axesModule.grid(axes[2].label, axes[2].index, axes[2].unit,
style=axesModule.boundariesGridToken,
values=valuesModule.values([1.0e-5, 0.100, 1.0, 20.0]))
axes[1] = axesModule.grid(axes[1].label, axes[1].index, axes[1].unit,
style=axesModule.linkGridToken,
values=linkModule.link(link=axes[2].values, relative=True))
myMatrix = arrayModule.full((3, 3), [4.0, 1.0, 9.0, 0.0, 0.0, 25.0],
symmetry=arrayModule.symmetryLowerToken)
a = covariances.covarianceMatrix.covarianceMatrix('eval', matrix=griddedModule.gridded2d(axes, myMatrix),
type=covariances.tokens.absoluteToken)
import numpy
fineBoundaries = numpy.linspace(1e-11, 20.0, num=100).tolist()
self.assertEqual( list( map(str, a.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [])
self.assertXMLListsEqual(a.toXMLList(),
'''<covarianceMatrix label="eval" type="absolute">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="MeV" style="boundaries">
<values>1e-5 0.1 1 20</values></grid>
<grid index="1" label="column_energy_bounds" unit="MeV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit="b**2"/></axes>
<array shape="3,3" symmetry="lower">
<values>4 1 9 0 0 25</values></array></gridded2d></covarianceMatrix>'''.split('\n'))
g = a.group(groupBoundaries=(fineBoundaries, fineBoundaries), groupUnit=('MeV', 'MeV'))
g.convertAxesToUnits(('b**2', 'keV', 'keV'))
self.assertEqual(g.matrix.axes[0].unit, 'b**2')
self.assertEqual(g.matrix.axes[1].unit, 'keV')
self.assertEqual(g.matrix.axes[2].unit, 'keV')
self.assertEqual( list( map(str, g.check(
{'checkUncLimits': False, 'negativeEigenTolerance': 0.0001, 'eigenvalueRatioTolerance': 0.0001})) ), [])
self.assertXMLListsEqual(g.toXMLList(),
"""<covarianceMatrix label="eval" type="absolute">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="keV" style="boundaries">
<values>1e-2 1e2 1e3 2e4</values></grid>
<grid index="1" label="column_energy_bounds" unit="keV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit="b**2"/></axes>
<array shape="99,99" symmetry="lower">
<values>3.77502899529869 5.03995050044352 9 5.03995050044352 9 9 5.03995050044352 9 9 9 4.78795297518434 8.54999999957678 8.54999999957678 8.54999999957678 8.18499999931344 0 0 0 0 1.25000000117562 25 0 0 0 0 1.25000000117562 25 25 0 0 0 0 1.25000000117562 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25
25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0
1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0
1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 0 0 0 0 1.25000000117562 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25
25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25</values></array></gridded2d></covarianceMatrix>""".split('\n'))
if False:
a.matrix.plot()
a.plot(xlog=True, ylog=True)
g.matrix.plot()
g.plot(xlog=True, ylog=True)
def test_removeExtraZeros(self):
x=axesModule.axes(rank=3)
x[0]=axesModule.axis(index="0", label="matrix_elements", unit="")
x[2]=axesModule.grid(index="2", label="row_energy_bounds", unit="eV", style="boundaries", values=valuesModule.values([1e-5,0.1e6,1.0e6,2.0e6,20.0e6]))
x[1]=axesModule.grid(index="1", label="column_energy_bounds", unit="eV", style="link", values=linkModule.link(x[2].values, relative=True))
a=arrayModule.diagonal(data=valuesModule.values([0.0,0.25,0.25,0.0]),shape=(4,4))
g=griddedModule.gridded2d(x,a)
m=covariances.covarianceMatrix.covarianceMatrix(label='0', matrix=g, type='relative')
m.removeExtraZeros()
self.assertXMLListsEqual(m.toXMLList(),"""<covarianceMatrix label="0" type="relative">
<gridded2d>
<axes>
<grid index="2" label="row_energy_bounds" unit="eV" style="boundaries">
<values>1e5 1e6 2e6</values></grid>
<grid index="1" label="column_energy_bounds" unit="eV" style="link">
<link href="../../grid[@index='2']/values"/></grid>
<axis index="0" label="matrix_elements" unit=""/></axes>
<array shape="2,2" symmetry="lower">
<values>0.25 0 0.25</values></array></gridded2d></covarianceMatrix>""".split('\n'))
def test_getUncertaintyVector(self):
# Test it as-is
asIs = HCovariance.covarianceSections[1]['eval'].getUncertaintyVector()
self.assertAlmostEqual(asIs[0][1], 0.0013097557024117131 ) # 1e-05 eV point
self.assertAlmostEqual(asIs[-1][1], 0.0013147246860084434 ) # 20000000.0 eV point
# Try making it absolute
absolute = HCovariance.covarianceSections[1]['eval'].getUncertaintyVector(relative=False)
self.assertAlmostEqual(absolute[0][1], 0.02676661285142459 ) # 1e-05 eV point
self.assertAlmostEqual(absolute[-1][1], 0.0006346783462167692 ) # 20000000.0 eV point
if __name__=="__main__":
unittest.main()
| 192.215842
| 1,294
| 0.694527
| 22,692
| 97,069
| 2.964437
| 0.178609
| 0.261101
| 0.382643
| 0.498536
| 0.475702
| 0.452779
| 0.446729
| 0.441897
| 0.434851
| 0.431283
| 0
| 0.628753
| 0.169704
| 97,069
| 504
| 1,295
| 192.597222
| 0.205891
| 0.012167
| 0
| 0.439252
| 0
| 0.168224
| 0.817397
| 0.018818
| 0
| 0
| 0
| 0
| 0.146417
| 1
| 0.05296
| false
| 0.003115
| 0.043614
| 0
| 0.105919
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
87b89daa3425c3d127adae6c9cdf1727a3046e56
| 46
|
py
|
Python
|
submodule/template_lib/warmup_scheduler/__init__.py
|
AnonymousGFR/wbgan.pytorch
|
d75cb6599852e901df0136db87520e3314f8ca71
|
[
"MIT"
] | 1
|
2022-03-07T12:34:59.000Z
|
2022-03-07T12:34:59.000Z
|
submodule/template_lib/warmup_scheduler/__init__.py
|
AnonymousGFR/wbgan.pytorch
|
d75cb6599852e901df0136db87520e3314f8ca71
|
[
"MIT"
] | 7
|
2020-01-28T23:01:23.000Z
|
2022-02-10T03:23:41.000Z
|
submodule/template_lib/warmup_scheduler/__init__.py
|
AnonymousGFR/wbgan.pytorch
|
d75cb6599852e901df0136db87520e3314f8ca71
|
[
"MIT"
] | null | null | null |
from .scheduler import GradualWarmupScheduler
| 23
| 45
| 0.891304
| 4
| 46
| 10.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086957
| 46
| 1
| 46
| 46
| 0.97619
| 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
|
87bb98ea593dfb8586f05bc1eb7dd789cda9859e
| 27
|
py
|
Python
|
xpprint/__init__.py
|
milucp/XPPRINT
|
14e6ffde492272164055a7646fafd886ecc9b37b
|
[
"Apache-2.0"
] | null | null | null |
xpprint/__init__.py
|
milucp/XPPRINT
|
14e6ffde492272164055a7646fafd886ecc9b37b
|
[
"Apache-2.0"
] | 2
|
2020-05-21T05:59:21.000Z
|
2020-12-16T05:59:10.000Z
|
xpprint/__init__.py
|
milucp/XPPRINT
|
14e6ffde492272164055a7646fafd886ecc9b37b
|
[
"Apache-2.0"
] | null | null | null |
from .xpprint import main
| 13.5
| 26
| 0.777778
| 4
| 27
| 5.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.185185
| 27
| 1
| 27
| 27
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 1
|
0
| 7
|
87d4cc2535a2647f1d335d226edf3f014ee42443
| 128
|
py
|
Python
|
src/clusto/drivers/locations/datacenters/__init__.py
|
thekad/clusto
|
c141ea3ef4931c6a21fdf42845c6e9de5ee08caa
|
[
"BSD-3-Clause"
] | 216
|
2015-01-10T17:03:25.000Z
|
2022-03-24T07:23:41.000Z
|
src/clusto/drivers/locations/datacenters/__init__.py
|
thekad/clusto
|
c141ea3ef4931c6a21fdf42845c6e9de5ee08caa
|
[
"BSD-3-Clause"
] | 23
|
2015-01-08T16:51:22.000Z
|
2021-03-13T12:56:04.000Z
|
src/clusto/drivers/locations/datacenters/__init__.py
|
thekad/clusto
|
c141ea3ef4931c6a21fdf42845c6e9de5ee08caa
|
[
"BSD-3-Clause"
] | 49
|
2015-01-08T00:13:17.000Z
|
2021-09-22T02:01:20.000Z
|
from clusto.drivers.locations.datacenters.basicdatacenter import *
from clusto.drivers.locations.datacenters.basiccage import *
| 42.666667
| 66
| 0.859375
| 14
| 128
| 7.857143
| 0.571429
| 0.181818
| 0.309091
| 0.472727
| 0.672727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.0625
| 128
| 2
| 67
| 64
| 0.916667
| 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
|
87fa690b1aace1c1e766476667dda10e8d62971b
| 160
|
py
|
Python
|
tests/test_atomstoys.py
|
craabreu/atomstoys
|
878da463f21d3443f74b2ad803ecd8dd047c25dc
|
[
"MIT"
] | null | null | null |
tests/test_atomstoys.py
|
craabreu/atomstoys
|
878da463f21d3443f74b2ad803ecd8dd047c25dc
|
[
"MIT"
] | null | null | null |
tests/test_atomstoys.py
|
craabreu/atomstoys
|
878da463f21d3443f74b2ad803ecd8dd047c25dc
|
[
"MIT"
] | null | null | null |
from atomstoys import longest
from atomstoys import main
def test_main():
pass
def test_longest():
assert longest([b'a', b'bc', b'abc']) == b'abc'
| 13.333333
| 51
| 0.6625
| 25
| 160
| 4.16
| 0.52
| 0.25
| 0.365385
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 160
| 11
| 52
| 14.545455
| 0.8125
| 0
| 0
| 0
| 0
| 0
| 0.056604
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 1
| 0.333333
| true
| 0.166667
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 8
|
357e71ab9e3b35d522f18af9d1546ab17eb59328
| 2,239
|
py
|
Python
|
lib/is_roca_test.py
|
matusso/RsaCtfTool
|
319acafc5375b473d0ec1d834ff5e0b59e88cdbf
|
[
"Beerware"
] | 1
|
2021-08-04T21:37:30.000Z
|
2021-08-04T21:37:30.000Z
|
lib/is_roca_test.py
|
swzhouu/RsaCtfTool
|
bf3c0148f7ab0592deba702160f502d29ca7dbe1
|
[
"Beerware"
] | null | null | null |
lib/is_roca_test.py
|
swzhouu/RsaCtfTool
|
bf3c0148f7ab0592deba702160f502d29ca7dbe1
|
[
"Beerware"
] | null | null | null |
tests = [(11, set([1, 10])), (13, set([1, 3, 4, 9, 10, 12])), (17, set([1, 2, 4, 8, 9, 13, 15, 16])), (19, set([1, 4, 5, 6, 7, 9, 11, 16, 17])), (37, set([1, 10, 26])), (53, set([1, 4, 6, 7, 9, 10, 11, 13, 15, 16, 17, 24, 25, 28, 29, 36, 37, 38, 40, 42, 43, 44, 46, 47, 49, 52])), (61, set([1, 34, 3, 37, 38, 33, 8, 9, 11, 60, 50, 20, 41, 23, 24, 52, 58, 27, 28, 53])), (71, set([1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 19, 20, 24, 25, 27, 29, 30, 32, 36, 37, 38, 40, 43, 45, 48, 49, 50, 54, 57, 58, 60, 64])), (73, set([1, 3, 7, 8, 9, 10, 17, 21, 22, 24, 27, 30, 43, 46, 49, 51, 52, 56, 63, 64, 65, 66, 70, 72])), (79, set([64, 1, 67, 38, 65, 8, 10, 46, 18, 52, 21, 22, 62])), (97, set([96, 1, 35, 36, 61, 62])), (103, set([1, 2, 4, 7, 8, 9, 13, 14, 15, 16, 17, 18, 19, 23, 25, 26, 28, 29, 30, 32, 33, 34, 36, 38, 41, 46, 49, 50, 52, 55, 56, 58, 59, 60, 61, 63, 64, 66, 68, 72, 76, 79, 81, 82, 83, 91, 92, 93, 97, 98, 100])), (107, set([1, 3, 4, 9, 10, 11, 12, 13, 14, 16, 19, 23, 25, 27, 29, 30, 33, 34, 35, 36, 37, 39, 40, 41, 42, 44, 47, 48, 49, 52, 53, 56, 57, 61, 62, 64, 69, 75, 76, 79, 81, 83, 85, 86, 87, 89, 90, 92, 99, 100, 101, 102, 105])), (109, set([1, 3, 4, 5, 7, 9, 12, 15, 16, 20, 21, 22, 25, 26, 27, 28, 29, 31, 34, 35, 36, 38, 43, 45, 46, 48, 49, 60, 61, 63, 64, 66, 71, 73, 74, 75, 78, 80, 81, 82, 83, 84, 87, 88, 89, 93, 94, 97, 100, 102, 104, 105, 106, 108])), (127, set([1, 2, 4, 5, 8, 10, 16, 19, 20, 25, 27, 32, 33, 38, 40, 47, 50, 51, 54, 61, 63, 64, 66, 73, 76, 77, 80, 87, 89, 94, 95, 100, 102, 107, 108, 111, 117, 119, 122, 123, 125, 126])), (151, set([1, 3, 132, 8, 9, 142, 143, 19, 20, 150, 24, 26, 27, 28, 29, 41, 44, 50, 53, 57, 59, 60, 64, 65, 67, 68, 70, 72, 73, 78, 79, 81, 83, 84, 86, 87, 91, 92, 94, 98, 131, 101, 107, 110, 148, 122, 123, 124, 125, 127])), (157, set([1, 130, 3, 4, 9, 10, 11, 12, 13, 14, 143, 16, 17, 146, 19, 148, 153, 25, 154, 27, 156, 30, 31, 33, 35, 36, 37, 39, 40, 42, 44, 46, 47, 48, 49, 51, 52, 56, 57, 58, 138, 64, 67, 68, 71, 140, 75, 76, 141, 81, 82, 147, 86, 89, 90, 93, 144, 99, 100, 101, 145, 105, 106, 108, 109, 110, 111, 113, 115, 117, 118, 132, 120, 121, 122, 124, 126, 127]))]
def is_roca_vulnerable(modulus):
return all(modulus % p in l for p, l in tests)
| 373.166667
| 2,152
| 0.497097
| 531
| 2,239
| 2.092279
| 0.293785
| 0.054005
| 0.022502
| 0.016202
| 0.071107
| 0.035104
| 0.023402
| 0.023402
| 0
| 0
| 0
| 0.579314
| 0.231353
| 2,239
| 5
| 2,153
| 447.8
| 0.066241
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0.333333
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 8
|
35a2217159e13a402a302f838efb80322fc6a890
| 10,680
|
py
|
Python
|
tests.py
|
fdelvalle/sdk-python
|
e8457644ca7dba94e3dc1cd3ba5a100887d75d26
|
[
"MIT"
] | 1
|
2019-06-04T19:18:00.000Z
|
2019-06-04T19:18:00.000Z
|
tests.py
|
fdelvalle/sdk-python
|
e8457644ca7dba94e3dc1cd3ba5a100887d75d26
|
[
"MIT"
] | null | null | null |
tests.py
|
fdelvalle/sdk-python
|
e8457644ca7dba94e3dc1cd3ba5a100887d75d26
|
[
"MIT"
] | 4
|
2018-07-09T12:56:09.000Z
|
2019-10-21T21:00:42.000Z
|
# coding: utf-8
import os
import unittest
from datetime import date
from maxipago import Maxipago, exceptions
from maxipago.utils import payment_processors
from random import randint
MAXIPAGO_ID = os.getenv('MAXIPAGO_ID')
MAXIPAGO_API_KEY = os.getenv('MAXIPAGO_API_KEY')
class MaxipagoTestCase(unittest.TestCase):
def setUp(self):
self.maxipago = Maxipago(MAXIPAGO_ID, MAXIPAGO_API_KEY, sandbox=True)
def test_add_customer(self):
CUSTOMER_ID = randint(1, 100000)
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name='Fulano',
last_name='de Tal',
)
self.assertTrue(getattr(response, 'id', False))
def test_add_customer_already_existing(self):
CUSTOMER_ID = randint(1, 100000)
# creating customer with random id.
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name='Fulano',
last_name='de Tal',
)
self.assertTrue(hasattr(response, 'id'))
# creating customer with the same id.
with self.assertRaises(exceptions.CustomerAlreadyExists):
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name='Fulano',
last_name='de Tal',
)
def test_delete_customer(self):
CUSTOMER_ID = randint(1, 100000)
# creating customer with random id.
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name='Fulano',
last_name='de Tal',
)
self.assertTrue(hasattr(response, 'id'))
maxipago_customer_id = response.id
response = self.maxipago.customer.delete(
id=maxipago_customer_id,
)
def test_update_customer(self):
CUSTOMER_ID = randint(1, 100000)
# creating customer with random id.
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name=u'Fulano',
last_name=u'de Tal',
)
self.assertTrue(hasattr(response, 'id'))
maxipago_customer_id = response.id
response = self.maxipago.customer.update(
id=maxipago_customer_id,
customer_id=CUSTOMER_ID,
first_name=u'Antonio',
)
def test_add_card(self):
CUSTOMER_ID = randint(1, 100000)
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name=u'Fulano',
last_name=u'de Tal',
)
self.assertTrue(hasattr(response, 'id'))
maxipago_customer_id = response.id
response = self.maxipago.card.add(
customer_id=maxipago_customer_id,
number=u'4111111111111111',
expiration_month=u'02',
expiration_year=date.today().year + 3,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20123456',
billing_country=u'BR',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
)
self.assertTrue(getattr(response, 'token', False))
def test_delete_card(self):
CUSTOMER_ID = randint(1, 100000)
customer_response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name=u'Fulano',
last_name=u'de Tal',
)
self.assertTrue(hasattr(customer_response, 'id'))
maxipago_customer_id = customer_response.id
card_response = self.maxipago.card.add(
customer_id=maxipago_customer_id,
number=u'4111111111111111',
expiration_month=u'02',
expiration_year=date.today().year + 3,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20345678',
billing_country=u'RJ',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
)
self.assertTrue(getattr(card_response, 'token', False))
token = card_response.token
response = self.maxipago.card.delete(
customer_id=maxipago_customer_id,
token=token,
)
self.assertTrue(getattr(response, 'success', False))
def test_payment_authorize(self):
REFERENCE = randint(1, 100000)
response = self.maxipago.payment.authorize(
processor_id=payment_processors.TEST,
reference_num=REFERENCE,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20345678',
billing_country=u'RJ',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
card_number='4111111111111111',
card_expiration_month=u'02',
card_expiration_year=date.today().year + 3,
card_cvv='123',
charge_total='100.00',
)
self.assertTrue(response.authorized)
self.assertFalse(response.captured)
def test_payment_direct(self):
REFERENCE = randint(1, 100000)
response = self.maxipago.payment.direct(
processor_id=payment_processors.TEST,
reference_num=REFERENCE,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20345678',
billing_country=u'RJ',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
card_number='4111111111111111',
card_expiration_month=u'02',
card_expiration_year=date.today().year + 3,
card_cvv='123',
charge_total='100.00',
)
self.assertTrue(response.authorized)
self.assertTrue(response.captured)
def test_payment_direct_declined(self):
REFERENCE = randint(1, 100000)
response = self.maxipago.payment.direct(
processor_id=payment_processors.TEST,
reference_num=REFERENCE,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20345678',
billing_country=u'RJ',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
card_number='4111111111111111',
card_expiration_month=u'02',
card_expiration_year=date.today().year + 3,
card_cvv='123',
charge_total='100.01',
)
self.assertFalse(response.authorized)
self.assertFalse(response.captured)
def test_payment_direct_with_token(self):
CUSTOMER_ID = randint(1, 100000)
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name=u'Fulano',
last_name=u'de Tal',
)
self.assertTrue(hasattr(response, 'id'))
maxipago_customer_id = response.id
response = self.maxipago.card.add(
customer_id=maxipago_customer_id,
number=u'4111111111111111',
expiration_month=u'02',
expiration_year=date.today().year + 3,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20123456',
billing_country=u'BR',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
)
self.assertTrue(getattr(response, 'token', False))
REFERENCE = randint(1, 100000)
response = self.maxipago.payment.direct(
processor_id=payment_processors.TEST,
reference_num=REFERENCE,
customer_id=maxipago_customer_id,
token=response.token,
charge_total='100.00',
)
self.assertTrue(response.authorized)
self.assertTrue(response.captured)
def test_payment_direct_with_token_decline(self):
CUSTOMER_ID = randint(1, 100000)
response = self.maxipago.customer.add(
customer_id=CUSTOMER_ID,
first_name=u'Fulano',
last_name=u'de Tal',
)
self.assertTrue(hasattr(response, 'id'))
maxipago_customer_id = response.id
response = self.maxipago.card.add(
customer_id=maxipago_customer_id,
number=u'4111111111111111',
expiration_month=u'02',
expiration_year=date.today().year + 3,
billing_name=u'Fulano de Tal',
billing_address1=u'Rua das Alamedas, 123',
billing_city=u'Rio de Janeiro',
billing_state=u'RJ',
billing_zip=u'20123456',
billing_country=u'BR',
billing_phone=u'552140634666',
billing_email=u'fulano@detal.com',
)
self.assertTrue(getattr(response, 'token', False))
REFERENCE = randint(1, 100000)
response = self.maxipago.payment.direct(
processor_id=payment_processors.TEST,
reference_num=REFERENCE,
customer_id=maxipago_customer_id,
token=response.token,
charge_total='100.01',
)
self.assertFalse(response.authorized)
self.assertFalse(response.captured)
def test_http_exception(self):
CUSTOMER_ID = randint(1, 100000)
customer_manager = self.maxipago.customer
customer_manager.uri_api = 'https://testapi.maxipago.net/UniversalAPI/WrongUri'
with self.assertRaises(exceptions.HttpErrorException):
customer_manager.add(
customer_id=CUSTOMER_ID,
first_name='Fulano',
last_name='de Tal',
)
if __name__ == '__main__':
unittest.main()
| 31.411765
| 88
| 0.580805
| 1,131
| 10,680
| 5.262599
| 0.102564
| 0.089046
| 0.070565
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| 0
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| 10,680
| 339
| 89
| 31.504425
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0
| 7
|
35cd56680a749b40447dff7da919f5509876da16
| 206
|
py
|
Python
|
trail/upload/process_uploads/processors/__init__.py
|
DinoBektesevic/trailblazer
|
31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06
|
[
"MIT"
] | 4
|
2021-11-17T22:54:10.000Z
|
2022-03-01T23:09:16.000Z
|
trail/upload/process_uploads/processors/__init__.py
|
DinoBektesevic/trailblazer
|
31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06
|
[
"MIT"
] | 35
|
2021-04-05T22:26:02.000Z
|
2022-03-31T23:10:44.000Z
|
trail/upload/process_uploads/processors/__init__.py
|
DinoBektesevic/trailblazer
|
31aeb2b2e3ab0cd97c4e4d2c0e26043f559abe06
|
[
"MIT"
] | 1
|
2022-02-11T17:03:25.000Z
|
2022-02-11T17:03:25.000Z
|
# This import is required to trigger init_subclass in UploadProcessor
from .single_extension_fits import *
from .multi_extension_fits import *
from .rubin_processor import *
from .decam_processor import *
| 29.428571
| 69
| 0.825243
| 28
| 206
| 5.821429
| 0.642857
| 0.184049
| 0.233129
| 0.282209
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.131068
| 206
| 6
| 70
| 34.333333
| 0.910615
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| 0
| true
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ea016d382f0129d3326578b553f15bd81d24fee5
| 18,193
|
py
|
Python
|
orders/tests/tests.py
|
Flict-dev/Boxes-api
|
a2aa76b2db6aaa5af1028138566dc3b2b251fcdb
|
[
"MIT"
] | 1
|
2021-12-13T22:03:58.000Z
|
2021-12-13T22:03:58.000Z
|
orders/tests/tests.py
|
Flict-dev/Boxes-api
|
a2aa76b2db6aaa5af1028138566dc3b2b251fcdb
|
[
"MIT"
] | null | null | null |
orders/tests/tests.py
|
Flict-dev/Boxes-api
|
a2aa76b2db6aaa5af1028138566dc3b2b251fcdb
|
[
"MIT"
] | null | null | null |
from random import randint
from django.utils import timezone
from rest_framework import status
from rest_framework.reverse import reverse
from rest_framework.test import APITestCase
from carts.models import Cart, CartItem
from items.tests.factories import ItemFactory
from orders.models import Order
from users.tests.factories import UserFactory
class OrderViewSetListTestCase(APITestCase):
@classmethod
def setUpTestData(cls):
cls.url = reverse('orders:order-list')
def setUp(self) -> None:
self.user = UserFactory()
self.cart = Cart.objects.create(user=self.user)
self.cart_items_id = []
self.cart_items_models = []
for _ in range(20):
item = ItemFactory()
cart_item = CartItem.objects.create(cart=self.cart, item=item, quantity=randint(1, 100), price=item.price)
self.cart_items_id.append(cart_item.id)
self.cart_items_models.append(cart_item)
self.cart.items.set(self.cart_items_id)
self.total_cost = 0
for item in self.cart_items_models:
self.total_cost += item.price * item.quantity
self.orders = [
Order.objects.create(
cart=self.cart,
address=f'test address - {_}',
delivery_at=timezone.now(),
total_cost=self.total_cost,
recipient=self.user
)
for _ in range(20)
]
def test_unauthorized(self):
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertEqual(response.json()["detail"], "Authentication credentials were not provided.")
def test(self):
self.client.force_authenticate(user=self.user)
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
results = response.json()['results']
self.assertEqual(
results,
[
{
"id": order.id,
"cart": {
"id": self.cart.id,
"cart_items": [
{
"id": cart_item.pk,
"item": {
"id": cart_item.item.id,
"title": f"{cart_item.item.title}",
"description": f"{cart_item.item.description}",
"image": f"{cart_item.item.image.url}",
"weight": cart_item.item.weight,
"price": f"{cart_item.item.price}"
},
"item_id": cart_item.item.id,
"quantity": cart_item.quantity,
"price": f'{cart_item.price}',
"total_price": float(cart_item.price * cart_item.quantity),
}
for cart_item in self.cart_items_models
],
"total_cost": float(self.total_cost)
},
"status": f"{order.status}",
"total_cost": float(self.total_cost),
"address": f"{order.address}",
"delivery_at": f"{str(order.delivery_at)[:19]}",
"created_at": f"{str(order.created_at)[:19]}"
}
for order in self.orders[:len(results)]
]
)
class OrderViewSetCreateTestCase(APITestCase):
@classmethod
def setUpTestData(cls):
cls.url = reverse('orders:order-list')
def setUp(self) -> None:
self.user = UserFactory()
self.cart = Cart.objects.create(user=self.user)
self.cart_items_id = []
self.cart_items_models = []
for _ in range(20):
item = ItemFactory()
cart_item = CartItem.objects.create(cart=self.cart, item=item, quantity=randint(1, 100), price=item.price)
self.cart_items_id.append(cart_item.id)
self.cart_items_models.append(cart_item)
self.cart.items.set(self.cart_items_id)
self.total_cost = 0
for item in self.cart_items_models:
self.total_cost += item.price * item.quantity
def test_unauthorized(self):
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertEqual(response.json()["detail"], "Authentication credentials were not provided.")
def test(self):
data = {
"address": "string",
"delivery_at": "2021-05-22 13:47:10.954Z"
}
self.client.force_authenticate(user=self.user)
response = self.client.post(self.url, data=data)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
order = Order.objects.get(id=response.json()['id'])
self.assertEqual(
response.json(),
{
"id": order.id,
"cart": {
"id": self.cart.id,
"cart_items": [
{
"id": cart_item.pk,
"item": {
"id": cart_item.item.id,
"title": f"{cart_item.item.title}",
"description": f"{cart_item.item.description}",
"image": f"http://testserver{cart_item.item.image.url}",
"weight": cart_item.item.weight,
"price": f"{cart_item.item.price}"
},
"item_id": cart_item.item.id,
"quantity": cart_item.quantity,
"price": f'{cart_item.price}',
"total_price": float(cart_item.price * cart_item.quantity),
}
for cart_item in self.cart_items_models
],
"total_cost": float(self.total_cost)
},
"status": f"{order.status}",
"total_cost": float(self.total_cost),
"address": f"{order.address}",
"delivery_at": f"{str(order.delivery_at)[:23]}Z",
"created_at": f"{str(order.created_at)[:19]}"
}
)
class OrderViewSetPartialUpdateTestCase(APITestCase):
@classmethod
def setUpTestData(cls):
cls.user = UserFactory()
cls.cart = Cart.objects.create(user=cls.user)
cls.cart_items_id = []
cls.cart_items_models = []
for _ in range(20):
item = ItemFactory()
cart_item = CartItem.objects.create(cart=cls.cart, item=item, quantity=randint(1, 100), price=item.price)
cls.cart_items_id.append(cart_item.id)
cls.cart_items_models.append(cart_item)
cls.cart.items.set(cls.cart_items_id)
cls.total_cost = 0
for item in cls.cart_items_models:
cls.total_cost += item.price * item.quantity
cls.ok_order = Order.objects.create(
cart=cls.cart,
address='test address',
delivery_at=timezone.now(),
total_cost=cls.total_cost,
recipient_id=cls.user.id,
)
cls.url = reverse('orders:order-detail', kwargs={"pk": cls.ok_order.id})
def setUp(self) -> None:
self.fail_order = Order.objects.create(
cart=self.cart,
address='test address',
delivery_at=timezone.now(),
total_cost=self.total_cost,
recipient=self.user,
recipient_id=self.user.id,
status='delivered',
)
def test_unauthorized(self):
data = {
"status": "created",
"address": "string",
"delivery_at": "2021-05-22T13:47:10.954Z"
}
response = self.client.patch(self.url, data=data)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertEqual(response.json()["detail"], "Authentication credentials were not provided.")
def test_order_status(self):
self.client.force_authenticate(user=self.user)
fail_url = reverse('orders:order-detail', kwargs={"pk": self.fail_order.id})
data = {
"status": "created",
"address": "string",
"delivery_at": "2021-05-22T13:47:10.954Z"
}
response = self.client.put(fail_url, data=data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
message = 'Вы не можете редактировать заказ в обработке/доставленный заказ/отмененный заказ'
self.assertEqual(response.json()['data'], message)
def test(self):
self.client.force_authenticate(user=self.user)
data = {
"status": "created",
"address": "string",
"delivery_dt": "2021-05-22T13:47:10.954Z"
}
response = self.client.patch(self.url, data=data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
order = Order.objects.get(id=response.json()['id'])
self.assertEqual(
response.json(),
{
"id": order.id,
"cart": {
"id": self.cart.id,
"cart_items": [
{
"id": cart_item.pk,
"item": {
"id": cart_item.item.id,
"title": f"{cart_item.item.title}",
"description": f"{cart_item.item.description}",
"image": f"{cart_item.item.image.url}",
"weight": cart_item.item.weight,
"price": f"{cart_item.item.price}"
},
"item_id": cart_item.item.id,
"quantity": cart_item.quantity,
"price": f'{cart_item.price}',
"total_price": float(cart_item.price * cart_item.quantity),
}
for cart_item in self.cart_items_models
],
"total_cost": float(self.total_cost)
},
"status": f"{order.status}",
"total_cost": float(self.total_cost),
"address": f"{order.address}",
"delivery_at": f"{str(order.delivery_at)[:19]}",
"created_at": f"{str(order.created_at)[:19]}"
}
)
class OrderViewSetUpdateTestCase(APITestCase):
@classmethod
def setUpTestData(cls):
cls.user = UserFactory()
cls.cart = Cart.objects.create(user=cls.user)
cls.cart_items_id = []
cls.cart_items_models = []
for _ in range(20):
item = ItemFactory()
cart_item = CartItem.objects.create(cart=cls.cart, item=item, quantity=randint(1, 100), price=item.price)
cls.cart_items_id.append(cart_item.id)
cls.cart_items_models.append(cart_item)
cls.cart.items.set(cls.cart_items_id)
cls.total_cost = 0
for item in cls.cart_items_models:
cls.total_cost += item.price * item.quantity
cls.ok_order = Order.objects.create(
cart=cls.cart,
address='test address',
delivery_at=timezone.now(),
total_cost=cls.total_cost,
recipient_id=cls.user.id,
)
cls.url = reverse('orders:order-detail', kwargs={"pk": cls.ok_order.id})
def setUp(self) -> None:
self.fail_order = Order.objects.create(
cart=self.cart,
address='test address',
delivery_at=timezone.now(),
total_cost=self.total_cost,
recipient=self.user,
recipient_id=self.user.id,
status='delivered',
)
def test_unauthorized(self):
data = {
"status": "created",
"address": "string",
"delivery_at": "2021-05-22T13:47:10.954Z"
}
response = self.client.put(self.url, data=data)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertEqual(response.json()["detail"], "Authentication credentials were not provided.")
def test_order_status(self):
self.client.force_authenticate(user=self.user)
fail_url = reverse('orders:order-detail', kwargs={"pk": self.fail_order.id})
data = {
"status": "created",
"address": "string",
"delivery_dt": "2021-05-22T13:47:10.954Z"
}
response = self.client.put(fail_url, data=data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
message = 'Вы не можете редактировать заказ в обработке/доставленный заказ/отмененный заказ'
self.assertEqual(response.json()['data'], message)
def test(self):
self.client.force_authenticate(user=self.user)
data = {
"status": "created",
"address": "string",
"delivery_at": "2021-05-22T13:47:10.954Z"
}
response = self.client.patch(self.url, data=data)
self.assertEqual(response.status_code, status.HTTP_200_OK)
order = Order.objects.get(id=response.json()['id'])
self.assertEqual(
response.json(),
{
"id": order.id,
"cart": {
"id": self.cart.id,
"cart_items": [
{
"id": cart_item.pk,
"item": {
"id": cart_item.item.id,
"title": f"{cart_item.item.title}",
"description": f"{cart_item.item.description}",
"image": f"{cart_item.item.image.url}",
"weight": cart_item.item.weight,
"price": f"{cart_item.item.price}"
},
"item_id": cart_item.item.id,
"quantity": cart_item.quantity,
"price": f'{cart_item.price}',
"total_price": float(cart_item.price * cart_item.quantity),
}
for cart_item in self.cart_items_models
],
"total_cost": float(self.total_cost)
},
"status": f"{order.status}",
"total_cost": float(self.total_cost),
"address": f"{order.address}",
"delivery_at": f"{str(order.delivery_at)[:19]}",
"created_at": f"{str(order.created_at)[:19]}"
}
)
class OrderViewSetRetrieveTestCase(APITestCase):
@classmethod
def setUpTestData(cls):
cls.user = UserFactory()
cls.cart = Cart.objects.create(user=cls.user)
cls.cart_items_id = []
cls.cart_items_models = []
for _ in range(20):
item = ItemFactory()
cart_item = CartItem.objects.create(cart=cls.cart, item=item, quantity=randint(1, 100), price=item.price)
cls.cart_items_id.append(cart_item.id)
cls.cart_items_models.append(cart_item)
cls.cart.items.set(cls.cart_items_id)
cls.total_cost = 0
for item in cls.cart_items_models:
cls.total_cost += item.price * item.quantity
cls.order = Order.objects.create(
cart=cls.cart,
address='test address',
delivery_at=timezone.now(),
total_cost=cls.total_cost,
recipient_id=cls.user.id,
)
cls.url = reverse('orders:order-detail', kwargs={"pk": cls.order.id})
def test_unauthorized(self):
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertEqual(response.json()["detail"], "Authentication credentials were not provided.")
def test(self):
self.client.force_authenticate(user=self.user)
response = self.client.get(self.url)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
response.json(),
{
"id": self.order.id,
"cart": {
"id": self.cart.id,
"cart_items": [
{
"id": cart_item.pk,
"item": {
"id": cart_item.item.id,
"title": f"{cart_item.item.title}",
"description": f"{cart_item.item.description}",
"image": f"{cart_item.item.image.url}",
"weight": cart_item.item.weight,
"price": f"{cart_item.item.price}"
},
"item_id": cart_item.item.id,
"quantity": cart_item.quantity,
"price": f'{cart_item.price}',
"total_price": float(cart_item.price * cart_item.quantity),
}
for cart_item in self.cart_items_models
],
"total_cost": float(self.total_cost)
},
"status": f"{self.order.status}",
"total_cost": float(self.total_cost),
"address": f"{self.order.address}",
"delivery_at": f"{str(self.order.delivery_at)[:19]}",
"created_at": f"{str(self.order.created_at)[:19]}"
}
)
| 41.347727
| 118
| 0.509372
| 1,871
| 18,193
| 4.7814
| 0.068413
| 0.076012
| 0.053655
| 0.02761
| 0.930584
| 0.925889
| 0.919741
| 0.915605
| 0.912251
| 0.908451
| 0
| 0.018548
| 0.371736
| 18,193
| 439
| 119
| 41.441913
| 0.764129
| 0
| 0
| 0.788698
| 0
| 0
| 0.156269
| 0.052108
| 0
| 0
| 0
| 0
| 0.058968
| 1
| 0.051597
| false
| 0
| 0.022113
| 0
| 0.085995
| 0
| 0
| 0
| 0
| null | 0
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| 1
| 1
| 1
| 1
| 1
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
ea0c26e4f921f2ac64032c95d7ef2363ca587720
| 77
|
py
|
Python
|
nq/__init__.py
|
societe-generale/nq
|
fd3f4d69ee5b2e5ffd02b082f13c7c98d453431c
|
[
"MIT"
] | null | null | null |
nq/__init__.py
|
societe-generale/nq
|
fd3f4d69ee5b2e5ffd02b082f13c7c98d453431c
|
[
"MIT"
] | null | null | null |
nq/__init__.py
|
societe-generale/nq
|
fd3f4d69ee5b2e5ffd02b082f13c7c98d453431c
|
[
"MIT"
] | null | null | null |
from nq.filters import *
from nq.nested_query import *
from nq.flags import *
| 25.666667
| 29
| 0.779221
| 13
| 77
| 4.538462
| 0.538462
| 0.305085
| 0.40678
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 77
| 3
| 30
| 25.666667
| 0.893939
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 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
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| null | 0
| 0
| 0
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| 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
ea3d6cfd114eb4d9d77b4b1ba3b38189c0710c8b
| 8,719
|
py
|
Python
|
tests/transforms/gen/test_spatial.py
|
ad12/meddlr
|
dda5a4ad7855de3a34331c60599e3253f980e989
|
[
"Apache-2.0"
] | 23
|
2021-11-05T02:00:01.000Z
|
2022-03-21T15:35:38.000Z
|
tests/transforms/gen/test_spatial.py
|
ad12/meddlr
|
dda5a4ad7855de3a34331c60599e3253f980e989
|
[
"Apache-2.0"
] | 29
|
2021-11-04T22:18:26.000Z
|
2022-03-24T01:04:53.000Z
|
tests/transforms/gen/test_spatial.py
|
ad12/meddlr
|
dda5a4ad7855de3a34331c60599e3253f980e989
|
[
"Apache-2.0"
] | 1
|
2022-01-25T22:34:51.000Z
|
2022-01-25T22:34:51.000Z
|
import unittest
import numpy as np
import torch
from meddlr.transforms.base.spatial import AffineTransform, TranslationTransform
from meddlr.transforms.gen import RandomAffine
from meddlr.transforms.gen.spatial import RandomTranslation
from meddlr.transforms.tf_scheduler import WarmupTF
from ..mock import MockIterTracker
class TestRandomAffine(unittest.TestCase):
def test_scheduler_basic(self):
iter_tracker = MockIterTracker()
affine = RandomAffine(angle=10, translate=1.0, scale=2.0)
scheduler = WarmupTF(affine, warmup_iters=400, params=["angle", "translate"])
scheduler.get_iteration = iter_tracker.get_iter
affine.register_schedulers([scheduler])
iter_tracker.step(100)
params = affine._get_param_values(use_schedulers=True)
assert params["angle"] == 2.5
assert params["translate"] == 0.25
assert params["scale"] == 2.0
def test_scheduler_p(self):
iter_tracker = MockIterTracker()
affine = RandomAffine(p=1.0, angle=10, translate=1.0, scale=2.0)
scheduler = WarmupTF(affine, warmup_iters=400, params=["p"])
scheduler.get_iteration = iter_tracker.get_iter
affine.register_schedulers([scheduler])
iter_tracker.step(100)
params = affine._get_param_values(use_schedulers=True)
p = params["p"]
assert all(v == 0.25 for v in p.values())
# Different probability for each.
iter_tracker = MockIterTracker()
affine = RandomAffine(
p={"angle": 1.0, "translate": 0.5, "scale": 0.2}, angle=10, translate=1.0, scale=2.0
)
p = affine.p
assert p["angle"] == 1.0
assert p["translate"] == 0.5
assert p["scale"] == 0.2
assert p["shear"] == 0
scheduler = WarmupTF(affine, warmup_iters=500, params=["p"])
scheduler.get_iteration = iter_tracker.get_iter
affine.register_schedulers([scheduler])
iter_tracker.step(100)
params = affine._get_param_values(use_schedulers=True)
p = params["p"]
assert np.allclose(p["angle"], 0.2)
assert np.allclose(p["translate"], 0.1)
assert np.allclose(p["scale"], 0.04)
assert np.allclose(p["shear"], 0.0)
def test_multi_arg_param_translate(self):
h, w = 100, 100
img = torch.randn(1, 1, h, w)
t_h, t_w = 0.1, 0.8
affine = RandomAffine(translate=(t_h, t_w), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert any(x < 0 for x in h_translate) and any(x > 0 for x in h_translate)
assert any(x < 0 for x in w_translate) and any(x > 0 for x in w_translate)
assert all(abs(x) <= h * t_h for x in h_translate)
assert all(abs(x) <= w * t_w for x in w_translate)
# Fixed range of values.
t_h1, t_h2, t_w1, t_w2 = 0.1, 0.2, -0.8, -0.6
affine = RandomAffine(translate=((t_h1, t_h2), (t_w1, t_w2)), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert all(x > 0 for x in h_translate)
assert all(x < 0 for x in w_translate)
assert all(h * t_h1 <= x <= h * t_h2 for x in h_translate)
assert all(w * t_w1 <= x <= w * t_w2 for x in w_translate)
# Mix of range and single value
t_h, t_w1, t_w2 = 0.1, -0.8, -0.6
affine = RandomAffine(translate=(t_h, (t_w1, t_w2)), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert any(x < 0 for x in h_translate) and any(x > 0 for x in h_translate)
assert all(x < 0 for x in w_translate)
assert all(abs(x) <= h * t_h for x in h_translate)
assert all(w * t_w1 <= x <= w * t_w2 for x in w_translate)
def test_multi_arg_param_shear(self):
h, w = 100, 100
img = torch.randn(1, 1, h, w)
s_h, s_w = 10, 80
affine = RandomAffine(shear=(s_h, s_w), p=1.0)
h_shear, w_shear = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_s, w_s = tuple(tfm.shear)
h_shear.append(h_s)
w_shear.append(w_s)
assert any(x < 0 for x in h_shear) and any(x > 0 for x in h_shear)
assert any(x < 0 for x in w_shear) and any(x > 0 for x in w_shear)
assert all(abs(x) <= s_h for x in h_shear)
assert all(abs(x) <= s_w for x in w_shear)
# Fixed range of values.
s_h1, s_h2, s_w1, s_w2 = 10, 20, -80, -60
affine = RandomAffine(shear=((s_h1, s_h2), (s_w1, s_w2)), p=1.0)
h_shear, w_shear = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_s, w_s = tuple(tfm.shear)
h_shear.append(h_s)
w_shear.append(w_s)
assert all(x > 0 for x in h_shear)
assert all(x < 0 for x in w_shear)
assert all(s_h1 <= x <= s_h2 for x in h_shear)
assert all(s_w1 <= x <= s_w2 for x in w_shear)
# Mix of range and single value
s_h, s_w1, s_w2 = 10, -80, -60
affine = RandomAffine(shear=(s_h, (s_w1, s_w2)), p=1.0)
h_shear, w_shear = [], []
for _ in range(100):
tfm: AffineTransform = affine.get_transform(img)
h_s, w_s = tuple(tfm.shear)
h_shear.append(h_s)
w_shear.append(w_s)
assert any(x < 0 for x in h_shear) and any(x > 0 for x in h_shear)
assert all(x < 0 for x in w_shear)
assert all(abs(x) <= s_h for x in h_shear)
assert all(s_w1 <= x <= s_w2 for x in w_shear)
class TestRandomTranslation(unittest.TestCase):
def test_scheduler_basic(self):
iter_tracker = MockIterTracker()
affine = RandomTranslation(translate=(0.1, 0.8))
scheduler = WarmupTF(affine, warmup_iters=400, params=["translate"])
scheduler.get_iteration = iter_tracker.get_iter
affine.register_schedulers([scheduler])
iter_tracker.step(100)
params = affine._get_param_values(use_schedulers=True)
assert tuple(params["translate"]) == (0.025, 0.2)
def test_multi_arg_param_translate(self):
h, w = 100, 100
img = torch.randn(1, 1, h, w)
t_h, t_w = 0.1, 0.8
affine = RandomTranslation(translate=(t_h, t_w), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: TranslationTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert any(x < 0 for x in h_translate) and any(x > 0 for x in h_translate)
assert any(x < 0 for x in w_translate) and any(x > 0 for x in w_translate)
assert all(abs(x) <= h * t_h for x in h_translate)
assert all(abs(x) <= w * t_w for x in w_translate)
# Fixed range of values.
t_h1, t_h2, t_w1, t_w2 = 0.1, 0.2, -0.8, -0.6
affine = RandomTranslation(translate=((t_h1, t_h2), (t_w1, t_w2)), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: TranslationTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert all(x > 0 for x in h_translate)
assert all(x < 0 for x in w_translate)
assert all(h * t_h1 <= x <= h * t_h2 for x in h_translate)
assert all(w * t_w1 <= x <= w * t_w2 for x in w_translate)
# Mix of range and single value
t_h, t_w1, t_w2 = 0.1, -0.8, -0.6
affine = RandomTranslation(translate=(t_h, (t_w1, t_w2)), p=1.0)
h_translate, w_translate = [], []
for _ in range(100):
tfm: TranslationTransform = affine.get_transform(img)
h_t, w_t = tuple(tfm.translate)
h_translate.append(h_t)
w_translate.append(w_t)
assert any(x < 0 for x in h_translate) and any(x > 0 for x in h_translate)
assert all(x < 0 for x in w_translate)
assert all(abs(x) <= h * t_h for x in h_translate)
assert all(w * t_w1 <= x <= w * t_w2 for x in w_translate)
| 38.074236
| 96
| 0.593761
| 1,381
| 8,719
| 3.546705
| 0.072411
| 0.03675
| 0.055125
| 0.033075
| 0.84953
| 0.841364
| 0.78236
| 0.773581
| 0.747652
| 0.747652
| 0
| 0.04685
| 0.290056
| 8,719
| 228
| 97
| 38.241228
| 0.744426
| 0.021791
| 0
| 0.704545
| 0
| 0
| 0.014318
| 0
| 0
| 0
| 0
| 0
| 0.278409
| 1
| 0.034091
| false
| 0
| 0.045455
| 0
| 0.090909
| 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
|
577607611e5cb624d74046d7f5f58432e1d2778d
| 163
|
py
|
Python
|
App/wizard/api/__init__.py
|
Wizard-collab/wizard
|
c2ec623fe011626716493c232b895fb0513f68ff
|
[
"MIT"
] | null | null | null |
App/wizard/api/__init__.py
|
Wizard-collab/wizard
|
c2ec623fe011626716493c232b895fb0513f68ff
|
[
"MIT"
] | null | null | null |
App/wizard/api/__init__.py
|
Wizard-collab/wizard
|
c2ec623fe011626716493c232b895fb0513f68ff
|
[
"MIT"
] | null | null | null |
from wizard.api import scene
from wizard.api import preferences
from wizard.api import assets
from gui import build
_wizard_stylesheet_ = build.load_stylesheet()
| 23.285714
| 45
| 0.834356
| 24
| 163
| 5.5
| 0.458333
| 0.227273
| 0.295455
| 0.431818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.122699
| 163
| 7
| 45
| 23.285714
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.8
| 0
| 0.8
| 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
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
578127fbbe6e2b83419cc0ebf06f3c9535063b71
| 65,514
|
py
|
Python
|
dingtalk/python/alibabacloud_dingtalk/crm_1_0/client.py
|
yndu13/dingtalk-sdk
|
700fb7bb49c4d3167f84afc5fcb5e7aa5a09735f
|
[
"Apache-2.0"
] | null | null | null |
dingtalk/python/alibabacloud_dingtalk/crm_1_0/client.py
|
yndu13/dingtalk-sdk
|
700fb7bb49c4d3167f84afc5fcb5e7aa5a09735f
|
[
"Apache-2.0"
] | null | null | null |
dingtalk/python/alibabacloud_dingtalk/crm_1_0/client.py
|
yndu13/dingtalk-sdk
|
700fb7bb49c4d3167f84afc5fcb5e7aa5a09735f
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
# This file is auto-generated, don't edit it. Thanks.
from Tea.core import TeaCore
from alibabacloud_tea_openapi.client import Client as OpenApiClient
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_tea_util.client import Client as UtilClient
from alibabacloud_dingtalk.crm_1_0 import models as dingtalkcrm__1__0_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_openapi_util.client import Client as OpenApiUtilClient
class Client(OpenApiClient):
"""
*\
"""
def __init__(
self,
config: open_api_models.Config,
):
super().__init__(config)
self._endpoint_rule = ''
if UtilClient.empty(self._endpoint):
self._endpoint = 'api.dingtalk.com'
def get_official_account_contacts(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountContactsRequest,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountContactsHeaders()
return self.get_official_account_contacts_with_options(request, headers, runtime)
async def get_official_account_contacts_async(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountContactsRequest,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountContactsHeaders()
return await self.get_official_account_contacts_with_options_async(request, headers, runtime)
def get_official_account_contacts_with_options(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountContactsRequest,
headers: dingtalkcrm__1__0_models.GetOfficialAccountContactsHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.next_token):
query['nextToken'] = request.next_token
if not UtilClient.is_unset(request.max_results):
query['maxResults'] = request.max_results
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse(),
self.do_roarequest('GetOfficialAccountContacts', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/contacts', 'json', req, runtime)
)
async def get_official_account_contacts_with_options_async(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountContactsRequest,
headers: dingtalkcrm__1__0_models.GetOfficialAccountContactsHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.next_token):
query['nextToken'] = request.next_token
if not UtilClient.is_unset(request.max_results):
query['maxResults'] = request.max_results
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountContactsResponse(),
await self.do_roarequest_async('GetOfficialAccountContacts', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/contacts', 'json', req, runtime)
)
def service_window_message_batch_push(
self,
request: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushRequest,
) -> dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushHeaders()
return self.service_window_message_batch_push_with_options(request, headers, runtime)
async def service_window_message_batch_push_async(
self,
request: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushRequest,
) -> dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushHeaders()
return await self.service_window_message_batch_push_with_options_async(request, headers, runtime)
def service_window_message_batch_push_with_options(
self,
request: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushRequest,
headers: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse(),
self.do_roarequest('ServiceWindowMessageBatchPush', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/messages/batchSend', 'json', req, runtime)
)
async def service_window_message_batch_push_with_options_async(
self,
request: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushRequest,
headers: dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.ServiceWindowMessageBatchPushResponse(),
await self.do_roarequest_async('ServiceWindowMessageBatchPush', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/messages/batchSend', 'json', req, runtime)
)
def delete_crm_form_instance(
self,
instance_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmFormInstanceRequest,
) -> dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DeleteCrmFormInstanceHeaders()
return self.delete_crm_form_instance_with_options(instance_id, request, headers, runtime)
async def delete_crm_form_instance_async(
self,
instance_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmFormInstanceRequest,
) -> dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DeleteCrmFormInstanceHeaders()
return await self.delete_crm_form_instance_with_options_async(instance_id, request, headers, runtime)
def delete_crm_form_instance_with_options(
self,
instance_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmFormInstanceRequest,
headers: dingtalkcrm__1__0_models.DeleteCrmFormInstanceHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
if not UtilClient.is_unset(request.name):
query['name'] = request.name
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse(),
self.do_roarequest('DeleteCrmFormInstance', 'crm_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/crm/formInstances/{instance_id}', 'json', req, runtime)
)
async def delete_crm_form_instance_with_options_async(
self,
instance_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmFormInstanceRequest,
headers: dingtalkcrm__1__0_models.DeleteCrmFormInstanceHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
if not UtilClient.is_unset(request.name):
query['name'] = request.name
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DeleteCrmFormInstanceResponse(),
await self.do_roarequest_async('DeleteCrmFormInstance', 'crm_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/crm/formInstances/{instance_id}', 'json', req, runtime)
)
def batch_send_official_account_otomessage(
self,
request: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageHeaders()
return self.batch_send_official_account_otomessage_with_options(request, headers, runtime)
async def batch_send_official_account_otomessage_async(
self,
request: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageHeaders()
return await self.batch_send_official_account_otomessage_with_options_async(request, headers, runtime)
def batch_send_official_account_otomessage_with_options(
self,
request: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse(),
self.do_roarequest('BatchSendOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/batchSend', 'json', req, runtime)
)
async def batch_send_official_account_otomessage_with_options_async(
self,
request: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.BatchSendOfficialAccountOTOMessageResponse(),
await self.do_roarequest_async('BatchSendOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/batchSend', 'json', req, runtime)
)
def get_official_account_contact_info(
self,
user_id: str,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountContactInfoHeaders()
return self.get_official_account_contact_info_with_options(user_id, headers, runtime)
async def get_official_account_contact_info_async(
self,
user_id: str,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountContactInfoHeaders()
return await self.get_official_account_contact_info_with_options_async(user_id, headers, runtime)
def get_official_account_contact_info_with_options(
self,
user_id: str,
headers: dingtalkcrm__1__0_models.GetOfficialAccountContactInfoHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse:
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse(),
self.do_roarequest('GetOfficialAccountContactInfo', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/contacts/{user_id}', 'json', req, runtime)
)
async def get_official_account_contact_info_with_options_async(
self,
user_id: str,
headers: dingtalkcrm__1__0_models.GetOfficialAccountContactInfoHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse:
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountContactInfoResponse(),
await self.do_roarequest_async('GetOfficialAccountContactInfo', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/contacts/{user_id}', 'json', req, runtime)
)
def query_all_customer(
self,
request: dingtalkcrm__1__0_models.QueryAllCustomerRequest,
) -> dingtalkcrm__1__0_models.QueryAllCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.QueryAllCustomerHeaders()
return self.query_all_customer_with_options(request, headers, runtime)
async def query_all_customer_async(
self,
request: dingtalkcrm__1__0_models.QueryAllCustomerRequest,
) -> dingtalkcrm__1__0_models.QueryAllCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.QueryAllCustomerHeaders()
return await self.query_all_customer_with_options_async(request, headers, runtime)
def query_all_customer_with_options(
self,
request: dingtalkcrm__1__0_models.QueryAllCustomerRequest,
headers: dingtalkcrm__1__0_models.QueryAllCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.QueryAllCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_corp_id):
body['dingCorpId'] = request.ding_corp_id
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.operator_user_id):
body['operatorUserId'] = request.operator_user_id
if not UtilClient.is_unset(request.max_results):
body['maxResults'] = request.max_results
if not UtilClient.is_unset(request.next_token):
body['nextToken'] = request.next_token
if not UtilClient.is_unset(request.object_type):
body['objectType'] = request.object_type
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.QueryAllCustomerResponse(),
self.do_roarequest('QueryAllCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/customerInstances', 'json', req, runtime)
)
async def query_all_customer_with_options_async(
self,
request: dingtalkcrm__1__0_models.QueryAllCustomerRequest,
headers: dingtalkcrm__1__0_models.QueryAllCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.QueryAllCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_corp_id):
body['dingCorpId'] = request.ding_corp_id
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.operator_user_id):
body['operatorUserId'] = request.operator_user_id
if not UtilClient.is_unset(request.max_results):
body['maxResults'] = request.max_results
if not UtilClient.is_unset(request.next_token):
body['nextToken'] = request.next_token
if not UtilClient.is_unset(request.object_type):
body['objectType'] = request.object_type
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.QueryAllCustomerResponse(),
await self.do_roarequest_async('QueryAllCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/customerInstances', 'json', req, runtime)
)
def send_official_account_otomessage(
self,
request: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageHeaders()
return self.send_official_account_otomessage_with_options(request, headers, runtime)
async def send_official_account_otomessage_async(
self,
request: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageHeaders()
return await self.send_official_account_otomessage_with_options_async(request, headers, runtime)
def send_official_account_otomessage_with_options(
self,
request: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse(),
self.do_roarequest('SendOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/send', 'json', req, runtime)
)
async def send_official_account_otomessage_with_options_async(
self,
request: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.detail):
body['detail'] = request.detail
if not UtilClient.is_unset(request.biz_id):
body['bizId'] = request.biz_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.SendOfficialAccountOTOMessageResponse(),
await self.do_roarequest_async('SendOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/send', 'json', req, runtime)
)
def get_official_account_otomessage_result(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultRequest,
) -> dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultHeaders()
return self.get_official_account_otomessage_result_with_options(request, headers, runtime)
async def get_official_account_otomessage_result_async(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultRequest,
) -> dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultHeaders()
return await self.get_official_account_otomessage_result_with_options_async(request, headers, runtime)
def get_official_account_otomessage_result_with_options(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultRequest,
headers: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.open_push_id):
query['openPushId'] = request.open_push_id
if not UtilClient.is_unset(request.account_id):
query['accountId'] = request.account_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse(),
self.do_roarequest('GetOfficialAccountOTOMessageResult', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/sendResults', 'json', req, runtime)
)
async def get_official_account_otomessage_result_with_options_async(
self,
request: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultRequest,
headers: dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.open_push_id):
query['openPushId'] = request.open_push_id
if not UtilClient.is_unset(request.account_id):
query['accountId'] = request.account_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.GetOfficialAccountOTOMessageResultResponse(),
await self.do_roarequest_async('GetOfficialAccountOTOMessageResult', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/sendResults', 'json', req, runtime)
)
def add_crm_personal_customer(
self,
request: dingtalkcrm__1__0_models.AddCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.AddCrmPersonalCustomerHeaders()
return self.add_crm_personal_customer_with_options(request, headers, runtime)
async def add_crm_personal_customer_async(
self,
request: dingtalkcrm__1__0_models.AddCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.AddCrmPersonalCustomerHeaders()
return await self.add_crm_personal_customer_with_options_async(request, headers, runtime)
def add_crm_personal_customer_with_options(
self,
request: dingtalkcrm__1__0_models.AddCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.AddCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.creator_user_id):
body['creatorUserId'] = request.creator_user_id
if not UtilClient.is_unset(request.creator_nick):
body['creatorNick'] = request.creator_nick
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.skip_duplicate_check):
body['skipDuplicateCheck'] = request.skip_duplicate_check
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse(),
self.do_roarequest('AddCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
async def add_crm_personal_customer_with_options_async(
self,
request: dingtalkcrm__1__0_models.AddCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.AddCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.creator_user_id):
body['creatorUserId'] = request.creator_user_id
if not UtilClient.is_unset(request.creator_nick):
body['creatorNick'] = request.creator_nick
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.skip_duplicate_check):
body['skipDuplicateCheck'] = request.skip_duplicate_check
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.AddCrmPersonalCustomerResponse(),
await self.do_roarequest_async('AddCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
def recall_official_account_otomessage(
self,
request: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageHeaders()
return self.recall_official_account_otomessage_with_options(request, headers, runtime)
async def recall_official_account_otomessage_async(
self,
request: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageRequest,
) -> dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageHeaders()
return await self.recall_official_account_otomessage_with_options_async(request, headers, runtime)
def recall_official_account_otomessage_with_options(
self,
request: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
if not UtilClient.is_unset(request.open_push_id):
body['openPushId'] = request.open_push_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse(),
self.do_roarequest('RecallOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/recall', 'json', req, runtime)
)
async def recall_official_account_otomessage_with_options_async(
self,
request: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageRequest,
headers: dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.ding_suite_key):
body['dingSuiteKey'] = request.ding_suite_key
if not UtilClient.is_unset(request.ding_org_id):
body['dingOrgId'] = request.ding_org_id
if not UtilClient.is_unset(request.ding_isv_org_id):
body['dingIsvOrgId'] = request.ding_isv_org_id
if not UtilClient.is_unset(request.ding_token_grant_type):
body['dingTokenGrantType'] = request.ding_token_grant_type
if not UtilClient.is_unset(request.account_id):
body['accountId'] = request.account_id
if not UtilClient.is_unset(request.open_push_id):
body['openPushId'] = request.open_push_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.RecallOfficialAccountOTOMessageResponse(),
await self.do_roarequest_async('RecallOfficialAccountOTOMessage', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/officialAccounts/oToMessages/recall', 'json', req, runtime)
)
def describe_crm_personal_customer_object_meta(self) -> dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaHeaders()
return self.describe_crm_personal_customer_object_meta_with_options(headers, runtime)
async def describe_crm_personal_customer_object_meta_async(self) -> dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaHeaders()
return await self.describe_crm_personal_customer_object_meta_with_options_async(headers, runtime)
def describe_crm_personal_customer_object_meta_with_options(
self,
headers: dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse:
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse(),
self.do_roarequest('DescribeCrmPersonalCustomerObjectMeta', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/personalCustomers/objectMeta', 'json', req, runtime)
)
async def describe_crm_personal_customer_object_meta_with_options_async(
self,
headers: dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse:
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DescribeCrmPersonalCustomerObjectMetaResponse(),
await self.do_roarequest_async('DescribeCrmPersonalCustomerObjectMeta', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/personalCustomers/objectMeta', 'json', req, runtime)
)
def delete_crm_personal_customer(
self,
data_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerHeaders()
return self.delete_crm_personal_customer_with_options(data_id, request, headers, runtime)
async def delete_crm_personal_customer_async(
self,
data_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerHeaders()
return await self.delete_crm_personal_customer_with_options_async(data_id, request, headers, runtime)
def delete_crm_personal_customer_with_options(
self,
data_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse(),
self.do_roarequest('DeleteCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/crm/personalCustomers/{data_id}', 'json', req, runtime)
)
async def delete_crm_personal_customer_with_options_async(
self,
data_id: str,
request: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.DeleteCrmPersonalCustomerResponse(),
await self.do_roarequest_async('DeleteCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'DELETE', 'AK', f'/v1.0/crm/personalCustomers/{data_id}', 'json', req, runtime)
)
def update_crm_personal_customer(
self,
request: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerHeaders()
return self.update_crm_personal_customer_with_options(request, headers, runtime)
async def update_crm_personal_customer_async(
self,
request: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerHeaders()
return await self.update_crm_personal_customer_with_options_async(request, headers, runtime)
def update_crm_personal_customer_with_options(
self,
request: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.instance_id):
body['instanceId'] = request.instance_id
if not UtilClient.is_unset(request.modifier_user_id):
body['modifierUserId'] = request.modifier_user_id
if not UtilClient.is_unset(request.modifier_nick):
body['modifierNick'] = request.modifier_nick
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.skip_duplicate_check):
body['skipDuplicateCheck'] = request.skip_duplicate_check
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse(),
self.do_roarequest('UpdateCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
async def update_crm_personal_customer_with_options_async(
self,
request: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.instance_id):
body['instanceId'] = request.instance_id
if not UtilClient.is_unset(request.modifier_user_id):
body['modifierUserId'] = request.modifier_user_id
if not UtilClient.is_unset(request.modifier_nick):
body['modifierNick'] = request.modifier_nick
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.skip_duplicate_check):
body['skipDuplicateCheck'] = request.skip_duplicate_check
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.UpdateCrmPersonalCustomerResponse(),
await self.do_roarequest_async('UpdateCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'PUT', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
def query_crm_personal_customer(
self,
request: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.QueryCrmPersonalCustomerHeaders()
return self.query_crm_personal_customer_with_options(request, headers, runtime)
async def query_crm_personal_customer_async(
self,
request: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerRequest,
) -> dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.QueryCrmPersonalCustomerHeaders()
return await self.query_crm_personal_customer_with_options_async(request, headers, runtime)
def query_crm_personal_customer_with_options(
self,
request: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
if not UtilClient.is_unset(request.next_token):
query['nextToken'] = request.next_token
if not UtilClient.is_unset(request.max_results):
query['maxResults'] = request.max_results
if not UtilClient.is_unset(request.query_dsl):
query['queryDsl'] = request.query_dsl
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse(),
self.do_roarequest('QueryCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
async def query_crm_personal_customer_with_options_async(
self,
request: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerRequest,
headers: dingtalkcrm__1__0_models.QueryCrmPersonalCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
if not UtilClient.is_unset(request.next_token):
query['nextToken'] = request.next_token
if not UtilClient.is_unset(request.max_results):
query['maxResults'] = request.max_results
if not UtilClient.is_unset(request.query_dsl):
query['queryDsl'] = request.query_dsl
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.QueryCrmPersonalCustomerResponse(),
await self.do_roarequest_async('QueryCrmPersonalCustomer', 'crm_1.0', 'HTTP', 'GET', 'AK', f'/v1.0/crm/personalCustomers', 'json', req, runtime)
)
def list_crm_personal_customers(
self,
request: dingtalkcrm__1__0_models.ListCrmPersonalCustomersRequest,
) -> dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.ListCrmPersonalCustomersHeaders()
return self.list_crm_personal_customers_with_options(request, headers, runtime)
async def list_crm_personal_customers_async(
self,
request: dingtalkcrm__1__0_models.ListCrmPersonalCustomersRequest,
) -> dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.ListCrmPersonalCustomersHeaders()
return await self.list_crm_personal_customers_with_options_async(request, headers, runtime)
def list_crm_personal_customers_with_options(
self,
request: dingtalkcrm__1__0_models.ListCrmPersonalCustomersRequest,
headers: dingtalkcrm__1__0_models.ListCrmPersonalCustomersHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query),
body=request.body
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse(),
self.do_roarequest('ListCrmPersonalCustomers', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/personalCustomers/batchQuery', 'json', req, runtime)
)
async def list_crm_personal_customers_with_options_async(
self,
request: dingtalkcrm__1__0_models.ListCrmPersonalCustomersRequest,
headers: dingtalkcrm__1__0_models.ListCrmPersonalCustomersHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse:
UtilClient.validate_model(request)
query = {}
if not UtilClient.is_unset(request.current_operator_user_id):
query['currentOperatorUserId'] = request.current_operator_user_id
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
query=OpenApiUtilClient.query(query),
body=request.body
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.ListCrmPersonalCustomersResponse(),
await self.do_roarequest_async('ListCrmPersonalCustomers', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/personalCustomers/batchQuery', 'json', req, runtime)
)
def create_customer(
self,
request: dingtalkcrm__1__0_models.CreateCustomerRequest,
) -> dingtalkcrm__1__0_models.CreateCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.CreateCustomerHeaders()
return self.create_customer_with_options(request, headers, runtime)
async def create_customer_async(
self,
request: dingtalkcrm__1__0_models.CreateCustomerRequest,
) -> dingtalkcrm__1__0_models.CreateCustomerResponse:
runtime = util_models.RuntimeOptions()
headers = dingtalkcrm__1__0_models.CreateCustomerHeaders()
return await self.create_customer_with_options_async(request, headers, runtime)
def create_customer_with_options(
self,
request: dingtalkcrm__1__0_models.CreateCustomerRequest,
headers: dingtalkcrm__1__0_models.CreateCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.CreateCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.object_type):
body['objectType'] = request.object_type
if not UtilClient.is_unset(request.instance_id):
body['instanceId'] = request.instance_id
if not UtilClient.is_unset(request.creator_user_id):
body['creatorUserId'] = request.creator_user_id
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.contacts):
body['contacts'] = request.contacts
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.save_option):
body['saveOption'] = request.save_option
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.CreateCustomerResponse(),
self.do_roarequest('CreateCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/customers', 'json', req, runtime)
)
async def create_customer_with_options_async(
self,
request: dingtalkcrm__1__0_models.CreateCustomerRequest,
headers: dingtalkcrm__1__0_models.CreateCustomerHeaders,
runtime: util_models.RuntimeOptions,
) -> dingtalkcrm__1__0_models.CreateCustomerResponse:
UtilClient.validate_model(request)
body = {}
if not UtilClient.is_unset(request.object_type):
body['objectType'] = request.object_type
if not UtilClient.is_unset(request.instance_id):
body['instanceId'] = request.instance_id
if not UtilClient.is_unset(request.creator_user_id):
body['creatorUserId'] = request.creator_user_id
if not UtilClient.is_unset(request.data):
body['data'] = request.data
if not UtilClient.is_unset(request.extend_data):
body['extendData'] = request.extend_data
if not UtilClient.is_unset(request.contacts):
body['contacts'] = request.contacts
if not UtilClient.is_unset(request.permission):
body['permission'] = request.permission
if not UtilClient.is_unset(request.save_option):
body['saveOption'] = request.save_option
real_headers = {}
if not UtilClient.is_unset(headers.common_headers):
real_headers = headers.common_headers
if not UtilClient.is_unset(headers.x_acs_dingtalk_access_token):
real_headers['x-acs-dingtalk-access-token'] = headers.x_acs_dingtalk_access_token
req = open_api_models.OpenApiRequest(
headers=real_headers,
body=OpenApiUtilClient.parse_to_map(body)
)
return TeaCore.from_map(
dingtalkcrm__1__0_models.CreateCustomerResponse(),
await self.do_roarequest_async('CreateCustomer', 'crm_1.0', 'HTTP', 'POST', 'AK', f'/v1.0/crm/customers', 'json', req, runtime)
)
| 52.537289
| 189
| 0.715694
| 6,998
| 65,514
| 6.27365
| 0.033438
| 0.011389
| 0.064255
| 0.093912
| 0.981641
| 0.966995
| 0.963055
| 0.940733
| 0.917887
| 0.904836
| 0
| 0.010825
| 0.203346
| 65,514
| 1,246
| 190
| 52.579454
| 0.830357
| 0.001221
| 0
| 0.82368
| 1
| 0
| 0.076888
| 0.044574
| 0
| 0
| 0
| 0
| 0
| 1
| 0.028109
| false
| 0
| 0.005963
| 0
| 0.089438
| 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
|
57b647ac4ecbe8e85e2bb36cd05290067d7cc26b
| 143
|
py
|
Python
|
app/main/views/misc.py
|
Jaydonjin/seer
|
adeb14601b07de93603176694115c9673f010989
|
[
"MIT"
] | null | null | null |
app/main/views/misc.py
|
Jaydonjin/seer
|
adeb14601b07de93603176694115c9673f010989
|
[
"MIT"
] | null | null | null |
app/main/views/misc.py
|
Jaydonjin/seer
|
adeb14601b07de93603176694115c9673f010989
|
[
"MIT"
] | null | null | null |
from flask import render_template
from app.main import main
@main.route("/faq.html")
def faq():
return render_template('main/faq.html')
| 15.888889
| 43
| 0.734266
| 22
| 143
| 4.681818
| 0.545455
| 0.271845
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.13986
| 143
| 8
| 44
| 17.875
| 0.837398
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.4
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 7
|
57d56faf4049d133a9ba1add9a03954bab505742
| 121
|
py
|
Python
|
malaya_speech/train/model/unet/__init__.py
|
ishine/malaya-speech
|
fd34afc7107af1656dff4b3201fa51dda54fde18
|
[
"MIT"
] | 111
|
2020-08-31T04:58:54.000Z
|
2022-03-29T15:44:18.000Z
|
malaya_speech/train/model/unet/__init__.py
|
ishine/malaya-speech
|
fd34afc7107af1656dff4b3201fa51dda54fde18
|
[
"MIT"
] | 14
|
2020-12-16T07:27:22.000Z
|
2022-03-15T17:39:01.000Z
|
malaya_speech/train/model/unet/__init__.py
|
ishine/malaya-speech
|
fd34afc7107af1656dff4b3201fa51dda54fde18
|
[
"MIT"
] | 29
|
2021-02-09T08:57:15.000Z
|
2022-03-12T14:09:19.000Z
|
from . import model
from .model import Model
from .model1d import Model as Model1D
from .model3d import Model as Model3D
| 24.2
| 37
| 0.801653
| 19
| 121
| 5.105263
| 0.315789
| 0.453608
| 0.309278
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.039604
| 0.165289
| 121
| 4
| 38
| 30.25
| 0.920792
| 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
|
17d9599860da14a1591ee953c492102676b12fde
| 56,712
|
py
|
Python
|
fixtures/main.py
|
angelolamonaca/mini_dicom_viewer
|
842b0585be879b8416a3d2d8ce8588ff57d175ae
|
[
"Apache-2.0"
] | null | null | null |
fixtures/main.py
|
angelolamonaca/mini_dicom_viewer
|
842b0585be879b8416a3d2d8ce8588ff57d175ae
|
[
"Apache-2.0"
] | null | null | null |
fixtures/main.py
|
angelolamonaca/mini_dicom_viewer
|
842b0585be879b8416a3d2d8ce8588ff57d175ae
|
[
"Apache-2.0"
] | null | null | null |
from graphqlclient import GraphQLClient
import pymysql
from random import randrange
client = GraphQLClient('http://172.17.0.1:3301/api')
def create_patient(name):
result = client.execute('''
mutation CreatePatient {
createPatient(name: "''' + name + '''") {
name
}
}
''')
print(result)
def create_study(name, id_patient):
result = client.execute('''
mutation CreateStudy {
createStudy(studyName: "''' + name + '''", idPatient: ''' + str(id_patient) + ''') {
studyName
idPatient
}
}
''')
print(result)
def create_modality(name):
result = client.execute('''
mutation CreateModality {
createModality(name: "''' + name + '''") {
name
}
}
''')
print(result)
def create_series(name, id_patient, id_study, id_modality):
result = client.execute('''
mutation CreateSeries {
createSeries(seriesName: "''' + name + '''", idPatient: ''' + str(id_patient) + ''', idStudy: ''' + str(
id_study) + ''', idModality: ''' + str(id_modality) + ''') {
seriesName
idPatient
idStudy
idModality
}
}
''')
print(result)
def create_file(filePath, id_patient, id_study, id_series):
result = client.execute('''
mutation CreateFile {
createFile(filePath: "''' + filePath + '''", idPatient: ''' + str(id_patient) + ''', idStudy: ''' + str(
id_study) + ''', idSeries: ''' + str(id_series) + ''') {
filePath
idPatient
idStudy
idSeries
}
}
''')
print(result)
create_modality("AR")
create_modality("ASMT")
create_modality("AU")
create_modality("BDUS")
create_modality("BI")
create_modality("BMD")
create_modality("CR")
create_modality("CT")
create_modality("DG")
create_modality("DOC")
create_modality("DX")
create_modality("ECG")
create_modality("EPS")
create_modality("ES")
create_modality("FID")
create_modality("GM")
create_modality("PLAN")
create_modality("PR")
create_modality("PT")
create_patient("HIPPOCRATES") # 1
create_study("CT_ABDOMEN_W_V_CONTRAST", 1) # 1
create_series("SER_HORIZONTAL_AX", 1, 1, 1) # 1
create_file("./documents/dicom/images/fake.dcm", 1, 1, 1)
create_file("./documents/dicom/images/fake.dcm", 1, 1, 1)
create_file("./documents/dicom/images/fake.dcm", 1, 1, 1)
create_file("./documents/dicom/images/fake.dcm", 1, 1, 1)
create_file("./documents/dicom/images/fake.dcm", 1, 1, 1)
create_study("TRANSREP_NOSE", 1) # 2
create_series("SER_HORIZONTAL_RF", 1, 2, 2) # 2
create_file("./documents/dicom/images/fake.dcm", 1, 2, 2)
create_file("./documents/dicom/images/fake.dcm", 1, 2, 2)
create_file("./documents/dicom/images/fake.dcm", 1, 2, 2)
create_file("./documents/dicom/images/fake.dcm", 1, 2, 2)
create_file("./documents/dicom/images/fake.dcm", 1, 2, 2)
create_study("TRANSREP_MOUTH", 1) # 3
create_series("SER_HORIZONTAL_RF", 1, 3, 2) # 3
create_file("./documents/dicom/images/fake.dcm", 1, 3, 3)
create_file("./documents/dicom/images/fake.dcm", 1, 3, 3)
create_file("./documents/dicom/images/fake.dcm", 1, 3, 3)
create_file("./documents/dicom/images/fake.dcm", 1, 3, 3)
create_patient("PERGAMON_GALEN") # 2
create_study("CT_ABDOMEN_W_WO_CONTRAST", 2) # 4
create_series("SER_W_CONTRAST_HORIZONTAL", 2, 4, 3) # 4
create_file("./documents/dicom/images/fake.dcm", 2, 4, 4)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 4)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 4)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 4)
create_series("SER_W_CONTRAST_VERTICAL", 2, 4, 3) # 5
create_file("./documents/dicom/images/fake.dcm", 2, 4, 5)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 5)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 5)
create_series("SER_HORIZONTAL_RF", 2, 4, 3) # 6
create_file("./documents/dicom/images/fake.dcm", 2, 4, 6)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 6)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 6)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 6)
create_file("./documents/dicom/images/fake.dcm", 2, 4, 6)
create_patient("IBN_SINA") # 3
create_study("CT_ABDOMEN_W_IV", 3) # 5
create_series("SER_RF_HORIZONTAL", 3, 5, 4) # 7
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 7)
create_series("SER_RF_W_HORIZONTAL", 3, 5, 5) # 8
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_file("./documents/dicom/images/fake.dcm", 3, 5, 8)
create_patient("ANDREA_VESALIO") # 4
create_study("CT_LEG", 4) # 6
create_series("SER_RF_CONTRAST_HORIZONTAL", 4, 6, 6) # 9
create_file("./documents/dicom/images/fake.dcm", 4, 6, 9)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 9)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 9)
create_series("SER_DP_W_HORIZONTAL", 4, 6, 7) # 10
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 10)
create_series("SER_W_CONTRAST_HORIZONTAL", 4, 6, 8) # 11
create_file("./documents/dicom/images/fake.dcm", 4, 6, 11)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 11)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 11)
create_file("./documents/dicom/images/fake.dcm", 4, 6, 11)
create_study("CT_ARM", 4) # 7
create_series("SER_RF_CONTRAST_HORIZONTAL", 4, 7, 6) # 12
create_file("./documents/dicom/images/fake.dcm", 4, 7, 12)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 12)
create_series("SER_DP_W_HORIZONTAL", 4, 7, 7) # 13
create_file("./documents/dicom/images/fake.dcm", 4, 7, 13)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 13)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 13)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 13)
create_series("SER_W_CONTRAST_HORIZONTAL", 4, 7, 8) # 14
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_file("./documents/dicom/images/fake.dcm", 4, 7, 14)
create_patient("RENÉ_LAËNNEC") # 5
create_study("CT_SHOULDER_DX", 5) # 8
create_series("SER_RF_CONTRAST_HORIZONTAL", 5, 8, 6) # 15
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 15)
create_series("SER_DP_W_HORIZONTAL", 5, 8, 7) # 16
create_file("./documents/dicom/images/fake.dcm", 5, 8, 16)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 16)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 16)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 16)
create_series("SER_W_CONTRAST_HORIZONTAL", 5, 8, 8) # 17
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_file("./documents/dicom/images/fake.dcm", 5, 8, 17)
create_patient("EDWARD_JENNER") # 6
create_study("CT_SHOULDER_DX", 6) # 9
create_series("SER_W_W_HORIZONTAL", 6, 9, 9) # 18
create_file("./documents/dicom/images/fake.dcm", 6, 9, 18)
create_file("./documents/dicom/images/fake.dcm", 6, 9, 18)
create_file("./documents/dicom/images/fake.dcm", 6, 9, 18)
create_series("SER_W_HORIZONTAL", 6, 9, 10) # 19
create_file("./documents/dicom/images/fake.dcm", 6, 9, 19)
create_file("./documents/dicom/images/fake.dcm", 6, 9, 19)
create_series("SER_F_W_HORIZONTAL", 6, 9, 11) # 20
create_file("./documents/dicom/images/fake.dcm", 6, 9, 20)
create_file("./documents/dicom/images/fake.dcm", 6, 9, 20)
create_file("./documents/dicom/images/fake.dcm", 6, 9, 20)
create_study("CT_SHOULDER_SX", 6) # 10
create_series("SER_W_W_HORIZONTAL", 6, 10, 9) # 21
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 21)
create_series("SER_W_HORIZONTAL", 6, 10, 10) # 22
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 22)
create_series("SER_F_W_HORIZONTAL", 6, 10, 11) # 23
create_file("./documents/dicom/images/fake.dcm", 6, 10, 23)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 23)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 23)
create_file("./documents/dicom/images/fake.dcm", 6, 10, 23)
create_patient("IGNAZ_SEMMELWEIS") # 7
create_study("CT_SHOULDER_DX", 7) # 11
create_series("FRS_W_CONTRAST_HORIZONTAL", 7, 11, 13) # 24
create_file("./documents/dicom/images/fake.dcm", 7, 11, 24)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 24)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 24)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 24)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 24)
create_series("VFB_W_HORIZONTAL", 7, 11, 14) # 25
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_file("./documents/dicom/images/fake.dcm", 7, 11, 25)
create_study("CT_SHOULDER_SX", 7) # 12
create_series("FRS_W_CONTRAST_HORIZONTAL", 7, 12, 13) # 26
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 26)
create_series("VFB_W_HORIZONTAL", 7, 12, 14) # 27
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_file("./documents/dicom/images/fake.dcm", 7, 12, 27)
create_study("TRANSREP_W_AX", 7) # 13
create_series("AWS_HORIZONTAL", 7, 13, 12) # 28
create_file("./documents/dicom/images/fake.dcm", 7, 13, 28)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 28)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 28)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 28)
create_series("FRS_W_CONTRAST_HORIZONTAL", 7, 13, 13) # 29
create_file("./documents/dicom/images/fake.dcm", 7, 13, 29)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 29)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 29)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 29)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 29)
create_series("VFB_W_HORIZONTAL", 7, 13, 14) # 30
create_file("./documents/dicom/images/fake.dcm", 7, 13, 30)
create_file("./documents/dicom/images/fake.dcm", 7, 13, 30)
create_patient("SIR_JOSEPH_LISTER") # 8
create_study("CT_ABDOMEN_W_V_CONTRAST", 8) # 14
create_series("RIGJ_W_HORIZONTAL", 8, 14, 16) # 31
create_file("./documents/dicom/images/fake.dcm", 8, 14, 31)
create_file("./documents/dicom/images/fake.dcm", 8, 14, 31)
create_file("./documents/dicom/images/fake.dcm", 8, 14, 31)
create_study("ABDOMEN_W", 8) # 15
create_series("RIGJ_W_HORIZONTAL", 8, 15, 16) # 32
create_file("./documents/dicom/images/fake.dcm", 8, 15, 32)
create_file("./documents/dicom/images/fake.dcm", 8, 15, 32)
create_file("./documents/dicom/images/fake.dcm", 8, 15, 32)
create_file("./documents/dicom/images/fake.dcm", 8, 15, 32)
create_file("./documents/dicom/images/fake.dcm", 8, 15, 32)
create_patient("JOHN_SNOW") # 9
create_study("CT_ABDOMEN_W_WO_CONTRAST", 9) # 16
create_series("VFB_HORIZONTAL", 9, 16, 15) # 33
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 33)
create_series("RIGJ_W_HORIZONTAL", 9, 16, 16) # 34
create_file("./documents/dicom/images/fake.dcm", 9, 16, 34)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 34)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 34)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 34)
create_series("RIGJ_HORIZONTAL", 9, 16, 17) # 35
create_file("./documents/dicom/images/fake.dcm", 9, 16, 35)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 35)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 35)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 35)
create_file("./documents/dicom/images/fake.dcm", 9, 16, 35)
create_study("TRANSREP_FX", 9) # 17
create_series("SERGG_CONTRAST_HORIZONTAL", 9, 17, 3) # 36
create_file("./documents/dicom/images/fake.dcm", 9, 17, 36)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 36)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 36)
create_series("SEWERR_HORIZONTAL", 9, 17, 4) # 37
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_file("./documents/dicom/images/fake.dcm", 9, 17, 37)
create_study("TRANSREP_GX", 9) # 18
create_series("SER", 9, 18, 1) # 38
create_file("./documents/dicom/images/fake.dcm", 9, 18, 38)
create_file("./documents/dicom/images/fake.dcm", 9, 18, 38)
create_file("./documents/dicom/images/fake.dcm", 9, 18, 38)
create_file("./documents/dicom/images/fake.dcm", 9, 18, 38)
create_series("SERGG_HORIZONTAL", 9, 18, 2) # 39
create_file("./documents/dicom/images/fake.dcm", 9, 18, 39)
create_file("./documents/dicom/images/fake.dcm", 9, 18, 39)
create_series("SEWERR_W_HORIZONTAL", 9, 18, 5) # 40
create_file("./documents/dicom/images/fake.dcm", 9, 18, 40)
create_file("./documents/dicom/images/fake.dcm", 9, 18, 40)
create_patient("SIGMUND_FREUD") # 10
create_study("CT_ABDOMEN_W_IV", 10) # 19
create_series("SEWERR_HORIZONTAL", 10, 19, 6) # 41
create_file("./documents/dicom/images/fake.dcm", 10, 19, 41)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 41)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 41)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 41)
create_series("SEWERR_W_HORIZONTAL", 10, 19, 7) # 42
create_file("./documents/dicom/images/fake.dcm", 10, 19, 42)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 42)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 42)
create_series("SER_GG_VERTICAL", 10, 19, 8) # 43
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_file("./documents/dicom/images/fake.dcm", 10, 19, 43)
create_study("CT_LEG", 10) # 20
create_series("SER_QWR_VERTICAL", 10, 20, 11) # 44
create_file("./documents/dicom/images/fake.dcm", 10, 20, 44)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 44)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 44)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 44)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 44)
create_series("SER_GBFGG_W_VERTICAL", 10, 20, 12) # 45
create_file("./documents/dicom/images/fake.dcm", 10, 20, 45)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 45)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 45)
create_file("./documents/dicom/images/fake.dcm", 10, 20, 45)
create_patient("SIR_WILLIAM_OSLER") # 11
create_study("CT_ARM", 11) # 21
create_series("SER_WEG_VERTICAL", 11, 21, 9) # 46
create_file("./documents/dicom/images/fake.dcm", 11, 21, 46)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 46)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 46)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 46)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 46)
create_series("SER_QWR_CONTRAST_VERTICAL", 11, 21, 10) # 47
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 47)
create_series("SER_WWRG_VERTICAL", 11, 21, 14) # 48
create_file("./documents/dicom/images/fake.dcm", 11, 21, 48)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 48)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 48)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 48)
create_file("./documents/dicom/images/fake.dcm", 11, 21, 48)
create_study("CT_SHOULDER_DX", 11) # 22
create_series("QERFF_SER_VERTICAL", 11, 22, 15) # 49
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 49)
create_series("QERFF_SER_CONTRAST_VERTICAL", 11, 22, 16) # 50
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 50)
create_series("QWFV_SER_VERTICAL", 11, 22, 17) # 51
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 51)
create_series("QWFV_SER_VERTICAL", 11, 22, 18) # 52
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 52)
create_series("WQFV_RUCK_RIGHT", 11, 22, 1) # 53
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_file("./documents/dicom/images/fake.dcm", 11, 22, 53)
create_patient("ROBERT_KOCH") # 12
create_study("CT_SHOULDER_SX", 12) # 23
create_series("WQFV_RUCK_CONTRAST_RIGHT", 12, 23, 2) # 54
create_file("./documents/dicom/images/fake.dcm", 12, 23, 54)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 54)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 54)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 54)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 54)
create_series("RUCK_WFV_RIGHT", 12, 23, 3) # 55
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 55)
create_series("RUCK_WFV_CONTRAST_RIGHT", 12, 23, 4) # 56
create_file("./documents/dicom/images/fake.dcm", 12, 23, 56)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 56)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 56)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 56)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 56)
create_series("QWFV_RUCK_RIGHT", 12, 23, 5) # 57
create_file("./documents/dicom/images/fake.dcm", 12, 23, 57)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 57)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 57)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 57)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 57)
create_series("EWV_RUCK_RIGHT", 12, 23, 6) # 58
create_file("./documents/dicom/images/fake.dcm", 12, 23, 58)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 58)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 58)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 58)
create_file("./documents/dicom/images/fake.dcm", 12, 23, 58)
create_study("TRANSREP_AX", 12) # 24
create_series("QWEFV_RUCK_CONTRAST_RIGHT", 12, 24, 7) # 59
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 59)
create_series("QWRBD_RUCK_RIGHT", 12, 24, 8) # 60
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 60)
create_series("QWFVDSA_RUCK_CONTRAST_RIGHT", 12, 24, 9) # 61
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 61)
create_series("QWSAZG_RUCK_RIGHT", 12, 24, 10) # 62
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 62)
create_series("SER_HORIZONTAL_AX", 12, 24, 1) # 63
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 63)
create_series("SER_HORIZONTAL_RF", 12, 24, 2) # 64
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 64)
create_series("SER_W_CONTRAST_HORIZONTAL", 12, 24, 3) # 65
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_file("./documents/dicom/images/fake.dcm", 12, 24, 65)
create_study("TRANSREP_PX", 12) # 25
create_series("SER_RF_HORIZONTAL", 12, 25, 4) # 66
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 66)
create_series("SER_RF_W_HORIZONTAL", 12, 25, 5) # 67
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 67)
create_series("SER_RF_CONTRAST_HORIZONTAL", 12, 25, 6) # 68
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 68)
create_series("SER_DP_W_HORIZONTAL", 12, 25, 7) # 69
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 69)
create_series("SER_W_CONTRAST_HORIZONTAL", 12, 25, 8) # 70
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 70)
create_series("SER_HORIZONTAL_AX", 12, 25, 1) # 71
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 71)
create_series("SER_HORIZONTAL_RF", 12, 25, 2) # 72
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_file("./documents/dicom/images/fake.dcm", 23, 36, 72)
create_series("SER_W_CONTRAST_HORIZONTAL", 12, 25, 3) # 73
create_file("./documents/dicom/images/fake.dcm", 12, 25, 73)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 73)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 73)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 73)
create_file("./documents/dicom/images/fake.dcm", 12, 25, 73)
create_patient("SIR_ALEXANDER_FLEMING") # 13
create_study("TRANSREP", 13) # 26
create_series("SER_F_W_HORIZONTAL", 13, 26, 11) # 74
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 74)
create_series("AWS_HORIZONTAL", 13, 26, 12) # 75
create_file("./documents/dicom/images/fake.dcm", 13, 26, 75)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 75)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 75)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 75)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 75)
create_series("FRS_W_CONTRAST_HORIZONTAL", 13, 26, 13) # 76
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 76)
create_series("VFB_W_HORIZONTAL", 13, 26, 14) # 77
create_file("./documents/dicom/images/fake.dcm", 13, 26, 77)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 77)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 77)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 77)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 77)
create_series("VFB_HORIZONTAL", 13, 26, 15) # 78
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 78)
create_series("RIGJ_W_HORIZONTAL", 13, 26, 16) # 79
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 79)
create_series("RIGJ_HORIZONTAL", 13, 26, 17) # 80
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 80)
create_series("SER", 13, 26, 1) # 81
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_file("./documents/dicom/images/fake.dcm", 13, 26, 81)
create_study("TRANSREP_THEET", 13) # 27
create_series("SERGG_HORIZONTAL", 13, 27, 2) # 82
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 82)
create_series("SERGG_CONTRAST_HORIZONTAL", 13, 27, 3) # 83
create_file("./documents/dicom/images/fake.dcm", 13, 27, 83)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 83)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 83)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 83)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 83)
create_series("SEWERR_HORIZONTAL", 13, 27, 4) # 84
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 84)
create_series("SEWERR_W_HORIZONTAL", 13, 27, 5) # 85
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 85)
create_series("SEWERR_HORIZONTAL", 13, 27, 6) # 86
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 86)
create_series("SEWERR_W_HORIZONTAL", 13, 27, 7) # 87
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 87)
create_series("SER_GG_VERTICAL", 13, 27, 8) # 88
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 88)
create_series("SER_WEG_VERTICAL", 13, 27, 9) # 89
create_file("./documents/dicom/images/fake.dcm", 13, 27, 89)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 89)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 89)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 89)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 89)
create_series("SER_QWR_CONTRAST_VERTICAL", 13, 27, 10) # 90
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_file("./documents/dicom/images/fake.dcm", 13, 27, 90)
create_patient("JONAS_SALK") # 14
create_study("AATRANSREP", 14) # 28
create_series("SER_RF_HORIZONTAL", 14, 28, 4) # 91
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 91)
create_series("SER_RF_W_HORIZONTAL", 14, 28, 5) # 92
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 92)
create_series("SER_RF_CONTRAST_HORIZONTAL", 14, 28, 6) # 93
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 93)
create_series("SER_DP_W_HORIZONTAL", 14, 28, 7) # 94
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 94)
create_series("SER_W_CONTRAST_HORIZONTAL", 14, 28, 8) # 95
create_file("./documents/dicom/images/fake.dcm", 14, 28, 95)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 95)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 95)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 95)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 95)
create_series("SER_W_W_HORIZONTAL", 14, 28, 9) # 96
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 96)
create_series("SER_W_HORIZONTAL", 14, 28, 10) # 97
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_file("./documents/dicom/images/fake.dcm", 14, 28, 97)
create_study("TRDDDANSREP", 14) # 29
create_series("SER_QWR_VERTICAL", 14, 29, 11) # 98
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 98)
create_series("SER_GBFGG_W_VERTICAL", 14, 29, 12) # 99
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 99)
create_series("SER_WWRG_W_VERTICAL", 14, 29, 13) # 100
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 100)
create_series("SEWERR_W_HORIZONTAL", 14, 29, 5) # 101
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 101)
create_series("SEWERR_HORIZONTAL", 14, 29, 6) # 102
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 102)
create_series("SEWERR_W_HORIZONTAL", 14, 29, 7) # 103
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 103)
create_series("SER_GG_VERTICAL", 14, 29, 8) # 104
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 104)
create_series("SER_WEG_VERTICAL", 14, 29, 9) # 105
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 105)
create_series("SER_QWR_CONTRAST_VERTICAL", 14, 29, 10) # 106
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_file("./documents/dicom/images/fake.dcm", 14, 29, 106)
create_study("TRANSGDAREP", 14) # 30
create_series("SER_QWR_VERTICAL", 14, 30, 11) # 107
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 107)
create_series("SER_GBFGG_W_VERTICAL", 14, 30, 12) # 108
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_file("./documents/dicom/images/fake.dcm", 10, 30, 108)
create_series("SER_WWRG_W_VERTICAL", 14, 30, 13) # 109
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 109)
create_series("SER_WWRG_VERTICAL", 14, 30, 14) # 110
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 110)
create_series("QERFF_SER_VERTICAL", 14, 30, 15) # 111
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 111)
create_series("QERFF_SER_CONTRAST_VERTICAL", 14, 30, 16) # 112
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 112)
create_series("QWFV_SER_VERTICAL", 14, 30, 17) # 113
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 113)
create_series("QWFV_SER_VERTICAL", 14, 30, 18) # 114
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
create_file("./documents/dicom/images/fake.dcm", 14, 30, 114)
# import the python mysql client
# Create a connection object to the MySQL Database Server
ipOfHost = "172.17.0.1"
dbUser = "root"
dbUserPassword = "angelo_lamonaca"
dbName = "dicom-viewer"
dbCharset = "utf8mb4"
cursorType = pymysql.cursors.DictCursor
databaseConnection = pymysql.connect(host=ipOfHost,
user=dbUser,
password=dbUserPassword,
db=dbName,
charset=dbCharset,
cursorclass=cursorType,
autocommit=True)
try:
# Cursor object creation
cursorObject = databaseConnection.cursor()
for x in range(1, 689):
updateStatement = "UPDATE `dicom-viewer`.Files t SET t.createdAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
updateStatement = "UPDATE `dicom-viewer`.Files t SET t.updatedAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
for x in range(1, 14):
updateStatement = "UPDATE `dicom-viewer`.Patients t SET t.createdAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
updateStatement = "UPDATE `dicom-viewer`.Patients t SET t.updatedAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
for x in range(1, 30):
updateStatement = "UPDATE `dicom-viewer`.Studies t SET t.createdAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
updateStatement = "UPDATE `dicom-viewer`.Studies t SET t.updatedAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
for x in range(1, 114):
updateStatement = "UPDATE `dicom-viewer`.Series t SET t.createdAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
updateStatement = "UPDATE `dicom-viewer`.Series t SET t.updatedAt = '2021-" + str(randrange(1, 11)) + "-" \
+ str(randrange(1, 28)) + " " + str(randrange(1, 23)) + ":" + str(
randrange(0, 59)) + ":" + str(randrange(0, 59)) + "' WHERE t.id = " + str(x)
cursorObject.execute(updateStatement)
except Exception as e:
print("Exeception occured:{}".format(e))
finally:
databaseConnection.close()
| 53.20075
| 117
| 0.702902
| 9,024
| 56,712
| 4.281139
| 0.034796
| 0.18404
| 0.349183
| 0.441074
| 0.924469
| 0.900267
| 0.873269
| 0.85005
| 0.841767
| 0.841767
| 0
| 0.100264
| 0.096752
| 56,712
| 1,065
| 118
| 53.250704
| 0.653919
| 0.010068
| 0
| 0.756
| 0
| 0
| 0.492935
| 0.436842
| 0
| 0
| 0
| 0
| 0
| 1
| 0.005
| false
| 0.002
| 0.003
| 0
| 0.008
| 0.006
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
aa0a8fd13a27dceb63d062096bc069f04f81fa5c
| 77
|
py
|
Python
|
px/__init__.py
|
zjid/pixel
|
ba0d73605873fcc3d16085458ede241e787b7854
|
[
"BSD-2-Clause"
] | null | null | null |
px/__init__.py
|
zjid/pixel
|
ba0d73605873fcc3d16085458ede241e787b7854
|
[
"BSD-2-Clause"
] | null | null | null |
px/__init__.py
|
zjid/pixel
|
ba0d73605873fcc3d16085458ede241e787b7854
|
[
"BSD-2-Clause"
] | null | null | null |
try: from . import jendela
except: pass
try: from . import snek
except: pass
| 15.4
| 26
| 0.74026
| 12
| 77
| 4.75
| 0.583333
| 0.245614
| 0.45614
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 77
| 4
| 27
| 19.25
| 0.904762
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 7
|
aa2c49767492a89c49934e85cba28b6c878814b7
| 15,255
|
bzl
|
Python
|
npm_deps.bzl
|
CodeIntelligenceTesting/rules_nodejs
|
39a440101f3c5f283cc6aff431a6799ce78174c5
|
[
"Apache-2.0"
] | null | null | null |
npm_deps.bzl
|
CodeIntelligenceTesting/rules_nodejs
|
39a440101f3c5f283cc6aff431a6799ce78174c5
|
[
"Apache-2.0"
] | null | null | null |
npm_deps.bzl
|
CodeIntelligenceTesting/rules_nodejs
|
39a440101f3c5f283cc6aff431a6799ce78174c5
|
[
"Apache-2.0"
] | null | null | null |
"""Define npm deps in yarn_install && npm_install repositories"""
load("//:index.bzl", "npm_install", "yarn_install")
def npm_deps():
"""Organize all yarn_install and npm_install calls here to prevent WORKSPACE bloat"""
yarn_install(
name = "npm",
data = [
"//:patches/jest-haste-map+25.3.0.patch",
"//internal/npm_install/test:postinstall.js",
],
environment = {
"SOME_USER_ENV": "yarn is great!",
},
links = {
"@test_multi_linker/lib-a": "//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-a2": "//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-b": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-b2": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-c": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-c2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-d": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_d",
"@test_multi_linker/lib-d2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_d",
},
package_json = "//:package.json",
yarn_lock = "//:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_deps",
links = {
"@test_multi_linker/lib-a": "@//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-a2": "@//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-b": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-b2": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-c": "@//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-c2": "@//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-d": "@//internal/linker/test/multi_linker/lib_d",
"@test_multi_linker/lib-d2": "@//internal/linker/test/multi_linker/lib_d",
},
package_json = "//internal/linker/test/multi_linker:package.json",
package_path = "internal/linker/test/multi_linker",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_test_a_deps",
links = {
"@test_multi_linker/lib-a": "@//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-a2": "@//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-b": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-b2": "@//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-c": "@//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-c2": "@//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-d": "@//internal/linker/test/multi_linker/lib_d",
"@test_multi_linker/lib-d2": "@//internal/linker/test/multi_linker/lib_d",
},
package_json = "//internal/linker/test/multi_linker/test_a:package.json",
package_path = "internal/linker/test/multi_linker/test_a",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/test_a:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_test_b_deps",
package_json = "//internal/linker/test/multi_linker/test_b:package.json",
package_path = "internal/linker/test/multi_linker/test_b",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/test_b:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_test_c_deps",
package_json = "//internal/linker/test/multi_linker/test_c:package.json",
package_path = "internal/linker/test/multi_linker/test_c",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/test_c:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_test_d_deps",
package_json = "//internal/linker/test/multi_linker/test_d:package.json",
package_path = "internal/linker/test/multi_linker/test_d",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/test_d:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_lib_b_deps",
# transitive deps for this first party lib should not include dev dependencies
args = ["--production"],
package_json = "//internal/linker/test/multi_linker/lib_b:package.json",
package_path = "internal/linker/test/multi_linker/lib_b",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/lib_b:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_lib_c_deps",
# transitive deps for this first party lib should not include dev dependencies
args = ["--production"],
package_json = "//internal/linker/test/multi_linker/lib_c:lib/package.json",
package_path = "internal/linker/test/multi_linker/lib_c/lib",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/lib_c:lib/yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_sub_dev_deps",
links = {
"@test_multi_linker/lib-a": "//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-a2": "//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-b": "//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-b2": "//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-c": "//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-c2": "//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-d": "//internal/linker/test/multi_linker/lib_d",
"@test_multi_linker/lib-d2": "//internal/linker/test/multi_linker/lib_d",
},
package_json = "//internal/linker/test/multi_linker/sub:package.json",
package_path = "internal/linker/test/multi_linker/sub/dev",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/sub:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_sub_deps",
# transitive deps for this first party lib should not include dev dependencies
args = ["--production"],
links = {
"@test_multi_linker/lib-a": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-a2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_a",
"@test_multi_linker/lib-b": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-b2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_b",
"@test_multi_linker/lib-c": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-c2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_c",
"@test_multi_linker/lib-d": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_d",
"@test_multi_linker/lib-d2": "@build_bazel_rules_nodejs//internal/linker/test/multi_linker/lib_d",
},
package_json = "//internal/linker/test/multi_linker/sub:package.json",
package_path = "internal/linker/test/multi_linker/sub",
yarn_lock = "//internal/linker/test/multi_linker/sub:yarn.lock",
)
yarn_install(
name = "internal_test_multi_linker_onep_a_deps",
# transitive deps for this first party lib should not include dev dependencies
args = ["--production"],
package_json = "//internal/linker/test/multi_linker/onep_a:package.json",
package_path = "internal/linker/test/multi_linker/onep_a",
symlink_node_modules = False,
yarn_lock = "//internal/linker/test/multi_linker/onep_a:yarn.lock",
)
yarn_install(
name = "fine_grained_deps_yarn",
data = [
"//:tools/npm_packages/local_module/yarn/BUILD.bazel",
"//:tools/npm_packages/local_module/yarn/index.js",
"//:tools/npm_packages/local_module/yarn/package.json",
"//internal/npm_install/test:postinstall.js",
],
environment = {
"SOME_USER_ENV": "yarn is great!",
},
generate_local_modules_build_files = False,
included_files = [
"",
".js",
".d.ts",
".json",
".proto",
],
package_json = "//:tools/fine_grained_deps_yarn/package.json",
symlink_node_modules = False,
yarn_lock = "//:tools/fine_grained_deps_yarn/yarn.lock",
)
npm_install(
name = "fine_grained_deps_npm",
data = [
"//:tools/npm_packages/local_module/npm/BUILD.bazel",
"//:tools/npm_packages/local_module/npm/index.js",
"//:tools/npm_packages/local_module/npm/package.json",
"//internal/npm_install/test:postinstall.js",
],
environment = {
"SOME_USER_ENV": "npm is cool!",
},
generate_local_modules_build_files = False,
included_files = [
"",
".js",
".d.ts",
".json",
".proto",
],
npm_command = "install",
package_json = "//:tools/fine_grained_deps_npm/package.json",
package_lock_json = "//:tools/fine_grained_deps_npm/package-lock.json",
symlink_node_modules = False,
)
yarn_install(
name = "fine_grained_no_bin",
package_json = "//:tools/fine_grained_no_bin/package.json",
symlink_node_modules = False,
yarn_lock = "//:tools/fine_grained_no_bin/yarn.lock",
)
yarn_install(
name = "fine_grained_goldens",
included_files = [
"",
".js",
".jst",
".ts",
".map",
".d.ts",
".json",
".proto",
],
links = {
"@some-scope/some-target-b": "@//some/target/b",
"@some-scope/some-target-b2": "@//some/target/b",
"some-target-a": "//some/target/a",
"some-target-a2": "//some/target/a",
},
manual_build_file_contents = """
filegroup(
name = "golden_files",
srcs = [
"//:BUILD.bazel",
"//:manual_build_file_contents",
"//:WORKSPACE",
"//@angular/core:BUILD.bazel",
"//@gregmagolan:BUILD.bazel",
"//@gregmagolan/test-a/bin:BUILD.bazel",
"//@gregmagolan/test-a:BUILD.bazel",
"//@gregmagolan/test-a:index.bzl",
"//@gregmagolan/test-b:BUILD.bazel",
"//ajv:BUILD.bazel",
"//jasmine/bin:BUILD.bazel",
"//jasmine:BUILD.bazel",
"//jasmine:index.bzl",
"//rxjs:BUILD.bazel",
"//unidiff:BUILD.bazel",
"//zone.js:BUILD.bazel",
"//some-target-a:BUILD.bazel",
"//some-target-a2:BUILD.bazel",
"//@some-scope/some-target-b:BUILD.bazel",
"//@some-scope/some-target-b2:BUILD.bazel",
],
)""",
package_json = "//:tools/fine_grained_goldens/package.json",
symlink_node_modules = False,
yarn_lock = "//:tools/fine_grained_goldens/yarn.lock",
)
yarn_install(
name = "internal_npm_install_test_patches_yarn",
package_json = "//internal/npm_install/test/patches_yarn:package.json",
package_path = "internal/npm_install/test/patches_yarn",
patch_args = ["-p0"],
patch_tool = "patch",
post_install_patches = ["//internal/npm_install/test/patches_yarn:semver+1.0.0.patch"],
pre_install_patches = ["//internal/npm_install/test/patches_yarn:package_json.patch"],
symlink_node_modules = False,
yarn_lock = "//internal/npm_install/test/patches_yarn:yarn.lock",
)
npm_install(
name = "internal_npm_install_test_patches_npm",
package_json = "//internal/npm_install/test/patches_npm:package.json",
package_lock_json = "//internal/npm_install/test/patches_npm:package-lock.json",
package_path = "internal/npm_install/test/patches_npm",
patch_args = ["-p0"],
patch_tool = "patch",
post_install_patches = ["//internal/npm_install/test/patches_npm:semver+1.0.0.patch"],
pre_install_patches = ["//internal/npm_install/test/patches_npm:package_json.patch"],
symlink_node_modules = False,
)
yarn_install(
name = "fine_grained_goldens_multi_linked",
included_files = [
"",
".js",
".jst",
".ts",
".map",
".d.ts",
".json",
".proto",
],
links = {
"@some-scope/some-target-b": "@//some/target/b",
"@some-scope/some-target-b2": "@//some/target/b",
"some-target-a": "@build_bazel_rules_nodejs//some/target/a",
"some-target-a2": "@build_bazel_rules_nodejs//some/target/a",
},
manual_build_file_contents = """
filegroup(
name = "golden_files",
srcs = [
"//:BUILD.bazel",
"//:manual_build_file_contents",
"//:WORKSPACE",
"//@angular/core:BUILD.bazel",
"//@gregmagolan:BUILD.bazel",
"//@gregmagolan/test-a/bin:BUILD.bazel",
"//@gregmagolan/test-a:BUILD.bazel",
"//@gregmagolan/test-a:index.bzl",
"//@gregmagolan/test-b:BUILD.bazel",
"//ajv:BUILD.bazel",
"//jasmine/bin:BUILD.bazel",
"//jasmine:BUILD.bazel",
"//jasmine:index.bzl",
"//rxjs:BUILD.bazel",
"//unidiff:BUILD.bazel",
"//zone.js:BUILD.bazel",
"//some-target-a:BUILD.bazel",
"//some-target-a2:BUILD.bazel",
"//@some-scope/some-target-b:BUILD.bazel",
"//@some-scope/some-target-b2:BUILD.bazel",
],
)""",
package_json = "//:tools/fine_grained_goldens/package.json",
package_path = "tools/fine_grained_goldens",
symlink_node_modules = False,
yarn_lock = "//:tools/fine_grained_goldens/yarn.lock",
)
npm_install(
name = "npm_node_patches",
package_json = "//packages/node-patches:package.json",
package_lock_json = "//packages/node-patches:package-lock.json",
)
npm_install(
name = "angular_deps",
package_json = "//packages/angular:package.json",
package_lock_json = "//packages/angular:package-lock.json",
)
yarn_install(
name = "cypress_deps",
package_json = "//packages/cypress/test:package.json",
yarn_lock = "//packages/cypress/test:yarn.lock",
)
yarn_install(
name = "rollup_test_multi_linker_deps",
package_json = "//packages/rollup/test/multi_linker:package.json",
package_path = "packages/rollup/test/multi_linker",
symlink_node_modules = False,
yarn_lock = "//packages/rollup/test/multi_linker:yarn.lock",
)
| 42.140884
| 110
| 0.626418
| 1,857
| 15,255
| 4.833064
| 0.06462
| 0.124345
| 0.207242
| 0.17649
| 0.939276
| 0.917326
| 0.873872
| 0.826072
| 0.782618
| 0.739721
| 0
| 0.003365
| 0.22078
| 15,255
| 361
| 111
| 42.257618
| 0.751661
| 0.029367
| 0
| 0.598187
| 0
| 0
| 0.600284
| 0.535929
| 0
| 0
| 0
| 0
| 0
| 1
| 0.003021
| true
| 0
| 0
| 0
| 0.003021
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
aa427ef0f740f5dc762c81c3ff8cb58e0eb663bd
| 7,131
|
py
|
Python
|
r2cmplr/_timestamp_config.py
|
manub686/atomix
|
80ca2b675f49d2aef1e078a36a282d7173e02805
|
[
"Apache-2.0"
] | 3
|
2015-04-21T21:04:48.000Z
|
2015-06-03T08:55:36.000Z
|
r2cmplr/_timestamp_config.py
|
manubansal/atomix
|
80ca2b675f49d2aef1e078a36a282d7173e02805
|
[
"Apache-2.0"
] | 1
|
2015-06-11T22:35:48.000Z
|
2015-06-11T22:35:48.000Z
|
r2cmplr/_timestamp_config.py
|
manub686/atomix
|
80ca2b675f49d2aef1e078a36a282d7173e02805
|
[
"Apache-2.0"
] | null | null | null |
'''
Atomix project, _timestamp_config.py, (TODO: summary)
Copyright (c) 2015 Stanford University
Released under the Apache License v2.0. See the LICENSE file for details.
Author(s): Manu Bansal
'''
def populate_timestamp_config(timestamp_conf):
if timestamp_conf == 'st':
TIMESTAMP_CONFIG = config_st()
elif timestamp_conf == 'at':
TIMESTAMP_CONFIG = config_at()
elif timestamp_conf == 'atii':
TIMESTAMP_CONFIG = config_atii()
elif timestamp_conf == 'none':
TIMESTAMP_CONFIG = config_none()
return TIMESTAMP_CONFIG
def config_none():
TIMESTAMP_CONFIG = {}
#==============================
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = False
#=================================
return TIMESTAMP_CONFIG
def config_ati():
TIMESTAMP_CONFIG = {}
#==============================
#config used for test6-ati
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,20)
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = False #just identify blocks
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = [\
'axnRxDataDecode54m_M_0', \
'axnRxDataDecode54m_M_1', \
'axnRxDataDecode54m_M_2', \
]
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [] #to internally timestamp all atoms
#=================================
return TIMESTAMP_CONFIG
def config_at():
TIMESTAMP_CONFIG = {}
#=================================
#probably the original config used for test6-at
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,20)
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = []
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [] #to internally timestamp all atoms
#=================================
return TIMESTAMP_CONFIG
def config_unknown():
TIMESTAMP_CONFIG = {}
#=================================
#unknown config
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,20)
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = True #to assign different atom ids (and therefore block classes) of the same block type if atoms are in different axns this will lead to code size blowup
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = [\
'axnRxDataDecode54m_M_0', \
'axnRxDataDecode54m_M_1', \
'axnRxDataDecode54m_M_2', \
'axnRxDataDecode54m_M_3', \
]
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [] #to internally timestamp all atoms
#=================================
return TIMESTAMP_CONFIG
def config_st():
TIMESTAMP_CONFIG = {}
#=================================
#config probably for test6-st
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = True
#TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,20)
#TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(9,14)
#-----------
#TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS']= True
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
#-----------
TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = False #just identify blocks
#TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = True #to assign different atom ids (and therefore block classes) of the same block type if atoms are in different axns this will lead to code size blowup
#-----------
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = False
#to profile atoms in all axns
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = []
#to profile atoms in selected axns
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = [\
'axnRxDataDecode54m_M_0', \
'axnRxDataDecode54m_M_1', \
'axnRxDataDecode54m_M_2', \
'axnRxDataDecode54m_M_3', \
]
#'axnRxDataDecode54m_M_3', \
#'axnRxDataDecode54m_T_0', \
#'axnRxDataDecode54m_T_1', \
#'axnRxDataDecode54m_T_2', \
#'axnRxDataDecode54m_T_3', \
#-------------
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = False
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = False
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [] #to internally timestamp all atoms
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [\
#103007, \
#]
#=================================
return TIMESTAMP_CONFIG
def config_atii():
TIMESTAMP_CONFIG = {}
#=================================
#config for set1 results, atii
TIMESTAMP_CONFIG['TIMESTAMP_STATES'] = False
#TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,20)
TIMESTAMP_CONFIG['TIMESTAMP_STATES_TO_TIMESTAMP'] = range(0,100)
TIMESTAMP_CONFIG['TIMESTAMP_ACTIONS'] = False
TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = False #just identify blocks
#TIMESTAMP_CONFIG['TIMESTAMP_IDENTIFY_ATOMS_PER_AXN'] = True #to assign different atom ids (and therefore block classes) of the same block type if atoms are in different axns this will lead to code size blowup
#-----------
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS'] = False
#to profile atoms in all axns
TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = []
#to profile atoms in selected axns
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMS_AXN_SCOPES_TO_TIMESTAMP'] = [\
#'axnRxDataDecode54m_M_0', \
#'axnRxDataDecode54m_M_1', \
#'axnRxDataDecode54m_M_2', \
#'axnRxDataDecode54m_M_3', \
#]
#'axnRxDataDecode54m_M_3', \
#'axnRxDataDecode54m_T_0', \
#'axnRxDataDecode54m_T_1', \
#'axnRxDataDecode54m_T_2', \
#'axnRxDataDecode54m_T_3', \
#-------------
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_ii_only'] = True
TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [] #to internally timestamp all atoms
#TIMESTAMP_CONFIG['TIMESTAMP_ATOMIMPLS_TO_TIMESTAMP'] = [\
#103007, \
#]
#=================================
return TIMESTAMP_CONFIG
| 41.947059
| 214
| 0.68658
| 765
| 7,131
| 6.003922
| 0.12549
| 0.267799
| 0.339647
| 0.145221
| 0.856739
| 0.841716
| 0.841716
| 0.841716
| 0.829305
| 0.813847
| 0
| 0.01983
| 0.158463
| 7,131
| 169
| 215
| 42.195266
| 0.745542
| 0.378629
| 0
| 0.76087
| 0
| 0
| 0.339458
| 0.244373
| 0
| 0
| 0
| 0.005917
| 0
| 1
| 0.076087
| false
| 0
| 0
| 0
| 0.152174
| 0
| 0
| 0
| 0
| null | 1
| 1
| 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
| 9
|
3516178c0ba8620ada68b4754a2905bfdcdae81c
| 2,368
|
py
|
Python
|
test-framework/test-suites/integration/tests/disable/test_disable_cart.py
|
knutsonchris/stacki
|
33087dd5fa311984a66ccecfeee6f9c2c25f665d
|
[
"BSD-3-Clause"
] | 123
|
2015-05-12T23:36:45.000Z
|
2017-07-05T23:26:57.000Z
|
test-framework/test-suites/integration/tests/disable/test_disable_cart.py
|
knutsonchris/stacki
|
33087dd5fa311984a66ccecfeee6f9c2c25f665d
|
[
"BSD-3-Clause"
] | 177
|
2015-06-05T19:17:47.000Z
|
2017-07-07T17:57:24.000Z
|
test-framework/test-suites/integration/tests/disable/test_disable_cart.py
|
knutsonchris/stacki
|
33087dd5fa311984a66ccecfeee6f9c2c25f665d
|
[
"BSD-3-Clause"
] | 32
|
2015-06-07T02:25:03.000Z
|
2017-06-23T07:35:35.000Z
|
import json
from textwrap import dedent
class TestDisableCart:
def test_no_args(self, host):
result = host.run('stack disable cart')
assert result.rc == 255
assert result.stderr == dedent('''\
error - "cart" argument is required
{cart ...} [box=string]
''')
def test_invalid_cart(self, host):
result = host.run('stack disable cart test')
assert result.rc == 255
assert result.stderr == dedent('''\
error - "test" argument is not a valid cart
{cart ...} [box=string]
''')
def test_invalid_box(self, host):
result = host.run('stack disable cart test box=test')
assert result.rc == 255
assert result.stderr == 'error - unknown box "test"\n'
def test_default_box(self, host, revert_etc, revert_export_stack_carts):
# Add our test cart
result = host.run('stack add cart test')
assert result.rc == 0
# Add the cart to the default box
result = host.run('stack enable cart test')
assert result.rc == 0
# Confirm it is in the box now
result = host.run('stack list cart test output-format=json')
assert result.rc == 0
assert json.loads(result.stdout) == [
{
'name': 'test',
'boxes': 'default'
}
]
# Disable the cart
result = host.run('stack disable cart test')
assert result.rc == 0
# Confirm it isn't in the box now
result = host.run('stack list cart test output-format=json')
assert result.rc == 0
assert json.loads(result.stdout) == [
{
'name': 'test',
'boxes': ''
}
]
def test_with_box(self, host, revert_etc, revert_export_stack_carts):
# Add our test box
result = host.run('stack add box test')
assert result.rc == 0
# Add our test cart
result = host.run('stack add cart test')
assert result.rc == 0
# Add the cart to the test box
result = host.run('stack enable cart test box=test')
assert result.rc == 0
# Confirm it is in the box now
result = host.run('stack list cart test output-format=json')
assert result.rc == 0
assert json.loads(result.stdout) == [
{
'name': 'test',
'boxes': 'test'
}
]
# Disable the cart
result = host.run('stack disable cart test box=test')
assert result.rc == 0
# Confirm it isn't in the box now
result = host.run('stack list cart test output-format=json')
assert result.rc == 0
assert json.loads(result.stdout) == [
{
'name': 'test',
'boxes': ''
}
]
| 24.666667
| 73
| 0.648226
| 352
| 2,368
| 4.309659
| 0.167614
| 0.134476
| 0.119974
| 0.166117
| 0.87739
| 0.875412
| 0.808833
| 0.808833
| 0.732367
| 0.670402
| 0
| 0.010817
| 0.219172
| 2,368
| 95
| 74
| 24.926316
| 0.809627
| 0.113598
| 0
| 0.57971
| 0
| 0
| 0.302827
| 0
| 0
| 0
| 0
| 0
| 0.304348
| 1
| 0.072464
| false
| 0
| 0.028986
| 0
| 0.115942
| 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
|
3519c9dc9072f8178e5eaf5a02bda3acc68dc6a7
| 139
|
py
|
Python
|
graphite_feeder/handler/event/enum/sun/twilight/__init__.py
|
majamassarini/automate-graphite-feeder
|
0f17f99bbdaab86e10e0b7d424d055ff44fc4ca0
|
[
"MIT"
] | null | null | null |
graphite_feeder/handler/event/enum/sun/twilight/__init__.py
|
majamassarini/automate-graphite-feeder
|
0f17f99bbdaab86e10e0b7d424d055ff44fc4ca0
|
[
"MIT"
] | null | null | null |
graphite_feeder/handler/event/enum/sun/twilight/__init__.py
|
majamassarini/automate-graphite-feeder
|
0f17f99bbdaab86e10e0b7d424d055ff44fc4ca0
|
[
"MIT"
] | null | null | null |
from graphite_feeder.handler.event.enum.sun.twilight import civil
from graphite_feeder.handler.event.enum.sun.twilight import astronomical
| 46.333333
| 72
| 0.870504
| 20
| 139
| 5.95
| 0.55
| 0.201681
| 0.302521
| 0.420168
| 0.857143
| 0.857143
| 0.857143
| 0.857143
| 0.857143
| 0
| 0
| 0
| 0.057554
| 139
| 2
| 73
| 69.5
| 0.908397
| 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
| 1
| 1
| 1
| 1
| 1
| 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
| 13
|
3536d983325de61d58271c3d74decac5790e87ea
| 1,600
|
py
|
Python
|
boxuegu/boxuegu/apps/users/forms.py
|
1111111111122222222223333333333/Django_boxuegu
|
cec4451bcc55f04013efd4e4cb7c4098a0fd5056
|
[
"MIT"
] | null | null | null |
boxuegu/boxuegu/apps/users/forms.py
|
1111111111122222222223333333333/Django_boxuegu
|
cec4451bcc55f04013efd4e4cb7c4098a0fd5056
|
[
"MIT"
] | 6
|
2021-02-08T20:30:13.000Z
|
2022-03-11T23:50:00.000Z
|
boxuegu/boxuegu/apps/users/forms.py
|
1111111111122222222223333333333/Django_boxuegu
|
cec4451bcc55f04013efd4e4cb7c4098a0fd5056
|
[
"MIT"
] | null | null | null |
from django import forms
from captcha.fields import CaptchaField
class RegisterForm(forms.Form):
"""
用户注册表单
"""
nick_name = forms.CharField(max_length=50, min_length=4, label='用户名')
mobile = forms.CharField(max_length=11, label='手机号')
email = forms.CharField(max_length=20, min_length=5, required=True, label='邮箱')
password = forms.CharField(max_length=20, min_length=8, required=True, label='密码')
# 增加验证码字段
captcha = CaptchaField(label='验证码')
class LoginForm(forms.Form):
"""
用户登录表单
"""
nick_name = forms.CharField(max_length=50, min_length=4, label='用户名')
# mobile = forms.CharField(max_length=11, required=True, label='手机号')
# email = forms.CharField(max_length=20, min_length=5, required=True, label='邮箱')
password = forms.CharField(max_length=20, min_length=6, required=True, label='密码')
class ForgetPwdForm(forms.Form):
"""
用户忘记密码
"""
email = forms.CharField(max_length=30, min_length=5, required=True, label='邮箱')
# 增加验证码字段
captcha = CaptchaField(label='验证码')
class ForgetPwdForm(forms.Form):
"""
用户忘记密码
"""
email = forms.CharField(max_length=30, min_length=5, required=True, label='邮箱')
# 增加验证码字段
captcha = CaptchaField(label='验证码')
class UserCoursesForm(forms.Form):
"""
我的课程
"""
email = forms.CharField(max_length=20, min_length=5, required=True, label='邮箱')
password = forms.CharField(max_length=20, min_length=6, required=True, label='密码')
# mobile = forms.CharField(max_length=11, min_length=11,required=True,label='手机号')
| 31.372549
| 86
| 0.674375
| 208
| 1,600
| 5.0625
| 0.206731
| 0.17284
| 0.209877
| 0.283951
| 0.845204
| 0.845204
| 0.740741
| 0.740741
| 0.740741
| 0.740741
| 0
| 0.028941
| 0.179375
| 1,600
| 50
| 87
| 32
| 0.773039
| 0.17875
| 0
| 0.65
| 0
| 0
| 0.026381
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.15
| 0.1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 1
| 0
|
0
| 10
|
10bc997a36788ceb3814813a3c543a61eb5c8a0e
| 246
|
py
|
Python
|
output/models/ms_data/particles/particles_q020_xsd/__init__.py
|
tefra/xsdata-w3c-tests
|
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
|
[
"MIT"
] | 1
|
2021-08-14T17:59:21.000Z
|
2021-08-14T17:59:21.000Z
|
output/models/ms_data/particles/particles_q020_xsd/__init__.py
|
tefra/xsdata-w3c-tests
|
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
|
[
"MIT"
] | 4
|
2020-02-12T21:30:44.000Z
|
2020-04-15T20:06:46.000Z
|
output/models/ms_data/particles/particles_q020_xsd/__init__.py
|
tefra/xsdata-w3c-tests
|
b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f
|
[
"MIT"
] | null | null | null |
from output.models.ms_data.particles.particles_q020_xsd.particles_q020 import (
B,
R,
Doc,
)
from output.models.ms_data.particles.particles_q020_xsd.particles_q020_imp import Bar
__all__ = [
"B",
"R",
"Doc",
"Bar",
]
| 17.571429
| 85
| 0.678862
| 34
| 246
| 4.529412
| 0.441176
| 0.337662
| 0.207792
| 0.233766
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0.779221
| 0
| 0.060914
| 0.199187
| 246
| 13
| 86
| 18.923077
| 0.720812
| 0
| 0
| 0
| 0
| 0
| 0.03252
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.166667
| 0
| 0.166667
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
5e1c7025e8e242e9b2cb5c0e7a185fe494b10c8b
| 233
|
py
|
Python
|
tests/article_tests.py
|
junhoyeo/fastdj
|
73284946270d1f7ae13f077931b360f70e5a0143
|
[
"MIT"
] | 7
|
2020-02-04T05:08:19.000Z
|
2022-01-01T15:49:38.000Z
|
tests/article_tests.py
|
junhoyeo/fastdj
|
73284946270d1f7ae13f077931b360f70e5a0143
|
[
"MIT"
] | null | null | null |
tests/article_tests.py
|
junhoyeo/fastdj
|
73284946270d1f7ae13f077931b360f70e5a0143
|
[
"MIT"
] | 5
|
2020-02-04T05:09:21.000Z
|
2021-11-26T00:46:41.000Z
|
import pytest
def write_post():
pass
def get_post():
pass
def edit_post():
pass
def get_post_list():
pass
def get_my_posts():
pass
def get_specific_user_posts():
pass
def remove_post():
pass
| 7.766667
| 30
| 0.630901
| 34
| 233
| 4
| 0.411765
| 0.308824
| 0.294118
| 0.205882
| 0.264706
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.27897
| 233
| 29
| 31
| 8.034483
| 0.809524
| 0
| 0
| 0.466667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.466667
| true
| 0.466667
| 0.066667
| 0
| 0.533333
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 7
|
5e2f92dce9d2d6263a666907bd9afe4f1d7157b1
| 66
|
py
|
Python
|
ips/ip/uart/__init__.py
|
zld012739/zldrepository
|
5635b78a168956091676ef4dd99fa564be0e5ba0
|
[
"MIT"
] | null | null | null |
ips/ip/uart/__init__.py
|
zld012739/zldrepository
|
5635b78a168956091676ef4dd99fa564be0e5ba0
|
[
"MIT"
] | null | null | null |
ips/ip/uart/__init__.py
|
zld012739/zldrepository
|
5635b78a168956091676ef4dd99fa564be0e5ba0
|
[
"MIT"
] | null | null | null |
from uart_partial import get_ip_name
from uart_partial import UART
| 33
| 36
| 0.893939
| 12
| 66
| 4.583333
| 0.583333
| 0.290909
| 0.545455
| 0.763636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.106061
| 66
| 2
| 37
| 33
| 0.932203
| 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
|
ead20639cc616de2d1ff60c3c8078bcc16ece779
| 25
|
py
|
Python
|
Chapter 04/ch41d.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
Chapter 04/ch41d.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
Chapter 04/ch41d.py
|
bpbpublications/TEST-YOUR-SKILLS-IN-PYTHON-LANGUAGE
|
f6a4194684515495d00aa38347a725dd08f39a0c
|
[
"MIT"
] | null | null | null |
print(8 ^ 2 * 4 + 3)
#3
| 12.5
| 22
| 0.4
| 6
| 25
| 1.666667
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.3125
| 0.36
| 25
| 2
| 23
| 12.5
| 0.3125
| 0.04
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 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
| 8
|
eadd788eb6b7606fb602cdb6cb38a986e640c269
| 253
|
py
|
Python
|
keyring/__init__.py
|
ewjoachim/keyring
|
5a887c13c9033951e9a7ac2d356cecc0cd95963f
|
[
"MIT"
] | null | null | null |
keyring/__init__.py
|
ewjoachim/keyring
|
5a887c13c9033951e9a7ac2d356cecc0cd95963f
|
[
"MIT"
] | null | null | null |
keyring/__init__.py
|
ewjoachim/keyring
|
5a887c13c9033951e9a7ac2d356cecc0cd95963f
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from .core import (set_keyring, get_keyring, set_password, get_password,
delete_password)
__all__ = (
'set_keyring', 'get_keyring', 'set_password', 'get_password',
'delete_password',
)
| 25.3
| 72
| 0.703557
| 29
| 253
| 5.482759
| 0.37931
| 0.125786
| 0.163522
| 0.251572
| 0.704403
| 0.704403
| 0.704403
| 0.704403
| 0.704403
| 0.704403
| 0
| 0
| 0.197628
| 253
| 9
| 73
| 28.111111
| 0.783251
| 0
| 0
| 0
| 0
| 0
| 0.241107
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.571429
| 0.285714
| 0
| 0.285714
| 0
| 1
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 9
|
eafca4a53ae72a658f500c45e79acafa8d76de5e
| 1,477
|
py
|
Python
|
tests/unit/test_json.py
|
alexbecker/dotlock
|
97521f5d40d1632bd2c8db0d6b47c76bbbb02413
|
[
"MIT"
] | 43
|
2018-05-25T13:35:38.000Z
|
2021-07-09T04:31:18.000Z
|
tests/unit/test_json.py
|
alexbecker/dotlock
|
97521f5d40d1632bd2c8db0d6b47c76bbbb02413
|
[
"MIT"
] | 6
|
2018-05-15T04:53:09.000Z
|
2019-06-14T02:58:11.000Z
|
tests/unit/test_json.py
|
alexbecker/dotlock
|
97521f5d40d1632bd2c8db0d6b47c76bbbb02413
|
[
"MIT"
] | null | null | null |
from dotlock import json
def test_strip_sharp_comments():
assert json.strip('# comment') == ''
assert json.strip(' # comment') == ' '
assert json.strip(' "foo": "bar" # baz') == ' "foo": "bar" '
assert json.strip(r' "foo": "bar\"" # baz') == r' "foo": "bar\"" '
assert json.strip(r' "foo": "bar # baz" # baz') == r' "foo": "bar # baz" '
def test_strip_slash_comments():
assert json.strip('// comment') == ''
assert json.strip(' // comment') == ' '
assert json.strip(' "foo": "bar" // baz') == ' "foo": "bar" '
assert json.strip(r' "foo": "bar\"" // baz') == r' "foo": "bar\"" '
assert json.strip(r' "foo": "bar // baz" // baz') == r' "foo": "bar // baz" '
def test_strip_no_comments():
assert json.strip(' "foo": "bar"') == ' "foo": "bar"'
assert json.strip(r' "foo": "bar\""') == r' "foo": "bar\""'
assert json.strip(r' "foo": "bar // baz"') == r' "foo": "bar // baz"'
assert json.strip(r' "foo": "bar # baz"') == r' "foo": "bar # baz"'
def test_loads():
assert json.loads(
r"""{
"a": 1,
"foo": true,
"bar//": {
"fun": "run\"in the sun", // remove me
"baz": "simple json" // me too
},
"array": [
1,
true,
"boo"
]
}""") == {
'a': 1,
'foo': True,
'bar//': {
'fun': 'run"in the sun',
'baz': 'simple json'
},
'array': [
1,
True,
"boo"
]
}
| 27.351852
| 83
| 0.448206
| 178
| 1,477
| 3.662921
| 0.179775
| 0.184049
| 0.322086
| 0.153374
| 0.78681
| 0.754601
| 0.754601
| 0.743865
| 0.70092
| 0.70092
| 0
| 0.003872
| 0.300609
| 1,477
| 53
| 84
| 27.867925
| 0.627299
| 0
| 0
| 0.121212
| 0
| 0
| 0.372111
| 0
| 0
| 0
| 0
| 0
| 0.454545
| 1
| 0.121212
| true
| 0
| 0.030303
| 0
| 0.151515
| 0
| 0
| 0
| 0
| null | 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
d82a0526e561038d48d2f3a844c9384c4c4c0e2e
| 11,542
|
py
|
Python
|
website/status/migrations/0001_initial.py
|
chiraggada2911/Credit-Society-Django
|
d52ccb1e6e3d6d64777dd0331fef1e0234b20368
|
[
"bzip2-1.0.6",
"MIT"
] | 4
|
2019-04-01T04:05:02.000Z
|
2020-08-13T03:55:40.000Z
|
website/status/migrations/0001_initial.py
|
jatindalvi/Credit-Society-Django
|
4b897090fe2e756258258dee5aee2a8f15bd68dc
|
[
"MIT"
] | 15
|
2019-02-16T17:06:58.000Z
|
2022-03-11T23:46:26.000Z
|
website/status/migrations/0001_initial.py
|
jatindalvi/Credit-Society-Django
|
4b897090fe2e756258258dee5aee2a8f15bd68dc
|
[
"MIT"
] | 4
|
2019-02-05T04:49:36.000Z
|
2019-07-30T10:57:13.000Z
|
# Generated by Django 2.2 on 2019-10-27 09:39
import datetime
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='interests',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('sharedividend', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('cddividend', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('fdinterest', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloaninterest', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloaninterest', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('year', models.CharField(default=0, max_length=50, null=True)),
],
),
migrations.CreateModel(
name='year',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('s_encode', models.CharField(blank=True, default=0, max_length=20, null=True)),
('cd_encode', models.CharField(blank=True, default=0, max_length=20, null=True)),
('year', models.CharField(blank=True, default=0, max_length=20, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='sharemonth',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('Janurary', models.IntegerField(blank=True, default=0, null=True)),
('February', models.IntegerField(blank=True, default=0, null=True)),
('March', models.IntegerField(blank=True, default=0, null=True)),
('April', models.IntegerField(blank=True, default=0, null=True)),
('May', models.IntegerField(blank=True, default=0, null=True)),
('June', models.IntegerField(blank=True, default=0, null=True)),
('July', models.IntegerField(blank=True, default=0, null=True)),
('August', models.IntegerField(blank=True, default=0, null=True)),
('September', models.IntegerField(blank=True, default=0, null=True)),
('October', models.IntegerField(blank=True, default=0, null=True)),
('November', models.IntegerField(blank=True, default=0, null=True)),
('December', models.IntegerField(blank=True, default=0, null=True)),
('encode', models.CharField(blank=True, default=0, max_length=20, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Notification',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('notimessage', models.CharField(default=0, max_length=50, null=True)),
('notidate', models.DateField(default=datetime.date(2019, 10, 27))),
('seen', models.BooleanField(default=False)),
('sender', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='HistorylongLoan',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('longloanamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloandate', models.DateField(blank=True, null=True)),
('longloanperiod', models.IntegerField(blank=True, default=0, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='HistoryFd',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('fdcapital', models.IntegerField(default=0, null=True)),
('fddate', models.DateField(blank=True, null=True)),
('fdmaturitydate', models.DateField(blank=True, null=True)),
('fdnumber', models.CharField(default=0, max_length=50, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='HistoryemerLoan',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('emerloanamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloanperiod', models.IntegerField(blank=True, default=0, null=True)),
('emerloandate', models.DateField(blank=True, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='FixedDeposits',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('fdcapital', models.IntegerField(default=0, null=True)),
('fddate', models.DateField(blank=True, null=True)),
('fdmaturitydate', models.DateField(blank=True, null=True)),
('fdnumber', models.CharField(default=0, max_length=50, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='cdmonth',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('Janurary', models.IntegerField(blank=True, default=0, null=True)),
('February', models.IntegerField(blank=True, default=0, null=True)),
('March', models.IntegerField(blank=True, default=0, null=True)),
('April', models.IntegerField(blank=True, default=0, null=True)),
('May', models.IntegerField(blank=True, default=0, null=True)),
('June', models.IntegerField(blank=True, default=0, null=True)),
('July', models.IntegerField(blank=True, default=0, null=True)),
('August', models.IntegerField(blank=True, default=0, null=True)),
('September', models.IntegerField(blank=True, default=0, null=True)),
('October', models.IntegerField(blank=True, default=0, null=True)),
('November', models.IntegerField(blank=True, default=0, null=True)),
('December', models.IntegerField(blank=True, default=0, null=True)),
('encode', models.CharField(blank=True, default=0, max_length=20, null=True)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
migrations.CreateModel(
name='Account',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('accountnumber', models.IntegerField(unique=True)),
('name', models.CharField(max_length=50)),
('sapid', models.IntegerField(unique=True)),
('dateofjoining', models.DateField()),
('sharevalue', models.IntegerField(default=0)),
('totalinvestment', models.IntegerField(default=0)),
('shareamount', models.IntegerField(default=0)),
('sharebalance', models.IntegerField(default=0)),
('cdamount', models.IntegerField(default=0)),
('cdbalance', models.IntegerField(default=0)),
('totalamount', models.DecimalField(decimal_places=2, default=0, max_digits=10)),
('Noofshares', models.IntegerField(default=0, null=True)),
('islongloantaken', models.BooleanField(default=False)),
('isloanemertaken', models.BooleanField(default=False)),
('longloandate', models.DateField(blank=True, null=True)),
('longloanamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloanprinciple', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloanperiod', models.IntegerField(blank=True, default=0, null=True)),
('longloaninterestamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloanbalance', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloanadditional', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('longloanemi', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloandate', models.DateField(blank=True, null=True)),
('emerloanamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloanprinciple', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloanperiod', models.IntegerField(blank=True, default=0, null=True)),
('emerloaninterestamount', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloanbalance', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('emerloanemi', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('downpayment', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('displaydownpayment', models.DecimalField(blank=True, decimal_places=2, default=0, max_digits=10, null=True)),
('FDCount', models.IntegerField(default=0)),
('teachingstaff', models.BooleanField(default=False)),
('nonteachingstaff', models.BooleanField(default=False)),
('username', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, unique=True)),
],
),
]
| 66.333333
| 146
| 0.613585
| 1,219
| 11,542
| 5.716161
| 0.111567
| 0.088404
| 0.075775
| 0.080511
| 0.804248
| 0.8031
| 0.79822
| 0.786739
| 0.782003
| 0.758467
| 0
| 0.019681
| 0.238434
| 11,542
| 173
| 147
| 66.716763
| 0.773038
| 0.003726
| 0
| 0.620482
| 1
| 0
| 0.094285
| 0.003827
| 0
| 0
| 0
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| 0
| false
| 0
| 0.024096
| 0
| 0.048193
| 0
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| null | 0
| 0
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| 1
| 1
| 1
| 1
| 1
| 1
| 0
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0
| 7
|
dc3fbb8a281bed5d68819549a4216811cd93cb00
| 12,622
|
py
|
Python
|
openunmix/__init__.py
|
wesleyr36/piano-unmix
|
839d380aa33d65898b61ad913c6f0c59af58ac62
|
[
"MIT"
] | 781
|
2019-05-15T17:01:33.000Z
|
2022-03-31T13:37:30.000Z
|
openunmix/__init__.py
|
wesleyr36/piano-unmix
|
839d380aa33d65898b61ad913c6f0c59af58ac62
|
[
"MIT"
] | 104
|
2019-08-14T15:43:02.000Z
|
2022-02-10T20:37:20.000Z
|
openunmix/__init__.py
|
wesleyr36/piano-unmix
|
839d380aa33d65898b61ad913c6f0c59af58ac62
|
[
"MIT"
] | 159
|
2019-08-14T03:46:17.000Z
|
2022-03-30T17:51:18.000Z
|
"""
Open-Unmix is a deep neural network reference implementation for music source separation, applicable for researchers, audio engineers and artists. Open-Unmix provides ready-to-use models that allow users to separate pop music into four stems: vocals, drums, bass and the remaining other instruments. The models were pre-trained on the MUSDB18 dataset. See details at apply pre-trained model.
This is the python package API documentation.
Please checkout [the open-unmix website](https://sigsep.github.io/open-unmix) for more information.
"""
from openunmix import utils
import torch.hub
def umxse_spec(targets=None, device="cpu", pretrained=True):
target_urls = {
"speech": "https://zenodo.org/api/files/765b45a3-c70d-48a6-936b-09a7989c349a/speech_f5e0d9f9.pth",
"noise": "https://zenodo.org/api/files/765b45a3-c70d-48a6-936b-09a7989c349a/noise_04a6fc2d.pth",
}
from .model import OpenUnmix
if targets is None:
targets = ["speech", "noise"]
# determine the maximum bin count for a 16khz bandwidth model
max_bin = utils.bandwidth_to_max_bin(rate=16000.0, n_fft=1024, bandwidth=16000)
# load open unmix models speech enhancement models
target_models = {}
for target in targets:
target_unmix = OpenUnmix(
nb_bins=1024 // 2 + 1, nb_channels=1, hidden_size=256, max_bin=max_bin
)
# enable centering of stft to minimize reconstruction error
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
target_urls[target], map_location=device
)
target_unmix.load_state_dict(state_dict, strict=False)
target_unmix.eval()
target_unmix.to(device)
target_models[target] = target_unmix
return target_models
def umxse(
targets=None,
residual=False,
niter=1,
device="cpu",
pretrained=True,
filterbank="torch",
):
"""
Open Unmix Speech Enhancemennt 1-channel BiLSTM Model
trained on the 28-speaker version of Voicebank+Demand
(Sampling rate: 16kHz)
Args:
targets (str): select the targets for the source to be separated.
a list including: ['speech', 'noise'].
If you don't pick them all, you probably want to
activate the `residual=True` option.
Defaults to all available targets per model.
pretrained (bool): If True, returns a model pre-trained on MUSDB18-HQ
residual (bool): if True, a "garbage" target is created
niter (int): the number of post-processingiterations, defaults to 0
device (str): selects device to be used for inference
filterbank (str): filterbank implementation method.
Supported are `['torch', 'asteroid']`. `torch` is about 30% faster
compared to `asteroid` on large FFT sizes such as 4096. However,
asteroids stft can be exported to onnx, which makes is practical
for deployment.
Reference:
Uhlich, Stefan, & Mitsufuji, Yuki. (2020).
Open-Unmix for Speech Enhancement (UMX SE).
Zenodo. http://doi.org/10.5281/zenodo.3786908
"""
from .model import Separator
target_models = umxse_spec(targets=targets, device=device, pretrained=pretrained)
separator = Separator(
target_models=target_models,
niter=niter,
residual=residual,
n_fft=1024,
n_hop=512,
nb_channels=1,
sample_rate=16000.0,
filterbank=filterbank,
).to(device)
return separator
def umxhq_spec(targets=None, device="cpu", pretrained=True):
from .model import OpenUnmix
# set urls for weights
target_urls = {
"bass": "https://zenodo.org/api/files/1c8f83c5-33a5-4f59-b109-721fdd234875/bass-8d85a5bd.pth",
"drums": "https://zenodo.org/api/files/1c8f83c5-33a5-4f59-b109-721fdd234875/drums-9619578f.pth",
"other": "https://zenodo.org/api/files/1c8f83c5-33a5-4f59-b109-721fdd234875/other-b52fbbf7.pth",
"vocals": "https://zenodo.org/api/files/1c8f83c5-33a5-4f59-b109-721fdd234875/vocals-b62c91ce.pth",
}
if targets is None:
targets = ["vocals", "drums", "bass", "other"]
# determine the maximum bin count for a 16khz bandwidth model
max_bin = utils.bandwidth_to_max_bin(rate=44100.0, n_fft=4096, bandwidth=16000)
target_models = {}
for target in targets:
# load open unmix model
target_unmix = OpenUnmix(
nb_bins=4096 // 2 + 1, nb_channels=2, hidden_size=512, max_bin=max_bin
)
# enable centering of stft to minimize reconstruction error
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
target_urls[target], map_location=device
)
target_unmix.load_state_dict(state_dict, strict=False)
target_unmix.eval()
target_unmix.to(device)
target_models[target] = target_unmix
return target_models
def umxhq(
targets=None,
residual=False,
niter=1,
device="cpu",
pretrained=True,
filterbank="torch",
):
"""
Open Unmix 2-channel/stereo BiLSTM Model trained on MUSDB18-HQ
Args:
targets (str): select the targets for the source to be separated.
a list including: ['vocals', 'drums', 'bass', 'other'].
If you don't pick them all, you probably want to
activate the `residual=True` option.
Defaults to all available targets per model.
pretrained (bool): If True, returns a model pre-trained on MUSDB18-HQ
residual (bool): if True, a "garbage" target is created
niter (int): the number of post-processingiterations, defaults to 0
device (str): selects device to be used for inference
filterbank (str): filterbank implementation method.
Supported are `['torch', 'asteroid']`. `torch` is about 30% faster
compared to `asteroid` on large FFT sizes such as 4096. However,
asteroids stft can be exported to onnx, which makes is practical
for deployment.
"""
from .model import Separator
target_models = umxhq_spec(targets=targets, device=device, pretrained=pretrained)
separator = Separator(
target_models=target_models,
niter=niter,
residual=residual,
n_fft=4096,
n_hop=1024,
nb_channels=2,
sample_rate=44100.0,
filterbank=filterbank,
).to(device)
return separator
def umx_spec(targets=None, device="cpu", pretrained=True):
from .model import OpenUnmix
# set urls for weights
target_urls = {
"bass": "https://zenodo.org/api/files/d6105b95-8c52-430c-84ce-bd14b803faaf/bass-646024d3.pth",
"drums": "https://zenodo.org/api/files/d6105b95-8c52-430c-84ce-bd14b803faaf/drums-5a48008b.pth",
"other": "https://zenodo.org/api/files/d6105b95-8c52-430c-84ce-bd14b803faaf/other-f8e132cc.pth",
"vocals": "https://zenodo.org/api/files/d6105b95-8c52-430c-84ce-bd14b803faaf/vocals-c8df74a5.pth",
}
if targets is None:
targets = ["vocals", "drums", "bass", "other"]
# determine the maximum bin count for a 16khz bandwidth model
max_bin = utils.bandwidth_to_max_bin(rate=44100.0, n_fft=4096, bandwidth=16000)
target_models = {}
for target in targets:
# load open unmix model
target_unmix = OpenUnmix(
nb_bins=4096 // 2 + 1, nb_channels=2, hidden_size=512, max_bin=max_bin
)
# enable centering of stft to minimize reconstruction error
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
target_urls[target], map_location=device
)
target_unmix.load_state_dict(state_dict, strict=False)
target_unmix.eval()
target_unmix.to(device)
target_models[target] = target_unmix
return target_models
def umx(
targets=None,
residual=False,
niter=1,
device="cpu",
pretrained=True,
filterbank="torch",
):
"""
Open Unmix 2-channel/stereo BiLSTM Model trained on MUSDB18
Args:
targets (str): select the targets for the source to be separated.
a list including: ['vocals', 'drums', 'bass', 'other'].
If you don't pick them all, you probably want to
activate the `residual=True` option.
Defaults to all available targets per model.
pretrained (bool): If True, returns a model pre-trained on MUSDB18-HQ
residual (bool): if True, a "garbage" target is created
niter (int): the number of post-processingiterations, defaults to 0
device (str): selects device to be used for inference
filterbank (str): filterbank implementation method.
Supported are `['torch', 'asteroid']`. `torch` is about 30% faster
compared to `asteroid` on large FFT sizes such as 4096. However,
asteroids stft can be exported to onnx, which makes is practical
for deployment.
"""
from .model import Separator
target_models = umx_spec(targets=targets, device=device, pretrained=pretrained)
separator = Separator(
target_models=target_models,
niter=niter,
residual=residual,
n_fft=4096,
n_hop=1024,
nb_channels=2,
sample_rate=44100.0,
filterbank=filterbank,
).to(device)
return separator
def umxl_spec(targets=None, device="cpu", pretrained=True):
from .model import OpenUnmix
# set urls for weights
target_urls = {
"bass": "https://zenodo.org/api/files/f8209c3e-ba60-48cf-8e79-71ae65beca61/bass-2ca1ce51.pth",
"drums": "https://zenodo.org/api/files/f8209c3e-ba60-48cf-8e79-71ae65beca61/drums-69e0ebd4.pth",
"other": "https://zenodo.org/api/files/f8209c3e-ba60-48cf-8e79-71ae65beca61/other-c8c5b3e6.pth",
"vocals": "https://zenodo.org/api/files/f8209c3e-ba60-48cf-8e79-71ae65beca61/vocals-bccbd9aa.pth",
}
if targets is None:
targets = ["vocals", "drums", "bass", "other"]
# determine the maximum bin count for a 16khz bandwidth model
max_bin = utils.bandwidth_to_max_bin(rate=44100.0, n_fft=4096, bandwidth=16000)
target_models = {}
for target in targets:
# load open unmix model
target_unmix = OpenUnmix(
nb_bins=4096 // 2 + 1, nb_channels=2, hidden_size=1024, max_bin=max_bin
)
# enable centering of stft to minimize reconstruction error
if pretrained:
state_dict = torch.hub.load_state_dict_from_url(
target_urls[target], map_location=device
)
target_unmix.load_state_dict(state_dict, strict=False)
target_unmix.eval()
target_unmix.to(device)
target_models[target] = target_unmix
return target_models
def umxl(
targets=None,
residual=False,
niter=1,
device="cpu",
pretrained=True,
filterbank="torch",
):
"""
Open Unmix Extra (UMX-L), 2-channel/stereo BLSTM Model trained on a private dataset
of ~400h of multi-track audio.
Args:
targets (str): select the targets for the source to be separated.
a list including: ['vocals', 'drums', 'bass', 'other'].
If you don't pick them all, you probably want to
activate the `residual=True` option.
Defaults to all available targets per model.
pretrained (bool): If True, returns a model pre-trained on MUSDB18-HQ
residual (bool): if True, a "garbage" target is created
niter (int): the number of post-processingiterations, defaults to 0
device (str): selects device to be used for inference
filterbank (str): filterbank implementation method.
Supported are `['torch', 'asteroid']`. `torch` is about 30% faster
compared to `asteroid` on large FFT sizes such as 4096. However,
asteroids stft can be exported to onnx, which makes is practical
for deployment.
"""
from .model import Separator
target_models = umxl_spec(targets=targets, device=device, pretrained=pretrained)
separator = Separator(
target_models=target_models,
niter=niter,
residual=residual,
n_fft=4096,
n_hop=1024,
nb_channels=2,
sample_rate=44100.0,
filterbank=filterbank,
).to(device)
return separator
| 36.37464
| 391
| 0.65164
| 1,600
| 12,622
| 5.044375
| 0.1625
| 0.035683
| 0.024284
| 0.029488
| 0.844877
| 0.83893
| 0.830752
| 0.816751
| 0.810928
| 0.810928
| 0
| 0.062394
| 0.253367
| 12,622
| 346
| 392
| 36.479769
| 0.794037
| 0.416574
| 0
| 0.73913
| 0
| 0.076087
| 0.194381
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043478
| false
| 0
| 0.054348
| 0
| 0.141304
| 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
|
dc6032d1d69c8e9e43c3687d2206c7e4c0bb9f1d
| 3,781
|
py
|
Python
|
insights/combiners/tests/test_dnsmasq_conf_all.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | 121
|
2017-05-30T20:23:25.000Z
|
2022-03-23T12:52:15.000Z
|
insights/combiners/tests/test_dnsmasq_conf_all.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | 1,977
|
2017-05-26T14:36:03.000Z
|
2022-03-31T10:38:53.000Z
|
insights/combiners/tests/test_dnsmasq_conf_all.py
|
lhuett/insights-core
|
1c84eeffc037f85e2bbf60c9a302c83aa1a50cf8
|
[
"Apache-2.0"
] | 244
|
2017-05-30T20:22:57.000Z
|
2022-03-26T10:09:39.000Z
|
from insights.parsers.dnsmasq_config import DnsmasqConf
from insights.combiners.dnsmasq_conf_all import DnsmasqConfTree
from insights.tests import context_wrap
DNSMASQ_CONF_MAIN = """
# Listen on this specific port instead of the standard DNS port
# (53). Setting this to zero completely disables DNS function,
# leaving only DHCP and/or TFTP.
port=5353
no-resolv
domain-needed
no-negcache
max-cache-ttl=1
enable-dbus
dns-forward-max=5000
cache-size=5000
bind-dynamic
except-interface=lo
server=/in-addr.arpa/127.0.0.1
server=/cluster.local/127.0.0.1
# End of config
""".strip()
DNSMASQ_CONF_MAIN_CONF_DIR = """
# Listen on this specific port instead of the standard DNS port
# (53). Setting this to zero completely disables DNS function,
# leaving only DHCP and/or TFTP.
port=5353
no-resolv
domain-needed
no-negcache
max-cache-ttl=1
enable-dbus
dns-forward-max=5000
cache-size=5000
bind-dynamic
except-interface=lo
server=/in-addr.arpa/127.0.0.1
server=/cluster.local/127.0.0.1
conf-dir=/etc/dnsmasq.d
# End of config
""".strip()
DNSMASQ_CONF_MAIN_EXCLUDE_CONF_DIR = """
# Listen on this specific port instead of the standard DNS port
# (53). Setting this to zero completely disables DNS function,
# leaving only DHCP and/or TFTP.
port=5353
no-resolv
domain-needed
no-negcache
max-cache-ttl=1
enable-dbus
dns-forward-max=5000
cache-size=5000
bind-dynamic
except-interface=lo
server=/in-addr.arpa/127.0.0.1
server=/cluster.local/127.0.0.1
conf-dir=/etc/dnsmasq.d,.conf
# End of config
""".strip()
DNSMASQ_CONF_MAIN_INCLUDE_CONF_DIR = """
enable-dbus
dns-forward-max=5000
cache-size=5000
bind-dynamic
except-interface=lo
server=/in-addr.arpa/127.0.0.1
server=/cluster.local/127.0.0.1
conf-dir=/etc/dnsmasq.d,*.conf
# End of config
""".strip()
DNSMASQ_CONF_FILE_1 = """
server=/in-addr.arpa/127.0.0.1
log-queries
txt-record=example.com,"v=spf1 a -all"
""".strip()
DNSMASQ_CONF_FILE_2 = """
dns-forward-max=10000
no-resolv
domain-needed
no-negcache
max-cache-ttl=1
enable-dbus
""".strip()
def test_no_conf_dir():
# no conf-dir
dnsmasq1 = DnsmasqConf(context_wrap(DNSMASQ_CONF_MAIN, path="/etc/dnsmasq.conf"))
dnsmasq2 = DnsmasqConf(context_wrap(DNSMASQ_CONF_FILE_1, path="/etc/dnsmasq.d/origin-dns.conf"))
result = DnsmasqConfTree([dnsmasq1, dnsmasq2])
assert "domain-needed" in result
assert "log-queries" not in result
assert len(result["server"]) == 2
def test_conf_dir():
dnsmasq1 = DnsmasqConf(context_wrap(DNSMASQ_CONF_MAIN_CONF_DIR, path="/etc/dnsmasq.conf"))
dnsmasq2 = DnsmasqConf(context_wrap(DNSMASQ_CONF_FILE_1, path="/etc/dnsmasq.d/origin-dns.conf"))
result = DnsmasqConfTree([dnsmasq1, dnsmasq2])
assert "txt-record" in result
assert len(result["server"]) == 3
dnsmasq1 = DnsmasqConf(context_wrap(DNSMASQ_CONF_MAIN_CONF_DIR, path="/etc/dnsmasq.conf"))
dnsmasq2 = DnsmasqConf(context_wrap(DNSMASQ_CONF_FILE_2, path="/etc/dnsmasq.d/dns-forward-max.conf"))
result = DnsmasqConfTree([dnsmasq1, dnsmasq2])
assert len(result["dns-forward-max"]) == 2
assert result["dns-forward-max"][-1].value == 10000
def test_exclude_conf_dir():
dnsmasq1 = DnsmasqConf(context_wrap(DNSMASQ_CONF_MAIN_EXCLUDE_CONF_DIR, path="/etc/dnsmasq.conf"))
dnsmasq2 = DnsmasqConf(context_wrap(DNSMASQ_CONF_FILE_1, path="/etc/dnsmasq.d/origin-dns.conf"))
result = DnsmasqConfTree([dnsmasq1, dnsmasq2])
assert "txt-record" not in result
assert len(result["server"]) == 2
def test_include_conf_dir():
dnsmasq1 = DnsmasqConf(context_wrap(DNSMASQ_CONF_MAIN_INCLUDE_CONF_DIR, path="/etc/dnsmasq.conf"))
dnsmasq2 = DnsmasqConf(context_wrap(DNSMASQ_CONF_FILE_2, path="/etc/dnsmasq.d/1id-dns.conf"))
result = DnsmasqConfTree([dnsmasq1, dnsmasq2])
assert len(result["dns-forward-max"]) == 2
| 28.643939
| 105
| 0.752446
| 590
| 3,781
| 4.683051
| 0.166102
| 0.087586
| 0.071661
| 0.087586
| 0.87586
| 0.86645
| 0.843286
| 0.818313
| 0.810351
| 0.751357
| 0
| 0.045631
| 0.113198
| 3,781
| 131
| 106
| 28.862595
| 0.778407
| 0.002909
| 0
| 0.732143
| 0
| 0
| 0.508493
| 0.148355
| 0
| 0
| 0
| 0
| 0.089286
| 1
| 0.035714
| false
| 0
| 0.026786
| 0
| 0.0625
| 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
|
dc9e98fdd28e538ffb6188f62ec65c24dbcb93a7
| 162
|
py
|
Python
|
emm/__init__.py
|
LucasKIJ/EMM
|
cf4ec0549956958fad8b3c1d1c5b8ca5b4143704
|
[
"MIT"
] | null | null | null |
emm/__init__.py
|
LucasKIJ/EMM
|
cf4ec0549956958fad8b3c1d1c5b8ca5b4143704
|
[
"MIT"
] | null | null | null |
emm/__init__.py
|
LucasKIJ/EMM
|
cf4ec0549956958fad8b3c1d1c5b8ca5b4143704
|
[
"MIT"
] | null | null | null |
from emm.reweighting import *
from emm.losses import *
from emm.regularizers import *
from emm.solvers import *
from emm.metrics import *
from emm.utils import *
| 23.142857
| 30
| 0.777778
| 24
| 162
| 5.25
| 0.375
| 0.333333
| 0.515873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 162
| 6
| 31
| 27
| 0.913043
| 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
|
f4b09c3cf043e4fac3849d6b73e00935050dc83d
| 6,545
|
py
|
Python
|
loldib/getratings/models/NA/na_darius/na_darius_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_darius/na_darius_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
loldib/getratings/models/NA/na_darius/na_darius_bot.py
|
koliupy/loldib
|
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
|
[
"Apache-2.0"
] | null | null | null |
from getratings.models.ratings import Ratings
class NA_Darius_Bot_Aatrox(Ratings):
pass
class NA_Darius_Bot_Ahri(Ratings):
pass
class NA_Darius_Bot_Akali(Ratings):
pass
class NA_Darius_Bot_Alistar(Ratings):
pass
class NA_Darius_Bot_Amumu(Ratings):
pass
class NA_Darius_Bot_Anivia(Ratings):
pass
class NA_Darius_Bot_Annie(Ratings):
pass
class NA_Darius_Bot_Ashe(Ratings):
pass
class NA_Darius_Bot_AurelionSol(Ratings):
pass
class NA_Darius_Bot_Azir(Ratings):
pass
class NA_Darius_Bot_Bard(Ratings):
pass
class NA_Darius_Bot_Blitzcrank(Ratings):
pass
class NA_Darius_Bot_Brand(Ratings):
pass
class NA_Darius_Bot_Braum(Ratings):
pass
class NA_Darius_Bot_Caitlyn(Ratings):
pass
class NA_Darius_Bot_Camille(Ratings):
pass
class NA_Darius_Bot_Cassiopeia(Ratings):
pass
class NA_Darius_Bot_Chogath(Ratings):
pass
class NA_Darius_Bot_Corki(Ratings):
pass
class NA_Darius_Bot_Darius(Ratings):
pass
class NA_Darius_Bot_Diana(Ratings):
pass
class NA_Darius_Bot_Draven(Ratings):
pass
class NA_Darius_Bot_DrMundo(Ratings):
pass
class NA_Darius_Bot_Ekko(Ratings):
pass
class NA_Darius_Bot_Elise(Ratings):
pass
class NA_Darius_Bot_Evelynn(Ratings):
pass
class NA_Darius_Bot_Ezreal(Ratings):
pass
class NA_Darius_Bot_Fiddlesticks(Ratings):
pass
class NA_Darius_Bot_Fiora(Ratings):
pass
class NA_Darius_Bot_Fizz(Ratings):
pass
class NA_Darius_Bot_Galio(Ratings):
pass
class NA_Darius_Bot_Gangplank(Ratings):
pass
class NA_Darius_Bot_Garen(Ratings):
pass
class NA_Darius_Bot_Gnar(Ratings):
pass
class NA_Darius_Bot_Gragas(Ratings):
pass
class NA_Darius_Bot_Graves(Ratings):
pass
class NA_Darius_Bot_Hecarim(Ratings):
pass
class NA_Darius_Bot_Heimerdinger(Ratings):
pass
class NA_Darius_Bot_Illaoi(Ratings):
pass
class NA_Darius_Bot_Irelia(Ratings):
pass
class NA_Darius_Bot_Ivern(Ratings):
pass
class NA_Darius_Bot_Janna(Ratings):
pass
class NA_Darius_Bot_JarvanIV(Ratings):
pass
class NA_Darius_Bot_Jax(Ratings):
pass
class NA_Darius_Bot_Jayce(Ratings):
pass
class NA_Darius_Bot_Jhin(Ratings):
pass
class NA_Darius_Bot_Jinx(Ratings):
pass
class NA_Darius_Bot_Kalista(Ratings):
pass
class NA_Darius_Bot_Karma(Ratings):
pass
class NA_Darius_Bot_Karthus(Ratings):
pass
class NA_Darius_Bot_Kassadin(Ratings):
pass
class NA_Darius_Bot_Katarina(Ratings):
pass
class NA_Darius_Bot_Kayle(Ratings):
pass
class NA_Darius_Bot_Kayn(Ratings):
pass
class NA_Darius_Bot_Kennen(Ratings):
pass
class NA_Darius_Bot_Khazix(Ratings):
pass
class NA_Darius_Bot_Kindred(Ratings):
pass
class NA_Darius_Bot_Kled(Ratings):
pass
class NA_Darius_Bot_KogMaw(Ratings):
pass
class NA_Darius_Bot_Leblanc(Ratings):
pass
class NA_Darius_Bot_LeeSin(Ratings):
pass
class NA_Darius_Bot_Leona(Ratings):
pass
class NA_Darius_Bot_Lissandra(Ratings):
pass
class NA_Darius_Bot_Lucian(Ratings):
pass
class NA_Darius_Bot_Lulu(Ratings):
pass
class NA_Darius_Bot_Lux(Ratings):
pass
class NA_Darius_Bot_Malphite(Ratings):
pass
class NA_Darius_Bot_Malzahar(Ratings):
pass
class NA_Darius_Bot_Maokai(Ratings):
pass
class NA_Darius_Bot_MasterYi(Ratings):
pass
class NA_Darius_Bot_MissFortune(Ratings):
pass
class NA_Darius_Bot_MonkeyKing(Ratings):
pass
class NA_Darius_Bot_Mordekaiser(Ratings):
pass
class NA_Darius_Bot_Morgana(Ratings):
pass
class NA_Darius_Bot_Nami(Ratings):
pass
class NA_Darius_Bot_Nasus(Ratings):
pass
class NA_Darius_Bot_Nautilus(Ratings):
pass
class NA_Darius_Bot_Nidalee(Ratings):
pass
class NA_Darius_Bot_Nocturne(Ratings):
pass
class NA_Darius_Bot_Nunu(Ratings):
pass
class NA_Darius_Bot_Olaf(Ratings):
pass
class NA_Darius_Bot_Orianna(Ratings):
pass
class NA_Darius_Bot_Ornn(Ratings):
pass
class NA_Darius_Bot_Pantheon(Ratings):
pass
class NA_Darius_Bot_Poppy(Ratings):
pass
class NA_Darius_Bot_Quinn(Ratings):
pass
class NA_Darius_Bot_Rakan(Ratings):
pass
class NA_Darius_Bot_Rammus(Ratings):
pass
class NA_Darius_Bot_RekSai(Ratings):
pass
class NA_Darius_Bot_Renekton(Ratings):
pass
class NA_Darius_Bot_Rengar(Ratings):
pass
class NA_Darius_Bot_Riven(Ratings):
pass
class NA_Darius_Bot_Rumble(Ratings):
pass
class NA_Darius_Bot_Ryze(Ratings):
pass
class NA_Darius_Bot_Sejuani(Ratings):
pass
class NA_Darius_Bot_Shaco(Ratings):
pass
class NA_Darius_Bot_Shen(Ratings):
pass
class NA_Darius_Bot_Shyvana(Ratings):
pass
class NA_Darius_Bot_Singed(Ratings):
pass
class NA_Darius_Bot_Sion(Ratings):
pass
class NA_Darius_Bot_Sivir(Ratings):
pass
class NA_Darius_Bot_Skarner(Ratings):
pass
class NA_Darius_Bot_Sona(Ratings):
pass
class NA_Darius_Bot_Soraka(Ratings):
pass
class NA_Darius_Bot_Swain(Ratings):
pass
class NA_Darius_Bot_Syndra(Ratings):
pass
class NA_Darius_Bot_TahmKench(Ratings):
pass
class NA_Darius_Bot_Taliyah(Ratings):
pass
class NA_Darius_Bot_Talon(Ratings):
pass
class NA_Darius_Bot_Taric(Ratings):
pass
class NA_Darius_Bot_Teemo(Ratings):
pass
class NA_Darius_Bot_Thresh(Ratings):
pass
class NA_Darius_Bot_Tristana(Ratings):
pass
class NA_Darius_Bot_Trundle(Ratings):
pass
class NA_Darius_Bot_Tryndamere(Ratings):
pass
class NA_Darius_Bot_TwistedFate(Ratings):
pass
class NA_Darius_Bot_Twitch(Ratings):
pass
class NA_Darius_Bot_Udyr(Ratings):
pass
class NA_Darius_Bot_Urgot(Ratings):
pass
class NA_Darius_Bot_Varus(Ratings):
pass
class NA_Darius_Bot_Vayne(Ratings):
pass
class NA_Darius_Bot_Veigar(Ratings):
pass
class NA_Darius_Bot_Velkoz(Ratings):
pass
class NA_Darius_Bot_Vi(Ratings):
pass
class NA_Darius_Bot_Viktor(Ratings):
pass
class NA_Darius_Bot_Vladimir(Ratings):
pass
class NA_Darius_Bot_Volibear(Ratings):
pass
class NA_Darius_Bot_Warwick(Ratings):
pass
class NA_Darius_Bot_Xayah(Ratings):
pass
class NA_Darius_Bot_Xerath(Ratings):
pass
class NA_Darius_Bot_XinZhao(Ratings):
pass
class NA_Darius_Bot_Yasuo(Ratings):
pass
class NA_Darius_Bot_Yorick(Ratings):
pass
class NA_Darius_Bot_Zac(Ratings):
pass
class NA_Darius_Bot_Zed(Ratings):
pass
class NA_Darius_Bot_Ziggs(Ratings):
pass
class NA_Darius_Bot_Zilean(Ratings):
pass
class NA_Darius_Bot_Zyra(Ratings):
pass
| 15.695444
| 46
| 0.766692
| 972
| 6,545
| 4.736626
| 0.151235
| 0.209818
| 0.389661
| 0.479583
| 0.803432
| 0.803432
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169748
| 6,545
| 416
| 47
| 15.733173
| 0.847258
| 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
|
57ff0f68d46d31db1717705fee0897a0f4f1e427
| 14,689
|
py
|
Python
|
agent/agent/process/tests/test_metadata_checks.py
|
woutersj/arxiv-submission-core
|
6077ce4e0685d67ce7010800083a898857158112
|
[
"MIT"
] | 14
|
2019-05-26T22:52:17.000Z
|
2021-11-05T12:26:46.000Z
|
agent/agent/process/tests/test_metadata_checks.py
|
woutersj/arxiv-submission-core
|
6077ce4e0685d67ce7010800083a898857158112
|
[
"MIT"
] | 30
|
2018-01-31T19:16:08.000Z
|
2018-12-08T08:41:04.000Z
|
agent/agent/process/tests/test_metadata_checks.py
|
cul-it/arxiv-submission-core
|
6077ce4e0685d67ce7010800083a898857158112
|
[
"MIT"
] | 8
|
2019-01-10T22:01:39.000Z
|
2021-11-20T21:44:51.000Z
|
"""Tests for automated metadata checks."""
from unittest import TestCase, mock
from datetime import datetime, timedelta
from pytz import UTC
import copy
from arxiv.submission.domain.event import SetTitle, SetAbstract, \
AddMetadataFlag, RemoveFlag
from arxiv.submission.domain.agent import Agent, User
from arxiv.submission.domain.flag import Flag, MetadataFlag
from arxiv.submission.domain.submission import Submission, SubmissionContent, \
SubmissionMetadata, Classification
from .. import CheckForSimilarTitles, CheckTitleForUnicodeAbuse, \
CheckAbstractForUnicodeAbuse, Failed
from .. import metadata_checks
from ...domain import Trigger
from ...factory import create_app
from .data import titles
class TestCheckForSimilarTitles(TestCase):
"""Tests for :func:`.metadata_checks.check_similar_titles`."""
def setUp(self):
"""We have a submission."""
self.app = create_app()
self.creator = User(native_id=1234, email='something@else.com')
self.submission = Submission(
submission_id=2347441,
creator=self.creator,
owner=self.creator,
created=datetime.now(UTC),
source_content=SubmissionContent(
identifier='5678',
source_format=SubmissionContent.Format('pdf'),
checksum='a1b2c3d4',
uncompressed_size=58493,
compressed_size=58493
)
)
self.process = CheckForSimilarTitles(self.submission.submission_id)
@mock.patch(f'{metadata_checks.__name__}.classic.get_titles')
def test_check_similar_titles(self, mock_get_titles):
"""Check for similar titles."""
mock_get_titles.return_value = titles.TITLES
user_id = 54321
title = 'a lepton qed of colliders or interactions with strong field' \
' electron laser'
event = SetTitle(creator=self.creator, title=title)
before = copy.deepcopy(self.submission)
after = copy.deepcopy(self.submission)
after.metadata = SubmissionMetadata(title=title)
events = []
trigger = Trigger(event=event, actor=self.creator,
before=before, after=after,
params={'TITLE_SIMILARITY_WINDOW': 60})
some_titles = self.process.get_candidates(None, trigger, events.append)
self.assertEqual(len(some_titles), len(titles.TITLES))
self.assertEqual(mock_get_titles.call_count, 1)
self.assertIsInstance(mock_get_titles.call_args[0][0], datetime)
def test_check_for_duplicates(self):
"""Look for similar titles."""
title = 'a lepton qed of colliders or interactions with strong field' \
' electron laser'
event = SetTitle(creator=self.creator, title=title)
before = copy.deepcopy(self.submission)
after = copy.deepcopy(self.submission)
after.metadata = SubmissionMetadata(title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=after,
params={'TITLE_SIMILARITY_THRESHOLD': 0.7})
events = []
self.process.check_for_duplicates(titles.TITLES, trigger,
events.append)
self.assertGreater(len(events), 0, "Generates some events")
for event in events:
self.assertIsInstance(event, AddMetadataFlag,
"Generates AddMetadataFlag events")
self.assertEqual(
event.flag_type,
MetadataFlag.FlagType.POSSIBLE_DUPLICATE_TITLE,
"Flag has type POSSIBLE_DUPLICATE_TITLE"
)
def test_check_with_existing_flags(self):
"""The submission already has possible dupe title flags."""
title = 'a lepton qed of colliders or interactions with strong field' \
' electron laser'
self.submission.flags['asdf1234'] = MetadataFlag(
event_id='asdf1234',
creator=self.creator,
created=datetime.now(UTC),
flag_type=MetadataFlag.FlagType.POSSIBLE_DUPLICATE_TITLE,
flag_data={'id': 5, 'title': title, 'owner': self.creator},
field='title',
comment='possible duplicate title'
)
event = SetTitle(creator=self.creator, title=title)
before = copy.deepcopy(self.submission)
after = copy.deepcopy(self.submission)
after.metadata = SubmissionMetadata(title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=after,
params={'TITLE_SIMILARITY_THRESHOLD': 1.0})
events = []
self.process.check_for_duplicates(titles.TITLES, trigger,
events.append)
self.assertGreater(len(events), 0, "Generates some events")
self.assertIsInstance(events[0], RemoveFlag)
self.assertEqual(events[0].flag_id, 'asdf1234')
def test_check_for_duplicates_with_strict_threshold(self):
"""Look for similar titles with an impossibly strict threshold."""
title = 'a lepton qed of colliders or interactions with strong field' \
' electron laser'
event = SetTitle(creator=self.creator, title=title)
before = copy.deepcopy(self.submission)
after = copy.deepcopy(self.submission)
after.metadata = SubmissionMetadata(title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=after,
params={'TITLE_SIMILARITY_THRESHOLD': 1.0})
events = []
self.process.check_for_duplicates(titles.TITLES, trigger,
events.append)
self.assertEqual(len(events), 0)
class TestCheckTitleForUnicodeAbuse(TestCase):
"""Tests for :func:`.CheckTitleForUnicodeAbuse.check_title`."""
def setUp(self):
"""We have a submission."""
self.app = create_app()
self.creator = User(native_id=1234, email='something@else.com')
self.submission = Submission(
submission_id=2347441,
creator=self.creator,
owner=self.creator,
created=datetime.now(UTC),
source_content=SubmissionContent(
identifier='5678',
source_format=SubmissionContent.Format('pdf'),
checksum='a1b2c3d4',
uncompressed_size=58493,
compressed_size=58493
)
)
self.process = CheckTitleForUnicodeAbuse(self.submission.submission_id)
def test_low_ascii(self):
"""Title has very few ASCII characters."""
before = copy.deepcopy(self.submission)
title = 'ⓕöö tïtłę'
self.submission.metadata = SubmissionMetadata(title=title)
event = SetTitle(creator=self.creator, title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.5})
events = []
self.process.check_title(None, trigger, events.append)
self.assertIsInstance(events[0], AddMetadataFlag, 'Adds metadata flag')
self.assertEqual(events[0].flag_type, MetadataFlag.FlagType.CHARACTER_SET)
self.assertEqual(events[0].field, 'title')
self.assertEqual(events[0].flag_data['ascii'], 3/9)
def test_plenty_of_ascii(self):
"""Title has very planty of ASCII characters."""
before = copy.deepcopy(self.submission)
title = 'A boring title with occâsional non-ASCII characters'
self.submission.metadata = SubmissionMetadata(title=title)
event = SetTitle(creator=self.creator, title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
self.process.check_title(None, trigger, events.append)
self.assertEqual(len(events), 0, 'No flags generated')
def test_no_metadata(self):
"""The submission has no metadata."""
self.submission.metadata = None
trigger = Trigger(actor=self.creator,
before=self.submission, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
with self.assertRaises(Failed):
self.process.check_title(None, trigger, events.append)
def test_no_abstract(self):
"""The submission has no title."""
self.submission.metadata = SubmissionMetadata(title=None)
trigger = Trigger(actor=self.creator,
before=self.submission, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
with self.assertRaises(Failed):
self.process.check_title(None, trigger, events.append)
def test_clear_previous_tags(self):
"""There were some previous flags."""
self.submission.flags['asdf1234'] = MetadataFlag(
event_id='asdf1234',
creator=self.creator,
created=datetime.now(UTC),
flag_type=MetadataFlag.FlagType.CHARACTER_SET,
flag_data={'ascii': 0},
field='title',
comment='something fishy'
)
before = copy.deepcopy(self.submission)
title = 'A boring title with occâsional non-ASCII characters'
self.submission.metadata = SubmissionMetadata(title=title)
event = SetTitle(creator=self.creator, title=title)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
self.process.check_title(None, trigger, events.append)
self.assertGreater(len(events), 0, "Generates some events")
self.assertIsInstance(events[0], RemoveFlag)
self.assertEqual(events[0].flag_id, 'asdf1234')
class TestCheckAbstractForUnicodeAbuse(TestCase):
"""Tests for :func:`.CheckAbstractForUnicodeAbuse.check_abstract`."""
def setUp(self):
"""We have a submission."""
self.app = create_app()
self.creator = User(native_id=1234, email='something@else.com')
self.submission = Submission(
submission_id=2347441,
creator=self.creator,
owner=self.creator,
created=datetime.now(UTC),
source_content=SubmissionContent(
identifier='5678',
source_format=SubmissionContent.Format('pdf'),
checksum='a1b2c3d4',
uncompressed_size=58493,
compressed_size=58493
)
)
self.process = \
CheckAbstractForUnicodeAbuse(self.submission.submission_id)
def test_low_ascii(self):
"""Abstract has very few ASCII characters."""
before = copy.deepcopy(self.submission)
abstract = 'ⓥéⓇÿ âⒷśⓣⓇāčⓣ'
self.submission.metadata = SubmissionMetadata(abstract=abstract)
event = SetAbstract(creator=self.creator, abstract=abstract)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.5})
events = []
self.process.check_abstract(None, trigger, events.append)
self.assertIsInstance(events[0], AddMetadataFlag, 'Adds metadata flag')
self.assertEqual(events[0].flag_type, MetadataFlag.FlagType.CHARACTER_SET)
self.assertEqual(events[0].field, 'abstract')
self.assertEqual(events[0].flag_data['ascii'], 1/13)
def test_plenty_of_ascii(self):
"""Abstract has very planty of ASCII characters."""
before = copy.deepcopy(self.submission)
abstract = 'what a boring abstract with no unicode characters'
self.submission.metadata = SubmissionMetadata(abstract=abstract)
event = SetAbstract(creator=self.creator, abstract=abstract)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
self.process.check_abstract(None, trigger, events.append)
self.assertEqual(len(events), 0, 'No flags generated')
def test_no_metadata(self):
"""The submission has no metadata."""
self.submission.metadata = None
trigger = Trigger(actor=self.creator,
before=self.submission, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
with self.assertRaises(Failed):
self.process.check_abstract(None, trigger, events.append)
def test_no_abstract(self):
"""The submission has no abstract."""
self.submission.metadata = SubmissionMetadata(abstract=None)
trigger = Trigger(actor=self.creator,
before=self.submission, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
with self.assertRaises(Failed):
self.process.check_abstract(None, trigger, events.append)
def test_clear_previous_tags(self):
"""There were some previous flags."""
self.submission.flags['asdf1234'] = MetadataFlag(
event_id='asdf1234',
creator=self.creator,
created=datetime.now(UTC),
flag_type=MetadataFlag.FlagType.CHARACTER_SET,
flag_data={'ascii': 0},
field='abstract',
comment='something fishy'
)
before = copy.deepcopy(self.submission)
abstract = 'what a boring abstract with no unicode characters'
self.submission.metadata = SubmissionMetadata(abstract=abstract)
event = SetAbstract(creator=self.creator, abstract=abstract)
trigger = Trigger(event=event, actor=self.creator,
before=before, after=self.submission,
params={'METADATA_ASCII_THRESHOLD': 0.1})
events = []
self.process.check_abstract(None, trigger, events.append)
self.assertGreater(len(events), 0, "Generates some events")
self.assertIsInstance(events[0], RemoveFlag)
self.assertEqual(events[0].flag_id, 'asdf1234')
| 43.45858
| 82
| 0.622166
| 1,488
| 14,689
| 6.036962
| 0.117608
| 0.073249
| 0.032061
| 0.040521
| 0.814873
| 0.790716
| 0.785372
| 0.773016
| 0.755093
| 0.745074
| 0
| 0.017664
| 0.275444
| 14,689
| 337
| 83
| 43.587537
| 0.825707
| 0.054599
| 0
| 0.76087
| 0
| 0
| 0.102895
| 0.029751
| 0
| 0
| 0
| 0
| 0.108696
| 1
| 0.061594
| false
| 0
| 0.047101
| 0
| 0.119565
| 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
|
52162283e2fb24691624b8fd28864476b6a8fc6d
| 3,278
|
py
|
Python
|
AI 이노베이션 스퀘어 언어지능 과정/20190502/03.py
|
donddog/AI_Innovation_Square_Codes
|
a04d50db011d25e00d8486146c24124c50242aa7
|
[
"MIT"
] | 1
|
2021-02-11T16:45:21.000Z
|
2021-02-11T16:45:21.000Z
|
AI 이노베이션 스퀘어 언어지능 과정/20190502/03.py
|
donddog/AI_Innovation_Square_Codes
|
a04d50db011d25e00d8486146c24124c50242aa7
|
[
"MIT"
] | null | null | null |
AI 이노베이션 스퀘어 언어지능 과정/20190502/03.py
|
donddog/AI_Innovation_Square_Codes
|
a04d50db011d25e00d8486146c24124c50242aa7
|
[
"MIT"
] | null | null | null |
<<<<<<< HEAD
from urllib import parse
import requests
import json
from requests.utils import quote, unquote
header = {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.108 Safari/537.36"}
def download(url, params={}, retries=3):
resp = None
try:
resp = requests.get(url, params=params, headers = header)
resp.raise_for_status()
except requests.exceptions.HTTPError as e:
if 500 <= e.response.status_code < 600 and retries > 0:
print(retries)
resp = download(url, params, retries-1)
else:
print("status_code: ", e.response.status_code, "\n")
print("response reason: ", e.response.reason, "\n")
print("headers: ", e.request.headers, "\n")
return resp
url = "http://openapi.airkorea.or.kr/openapi/services/rest/ArpltnInforInqireSvc/getCtprvnRltmMesureDnsty"
params = {
"serviceKey": "SOiHe0eIo6uoLf60XK0keAAwBXmx33hU4bEEdZZ0rYoCbbWRYswGsk2JdpJsUcHRQbvdp0ACEbmu5YcpfiJcKw%3D%3D",
"numOfRows": 10,
"pageNo": 1,
"sidoName": "경기",
"ver": 1.3,
"_returnType": "json"
}
result = download(url, params)
print("result text: ", result.text, "\n")
print("result url: ", result.url, "\n")
#params["serviceKey"] = unquote(params["serviceKey"])
#quote(unquote(params["serviceKey"]))
org = requests.utils.unquote(params["serviceKey"])
params['serviceKey'] = org
result = download(url, params)
result = json.loads(result.text)
print("result: ", result)
for row in result['list']:
=======
from urllib import parse
import requests
import json
from requests.utils import quote, unquote
header = {"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.108 Safari/537.36"}
def download(url, params={}, retries=3):
resp = None
try:
resp = requests.get(url, params=params, headers = header)
resp.raise_for_status()
except requests.exceptions.HTTPError as e:
if 500 <= e.response.status_code < 600 and retries > 0:
print(retries)
resp = download(url, params, retries-1)
else:
print("status_code: ", e.response.status_code, "\n")
print("response reason: ", e.response.reason, "\n")
print("headers: ", e.request.headers, "\n")
return resp
url = "http://openapi.airkorea.or.kr/openapi/services/rest/ArpltnInforInqireSvc/getCtprvnRltmMesureDnsty"
params = {
"serviceKey": "SOiHe0eIo6uoLf60XK0keAAwBXmx33hU4bEEdZZ0rYoCbbWRYswGsk2JdpJsUcHRQbvdp0ACEbmu5YcpfiJcKw%3D%3D",
"numOfRows": 10,
"pageNo": 1,
"sidoName": "경기",
"ver": 1.3,
"_returnType": "json"
}
result = download(url, params)
print("result text: ", result.text, "\n")
print("result url: ", result.url, "\n")
#params["serviceKey"] = unquote(params["serviceKey"])
#quote(unquote(params["serviceKey"]))
org = requests.utils.unquote(params["serviceKey"])
params['serviceKey'] = org
result = download(url, params)
result = json.loads(result.text)
print("result: ", result)
for row in result['list']:
>>>>>>> 125e15a4c5fcf711dd279c9b18e149867466699e
print("동이름: ",row['stationName'], "/", "미세먼지 농도: ",row['pm25Value'], "/", "통합대기 등급: ", row['khaiGrade'])
| 33.44898
| 142
| 0.663819
| 388
| 3,278
| 5.57732
| 0.252577
| 0.088725
| 0.062847
| 0.044362
| 0.952865
| 0.952865
| 0.952865
| 0.952865
| 0.952865
| 0.952865
| 0
| 0.053235
| 0.174802
| 3,278
| 98
| 143
| 33.44898
| 0.746765
| 0.053691
| 0
| 0.923077
| 0
| 0.025641
| 0.322685
| 0.059374
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.102564
| null | null | 0.192308
| 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
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
5216b3b36242c73754a0967eaf56949a5a96d1b1
| 92
|
py
|
Python
|
parameters_8000.py
|
Nuevalgo/Feedbot
|
96bdd150fcd92fa155dfc7b13d930bab394e8e47
|
[
"BSD-3-Clause"
] | 1
|
2017-06-05T10:28:32.000Z
|
2017-06-05T10:28:32.000Z
|
parameters_8000.py
|
Nuevalgo/Feedbot
|
96bdd150fcd92fa155dfc7b13d930bab394e8e47
|
[
"BSD-3-Clause"
] | null | null | null |
parameters_8000.py
|
Nuevalgo/Feedbot
|
96bdd150fcd92fa155dfc7b13d930bab394e8e47
|
[
"BSD-3-Clause"
] | null | null | null |
password="pbkdf2(1000,20,sha512)$ae079c735f468745$3bf00655fa79db2ea893e8f068703c259413ab0f"
| 46
| 91
| 0.891304
| 7
| 92
| 11.714286
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.527473
| 0.01087
| 92
| 1
| 92
| 92
| 0.373626
| 0
| 0
| 0
| 0
| 0
| 0.869565
| 0.869565
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
52319425521175d5c0edfbee130ee0238f4f3514
| 952
|
py
|
Python
|
scipy/linalg/HOSVD.py
|
mertall/scipy
|
b3d4b18a61f6020b2fe02f214cea903fc58d505e
|
[
"BSD-3-Clause"
] | null | null | null |
scipy/linalg/HOSVD.py
|
mertall/scipy
|
b3d4b18a61f6020b2fe02f214cea903fc58d505e
|
[
"BSD-3-Clause"
] | null | null | null |
scipy/linalg/HOSVD.py
|
mertall/scipy
|
b3d4b18a61f6020b2fe02f214cea903fc58d505e
|
[
"BSD-3-Clause"
] | null | null | null |
def HOSVD(A):
#A is n(1) x ... x n(d) tensor
#U is d cell array with U(k) being the left singular vector of a's mode-k unfolding
#S is n(1) x ... x n(d) tensor : A x1 U(1) x2 U(2) ... xd U(d)
S = A
for k in range(A.size()):
C = A[k,:].numpy() ##may need to make sure columns are arranged in increasing order
[U[k],Sig,V] = svd(C)
t_U(k) = numpyu.transpose(U(k))
S = numpy.tensordot(S,t_U(k),k)
return S, U
def SHOSVD(A):
#A is n(1) x ... x n(d) tensor
#U is d cell array with U(k) being the left singular vector of a's mode-k unfolding
#S is n(1) x ... x n(d) tensor : A x1 U(1) x2 U(2) ... xd U(d)
S = A
for k in range(A.size()):
C = A[k,:].numpy() ##may need to make sure columns are arranged in increasing order
[U[k],Sig,V] = svds(C)
t_U(k) = numpyu.transpose(U(k))
S = numpy.tensordot(S,t_U(k),k)
return S, U
| 38.08
| 92
| 0.533613
| 194
| 952
| 2.597938
| 0.273196
| 0.039683
| 0.031746
| 0.039683
| 0.952381
| 0.952381
| 0.952381
| 0.952381
| 0.952381
| 0.952381
| 0
| 0.018127
| 0.304622
| 952
| 25
| 93
| 38.08
| 0.743202
| 0.491597
| 0
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| 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
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
8708d903405b4c6313b57883408d6760699815ce
| 28,794
|
py
|
Python
|
pcdswidgets/vacuum/valves.py
|
klauer/pcdswidgets
|
a6f50fdb41b4d7a991c86fec9bce06a4f09a80af
|
[
"BSD-3-Clause-LBNL"
] | null | null | null |
pcdswidgets/vacuum/valves.py
|
klauer/pcdswidgets
|
a6f50fdb41b4d7a991c86fec9bce06a4f09a80af
|
[
"BSD-3-Clause-LBNL"
] | null | null | null |
pcdswidgets/vacuum/valves.py
|
klauer/pcdswidgets
|
a6f50fdb41b4d7a991c86fec9bce06a4f09a80af
|
[
"BSD-3-Clause-LBNL"
] | null | null | null |
from qtpy.QtCore import QSize, Property
from .base import PCDSSymbolBase, ContentLocation
from .mixins import (InterlockMixin, ErrorMixin, StateMixin, ButtonControl)
from ..icons.valves import (ApertureValveSymbolIcon, PneumaticValveSymbolIcon,
FastShutterSymbolIcon, NeedleValveSymbolIcon,
ProportionalValveSymbolIcon,
RightAngleManualValveSymbolIcon,
ControlValveSymbolIcon,
ControlOnlyValveSymbolIcon)
class PneumaticValve(InterlockMixin, ErrorMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Pneumatic Valve with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------+
|Property |Values |
+===========+=======================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------+
|error |`Vented`, `At Vacuum`, `Differential Pressure` or |
| |`Lost Vacuum` |
+-----------+-------------------------------------------------------+
|state |`Open`, `Closed`, `Moving`, `Invalid` |
+-----------+-------------------------------------------------------+
Examples
--------
.. code-block:: css
PneumaticValve[interlocked="true"] #interlock {
border: 5px solid red;
}
PneumaticValve[interlocked="false"] #interlock {
border: 0px;
}
PneumaticValve[interlocked="true"] #icon {
qproperty-interlockBrush: #FF0000;
}
PneumaticValve[interlocked="false"] #icon {
qproperty-interlockBrush: #00FF00;
}
PneumaticValve[error="Lost Vacuum"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
PneumaticValve[state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
_interlock_suffix = ":OPN_OK_RBV"
_error_suffix = ":STATE_RBV"
_state_suffix = ":POS_STATE_RBV"
_command_suffix = ":OPN_SW"
NAME = "Pneumatic Valve"
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VGC"
def __init__(self, parent=None, **kwargs):
super(PneumaticValve, self).__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
error_suffix=self._error_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = PneumaticValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class ApertureValve(InterlockMixin, ErrorMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing an Aperture Valve with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------+
|Property |Values |
+===========+=======================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------+
|error |`Vented`, `At Vacuum`, `Differential Pressure` or |
| |`Lost Vacuum` |
+-----------+-------------------------------------------------------+
|state |`Open`, `Close`, `Moving` or `INVALID` |
+-----------+-------------------------------------------------------+
Examples
--------
.. code-block:: css
ApertureValve [interlocked="true"] #interlock {
border: 5px solid red;
}
ApertureValve [interlocked="false"] #interlock {
border: 0px;
}
ApertureValve [interlocked="true"] #icon {
qproperty-interlockBrush: #FF0000;
}
ApertureValve [interlocked="false"] #icon {
qproperty-interlockBrush: #00FF00;
}
ApertureValve [error="Lost Vacuum"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
ApertureValve [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
_interlock_suffix = ":OPN_OK_RBV"
_error_suffix = ":STATE_RBV"
_state_suffix = ":POS_STATE_RBV"
_command_suffix = ":OPN_SW"
NAME = "Aperture Valve"
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VRC"
def __init__(self, parent=None, **kwargs):
super(ApertureValve, self).__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
error_suffix=self._error_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = ApertureValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class FastShutter(InterlockMixin, ErrorMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Fast Shutter with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------------+
|Property |Values |
+===========+=============================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------------+
|error |`true`, or `false` |
+-----------+-------------------------------------------------------------+
|state |`Open`, `Close` `Moving` or `INVALID` |
+-----------+-------------------------------------------------------------+
Examples
--------
.. code-block:: css
FastShutter [interlocked="true"] #interlock {
border: 5px solid red;
}
FastShutter [interlocked="false"] #interlock {
border: 0px;
}
FastShutter [error="true"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
FastShutter [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
_interlock_suffix = ":VAC_FAULT_OK_RBV"
_error_suffix = ":ERROR_RBV"
_state_suffix = ":POS_STATE_RBV"
_command_suffix = ":OPN_SW"
NAME = "Fast Shutter"
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VRC"
def __init__(self, parent=None, **kwargs):
super(FastShutter, self).__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
error_suffix=self._error_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = FastShutterSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class NeedleValve(InterlockMixin, StateMixin, ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Needle Valve with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------------+
|Property |Values |
+===========+=============================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------------+
|state |`Close`, `Open`, `PressureControl`, `ManualControl` |
+-----------+-------------------------------------------------------------+
Examples
--------
.. code-block:: css
NeedleValve [interlocked="true"] #interlock {
border: 5px solid red;
}
NeedleValve [interlocked="false"] #interlock {
border: 0px;
}
FastShutter [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
_interlock_suffix = ":ILK_OK_RBV"
_state_suffix = ":STATE_RBV"
_command_suffix = ":OPN_SW"
NAME = "Needle Valve"
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VCN"
def __init__(self, parent=None, **kwargs):
super(NeedleValve, self).__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = NeedleValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class ProportionalValve(InterlockMixin, StateMixin, ButtonControl,
PCDSSymbolBase):
"""
A Symbol Widget representing a Proportional Valve with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------------+
|Property |Values |
+===========+=============================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------------+
|state |`Close`, `Open`, `PressureControl`, `ManualControl` |
+-----------+-------------------------------------------------------------+
Examples
--------
.. code-block:: css
ProportionalValve [interlocked="true"] #interlock {
border: 5px solid red;
}
ProportionalValve [interlocked="false"] #interlock {
border: 0px;
}
ProportionalValve [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
_interlock_suffix = ":ILK_OK_RBV"
_state_suffix = ":STATE_RBV"
_command_suffix = ":OPN_SW"
NAME = "Proportional Valve"
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VRC"
def __init__(self, parent=None, **kwargs):
super(ProportionalValve, self).__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = ProportionalValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class RightAngleManualValve(PCDSSymbolBase):
"""
A Symbol Widget representing a Right Angle Manual Valve with the proper
icon.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
"""
NAME = "Right Angle Manual Valve"
def __init__(self, parent=None, **kwargs):
self._controls_location = ContentLocation.Hidden
super(RightAngleManualValve, self).__init__(parent=parent, **kwargs)
self.icon = RightAngleManualValveSymbolIcon(parent=self)
def sizeHint(self):
"""
Suggested initial size for the widget.
Returns
-------
size : QSize
"""
return QSize(40, 40)
@Property(str, designable=False)
def channelsPrefix(self):
return super().channelsPrefix
@Property(bool, designable=False)
def showIcon(self):
return super().showIcon
@Property(ContentLocation, designable=False)
def controlsLocation(self):
return super().controlsLocation
class ControlValve(InterlockMixin, ErrorMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Control Valve with the proper icon and
controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------+
|Property |Values |
+===========+=======================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------+
|error |`Vented`, `At Vacuum`, `Differential Pressure` or |
| |`Lost Vacuum` |
+-----------+-------------------------------------------------------+
|state |`Open`, `Close` `Moving` or `INVALID` |
+-----------+-------------------------------------------------------+
Examples
--------
.. code-block:: css
ControlValve [interlocked="true"] #interlock {
border: 5px solid red;
}
ControlValve [interlocked="false"] #interlock {
border: 0px;
}
ControlValve [interlocked="true"] #icon {
qproperty-interlockBrush: #FF0000;
}
ControlValve [interlocked="false"] #icon {
qproperty-interlockBrush: #00FF00;
}
ControlValve [error="Lost Vacuum"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
ControlValve [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
NAME = 'Control Valve with Readback'
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VVC"
_interlock_suffix = ":OPN_OK_RBV"
_error_suffix = ":STATE_RBV"
_state_suffix = ":POS_STATE_RBV"
_command_suffix = ":OPN_SW"
def __init__(self, parent=None, **kwargs):
super().__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
error_suffix=self._error_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = ControlValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class ControlOnlyValveNC(InterlockMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Normally Closed Control Valve with the
proper icon and controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------+
|Property |Values |
+===========+=======================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------+
|state |`Open`, `Close` or `INVALID` |
+-----------+-------------------------------------------------------+
Examples
--------
.. code-block:: css
ControlOnlyValveNC [interlocked="true"] #interlock {
border: 5px solid red;
}
ControlOnlyValveNC [interlocked="false"] #interlock {
border: 0px;
}
ControlOnlyValveNC [interlocked="true"] #icon {
qproperty-interlockBrush: #FF0000;
}
ControlOnlyValveNC [interlocked="false"] #icon {
qproperty-interlockBrush: #00FF00;
}
ControlOnlyValveNC [error="Lost Vacuum"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
ControlOnlyValveNC [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
NAME = 'Normally Closed Control Valve with No Readback'
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VVC"
_interlock_suffix = ":OPN_OK_RBV"
_state_suffix = ':OPN_DO_RBV'
_command_suffix = ":OPN_SW"
def __init__(self, parent=None, **kwargs):
super().__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = ControlOnlyValveSymbolIcon(parent=self)
def sizeHint(self):
return QSize(180, 70)
class ControlOnlyValveNO(InterlockMixin, StateMixin,
ButtonControl, PCDSSymbolBase):
"""
A Symbol Widget representing a Normally Open Control Valve with the
proper icon and controls.
Parameters
----------
parent : QWidget
The parent widget for the symbol
Notes
-----
This widget allow for high customization through the Qt Stylesheets
mechanism.
As this widget is composed by internal widgets, their names can be used as
selectors when writing your stylesheet to be used with this widget.
Properties are also available to offer wider customization possibilities.
**Internal Components**
+-----------+--------------+---------------------------------------+
|Widget Name|Type |What is it? |
+===========+==============+=======================================+
|interlock |QFrame |The QFrame wrapping this whole widget. |
+-----------+--------------+---------------------------------------+
|controls |QFrame |The QFrame wrapping the controls panel.|
+-----------+--------------+---------------------------------------+
|icon |BaseSymbolIcon|The widget containing the icon drawing.|
+-----------+--------------+---------------------------------------+
**Additional Properties**
+-----------+-------------------------------------------------------+
|Property |Values |
+===========+=======================================================+
|interlocked|`true` or `false` |
+-----------+-------------------------------------------------------+
|state |`Open`, `Close` or `INVALID` |
+-----------+-------------------------------------------------------+
Examples
--------
.. code-block:: css
ControlOnlyValveNO [interlocked="true"] #interlock {
border: 5px solid red;
}
ControlOnlyValveNO [interlocked="false"] #interlock {
border: 0px;
}
ControlOnlyValveNO [interlocked="true"] #icon {
qproperty-interlockBrush: #FF0000;
}
ControlOnlyValveNO [interlocked="false"] #icon {
qproperty-interlockBrush: #00FF00;
}
ControlOnlyValveNO [error="Lost Vacuum"] #icon {
qproperty-penStyle: "Qt::DotLine";
qproperty-penWidth: 2;
qproperty-brush: red;
}
ControlOnlyValveNO [state="Open"] #icon {
qproperty-penColor: green;
qproperty-penWidth: 2;
}
"""
NAME = 'Normally Open Control Valve with No Readback'
EXPERT_OPHYD_CLASS = "pcdsdevices.valve.VVCNO"
_interlock_suffix = ":CLS_OK_RBV"
_state_suffix = ':CLS_DO_RBV'
_command_suffix = ":CLS_SW"
def __init__(self, parent=None, **kwargs):
super().__init__(
parent=parent,
interlock_suffix=self._interlock_suffix,
state_suffix=self._state_suffix,
command_suffix=self._command_suffix,
**kwargs)
self.icon = ControlOnlyValveSymbolIcon(parent=self)
| 37.346304
| 79
| 0.461971
| 2,122
| 28,794
| 6.139962
| 0.090952
| 0.021491
| 0.020723
| 0.031775
| 0.850257
| 0.827385
| 0.772431
| 0.742344
| 0.742344
| 0.723924
| 0
| 0.005212
| 0.273633
| 28,794
| 770
| 80
| 37.394805
| 0.617738
| 0.684657
| 0
| 0.634731
| 0
| 0
| 0.093838
| 0.02381
| 0
| 0
| 0
| 0
| 0
| 1
| 0.11976
| false
| 0
| 0.023952
| 0.05988
| 0.532934
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
8718a7b6a169e17cd1c14cfa3f4bde061297a271
| 14,981
|
py
|
Python
|
relevanceai/api/endpoints/centroids.py
|
boba-and-beer/RelevanceAI
|
467a9e58085306e84e4b5ee91422496f8fbfc08a
|
[
"Apache-2.0"
] | null | null | null |
relevanceai/api/endpoints/centroids.py
|
boba-and-beer/RelevanceAI
|
467a9e58085306e84e4b5ee91422496f8fbfc08a
|
[
"Apache-2.0"
] | null | null | null |
relevanceai/api/endpoints/centroids.py
|
boba-and-beer/RelevanceAI
|
467a9e58085306e84e4b5ee91422496f8fbfc08a
|
[
"Apache-2.0"
] | null | null | null |
from relevanceai.base import _Base
from typing import Optional, Dict, Any, List
class CentroidsClient(_Base):
def __init__(self, project, api_key):
self.project = project
self.api_key = api_key
super().__init__(project, api_key)
def list(
self,
dataset_id: str,
vector_fields: List,
alias: str = "default",
page_size: int = 5,
cursor: str = None,
include_vector: bool = False,
base_url="https://gateway-api-aueast.relevance.ai/latest",
):
"""
Retrieve the cluster centroid
Parameters
----------
dataset_id : string
Unique name of dataset
vector_fields: list
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
page_size: int
Size of each page of results
cursor: string
Cursor to paginate the document retrieval
include_vector: bool
Include vectors in the search results
"""
return self.make_http_request(
"/services/cluster/centroids/list",
method="POST",
parameters={
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
"page_size": page_size,
"cursor": cursor,
"include_vector": include_vector,
},
base_url=base_url,
)
def get(
self,
dataset_id: str,
cluster_ids: List,
vector_fields: List,
alias: str = "default",
page_size: int = 5,
cursor: str = None,
):
"""
Retrieve the cluster centroids by IDs
Parameters
----------
dataset_id : string
Unique name of dataset
cluster_ids : list
List of cluster IDs
vector_field: string
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
page_size: int
Size of each page of results
cursor: string
Cursor to paginate the document retrieval
"""
return self.make_http_request(
"/services/cluster/centroids/get",
method="GET",
parameters={
"dataset_id": dataset_id,
"cluster_ids": cluster_ids,
"vector_fields": vector_fields,
"alias": alias,
"page_size": page_size,
"cursor": cursor,
},
)
def insert(
self,
dataset_id: str,
cluster_centers: List,
vector_fields: List,
alias: str = "default",
):
"""
Insert your own cluster centroids for it to be used in approximate search settings and cluster aggregations.
Parameters
----------
dataset_id : string
Unique name of dataset
cluster_centers : list
Cluster centers with the key being the index number
vector_field: string
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
"""
return self.make_http_request(
"/services/cluster/centroids/insert",
method="POST",
parameters={
"dataset_id": dataset_id,
"cluster_centers": cluster_centers,
"vector_fields": vector_fields,
"alias": alias,
},
)
def documents(
self,
dataset_id: str,
cluster_ids: List,
vector_fields: List,
alias: str = "default",
page_size: int = 5,
cursor: str = None,
page: int = 1,
include_vector: bool = False,
similarity_metric: str = "cosine",
):
"""
Retrieve the cluster centroids by IDs
Parameters
----------
dataset_id : string
Unique name of dataset
cluster_ids : list
List of cluster IDs
vector_fields: list
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
page_size: int
Size of each page of results
cursor: string
Cursor to paginate the document retrieval
page: int
Page of the results
include_vector: bool
Include vectors in the search results
similarity_metric: string
Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
"""
return self.make_http_request(
"/services/cluster/centroids/documents",
method="POST",
parameters={
"dataset_id": dataset_id,
"cluster_ids": cluster_ids,
"vector_fields": vector_fields,
"alias": alias,
"page_size": page_size,
"cursor": cursor,
"page": page,
"include_vector": include_vector,
"similarity_metric": similarity_metric,
},
)
def metadata(
self,
dataset_id: str,
vector_fields: List,
alias: str = "default",
metadata: Optional[Dict[str, Any]] = None,
):
"""
If metadata is none, retrieves metadata about a dataset. notably description, data source, etc
Otherwise, you can store the metadata about your cluster here.
Parameters
----------
dataset_id: string
Unique name of dataset
vector_field: string
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
metadata: Optional[dict]
If None, it will retrieve the metadata, otherwise
it will overwrite the metadata of the cluster
"""
if metadata is None:
return self.make_http_request(
"/services/cluster/centroids/metadata",
method="POST",
parameters={
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
},
)
else:
return self.make_http_request(
"/services/cluster/centroids/metadata",
method="POST",
parameters={
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
"metadata": metadata,
},
)
def list_closest_to_center(
self,
dataset_id: str,
vector_fields: List,
cluster_ids: List = [],
alias: str = "default",
centroid_vector_fields: List = ["centroid_vector_"],
select_fields: List = [],
approx: int = 0,
sum_fields: bool = True,
page_size: int = 1,
page: int = 1,
similarity_metric: str = "cosine",
filters: List = [],
facets: List = [],
min_score: int = 0,
include_vector: bool = False,
include_count: bool = True,
include_facets: bool = False,
):
"""
List of documents closest from the centre.
Parameters
----------
dataset_id: string
Unique name of dataset
vector_field: string
The vector field where a clustering task was run.
cluster_ids: lsit
Any of the cluster ids
alias: string
Alias is used to name a cluster
centroid_vector_fields: list
Vector fields stored
select_fields: list
Fields to include in the search results, empty array/list means all fields
approx: int
Used for approximate search to speed up search. The higher the number, faster the search but potentially less accurate
sum_fields: bool
Whether to sum the multiple vectors similarity search score as 1 or seperate
page_size: int
Size of each page of results
page: int
Page of the results
similarity_metric: string
Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: list
Query for filtering the search results
facets: list
Fields to include in the facets, if [] then all
min_score: int
Minimum score for similarity metric
include_vectors: bool
Include vectors in the search results
include_count: bool
Include the total count of results in the search results
include_facets: bool
Include facets in the search results
"""
parameters = {
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
"cluster_ids": cluster_ids,
"centroid_vector_fields": centroid_vector_fields,
"select_fields": select_fields,
"approx": approx,
"sum_fields": sum_fields,
"page_size": page_size,
"page": page,
"similarity_metric": similarity_metric,
"filters": filters,
"facets": facets,
"min_score": min_score,
"include_vector": include_vector,
"include_count": include_count,
"include_facets": include_facets,
}
endpoint = "/services/cluster/centroids/list_closest_to_center"
method = "POST"
self._log_to_dashboard(
method=method,
parameters=parameters,
endpoint=endpoint,
dashboard_type="cluster_centroids_closest",
)
return self.make_http_request(endpoint, method=method, parameters=parameters)
docs_closest_to_center = list_closest_to_center
def list_furthest_from_center(
self,
dataset_id: str,
vector_fields: str,
cluster_ids: List = [],
alias: str = "default",
select_fields: List = [],
approx: int = 0,
sum_fields: bool = True,
page_size: int = 1,
page: int = 1,
similarity_metric: str = "cosine",
filters: List = [],
facets: List = [],
min_score: int = 0,
include_vector: bool = False,
include_count: bool = True,
include_facets: bool = False,
):
"""
List of documents furthest from the centre.
Parameters
----------
dataset_id: string
Unique name of dataset
vector_fields: list
The vector field where a clustering task was run.
cluster_ids: list
Any of the cluster ids
alias: string
Alias is used to name a cluster
select_fields: list
Fields to include in the search results, empty array/list means all fields
approx: int
Used for approximate search to speed up search. The higher the number, faster the search but potentially less accurate
sum_fields: bool
Whether to sum the multiple vectors similarity search score as 1 or seperate
page_size: int
Size of each page of results
page: int
Page of the results
similarity_metric: string
Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: list
Query for filtering the search results
facets: list
Fields to include in the facets, if [] then all
min_score: int
Minimum score for similarity metric
include_vectors: bool
Include vectors in the search results
include_count: bool
Include the total count of results in the search results
include_facets: bool
Include facets in the search results
"""
endpoint = "/services/cluster/centroids/list_furthest_from_center"
method = "POST"
parameters = {
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
"cluster_ids": cluster_ids,
"select_fields": select_fields,
"approx": approx,
"sum_fields": sum_fields,
"page_size": page_size,
"page": page,
"similarity_metric": similarity_metric,
"filters": filters,
"facets": facets,
"min_score": min_score,
"include_vector": include_vector,
"include_count": include_count,
"include_facets": include_facets,
}
self._log_to_dashboard(
method=method,
parameters=parameters,
endpoint=endpoint,
dashboard_type="cluster_centroids_furthest",
)
response = self.make_http_request(
endpoint, method=method, parameters=parameters
)
return response
docs_furthest_from_center = list_furthest_from_center
def delete(
self,
dataset_id: str,
vector_fields: List,
alias: str = "default",
):
"""
Delete centroids by dataset ID, vector field and alias
Parameters
----------
dataset_id : string
Unique name of dataset
vector_field: string
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
"""
return self.make_http_request(
"/services/cluster/centroids/delete",
method="POST",
parameters={
"dataset_id": dataset_id,
"vector_field": vector_fields,
"alias": alias,
},
)
def update(
self,
dataset_id: str,
vector_fields: List,
id: str,
update: dict = {},
alias: str = "default",
):
"""
Delete centroids by dataset ID, vector field and alias
Parameters
----------
dataset_id : string
Unique name of dataset
vector_field: List
The vector field where a clustering task was run.
alias: string
Alias is used to name a cluster
id: string
The centroid ID
update: dict
The update to be applied to the document
"""
return self.make_http_request(
"/services/cluster/centroids/update",
method="POST",
parameters={
"dataset_id": dataset_id,
"vector_fields": vector_fields,
"alias": alias,
"id": id,
"update": update,
},
)
| 31.806794
| 130
| 0.539951
| 1,538
| 14,981
| 5.085826
| 0.108583
| 0.046024
| 0.046152
| 0.023012
| 0.817182
| 0.80069
| 0.785988
| 0.768218
| 0.730504
| 0.697264
| 0
| 0.002174
| 0.385822
| 14,981
| 470
| 131
| 31.874468
| 0.847951
| 0.346639
| 0
| 0.726563
| 0
| 0
| 0.159595
| 0.054243
| 0
| 0
| 0
| 0
| 0
| 1
| 0.039063
| false
| 0
| 0.007813
| 0
| 0.097656
| 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
|
8724cd434e94b5fce277c155068c3e5e4e7320fe
| 12,095
|
py
|
Python
|
extensions/.stubs/clrclasses/Autodesk/AutoCAD/GraphicsInterface/__init__.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | 1
|
2020-03-25T03:27:24.000Z
|
2020-03-25T03:27:24.000Z
|
extensions/.stubs/clrclasses/Autodesk/AutoCAD/GraphicsInterface/__init__.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | null | null | null |
extensions/.stubs/clrclasses/Autodesk/AutoCAD/GraphicsInterface/__init__.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | null | null | null |
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ArcType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import AttenuationType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import AutoTransform
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ChannelFlags
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ClipBoundary
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ColorRGB
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ColorRGBA
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import CommonDraw
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Context
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ContextualColors
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DefaultLightingType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DeviationType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DiagnosticBSPMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DiagnosticGridMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DiagnosticMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DiagnosticPhotonMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DistantLightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Drawable
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawableAttributes
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawableOverrule
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawableTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawableType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawFlags
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import DrawStream
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import EdgeData
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ExposureType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ExtendedLightShape
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ExteriorDaylightMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FaceData
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Fill
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FillType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Filter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FinalGatheringMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FinalGatherMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FontDescriptor
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import FrontAndBackClipping
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GdiDrawObject
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GenericTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Geometry
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GlobalIlluminationMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Glyph
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GradientBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GradientFill
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GradientType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import GroundPlaneBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import HatchPattern
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import HatchPatternDefinition
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import HighlightStyle
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import IBLBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import IlluminationModel
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageBGRA32
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageFileTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageOrg
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageSource
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ImageTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import LightAttenuation
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import LightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Linetype
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import LinetypeCollection
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import LuminanceMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MapChannel
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MapFilter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Mapper
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MarbleTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialColor
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialDiffuseComponent
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialMap
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialNormalMapComponent
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialOpacityComponent
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialRefractionComponent
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialSpecularComponent
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MaterialTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsBoolParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsDiagnosticGridModeParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsDoubleRangeParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsFloatParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsIntegerRangeParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsSamplingParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MentalRayRenderSettingsTraitsTraceParameter
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Method
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Mode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import MrExportMIMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import NonEntityTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import NormalMapMethod
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import OrientationBehavior
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import OrientationType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import PhotographicExposureParameters
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import PixelBGRA32
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import PointLightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Polyline
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import PolylineCollection
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import PositionBehavior
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ProceduralTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ProceduralTextureType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Projection
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RapidRTFilterType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RapidRTLightingMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RapidRTQuitCondition
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RapidRTRenderSettingsTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RegenType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RenderEnvironmentTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import RenderSettingsTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ScaleBehavior
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SelectionFlags
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ShadowFlags
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ShadowMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ShadowParameters
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ShadowType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SkyBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SkyParameters
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SolidBackgroundTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Source
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SpotLightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import StandardLightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import SubEntityTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import TextStyle
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import TileOrder
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Tiling
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ToneOperatorParameters
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import TransientDrawingMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import TransientManager
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import TransparencyMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VariantType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VertexData
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import Viewport
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ViewportDraw
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ViewportGeometry
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import ViewportTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VisualStyle
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VisualStyleOperation
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VisualStyleProperty
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VisualStyleTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VisualStyleType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSDisplayShadowType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSDisplayStyles
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSEdgeJitterAmount
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSEdgeLinePattern
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSEdgeModel
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSEdgeModifiers
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSEdgeStyles
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSFaceColorMode
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSFaceLightingModel
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSFaceLightingQuality
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import VSFaceModifiers
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WebFileType
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WebLightTraits
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WebSymmetry
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WoodTexture
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WorldDraw
from __clrclasses__.Autodesk.AutoCAD.GraphicsInterface import WorldGeometry
| 78.538961
| 118
| 0.911451
| 1,071
| 12,095
| 9.721755
| 0.148459
| 0.205724
| 0.323281
| 0.426143
| 0.764118
| 0.764118
| 0
| 0
| 0
| 0
| 0
| 0.000348
| 0.050599
| 12,095
| 153
| 119
| 79.052288
| 0.906383
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 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
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
872b73a4a22262466d765667a6c7d4125f5da27b
| 272
|
py
|
Python
|
Codewars/8kyu/easy-logs/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | 7
|
2017-09-20T16:40:39.000Z
|
2021-08-31T18:15:08.000Z
|
Codewars/8kyu/easy-logs/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | null | null | null |
Codewars/8kyu/easy-logs/Python/test.py
|
RevansChen/online-judge
|
ad1b07fee7bd3c49418becccda904e17505f3018
|
[
"MIT"
] | null | null | null |
# Python - 3.6.0
test.describe('Example tests')
test.it('Should pass basic tests')
test.assert_approx_equals(logs(10, 2, 3), 0.7781512503836435)
test.assert_approx_equals(logs(5, 2, 3), 1.1132827525593785)
test.assert_approx_equals(logs(1000, 2, 3), 0.25938375012788123)
| 34
| 64
| 0.764706
| 44
| 272
| 4.590909
| 0.545455
| 0.148515
| 0.237624
| 0.326733
| 0.386139
| 0
| 0
| 0
| 0
| 0
| 0
| 0.273092
| 0.084559
| 272
| 7
| 65
| 38.857143
| 0.538153
| 0.051471
| 0
| 0
| 0
| 0
| 0.140625
| 0
| 0
| 0
| 0
| 0
| 0.6
| 1
| 0
| true
| 0.2
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
8748441eb45a4c2f4f7dbaf76f5561c1b68a54ce
| 1,301
|
py
|
Python
|
syntax/string4.py
|
sangumee/Opentutorials-Webn-Python
|
9f813f8f342ea99ffee6e31f363f175fa023c489
|
[
"MIT"
] | null | null | null |
syntax/string4.py
|
sangumee/Opentutorials-Webn-Python
|
9f813f8f342ea99ffee6e31f363f175fa023c489
|
[
"MIT"
] | null | null | null |
syntax/string4.py
|
sangumee/Opentutorials-Webn-Python
|
9f813f8f342ea99ffee6e31f363f175fa023c489
|
[
"MIT"
] | null | null | null |
# Positional Formatting
print('To {} Apple. Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry\'s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book {}. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem.'.format('Apple', 'Good'))
# Named Placeholder
print('To {name} Apple. Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry\'s standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book {number}. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem.'.format(name='Apple', number=12))
| 216.833333
| 636
| 0.805534
| 202
| 1,301
| 5.188119
| 0.381188
| 0.057252
| 0.028626
| 0.032443
| 0.917939
| 0.917939
| 0.917939
| 0.917939
| 0.917939
| 0.917939
| 0
| 0.016304
| 0.151422
| 1,301
| 5
| 637
| 260.2
| 0.932971
| 0.029977
| 0
| 0
| 0
| 1
| 0.21525
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
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| null | 0
| 0
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| 0
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| 1
| 1
| 0
| 0
| 0
| 1
|
0
| 12
|
874940493ad61e9272dad30cd8f0b078414e9625
| 3,109
|
py
|
Python
|
migrations/versions/42c343872516_.py
|
SRCF/lightbluetent
|
615b7183b480c26562c9daa3e156fbfc70e0e75a
|
[
"MIT"
] | 8
|
2020-07-27T22:34:05.000Z
|
2020-10-05T10:56:28.000Z
|
migrations/versions/42c343872516_.py
|
SRCF/lightbluetent
|
615b7183b480c26562c9daa3e156fbfc70e0e75a
|
[
"MIT"
] | 80
|
2020-07-27T22:39:34.000Z
|
2020-10-07T10:07:01.000Z
|
migrations/versions/42c343872516_.py
|
SRCF/lightbluetent
|
615b7183b480c26562c9daa3e156fbfc70e0e75a
|
[
"MIT"
] | 8
|
2020-08-01T09:07:08.000Z
|
2021-09-01T11:56:51.000Z
|
"""empty message
Revision ID: 42c343872516
Revises: 6acc09e8039c
Create Date: 2020-10-07 08:53:15.546502
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '42c343872516'
down_revision = '6acc09e8039c'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('whitelist', 'room_id',
existing_type=sa.String(length=20),
type_=sa.VARCHAR(length=28),
existing_nullable=True)
op.alter_column('whitelist', 'group_id',
existing_type=sa.String(length=12),
type_=sa.VARCHAR(length=20),
existing_nullable=True)
op.alter_column('users_groups', 'group_id',
existing_type=sa.String(length=12),
type_=sa.VARCHAR(length=20))
op.alter_column('sessions', 'room_id',
existing_type=sa.String(length=16),
type_=sa.VARCHAR(length=28),
existing_nullable=True)
op.alter_column('rooms', 'id',
existing_type=sa.String(length=20),
type_=sa.VARCHAR(length=28))
op.alter_column('links', 'room_id',
existing_type=sa.String(length=16),
type_=sa.VARCHAR(length=28),
existing_nullable=True)
op.alter_column('links', 'group_id',
existing_type=sa.String(length=16),
type_=sa.VARCHAR(length=20),
existing_nullable=True)
op.alter_column('groups', 'id',
existing_type=sa.String(length=12),
type_=sa.VARCHAR(length=20))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.alter_column('groups', 'id',
existing_type=sa.VARCHAR(length=20),
type_=sa.String(length=12))
op.alter_column('links', 'group_id',
existing_type=sa.VARCHAR(length=20),
type_=sa.String(length=16),
existing_nullable=True)
op.alter_column('links', 'room_id',
existing_type=sa.VARCHAR(length=28),
type_=sa.String(length=16),
existing_nullable=True)
op.alter_column('rooms', 'id',
existing_type=sa.VARCHAR(length=28),
type_=sa.String(length=20))
op.alter_column('sessions', 'room_id',
existing_type=sa.VARCHAR(length=28),
type_=sa.String(length=16),
existing_nullable=True)
op.alter_column('users_groups', 'group_id',
existing_type=sa.VARCHAR(length=20),
type_=sa.String(length=12))
op.alter_column('whitelist', 'group_id',
existing_type=sa.VARCHAR(length=20),
type_=sa.String(length=12),
existing_nullable=True)
op.alter_column('whitelist', 'room_id',
existing_type=sa.VARCHAR(length=28),
type_=sa.String(length=20),
existing_nullable=True)
# ### end Alembic commands ###
| 36.576471
| 65
| 0.591187
| 364
| 3,109
| 4.843407
| 0.17033
| 0.108905
| 0.117981
| 0.145207
| 0.838344
| 0.826999
| 0.826999
| 0.815655
| 0.808849
| 0.757232
| 0
| 0.053548
| 0.279189
| 3,109
| 84
| 66
| 37.011905
| 0.733155
| 0.094886
| 0
| 0.818182
| 0
| 0
| 0.086424
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.030303
| false
| 0
| 0.030303
| 0
| 0.060606
| 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
|
5e9a36abeb288dea9488bab86e8d6ca418f9d417
| 15,939
|
py
|
Python
|
scripts/em_alg_lib.py
|
hanshengjiang/npmle_git
|
fcaae0d1a948ee4cf94c773e8b763a1d604d2d8d
|
[
"MIT"
] | 1
|
2021-08-24T14:04:35.000Z
|
2021-08-24T14:04:35.000Z
|
scripts/em_alg_lib.py
|
hanshengjiang/npmle_git
|
fcaae0d1a948ee4cf94c773e8b763a1d604d2d8d
|
[
"MIT"
] | null | null | null |
scripts/em_alg_lib.py
|
hanshengjiang/npmle_git
|
fcaae0d1a948ee4cf94c773e8b763a1d604d2d8d
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: hanshengjiang
"""
'''
---------------------------------------------
We deprecate this python script and use R package mixtools instead
though implementation also follows the guideline of R paclage
---------------------------------------------
'''
from package_import import *
import sys
def EMA_true(X,y,k,B_true,alpha_true,sigma_list,iter,BL,BR,sigmaL,sigmaR):
'''
Use EM algorithm to fit (fixed component number) mixture of linear regression
Initialized with true parameters
---------------------------------------------
Input
X: n * p, covariate matrix
y: n * 1, response variable
k: number of components
iter: number of iterations
b1, b2, b3,pi1,pi2, sigma_list:
---------------------------------------------
Output
f: n * k, atomic likelihood vectors in active set
B: p * k, coefficients corresponding to vectors in active set
alpha: k*1, mixing proportions of vectors in active set
sigma: std of noise
L_rec: neg-log likelihood over iterations
L_temp: final neg-log likelihood
---------------------------------------------
'''
n = len(X)
p = len(X[0])
wden = np.zeros((n,k)) # unweighted posterior probability
w = np.zeros((n,k)) # posterior probability
sigma_array = np.zeros((k,))
z = np.zeros((n,)) #latent variable, class of each data point
f = np.zeros((n,))
L_rec = []
B = np.zeros((p,k))
alpha = np.zeros(k)
# initialize with true values
B = np.array(B_true)
alpha = np.array(alpha_true)
sigma_array = np.array(sigma_list)
for r in range(iter):
# update posteriors
for i in range(n):
for j in range(k):
wden[i][j] = (alpha[j]*np.exp(-0.5*((y[i]- np.dot(B[:,j],X[i]))**2\
)/(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma_array[j]).ravel()
f[i] = np.sum(wden[i])
#record negative log likelihood
L_temp = np.sum(np.log(1/f))
L_rec.append(L_temp)
if r> 1:
print("EM iteration {}:".format(r))
print("recent EM neg-log likelihood difference", L_rec[-1] - L_rec[-2])
# normalize
for i in range(n):
for j in range(k):
temp = np.sum(wden[i][:j])
if j+1 <k:
temp = temp + np.sum(wden[i][j+1:])
# caution! potential underflow might happen at this step
w[i][j] = 1/(1+temp/wden[i][j])
#M step: update B, alpha, and sigma
for j in range(k):
alpha[j] = np.sum(w[:,j])/n
w_diag = np.diag(w[:,j])
#update B
temp1 = np.linalg.inv(np.matmul(np.matmul(X.T, w_diag),X))
temp2 = np.matmul(np.matmul(X.T,w_diag),y)
B[:,j] = np.matmul(temp1, temp2).ravel()
# update sigma
sigma_temp = 0
for i in range(n):
sigma_temp = sigma_temp + w[i][j]*(y[i] - np.dot(B[:,j],X[i]))**2
sigma_array[j] = np.sqrt(sigma_temp/np.sum(w[:,j]))
return f, B, alpha, sigma_array, L_rec, temp
def EMA(X,y,k,iter,BL,BR,sigmaL,sigmaR):
'''
Use EM algorithm to fit (fixed component number) mixture of linear regression
without knowing sigma value
test multiple random initializations
---------------------------------------------
Input
X: n * p, covariate matrix
y: n * 1, response variable
k: number of components
iter: number of iterations
[BL,BR]^p: randomization range of parameters, used only in initialization
[sigmaL,sigmaR]: randomization range of sigma, used only initialization
---------------------------------------------
Output
f: n * k, atomic likelihood vectors in active set
B: p * k, coefficients corresponding to vectors in active set
alpha: k*1, mixing proportions of vectors in active set
sigma: std of noise
L_rec: neg-log likelihood over iterations
L_temp: final neg-log likelihood
---------------------------------------------
'''
n = len(X)
p = len(X[0])
wden = np.zeros((n,k)) # unweighted posterior probability
w = np.zeros((n,k)) # posterior probability
sigma_array = np.zeros((k,))
z = np.zeros((n,)) #latent variable, class of each data point
f = np.zeros((n,))
L_rec = []
#intialization
B = np.zeros((p,k))
alpha = np.zeros((k,1))
while min(f) == 0:
for j in range(k):
B[:,j] = np.random.uniform(BL,BR,(p,))
alpha[j][0] = 1/k
sigma_array[j] = np.random.uniform(sigmaL,sigmaR)
for i in range(n):
for j in range(k):
wden[i][j] = (alpha[j]*np.exp(-0.5*((y[i]- np.dot(B[:,j],X[i]))**2\
)/(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma_array[j]).ravel()
f[i] = np.sum(wden[i])
for r in range(iter):
# update posteriors
for i in range(n):
for j in range(k):
wden[i][j] = (alpha[j]*np.exp(-0.5*((y[i]- np.dot(B[:,j],X[i]))**2\
)/(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma_array[j]).ravel()
f[i] = np.sum(wden[i])
#record negative log likelihood
L_temp = np.sum(np.log(1/f))
L_rec.append(L_temp)
if r> 1:
print("recent EM neg-log likelihood difference", L_rec[-1] - L_rec[-2])
# normalize
for i in range(n):
temp = np.sum(wden[i])
for j in range(k):
w[i][j] = wden[i][j]/temp
#M step: update B, alpha, and sigma
for j in range(k):
alpha[j] = np.sum(w[:,j])/n
w_diag = np.diag(w[:,j])
#update B
temp1 = np.linalg.inv(np.matmul(np.matmul(X.T, w_diag),X))
temp2 = np.matmul(np.matmul(X.T,w_diag),y)
B[:,j] = np.dot(temp1, temp2).ravel()
# update sigma
sigma_temp = 0
for i in range(n):
sigma_temp = sigma_temp + w[i][j]*(y[i] - np.dot(B[:,j],X[i]))**2
sigma_array[j] = np.sqrt(sigma_temp/np.sum(w[:,j]))
return f, B, alpha, sigma_array, L_rec, temp
def EMA_sigma(X,y,k,iter,BL,BR,sigma):
'''
Use EM algorithm to fit (fixed component number) mixture of linear regression
Use known fixed sigma value
---------------------------------------------
Input
X: n * p, covariate matrix
y: n * 1, response variable
k: number of components
iter: number of iterations
[BL,BR]^p: randomization range of regression parameters
sigma: std of noise
---------------------------------------------
Output
f: n * k, atomic likelihood vectors in active set
B: p * k, coefficients corresponding to vectors in active set
alpha: k*1, mixing proportions of vectors in active set
sigma_array: k*1, sigma value of each component
L_rec: neg-log likelihood over iterations
L_temp: final neg-log likelihood
---------------------------------------------
'''
n = len(X)
p = len(X[0])
wden = np.zeros((n,k)) # unweighted posterior probability
w = np.zeros((n,k)) # posterior probability
sigma_array = np.zeros((k,))
z = np.zeros((n,)) #latent variable, class of each data point
f = np.zeros((n,))
L_rec = []
#intialization
B = np.zeros((p,k))
alpha = np.zeros((k,1))
for j in range(k):
B[:,j] = np.random.uniform(BL,BR,(p,))
alpha[j][0] = 1/k
sigma_array[j] = sigma # use know sigma
for r in range(iter):
# C step
for i in range(n):
wden_temp = 0
for j in range(k):
wden_temp = max(wden_temp, (y[i]- np.dot(B[:,j],X[i]))**2 )
for j in range(k):
if ((y[i]- np.dot(B[:,j],X[i]))**2-wden_temp) < -20:
wden[i][j] = 0
else:
wden[i][j] = (alpha[j]*np.exp(-0.5*((y[i]- np.dot(B[:,j],X[i]))**2 - wden_temp)/(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma).ravel()
f[i] = np.sum(wden[i])*np.exp(-wden_temp)
#record negative log likelihood
L_temp = np.sum(np.log(1/f))
L_rec.append(L_temp)
for i in range(n):
temp = np.sum(wden[i])
for j in range(k):
w[i][j] = wden[i][j]/temp
#M step: update B, alpha
for j in range(k):
alpha[j] = np.sum(w[:,j])/n
w_diag = np.diag(w[:,j])
#update B
temp1 = np.linalg.inv(np.matmul(np.matmul(X.T, w_diag),X))
temp2 = np.matmul(np.matmul(X.T,w_diag),y)
B[:,j] = np.dot(temp1, temp2).ravel()
#update sigma
#sigma_temp = 0
#for i in range(n):
#sigma_temp = sigma_temp + w[i][j]*(y[i] - np.dot(B[:,j],X[i]))**2
#sigma_array[j] = np.sqrt(sigma_temp/np.sum(w[:,j]))
return f, B, alpha, sigma_array, L_rec, temp
#----------------------------------------------#
# Alternatives: A variant of standard EM algorithm
# Classification EM
# See reference
# "A classification EM algorithm for clustering and two stochastic versions"
#----------------------------------------------#
def CEMA_sigma(X,y,k,iter,BL,BR,sigma):
'''
Use classification EM algorithm to fit (fixed component number) mixture of linear regression
Use known fixed sigma value
---------------------------------------------
Input
X: n * p, covariate matrix
y: n * 1, response variable
k: number of components
iter: number of iterations
[BL,BR]^p: randomization range of regression parameters
sigma: std of noise
---------------------------------------------
Output
f: n * k, atomic likelihood vectors in active set
B: p * k, coefficients corresponding to vectors in active set
alpha: k*1, mixing proportions of vectors in active set
sigma_array: k*1, sigma value of each component
L_rec: neg-log likelihood over iterations
L_temp: final neg-log likelihood
---------------------------------------------
'''
n = len(X)
p = len(X[0])
wden = np.zeros((n,k)) # unweighted posterior probability
w = np.zeros((n,k)) # posterior probability
sigma_array = np.zeros((k,))
z = np.zeros((n,)) #latent variable, class of each data point
f = np.zeros((n,))
L_rec = []
#intialization
B = np.zeros((p,k))
alpha = np.zeros((k,1))
for j in range(k):
B[:,j] = np.random.uniform(BL,BR,(p,))
alpha[j][0] = 1/k
sigma_array[j] = sigma # use know sigma
for r in range(iter):
# Expectation step
for i in range(n):
for j in range(k):
wden[i][j] = (alpha[j][0]*np.exp(-0.5*(y[i]- np.dot(B[:,j],X[i]))**2/(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma).ravel()
for i in range(n):
temp = np.sum(wden[i])
for j in range(k):
w[i][j] = wden[i][j]/temp
z[i] = np.argmax(np.reshape(w[i],(k,)))
# record neg-log likelihood
for i in range(n):
f[i] = np.sum(wden[i])
L_temp = np.sum(np.log(1/f))
# print(L_temp)
L_rec.append(L_temp)
#M step: update B, alpha, (and sigma)
for j in range(k):
alpha[j] = sum(z == j)/n
X_temp = np.zeros((sum(z == j),p))
y_temp = np.zeros((sum(z == j),1))
W_temp = np.zeros((sum(z == j), sum(z == j)))
count = 0
for i in range(n):
if z[i] == j:
X_temp[count] = X[i].ravel()
y_temp[count] = y[i]
W_temp[count][count] = w[i][j]
count = count + 1
temp1 = np.copy(np.linalg.inv(np.matmul(np.matmul(X_temp.T, W_temp),X_temp))) #used generalized inverse
temp2 = np.copy(np.matmul(np.matmul(X_temp.T, W_temp),y_temp))
B[:,j] = (np.matmul(temp1, temp2)).ravel()
return f, B, alpha, sigma_array, L_rec, temp
def CEMA(X,y,k,iter,BL,BR,sigmaL,sigmaR):
'''
Use classification EM algorithm to fit (fixed component number) mixture of linear regression
without knowing sigma value
---------------------------------------------
Input
X: n * p, covariate matrix
y: n * 1, response variable
k: number of components
iter: number of iterations
[BL,BR]^p: randomization range of parameters, used only in initialization
[sigmaL,sigmaR]: randomization range of sigma, used only initialization
---------------------------------------------
Output
f: n * k, atomic likelihood vectors in active set
B: p * k, coefficients corresponding to vectors in active set
alpha: k*1, mixing proportions of vectors in active set
sigma: std of noise
L_rec: neg-log likelihood over iterations
L_temp: final neg-log likelihood
---------------------------------------------
'''
n = len(X)
p = len(X[0])
wden = np.zeros((n,k)) # unweighted posterior probability
w = np.zeros((n,k)) # posterior probability
sigma_array = np.zeros((k,))
z = np.zeros((n,)) #latent variable, class of each data point
f = np.zeros((n,))
L_rec = []
#intialization
B = np.zeros((p,k))
alpha = np.zeros((k,1))
for j in range(k):
B[:,j] = np.random.uniform(BL,BR,(p,))
alpha[j][0] = 1/k
sigma_array[j] = np.random.uniform(sigmaL,sigmaR)
for r in range(iter):
# Expectation step
for i in range(n):
for j in range(k):
wden[i][j] = (alpha[j][0]*np.exp(-0.5*(y[i]- np.dot(B[:,j],X[i]))**2/\
(sigma_array[j]**2))/np.sqrt(2*np.pi)/sigma_array[j]).ravel()
for i in range(n):
temp = np.sum(wden[i])
for j in range(k):
w[i][j] = wden[i][j]/temp
z[i] = np.argmax(np.reshape(w[i],(k,)))
# record neg-log likelihood
for i in range(n):
f[i] = np.sum(wden[i])
L_temp = np.sum(np.log(1/f))
# print(L_temp)
L_rec.append(L_temp)
#M step: update B, alpha, (and sigma)
for j in range(k):
alpha[j] = sum(z == j)/n # this is classification EM
X_temp = np.zeros((sum(z == j),p))
y_temp = np.zeros((sum(z == j),1))
W_temp = np.zeros((sum(z == j), sum(z == j)))
count = 0
for i in range(n):
if z[i] == j:
X_temp[count] = X[i].ravel()
y_temp[count] = y[i]
W_temp[count][count] = w[i][j]
count = count + 1
temp1 = np.copy(np.linalg.inv(np.matmul(np.matmul(X_temp.T, W_temp),X_temp))) #used generalized inverse
temp2 = np.copy(np.matmul(np.matmul(X_temp.T, W_temp),y_temp))
B[:,j] = (np.matmul(temp1, temp2)).ravel()
if count == 0:
sys.exit("Opps, no data is assgined to component {}".format(beta))
else:
sigma_temp = 0
w_sum = 0
for i in range(n):
if z[i] == j:
sigma_temp = sigma_temp + w[i][j]*(y[i] - np.dot(B[:,j],X[i]))**2
w_sum = w_sum + w[i][j]
sigma_array[j] = np.sqrt(sigma_temp/w_sum)
return f, B, alpha, sigma_array, L_rec, temp
| 35.657718
| 152
| 0.494949
| 2,288
| 15,939
| 3.385927
| 0.086976
| 0.040661
| 0.016264
| 0.029818
| 0.897638
| 0.896089
| 0.887828
| 0.882148
| 0.882148
| 0.864851
| 0
| 0.011532
| 0.314512
| 15,939
| 447
| 153
| 35.657718
| 0.697511
| 0.34036
| 0
| 0.859649
| 0
| 0
| 0.013839
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.02193
| false
| 0
| 0.008772
| 0
| 0.052632
| 0.013158
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| null | 0
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|
0
| 7
|
5e9cf9127111b3ab087e87495677e0d4b57aa188
| 464,210
|
py
|
Python
|
heals2vis/config.py
|
cancer-staging-ontology/cancer-staging-ontology.github.io
|
a0ec2db1232f3d9a6ad7d46eb1491fcefca92512
|
[
"Apache-2.0"
] | null | null | null |
heals2vis/config.py
|
cancer-staging-ontology/cancer-staging-ontology.github.io
|
a0ec2db1232f3d9a6ad7d46eb1491fcefca92512
|
[
"Apache-2.0"
] | null | null | null |
heals2vis/config.py
|
cancer-staging-ontology/cancer-staging-ontology.github.io
|
a0ec2db1232f3d9a6ad7d46eb1491fcefca92512
|
[
"Apache-2.0"
] | 3
|
2018-06-01T01:30:49.000Z
|
2021-12-27T18:03:34.000Z
|
# -*- config:utf-8 -*-
import os
import logging
from datetime import timedelta
project_name = "heals2vis"
import importer
import autonomic
import agents.nlp as nlp
import rdflib
from datetime import datetime
# Set to be custom for your project
LOD_PREFIX = 'http://localhost'
#os.getenv('lod_prefix') if os.getenv('lod_prefix') else 'http://hbgd.tw.rpi.edu'
skos = rdflib.Namespace("http://www.w3.org/2004/02/skos/core#")
from heals2vis.agent import *
tnm_where='''?Tumor sio:isPartOf ?Subject .
?Tumor prov:wasGeneratedBy ?Cancer .
?Tumor rdf:type ncit:C3262 .
?T rdf:type [ rdfs:subClassOf sio:Diameter ;
rdfs:subClassOf ncit:C120284 ] .
?T sio:isAttributeOf ?Tumor .
?N rdf:type [ rdfs:subClassOf sio:Count ] .
?N sio:isAttributeOf [ rdf:type ncit:C12745 ;
prov:wasDerivedFrom ?Subject ] .
?M rdf:type [ rdfs:subClassOf sio:StatusDescriptor ] .
?M sio:isAttributeOf [ rdf:type ncit:C19151 ;
sio:inRelationTo ?Tumor ] .
?Grade sio:isAttributeOf ?Tumor .
?Grade rdf:type [ rdfs:subClassOf ncit:C135461 ] .
?ER rdf:type [ rdfs:subClassOf sio:StatusDescriptor ] .
?ER sio:isAttributeOf [ rdf:type ncit:C38361 ;
prov:wasDerivedFrom ?Subject ] .
?PR rdf:type [ rdfs:subClassOf sio:StatusDescriptor ] .
?PR sio:isAttributeOf [ rdf:type ncit:C17075 ;
prov:wasDerivedFrom ?Subject ] .
?HER2 rdf:type [ rdfs:subClassOf sio:StatusDescriptor ] .
?HER2 sio:isAttributeOf [ rdf:type ncit:C17756 ;
prov:wasDerivedFrom ?Subject ] .
'''
# base config class; extend it to your needs.
Config = dict(
# use DEBUG mode?
DEBUG = False,
site_name = "heals2vis",
root_path = '/apps/whyis',
# use TESTING mode?
TESTING = False,
# use server x-sendfile?
USE_X_SENDFILE = False,
WTF_CSRF_ENABLED = True,
SECRET_KEY = "oiaXTKsVmvxwDiOsPp/MJdL2+fSPbCwF",
nanopub_archive = {
'depot.storage_path' : "/data/nanopublications",
},
file_archive = {
'depot.storage_path' : '/data/files',
'cache_max_age' : 3600*24*7,
},
vocab_file = "/apps/heals2vis/vocab.ttl",
WHYIS_TEMPLATE_DIR = [
"/apps/heals2vis/templates",
],
WHYIS_CDN_DIR = "/apps/heals2vis/static",
# LOGGING
LOGGER_NAME = "%s_log" % project_name,
LOG_FILENAME = "/var/log/%s/output-%s.log" % (project_name,str(datetime.now()).replace(' ','_')),
LOG_LEVEL = logging.INFO,
LOG_FORMAT = "%(asctime)s %(levelname)s\t: %(message)s", # used by logging.Formatter
PERMANENT_SESSION_LIFETIME = timedelta(days=7),
# EMAIL CONFIGURATION
## MAIL_SERVER = "",
## MAIL_PORT = 587,
## MAIL_USE_TLS = True,
## MAIL_USE_SSL = False,
## MAIL_DEBUG = False,
## MAIL_USERNAME = '',
## MAIL_PASSWORD = '',
## DEFAULT_MAIL_SENDER = "Shruthi <charis@rpi.edu",
# see example/ for reference
# ex: BLUEPRINTS = ['blog'] # where app is a Blueprint instance
# ex: BLUEPRINTS = [('blog', {'url_prefix': '/myblog'})] # where app is a Blueprint instance
BLUEPRINTS = [],
lod_prefix = LOD_PREFIX,
SECURITY_EMAIL_SENDER = "Shruthi <charis@rpi.edu",
SECURITY_FLASH_MESSAGES = True,
SECURITY_CONFIRMABLE = False,
SECURITY_CHANGEABLE = True,
SECURITY_TRACKABLE = True,
SECURITY_RECOVERABLE = True,
SECURITY_REGISTERABLE = True,
SECURITY_PASSWORD_HASH = 'sha512_crypt',
SECURITY_PASSWORD_SALT = 'JzjOQ1lCUZC0C6Es589H7uYPveUh0H5F',
SECURITY_SEND_REGISTER_EMAIL = False,
SECURITY_POST_LOGIN_VIEW = "/",
SECURITY_SEND_PASSWORD_CHANGE_EMAIL = False,
SECURITY_DEFAULT_REMEMBER_ME = True,
ADMIN_EMAIL_RECIPIENTS = [],
db_defaultGraph = LOD_PREFIX + '/',
admin_queryEndpoint = 'http://localhost:8080/blazegraph/namespace/admin/sparql',
admin_updateEndpoint = 'http://localhost:8080/blazegraph/namespace/admin/sparql',
knowledge_queryEndpoint = 'http://localhost:8080/blazegraph/namespace/knowledge/sparql',
knowledge_updateEndpoint = 'http://localhost:8080/blazegraph/namespace/knowledge/sparql',
LOGIN_USER_TEMPLATE = "auth/login.html",
CELERY_BROKER_URL = 'redis://localhost:6379/0',
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0',
default_language = 'en',
namespaces = [
importer.LinkedData(
prefix = LOD_PREFIX+'/doi/',
url = 'http://dx.doi.org/%s',
headers={'Accept':'text/turtle'},
format='turtle',
postprocess_update= '''insert {
graph ?g {
?pub a <http://purl.org/ontology/bibo/AcademicArticle>.
}
} where {
graph ?g {
?pub <http://purl.org/ontology/bibo/doi> ?doi.
}
}
'''
),
importer.LinkedData(
prefix = LOD_PREFIX+'/dbpedia/',
url = 'http://dbpedia.org/resource/%s',
headers={'Accept':'text/turtle'},
format='turtle',
postprocess_update= '''insert {
graph ?g {
?article <http://purl.org/dc/terms/abstract> ?abstract.
}
} where {
graph ?g {
?article <http://dbpedia.org/ontology/abstract> ?abstract.
}
}
'''
)
],
inferencers = {
"SETLr": autonomic.SETLr(),
"Class Subsumption Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource rdfs:subClassOf ?class .\n\t?class rdfs:subClassOf+ ?superClass .",
construct="?resource rdfs:subClassOf ?superClass .",
explanation="Since {{class}} is a subclass of {{superClass}}, any class that is a subclass of {{class}} is also a subclass of {{superClass}}. Therefore, {{resource}} is a subclass of {{superClass}}."),
"Instance Subsumbtion Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource rdf:type ?class .\n\t?class rdfs:subClassOf+ ?superClass .",
construct="?resource rdf:type ?superClass .",
explanation="Any instance of {{class}} is also an instance of {{superClass}}. Therefore, since {{resource}} is a {{class}}, it also is a {{superClass}}."),
"Object Property Subsumbtion Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource ?p ?o .\n\t?p rdf:type owl:ObjectPropery .\n\t?p owl:subPropertyOf+ ?superProperty .",
construct="?resource ?superProperty ?o .",
explanation="Any subject and object related by the property {{p}} is also related by {{superProperty}}. Therefore, since {{resource}} {{p}} {{o}}, it is implied that {{resource}} {{superProperty}} {{o}}."),
"Datatype Property Subsumbtion Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource ?p ?o .\n\t?p rdf:type owl:DatatypePropery .\n\t?p owl:subPropertyOf+ ?superProperty .",
construct="?resource ?superProperty ?o .",
explanation="Any subject and object related by the property {{p}} is also related by {{superProperty}}. Therefore, since {{resource}} {{p}} {{o}}, it is implied that {{resource}} {{superProperty}} {{o}}."),
"Class Equivalency Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource a ?superClass.\n\t?superClass owl:equivalentClass ?equivClass .",
construct="?resource a ?equivClass .",
explanation="{{superClass}} is equivalent to {{equivClass}}, so since {{resource}} is a {{superClass}}, it is also a {{equivClass}}."),
"Property Equivalency Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource ?p ?o .\n\t?p owl:equivalentProperty ?equivProperty .",
construct="?resource ?equivProperty ?o .",
explanation="The properties {{p}} and {{equivProperty}} are equivalent. Therefore, since {{resource}} {{p}} {{o}}, it is implied that {{resource}} {{equivProperty}} {{o}}."),
"Property Inversion Closure": autonomic.Deductor(
resource="?resource",
prefixes="",
where = "\t?resource ?p ?o .\n\t?p owl:inverseOf ?inverseProperty .",
construct="?o ?inverseProperty ?resource .",
explanation="The properties {{p}} and {{inverseProperty}} are inversely related to eachother. Therefore, since {{resource}} {{p}} {{o}}, it is implied that {{o}} {{inverseProperty}} {{resource}}."),
#"Some Values From": autonomic.Deductor(resource="?resource", prefixes="", where = "", construct="", explanation=""),
#"Property Path Closure": autonomic.Deductor(resource="?resource", prefixes="", where = "", construct="", explanation=""),
"Triple Negative Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"efo": "http://www.ebi.ac.uk/efo/EFO_"},
construct="?Subject cst:hasDisease efo:0005537 .",
where=tnm_where + """
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Subject}} was found to have Triple Negative Breast Cancer since the following are true:
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"Estrogen-receptor Positive Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"efo": "http://www.ebi.ac.uk/efo/EFO_"},
construct="?Subject cst:hasDisease efo:1000649 .",
where=tnm_where + """
?ER rdf:type cst:ER_Pos .
""",
explanation="""{{Subject}} was found to have Estrogen-receptor Positive Breast Cancer since the following are true:
Estrogen receptor status is ER_Pos .
"""),
"Estrogen-receptor Negative Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"efo": "http://www.ebi.ac.uk/efo/EFO_"},
construct="?Subject cst:hasDisease efo:1000650 .",
where=tnm_where + """
?ER rdf:type cst:ER_Neg .
""",
explanation="""{{Subject}} was found to have Estrogen-receptor Negative Breast Cancer since the following are true:
Estrogen receptor status is ER_Neg .
"""),
"Progesterone-receptor Positive Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"doid": "http://purl.obolibrary.org/obo/DOID_"},
construct="?Subject cst:hasDisease doid:0060077 .",
where=tnm_where + """
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Subject}} was found to have Progesterone-receptor Positive Breast Cancer since the following are true:
Progesterone receptor status is PR_Pos .
"""),
"Progesterone-receptor Negative Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"doid": "http://purl.obolibrary.org/obo/DOID_"},
construct="?Subject cst:hasDisease doid:0060078 .",
where=tnm_where + """
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Subject}} was found to have Progesterone-receptor Negative Breast Cancer since the following are true:
Progesterone receptor status is PR_Neg .
"""),
"HER2-receptor Positive Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"doid": "http://purl.obolibrary.org/obo/DOID_"},
construct="?Subject cst:hasDisease doid:0060079 .",
where=tnm_where + """
?HER2 rdf:type cst:HER2_Pos .
""",
explanation="""{{Subject}} was found to have HER2-receptor Positive Breast Cancer since the following are true:
Progesterone receptor status is HER2_Pos .
"""),
"HER2-receptor Negative Finding":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"doid": "http://purl.obolibrary.org/obo/DOID_"},
construct="?Subject cst:hasDisease doid:0060080 .",
where=tnm_where + """
?HER2 rdf:type cst:HER2_Neg .
""",
explanation="""{{Subject}} was found to have HER2-receptor Negative Breast Cancer since the following are true:
Progesterone receptor status is HER2_Neg .
"""),
"OncoTypeDX Testing":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#",
"doid": "http://purl.obolibrary.org/obo/DOID_"},
construct="?Subject cst:hasRecommendedTest 'OncoTypeDX Testing'^^xsd:string .",
where=tnm_where + """
?ER rdf:type cst:ER_Pos .
?N rdf:type cst:N0 .
""",
explanation="""{{Subject}} can be prescribed OncoTypeDX Testing:
Estrogen receptor status is ER_Pos .
Degree of spread to lymph nodes is N0 .
"""),
"R7 Stage 0 (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage I (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_I .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage I since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage I (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_I .",
where=tnm_where + """
?T rdf:type cst:T1mic .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage I since the following are true:
Primary Tumor size is T1mic .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R7 Stage IB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
"""),
"R7 Stage IIB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst":"http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (4)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (5)":autonomic.Deductor(
resource="?Tumor",
prefixes={
"ncit": "http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#",
"cst": "http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#"},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage 0 (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage I (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_I .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage I since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage I (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_I .",
where=tnm_where + """
?T rdf:type cst:T1mic .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage I since the following are true:
Primary Tumor size is T1mic .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R7 Stage IB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
"""),
"R7 Stage IIB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIA (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIA (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R7 Stage IIIB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIB (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IIIC (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IIIC .",
where=tnm_where + """
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IIIC since the following are true:
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
"""),
"R7 Stage IV (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R7_Stage_IV .",
where=tnm_where + """
?M rdf:type cst:M1 .
""",
explanation="""{{Tumor}} was found to have stage R7 Stage IV since the following are true:
Presence of distant metastasis is M1 .
"""),
"R8 Stage 0 (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage 0 (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage 0 (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_0 .",
where=tnm_where + """
?T rdf:type cst:Tis .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage 0 since the following are true:
Primary Tumor size is Tis .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (33)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (34)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (35)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (36)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (37)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (38)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (39)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (40)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (41)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (42)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (43)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (44)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (45)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (46)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (47)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (48)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (49)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (50)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (51)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (52)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (53)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IA (54)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (55)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IA (56)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IB (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1mi .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1mi .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IB (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (33)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (34)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (35)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (36)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (37)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (38)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (39)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (40)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (41)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (42)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (43)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (44)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (45)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (46)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (47)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (48)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (49)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (50)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (51)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (52)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIA (53)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (54)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (55)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (56)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (57)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (58)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (59)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (60)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (61)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (62)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (63)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (64)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (65)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (66)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (67)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (68)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (69)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (70)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (71)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (72)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (73)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (74)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (75)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIA (76)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIB (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (33)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (34)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (35)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (36)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (37)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIB (38)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (33)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (34)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (35)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (36)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (37)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (38)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (39)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (40)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (41)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (42)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (43)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (44)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (45)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (46)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (47)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (48)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (49)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (50)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (51)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (52)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (53)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (54)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (55)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (56)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (57)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (58)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (59)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (60)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (61)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (62)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (63)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (64)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (65)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (66)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (67)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (68)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIA (69)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (70)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (71)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (72)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (73)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (74)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (75)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (76)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (77)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (78)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (79)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (80)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIA (81)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIA .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIA since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (25)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (26)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (27)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (28)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (29)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (30)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (31)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (32)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (33)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (34)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (35)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (36)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (37)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (38)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (39)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (40)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (41)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (42)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (43)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (44)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (45)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (46)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (47)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (48)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (49)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (50)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (51)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (52)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (53)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (54)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (55)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (56)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (57)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (58)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (59)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (60)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (61)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (62)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (63)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (64)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (65)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (66)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (67)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (68)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (69)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (70)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (71)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (72)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (73)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (74)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (75)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (76)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (77)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (78)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (79)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (80)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (81)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (82)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (83)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (84)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (85)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (86)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (87)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIB (88)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (89)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (90)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIB (91)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIB .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIB since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIC (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T0 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T0 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T1 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T1 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T2 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T2 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T3 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T3 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIC (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIC (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIC (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IIIC (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N0 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N0 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N1 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N1 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T4 .
?N rdf:type cst:N2 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T4 .
Degree to spread of lymph nodes is N2 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IIIC (24)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IIIC .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N3 .
?M rdf:type cst:M0 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IIIC since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N3 .
Presence of distant metastasis is M0 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (0)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (1)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (2)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (3)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (4)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (5)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (6)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (7)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade1 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade1 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (8)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (9)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (10)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (11)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (12)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (13)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (14)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (15)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade2 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade2 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (16)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (17)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (18)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (19)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Pos .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Pos .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (20)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (21)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Pos .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Pos .
Progesterone receptor status is PR_Neg .
"""),
"R8 Stage IV (22)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Pos .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Pos .
"""),
"R8 Stage IV (23)":autonomic.Deductor(
resource="?Tumor",
prefixes= {'cst': 'http://idea.rpi.edu/ontologies/cancer_staging_terms.owl#', 'ncit': 'http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#', 'efo': 'http://www.ebi.ac.uk/efo/EFO_'},
construct="?Tumor cst:hasAJCCStage cst:R8_Stage_IV .",
where=tnm_where + """
?T rdf:type cst:T .
?N rdf:type cst:N .
?M rdf:type cst:M1 .
?Grade rdf:type cst:Grade3 .
?HER2 rdf:type cst:HER2_Neg .
?ER rdf:type cst:ER_Neg .
?PR rdf:type cst:PR_Neg .
""",
explanation="""{{Tumor}} was found to have stage R8 Stage IV since the following are true:
Primary Tumor size is T .
Degree to spread of lymph nodes is N .
Presence of distant metastasis is M1 .
Grade is Grade3 .
HER2 status is HER2_Neg .
Estrogen receptor status is ER_Neg .
Progesterone receptor status is PR_Neg .
"""),
# "HTML2Text" : nlp.HTML2Text(),
# "EntityExtractor" : nlp.EntityExtractor(),
# "EntityResolver" : nlp.EntityResolver(),
# "TF-IDF Calculator" : nlp.TFIDFCalculator(),
# "SKOS Crawler" : autonomic.Crawler(predicates=[skos.broader, skos.narrower, skos.related])
},
inference_tasks = [
# dict ( name="SKOS Crawler",
# service=autonomic.Crawler(predicates=[skos.broader, skos.narrower, skos.related]),
# schedule=dict(hour="1")
# )
]
)
# config class for development environment
Dev = dict(Config)
Dev.update(dict(
DEBUG = True, # we want debug level output
MAIL_DEBUG = True,
EXPLAIN_TEMPLATE_LOADING = True,
DEBUG_TB_INTERCEPT_REDIRECTS = False
))
# config class used during tests
Test = dict(Config)
Test.update(dict(
TESTING = True,
WTF_CSRF_ENABLED = False
))
| 39.911444
| 218
| 0.643903
| 70,777
| 464,210
| 4.139212
| 0.00698
| 0.081096
| 0.115271
| 0.052328
| 0.9872
| 0.985292
| 0.984134
| 0.983226
| 0.981523
| 0.979762
| 0
| 0.022607
| 0.214914
| 464,210
| 11,630
| 219
| 39.914875
| 0.781248
| 0.003152
| 0
| 0.942849
| 0
| 0.000993
| 0.767951
| 0.000834
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.000271
| 0.000993
| 0
| 0.000993
| 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
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
0d5d1d455caa3125576d83564b4bcb0083c82879
| 784
|
py
|
Python
|
eval/utils/metrics.py
|
mbatchkarov/dc_evaluation
|
83fad49d597952957420b23c0d39c264c581e750
|
[
"BSD-3-Clause"
] | null | null | null |
eval/utils/metrics.py
|
mbatchkarov/dc_evaluation
|
83fad49d597952957420b23c0d39c264c581e750
|
[
"BSD-3-Clause"
] | null | null | null |
eval/utils/metrics.py
|
mbatchkarov/dc_evaluation
|
83fad49d597952957420b23c0d39c264c581e750
|
[
"BSD-3-Clause"
] | null | null | null |
# coding=utf-8
"""
Created on Oct 17, 2012
@author: ml249
"""
from sklearn.metrics import f1_score, precision_score, recall_score
def macroavg_f1(y_true, y_pred):
return f1_score(y_true, y_pred, average='macro', pos_label=None)
def macroavg_prec(y_true, y_pred):
return precision_score(y_true, y_pred, average='macro', pos_label=None)
def macroavg_rec(y_true, y_pred):
return recall_score(y_true, y_pred, average='macro', pos_label=None)
def microavg_f1(y_true, y_pred):
return f1_score(y_true, y_pred, average='micro', pos_label=None)
def microavg_prec(y_true, y_pred):
return precision_score(y_true, y_pred, average='micro', pos_label=None)
def microavg_rec(y_true, y_pred):
return recall_score(y_true, y_pred, average='micro', pos_label=None)
| 23.757576
| 75
| 0.75
| 134
| 784
| 4.052239
| 0.246269
| 0.110497
| 0.132597
| 0.220994
| 0.804788
| 0.790055
| 0.790055
| 0.790055
| 0.790055
| 0.790055
| 0
| 0.022026
| 0.131378
| 784
| 32
| 76
| 24.5
| 0.77533
| 0.067602
| 0
| 0
| 0
| 0
| 0.041494
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.461538
| false
| 0
| 0.076923
| 0.461538
| 1
| 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
|
2174a357b8849ba48c23498ac1b8df11ab9516c7
| 278
|
py
|
Python
|
django-rest-framework-fast/characters/constants.py
|
mervatkheir/kite-python-blog-post-code
|
9a331e5d327cd27c6ecd72926f3e74afd252efb5
|
[
"MIT"
] | 238
|
2018-10-10T18:50:40.000Z
|
2022-02-09T21:26:24.000Z
|
django-rest-framework-fast/characters/constants.py
|
mrrizal/kite-python-blog-post-code
|
597f2d75b2ad5dda97e9b19f6e9c7195642e1739
|
[
"MIT"
] | 38
|
2019-12-04T22:42:45.000Z
|
2022-03-12T00:04:57.000Z
|
django-rest-framework-fast/characters/constants.py
|
mrrizal/kite-python-blog-post-code
|
597f2d75b2ad5dda97e9b19f6e9c7195642e1739
|
[
"MIT"
] | 154
|
2018-11-11T22:48:09.000Z
|
2022-03-22T07:12:18.000Z
|
HERO = 'hero'
VILLAIN = 'villain'
SIDEKICK = 'sidekick'
COMEDIC = 'comedic'
EXTRA = 'extra'
SOLO = 'solo'
CHARACTER_TYPES = (
(HERO, 'Hero'),
(VILLAIN, 'Villain'),
(SIDEKICK, 'Sidekick'),
(COMEDIC, 'Comedic'),
(EXTRA, 'Extra'),
(SOLO, 'Solo Artist'),
)
| 17.375
| 27
| 0.579137
| 27
| 278
| 5.925926
| 0.333333
| 0.1
| 0.1875
| 0.275
| 0.875
| 0.875
| 0.875
| 0.875
| 0.875
| 0.875
| 0
| 0
| 0.215827
| 278
| 15
| 28
| 18.533333
| 0.733945
| 0
| 0
| 0
| 0
| 0
| 0.276978
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 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
|
21e85071dceb79312978b6217c03401858afdbc6
| 11,065
|
py
|
Python
|
coniii/ising_eqn/ising_eqn_5.py
|
bcdaniels/coniii
|
50218dc571135dd08b441361da33fed64a8eebc4
|
[
"MIT"
] | 10
|
2018-01-26T09:52:17.000Z
|
2019-04-02T13:34:53.000Z
|
coniii/ising_eqn/ising_eqn_5.py
|
bcdaniels/coniii
|
50218dc571135dd08b441361da33fed64a8eebc4
|
[
"MIT"
] | 19
|
2017-04-19T17:05:50.000Z
|
2019-01-20T20:54:06.000Z
|
coniii/ising_eqn/ising_eqn_5.py
|
bcdaniels/coniii
|
50218dc571135dd08b441361da33fed64a8eebc4
|
[
"MIT"
] | 3
|
2017-04-19T16:58:05.000Z
|
2018-10-22T19:14:04.000Z
|
# Equations of 5-spin Ising model.
# 30/12/2017
from numpy import zeros, exp
def calc_observables(params):
"""
Give each set of parameters concatenated into one array.
"""
Cout = zeros((15))
H = params[0:5]
J = params[5:15]
Z = +exp(+0)+exp(+H[4]+0)+exp(+H[3]+0)+exp(+H[3]+H[4]+J[9])+exp(+H[2]+0)+exp(+H[2]+H[4]+J[8])+exp(+H[2]+H[3]+J[7])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+0)+exp(+H[1]+H[4]+J[6])+exp(+H[1]+H[3]+J[5])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+J[4])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+0)+exp(+H[0]+H[4]+J[3])+exp(+H[0]+H[3]+J[2])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+J[1])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+J[0])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])
Cout[0] = (+exp(+H[0]+0)+exp(+H[0]+H[4]+J[3])+exp(+H[0]+H[3]+J[2])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+J[1])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+J[0])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[1] = (+exp(+H[1]+0)+exp(+H[1]+H[4]+J[6])+exp(+H[1]+H[3]+J[5])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+J[4])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+J[0])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[2] = (+exp(+H[2]+0)+exp(+H[2]+H[4]+J[8])+exp(+H[2]+H[3]+J[7])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[2]+J[4])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[2]+J[1])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[3] = (+exp(+H[3]+0)+exp(+H[3]+H[4]+J[9])+exp(+H[2]+H[3]+J[7])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[3]+J[5])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[3]+J[2])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[4] = (+exp(+H[4]+0)+exp(+H[3]+H[4]+J[9])+exp(+H[2]+H[4]+J[8])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[4]+J[6])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[4]+J[3])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[5] = (+exp(+H[0]+H[1]+J[0])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[6] = (+exp(+H[0]+H[2]+J[1])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[7] = (+exp(+H[0]+H[3]+J[2])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[8] = (+exp(+H[0]+H[4]+J[3])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[9] = (+exp(+H[1]+H[2]+J[4])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[10] = (+exp(+H[1]+H[3]+J[5])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[11] = (+exp(+H[1]+H[4]+J[6])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[12] = (+exp(+H[2]+H[3]+J[7])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[13] = (+exp(+H[2]+H[4]+J[8])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
Cout[14] = (+exp(+H[3]+H[4]+J[9])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9]))/Z
return(Cout)
def p(params):
"""
Give each set of parameters concatenated into one array.
"""
Cout = zeros((15))
H = params[0:5]
J = params[5:15]
H = params[0:5]
J = params[5:15]
Pout = zeros((32))
Z = +exp(+0)+exp(+H[4]+0)+exp(+H[3]+0)+exp(+H[3]+H[4]+J[9])+exp(+H[2]+0)+exp(+H[2]+H[4]+J[8])+exp(+H[2]+H[3]+J[7])+exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])+exp(+H[1]+0)+exp(+H[1]+H[4]+J[6])+exp(+H[1]+H[3]+J[5])+exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])+exp(+H[1]+H[2]+J[4])+exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])+exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])+exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])+exp(+H[0]+0)+exp(+H[0]+H[4]+J[3])+exp(+H[0]+H[3]+J[2])+exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])+exp(+H[0]+H[2]+J[1])+exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])+exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])+exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])+exp(+H[0]+H[1]+J[0])+exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])+exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])+exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])+exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])+exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])+exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])+exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])
Pout[0] = +exp(+0)/Z
Pout[1] = +exp(+H[4]+0)/Z
Pout[2] = +exp(+H[3]+0)/Z
Pout[3] = +exp(+H[3]+H[4]+J[9])/Z
Pout[4] = +exp(+H[2]+0)/Z
Pout[5] = +exp(+H[2]+H[4]+J[8])/Z
Pout[6] = +exp(+H[2]+H[3]+J[7])/Z
Pout[7] = +exp(+H[2]+H[3]+H[4]+J[7]+J[8]+J[9])/Z
Pout[8] = +exp(+H[1]+0)/Z
Pout[9] = +exp(+H[1]+H[4]+J[6])/Z
Pout[10] = +exp(+H[1]+H[3]+J[5])/Z
Pout[11] = +exp(+H[1]+H[3]+H[4]+J[5]+J[6]+J[9])/Z
Pout[12] = +exp(+H[1]+H[2]+J[4])/Z
Pout[13] = +exp(+H[1]+H[2]+H[4]+J[4]+J[6]+J[8])/Z
Pout[14] = +exp(+H[1]+H[2]+H[3]+J[4]+J[5]+J[7])/Z
Pout[15] = +exp(+H[1]+H[2]+H[3]+H[4]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])/Z
Pout[16] = +exp(+H[0]+0)/Z
Pout[17] = +exp(+H[0]+H[4]+J[3])/Z
Pout[18] = +exp(+H[0]+H[3]+J[2])/Z
Pout[19] = +exp(+H[0]+H[3]+H[4]+J[2]+J[3]+J[9])/Z
Pout[20] = +exp(+H[0]+H[2]+J[1])/Z
Pout[21] = +exp(+H[0]+H[2]+H[4]+J[1]+J[3]+J[8])/Z
Pout[22] = +exp(+H[0]+H[2]+H[3]+J[1]+J[2]+J[7])/Z
Pout[23] = +exp(+H[0]+H[2]+H[3]+H[4]+J[1]+J[2]+J[3]+J[7]+J[8]+J[9])/Z
Pout[24] = +exp(+H[0]+H[1]+J[0])/Z
Pout[25] = +exp(+H[0]+H[1]+H[4]+J[0]+J[3]+J[6])/Z
Pout[26] = +exp(+H[0]+H[1]+H[3]+J[0]+J[2]+J[5])/Z
Pout[27] = +exp(+H[0]+H[1]+H[3]+H[4]+J[0]+J[2]+J[3]+J[5]+J[6]+J[9])/Z
Pout[28] = +exp(+H[0]+H[1]+H[2]+J[0]+J[1]+J[4])/Z
Pout[29] = +exp(+H[0]+H[1]+H[2]+H[4]+J[0]+J[1]+J[3]+J[4]+J[6]+J[8])/Z
Pout[30] = +exp(+H[0]+H[1]+H[2]+H[3]+J[0]+J[1]+J[2]+J[4]+J[5]+J[7])/Z
Pout[31] = +exp(+H[0]+H[1]+H[2]+H[3]+H[4]+J[0]+J[1]+J[2]+J[3]+J[4]+J[5]+J[6]+J[7]+J[8]+J[9])/Z
return(Pout)
| 143.701299
| 1,009
| 0.42639
| 3,818
| 11,065
| 1.235464
| 0.016501
| 0.214543
| 0.161119
| 0.188255
| 0.934492
| 0.933008
| 0.932796
| 0.916472
| 0.902268
| 0.89824
| 0
| 0.165785
| 0.025847
| 11,065
| 76
| 1,010
| 145.592105
| 0.271825
| 0.014279
| 0
| 0.15873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.031746
| false
| 0
| 0.015873
| 0
| 0.047619
| 0
| 0
| 0
| 1
| null | 1
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 12
|
df12739e7c4df23e8cf35f1cddd3a25106eed362
| 351,885
|
py
|
Python
|
import-tests/tests/test_ops.py
|
KonduitAI/spark-k8s
|
2bb49019661374a79686374861b7e2b17b26996b
|
[
"Apache-2.0"
] | 6
|
2019-11-24T01:48:22.000Z
|
2021-09-26T12:49:00.000Z
|
import-tests/tests/test_ops.py
|
KonduitAI/spark-k8s
|
2bb49019661374a79686374861b7e2b17b26996b
|
[
"Apache-2.0"
] | 19
|
2019-11-19T23:12:53.000Z
|
2022-02-10T00:27:44.000Z
|
import-tests/tests/test_ops.py
|
KonduitAI/spark-k8s
|
2bb49019661374a79686374861b7e2b17b26996b
|
[
"Apache-2.0"
] | 1
|
2019-11-19T08:27:51.000Z
|
2019-11-19T08:27:51.000Z
|
import tensorflow as tf
from tfoptests.persistor import TensorFlowPersistor
from tfoptests.test_graph import TestGraph
from tfoptests.reduce_ops import ReduceOps
from tfoptests.ops import OpCreator
from tfoptests.var_initializer import VarInitializer
class OpTest(TestGraph):
def __init__(self, op, *args, **kwargs):
super(OpTest, self).__init__(*args, **kwargs)
self.op = op
def list_inputs(self):
return self.op.get("phNames", [])
def _get_placeholder_shape(self, name):
'''Get input tensor shape for given node name'''
return self.op.get("phShapes", {})
def get_placeholder_input(self, name):
'''Get input tensor for given node name'''
return self.invals[name]
def createVars(self, shapes, dtypes, init):
print("Creating vars: shapes=", shapes, ", dtypes=", dtypes, ", init=", init)
out = []
initializer = VarInitializer()
# for(s in shapes):
for i in range(len(shapes)):
s = shapes[i]
d = tf.float32
if(dtypes is not None):
d = dtypes[i]
n = "in_" + str(i)
varInit = "uniform"
if(init is not None and init[i] is not None):
varInit = init[i]
out.append(initializer.newVar(varInit, s, d, n))
return out
def createPlaceholders(self, shapes, dtypes, init):
print("Creating vars: shapes=", shapes, ", dtypes=", dtypes, ", init=", init)
out = []
initializer = VarInitializer()
for i in range(len(shapes)):
s = shapes[i]
d = tf.float32
if(dtypes is not None):
d = dtypes[i]
n = "in_ph_" + str(i)
varInit = "uniform"
if(init is not None and init[i] is not None):
varInit = init[i]
out.append(initializer.newPlaceholder(varInit, s, d, n))
return out
def test_mathtransform():
ops = [
#Format:
#{"opName": "segment_max", "outName": "segment/segment_max_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment5"]},
# {"opName": "segment_mean", "outName": "segment/segment_mean_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment5"]},
# {"opName": "segment_mean", "outName": "segment/segment_mean_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_min", "outName": "segment/segment_min_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment5"]},
# {"opName": "segment_prod", "outName": "segment/segment_prod_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment5"]},
# {"opName": "segment_sum", "outName": "segment/segment_sum_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment5"]},
# {"opName": "segment_max", "outName": "segment/segment_max_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_min", "outName": "segment/segment_min_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_prod", "outName": "segment/segment_prod_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_sum", "outName": "segment/segment_sum_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_max", "outName": "segment/segment_max_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_mean", "outName": "segment/segment_mean_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_min", "outName": "segment/segment_min_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_prod", "outName": "segment/segment_prod_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "segment_sum", "outName": "segment/segment_sum_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "segment3"]},
# {"opName": "space_to_batch", "outName": "space_to_batch/rank4nhwc", "varShapes":[[2,4,4,4], [2,2]], "varTypes":["float32", "int32"], "varInit":["range", "zero"]},
# {"opName": "space_to_batch", "outName": "space_to_batch/rank4nhwc_pad", "varShapes":[[2,2,2,4], [2,2]], "varTypes":["float32", "int32"], "varInit":["range", "one"]},
# {"opName": "space_to_depth", "outName": "space_to_depth/rank4nhwc", "varShapes":[[2,4,4,4]], "varTypes":["float32", "int32"], "varInit":["range", "zero"], "data_format":"NHWC"},
# {"opName": "space_to_depth", "outName": "space_to_depth/rank4nchw", "varShapes":[[2,4,4,4]], "varTypes":["float32", "int32"], "varInit":["range", "zero"], "data_format":"NCHW"},
# {"opName": "batch_to_space", "outName": "batch_to_space/rank4nhwc", "varShapes":[[8,2,2,4], [2,2]], "varTypes":["float32", "int32"], "varInit":["range", "zero"]},
# {"opName": "batch_to_space", "outName": "batch_to_space/rank4nhwc_crop", "varShapes":[[8,2,2,4], [2,2]], "varTypes":["float32", "int32"], "varInit":["range", "one"]},
# {"opName": "depth_to_space", "outName": "depth_to_space/rank4nhwc", "varShapes":[[2,4,4,4]], "varTypes":["float32", "int32"], "varInit":["range", "zero"], "data_format":"NHWC"},
#{"opName": "depth_to_space", "outName": "depth_to_space/rank4nchw", "varShapes":[[2,4,4,4]], "varTypes":["float32", "int32"], "varInit":["range", "zero"], "data_format":"NCHW"}, #Only NHWC format supported on CPU!?
# {"opName": "size", "outName": "size_rank2", "varShapes":[[3,4]], "varTypes":["float32"]},
# {"opName": "size", "outName": "size_rank3", "varShapes":[[2,3,4]], "varTypes":["float32"]},
# {"opName": "shape", "outName": "shape_rank2", "varShapes":[[3,4]], "varTypes":["float32"]},
# {"opName": "shape", "outName": "shape_rank3", "varShapes":[[2,3,4]], "varTypes":["float32"]}
# {"opName": "shapen", "outName": "shapen_3x2", "varShapes":[[3,4], [1,2], [2,4]], "varTypes":["float32", "float32", "float32"]},
# {"opName": "shapen", "outName": "shapen_3x3", "varShapes":[[2,3,4], [1,2,3], [2,1,2]], "varTypes":["float32", "float32", "float32"]}
# {"opName": "matrix_inverse", "outName": "matrix_inverse/rank2", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_inverse", "outName": "matrix_inverse/rank3", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_inverse", "outName": "matrix_inverse/rank4", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"]}
# {"opName": "pad", "outName": "pad/rank1Pzero_const0", "varShapes":[[5],[1,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank1Pzero_const10", "varShapes":[[5],[1,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank1Pone_const0", "varShapes":[[5],[1,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank1Pone_const10", "varShapes":[[5],[1,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank1Pone_reflect", "varShapes":[[5],[1,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"REFLECT"},
# {"opName": "pad", "outName": "pad/rank1Pone_symmetric", "varShapes":[[5],[1,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"SYMMETRIC"}
# {"opName": "pad", "outName": "pad/rank2Pzero_const0", "varShapes":[[3,4],[2,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank2Pzero_const10", "varShapes":[[3,4],[2,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank2Pone_const0", "varShapes":[[3,4],[2,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank2Pone_const10", "varShapes":[[3,4],[2,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank2Pone_reflect", "varShapes":[[3,4],[2,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"REFLECT"},
# {"opName": "pad", "outName": "pad/rank2Pone_symmetric", "varShapes":[[3,4],[2,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"SYMMETRIC"},
# {"opName": "pad", "outName": "pad/rank3Pzero_const0", "varShapes":[[2,3,4],[3,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank3Pzero_const10", "varShapes":[[2,3,4],[3,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","zero","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank3Pone_const0", "varShapes":[[2,3,4],[3,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","zero"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank3Pone_const10", "varShapes":[[2,3,4],[3,2],[]], "varTypes":["float32", "int32", "float32"], "varInit":["uniform","one","ten"], "mode":"CONSTANT"},
# {"opName": "pad", "outName": "pad/rank3Pone_reflect", "varShapes":[[2,3,4],[3,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"REFLECT"},
# {"opName": "pad", "outName": "pad/rank3Pone_symmetric", "varShapes":[[2,3,4],[3,2]], "varTypes":["float32", "int32"], "varInit":["uniform","one"], "mode":"SYMMETRIC"},
# {"opName": "unique", "outName": "unique10-5", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform_int5"]},
# {"opName": "unique_with_counts", "outName": "uniqueWithCounts10-5", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform_int5"]},
# {"opName": "topk", "outName": "topk/rank1_k1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "k":1, "sorted":False},
# {"opName": "topk", "outName": "topk/rank1_k1_sorted", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "k":1, "sorted": True},
# {"opName": "topk", "outName": "topk/rank1_k5", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "k":5, "sorted":False},
# {"opName": "topk", "outName": "topk/rank1_k5_sorted", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "k":5, "sorted":True},
# {"opName": "topk", "outName": "topk/rank2_k1", "varShapes":[[3,6]], "varTypes":["float32"], "varInit":["uniform"], "k":1, "sorted":False},
# {"opName": "topk", "outName": "topk/rank2_k1_sorted", "varShapes":[[3,6]], "varTypes":["float32"], "varInit":["uniform"], "k":1, "sorted": True},
# {"opName": "topk", "outName": "topk/rank2_k5", "varShapes":[[3,6]], "varTypes":["float32"], "varInit":["uniform"], "k":5, "sorted":False},
# {"opName": "topk", "outName": "topk/rank2_k5_sorted", "varShapes":[[3,6]], "varTypes":["float32"], "varInit":["uniform"], "k":5, "sorted":True},
# {"opName": "topk", "outName": "topk/rank3_k3", "varShapes":[[3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "k":3, "sorted":False},
# {"opName": "topk", "outName": "topk/rank3_k3_sorted", "varShapes":[[3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "k":3, "sorted":True}
# {"opName": "in_top_k", "outName": "in_top_k/test_4,5_k1", "varShapes":[[4,5], [4]], "varTypes":["float32", "int32"], "varInit":["uniform", "uniform_int5"], "k":1},
# {"opName": "in_top_k", "outName": "in_top_k/test_4,5_k3", "varShapes":[[4,5], [4]], "varTypes":["float32", "int32"], "varInit":["uniform", "uniform_int5"], "k":3}
# {"opName": "matrix_determinant", "outName": "matrix_determinant/rank2_5,5", "varShapes":[[5,5]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_determinant", "outName": "matrix_determinant/rank3_2,3,3", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_determinant", "outName": "matrix_determinant/rank4_2,2,3,3", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank2_5,5", "varShapes":[[5,5], [5]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank2_5,4", "varShapes":[[5,4], [4]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank2_4,5", "varShapes":[[5,4], [4]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank3_2,3,3", "varShapes":[[2,3,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank3_2,3,4", "varShapes":[[2,3,4], [2,3]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]},
# {"opName": "matrix_set_diag", "outName": "matrix_set_diag/rank4_2,2,3,3", "varShapes":[[2,2,3,3], [2,2,3]], "varTypes":["float32", "float32"], "varInit":["zeros", "uniform"]}
# {"opName": "identity_n", "outName": "identity_n_2", "varShapes":[[2,3], [2]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "identity_n", "outName": "identity_n_4", "varShapes":[[2,3], [2], [], [2,1,3]], "varTypes":["float32", "float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform", "uniform"]}
# {"opName": "zeta", "outName": "zeta_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "zeta", "outName": "zeta_rank3", "varShapes":[[2,3,2], [2,3,2]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniformt"]},
#{"opName": "confusion_matrix", "outName": "confusion/no_num_classes", "varShapes":[[5], [5]], "varTypes":["int32", "int32"], "varInit":["uniform_int5", "uniform_int5"], "num_classes":None},
#{"opName": "confusion_matrix", "outName": "confusion/with_num_classes", "varShapes":[[5], [5]], "varTypes":["int32", "int32"], "varInit":["uniform_int5", "uniform_int5"], "num_classes":5},
#{"opName": "confusion_matrix", "outName": "confusion/no_num_classes_with_weights", "varShapes":[[5], [5], [5]], "varTypes":["int32", "int32", "float32"], "varInit":["uniform_int5", "uniform_int5", "uniform"], "num_classes":None},
#{"opName": "confusion_matrix", "outName": "confusion/with_num_classes_with_weights", "varShapes":[[5], [5], [5]], "varTypes":["int32", "int32", "float32"], "varInit":["uniform_int5", "uniform_int5", "uniform"], "num_classes":5}
# {"opName": "stack", "outName": "stack/rank0_axis-1", "varShapes":[[], []], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-1},
# {"opName": "stack", "outName": "stack/rank0_axis0", "varShapes":[[], []], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":0},
# {"opName": "stack", "outName": "stack/rank1_axis-2", "varShapes":[[3], [3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-2},
# {"opName": "stack", "outName": "stack/rank1_axis-1", "varShapes":[[3], [3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-1},
# {"opName": "stack", "outName": "stack/rank1_axis-0", "varShapes":[[3], [3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":0},
# {"opName": "stack", "outName": "stack/rank1_axis1", "varShapes":[[3], [3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":1},
# {"opName": "stack", "outName": "stack/rank2_axis-3", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-3},
# {"opName": "stack", "outName": "stack/rank2_axis-2", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-2},
# {"opName": "stack", "outName": "stack/rank2_axis-1", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-1},
# {"opName": "stack", "outName": "stack/rank2_axis-0", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":0},
# {"opName": "stack", "outName": "stack/rank2_axis1", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":1},
# {"opName": "stack", "outName": "stack/rank2_axis2", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":2},
# {"opName": "stack", "outName": "stack/rank3_axis-2", "varShapes":[[2,1,3], [2,1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":-2},
# {"opName": "stack", "outName": "stack/rank3_axis0", "varShapes":[[2,1,3], [2,1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":0},
# {"opName": "stack", "outName": "stack/rank3_axis3", "varShapes":[[2,1,3], [2,1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axis":3},
#Note that parallel_stack doesn't support axis arg - equivalent to stack with axis=0
# {"opName": "parallel_stack", "outName": "parallel_stack/rank0", "varShapes":[[], []], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "parallel_stack", "outName": "parallel_stack/rank1", "varShapes":[[3], [3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "parallel_stack", "outName": "parallel_stack/rank2", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "parallel_stack", "outName": "parallel_stack/rank3", "varShapes":[[2,1,3], [2,1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "accumulate_n", "outName": "accumulate_n/rank0", "varShapes":[[], [], []], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"]},
# {"opName": "accumulate_n", "outName": "accumulate_n/rank1", "varShapes":[[3], [3], [3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"]},
# {"opName": "accumulate_n", "outName": "accumulate_n/rank2", "varShapes":[[2,3], [2,3], [2,3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"]},
# {"opName": "accumulate_n", "outName": "accumulate_n/rank3", "varShapes":[[2,3,4], [2,3,4], [2,3,4]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"]},
#{"opName": "angle", "outName": "angle_scalar", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
#{"opName": "angle", "outName": "angle_rank1", "varShapes":[[5]], "varTypes":["float32"], "varInit":["uniform"]},
#{"opName": "angle", "outName": "angle_rank2", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"]},
#TODO how to create ApproximateEqual class??
# {"opName": "approximate_equal", "outName": "approximate_equal_scalar", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "tolerance":0.1},
# {"opName": "matmul", "outName": "matmul/rank2", "varShapes":[[3,4],[4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":False, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/emptyArrayTest/rank2", "varShapes":[[0,4],[4,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "transpose_a":False, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/rank2_ta", "varShapes":[[4,3],[4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/rank2_tb", "varShapes":[[3,4],[5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":False, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/rank3_batch1", "varShapes":[[1,3,4],[1,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "matmul", "outName": "matmul/rank3_batch2", "varShapes":[[2,3,4],[2,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "matmul", "outName": "matmul/rank3_batch2_ta", "varShapes":[[2,4,3],[2,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/rank3_batch2_tb", "varShapes":[[2,3,4],[2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":False, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/rank3_batch2_ta_tb", "varShapes":[[2,4,3],[2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/rank3_batch3", "varShapes":[[3,3,4],[3,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "matmul", "outName": "matmul/rank4_batch2,2", "varShapes":[[2,2,3,4],[2,2,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "matmul", "outName": "matmul/rank4_batch2,2_ta", "varShapes":[[2,2,4,3],[2,2,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/rank4_batch2,2_tb", "varShapes":[[2,2,3,4],[2,2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":False, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/rank4_batch2,2_ta_tb", "varShapes":[[2,2,4,3],[2,2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/emptyArrayTest/rank4", "varShapes":[[2,2,4,0],[2,2,0,4]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "transpose_a":True, "transpose_b":True},
# {"opName": "matmul", "outName": "matmul/rank5_batch2,2,2", "varShapes":[[2,2,2,3,4],[2,2,2,4,5]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":False, "transpose_b":False},
# {"opName": "matmul", "outName": "matmul/rank5_batch2,2,2_ta_tb", "varShapes":[[2,2,2,4,3],[2,2,2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "transpose_a":True, "transpose_b":True},
# {"opName": "matrix_diag_part", "outName": "matrix_diag_part/rank2", "varShapes":[[4,4]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_diag_part", "outName": "matrix_diag_part/rank3", "varShapes":[[3,4,4]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "matrix_diag_part", "outName": "matrix_diag_part/rank4", "varShapes":[[2,2,4,4]], "varTypes":["float32"], "varInit":["uniform"]},
#{"opName": "svd", "outName": "svd/rank2_3,3_noFull_noUv", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank2_3,3_full_noUv", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank2_3,3_noFull_uv", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":True},
#{"opName": "svd", "outName": "svd/rank2_3,3_full_uv", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank2_4,3_noFull_noUv", "varShapes":[[4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank2_4,3_full_noUv", "varShapes":[[4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank2_4,3_noFull_uv", "varShapes":[[4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":True},
#{"opName": "svd", "outName": "svd/rank2_4,3_full_uv", "varShapes":[[4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,3,3_noFull_noUv", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,3,3_full_noUv", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,3,3_noFull_uv", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":True},
#{"opName": "svd", "outName": "svd/rank3_2,3,3_full_uv", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,4,3_noFull_noUv", "varShapes":[[2,4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,4,3_full_noUv", "varShapes":[[2,4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank3_2,4,3_noFull_uv", "varShapes":[[2,4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":True},
#{"opName": "svd", "outName": "svd/rank3_2,4,3_full_uv", "varShapes":[[2,4,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank4_2,2,3,3_noFull_noUv", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank4_2,2,3,3_full_noUv", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
#{"opName": "svd", "outName": "svd/rank4_2,2,3,3_noFull_uv", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":False, "compute_uv":True},
#{"opName": "svd", "outName": "svd/rank4_2,2,3,3_full_uv", "varShapes":[[2,2,3,3]], "varTypes":["float32"], "varInit":["uniform"], "full_matrices":True, "compute_uv":False},
# {"opName": "pow", "outName": "pow/rank0", "varShapes": [[], []],"varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "pow", "outName": "pow/rank1", "varShapes":[[2], [2]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "pow", "outName": "pow/rank2", "varShapes":[[3,2], [3,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"]},
# {"opName": "pow", "outName": "pow/rank2bc0", "varShapes":[[2,5], []], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"]},
# {"opName": "pow", "outName": "pow/rank2bc", "varShapes":[[2,5], [2, 1]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank2", "varShapes":[[3,4],[3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank1_weights_1", "varShapes":[[5],[5],[]], "varTypes":["float32","float32", "float32"], "varInit":["uniform","uniform", "uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank2_weights_1", "varShapes":[[3,4],[3,4],[]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank2_weights_2", "varShapes":[[3,4],[3,4],[1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank2_weights_3", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank3_weights_1", "varShapes":[[2,3,4],[2,3,4],[]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank3_weights_2", "varShapes":[[2,3,4],[2,3,4],[1,1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank3_weights_3", "varShapes":[[2,3,4],[2,3,4],[2,1,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "mean_squared_error", "outName": "losses/mse_rank3_weights_4", "varShapes":[[2,3,4],[2,3,4],[2,3,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]}
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank2", "varShapes":[[3,4],[3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank0_weights", "varShapes":[[],[],[]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank1_weights_1", "varShapes":[[5],[5],[]], "varTypes":["float32","float32", "float32"], "varInit":["uniform","uniform", "uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank2_weights_1", "varShapes":[[3,4],[3,4],[]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank2_weights_2", "varShapes":[[3,4],[3,4],[1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank3_weights_1", "varShapes":[[2,3,4],[2,3,4],[]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "absolute_difference", "outName": "losses/absdiff_rank3_weights_2", "varShapes":[[2,3,4],[2,3,4],[2,1,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
#{"opName": "cosine_distance", "outName": "losses/cosine_diff_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "axis":None}, #Cosine doesn't like rank 0 input, it seems...
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "axis":0},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis0_SUM", "varShapes":[[3,4],[3,4],[1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM, "axis":0},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis1_NONE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.NONE, "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis1_SUM", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM, "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis1_MEAN", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN, "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis1_SUM_OVER_BATCH_SIZE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE, "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank2_axis1_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS, "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "axis":0},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_axis1", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "axis":1},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_axis2", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "axis":2},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_weights0", "varShapes":[[2,3,4],[2,3,4],[1,1,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "axis":0}, #Can't have weights [2,1,1]? Maybe weights must match post reduce...
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_weights1", "varShapes":[[2,3,4],[2,3,4],[1,3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "axis":0},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_weights2", "varShapes":[[2,3,4],[2,3,4],[1,1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "axis":0},
# {"opName": "cosine_distance", "outName": "losses/cosine_diff_rank3_weightsAll", "varShapes":[[2,3,4],[2,3,4],[1,3,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "axis":0},
#Hinge: need bernoulli (0 or 1) labels, and 0 centered predictions (<0 negative, > 0 positive)
# {"opName": "hinge_loss", "outName": "losses/hinge_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["bernoulli","uniform_m1_1"]},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank2_NONE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank2_SUM", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank2_MEAN", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank2_SUM_OVER_BATCH_SIZE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank2_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["bernoulli","uniform_m1_1"]},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank3_weights0", "varShapes":[[2,3,4],[2,3,4],[2,1,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform"]},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank3_weights1", "varShapes":[[2,3,4],[2,3,4],[1,3,1]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform"]},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank3_weights2", "varShapes":[[2,3,4],[2,3,4],[1,1,4]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform"]},
# {"opName": "hinge_loss", "outName": "losses/hinge_rank3_weightsAll", "varShapes":[[2,3,4],[2,3,4],[2,3,4]], "varTypes":["float32","float32","float32"], "varInit":["bernoulli","uniform_m1_1","uniform"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank1_d05", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"],"delta":0.5},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank1_d2", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"],"delta":2.0},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis0_SUM", "varShapes":[[3,4],[3,4],[1,4]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis1_NONE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis1_SUM", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis1_MEAN", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis1_SUM_OVER_BATCH_SIZE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank2_axis1_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_axis1", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_axis2", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["stdnormal","stdnormal"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_weights0", "varShapes":[[2,3,4],[2,3,4],[1,1,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_weights1", "varShapes":[[2,3,4],[2,3,4],[1,3,1]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_weights2", "varShapes":[[2,3,4],[2,3,4],[1,1,4]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform"]},
# {"opName": "huber_loss", "outName": "losses/huber_diff_rank3_weightsAll", "varShapes":[[2,3,4],[2,3,4],[1,3,4]], "varTypes":["float32","float32","float32"], "varInit":["stdnormal","stdnormal","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank0", "varShapes":[[],[]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank1", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank1_eps01", "varShapes":[[5],[5]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"],"epsilon":0.1},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis0_SUM", "varShapes":[[3,4],[3,4],[1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis1_NONE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis1_SUM", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis1_MEAN", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis1_SUM_OVER_BATCH_SIZE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "log_loss", "outName": "losses/log_loss_rank2_axis1_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_axis1", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_axis2", "varShapes":[[2,3,4],[2,3,4]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_weights0", "varShapes":[[2,3,4],[2,3,4],[1,1,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_weights1", "varShapes":[[2,3,4],[2,3,4],[1,3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_weights2", "varShapes":[[2,3,4],[2,3,4],[1,1,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
# {"opName": "log_loss", "outName": "losses/log_loss_rank3_weightsAll", "varShapes":[[2,3,4],[2,3,4],[1,3,4]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"]},
#sigmoid_cross_entropy: seems to only support [batch_size, num_classes] shapes?
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce", "varShapes":[[3,4],[3,4]], "varTypes":["float32","float32"], "varInit":["uniform","stdnormal"]},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_smooth01", "varShapes":[[3,4],[3,4]], "varTypes":["float32","float32"], "varInit":["uniform","stdnormal"],"label_smoothing":0.1},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_smooth05", "varShapes":[[3,4],[3,4]], "varTypes":["float32","float32"], "varInit":["uniform","stdnormal"],"label_smoothing":0.5},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_NONE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_SUM", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_MEAN", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_SUM_OVER_BATCH_SIZE", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "sigmoid_cross_entropy", "outName": "losses/sigmoid_ce_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[3,4],[3,4],[3,1]], "varTypes":["float32","float32","float32"], "varInit":["uniform","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce", "varShapes":[[10,4],[10,4]], "varTypes":["float32","float32"], "varInit":["onehot","stdnormal"]},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_smooth01", "varShapes":[[10,4],[10,4]], "varTypes":["float32","float32"], "varInit":["onehot","stdnormal"],"label_smoothing":0.1},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_smooth05", "varShapes":[[10,4],[10,4]], "varTypes":["float32","float32"], "varInit":["onehot","stdnormal"],"label_smoothing":0.5},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_NONE", "varShapes":[[10,4],[10,4],[10]], "varTypes":["float32","float32","float32"], "varInit":["onehot","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_SUM", "varShapes":[[10,4],[10,4],[10]], "varTypes":["float32","float32","float32"], "varInit":["onehot","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_MEAN", "varShapes":[[10,4],[10,4],[10]], "varTypes":["float32","float32","float32"], "varInit":["onehot","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_SUM_OVER_BATCH_SIZE", "varShapes":[[10,4],[10,4],[10]], "varTypes":["float32","float32","float32"], "varInit":["onehot","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "softmax_cross_entropy", "outName": "losses/softmax_ce_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[10,4],[10,4],[10]], "varTypes":["float32","float32","float32"], "varInit":["onehot","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce", "varShapes":[[10],[10,5]], "varTypes":["int32","float32"], "varInit":["uniform_int5","stdnormal"]},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_NONE", "varShapes":[[10],[10,5],[10,1]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.NONE},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_SUM", "varShapes":[[10],[10,5],[10,1]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_MEAN", "varShapes":[[10],[10,5],[10,1]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.MEAN},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_SUM_OVER_BATCH_SIZE", "varShapes":[[10],[10,5],[10]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_OVER_BATCH_SIZE},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_SUM_BY_NONZERO_WEIGHTS", "varShapes":[[10],[10,5],[10]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName": "sparse_softmax_cross_entropy", "outName": "losses/sparse_softmax_ce_SUM_BY_NONZERO_WEIGHTS_1", "varShapes":[[10],[10,5],[1]], "varTypes":["int32","float32","float32"], "varInit":["uniform_int5","stdnormal","uniform_sparse"], "reduction":tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS},
# {"opName":"l2_loss", "outName":"losses/l2_loss_rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"l2_loss", "outName":"losses/l2_loss_rank1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"l2_loss", "outName":"losses/l2_loss_rank2", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"l2_loss", "outName":"losses/l2_loss_rank3", "varShapes":[[2,3,4]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"l2_loss", "outName":"losses/l2_loss_rank4", "varShapes":[[2,3,4,5]], "varTypes":["float32"], "varInit":["uniform"]}
#tf.nn.conv1d
#CNN 1D layers: value, filters
#value: NCW: [batch, channels, width], NWC: [batch, width, channels]
#filters are [kernel, inChannels, outChannels] for both
#Can't run the ncw tests: "UnimplementedError (see above for traceback): Generic conv implementation only supports NHWC tensor format for now" :/
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/ncw_b1_k2_s1_SAME", "varShapes":[[1, 2, 5], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"SAME", "data_format":"NCW"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/ncw_b2_k2_s1_SAME", "varShapes":[[2, 2, 5], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"SAME", "data_format":"NCW"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/ncw_b2_k2_s1_VALID", "varShapes":[[2, 2, 5], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"VALID", "data_format":"NCW"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/ncw_b1_k2_s2_SAME", "varShapes":[[1, 2, 5], [5, 2, 3]], "varTypes":["float32","float32"], "stride":2, "padding":"SAME", "data_format":"NCW"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/nwc_b1_k2_s1_SAME", "varShapes":[[1, 5, 2], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"SAME", "data_format":"NWC"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/nwc_b2_k2_s1_SAME", "varShapes":[[2, 5, 2], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"SAME", "data_format":"NWC"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/nwc_b2_k2_s1_VALID", "varShapes":[[2, 5, 2], [5, 2, 3]], "varTypes":["float32","float32"], "stride":1, "padding":"VALID", "data_format":"NWC"},
# {"opName":"nn_cnn1d", "outName":"cnn1d_nn/nwc_b1_k2_s2_SAME", "varShapes":[[1, 5, 2], [5, 2, 3]], "varTypes":["float32","float32"], "stride":2, "padding":"SAME", "data_format":"NWC"},
#tf.layers.conv1d
#Note that the tf.layers version seems to add the variables directly - you don't provide the kernel params as a variable...
#Also can't run channels_first here: "Generic conv implementation only supports NHWC tensor format for now." :/
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_first_b1_k2_s1_d1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32","float32"], "filters":2, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_first", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_first_b1_k3_s1_d1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_first", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_first_b2_k2_s1_d2_SAME", "varShapes":[[2, 2, 5]], "varTypes":["float32","float32"], "filters":4, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_first", "dilation_rate":2},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_first_b2_k2_s1_d1_VALID", "varShapes":[[2, 2, 5]], "varTypes":["float32","float32"], "filters":1, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_first", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_first_b1_k2_s2_d1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32","float32"], "filters":2, "kernel_size":2, "strides":2, "padding":"SAME", "data_format":"channels_first", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d2_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":2},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_SAME", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":2, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_sigmoid", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.relu},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_sigmoid", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.sigmoid},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_elu", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.elu},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_relu6", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.relu6},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_relu6", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.selu},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b1_k2_s1_d1_SAME_crelu", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "activation":tf.nn.crelu},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_SAME_regularizers", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1,
# "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":tf.contrib.layers.l1_regularizer(scale=0.2), "activity_regularizer":tf.contrib.layers.l1_l2_regularizer(scale_l1=0.1,scale_l2=0.2)},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_SAME_constraints1", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1,
# "kernel_constraint":tf.keras.constraints.MaxNorm(max_value=2), "bias_constraint":tf.keras.constraints.NonNeg()},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_SAME_constraints2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1,
# "kernel_constraint":tf.keras.constraints.MinMaxNorm(min_value=1, max_value=2)},
# {"opName":"layers_cnn1d", "outName":"cnn1d_layers/channels_last_b2_k2_s1_SAME_constraints3", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1,
# "kernel_constraint":tf.keras.constraints.UnitNorm()},
#TODO TF constraints don't appear to get saved with the model...
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b1_k2_s1_d1_SAME_dm1", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_SAME_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s2_d1_SAME_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":2, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d2_SAME_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":2, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_SAME_dm1_sigm", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1, "activation":tf.nn.tanh},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_SAME_dm2_sigm_nobias", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2, "activation":tf.nn.tanh, "use_bias":False},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b1_k2_s1_d1_VALID_dm1", "varShapes":[[1, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_VALID_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s2_d1_VALID_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":2, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d2_VALID_dm2", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":2, "depth_multiplier":2},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_VALID_dm1_sigm", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1, "activation":tf.nn.tanh},
# {"opName":"layers_sepconv1d", "outName":"sepconv1d_layers/channels_last_b2_k2_s1_d1_VALID_dm2_sigm_nobias", "varShapes":[[2, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2, "activation":tf.nn.tanh, "use_bias":False},
#Max/avg pool 1d:
#channels_first: "Default MaxPoolingOp only supports NHWC on device type CPU" :/
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_first_b1_k2_s1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_first_b1_k3_s1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":3, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_first_b2_k2_s1_SAME", "varShapes":[[2, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_first_b2_k2_s1_VALID", "varShapes":[[2, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_first"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_first_b1_k2_s2_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_last_b1_k2_s1_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_last_b2_k2_s1_SAME", "varShapes":[[2, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_last"},
# {"opName":"max_pooling1d", "outName":"max_pooling1d/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_last"},
#{"opName":"max_pool_with_argmax", "outName":"max_pool_with_argmax/float32_to_int32_nhwc", "varShapes":[[1, 2, 2, 5]], "varTypes":["int32"], "varInit":["uniform_int10"], "ksizes":[1,2,1,1],"strides":[1,1,2,1], "padding":"SAME", "data_format":"NHWC", "output_dtype":tf.int32},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/float32_to_int64", "varShapes": [[1, 2, 2, 5]], "varTypes": ["float32"], "varInit": ["uniform"], "ksizes": [1,2,1,1], "strides": [1,1,2,1], "padding": "SAME", "data_format": "NHWC", "output_dtype": tf.int64},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/float64_to_int64", "varShapes": [[1, 2, 2, 5]], "varTypes": ["float64"], "varInit": ["uniform"], "ksizes": [1, 2,1,1], "strides": [1, 1,2,1], "padding": "VALID", "data_format": "NHWC", "output_dtype": tf.int64},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/int32_to_int64", "varShapes": [[1, 2, 2, 5]], "varTypes": ["int32"], "varInit": ["uniform"], "ksizes": [1, 2,1,1], "strides": [1, 1,2,1], "padding": "VALID","data_format": "NHWC", "output_dtype": tf.int64},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/int64_to_int32", "varShapes": [[1, 2, 2, 5]], "varTypes": ["int64"], "varInit": ["uniform"], "ksizes": [1, 2,1,1], "strides": [1, 1,2,1], "padding": "VALID", "data_format": "NHWC", "output_dtype": tf.int32},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/bfloat16_to_int32", "varShapes": [[1, 2, 2, 5]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "ksizes": [1, 2,1,1], "strides": [1, 1,2,1], "padding": "VALID", "data_format": "NHWC", "output_dtype": tf.int32},
#{"opName": "max_pool_with_argmax", "outName": "max_pool_with_argmax/half_to_int32", "varShapes": [[1, 2, 2, 5]], "varTypes": ["half"], "varInit": ["uniform"], "ksizes": [1, 2,1,1], "strides": [1, 1,1,2], "padding": "VALID","data_format": "NHWC", "output_dtype": tf.int32},
#Default AvgPoolingOp only supports NHWC on device type CPU
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_first_b1_k2_s1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_first_b1_k2_s1_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":3, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_first_b2_k2_s1_SAME", "varShapes":[[2, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_first_b2_k2_s1_VALID", "varShapes":[[2, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_first"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_first_b1_k2_s2_SAME", "varShapes":[[1, 2, 5]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_last_b1_k2_s1_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_last_b2_k2_s1_SAME", "varShapes":[[2, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_last"},
# {"opName":"avg_pooling1d", "outName":"avg_pooling1d/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"dense", "outName":"dense/dense5", "varShapes":[[5,4]], "varTypes":["float32"], "units":5, "activation":None, "use_bias":True, "kernel_regularizer":None, "bias_regularizer":None},
# {"opName":"dense", "outName":"dense/dense5_sigmoid_nobias", "varShapes":[[5,4]], "varTypes":["float32"], "units":5, "activation":tf.nn.sigmoid, "use_bias":False, "kernel_regularizer":None, "bias_regularizer":None},
# {"opName":"dense", "outName":"dense/dense5_tanh_regularizer", "varShapes":[[5,4]], "varTypes":["float32"], "units":5, "activation":tf.nn.tanh, "use_bias":True, "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":None},
# {"opName":"flatten", "outName":"flatten/rank2", "varShapes":[[3,4]], "varTypes":["float32"]},
# {"opName":"flatten", "outName":"flatten/rank3", "varShapes":[[2,3,4]], "varTypes":["float32"]},
# {"opName":"flatten", "outName":"flatten/rank4", "varShapes":[[2,3,2,4]], "varTypes":["float32"]},
# {"opName":"flatten", "outName":"flatten/rank5", "varShapes":[[2,3,2,4,2]], "varTypes":["float32"]},
# NHWC format: kernel format is [kH,kW,cIn,cOut]
#Also, strides and dilation are 4d for some reason, strides should be [1, sH, sW, 1]
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b1_k2_s1_d1_SAME", "varShapes":[[1, 5, 5, 2], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NHWC"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b2_k3_s1_d1_SAME", "varShapes":[[2, 5, 5, 2], [3, 3, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NHWC"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b2_k2_s1_d1_SAME", "varShapes":[[2, 5, 5, 2], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NHWC"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b2_k2_s1_d2_SAME", "varShapes":[[2, 5, 5, 2], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NHWC", "dilation":[1,2,2,1]},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b2_k2_s1_d1_VALID", "varShapes":[[2, 5, 5, 2], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"VALID", "data_format":"NHWC"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_b1_k2_s2_SAME", "varShapes":[[2, 5, 5, 2], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,2,2,1], "padding":"SAME", "data_format":"NHWC"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b1_k2_s1_d1_SAME", "varShapes":[[1, 2, 5, 5], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NCHW"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b2_k3_s1_d1_SAME", "varShapes":[[2, 2, 5, 5], [3, 3, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NCHW"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b2_k2_s1_d1_SAME", "varShapes":[[2, 2, 5, 5], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NCHW"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b2_k2_s1_d2_SAME", "varShapes":[[2, 2, 5, 5], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NCHW", "dilation":[1,1,2,2]},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b2_k2_s1_d1_VALID", "varShapes":[[2, 2, 5, 5], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"VALID", "data_format":"NCHW"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nchw_b1_k2_s2_SAME", "varShapes":[[2, 2, 5, 5], [2, 2, 2, 3]], "varTypes":["float32","float32"], "strides":[1,1,1,2], "padding":"SAME", "data_format":"NCHW"},
# {"opName":"nn_conv2d", "outName":"cnn2d_nn/nhwc_35_35_32_b1_k3_s1_SAME", "varShapes":[[1, 35, 35, 3], [3, 3, 3, 192]], "varTypes":["float32","float32"], "strides":[1,1,1,1], "padding":"SAME", "data_format":"NHWC"},
#Again, no channels_first on CPU
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1]},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d2_SAME", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[2,2]},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_SAME_nobias", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "use_bias":False},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2], "strides":[1,1], "padding":"VALID", "data_format":"channels_last", "dilation_rate":[1,1]},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[2,2], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1]},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_sigmoid", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.relu},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_sigmoid", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.sigmoid},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_elu", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.elu},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_relu6", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.relu6},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_selu_nobias", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.selu, "use_bias":False},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b1_k2_s1_d1_SAME_crelu", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1], "activation":tf.nn.crelu},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_SAME_regularizers", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1],
# "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":tf.contrib.layers.l1_regularizer(scale=0.2), "activity_regularizer":tf.contrib.layers.l1_l2_regularizer(scale_l1=0.1,scale_l2=0.2)},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_SAME_constraints1", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1],
# "kernel_constraint":tf.keras.constraints.MaxNorm(max_value=2), "bias_constraint":tf.keras.constraints.NonNeg()},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_SAME_constraints2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1],
# "kernel_constraint":tf.keras.constraints.MinMaxNorm(min_value=1, max_value=2)},
# {"opName":"layers_conv2d", "outName":"cnn2d_layers/channels_last_b2_k2_s1_SAME_constraints3", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1],
# "kernel_constraint":tf.keras.constraints.UnitNorm()},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last"},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b2_k2_s1_SAME_nobias", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "use_bias":False},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2], "strides":[1,1], "padding":"VALID", "data_format":"channels_last"},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[2,2], "padding":"SAME", "data_format":"channels_last"},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_sigmoid", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.relu},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_sigmoid", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.sigmoid},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_elu", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.elu},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_relu6", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.relu6},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_selu_nobias", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.selu, "use_bias":False},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b1_k2_s1_SAME_crelu", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.crelu},
# {"opName":"layers_conv2d_transpose", "outName":"conv2d_transpose/channels_last_b2_k2_s1_SAME_regularizers", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2], "strides":[1,1], "padding":"SAME", "data_format":"channels_last",
# "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":tf.contrib.layers.l1_regularizer(scale=0.2), "activity_regularizer":tf.contrib.layers.l1_l2_regularizer(scale_l1=0.1,scale_l2=0.2)},
# {"opName":"Conv2DTranspose", "outName":"Conv2DTranspose", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "kernel_size":[2,2], "filters":2},
# Data format: ch_last: NDHWC, ch_first: NCDHW
# "CPU implementation of Conv3D currently only supports the NHWC tensor format."
#{"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_first_b1_k2_s1_d1_SAME", "varShapes":[[1, 2, 5, 5, 5]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_first", "dilation_rate":[1,1,1]},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b1_k2_s1_d1_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1]},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b1_k2_s1_d2_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[2,2,2]},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b2_k3_s1_SAME_nobias", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[3,3,3], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1], "use_bias":False},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"VALID", "data_format":"channels_last", "dilation_rate":[1,1,1]},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[2,2,2], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1]},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b1_k2_s1_d1_SAME_sigmoid", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1], "activation":tf.nn.relu},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b2_k2_s1_SAME_regularizers", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1],
# "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":tf.contrib.layers.l1_regularizer(scale=0.2), "activity_regularizer":tf.contrib.layers.l1_l2_regularizer(scale_l1=0.1,scale_l2=0.2)},
# {"opName":"layers_conv3d", "outName":"cnn3d_layers/channels_last_b2_k2_s1_SAME_constraints1", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "dilation_rate":[1,1,1],
# "kernel_constraint":tf.keras.constraints.MaxNorm(max_value=2), "bias_constraint":tf.keras.constraints.NonNeg()},
# Max/avg pool 3d:
# channels_first: "Default MaxPoolingOp only supports NHWC on device type CPU" :/
# {"opName":"max_pooling3d", "outName":"max_pooling3d/channels_first_b1_k2_s1_SAME", "varShapes":[[1, 2, 5, 5, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"max_pooling3d", "outName":"max_pooling3d/channels_last_b1_k2_s1_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"max_pooling3d", "outName":"max_pooling3d/channels_last_b2_k2_s1_SAME", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"max_pooling3d", "outName":"max_pooling3d/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_last"},
# {"opName":"max_pooling3d", "outName":"max_pooling3d/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_last"},
#
# #Default AvgPoolingOp only supports NHWC on device type CPU
# {"opName":"avg_pooling3d", "outName":"avg_pooling3d/channels_first_b1_k2_s1_SAME", "varShapes":[[1, 2, 5, 5, 5]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_first"},
# {"opName":"avg_pooling3d", "outName":"avg_pooling3d/channels_last_b1_k2_s1_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"avg_pooling3d", "outName":"avg_pooling3d/channels_last_b2_k2_s1_SAME", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"avg_pooling3d", "outName":"avg_pooling3d/channels_last_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":1, "padding":"VALID", "data_format":"channels_last"},
# {"opName":"avg_pooling3d", "outName":"avg_pooling3d/channels_last_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32"], "pooling_size":2, "stride":2, "padding":"SAME", "data_format":"channels_last"},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b1_k2_s1_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last"},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NCDHW_b2_k2_s1_SAME_nobias", "varShapes":[[2, 2, 5, 5, 5]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_first", "use_bias":False},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b2_k2_s1_VALID", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":2, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"VALID", "data_format":"channels_last"},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b1_k2_s2_SAME", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[2,2,2], "padding":"SAME", "data_format":"channels_last"},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b1_k2_s1_SAME_sigmoid", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.relu},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NCDHW_b1_k2_s2_SAME_sigmoid", "varShapes":[[1, 2, 6, 6, 6,]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[2,2,2], "padding":"SAME", "data_format":"channels_first", "activation":tf.nn.sigmoid},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b1_k2_s1_SAME_elu", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_first", "activation":tf.nn.elu},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NCDHW_b1_k3_s2_SAME_relu6", "varShapes":[[1, 2, 6, 6, 6]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[3,3,3], "strides":[2,2,2], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.relu6},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b1_k2_s1_SAME_selu_nobias", "varShapes":[[1, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last", "activation":tf.nn.selu, "use_bias":False},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NCDHW_b1_k2_s1_VALID_crelu", "varShapes":[[1, 2, 6, 6, 6]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"VALID", "data_format":"channels_first", "activation":tf.nn.crelu},
# {"opName":"conv3d_transpose_layers", "outName":"conv3d_transpose_layers/NDHWC_b2_k2_s1_SAME_regularizers", "varShapes":[[2, 5, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":[2,2,2], "strides":[1,1,1], "padding":"SAME", "data_format":"channels_last",
# "kernel_regularizer":tf.contrib.layers.l2_regularizer(scale=0.1), "bias_regularizer":tf.contrib.layers.l1_regularizer(scale=0.2), "activity_regularizer":tf.contrib.layers.l1_l2_regularizer(scale_l1=0.1,scale_l2=0.2)},
# variables: value (NCDHW / NDHWC) and filter ([kD, kH, kW, oC, iC])
# {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NDHWC_b1_k2_s1_d1_SAME", "varShapes":[[1, 3, 3, 3, 2], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,3,3,3,3], "strides":[1,1,1,1,1], "padding":"SAME", "data_format":"NDHWC", "dilations":1},
# # Next 2: "Operation received an exception:Status: 3, message: could not create a convolution backward data descriptor, in file tensorflow/core/kernels/mkl_conv_grad_input_ops.cc:456"
# # {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NCDHW_b2_k2_s2_d1_VALID_out8", "varShapes":[[2, 2, 4, 4, 4], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,3,8,8,8], "strides":[1,1,2,2,2], "padding":"VALID", "data_format":"NCDHW", "dilations":1},
# # {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NDHWC_b2_k2_s2_d1_VALID_out9", "varShapes":[[2, 4, 4, 4, 2], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,9,9,9,3], "strides":[1,2,2,2,1], "padding":"VALID", "data_format":"NDHWC", "dilations":1},
# {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NDHWC_b1_k2_s2_d1_SAME", "varShapes":[[1, 2, 2, 2, 2], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,4,4,4,3], "strides":[1,2,2,2,1], "padding":"SAME", "data_format":"NDHWC", "dilations":[1,1,1,1,1]},
# {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NCDHW_b1_k2_s2_d1_SAME_out6", "varShapes":[[1, 2, 3, 3, 3,], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,3,6,6,6], "strides":[1,1,2,2,2], "padding":"SAME", "data_format":"NCDHW", "dilations":[1,1,1]},
# {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NCDHW_b1_k2_s2_d1_SAME_out5", "varShapes":[[1, 2, 3, 3, 3,], [2, 2, 2, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,3,5,5,5], "strides":[1,1,2,2,2], "padding":"SAME", "data_format":"NCDHW", "dilations":[1,1,1]},
# {"opName":"conv3d_transpose_nn", "outName":"conv3d_transpose_nn/NCDHW_b1_k3_s3_d1_SAME", "varShapes":[[1, 2, 1, 1, 1], [3, 3, 3, 3, 2]], "varTypes":["float32","float32"], "output_shape":[1,3,3,3,3], "strides":[1,1,3,3,3], "padding":"SAME", "data_format":"NCDHW", "dilations":[1,1,1]},
#Separable conv 2d - channels_last = NHWC
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b1_k2_s1_d1_SAME_dm1", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_SAME_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s2_d1_SAME_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":2, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d2_SAME_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":2, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_SAME_dm1_sigm", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1, "activation":tf.nn.tanh},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_SAME_dm2_sigm_nobias", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"SAME", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2, "activation":tf.nn.tanh, "use_bias":False},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b1_k2_s1_d1_VALID_dm1", "varShapes":[[1, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_VALID_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s2_d1_VALID_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":2, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d2_VALID_dm2", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":2, "depth_multiplier":2},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_VALID_dm1_sigm", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":1, "activation":tf.nn.tanh},
# {"opName":"layers_sepconv2d", "outName":"sepconv2d_layers/channels_last_b2_k2_s1_d1_VALID_dm2_sigm_nobias", "varShapes":[[2, 5, 5, 2]], "varTypes":["float32","float32"], "filters":3, "kernel_size":2, "strides":1, "padding":"VALID", "data_format":"channels_last", "dilation_rate":1, "depth_multiplier":2, "activation":tf.nn.tanh, "use_bias":False},
#Batch norm - 2d
# {"opName":"batchnorm", "outName":"batchnorm/rank2_batch2_sz5_fused", "varShapes":[[2, 5]], "varTypes":["float32"], "axis":1, "fused":True},
# {"opName":"batchnorm", "outName":"batchnorm/rank2_batch4_sz5_noFused", "varShapes":[[4, 5]], "varTypes":["float32"], "axis":1, "fused":False},
# {"opName":"batchnorm", "outName":"batchnorm/rank2_batch3_sz5_fused_m50_e01", "varShapes":[[3, 5]], "varTypes":["float32"], "axis":1, "fused":True, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank2_batch4_sz5_noFused_m50e01", "varShapes":[[4, 5]], "varTypes":["float32"], "axis":1, "fused":False, "momentum":0.5, "epsilon":0.1},
#
# #Batch norm - 3d input (time series) - NCW = [mb, size, length]
# {"opName":"batchnorm", "outName":"batchnorm/rank3_ncw_batch2_sz5_fused", "varShapes":[[2, 5, 5]], "varTypes":["float32"], "axis":1, "fused":True},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_ncw_batch4_sz5_noFused", "varShapes":[[4, 5, 5]], "varTypes":["float32"], "axis":1, "fused":False},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_ncw_batch3_sz5_fused_m50_e01", "varShapes":[[3, 5, 3]], "varTypes":["float32"], "axis":1, "fused":True, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_ncw_batch4_sz5_noFused_m50e01", "varShapes":[[4, 5, 1]], "varTypes":["float32"], "axis":1, "fused":False, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_nwc_batch2_sz5_fused", "varShapes":[[2, 5, 5]], "varTypes":["float32"], "axis":2, "fused":True},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_nwc_batch4_sz5_noFused", "varShapes":[[4, 5, 5]], "varTypes":["float32"], "axis":2, "fused":False},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_nwc_batch3_sz5_fused_m50_e01", "varShapes":[[3, 5, 3]], "varTypes":["float32"], "axis":2, "fused":True, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank3_nwc_batch4_sz5_noFused_m50e01", "varShapes":[[4, 5, 1]], "varTypes":["float32"], "axis":2, "fused":False, "momentum":0.5, "epsilon":0.1},
#Batch norm - 4d input (2d CNN)
#Can't do fused + nchw(axis=1)
# # {"opName":"batchnorm", "outName":"batchnorm/rank4_nchw_batch2_sz5_fused", "varShapes":[[2, 5, 5, 3]], "varTypes":["float32"], "axis":1, "fused":True},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nchw_batch4_sz5_noFused", "varShapes":[[4, 5, 5, 3]], "varTypes":["float32"], "axis":1, "fused":False},
# # {"opName":"batchnorm", "outName":"batchnorm/rank4_nchw_batch3_sz5_fused_m50_e01", "varShapes":[[3, 5, 3, 5]], "varTypes":["float32"], "axis":1, "fused":True, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nchw_batch4_sz5_noFused_m50e01", "varShapes":[[4, 5, 5, 1]], "varTypes":["float32"], "axis":1, "fused":False, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nhwc_batch2_sz5_fused", "varShapes":[[2, 5, 5, 5]], "varTypes":["float32"], "axis":3, "fused":True},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nhwc_batch4_sz5_noFused", "varShapes":[[4, 5, 5, 5]], "varTypes":["float32"], "axis":3, "fused":False},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nhwc_batch3_sz5_fused_m50_e01", "varShapes":[[3, 5, 5, 3]], "varTypes":["float32"], "axis":3, "fused":True, "momentum":0.5, "epsilon":0.1},
# {"opName":"batchnorm", "outName":"batchnorm/rank4_nhwc_batch4_sz5_noFused_m50e01", "varShapes":[[4, 5, 5, 1]], "varTypes":["float32"], "axis":3, "fused":False, "momentum":0.5, "epsilon":0.1},
#Leaky RELU
#{"opName":"leaky_relu", "outName":"leaky_relu/rank2_a0", "varShapes":[[4, 5]], "varTypes":["float32"], "varInit":["stdnormal"], "alpha":0.0},
#{"opName":"leaky_relu", "outName":"leaky_relu/rank4_a05", "varShapes":[[4, 5]], "varTypes":["float32"], "varInit":["stdnormal"], "alpha":0.5},
#{"opName":"leaky_relu", "outName":"leaky_relu/rank4_a0", "varShapes":[[4, 5, 5, 1]], "varTypes":["float32"], "varInit":["stdnormal"], "alpha":0.0},
#{"opName":"leaky_relu", "outName":"leaky_relu/rank4_a02", "varShapes":[[4, 5, 5, 1]], "varTypes":["float32"], "varInit":["stdnormal"], "alpha":0.2},
#Embedding lookup
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank2_single_div_nomaxnorm", "varShapes":[[10, 5],[4]], "varTypes":["float32","int32"], "varInit":["uniform","uniform_int10"], "partition_strategy":"div", "max_norm":None},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank2_single_mod_maxnorm1", "varShapes":[[10, 5],[4]], "varTypes":["float32","int32"], "varInit":["uniform","uniform_int10"], "partition_strategy":"mod", "max_norm":1.0},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank2_multiple_div_nomaxnorm", "varShapes":[[4, 5],[3,5],[3,5],[4]], "varTypes":["float32","float32","float32","int32"], "varInit":["uniform","uniform","uniform","uniform_int10"], "partition_strategy":"div", "max_norm":None},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank2_multiple_mod_maxnorm1", "varShapes":[[4, 5],[3,5],[3,5],[4]], "varTypes":["float32","float32","float32","int32"], "varInit":["uniform","uniform","uniform","uniform_int10"], "partition_strategy":"mod", "max_norm":1.0},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank4_single_div_nomaxnorm", "varShapes":[[10, 5],[4]], "varTypes":["float32","int32"], "varInit":["uniform","uniform_int10"], "partition_strategy":"div", "max_norm":None},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank4_single_mod_maxnorm1", "varShapes":[[10, 2, 3, 4],[4]], "varTypes":["float32","int32"], "varInit":["uniform","uniform_int10"], "partition_strategy":"mod", "max_norm":1.0},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank4_multiple_div_nomaxnorm", "varShapes":[[4,2,3,4],[3,2,3,4],[3,2,3,4],[4]], "varTypes":["float32","float32","float32","int32"], "varInit":["uniform","uniform","uniform","uniform_int10"], "partition_strategy":"div", "max_norm":None},
# {"opName":"embedding_lookup", "outName":"embedding_lookup/rank4_multiple_mod_maxnorm1", "varShapes":[[4,2,3,4],[3,2,3,4],[3,2,3,4],[4]], "varTypes":["float32","float32","float32","int32"], "varInit":["uniform","uniform","uniform","uniform_int10"], "partition_strategy":"mod", "max_norm":1.0},
# {"opName":"l2_normalize", "outName":"l2_normalize/rank2_e0", "varShapes":[[4, 5]], "varTypes":["float32"], "varInit":["uniform"], "axis":1, "epsilon":0.0},
# {"opName":"l2_normalize", "outName":"l2_normalize/rank2_e05", "varShapes":[[4, 5]], "varTypes":["float32"], "varInit":["uniform"], "axis":1, "epsilon":0.5},
# {"opName":"l2_normalize", "outName":"l2_normalize/rank4_e0_d1", "varShapes":[[3,2,3,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":1, "epsilon":0.0},
# {"opName":"l2_normalize", "outName":"l2_normalize/rank4_e05_d123", "varShapes":[[3,3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":[1,2,3], "epsilon":0.5}
# {"opName":"lrn", "outName":"lrn/dr5_b1_a1_b05", "varShapes":[[2, 4, 4, 8]], "varTypes":["float32"], "varInit":["uniform"], "depth_radius":5, "bias":1.0, "alpha":1.0, "beta":0.5},
# {"opName":"lrn", "outName":"lrn/dr3_b05_a05_b02", "varShapes":[[2, 4, 4, 8]], "varTypes":["float32"], "varInit":["uniform"], "depth_radius":3, "bias":0.5, "alpha":0.5, "beta":0.2},
#Dropouts. Note that due to random nature - we need to validate these differently than simply "samediff output equals tensorflow output"
# {"opName":"layers_dropout", "outName":"layers_dropout/rank2_d05_train", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":True},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank2_d05_test", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":False},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank2_d01_train", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.1, "training":True},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank2_d01_test", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.1, "training":False},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank2_d09_train", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.9, "training":True},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank3_d05_train_mask1", "varShapes":[[4,5,6]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":True, "noise_shape":[4,1,6]},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank3_d05_train_mask2", "varShapes":[[4,5,6]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":True, "noise_shape":[4,5,1]},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank3_d05_test", "varShapes":[[4,5,6]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":False},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank4_d05_train", "varShapes":[[2,5,5,3]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":True},
# {"opName":"layers_dropout", "outName":"layers_dropout/rank4_d05_train_mask", "varShapes":[[2,5,5,3]], "varTypes":["float32"], "varInit":["uniform"], "rate":0.5, "training":True, "noise_shape":[2,1,1,3]},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank2_p05", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.5},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank2_p01", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.1},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank2_p09", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.9},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank3_p05_mask1", "varShapes":[[4,5,6]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.5, "noise_shape":[4,1,6]},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank3_p05_mask2", "varShapes":[[4,5,6]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.5, "noise_shape":[4,5,1]},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank4_p05", "varShapes":[[2,5,5,3]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.5},
# {"opName":"contrib_nn_alpha_dropout", "outName":"alpha_dropout/rank4_p05_mask", "varShapes":[[2,5,5,3]], "varTypes":["float32"], "varInit":["uniform"], "keep_prob":0.5, "noise_shape":[2,1,1,3]},
#Meshgrid - seems like TF doesn't like things like [[3], [4]] - "ValueError: Dimension 0 in both shapes must be equal, but are 3 and 4. Shapes are [3] and [4]."
# {"opName":"meshgrid", "outName":"meshgrid/n1_xy", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "indexing":"xy"},
# {"opName":"meshgrid", "outName":"meshgrid/n1_ij", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "indexing":"ij"},
# {"opName":"meshgrid", "outName":"meshgrid/n2_xy", "varShapes":[[3],[3]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "indexing":"xy"},
# {"opName":"meshgrid", "outName":"meshgrid/n2_ij", "varShapes":[[3],[3]], "varTypes":["float32","float32"], "varInit":["uniform","uniform"], "indexing":"ij"},
# {"opName":"meshgrid", "outName":"meshgrid/n3_xy", "varShapes":[[3],[3],[3]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "indexing":"xy"},
# {"opName":"meshgrid", "outName":"meshgrid/n3_ij", "varShapes":[[3],[3],[3]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"], "indexing":"ij"},
# {"opName":"meshgrid", "outName":"meshgrid/n4_xy", "varShapes":[[3],[3],[3],[3]], "varTypes":["float32","float32","float32","float32"], "varInit":["uniform","uniform","uniform","uniform"],"indexing":"xy"},
# {"opName":"meshgrid", "outName":"meshgrid/n4_ij", "varShapes":[[3],[3],[3],[3]], "varTypes":["float32","float32","float32","float32"], "varInit":["uniform","uniform","uniform","uniform"],"indexing":"ij"}
#{"opName":"eye", "outName":"eye/e22", "varShapes":[], "varTypes":[], "varInit":[], "num_rows":2, "num_columns":2},
#{"opName":"eye", "outName":"eye/e23", "varShapes":[], "varTypes":[], "varInit":[], "num_rows":2, "num_columns":3},
#{"opName":"eye", "outName":"eye/e32", "varShapes":[], "varTypes":[], "varInit":[], "num_rows":3, "num_columns":2},
#{"opName":"eye", "outName":"eye/e22_b1", "varShapes":[[1]], "varTypes":["int32"], "varInit":["one"], "num_rows":2, "num_columns":2},
#{"opName":"eye", "outName":"eye/e23_b2", "varShapes":[[1]], "varTypes":["int32"], "varInit":["two"], "num_rows":2, "num_columns":3},
#{"opName":"eye", "outName":"eye/e32_b22", "varShapes":[[2]], "varTypes":["int32"], "varInit":["two"], "num_rows":3, "num_columns":2},
#log determinant op: requires SYMMETRIC matrix. slogdet uses log determinant internally - so same thing...
# {"opName":"log_determinant", "outName":"log_determinant/rank2", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["positive_def_symmetric_33"]},
# {"opName":"log_determinant", "outName":"log_determinant/rank3", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["positive_def_symmetric_233"]},
# {"opName":"slog_determinant", "outName":"slogdet/rank2", "varShapes":[[3,3]], "varTypes":["float32"], "varInit":["positive_def_symmetric_33"]},
# {"opName":"slog_determinant", "outName":"slogdet/rank3", "varShapes":[[2,3,3]], "varTypes":["float32"], "varInit":["positive_def_symmetric_233"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank1_auto_maxlen", "varShapes":[[5]], "varTypes":["int32"], "varInit":["uniform_int5"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank1_provided_maxlen", "varShapes":[[5], []], "varTypes":["int32", "int32"], "varInit":["uniform_int5", "ten"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank2_auto_maxlen", "varShapes":[[3,4]], "varTypes":["int32"], "varInit":["uniform_int5"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank2_provided_maxlen", "varShapes":[[3,4], []], "varTypes":["int32", "int32"], "varInit":["uniform_int5", "ten"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank3_auto_maxlen", "varShapes":[[2,3,4]], "varTypes":["int32"], "varInit":["uniform_int5"]},
# {"opName":"sequence_mask", "outName":"sequence_mask/rank3_provided_maxlen", "varShapes":[[2,3,4], []], "varTypes":["int32", "int32"], "varInit":["uniform_int5", "ten"]},
# {"opName":"rint", "outName":"rint/rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["stdnormal"]},
# {"opName":"rint", "outName":"rint/rank1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["stdnormal"]},
# {"opName":"rint", "outName":"rint/rank2", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["stdnormal"]},
# {"opName":"histogram_fixed_width", "outName":"histogram_fixed_width/rank0", "varShapes":[[], [2]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "fixed_m1_1"], "nbins":5},
# {"opName":"histogram_fixed_width", "outName":"histogram_fixed_width/rank1", "varShapes":[[10], [2]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "fixed_m1_1"], "nbins":5},
# {"opName":"histogram_fixed_width", "outName":"histogram_fixed_width/rank2", "varShapes":[[10,10], [2]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "fixed_m1_1"], "nbins":20}
# {"opName":"bincount", "outName":"bincount/rank0", "varShapes":[[]], "varTypes":["int32"], "varInit":["uniform_int10"], "minlength":None, "maxlength":None},
# {"opName":"bincount", "outName":"bincount/rank0_minmax", "varShapes":[[]], "varTypes":["int32"], "varInit":["uniform_int10"], "minlength":3, "maxlength":8},
# {"opName":"bincount", "outName":"bincount/rank0_weights", "varShapes":[[],[]], "varTypes":["int32", "float32"], "varInit":["uniform_int10","uniform"], "minlength":None, "maxlength":None},
# {"opName":"bincount", "outName":"bincount/rank1", "varShapes":[[10]], "varTypes":["int32"], "varInit":["uniform_int10"], "minlength":None, "maxlength":None},
# {"opName":"bincount", "outName":"bincount/rank1_minmax", "varShapes":[[10]], "varTypes":["int32"], "varInit":["uniform_int10","uniform"], "minlength":3, "maxlength":8},
# {"opName":"bincount", "outName":"bincount/rank1_min10", "varShapes":[[10]], "varTypes":["int32"], "varInit":["uniform_int10","uniform"], "minlength":10, "maxlength":None},
# {"opName":"bincount", "outName":"bincount/rank1_max5", "varShapes":[[10]], "varTypes":["int32"], "varInit":["uniform_int10","uniform"], "minlength":None, "maxlength":5},
# {"opName":"bincount", "outName":"bincount/rank1_weights", "varShapes":[[10],[10]], "varTypes":["int32", "float32"], "varInit":["uniform_int10","uniform"], "minlength":None, "maxlength":None},
# #TF bug? Next one doess't work
# #{"opName":"bincount", "outName":"bincount/rank1_minmax_weights", "varShapes":[[10],[10]], "varTypes":["int32", "float32"], "varInit":["uniform_int10","uniform"], "minlength":3, "maxlength":8},
# #TF bug? Next one doess't work
# # {"opName":"bincount", "outName":"bincount/rank2_minmax_weights", "varShapes":[[5,5],[5,5]], "varTypes":["int32", "float32"], "varInit":["uniform_int10","uniform"], "minlength":3, "maxlength":8},
# {"opName":"bincount", "outName":"bincount/rank2_weights", "varShapes":[[5,5],[5,5]], "varTypes":["int32", "float32"], "varInit":["uniform_int10","uniform"], "minlength":None, "maxlength":None},
#Scatter ND: arrays are indices, updates. Updates has shape indices.shape[:-1] + shape[indices.shape[-1]:]
#This case: update has shape [4]+[] = 4
# {"opName":"scatter_nd", "outName":"scatter_nd/rank1shape_1indices", "varShapes":[[4,1],[4]], "varTypes":["int32", "float32"], "varInit":["fixed_3_1_4_2", "uniform"], "shape":[10]},
# #This case: 2 indices, 2 shape -> updates are individual elements
# {"opName":"scatter_nd", "outName":"scatter_nd/rank2shape_2indices", "varShapes":[[4,2],[4]], "varTypes":["int32", "float32"], "varInit":["unique_rand_10", "uniform"], "shape":[10,10]},
# #This case: 1 indices, 2 shape -> updates are slices from shape [4]+[7]=[4,7]
# {"opName":"scatter_nd", "outName":"scatter_nd/rank2shape_1indices", "varShapes":[[4,1],[4,7]], "varTypes":["int32", "float32"], "varInit":["unique_rand_10", "uniform"], "shape":[10,7]},
# #This case: 2 indices, 3 shape -> updates are slices of shape [2]+[5] = [2,5]
# {"opName":"scatter_nd", "outName":"scatter_nd/rank3shape_2indices", "varShapes":[[2,2],[2,5]], "varTypes":["int32", "float32"], "varInit":["unique_rand_5", "uniform"], "shape":[10,7,5]},
# #This case: 1 indices, 3 shape -> updates are slices of shape [4]+[7,5] = [4,7,5]
# {"opName":"scatter_nd", "outName":"scatter_nd/rank3shape_1indices", "varShapes":[[4,1],[4,7,5]], "varTypes":["int32", "float32"], "varInit":["uniform_int10", "uniform"], "shape":[10,7,5]},
# #Scatter ND ADD: arrays are ref, indices, updates. Updates has shape indices.shape[:-1] + shape[indices.shape[-1]:]
# #This case: update has shape [4]+[] = 4
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/locking/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/unique_idxs/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# #This case: 2 indices, 2 shape -> updates are individual elements
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/locking/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/unique_idxs/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# #This case: 1 indices, 2 shape -> updates are slices of shape [4]+[7]
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/locking/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/unique_idxs/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# # This case: 2 indices, 3 shape -> updates are slices of shape [4]+[5] = [4,5]
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/locking/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/unique_idxs/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# #This case: 1 indices, 3 shape -> updates are slices of shape [4]+[7,5] = [4,7,5]
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/locking/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_add", "outName":"scatter_nd_add/unique_idxs/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
#
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/locking/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/locking/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/locking/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/locking/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/locking/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/unique_idxs/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/unique_idxs/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/unique_idxs/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/unique_idxs/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_sub", "outName":"scatter_nd_sub/unique_idxs/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/locking/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/locking/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/locking/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/locking/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/locking/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "uniform_int10", "uniform"], "use_locking":True},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/unique_idxs/rank1shape_1indices", "varShapes":[[10], [4,1],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/unique_idxs/rank2shape_2indices", "varShapes":[[10,10], [4,2],[4]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/unique_idxs/rank2shape_1indices", "varShapes":[[10,7], [4,1],[4,7]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/unique_idxs/rank3shape_2indices", "varShapes":[[10,7,5], [4,2],[4,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
# {"opName":"scatter_nd_update", "outName":"scatter_nd_update/unique_idxs/rank3shape_1indices", "varShapes":[[10,7,5], [4,1],[4,7,5]], "varTypes":["float32", "int32", "float32"], "varInit":["one", "unique_rand_10", "uniform"], "use_locking":False},
#Seems like shift=None is NOT supported: "TypeError: Fetch argument None has invalid type <class 'NoneType'>"
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0], "shift":0.0, "keep_dims":False},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank1_keep", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0], "shift":0.0, "keep_dims":True},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank1_keep_shift05", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0], "shift":0.5, "keep_dims":True},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank2_d0", "varShapes":[[5,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0], "shift":0.0, "keep_dims":False},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank2_d1_keep", "varShapes":[[5,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[1], "shift":0.0, "keep_dims":True},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank2_d01_keep_shift05", "varShapes":[[5,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0,1], "shift":0.5, "keep_dims":True},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank3_d0", "varShapes":[[3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[0], "shift":0.0, "keep_dims":False},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank3_d1", "varShapes":[[3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[1], "shift":0.0, "keep_dims":False},
# {"opName":"sufficient_statistics", "outName":"sufficient_statistics/rank3_d2", "varShapes":[[3,4,5]], "varTypes":["float32"], "varInit":["uniform"], "axes":[2], "shift":0.0, "keep_dims":False},
# {"opName":"split", "outName":"split/rank1_10_num2_axis-1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":2, "axis":-1},
# {"opName":"split", "outName":"split/rank1_9_num3_axis0", "varShapes":[[9]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":3, "axis":0},
# {"opName":"split", "outName":"split/rank2_3,8_num2_axis-1", "varShapes":[[3,8]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":2, "axis":-1},
# {"opName":"split", "outName":"split/rank2_8,4_num2_axis0", "varShapes":[[8,4]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":2, "axis":0},
# {"opName":"split", "outName":"split/rank2_3,8_num2_axis1", "varShapes":[[3,8]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":2, "axis":1},
# {"opName":"split", "outName":"split/rank2_8,7_sz5,3_axis-2", "varShapes":[[8,7]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":[5,3], "axis":-2},
# {"opName":"split", "outName":"split/rank2_8,7_sz5,3_axis0", "varShapes":[[8,7]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":[5,3], "axis":0},
# {"opName":"split", "outName":"split/rank2_8,7_sz2,1,4_axis-1", "varShapes":[[8,7]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":[2,1,4], "axis":-1},
# {"opName":"split", "outName":"split/rank2_8,7_sz2,1,4_axis1", "varShapes":[[8,7]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":[2,1,4], "axis":1},
# {"opName":"split", "outName":"split/rank3_9,3,4_num3_axis0", "varShapes":[[9,3,4]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":3, "axis":0},
# {"opName":"split", "outName":"split/rank3_3,9,4_num3_axis1", "varShapes":[[3,9,4]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":3, "axis":1},
# {"opName":"split", "outName":"split/rank3_3,4,9_num3_axis2", "varShapes":[[3,4,9]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":3, "axis":2},
# {"opName":"split", "outName":"split/rank3_10,3,4_sz2,3,5_axis0", "varShapes":[[10,3,4]], "varTypes":["float32"], "varInit":["uniform"], "num_or_size_split":[2,3,5], "axis":0},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"], "axis":None, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axis":None, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank1_d0", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axis":0, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank1_d-1", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axis":-1, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank1_d0_keep", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axis":0, "keep_dims":True},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank1_keep", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "axis":None, "keep_dims":True},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":None, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2_d0", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":0, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2_d1", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":1, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2_d-1", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":-1, "keep_dims":False},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2_d0_keep", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":0, "keep_dims":True},
# {"opName":"reduce_logsumexp", "outName":"logsumexp/rank2_keep", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "axis":None, "keep_dims":True},
# {"opName":"nth_element", "outName":"nth_element/rank1_n0", "varShapes":[[10],[]], "varTypes":["float32","int32"], "varInit":["uniform","zero"], "reverse":False},
# {"opName":"nth_element", "outName":"nth_element/rank1_n3_reverse", "varShapes":[[10],[]], "varTypes":["float32","int32"], "varInit":["uniform","three"], "reverse":True},
# {"opName":"nth_element", "outName":"nth_element/rank2_n0", "varShapes":[[10],[]], "varTypes":["float32","int32"], "varInit":["uniform","zero"], "reverse":False},
# {"opName":"nth_element", "outName":"nth_element/rank2_n4", "varShapes":[[5,6],[]], "varTypes":["float32","int32"], "varInit":["uniform","four"], "reverse":False},
# {"opName":"nth_element", "outName":"nth_element/rank2_n3_reverse", "varShapes":[[5,5],[]], "varTypes":["float32","int32"], "varInit":["uniform","three"], "reverse":True},
# {"opName":"nth_element", "outName":"nth_element/rank3_n2", "varShapes":[[2,3,4],[]], "varTypes":["float32","int32"], "varInit":["uniform","two"], "reverse":False},
#
# {"opName":"reduce_any", "outName":"reduce_any/rank0", "varShapes":[[]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_any", "outName":"reduce_any/rank1", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_any", "outName":"reduce_any/rank1_d0_keep", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":0, "keep_dims":True},
# {"opName":"reduce_any", "outName":"reduce_any/rank2", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_any", "outName":"reduce_any/rank2_d0_keep", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":0, "keep_dims":True},
# {"opName":"reduce_any", "outName":"reduce_any/rank2_d1_keep", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":1, "keep_dims":False},
# {"opName":"reduce_any", "outName":"reduce_any/rank3_d01_keep", "varShapes":[[2,3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":[0,1], "keep_dims":True},
#
# {"opName":"reduce_all", "outName":"reduce_all/rank0", "varShapes":[[]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_all", "outName":"reduce_all/rank1", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_all", "outName":"reduce_all/rank1_d0_keep", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":0, "keep_dims":True},
# {"opName":"reduce_all", "outName":"reduce_all/rank2", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":None, "keep_dims":False},
# {"opName":"reduce_all", "outName":"reduce_all/rank2_d0_keep", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":0, "keep_dims":True},
# {"opName":"reduce_all", "outName":"reduce_all/rank2_d1_keep", "varShapes":[[3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":1, "keep_dims":False},
# {"opName":"reduce_all", "outName":"reduce_all/rank3_d01_keep", "varShapes":[[2,3,4]], "varTypes":["bool"], "varInit":["boolean"], "axis":[0,1], "keep_dims":True},
#{"opName": "reduce_all", "outName": "reduce_all/empty_axis0", "varShapes": [[0, 0, 0]], "varTypes": ["bool"], "varInit": ["boolean"], "axis": [0], "keep_dims": True},
#{"opName": "reduce_all", "outName": "reduce_all/empty_axis1", "varShapes": [[0, 0, 3]], "varTypes": ["bool"], "varInit": ["boolean"], "axis": [1], "keep_dims": True},
#{"opName":"reduce_all", "outName":"reduce_all/empty_axis2", "varShapes":[[2,0,3]], "varTypes":["bool"], "varInit":["boolean"], "axis":[2], "keep_dims":True},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank1_mask1", "varShapes":[[10],[10]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank2_mask1", "varShapes":[[5,4],[5]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank2_mask2", "varShapes":[[5,4],[5,4]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank3_mask1", "varShapes":[[5,4,3],[5]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank3_mask2", "varShapes":[[5,4,3],[5,4]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
# {"opName":"boolean_mask", "outName":"boolean_mask/rank3_mask2", "varShapes":[[5,4,3],[5,4,3]], "varTypes":["float32", "bool"], "varInit":["uniform", "boolean"]},
#
# {"opName":"where", "outName":"where/cond_only_rank1", "varShapes":[[5]], "varTypes":["float32"], "varInit":["fixed_5"]},
# {"opName":"where", "outName":"where/cond_only_rank2", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["fixed_2_3"]},
# {"opName":"where", "outName":"where/cond_only_rank3", "varShapes":[[2,3,4]], "varTypes":["float32"], "varInit":["fixed_2_3_4"]},
# {"opName":"where", "outName":"where/cond_rank1_xy_rank1", "varShapes":[[5],[5],[5]], "varTypes":["bool", "float32", "float32"], "varInit":["boolean", "uniform", "uniform"]},
# {"opName":"where", "outName":"where/cond_rank2_xy_rank2", "varShapes":[[3,4],[3,4],[3,4]], "varTypes":["bool", "float32", "float32"], "varInit":["boolean", "uniform", "uniform"]},
# {"opName":"where", "outName":"where/cond_rank3_xy_rank3", "varShapes":[[2,3,4],[2,3,4],[2,3,4]], "varTypes":["bool", "float32", "float32"], "varInit":["boolean", "uniform", "uniform"]},
#"If condition is a vector and x and y are higher rank matrices, then it chooses which row (outer dimension) to copy from x and y"
#"If condition is rank 1, x may have higher rank, but its first dimension must match the size of condition."
# {"opName":"where", "outName":"where/cond_rank1_xy_rank2", "varShapes":[[5],[5,4],[5,4]], "varTypes":["bool", "float32", "float32"], "varInit":["boolean", "uniform", "uniform"]},
# {"opName":"where", "outName":"where/cond_rank1_xy_rank3", "varShapes":[[5],[5,3,4],[5,3,4]], "varTypes":["bool", "float32", "float32"], "varInit":["boolean", "uniform", "uniform"]},
# {"opName":"broadcast_dynamic_shape", "outName":"broadcast_dynamic_shape/1_4", "varShapes":[[1],[1]], "varTypes":["int32", "int32"], "varInit":["one", "four"]},
# {"opName":"broadcast_dynamic_shape", "outName":"broadcast_dynamic_shape/1,1_4", "varShapes":[[2],[1]], "varTypes":["int32", "int32"], "varInit":["one", "four"]},
# {"opName":"broadcast_dynamic_shape", "outName":"broadcast_dynamic_shape/2,2_1", "varShapes":[[2],[1]], "varTypes":["int32", "int32"], "varInit":["two", "one"]},
# {"opName":"broadcast_dynamic_shape", "outName":"broadcast_dynamic_shape/2,1_4", "varShapes":[[2],[1]], "varTypes":["int32", "int32"], "varInit":["fixed_2_1", "four"]},
# {"opName":"broadcast_dynamic_shape", "outName":"broadcast_dynamic_shape/2,1,4_2,2,4", "varShapes":[[3],[3]], "varTypes":["int32", "int32"], "varInit":["fixed_2_1_4", "fixed_2_2_4"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/1_4", "varShapes":[[],[1]], "varTypes":["int32", "int32"], "varInit":["uniform", "four"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/1_3,3", "varShapes":[[1],[2]], "varTypes":["int32", "int32"], "varInit":["uniform", "three"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/1_2,1", "varShapes":[[],[2]], "varTypes":["int32", "int32"], "varInit":["uniform", "fixed_2_1"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/3_5,3", "varShapes":[[3],[2]], "varTypes":["int32", "int32"], "varInit":["uniform", "fixed_5_3"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/1,3_5,3", "varShapes":[[1,3],[2]], "varTypes":["int32", "int32"], "varInit":["uniform", "fixed_5_3"]},
# {"opName":"broadcast_to", "outName":"broadcast_to/2,1,4_2,2,4", "varShapes":[[2,1,4],[3]], "varTypes":["int32", "int32"], "varInit":["uniform", "fixed_2_2_4"]},
# {"opName": "unsorted_segment_max", "outName": "unsorted_segment/unsorted_segment_max_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_mean", "outName": "unsorted_segment/unsorted_segment_mean_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_min", "outName": "unsorted_segment/unsorted_segment_min_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_prod", "outName": "unsorted_segment/unsorted_segment_prod_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_sum", "outName": "unsorted_segment/unsorted_segment_sum_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_sqrt_n", "outName": "unsorted_segment/unsorted_segment_sqrt_n_rank1", "varShapes":[[20], [20]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int5"], "num_segments":5},
# {"opName": "unsorted_segment_max", "outName": "unsorted_segment/unsorted_segment_max_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_mean", "outName": "unsorted_segment/unsorted_segment_mean_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_min", "outName": "unsorted_segment/unsorted_segment_min_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_prod", "outName": "unsorted_segment/unsorted_segment_prod_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_sum", "outName": "unsorted_segment/unsorted_segment_sum_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_sqrt_n", "outName": "unsorted_segment/unsorted_segment_sqrt_n_rank2", "varShapes":[[6,3], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_max", "outName": "unsorted_segment/unsorted_segment_max_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_mean", "outName": "unsorted_segment/unsorted_segment_mean_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_min", "outName": "unsorted_segment/unsorted_segment_min_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_prod", "outName": "unsorted_segment/unsorted_segment_prod_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_sum", "outName": "unsorted_segment/unsorted_segment_sum_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
# {"opName": "unsorted_segment_sqrt_n", "outName": "unsorted_segment/unsorted_segment_sqrt_n_rank3", "varShapes":[[6,3,2], [6]], "varTypes":["float32", "int32"], "varInit":["uniform_int10", "uniform_int3"], "num_segments":3},
#
# {"opName":"truncatemod", "outName":"truncatemod/1_4", "varShapes":[[],[4]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "stdnormal"]},
# {"opName":"truncatemod", "outName":"truncatemod/4_4", "varShapes":[[4],[4]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "stdnormal"]},
# {"opName":"truncatemod", "outName":"truncatemod/3,4_3,1", "varShapes":[[3,4],[3,1]], "varTypes":["float32", "float32"], "varInit":["stdnormal", "stdnormal"]},
#
# #axes=1: equivalent to mmul, as is axes=[[1],[0]]
# {"opName":"tensordot", "outName":"tensordot/4,3_3,2_a1", "varShapes":[[4,3],[3,2]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axes":1},
# {"opName":"tensordot", "outName":"tensordot/emptyArrayTest/4,3_3,2_a1", "varShapes":[[0,3],[3,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axes":0},
# {"opName":"tensordot", "outName":"tensordot/4,3_3,2_a1-0", "varShapes":[[4,3],[3,2]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axes":[[1],[0]]},
# {"opName":"tensordot", "outName":"tensordot/4,3_2,3_a1-1", "varShapes":[[4,3],[2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axes":[[1],[1]]},
# {"opName":"tensordot", "outName":"tensordot/3,4_2,3_a1-1", "varShapes":[[4,3],[2,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axes":[[1],[1]]},
# {"opName":"tensordot", "outName":"tensordot/4,3,2_2,5,4_a2,0-0,2", "varShapes":[[4,3,2],[2,5,4]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "axes":[[2,0],[0,2]]},
# {"opName":"assert_equal", "outName":"assert_equal/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["one", "one"]},
# {"opName":"assert_equal", "outName":"assert_equal/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["one", "one"]},
# {"opName":"assert_equal", "outName":"assert_equal/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["two", "two"]},
# {"opName":"assert_equal", "outName":"assert_equal/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "one"]},
# {"opName":"assert_equal", "outName":"assert_equal/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["four", "four"]},
# {"opName":"assert_equal", "outName":"assert_equal/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["three", "three"]},
# {"opName":"assert_greater", "outName":"assert_greater/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["two", "one"]},
# {"opName":"assert_greater", "outName":"assert_greater/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["two", "one"]},
# {"opName":"assert_greater", "outName":"assert_greater/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["three", "two"]},
# {"opName":"assert_greater", "outName":"assert_greater/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "zero"]},
# {"opName":"assert_greater", "outName":"assert_greater/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["four", "one"]},
# {"opName":"assert_greater", "outName":"assert_greater/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["three", "one"]},
#
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["two", "one"]},
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["one", "one"]},
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["three", "two"]},
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "zero"]},
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["four", "four"]},
# {"opName":"assert_greater_equal", "outName":"assert_greater_equal/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["three", "one"]},
#
# {"opName":"assert_less", "outName":"assert_less/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["zero", "one"]},
# {"opName":"assert_less", "outName":"assert_less/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["one", "two"]},
# {"opName":"assert_less", "outName":"assert_less/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["zero", "two"]},
# {"opName":"assert_less", "outName":"assert_less/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "three"]},
# {"opName":"assert_less", "outName":"assert_less/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["two", "four"]},
# {"opName":"assert_less", "outName":"assert_less/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["three", "four"]},
#
# {"opName":"assert_less_equal", "outName":"assert_less_equal/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["zero", "one"]},
# {"opName":"assert_less_equal", "outName":"assert_less_equal/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["one", "one"]},
# {"opName":"assert_less_equal", "outName":"assert_less_equal/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["two", "two"]},
# {"opName":"assert_less_equal", "outName":"assert_less_equal/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "three"]},
# {"opName":"assert_less_equal", "outName":"assert_less_equal/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["two", "four"]},
# {"opName":"assert_less_equal", "outName":"assert_less_equal/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["two", "three"]},
#
# {"opName":"assert_none_equal", "outName":"assert_none_equal/scalar_float32", "varShapes":[[],[]], "varTypes":["float32", "float32"], "varInit":["one", "two"]},
# {"opName":"assert_none_equal", "outName":"assert_none_equal/scalar_int32", "varShapes":[[],[]], "varTypes":["int32", "int32"], "varInit":["two", "one"]},
# {"opName":"assert_none_equal", "outName":"assert_none_equal/scalar_rank1_float32", "varShapes":[[],[1]], "varTypes":["float32", "float32"], "varInit":["two", "three"]},
# {"opName":"assert_none_equal", "outName":"assert_none_equal/scalar_rank1_int32", "varShapes":[[1],[]], "varTypes":["int32", "int32"], "varInit":["one", "four"]},
# {"opName":"assert_none_equal", "outName":"assert_none_equal/3,4_3,4_float32", "varShapes":[[3,4],[3,4]], "varTypes":["float32", "float32"], "varInit":["zero", "four"]},
# {"opName":"assert_none_equal", "outName":"assert_none_equal/3,4_1,4_int32", "varShapes":[[3,4],[1,4]], "varTypes":["int32", "int32"], "varInit":["three", "zero"]},
# {"opName":"assert_integer", "outName":"assert_integer/scalar_int32", "varShapes":[[]], "varTypes":["int32"], "varInit":["one"]},
# {"opName":"assert_integer", "outName":"assert_integer/scalar_int64", "varShapes":[[]], "varTypes":["int64"], "varInit":["two"]},
# {"opName":"assert_integer", "outName":"assert_integer/scalar_rank1_int32", "varShapes":[[3]], "varTypes":["int32"], "varInit":["three"]},
# {"opName":"assert_integer", "outName":"assert_integer/3,4_3,4_int64", "varShapes":[[3,4]], "varTypes":["int64"], "varInit":["four"]},
#
# {"opName":"assert_negative", "outName":"assert_negative/scalar_int32", "varShapes":[[]], "varTypes":["int32"], "varInit":["minus_one"]},
# {"opName":"assert_negative", "outName":"assert_negative/scalar_int64", "varShapes":[[]], "varTypes":["int64"], "varInit":["minus_two"]},
# {"opName":"assert_negative", "outName":"assert_negative/scalar_rank1_float32", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_m1_0"]},
# {"opName":"assert_negative", "outName":"assert_negative/3,4_3,4_float64", "varShapes":[[3,4]], "varTypes":["float64"], "varInit":["uniform_m1_0"]},
# {"opName":"assert_positive", "outName":"assert_positive/scalar_float32", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"assert_positive", "outName":"assert_positive/scalar_int64", "varShapes":[[]], "varTypes":["int64"], "varInit":["two"]},
# {"opName":"assert_positive", "outName":"assert_positive/rank1_int32", "varShapes":[[3]], "varTypes":["int32"], "varInit":["uniform_int3"]},
# {"opName":"assert_positive", "outName":"assert_positive/3,4_3,4_int64", "varShapes":[[3,4]], "varTypes":["int64"], "varInit":["uniform"]},
# {"opName":"assert_rank", "outName":"assert_rank/rank0_int32", "varShapes":[[], []], "varTypes":["int32", "int32"], "varInit":["one", "zero"]},
# {"opName":"assert_rank", "outName":"assert_rank/rank1_float64", "varShapes":[[3], []], "varTypes":["float64", "int32"], "varInit":["uniform", "one"]},
# {"opName":"assert_rank", "outName":"assert_rank/rank2_float32", "varShapes":[[2,3], []], "varTypes":["float32", "int32"], "varInit":["three", "two"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank0_int32", "varShapes":[[], []], "varTypes":["int32", "int32"], "varInit":["one", "zero"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank1_float64", "varShapes":[[3], []], "varTypes":["float64", "int32"], "varInit":["uniform", "zero"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank1_float32", "varShapes":[[3], []], "varTypes":["float32", "int32"], "varInit":["uniform", "one"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank2_float32", "varShapes":[[2,3], []], "varTypes":["float32", "int32"], "varInit":["three", "two"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank2_float64", "varShapes":[[2,3], []], "varTypes":["float64", "int32"], "varInit":["three", "one"]},
# {"opName":"assert_rank_at_least", "outName":"assert_rank_at_least/rank2_float32", "varShapes":[[2,3], []], "varTypes":["float32", "int32"], "varInit":["three", "zero"]},
# {"opName":"assert_type", "outName":"assert_type/rank0_int32", "varShapes":[[]], "varTypes":["int32"], "varInit":["one"], "tf_type":tf.int32},
# {"opName":"assert_type", "outName":"assert_type/rank1_float32", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "tf_type":tf.float32},
# {"opName":"assert_type", "outName":"assert_type/rank2_float32", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["three"], "tf_type":tf.float32},
# {"opName":"assert_type", "outName":"assert_type/rank2_int64", "varShapes":[[2,2]], "varTypes":["int64"], "varInit":["zero"], "tf_type":tf.int64},
# {"opName":"cond", "outName":"cond/cond_true", "varShapes":[[]], "varTypes":["bool"], "varInit":["booleanTrue"]},
# {"opName":"cond", "outName":"cond/cond_false", "varShapes":[[]], "varTypes":["bool"], "varInit":["booleanFalse"]},
# {"opName":"case", "outName":"case/cond_1", "varShapes":[[]], "varTypes":["float32"], "varInit":["zero"]},
# {"opName":"case", "outName":"case/cond_1b", "varShapes":[[]], "varTypes":["float32"], "varInit":["one"]},
# {"opName":"case", "outName":"case/cond_2", "varShapes":[[]], "varTypes":["float32"], "varInit":["two"]},
# {"opName":"case", "outName":"case/cond_3", "varShapes":[[]], "varTypes":["float32"], "varInit":["three"]},
# {"opName":"case", "outName":"case/cond_default", "varShapes":[[]], "varTypes":["float32"], "varInit":["four"]},
# {"opName":"while1", "outName":"while1/iter_1", "varShapes":[[]], "varTypes":["float32"], "varInit":["one"]},
# {"opName":"while1", "outName":"while1/iter_3", "varShapes":[[]], "varTypes":["float32"], "varInit":["three"]},
# {"opName":"while2", "outName":"while2/a", "varShapes":[[2],[2]], "varTypes":["float32", "float32"], "varInit":["one", "fixed_5_3"]},
# {"opName":"while2", "outName":"while2/b", "varShapes":[[2,3],[2,3]], "varTypes":["float32", "float32"], "varInit":["two", "ten"]},
#Reductions with dynamic reduce dimensions
# {"opName":"sum_dynamic_axis", "outName":"reduce_dynamic_axis/sum_rank2_argmin_shape", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"], "axistype":"argmin", "keepdims":False},
# {"opName":"sum_dynamic_axis", "outName":"reduce_dynamic_axis/sum_rank2_argmax_shape", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"], "axistype":"argmax", "keepdims":True}
#TensorArray - get and set ops
# {"opName":"tensorarray_identity", "outName":"tensor_array/getset_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"],\
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_getset", "outName":"tensor_array/getset_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_getset", "outName":"tensor_array/getset_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# #Next test: initially size 1, but dynamic so add 3
# {"opName":"tensorarray_getset", "outName":"tensor_array/getset_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3]},
#TensorArray - size
# {"opName":"tensorarray_size", "outName":"tensor_array/size_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], \
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_size", "outName":"tensor_array/size_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_size", "outName":"tensor_array/size_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_size", "outName":"tensor_array/size_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3]},
#TensorArray - Concat (note that shapes must match, but first dim can differ - but not possible in practice, just gives inconsistent shapes exception???)
# {"opName":"tensorarray_concat", "outName":"tensor_array/concat_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], \
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_concat", "outName":"tensor_array/concat_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_concat", "outName":"tensor_array/concat_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_concat", "outName":"tensor_array/concat_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3], "infer_shape":False},
#TensorArray - Stack
# {"opName":"tensorarray_stack", "outName":"tensor_array/stack_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], \
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_stack", "outName":"tensor_array/stack_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_stack", "outName":"tensor_array/stack_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_stack", "outName":"tensor_array/stack_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3], "infer_shape":False},
#TensorArray - Unstack
#{"opName":"tensorarray_unstack", "outName":"tensor_array/unstack_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], \
#"dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
#{"opName":"tensorarray_unstack", "outName":"tensor_array/unstack_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
#"dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
#{"opName":"tensorarray_unstack", "outName":"tensor_array/unstack_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
#{"opName":"tensorarray_unstack", "outName":"tensor_array/unstack_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3], "infer_shape":False},
#TensorArray - identity (set, identity, get)
# {"opName":"tensorarray_identity", "outName":"tensor_array/identity_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"],\
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_identity", "outName":"tensor_array/identity_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_identity", "outName":"tensor_array/identity_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# #Next test: initially size 1, but dynamic so add 3
# {"opName":"tensorarray_identity", "outName":"tensor_array/identity_sz3-1_int32_dynamic_name_shape", "varShapes":[[2,3],[2,3],[2,3]], "varTypes":["int32","int32","int32"], "varInit":["uniform_int10","uniform_int10","uniform_int10"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[2,3]},
# #TensorArray - Split (basically, standard Split op + tensor array scatter - i.e., split then put results into TensorArray)
# # In these tests, first variable is the values, second is the lengths of each split. Note that size of each has to be same size...
# {"opName":"tensorarray_split", "outName":"tensor_array/split_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3],[1]], "varTypes":["float32", "int32"], "varInit":["uniform", "two"],\
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_split", "outName":"tensor_array/split_sz2_float64_nodynamic_name_noshape", "varShapes":[[4,3],[2]], "varTypes":["float64","int32"], "varInit":["uniform","two"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_split", "outName":"tensor_array/split_sz3-1_int32_dynamic_name_shape", "varShapes":[[9,3],[3]], "varTypes":["int32","int32"], "varInit":["uniform_int10","three"], \
# "dtype":tf.int32, "size":1, "dynamic_size":True, "tensor_array_name":None, "element_shape":[3,3]},
# TensorArray - close
# {"opName":"tensorarray_close", "outName":"tensor_array/close_sz1_float32_nodynamic_noname_noshape", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"],\
# "dtype":tf.float32, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
# {"opName":"tensorarray_close", "outName":"tensor_array/close_sz1_int64_nodynamic_noname_shape2-3", "varShapes":[[2,3]], "varTypes":["int64"], "varInit":["uniform_int5"], \
# "dtype":tf.int64, "size":1, "dynamic_size":False, "tensor_array_name":None, "element_shape":[2,3]},
# {"opName":"tensorarray_close", "outName":"tensor_array/close_sz2_float64_nodynamic_name_noshape", "varShapes":[[2,3],[2,3]], "varTypes":["float64","float64"], "varInit":["uniform","uniform"], \
# "dtype":tf.float64, "size":2, "dynamic_size":False, "tensor_array_name":None, "element_shape":None},
#ExtractImagePatches
# {"opName":"extractImagePatches", "outName":"extractImagePatches/sz1-6-6-2_float32_k3_s1_r1_SAME", "varShapes":[[1,6,6,2]], "varTypes":["float32"], "varInit":["uniform"],\
# "ksizes":[1,3,3,1], "strides":[1,1,1,1], "rates":[1,1,1,1], "padding":"SAME"},
# {"opName":"extractImagePatches", "outName":"extractImagePatches/sz1-6-6-2_float32_k3_s1_r1_VALID", "varShapes":[[1,6,6,2]], "varTypes":["float32"], "varInit":["uniform"], \
# "ksizes":[1,3,3,1], "strides":[1,1,1,1], "rates":[1,1,1,1], "padding":"VALID"},
# {"opName":"extractImagePatches", "outName":"extractImagePatches/sz1-8-8-2_int32_k2_s2_r1_SAME", "varShapes":[[1,8,8,2]], "varTypes":["int32"], "varInit":["uniform_int10"], \
# "ksizes":[1,2,2,1], "strides":[1,2,2,1], "rates":[1,1,1,1], "padding":"SAME"},
# {"opName":"extractImagePatches", "outName":"extractImagePatches/sz1-8-8-2_int64_k2_s1_r2_SAME", "varShapes":[[1,8,8,2]], "varTypes":["int64"], "varInit":["uniform_int10"], \
# "ksizes":[1,2,2,1], "strides":[1,1,1,1], "rates":[1,2,2,1], "padding":"SAME"},
#Stop gradient op
# {"opName":"stopGradient", "outName":"stopGradient/rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName":"stopGradient", "outName":"stopGradient/rank1", "varShapes":[[3]], "varTypes":["float64"], "varInit":["uniform"]},
# {"opName":"stopGradient", "outName":"stopGradient/rank2", "varShapes":[[3,4]], "varTypes":["float64"], "varInit":["uniform"]}
#Note: For RNNs, TF uses [batch, seqLength, nIn]
#LSTM - Static
# {"opName":"lstmcell", "outName":"rnn/lstmcell/static_batch1_n5-3_tsLength4_noPH_noClip_fBias1_Tanh_noInitState_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,\
# "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float32},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/static_batch2_nIn2_nOut3_tsLength4_withPH_noClip_fBias1_Tanh_noInitState_double", "varShapes":[[2,4,2]], "varTypes":["float64"], "varInit":["uniform"], "static":True, "timeSteps":4, \
# "num_units":3, "use_peepholes":True, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float64},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/static_batch1_n5-3_tsLength4_noPH_clip-0.3-0.4_fBias1_Tanh_noInitState_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4, \
# "num_units":3, "use_peepholes":False, "cell_clip":0.3, "proj_clip":0.4, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float32},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/static_batch1_nIn5_nOut3_tsLength4_noPH_noClip_fBias1_Softsign_withInitState_float", "varShapes":[[1,4,5],[1,3],[1,3]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"],
# "static":True, "timeSteps":4, "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"softsign", "dtype":tf.float32},
#LSTM - Dynamic. Supports time_major: if true, [max_time, batch_size, depth]; If false [batch_size, max_time, depth]
# {"opName":"lstmcell", "outName":"rnn/lstmcell/dynamic_b1_nIn5_nOut3_ts4_noPH_noClip_fB1_Tanh_noInitState_float_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float32, "time_major":False},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/dynamic_b1_nIn5_nOut3_ts4_noPH_noClip_fB1_Tanh_noIS_float_withTM", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float32, "time_major":False},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/dynamic_b2_nIn2_nOut3_ts4_withPH_noClip_fB1_Tanh_noIS_double_noTM", "varShapes":[[2,4,2]], "varTypes":["float64"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":True, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float64, "time_major":False},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/dynamic_b1_nIn5_nOut3_ts4_noPH_clip-0.3-0.4_fB1_Tanh_noIS_float_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":0.3, "proj_clip":0.4, "forget_bias":1.0, "activation":"tanh", "dtype":tf.float32, "time_major":False},
# {"opName":"lstmcell", "outName":"rnn/lstmcell/dynamic_b1_nIn5_nOut3_ts4_noPH_noClip_fB2_Softsign_withIS_float_noTM", "varShapes":[[1,4,5],[1,3],[1,3]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"],
# "static":False, "timeSteps":4, "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":2.0, "activation":"softsign", "dtype":tf.float32, "time_major":False},
#BasicRNNCell - Static
# {"opName":"basicrnncell", "outName":"rnn/basicrnncell/static_b1_nIn5_nOut3_ts4_tanh_noIS_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32},
# {"opName":"basicrnncell", "outName":"rnn/basicrnncell/static_b1_nIn5_nOut3_ts4_sigmoid_withIS_double", "varShapes":[[1,4,5], [1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"sigmoid", "dtype":tf.float64},
#BasicRNNCell - dynamic
# {"opName":"basicrnncell", "outName":"rnn/basicrnncell/dynamic_b1_nIn5_nOut3_ts4_relu_noIS_noTM_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":False},
# {"opName":"basicrnncell", "outName":"rnn/basicrnncell/dynamic_b1_nIn5_nOut3_ts4_relu_noIS_withTM_float", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":True},
# {"opName":"basicrnncell", "outName":"rnn/basicrnncell/dynamic_b1_nIn5_nOut3_ts4_sigmoid_withIS_noTM_double", "varShapes":[[1,4,5], [1,3]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"softsign", "dtype":tf.float64, "time_major":False},
#BasicLSTMCell - Static
# {"opName":"basiclstmcell", "outName":"rnn/basiclstmcell/static_b1_nIn5_nOut3_ts4_tanh_noIS_fb1_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32, "forget_bias":1.0},
# {"opName":"basiclstmcell", "outName":"rnn/basiclstmcell/static_b1_nIn5_nOut3_ts4_sigmoid_withIS_fb2_double", "varShapes":[[1,4,5], [1,3], [1,3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"sigmoid", "dtype":tf.float64, "forget_bias":2.0},
#BasicLSTMCell - dynamic
# {"opName":"basiclstmcell", "outName":"rnn/basiclstmcell/dynamic_b1_nIn5_nOut3_ts4_tanh_noIS_noTM_fb1_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32, "time_major":False, "forget_bias":1.0},
# {"opName":"basiclstmcell", "outName":"rnn/basiclstmcell/dynamic_b1_nIn5_nOut3_ts4_tanh_noIS_withTM_fb1_float", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32, "time_major":True, "forget_bias":1.0},
# {"opName":"basiclstmcell", "outName":"rnn/basiclstmcell/dynamic_b1_nIn5_nOut3_ts4_softsign_withIS_noTM_fb2_double", "varShapes":[[1,4,5], [1,3], [1,3]], "varTypes":["float64", "float64", "float64"], "varInit":["uniform", "uniform", "uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"softsign", "dtype":tf.float64, "time_major":False, "forget_bias":2.0},
#GRUCell - Static
# {"opName":"grucell", "outName":"rnn/grucell/static_b1_nIn5_nOut3_ts4_tanh_noIS_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32},
# {"opName":"grucell", "outName":"rnn/grucell/static_b1_nIn5_nOut3_ts4_softsign_withIS_double", "varShapes":[[1,4,5], [1,3]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"softsign", "dtype":tf.float64},
#GRUCell - dynamic
# {"opName":"grucell", "outName":"rnn/grucell/dynamic_b1_nIn5_nOut3_ts4_relu_noIS_noTM_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":False},
# {"opName":"grucell", "outName":"rnn/grucell/dynamic_b1_nIn5_nOut3_ts4_relu_noIS_withTM_float", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":True},
# {"opName":"grucell", "outName":"rnn/grucell/dynamic_b1_nIn5_nOut3_ts4_sigmoid_withIS_noTM_double", "varShapes":[[1,4,5], [1,3]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"softsign", "dtype":tf.float64, "time_major":False},
#GRUBlockCellV2 - Static
#Note: GRUBlockCellV2: "Only differs from GRUBlockCell by variable names." - GRUBlockCell is deprecated
# {"opName":"grublockcellv2", "outName":"rnn/grublockcellv2/static_b1_n5-3_ts4_noIS_f32", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "dtype":tf.float32},
# {"opName":"grublockcellv2", "outName":"rnn/grublockcellv2/static_b1_n5-3_ts4_withIS_f32", "varShapes":[[1,4,5], [1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "dtype":tf.float32},
#GRUBlockCellV2 - dynamic
# {"opName":"grublockcellv2", "outName":"rnn/grublockcellv2/dynamic_b1_n3-2_ts1_noIS_noTM", "varShapes":[[1,1,3]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":1,
# "num_units":2, "dtype":tf.float32, "time_major":False},
# {"opName":"grublockcellv2", "outName":"rnn/grublockcellv2/dynamic_b1_n5-3_ts4_noIS_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "time_major":False},
# {"opName":"grublockcellv2", "outName":"rnn/grublockcellv2/dynamic_b1_n5-3_ts4_noIS_withTM", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "time_major":True},
#LSTMBlockCell - Static. Note: float32 and float16 only
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/static_batch1_n3-2_tsLength1_noPH_noClip_fBias1_noIS", "varShapes":[[1,1,3]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":1,
# "num_units":2, "use_peepholes":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/static_batch1_n5-3_tsLength4_noPH_noClip_fBias1_noIS", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/static_batch2_n2-3_tsLength4_withPH_noClip_fBias1_noIS", "varShapes":[[2,4,2]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peepholes":True, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/static_batch1_n5-3_tsLength4_noPH_clip-0.3_fBias2_noIS", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":0.3, "forget_bias":2.0, "dtype":tf.float32},
#LSTMBlockCell - Dynamic. Supports time_major: if true, [max_time, batch_size, depth]; If false [batch_size, max_time, depth]
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/dynamic_b1_n5-3_ts4_noPH_noClip_fB1_noIS_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32, "time_major":False},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/dynamic_b1_n5-3_ts4_noPH_noClip_fB1_noIS_withTM", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":1.0, "dtype":tf.float32, "time_major":False},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/dynamic_b1_n5-3_ts4_noPH_clip-0.3-0.4_fB1_Tanh_noIS_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "use_peepholes":False, "cell_clip":0.3, "proj_clip":0.4, "forget_bias":1.0, "dtype":tf.float32, "time_major":False},
# {"opName":"lstmblockcell", "outName":"rnn/lstmblockcell/dynamic_b1_n5-3_ts4_noPH_noClip_fB2_withIS_noTM", "varShapes":[[1,4,5],[1,3],[1,3]], "varTypes":["float32","float32","float32"], "varInit":["uniform","uniform","uniform"],
# "static":False, "timeSteps":4, "num_units":3, "use_peepholes":False, "cell_clip":None, "proj_clip":None, "forget_bias":2.0, "dtype":tf.float32, "time_major":False},
#SRUCell - Static
# {"opName":"srucell", "outName":"rnn/srucell/static_b1_n5-3_tanh_ts4_noIS_f32", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "activation":tf.nn.tanh},
# {"opName":"srucell", "outName":"rnn/srucell/static_b1_n5-3_relu_ts4_withIS_f32", "varShapes":[[1,4,5], [1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "activation":tf.nn.relu},
#SRUCell - dynamic
# {"opName":"srucell", "outName":"rnn/srucell/dynamic_b1_n5-3_tanh_ts4_noIS_noTM", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "time_major":False, "activation":tf.nn.tanh},
# {"opName":"srucell", "outName":"rnn/srucell/dynamic_b1_n5-3_elu_ts4_noIS_withTM", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "dtype":tf.float32, "time_major":True, "activation":tf.nn.elu},
#LSTMBlockFusedCell. Note: these don't use rnn static/dynamic, the whole RNN is one op. Also note they expect [time,batch,inSize] inputs only (not configurable)
# {"opName":"lstmblockfusedcell", "outName":"rnn/lstmblockfusedcell/batch1_n3-2_tsLength1_noPH_noClip_fBias1_noIS", "varShapes":[[3,1,1]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":1,
# "num_units":2, "use_peephole":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockfusedcell", "outName":"rnn/lstmblockfusedcell/batch1_n5-3_tsLength4_noPH_noClip_fBias1_noIS", "varShapes":[[5,1,4]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockfusedcell", "outName":"rnn/lstmblockfusedcell/batch2_n2-3_tsLength4_withPH_noClip_fBias1_noIS", "varShapes":[[4,2,2]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":True, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"lstmblockfusedcell", "outName":"rnn/lstmblockfusedcell/batch1_n5-3_tsLength4_noPH_clip-0.3_fBias2_withIS", "varShapes":[[5,1,4], [1,3], [1,3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":False, "cell_clip":0.3, "forget_bias":2.0, "dtype":tf.float32},
# Bidirectional dynamic RNN + BasicRNNCell
# {"opName":"bidirectional_basicrnncell", "outName":"rnn/bidir_basic/static_b1_nIn5_nOut3_ts4_tanh_noIS_float", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32},
# {"opName":"bidirectional_basicrnncell", "outName":"rnn/bidir_basic/static_b1_nIn5_nOut3_ts4_sigmoid_withIS_double", "varShapes":[[1,4,5], [1,3], [1,3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"sigmoid", "dtype":tf.float64},
# Bidirectional static RNN + BasicRNNCell
# {"opName":"bidirectional_basicrnncell", "outName":"rnn/bidir_basic/dynamic_b1_n5-3_ts4_relu_noIS_noTM_f32", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":False},
# {"opName":"bidirectional_basicrnncell", "outName":"rnn/bidir_basic/dynamic_b1_n5-3_ts4_relu_noIS_withTM_f32", "varShapes":[[4,1,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":True},
# {"opName":"bidirectional_basicrnncell", "outName":"rnn/bidir_basic/dynamic_b1_n5-3_ts4_sig_withIS_noTM_f64", "varShapes":[[1,4,5], [1,3], [1,3]], "varTypes":["float64", "float64", "float64"], "varInit":["uniform", "uniform", "uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"softsign", "dtype":tf.float64, "time_major":False},
#TimeReversedFusedRNN + LSTMBlockFusedCell. Note: these don't use rnn static/dynamic, the whole RNN is one op. Also note they expect [time,batch,inSize] inputs only (not configurable)
# {"opName":"timereversed_lstmblockfusedcell", "outName":"rnn/tr_lstmbfc/batch1_n5-3_tsLength4_noPH_noClip_fBias1_noIS", "varShapes":[[5,1,4]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":False, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"timereversed_lstmblockfusedcell", "outName":"rnn/tr_lstmbfc/batch2_n2-3_tsLength4_withPH_noClip_fBias1_noIS", "varShapes":[[4,2,2]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":True, "cell_clip":None, "forget_bias":1.0, "dtype":tf.float32},
# {"opName":"timereversed_lstmblockfusedcell", "outName":"rnn/tr_lstmbfc/batch1_n5-3_tsLength4_noPH_clip-0.3_fBias2_withIS", "varShapes":[[5,1,4], [1,3], [1,3]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "use_peephole":False, "cell_clip":0.3, "forget_bias":2.0, "dtype":tf.float32},
# FusedRNNCellAdaptor + BasicRNNCell. Again, fused uses [time,batch,inSize]
# {"opName":"fused_adaptor_basicrnncell", "outName":"rnn/fused_adapt_basic/static_b1_n5-3_ts4_tanh_noIS_float", "varShapes":[[5,1,4]], "varTypes":["float32"], "varInit":["uniform"], "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32, "use_dynamic_rnn":False},
# {"opName":"fused_adaptor_basicrnncell", "outName":"rnn/fused_adapt_basic/static_b1_n5-3_ts4_sigmoid_withIS_double", "varShapes":[[5,1,4], [1,3]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"], "timeSteps":4,
# "num_units":3, "activation":"sigmoid", "dtype":tf.float64, "use_dynamic_rnn":False},
# {"opName":"fused_adaptor_basicrnncell", "outName":"rnn/fused_adapt_basic/dynamic_b1_n5-3_ts4_relu_noIS_float", "varShapes":[[5,1,4]], "varTypes":["float32"], "varInit":["uniform"], "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "use_dynamic_rnn":True},
# {"opName":"fused_adaptor_basicrnncell", "outName":"rnn/fused_adapt_basic/dynamic_b1_n5-3_ts4_elu_withIS_double", "varShapes":[[5,1,4], [1,3]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"], "timeSteps":4,
# "num_units":3, "activation":"elu", "dtype":tf.float64, "use_dynamic_rnn":True},
# Stacked Bidirectional dynamic RNN + BasicRNNCell
# {"opName":"stack_bidir_basicrnncell", "outName":"rnn/bstack/sta_b1_n5-3_ts4_tanh_noIS_f32_n3", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"tanh", "dtype":tf.float32, "size":3},
# {"opName":"stack_bidir_basicrnncell", "outName":"rnn/bstack/sta_b1_n5-3_ts4_sig_IS_f64_n2", "varShapes":[[1,4,5], [1,3], [1,3], [1,3], [1,3]], "varTypes":["float32", "float32", "float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform", "uniform", "uniform"], "static":True, "timeSteps":4,
# "num_units":3, "activation":"sigmoid", "dtype":tf.float64, "size":2},
# Stacked Bidirectional static RNN + BasicRNNCell
# {"opName":"stack_bidir_basicrnncell", "outName":"rnn/bstack/d_b1_n3", "varShapes":[[1,4,5]], "varTypes":["float32"], "varInit":["uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":False, "size":3},
# {"opName":"stack_bidir_basicrnncell", "outName":"rnn/bstack/d_n2", "varShapes":[[4,1,5], [1,3], [1,3], [1,3], [1,3]], "varTypes":["float32", "float32", "float32", "float32", "float32"], "varInit":["uniform", "uniform", "uniform", "uniform", "uniform"], "static":False, "timeSteps":4,
# "num_units":3, "activation":"relu", "dtype":tf.float32, "time_major":True, "size":2},
# {"opName": "arg_max", "outName": "arg_max/rank1_dim0", "varShapes":[[4]], "varTypes":["float32"], "varInit":["uniform"], "dimension":0},
# {"opName": "arg_max", "outName": "arg_max/rank2_dim1", "varShapes":[[0,1]], "varTypes":["float32"], "varInit":["uniform"], "dimension":1},
# {"opName": "arg_min", "outName": "arg_min/rank1_dim0", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "dimension":0},
# {"opName": "arg_min", "outName": "arg_min/rank2_dim1", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], "dimension":1},
# {"opName": "expand_dims", "outName": "expand_dims/rank1_axis0", "varShapes":[[1]], "varTypes":["float32"], "varInit":["uniform"], "axis":0},
# {"opName": "expand_dims", "outName": "expand_dims/rank1_axis-1", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform"], "axis":-1},
# {"opName": "expand_dims", "outName": "expand_dims/rank1_axis1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "axis":1},
# {"opName": "expand_dims", "outName": "expand_dims/rank2_axis0", "varShapes":[[1,2]], "varTypes":["float32"], "varInit":["uniform"], "axis":0},
# {"opName": "expand_dims", "outName": "expand_dims/rank2_axis-1", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform"], "axis":-1},
# {"opName": "expand_dims", "outName": "expand_dims/rank2_axis1", "varShapes":[[3,1]], "varTypes":["float32"], "varInit":["uniform"], "axis":1},
# {"opName": "expand_dims", "outName": "expand_dims/rank2_axis2", "varShapes":[[1,3]], "varTypes":["float32"], "varInit":["uniform"], "axis":2},
# {"opName": "fill", "outName": "fill/fill_3-1_val3", "varShapes":[[2], []], "varTypes":["int32", "float"], "varInit":["fixed_3_1", "three"]},
# {"opName": "identity", "outName": "identity/identity_rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "identity", "outName": "identity/identity_rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "identity", "outName": "identity/identity_rank2", "varShapes":[[2,3]], "varTypes":["int32"], "varInit":["uniform_int10"]},
# {"opName": "gather", "outName": "gather/gather_3-3-1_2_axis0", "varShapes":[[3,3,1], [2]], "varTypes":["float32", "int32"], "varInit":["uniform", "uniform_int3"], "axis":0},
# {"opName": "gather", "outName": "gather/gather_4-4_1_axis1", "varShapes":[[4,4], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "uniform_int3"], "axis":1},
# {"opName": "gather", "outName": "gather/gather_2-2-3_3_axis-1", "varShapes":[[2,2,3], [3]], "varTypes":["float32", "int32"], "varInit":["uniform", "uniform_int3"], "axis":-1},
# {"opName": "one", "outName": "ones/ones_rank0", "varShapes":[[1]], "varTypes":["int32"], "varInit":["one"], "dtype":tf.int64},
# {"opName": "one", "outName": "ones/ones_rank2", "varShapes":[[2]], "varTypes":["int32"], "varInit":["fixed_2_1"], "dtype":tf.float64},
# {"opName": "ones_like", "outName": "ones_like/rank0", "varShapes":[[]], "varTypes":["float32"], "varInit":["uniform"]},
# {"opName": "ones_like", "outName": "ones_like/rank2", "varShapes":[[2,2]], "varTypes":["float64"], "varInit":["uniform"]},
# {"opName": "reverse", "outName": "reverse/rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform"], "axis":[0]},
# {"opName": "reverse", "outName": "reverse/rank2_axis0", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":[0]},
# {"opName": "reverse", "outName": "reverse/rank2_axis1", "varShapes":[[3,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":[1]},
# {"opName": "reverse", "outName": "reverse/rank3_axis0-2", "varShapes":[[2,3,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":[0,2]},
#{"opName": "fake_quant_with_min_max_vars", "outName": "fake_quant/min_max_vars/rank1_0_1_8bit", "varShapes":[[10],[],[]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "zero", "one"], "num_bits":8, "narrow_range":False},
#{"opName": "fake_quant_with_min_max_vars", "outName": "fake_quant/min_max_vars/rank1_0_1_8bit_narrow", "varShapes":[[10],[],[]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "zero", "one"], "num_bits":8, "narrow_range":True},
#{"opName": "fake_quant_with_min_max_vars", "outName": "fake_quant/min_max_vars/rank1_0_1_4bit", "varShapes":[[10],[],[]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "zero", "one"], "num_bits":4, "narrow_range":False},
#{"opName": "fake_quant_with_min_max_vars", "outName": "fake_quant/min_max_vars/rank2_0_2_8bit", "varShapes":[[4,5],[],[]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "zero", "two"], "num_bits":8, "narrow_range":False},
#{"opName": "fake_quant_with_min_max_vars", "outName": "fake_quant/min_max_vars/rank1_1_5_4bit_narrow", "varShapes":[[5,5],[],[]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform10", "one", "five"], "num_bits":4, "narrow_range":True},
#
#{"opName": "fake_quant_with_min_max_args", "outName": "fake_quant/min_max_args/rank1_0_1_8bit", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "num_bits":8, "narrow_range":False, "min":0.0, "max":1.0},
#{"opName": "fake_quant_with_min_max_args", "outName": "fake_quant/min_max_args/rank1_0_1_8bit_narrow", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "num_bits":8, "narrow_range":True, "min":0.0, "max":1.0},
#{"opName": "fake_quant_with_min_max_args", "outName": "fake_quant/min_max_args/rank1_0_1_4bit", "varShapes":[[10]], "varTypes":["float32"], "varInit":["uniform"], "num_bits":4, "narrow_range":False, "min":0.0, "max":1.0},
#{"opName": "fake_quant_with_min_max_args", "outName": "fake_quant/min_max_args/rank2_0_2_8bit", "varShapes":[[4,5]], "varTypes":["float32"], "varInit":["uniform"], "num_bits":8, "narrow_range":False, "min":0.0, "max":2.0},
#{"opName": "fake_quant_with_min_max_args", "outName": "fake_quant/min_max_args/rank1_1_5_4bit_narrow", "varShapes":[[5,5]], "varTypes":["float32"], "varInit":["uniform10"], "num_bits":4, "narrow_range":True, "min":1.0, "max":5.0},
# {"opName": "fake_quant_with_min_max_vars_per_channel", "outName": "fake_quant/min_max_args_per_channel/rank1_8bit", "varShapes":[[5], [5], [5]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform_m1_0", "uniform"], "num_bits":8, "narrow_range":False},
# {"opName": "fake_quant_with_min_max_vars_per_channel", "outName": "fake_quant/min_max_args_per_channel/rank2_8bit_narrow", "varShapes":[[3, 5], [5], [5]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform_m1_0", "uniform"], "num_bits":8, "narrow_range":True},
# {"opName": "fake_quant_with_min_max_vars_per_channel", "outName": "fake_quant/min_max_args_per_channel/rank4_6bit", "varShapes":[[3, 2, 2, 5], [5], [5]], "varTypes":["float32", "float32", "float32"], "varInit":["uniform", "uniform_m1_0", "uniform"], "num_bits":6, "narrow_range":False},
#{"opName": "adjust_saturation", "outName": "adjust_saturation/rank3_float32", "varShapes": [[8, 8, 3]], "varTypes": ["float32"], "varInit": ["uniform"], "factor": 2.0},
#{"opName": "adjust_saturation", "outName": "adjust_saturation/rank4_float32", "varShapes": [[8, 8, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"], "factor": 0.5},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/rank3_float32", "varShapes": [[8,8,3]],"varTypes": ["float32"], "varInit": ["uniform"],"contrast_factor":2.0},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/rank4_float32", "varShapes": [[8,8,3,1]],"varTypes": ["float32"], "varInit": ["uniform"],"contrast_factor":2.0},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/rank4_float64","varShapes": [[8, 8, 3, 4]], "varTypes": ["float64"], "varInit": ["uniform"],"contrast_factor":2.0},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/rank3_float32", "varShapes": [[8, 8, 3]], "varTypes": ["float32"], "varInit": ["uniform"], "contrast_factor": 2.0},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/rank3_float64", "varShapes": [[8, 8, 3]], "varTypes": ["float64"], "varInit": ["uniform"], "contrast_factor": 2.0},
#{"opName": "adjust_contrast", "outName": "adjust_contrast_v2/emptyArrayTests/rank3_float32", "varShapes": [[0,0,0]],"varTypes": ["float32"], "varInit": ["empty"],"contrast_factor":0.0},
#{"opName": "adjust_hue", "outName": "adjust_hue/rank3_float32", "varShapes": [[8, 8, 3]], "varTypes": ["float32"], "varInit": ["stdnormal"], "delta": 0.5},
#{"opName": "adjust_hue", "outName": "adjust_hue/rank3_float64", "varShapes": [[8, 8, 3]], "varTypes": ["float64"], "varInit": ["uniform"], "delta": 0.5},
#{"opName": "adjust_hue", "outName": "adjust_hue/rank3_float64_zero_delta", "varShapes": [[8, 8, 3]], "varTypes": ["float64"], "varInit": ["uniform"], "delta": 0.5},
# {"opName": "adjust_hue", "outName": "adjust_hue/rank3_float32_test", "varShapes": [[8, 8, 3]], "varTypes": ["float32"], "varInit": ["stdnormal"], "delta": 0.5},
#{"opName": "crop_and_resize", "outName": "crop_and_resize", "varShapes": [[1,2,2,1], [2,4], [2], [2]],"varTypes": ["float32", "float32","int32","int32"], "varInit": ["uniform", "uniform","uniform_int10","uniform_int10"], "method":"bilinear","ext_value":0},
#{"opName": "crop_and_resize", "outName": "crop_and_resize", "varShapes": [[1,2,2,1], [2,4], [2], [2]],"varTypes": ["float32", "float32","int32","int32"], "varInit": ["uniform", "uniform","uniform_int10","uniform_int10"], "method":"nearest","ext_value":0},
# {"opName": "crop_and_resize", "outName": "crop_and_resize", "varShapes": [[1, 2, 2, 1], [2, 4], [2], [2]], "varTypes": ["float32", "float32", "int32", "int32"], "varInit": ["uniform", "uniform", "uniform_int10", "uniform_int10"], "method": "nearest", "ext_value": 0.5},
#{"opName": "draw_bounding_boxes", "outName": "draw_bounding_boxes/float32_input", "varShapes": [[2,5,5,1], [2,2,4], [1,2]],"varTypes": ["float32", "float32","float32"], "varInit": ["uniform", "uniform","uniform"]},
#{"opName": "draw_bounding_boxes", "outName": "draw_bounding_boxes/half_input", "varShapes": [[2,5,5,1], [2,2,4],[1,2]],"varTypes": ["half", "float32","float32"], "varInit": ["uniform", "uniform","uniform"]},
#{"opName": "resize_bilinear", "outName": "resize_bilinear/float32", "varShapes": [[2,5,5,3], [2]],"varTypes": ["float32", "int32"], "varInit": ["uniform", "uniform_int10"], "align_corners":True, "half_pixel_centers":False},
#{"opName": "resize_bilinear", "outName": "resize_bilinear/float64", "varShapes": [[2, 5, 5, 1], [2]], "varTypes": ["float64", "int32"], "varInit": ["uniform", "uniform_int10"],"align_corners":True, "half_pixel_centers":False},
#{"opName": "resize_bilinear", "outName": "resize_bilinear/int32", "varShapes": [[2, 5, 5, 1], [2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"],"align_corners":False, "half_pixel_centers":True},
#{"opName": "resize_bilinear", "outName": "resize_bilinear/float32_1", "varShapes": [[2, 5, 5, 3], [2]], "varTypes": ["float32", "int32"], "varInit": ["uniform", "uniform_int10"],"align_corners":False, "half_pixel_centers":False},
#{"opName": "resize_nearest_neighbor", "outName": "resize_nearest_neighbor/float32", "varShapes": [[2,5,5,3], [2]],"varTypes": ["float32", "int32"], "varInit": ["uniform", "uniform_int10"], "align_corners":True, "half_pixel_centers":False},
#{"opName": "resize_nearest_neighbor", "outName": "resize_nearest_neighbor/float64", "varShapes": [[2,5,5,3], [2]],"varTypes": ["float64", "int32"], "varInit": ["uniform", "uniform_int10"],"align_corners":True, "half_pixel_centers":False},
#{"opName": "resize_nearest_neighbor", "outName": "resize_nearest_neighbor/int32", "varShapes": [[2, 5, 5, 1], [2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"],"align_corners":False, "half_pixel_centers":True},
#{"opName": "resize_nearest_neighbor", "outName": "resize_nearest_neighbor/float32_1", "varShapes": [[2, 5, 5, 3], [2]], "varTypes": ["float32", "int32"], "varInit": ["uniform", "uniform_int10"],"align_corners":False, "half_pixel_centers":False},
# {"opName": "resize_bicubic", "outName": "resize_bicubic/float32", "varShapes": [[2,5,5,3],[2]],"varTypes": ["float32","int32"], "varInit": ["uniform","uniform_int10"], "align_corners":True, "half_pixel_centers":False},
# {"opName": "resize_bicubic", "outName": "resize_bicubic/float64", "varShapes": [[2, 5, 5, 1],[2]], "varTypes": ["float64","int32"], "varInit": ["uniform","uniform_int10"], "size":10, "align_corners":True, "half_pixel_centers":False},
# {"opName": "resize_bicubic", "outName": "resize_bicubic/int32", "varShapes": [[2, 5, 5, 1],[2]], "varTypes": ["int32","int32"], "varInit": ["uniform_int10","uniform_int10"], "size":0, "align_corners":False, "half_pixel_centers":True},
# {"opName": "resize_bicubic", "outName": "resize_bicubic/float32_1", "varShapes": [[2, 5, 5, 3],[2]], "varTypes": ["float32","int32"], "varInit": ["uniform","uniform_int10"], "size":100, "align_corners":False, "half_pixel_centers":False},
# TODO: more tests
# {"opName": "resize_area", "outName": "resize_area/float32", "varShapes": [[2,5,5,3],[2]],"varTypes": ["float32","int32"], "varInit": ["uniform","uniform_int10"], "align_corners":True},
# {"opName": "resize_area", "outName": "resize_area/float64", "varShapes": [[2, 5, 5, 1],[2]], "varTypes": ["float64","int32"], "varInit": ["uniform","uniform_int10"], "size":10, "align_corners":True},
# {"opName": "resize_area", "outName": "resize_area/int32", "varShapes": [[2, 5, 5, 1],[2]], "varTypes": ["int32","int32"], "varInit": ["uniform_int10","uniform_int10"], "size":0, "align_corners":False},
# {"opName": "resize_area", "outName": "resize_area/float32_1", "varShapes": [[2, 5, 5, 3],[2]], "varTypes": ["float32","int32"], "varInit": ["uniform","uniform_int10"], "size":100, "align_corners":False},
# {"opName": "resize_area", "outName": "resize_area/zeroArrayTest/float32", "varShapes": [[2, 5, 5, 3], [2]], "varTypes": ["float32", "int32"], "varInit": ["zeros", "uniform_int10"], "align_corners": True},
# {"opName": "check_numerics", "outName": "check_numerics/rank1_float16", "varShapes":[[5]], "varTypes":["float16", "string"], "varInit":["uniform", "string_scalar"], "message":"This is a test string."},
# {"opName": "check_numerics", "outName": "check_numerics/rank1_float32", "varShapes":[[5]], "varTypes":["float32", "string"], "varInit":["uniform", "string_scalar"], "message":"This is a test string."},
# {"opName": "check_numerics", "outName": "check_numerics/rank2_float64", "varShapes":[[5]], "varTypes":["float64", "string"], "varInit":["uniform", "string_scalar"], "message":"This is a test string."},
# {"opName": "check_numerics", "outName": "check_numerics/rank4_bfloat16", "varShapes":[[5]], "varTypes":["bfloat16", "string"], "varInit":["uniform", "string_scalar"], "message":"This is a test string."}
# {"opName": "dropout", "outName": "dropout", "varShapes":[[1,100]], "varTypes":["float32"], "varInit":["uniform"]}
#####################################################################################################################################
# Empty array tests
# {"opName": "arg_max", "outName": "emptyArrayTests/arg_max/rank1_dim0", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "dimension":0},
# {"opName": "arg_max", "outName": "emptyArrayTests/arg_max/rank2_dim0", "varShapes":[[0,1]], "varTypes":["float32"], "varInit":["empty"], "dimension":0},
# {"opName": "arg_min", "outName": "emptyArrayTests/arg_min/rank1_dim0", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "dimension":0},
# {"opName": "arg_min", "outName": "emptyArrayTests/arg_min/rank2_dim1", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"], "dimension":1},
# "AttributeError: 'list' object has no attribute 'dtype'" ???
# {"opName": "assign", "outName": "emptyArrayTests/assign/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "assign", "outName": "emptyArrayTests/assign/rank2", "varShapes":[[2,0], [0,3]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "concat", "outName": "emptyArrayTests/concat/rank1_dim0", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "concat", "outName": "emptyArrayTests/concat/rank2_dim0", "varShapes":[[0,1], [0,1]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "concat", "outName": "emptyArrayTests/concat/rank2_dim1", "varShapes":[[0,2], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":1},
# {"opName": "concat", "outName": "emptyArrayTests/concat/rank2_dim0b", "varShapes":[[2,0], [2,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "concat", "outName": "emptyArrayTests/concat/rank2_dim1b", "varShapes":[[2,0], [3,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":1},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank1_axis0", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank1_axis-1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":-1},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank1_axis1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":1},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":0},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank2_axis-1", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"], "axis":-1},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank2_axis1", "varShapes":[[3,0]], "varTypes":["float32"], "varInit":["empty"], "axis":1},
# {"opName": "expand_dims", "outName": "emptyArrayTests/expand_dims/rank2_axis2", "varShapes":[[0,0]], "varTypes":["float32"], "varInit":["empty"], "axis":2},
# {"opName": "fill", "outName": "emptyArrayTests/fill/fill_2-0_val3", "varShapes":[[2], []], "varTypes":["int32", "float"], "varInit":["fixed_2_0", "three"]},
# {"opName": "fill", "outName": "emptyArrayTests/fill/fill_0-0-3_val3", "varShapes":[[3], []], "varTypes":["int32", "float"], "varInit":["fixed_0_0_3", "three"]},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_2-3_emptyIndicesR1_axis0", "varShapes":[[2,3], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":0},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_2-3_emptyIndicesR1_axis1", "varShapes":[[2,3], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":1},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_2-3-1_emptyIndicesR1_axis0", "varShapes":[[2,3,1], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":0},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_2-3-1_emptyIndicesR1_axis2", "varShapes":[[2,3,1], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":2},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_emptyR2_emptyR1_axis0", "varShapes":[[2,0], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":0},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_emptyR2_emptyR1_axis1", "varShapes":[[2,0], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":1},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_emptyR3_emptyR1_axis2", "varShapes":[[2,0,3], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":2},
# {"opName": "gather", "outName": "emptyArrayTests/gather/gather_emptyR3_emptyR1_axis-1", "varShapes":[[2,0,3], [0]], "varTypes":["float32", "int32"], "varInit":["uniform", "empty"], "axis":-1},
# {"opName": "identity", "outName": "emptyArrayTests/identity/identity_rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "identity", "outName": "emptyArrayTests/identity/identity_rank2", "varShapes":[[2,0]], "varTypes":["int32"], "varInit":["empty"]},
# {"opName": "identity", "outName": "emptyArrayTests/identity/identity_rank3", "varShapes":[[0,1,0]], "varTypes":["int64"], "varInit":["empty"]},
# {"opName": "identity_n", "outName": "emptyArrayTests/identity_n/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "identity_n", "outName": "emptyArrayTests/identity_n/rank2", "varShapes":[[2,0], [0,3]], "varTypes":["int32", "int32"], "varInit":["empty", "empty"]},
# {"opName": "identity_n", "outName": "emptyArrayTests/identity_n/rank3", "varShapes":[[0,1,0], [0,0,2]], "varTypes":["int64", "int64"], "varInit":["empty", "empty"]},
# {"opName": "one", "outName": "emptyArrayTests/ones/ones_rank1", "varShapes":[[1]], "varTypes":["int32"], "varInit":["zero"], "dtype":tf.int32},
# {"opName": "one", "outName": "emptyArrayTests/ones/ones_rank2", "varShapes":[[2]], "varTypes":["int32"], "varInit":["fixed_2_0"], "dtype":tf.float32},
# {"opName": "one", "outName": "emptyArrayTests/ones/ones_rank3", "varShapes":[[3]], "varTypes":["int32"], "varInit":["fixed_0_0_3"], "dtype":tf.int64},
# {"opName": "ones_like", "outName": "emptyArrayTests/ones_like/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "ones_like", "outName": "emptyArrayTests/ones_like/rank2", "varShapes":[[2,0]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "range", "outName": "emptyArrayTests/range/0_0_1", "varShapes":[[],[],[]], "varTypes":["float32","float32","float32"], "varInit":["zero","zero","one"]},
# {"opName": "range", "outName": "emptyArrayTests/range/2_2_2", "varShapes":[[],[],[]], "varTypes":["float32","float32","float32"], "varInit":["two","two","two"]},
# {"opName": "rank", "outName": "emptyArrayTests/rank/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "rank", "outName": "emptyArrayTests/rank/rank2a", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "rank", "outName": "emptyArrayTests/rank/rank2b", "varShapes":[[1,0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "rank", "outName": "emptyArrayTests/rank/rank3", "varShapes":[[1,0,2]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "realdiv", "outName": "emptyArrayTests/realdiv/scalar_empty1", "varShapes":[[],[0]], "varTypes":["float32","float32"], "varInit":["two", "empty"]},
# {"opName": "realdiv", "outName": "emptyArrayTests/realdiv/empty1_scalar", "varShapes":[[0],[]], "varTypes":["float32","float32"], "varInit":["empty", "two"]},
# {"opName": "realdiv", "outName": "emptyArrayTests/realdiv/empty1_empty1", "varShapes":[[0],[0]], "varTypes":["float32","float32"], "varInit":["empty", "empty"]},
# {"opName": "realdiv", "outName": "emptyArrayTests/realdiv/empty2_rank2", "varShapes":[[0,1],[1,2]], "varTypes":["float32","float32"], "varInit":["empty", "two"]},
# {"opName": "reshape", "outName": "emptyArrayTests/reshape/rank2_shape2-0_0-1-2", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty", "empty"], "shape":[0,1,2]},
# {"opName": "reshape", "outName": "emptyArrayTests/reshape/rank3_shape0-1-2_10-0", "varShapes":[[0,1,2]], "varTypes":["float32"], "varInit":["empty", "empty"], "shape":[10,0]},
# {"opName": "reverse", "outName": "emptyArrayTests/reverse/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":[0]},
# {"opName": "reverse", "outName": "emptyArrayTests/reverse/rank2_axis0", "varShapes":[[0,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":[0]},
# {"opName": "reverse", "outName": "emptyArrayTests/reverse/rank2_axis1", "varShapes":[[3,0]], "varTypes":["float32"], "varInit":["uniform"], "axis":[1]},
# {"opName": "reverse", "outName": "emptyArrayTests/reverse/rank3_axis0-2", "varShapes":[[2,0,4]], "varTypes":["float32"], "varInit":["uniform"], "axis":[0,2]},
# {"opName": "scatter_add", "outName": "emptyArrayTests/scatter_add/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_add", "outName": "emptyArrayTests/scatter_add/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_div", "outName": "emptyArrayTests/scatter_div/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_div", "outName": "emptyArrayTests/scatter_div/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_max", "outName": "emptyArrayTests/scatter_max/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_max", "outName": "emptyArrayTests/scatter_max/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_min", "outName": "emptyArrayTests/scatter_min/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_min", "outName": "emptyArrayTests/scatter_min/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_mul", "outName": "emptyArrayTests/scatter_mul/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_mul", "outName": "emptyArrayTests/scatter_mul/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_div", "outName": "emptyArrayTests/scatter_div/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_div", "outName": "emptyArrayTests/scatter_div/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_update", "outName": "emptyArrayTests/scatter_update/rank1_emptyIndices_emptyUpdates", "varShapes":[[10],[0],[0]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "scatter_update", "outName": "emptyArrayTests/scatter_update/rank2_emptyIndices_emptyUpdates", "varShapes":[[3,4],[0],[0,4]], "varTypes":["float32","int32","float32"], "varInit":["uniform","empty","empty"]},
# {"opName": "shape", "outName": "emptyArrayTests/shape/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "shape", "outName": "emptyArrayTests/shape/rank2a", "varShapes":[[0,3]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "shape", "outName": "emptyArrayTests/shape/rank2b", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "shape", "outName": "emptyArrayTests/shape/rank3a", "varShapes":[[2,0,3]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "shape", "outName": "emptyArrayTests/shape/rank3b", "varShapes":[[0,0,3]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "shapen", "outName": "emptyArrayTests/shape_n/rank1-2-3", "varShapes":[[0],[1,0],[0,2,0]], "varTypes":["float32","float32","float32"], "varInit":["empty","empty","empty"]},
# {"opName": "size", "outName": "emptyArrayTests/size/rank2", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "size", "outName": "emptyArrayTests/size/rank3", "varShapes":[[0,0,3]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "slice", "outName": "emptyArrayTests/slice/rank1_size0", "varShapes":[[3],[1],[1]], "varTypes":["float32","int32","int32"], "varInit":["uniform","zero","zero"]},
# {"opName": "slice", "outName": "emptyArrayTests/slice/rank2_size0", "varShapes":[[3,4],[2],[2]], "varTypes":["float32","int32","int32"], "varInit":["uniform","one","zero"]},
# {"opName": "slice", "outName": "emptyArrayTests/slice/rank3_size0", "varShapes":[[3,4,2],[3],[3]], "varTypes":["float32","int32","int32"], "varInit":["uniform","one","zero"]},
#Squeeze here is odd: input [2,1,0] axis 1 gives error: "Can not squeeze dim[1], expected a dimension of 1, got 2 for 'Squeeze' (op: 'Squeeze') with input shapes: [1,2,1,0]."
# {"opName": "squeeze", "outName": "emptyArrayTests/squeeze/in2-1-0_axis2", "varShapes":[[2,1,0]], "varTypes":["float32"], "varInit":["empty"], "axis":2},
# {"opName": "squeeze", "outName": "emptyArrayTests/squeeze/in1-0_axis1", "varShapes":[[1,0]], "varTypes":["float32"], "varInit":["empty"], "axis":1},
# {"opName": "stack", "outName": "emptyArrayTests/stack/rank1_axis0", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "stack", "outName": "emptyArrayTests/stack/rank2_shape0-1_axis0", "varShapes":[[0,2], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "stack", "outName": "emptyArrayTests/stack/rank2_shape0-1_axis1", "varShapes":[[0,1], [0,1]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":1},
# {"opName": "stack", "outName": "emptyArrayTests/stack/rank2_shape2-0_axis0", "varShapes":[[2,0], [2,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":0},
# {"opName": "stack", "outName": "emptyArrayTests/stack/rank2_shape1-0_axis1", "varShapes":[[1,0], [1,0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"], "axis":1},
# {"opName": "strided_slice", "outName": "emptyArrayTests/strided_slice/in2-3-4_out2-0-2", "varShapes":[[2,3,4]], "varTypes":["float32"], "varInit":["uniform"], "begin":[0,0,0], "end":[2,0,2], "strides":[1,1,1],\
# "begin_mask":0, "end_mask":0},
# {"opName": "strided_slice", "outName": "emptyArrayTests/strided_slice/in2-0-4_out2-0-2", "varShapes":[[2,0,4]], "varTypes":["float32"], "varInit":["empty"], "begin":[0,0,0], "end":[2,0,2], "strides":[1,1,1],\
# "begin_mask":0, "end_mask":0}
# {"opName": "strided_slice", "outName": "emptyArrayTests/strided_slice/in2-0-4_out2-0-2_v2", "varShapes":[[2,0,4]], "varTypes":["float32"], "varInit":["empty"], "begin":[0,-1,0], "end":[2,-1,2], "strides":[1,1,1], \
# "begin_mask":2, "end_mask":2}
# {"opName": "strided_slice", "outName": "strided_slice/in2-3-4_out2-0-2","varShapes": [[20, 30, 40]], "varTypes": ["float32"], "varInit": ["uniform"], "begin": [0, 0, 0],"end": [15,10, 12], "strides": [1, 1, 1],
# "begin_mask": 0, "end_mask": 0},
#{"opName": "strided_slice", "outName": "strided_slice/largein_largeout", "varShapes": [[200, 300, 400]],
# "varTypes": ["float32"], "varInit": ["uniform"], "begin": [10, 10, 20], "end": [15, 10, 12], "strides": [10, 10, 10],
# "begin_mask": 0, "end_mask": 0},
#{"opName": "strided_slice", "outName": "strided_slice/largein_largeout_1", "varShapes": [[50, 30, 40]], "varTypes": ["float64"], "varInit": ["uniform"], "begin": [10, 10, 20], "end": [50, 5, 7],
# "strides": [10, 10, 10],
# "begin_mask": 0, "end_mask": 0},
#{"opName": "strided_slice", "outName": "strided_slice/largein_largeout_2", "varShapes": [[1000, 200, 100]],
# "varTypes": ["float64"], "varInit": ["uniform"], "begin": [50, 20, 20], "end": [50, 20, 40],
# "strides": [20, 20, 20],
# "begin_mask": 0, "end_mask": 0},
#Again, somehow TF is appending a 1 here: InvalidArgumentError: Dimension must be 3 but is 2 for 'transpose' (op: 'Transpose') with input shapes: [1,2,0], [2].
# {"opName": "transpose", "outName": "emptyArrayTests/transpose/in2-0_perm1-0", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"], "perm":[1,0,2]},
# {"opName": "transpose", "outName": "emptyArrayTests/transpose/in2-1-0_perm1-2-0", "varShapes":[[2,1,0]], "varTypes":["float32"], "varInit":["empty"], "perm":[1,2,0,3]},
# {"opName": "unstack", "outName": "emptyArrayTests/unstack/in2-0-4_axis2", "varShapes":[[2,0,4]], "varTypes":["float32"], "varInit":["empty"], "num":4, "axis":2},
# {"opName": "unstack", "outName": "emptyArrayTests/unstack/in3-0_axis0", "varShapes":[[3,0]], "varTypes":["float32"], "varInit":["empty"], "num":3, "axis":0},
# {"opName": "zeros", "outName": "emptyArrayTests/zeros/ones_rank1", "varShapes":[[1]], "varTypes":["int32"], "varInit":["zero"], "dtype":tf.int32},
# {"opName": "zeros", "outName": "emptyArrayTests/zeros/ones_rank2", "varShapes":[[2]], "varTypes":["int32"], "varInit":["fixed_2_0"], "dtype":tf.float32},
# {"opName": "zeros", "outName": "emptyArrayTests/zeros/ones_rank3", "varShapes":[[3]], "varTypes":["int32"], "varInit":["fixed_0_0_3"], "dtype":tf.int64},
# {"opName": "zeros_like", "outName": "emptyArrayTests/zeros_like/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "zeros_like", "outName": "emptyArrayTests/zeros_like/rank2", "varShapes":[[2,0]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_float16", "varShapes": [[3, 12]],"varTypes": ["float16"], "varInit": ["uniform"]},
#{"opName": "zeros_like", "outName": "zeros_like/rank2_float32", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "zeros_like", "outName": "zeros_like/rank2_float64", "varShapes": [[3, 12]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "zeros_like", "outName": "zeros_like/rank2_int32", "varShapes": [[3, 10]], "varTypes": ["int32"], "varInit": ["uniform_int10"]},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_int64", "varShapes": [[3, 10]], "varTypes": ["int64"], "varInit": ["uniform_int10"]},
#{"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_int8", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.int8},
#{"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_int16", "varShapes": [[3, 12]],"varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.int16},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_int32", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype":tf.int32},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_float16", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.float16},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_float32", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.float32},
# {"opName": "zeros_like", "outName": "zeros_like/rank2_float32_dtype_float64", "varShapes": [[3, 12]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.float64},
# {"opName": "cast", "outName": "emptyArrayTests/cast/rank2_shape2-0", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty", "empty"], "dtype":tf.int32},
# {"opName": "cast", "outName": "emptyArrayTests/cast/rank1_shape0", "varShapes":[[0]], "varTypes":["int32"], "varInit":["empty", "empty"], "dtype":tf.int64},
#{"opName": "abs", "outName": "emptyArrayTests/abs/rank1", "varShapes":[[0]], "varTypes":["int32"], "varInit":["empty"], "dtype":tf.int32},
#{"opName": "abs", "outName": "emptyArrayTests/abs/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "dtype":tf.float64},
#{"opName": "abs", "outName": "abs/rank2_float64", "varShapes": [[1, 10]], "varTypes": ["float64"], "varInit": ["uniform"], "dtype": tf.float64},
#{"opName": "abs", "outName": "abs/rank2_float32", "varShapes": [[1, 10]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.float32},
#{"opName": "abs", "outName": "abs/rank2_float16", "varShapes": [[1, 10]], "varTypes": ["float16"], "varInit": ["uniform"], "dtype": tf.float16},
#{"opName": "abs", "outName": "abs/rank2_int32", "varShapes": [[1, 10]], "varTypes": ["int32"], "varInit": ["uniform_int10"], "dtype": tf.int32},
#{"opName": "abs", "outName": "abs/rank2_int64", "varShapes": [[1, 10]], "varTypes": ["int64"], "varInit": ["uniform_int10"],"dtype": tf.int64},
#{"opName": "abs", "outName": "abs/rank3_float64", "varShapes": [[1, 10, 20]], "varTypes": ["float64"], "varInit": ["uniform"], "dtype": tf.float64},
#{"opName": "abs", "outName": "abs/rank3_float32", "varShapes": [[1, 10, 20]], "varTypes": ["float32"], "varInit": ["uniform"], "dtype": tf.float32},
#{"opName": "abs", "outName": "abs/rank3_float16", "varShapes": [[1, 10, 20]], "varTypes": ["float16"], "varInit": ["uniform"], "dtype": tf.float16},
#{"opName": "abs", "outName": "abs/rank3_int32", "varShapes": [[1, 10, 20]], "varTypes": ["int32"], "varInit": ["uniform_int10"], "dtype": tf.int32},
# {"opName": "abs", "outName": "abs/rank3_int64", "varShapes": [[1, 10, 20]], "varTypes": ["int64"], "varInit": ["uniform_int10"], "dtype": tf.int64},
# {"opName": "abs", "outName": "abs/rank2_float32_normal", "varShapes": [[1, 10]], "varTypes": ["float32"], "varInit": ["stdnormal"], "dtype": tf.float32},
# {"opName": "abs", "outName": "abs/rank2_float16_normal", "varShapes": [[1, 10]], "varTypes": ["float16"], "varInit": ["stdnormal"], "dtype": tf.float16},
# {"opName": "accumulate_n", "outName": "emptyArrayTests/accumulate_n/rank1_1", "varShapes":[[0]], "varTypes":["int64"], "varInit":["empty"], "dtype":tf.int64},
# {"opName": "accumulate_n", "outName": "emptyArrayTests/accumulate_n/rank1_3", "varShapes":[[0], [0], [0]], "varTypes":["int64", "int64", "int64"], "varInit":["empty","empty","empty"], "dtype":tf.int64},
# {"opName": "accumulate_n", "outName": "emptyArrayTests/accumulate_n/rank2_1", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "dtype":tf.float64},
# {"opName": "accumulate_n", "outName": "emptyArrayTests/accumulate_n/rank2_2", "varShapes":[[0,2], [0,2], [0,2]], "varTypes":["float64", "float64", "float64"], "varInit":["empty", "empty", "empty"], "dtype":tf.float64},
# {"opName": "add", "outName": "emptyArrayTests/add/rank1", "varShapes":[[0], [0]], "varTypes":["int64", "int64"], "varInit":["empty","empty"], "dtype":tf.int64},
# {"opName": "add", "outName": "emptyArrayTests/add/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "dtype":tf.float64},
# {"opName": "sub", "outName": "emptyArrayTests/sub/rank1", "varShapes":[[0], [0]], "varTypes":["int64", "int64"], "varInit":["empty","empty"], "dtype":tf.int64},
# {"opName": "sub", "outName": "emptyArrayTests/sub/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "dtype":tf.float64},
# {"opName": "mul", "outName": "emptyArrayTests/mul/rank1", "varShapes":[[0], [0]], "varTypes":["int64", "int64"], "varInit":["empty","empty"], "dtype":tf.int64},
# {"opName": "mul", "outName": "emptyArrayTests/mul/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "dtype":tf.float64},
# TODO: Div just gives "TypeError: unsupported operand type(s) for /: 'list' and 'list'" event through other ops work :/
# {"opName": "div", "outName": "emptyArrayTests/div/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty","empty"], "dtype":tf.float32},
# {"opName": "div", "outName": "emptyArrayTests/div/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "dtype":tf.float64},
# {"opName": "div", "outName": "div/float64", "varShapes":[[1,10], [1,10]], "varTypes":["float64", "float64"], "varInit":["uniform", "uniform"], "dtype":tf.float64},
# {"opName": "div", "outName": "div/float32", "varShapes": [[1, 10], [1, 10]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"], "dtype": tf.float32},
# {"opName": "div", "outName": "div/int32", "varShapes": [[1, 10], [1, 10]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"], "dtype": tf.int32},
#{"opName": "div_no_nan", "outName": "div_no_nan", "varShapes":[[1,10], [1,10]], "varTypes":["float32", "float32"], "varInit":["uniform","uniform"], "dtype":tf.float32},
# {"opName": "div_no_nan", "outName": "div_no_nan", "varShapes":[[], [1,10]], "varTypes":["float32", "float32"], "varInit":["uniform","uniform"], "dtype":tf.float32},
#{"opName": "div_no_nan", "outName": "div_no_nan", "varShapes": [[1, 10], [1, 10]],"varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"], "dtype": tf.float64},
# {"opName": "add_n", "outName": "emptyArrayTests/add_n/rank1", "varShapes":[[0], [0]], "varTypes":["int64", "int64"], "varInit":["empty","empty"], "dtype":tf.int64},
# {"opName": "add_n", "outName": "emptyArrayTests/add_n/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"], "dtype":tf.float64},
# {"opName": "cos", "outName": "emptyArrayTests/cos/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "cos", "outName": "emptyArrayTests/cos/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "sin", "outName": "emptyArrayTests/sin/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "sin", "outName": "emptyArrayTests/sin/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "tan", "outName": "emptyArrayTests/tan/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "tan", "outName": "emptyArrayTests/tan/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "cosh", "outName": "emptyArrayTests/cosh/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "cosh", "outName": "emptyArrayTests/cosh/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "acos", "outName": "emptyArrayTests/acos/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "acos", "outName": "emptyArrayTests/acos/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "acosh", "outName": "emptyArrayTests/acosh/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "acosh", "outName": "emptyArrayTests/acosh/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "asin", "outName": "emptyArrayTests/asin/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "asin", "outName": "emptyArrayTests/asin/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "asinh", "outName": "emptyArrayTests/asinh/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "asinh", "outName": "emptyArrayTests/asinh/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "atan", "outName": "emptyArrayTests/atan/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "atan", "outName": "emptyArrayTests/atan/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "atanh", "outName": "emptyArrayTests/atanh/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "atanh", "outName": "emptyArrayTests/atanh/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "ceil", "outName": "emptyArrayTests/ceil/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "ceil", "outName": "emptyArrayTests/ceil/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "count_nonzero", "outName": "emptyArrayTests/count_nonzero/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keep_dims":False},
# {"opName": "count_nonzero", "outName": "emptyArrayTests/count_nonzero/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":0, "keep_dims":False},
# {"opName": "count_nonzero", "outName": "emptyArrayTests/count_nonzero/rank2_axis1", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":1, "keep_dims":False},
# {"opName": "count_nonzero", "outName": "emptyArrayTests/count_nonzero/rank2_axis1_keep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":1, "keep_dims":True},
# {"opName": "cumprod", "outName": "emptyArrayTests/cumprod/rank1_axis0", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0},
# {"opName": "cumprod", "outName": "emptyArrayTests/cumprod/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":0},
# {"opName": "cumprod", "outName": "emptyArrayTests/cumprod/rank2_axis1", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":1},
# {"opName": "cumsum", "outName": "emptyArrayTests/cumsum/rank1_axis0", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0},
# {"opName": "cumsum", "outName": "emptyArrayTests/cumsum/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":0},
# {"opName": "cumsum", "outName": "emptyArrayTests/cumsum/rank2_axis1", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":1},
# {"opName": "equal", "outName": "emptyArrayTests/equal/rank1", "varShapes":[[0], [0]], "varTypes":["int64", "int64"], "varInit":["empty","empty"], "dtype":tf.int64},
# {"opName": "equal", "outName": "emptyArrayTests/equal/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "equal", "outName": "emptyArrayTests/equal/rank2bc", "varShapes":[[1,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "exp", "outName": "emptyArrayTests/exp/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "exp", "outName": "emptyArrayTests/exp/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "floor", "outName": "emptyArrayTests/floor/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "floor", "outName": "emptyArrayTests/floor/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "floordiv", "outName": "emptyArrayTests/floordiv/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "floordiv", "outName": "emptyArrayTests/floordiv/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "floordiv", "outName": "emptyArrayTests/floordiv/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "floordiv", "outName": "emptyArrayTests/floordiv/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "log", "outName": "emptyArrayTests/log/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "log", "outName": "emptyArrayTests/log/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "log_sigmoid", "outName": "emptyArrayTests/log_sigmoid/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "log_sigmoid", "outName": "emptyArrayTests/log_sigmoid/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "negative", "outName": "emptyArrayTests/negative/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "negative", "outName": "emptyArrayTests/negative/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "reciprocal", "outName": "emptyArrayTests/reciprocal/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "reciprocal", "outName": "emptyArrayTests/reciprocal/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "sign", "outName": "emptyArrayTests/sign/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "sign", "outName": "emptyArrayTests/sign/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "softplus", "outName": "emptyArrayTests/softplus/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "softplus", "outName": "emptyArrayTests/softplus/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "sqrt", "outName": "emptyArrayTests/sqrt/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "sqrt", "outName": "emptyArrayTests/sqrt/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "square", "outName": "emptyArrayTests/square/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "square", "outName": "emptyArrayTests/square/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "rsqrt", "outName": "emptyArrayTests/rsqrt/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "rsqrt", "outName": "emptyArrayTests/rsqrt/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "round", "outName": "emptyArrayTests/round/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "round", "outName": "emptyArrayTests/round/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "sigmoid", "outName": "emptyArrayTests/sigmoid/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "sigmoid", "outName": "emptyArrayTests/sigmoid/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
# {"opName": "greater", "outName": "emptyArrayTests/greater/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "greater", "outName": "emptyArrayTests/greater/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "greater", "outName": "emptyArrayTests/greater/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "greater", "outName": "emptyArrayTests/greater/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "greater_equal", "outName": "emptyArrayTests/greater_equal/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "greater_equal", "outName": "emptyArrayTests/greater_equal/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "greater_equal", "outName": "emptyArrayTests/greater_equal/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "greater_equal", "outName": "emptyArrayTests/greater_equal/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "less", "outName": "emptyArrayTests/less/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "less", "outName": "emptyArrayTests/less/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "less", "outName": "emptyArrayTests/less/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "less", "outName": "emptyArrayTests/less/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "less_equal", "outName": "emptyArrayTests/less_equal/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "less_equal", "outName": "emptyArrayTests/less_equal/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "less_equal", "outName": "emptyArrayTests/less_equal/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "less_equal", "outName": "emptyArrayTests/less_equal/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "not_equal", "outName": "emptyArrayTests/not_equal/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "not_equal", "outName": "emptyArrayTests/not_equal/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "not_equal", "outName": "emptyArrayTests/not_equal/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "not_equal", "outName": "emptyArrayTests/not_equal/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "truediv", "outName": "emptyArrayTests/truediv/rank1", "varShapes":[[0], [0]], "varTypes":["int32", "int32"], "varInit":["empty", "empty"]},
# {"opName": "truediv", "outName": "emptyArrayTests/truediv/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["int64", "int64"], "varInit":["empty", "empty"]},
# {"opName": "truediv", "outName": "emptyArrayTests/truediv/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "truediv", "outName": "emptyArrayTests/truediv/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["int32", "int32"], "varInit":["empty", "empty"]},
# {"opName": "zero_fraction", "outName": "emptyArrayTests/zero_fraction/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "zero_fraction", "outName": "emptyArrayTests/zero_fraction/rank2", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"]},
#{"opName": "maximum", "outName": "emptyArrayTests/maximum/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "maximum", "outName": "emptyArrayTests/maximum/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "maximum", "outName": "emptyArrayTests/maximum/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "maximum", "outName": "emptyArrayTests/maximum/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "minimum", "outName": "emptyArrayTests/minimum/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "minimum", "outName": "emptyArrayTests/minimum/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "minimum", "outName": "emptyArrayTests/minimum/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "minimum", "outName": "emptyArrayTests/minimum/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "max", "outName": "max/rank2_float32", "varShapes":[[2,12], [2,12]], "varTypes":["float32", "float32"], "varInit":["uniform", "uniform"]},
# {"opName": "min", "outName": "min/rank2_float32", "varShapes": [[2, 12], [2, 12]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "mod", "outName": "mod/rank2_float32", "varShapes": [[2, 1,10], [2, 3,1]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/scalar_broadcasting_float64", "varShapes": [[], [2, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "min", "outName": "min/scalar_broadcasting_float64", "varShapes": [[], [2, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "mod", "outName": "mod/rank2_float64", "varShapes": [[2, 12], [2, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank2_int32", "varShapes": [[2, 12], [2, 12]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
# {"opName": "min", "outName": "min/rank2_int32", "varShapes": [[2, 12], [2, 12]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
# {"opName": "mod", "outName": "mod/rank2_float32", "varShapes": [[2, 12], [2, 12]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "mod", "outName": "mod/rank2_float64", "varShapes": [[2, 12], [2, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_int32", "varShapes":[[2,3,10], [2,3,10]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int10"]},
# {"opName": "max", "outName": "max/rank3_float32", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_float32", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_float64", "varShapes": [[2, 3, 10], [2, 3, 10]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_float64", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_float64", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"]},
#{"opName": "max", "outName": "max/rank3_half", "varShapes": [[2, 3, 10], [2, 3, 10]], "varTypes": ["half", "half"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_half", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["half", "half"], "varInit": ["uniform", "uniform"]},
# {"opName": "max", "outName": "max/rank3_half", "varShapes": [[2, 3, 12], [2, 3, 12]], "varTypes": ["half", "half"], "varInit": ["uniform", "uniform"]},
# {"opName": "pow", "outName": "emptyArrayTests/pow/rank1", "varShapes":[[0], [0]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
# {"opName": "pow", "outName": "emptyArrayTests/pow/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["float64", "float64"], "varInit":["empty", "empty"]},
# {"opName": "pow", "outName": "emptyArrayTests/pow/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["float64", "float64"], "varInit":["uniform", "empty"]},
# {"opName": "pow", "outName": "emptyArrayTests/pow/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["float32", "float32"], "varInit":["empty", "empty"]},
#Not sure if segment ops are possible with empty inputs? "ValueError: Shape must be rank 1 but is rank 2 for 'SegmentMax' (op: 'SegmentMax') with input shapes: [1,0], [1,0]."??
# {"opName": "segment_max", "outName": "emptyArrayTests/segment/segment_max_rank1", "varShapes":[[0], [0]], "varTypes":["float32", "int32"], "varInit":["empty", "empty"]},
# {"opName": "logical_and", "outName": "emptyArrayTests/logical_and/rank1", "varShapes":[[0], [0]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_and", "outName": "emptyArrayTests/logical_and/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_and", "outName": "emptyArrayTests/logical_and/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["boolean", "empty"]},
# {"opName": "logical_and", "outName": "emptyArrayTests/logical_and/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_or", "outName": "emptyArrayTests/logical_or/rank1", "varShapes":[[0], [0]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_or", "outName": "emptyArrayTests/logical_or/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_or", "outName": "emptyArrayTests/logical_or/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["boolean", "empty"]},
# {"opName": "logical_or", "outName": "emptyArrayTests/logical_or/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_xor", "outName": "emptyArrayTests/logical_xor/rank1", "varShapes":[[0], [0]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_xor", "outName": "emptyArrayTests/logical_xor/rank2", "varShapes":[[0,2], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_xor", "outName": "emptyArrayTests/logical_xor/rank2bc", "varShapes":[[1,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["boolean", "empty"]},
# {"opName": "logical_xor", "outName": "emptyArrayTests/logical_xor/rank2bc2", "varShapes":[[0,1], [0,2]], "varTypes":["bool", "bool"], "varInit":["empty", "empty"]},
# {"opName": "logical_not", "outName": "emptyArrayTests/logical_not/rank1", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"]},
# {"opName": "logical_not", "outName": "emptyArrayTests/logical_not/rank2", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"]},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank1_axisNone", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank2_axis0", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyArrayTests/reduce_all/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank1_axisNone", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank2_axis0", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyArrayTests/reduce_any/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp", "outName": "emptyArrayTests/reduce_logsumexp/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_max", "outName": "emptyArrayTests/reduce_max/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_min", "outName": "emptyArrayTests/reduce_min/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyArrayTests/reduce_prod/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyArrayTests/reduce_mean/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_sum", "outName": "emptyArrayTests/reduce_sum/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "l2_normalize", "outName": "emptyArrayTests/l2_normalize/rank1", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "epsilon":0.1, "axis":None},
# {"opName": "l2_normalize", "outName": "emptyArrayTests/l2_normalize/rank2", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "epsilon":0.1, "axis":None},
# {"opName": "l2_normalize", "outName": "emptyArrayTests/l2_normalize/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "epsilon":0.1, "axis":0},
# {"opName": "l2_normalize", "outName": "emptyArrayTests/l2_normalize/rank2_axis1", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "epsilon":0.1, "axis":1},
#Not possible?
# {"opName": "diag_part", "outName": "emptyArrayTests/diag_part/rank2", "varShapes":[[0,0]], "varTypes":["float32"], "varInit":["empty"]},
# {"opName": "diag_part", "outName": "emptyArrayTests/diag_part/rank3", "varShapes":[[2,2,0]], "varTypes":["float32"], "varInit":["empty"]},
# TODO BOOL
# {"opName": "add", "outName": "dtype_tests/add/bfloat16_rank0_1", "varShapes":[[], [1]], "varTypes":["bfloat16", "bfloat16"], "varInit":["one","one"], "dtype":tf.bfloat16},
# {"opName": "add", "outName": "dtype_tests/add/bfloat16_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["bfloat16", "bfloat16"], "varInit":["one","one"], "dtype":tf.bfloat16},
# {"opName": "add", "outName": "dtype_tests/add/double_rank0_1", "varShapes":[[], [1]], "varTypes":["double", "double"], "varInit":["one","one"], "dtype":tf.double},
# {"opName": "add", "outName": "dtype_tests/add/double_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["double", "double"], "varInit":["one","one"], "dtype":tf.double},
# {"opName": "add", "outName": "dtype_tests/add/float16_rank0_1", "varShapes":[[], [1]], "varTypes":["float16", "float16"], "varInit":["one","one"], "dtype":tf.float16},
# {"opName": "add", "outName": "dtype_tests/add/float16_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["float16", "float16"], "varInit":["one","one"], "dtype":tf.float16},
# {"opName": "add", "outName": "dtype_tests/add/float32_rank0_1", "varShapes":[[], [1]], "varTypes":["float32", "float32"], "varInit":["one","one"], "dtype":tf.float32},
# {"opName": "add", "outName": "dtype_tests/add/float32_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["float32", "float32"], "varInit":["one","one"], "dtype":tf.float32},
# {"opName": "add", "outName": "dtype_tests/add/float64_rank0_1", "varShapes":[[], [1]], "varTypes":["float64", "float64"], "varInit":["one","one"], "dtype":tf.float64},
# {"opName": "add", "outName": "dtype_tests/add/float64_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["float64", "float64"], "varInit":["one","one"], "dtype":tf.float64},
# {"opName": "add", "outName": "dtype_tests/add/half_rank0_1", "varShapes":[[], [1]], "varTypes":["half", "half"], "varInit":["one","one"], "dtype":tf.half},
# {"opName": "add", "outName": "dtype_tests/add/half_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["half", "half"], "varInit":["one","one"], "dtype":tf.half},
# {"opName": "add", "outName": "dtype_tests/add/int16_rank0_1", "varShapes":[[], [1]], "varTypes":["int16", "int16"], "varInit":["one","one"], "dtype":tf.int16},
# {"opName": "add", "outName": "dtype_tests/add/int16_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["int16", "int16"], "varInit":["one","one"], "dtype":tf.int16},
# {"opName": "add", "outName": "dtype_tests/add/int32_rank0_1", "varShapes":[[], [1]], "varTypes":["int32", "int32"], "varInit":["one","one"], "dtype":tf.int32},
# {"opName": "add", "outName": "dtype_tests/add/int32_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["int32", "int32"], "varInit":["one","one"], "dtype":tf.int32},
# {"opName": "add", "outName": "dtype_tests/add/int64_rank0_1", "varShapes":[[], [1]], "varTypes":["int64", "int64"], "varInit":["one","one"], "dtype":tf.int64},
# {"opName": "add", "outName": "dtype_tests/add/int64_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["int64", "int64"], "varInit":["one","one"], "dtype":tf.int64},
# {"opName": "add", "outName": "dtype_tests/add/int8_rank0_1", "varShapes":[[], [1]], "varTypes":["int8", "int8"], "varInit":["one","one"], "dtype":tf.int8},
# {"opName": "add", "outName": "dtype_tests/add/int8_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["int8", "int8"], "varInit":["one","one"], "dtype":tf.int8},
# {"opName": "concat", "outName": "dtype_tests/concat/uint16_rank1", "varShapes":[[2], [1]], "varTypes":["uint16", "uint16"], "varInit":["one","one"], "dtype":tf.uint16, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint16_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["uint16", "uint16"], "varInit":["one","one"], "dtype":tf.uint16, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint8_rank1", "varShapes":[[2], [1]], "varTypes":["uint8", "uint8"], "varInit":["one","one"], "dtype":tf.uint8, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint8_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["uint8", "uint8"], "varInit":["one","one"], "dtype":tf.uint8, "axis":0},
#UINT32 and 64 don't support concat for some reason :/
# {"opName": "concat", "outName": "dtype_tests/concat/uint32_rank1", "varShapes":[[2], [1]], "varTypes":["uint32", "uint32"], "varInit":["one","one"], "dtype":tf.uint32, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint32_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["uint32", "uint32"], "varInit":["one","one"], "dtype":tf.uint32, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint64_rank1", "varShapes":[[2], [1]], "varTypes":["uint64", "uint64"], "varInit":["one","one"], "dtype":tf.uint64, "axis":0},
# {"opName": "concat", "outName": "dtype_tests/concat/uint64_rank2", "varShapes":[[2,3], [2,3]], "varTypes":["uint64", "uint64"], "varInit":["one","one"], "dtype":tf.uint64, "axis":0},
# This doesn't work either... :/
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint32_rank1", "varShapes":[[4]], "varTypes":["uint32", "uint32"], "varInit":["one"], "dtype":tf.uint32, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint32_rank2", "varShapes":[[3,4]], "varTypes":["uint32", "uint32"], "varInit":["one"], "dtype":tf.uint32, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint64_rank1", "varShapes":[[4]], "varTypes":["uint64", "uint64"], "varInit":["one"], "dtype":tf.uint64, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint64_rank2", "varShapes":[[3,4]], "varTypes":["uint64", "uint64"], "varInit":["one"], "dtype":tf.uint64, "dimension":0},
#Also doesn't work: zero, range, four, uniform10, etc inits - always fail with "No OpKernel was registered to support Op 'Assign'"
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint32_rank1", "varShapes":[[3]], "varTypes":["uint32", "uint32"], "varInit":["fixed_2_2_4"], "dtype":tf.uint32, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint32_rank2", "varShapes":[[3,4]], "varTypes":["uint32", "uint32"], "varInit":["one"], "dtype":tf.uint32, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint64_rank1", "varShapes":[[4]], "varTypes":["uint64", "uint64"], "varInit":["one"], "dtype":tf.uint64, "dimension":0},
# {"opName": "arg_max", "outName": "dtype_tests/argmax/uint64_rank2", "varShapes":[[3,4]], "varTypes":["uint64", "uint64"], "varInit":["one"], "dtype":tf.uint64, "dimension":0},
# {"opName": "cast", "outName": "dtype_tests/cast/uint32_rank1", "varShapes":[[4]], "varTypes":["int32"], "varInit":["uniform10"], "dtype":tf.int32, "dtype":tf.uint32},
# {"opName": "cast", "outName": "dtype_tests/cast/uint64_rank1", "varShapes":[[4]], "varTypes":["int32"], "varInit":["uniform10"], "dtype":tf.int32, "dtype":tf.uint64},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank1_axisNone", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2_axis0", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank1_axisNone", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["bool"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2_axis0", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["bool"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_logsumexp_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_max_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_max/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_max_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_max/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_max_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_max/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_max_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_max/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_min_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_min/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_min_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_min/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_min_dynamicaxis", "outName": "emptyReduceAxisTests/reduce_min/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank1_axisNone", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank1_axis0_keep", "varShapes":[[0]], "varTypes":["float32"], "varInit":["empty"], "axis":0, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank2_axisNone", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank2_axisNoneKeep", "varShapes":[[0,2]], "varTypes":["float64"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank2_axis0", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":False},
# {"opName": "reduce_mean", "outName": "emptyReduceAxisTests/reduce_mean/rank2_axis1Keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":None, "keepdims":True},
# {"opName": "reduce_sum", "outName": "emptyReduceAxisTests/reduce_sum/rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_sum", "outName": "emptyReduceAxisTests/reduce_sum/rank1_keep", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":True},
# {"opName": "reduce_sum", "outName": "emptyReduceAxisTests/reduce_sum/rank3", "varShapes":[[2,3,4]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# #Can't save next 2... ""No variables to save"" in TF persistor
# {"opName": "reduce_sum", "outName": "emptyReduceAxisTests/reduce_sum/rank2_empty", "varShapes":[[2,0]], "varTypes":["float32"], "varInit":["empty"], "axis":(), "keepdims":False},
# {"opName": "reduce_sum", "outName": "emptyReduceAxisTests/reduce_sum/rank2_empty_keep", "varShapes":[[0,2]], "varTypes":["float32"], "varInit":["empty"], "axis":(), "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank1_keep", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":True},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_prod", "outName": "emptyReduceAxisTests/reduce_prod/rank2_keep", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":True},
# {"opName": "reduce_logsumexp", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_logsumexp", "outName": "emptyReduceAxisTests/reduce_logsumexp/rank2", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank1", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank1_keep", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":True},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2", "varShapes":[[2,3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":False},
# {"opName": "reduce_all", "outName": "emptyReduceAxisTests/reduce_all/rank2_keep", "varShapes":[[2,3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank1", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank1_keep", "varShapes":[[3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":True},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2", "varShapes":[[2,3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":False},
# {"opName": "reduce_any", "outName": "emptyReduceAxisTests/reduce_any/rank2_keep", "varShapes":[[2,3]], "varTypes":["bool"], "varInit":["boolean"], "axis":(), "keepdims":True},
# {"opName": "reduce_min", "outName": "emptyReduceAxisTests/reduce_min/rank1", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_min", "outName": "emptyReduceAxisTests/reduce_min/rank1_keep", "varShapes":[[3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":True},
# {"opName": "reduce_max", "outName": "emptyReduceAxisTests/reduce_max/rank2", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":False},
# {"opName": "reduce_max", "outName": "emptyReduceAxisTests/reduce_max/rank2_keep", "varShapes":[[2,3]], "varTypes":["float32"], "varInit":["uniform_int5"], "axis":(), "keepdims":True},
# {"opName": "multinomial", "outName": "multinomial/logits/sample/rank1", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform_int5"], "total_count":4., "sample_shape": 5},
# {"opName": "multinomial", "outName": "multinomial/logits/sample/rank2", "varShapes":[[2, 3]], "varTypes":["float32"], "varInit":["uniform_int5"], "total_count":[4., 2], "sample_shape": 5},
# {"opName": "multinomial_with_p", "outName": "multinomial/prob/sample/rank1", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform_int5"], "total_count":4., "sample_shape": 5},
# {"opName": "multinomial_with_p", "outName": "multinomial/prob/sample/rank2", "varShapes":[[2, 3]], "varTypes":["float32"], "varInit":["uniform_int5"], "total_count":[4., 2], "sample_shape": 5},
# {"opName": "add", "outName": "ragged/add/2d", "varShapes":[[], []], "varTypes":["int32", "int32"], "varInit":["ragged2d", "one"]},
# {"opName": "identity", "outName": "ragged/identity/2d", "varShapes":[[]], "varTypes":["float32"], "varInit":["ragged2d"]},
# {"opName": "reduce_mean", "outName": "ragged/reduce_mean/2d_a0", "varShapes":[[]], "varTypes":["float32"], "varInit":["ragged2d"], "axis":0, "keepdims":False},
# {"opName": "reduce_mean", "outName": "ragged/reduce_mean/2d_a1", "varShapes":[[]], "varTypes":["float32"], "varInit":["ragged2d"], "axis":1, "keepdims":False},
# {"opName": "sqrt", "outName": "ragged/sqrt/2d", "varShapes":[[]], "varTypes":["float32"], "varInit":["ragged2d"]},
# {"opName": "strings_split", "outName": "ragged/sqrt/2d", "varShapes":[[]], "varTypes":["string"], "varInit":["string2"], "split":" "},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_int8", "varShapes":[[1]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int8", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_int8", "varShapes": [[1]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_int8", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_int8", "varShapes": [[1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_int8", "varShapes": [[1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float32_to_int8", "varShapes":[[1,1]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_bfloat16_to_int8", "varShapes": [[2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_half_to_int8", "varShapes": [[1,1]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float64_to_int8", "varShapes": [[2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int32_to_int8", "varShapes": [[1,1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int64_to_int8", "varShapes": [[1,1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float32_to_int8", "varShapes":[[1,1,1]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_bfloat16_to_int8", "varShapes": [[2,2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_half_to_int8", "varShapes": [[1,1,1]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float64_to_int8", "varShapes": [[2,2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int32_to_int8", "varShapes": [[1,1,1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int64_to_int8", "varShapes": [[1,1,1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_int8", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int8},
# {"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_rank2_uint32_to_int8", "varShapes": [[0,0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int8},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_rank3_uint32_to_int8", "varShapes": [[0, 0, 0]], "varTypes": ["uint32"], "varInit": ["empty"], "output": tf.int8},
# {"opName": "bitcast", "outName": "bitcast/from_float32_to_int16", "varShapes":[[1]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int16", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_half_to_int16", "varShapes": [[1]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_float64_to_int16", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_int32_to_int16", "varShapes": [[1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_int64_to_int16", "varShapes": [[1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float32_to_int16", "varShapes":[[1,1]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_bfloat16_to_int16", "varShapes": [[2,1]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_half_to_int16", "varShapes": [[1,1]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float64_to_int16", "varShapes": [[2,1]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int32_to_int16", "varShapes": [[1,1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int64_to_int16", "varShapes": [[1,1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float32_to_int16", "varShapes": [[1, 1, 1]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int16},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_bfloat16_to_int16", "varShapes": [[2, 1, 1]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output": tf.int16},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_half_to_int16", "varShapes": [[1, 1, 1]], "varTypes": ["half"], "varInit": ["uniform"], "output": tf.int16},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_float64_to_int16", "varShapes": [[2, 1, 1]],"varTypes": ["float64"], "varInit": ["uniform"], "output": tf.int16},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_int32_to_int16", "varShapes": [[1, 1, 1]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output": tf.int16},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_int64_to_int16", "varShapes": [[1, 1, 1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output": tf.int16},
# {"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_int16", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int16},
# {"opName": "bitcast", "outName": "bitcast/from_float32_to_int32", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int32", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_half_to_int32", "varShapes": [[2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_float64_to_int32", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_int32_to_int32", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_int64_to_int32", "varShapes": [[2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_float32_to_int32", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int32", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_half_to_int32", "varShapes": [[2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_float64_to_int32", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_int32_to_int32", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_int64_to_int32", "varShapes": [[2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_float32_to_int32", "varShapes": [[2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_bfloat16_to_int32", "varShapes": [[2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_half_to_int32", "varShapes": [[2,2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float64_to_int32", "varShapes": [[2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int32_to_int32", "varShapes": [[2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int64_to_int32", "varShapes": [[2,2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_float32_to_int32", "varShapes": [[2,2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_bfloat16_to_int32", "varShapes": [[2,2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_half_to_int32", "varShapes": [[2,2,2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float64_to_int32", "varShapes": [[2,2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int32_to_int32", "varShapes": [[2,2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int64_to_int32", "varShapes": [[2,2,2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_int32", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_int32", "varShapes":[[2]], "varTypes":["float32"], "varInit":["uniform"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int32", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_int32", "varShapes": [[2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_int32", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_int32", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_int32", "varShapes": [[2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float32_to_int32", "varShapes": [[2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_bfloat16_to_int32", "varShapes": [[2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_half_to_int32", "varShapes": [[2,2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_float64_to_int32", "varShapes": [[2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int32_to_int32", "varShapes": [[2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank2_int64_to_int32", "varShapes": [[2,2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float32_to_int32", "varShapes": [[2,2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_bfloat16_to_int32", "varShapes": [[2,2,2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_half_to_int32", "varShapes": [[2,2,2]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_float64_to_int32", "varShapes": [[2,2,2]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int32_to_int32", "varShapes": [[2,2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int32},
# {"opName": "bitcast", "outName": "bitcast/from_rank3_int64_to_int32", "varShapes": [[2,2,2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_int32", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int32},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_int64", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_int64", "varShapes": [[4]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_int64", "varShapes": [[4]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int64},
#{"opName": "bitcast_float64", "outName": "bitcast/from_float64_to_int64", "varShapes": [[1]], "varTypes": ["float64"], "varInit": ["uniform_int10"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_int64", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_int64", "varShapes": [[1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_float32_to_int64", "varShapes": [[2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_bfloat16_to_int64", "varShapes": [[4,4]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_half_to_int64", "varShapes": [[4,4]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.int64},
#{"opName": "bitcast_float64", "outName": "bitcast/from_rank2_float64_to_int64", "varShapes": [[1,1]], "varTypes": ["float64"], "varInit": ["uniform_int10"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_int32_to_int64", "varShapes": [[2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank2_int64_to_int64", "varShapes": [[1,1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_float32_to_int64", "varShapes": [[2, 2,2]],"varTypes": ["float32"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_bfloat16_to_int64", "varShapes": [[4, 4,4]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_half_to_int64", "varShapes": [[4, 4,4]], "varTypes": ["half"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_float64_to_int64", "varShapes": [[1, 1,1]], "varTypes": ["float64"], "varInit": ["uniform"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_int32_to_int64", "varShapes": [[2, 2,2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_rank3_int64_to_int64", "varShapes": [[1, 1,1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output": tf.int64},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_int64", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.int64},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_uint8", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_uint8", "varShapes": [[4]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_uint8", "varShapes": [[4]], "varTypes": ["half"], "varInit": ["uniform"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_uint8", "varShapes": [[1]], "varTypes": ["float64"], "varInit": ["uniform"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_uint8", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_uint8", "varShapes": [[1]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_uint8", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output":tf.uint8},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_uint32", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_uint32", "varShapes": [[2]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_uint32", "varShapes": [[2]], "varTypes": ["half"],"varInit": ["uniform"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_uint32", "varShapes": [[1]], "varTypes": ["float64"],"varInit": ["uniform"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_uint32", "varShapes": [[1]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_uint32", "varShapes": [[1]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_uint16", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.uint32},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_uint64", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.uint64},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_uint64", "varShapes": [[4]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.uint64},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_uint64", "varShapes": [[4]], "varTypes": ["half"],"varInit": ["uniform"], "output": tf.uint64},
#{"opName": "bitcast_float64", "outName": "bitcast/from_float64_to_uint64", "varShapes": [[1]], "varTypes": ["int64"],"varInit": ["uniform_int10"], "output": tf.uint64},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_uint64", "varShapes": [[2]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.uint64},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_uint64", "varShapes": [[1]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.uint64},
# {"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_uint16", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.uint32},
# {"opName": "bitcast", "outName": "bitcast/from_float32_to_uint16", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_uint16", "varShapes": [[4]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/from_half_to_uint16", "varShapes": [[4]], "varTypes": ["half"],"varInit": ["uniform"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/from_float64_to_uint16", "varShapes": [[1]], "varTypes": ["float64"],"varInit": ["uniform"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/from_int32_to_uint16", "varShapes": [[2]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/from_int64_to_uint16", "varShapes": [[1]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.uint16},
# {"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_uint16", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.uint16},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_bfloat16", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_bfloat16", "varShapes": [[2]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_bfloat16", "varShapes": [[2]], "varTypes": ["half"],"varInit": ["uniform"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_bfloat16", "varShapes": [[2]], "varTypes": ["float64"],"varInit": ["uniform"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_bfloat16", "varShapes": [[2]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_bfloat16", "varShapes": [[2]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_bfloat16", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.bfloat16},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_half", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_half", "varShapes": [[2]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_half", "varShapes": [[2]], "varTypes": ["half"],"varInit": ["uniform"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_half", "varShapes": [[2]], "varTypes": ["float64"],"varInit": ["uniform"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_half", "varShapes": [[2]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_half", "varShapes": [[2]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_half", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.half},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_float32", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_float32", "varShapes": [[2]], "varTypes": ["bfloat16"], "varInit": ["uniform"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_float32", "varShapes": [[2]], "varTypes": ["half"], "varInit": ["uniform"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_float32", "varShapes": [[2]], "varTypes": ["float64"], "varInit": ["uniform"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_float32", "varShapes": [[2]], "varTypes": ["int32"],"varInit": ["uniform_int2"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_float32", "varShapes": [[2]], "varTypes": ["int64"],"varInit": ["uniform_int2"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_float32", "varShapes": [[0]],"varTypes": ["uint32"], "varInit": ["empty"], "output": tf.float32},
#{"opName": "bitcast", "outName": "bitcast/from_float32_to_float64", "varShapes": [[2]], "varTypes": ["float32"], "varInit": ["uniform"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/from_bfloat16_to_float64", "varShapes": [[4]], "varTypes": ["bfloat16"],"varInit": ["uniform"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/from_half_to_float64", "varShapes": [[4]], "varTypes": ["half"], "varInit": ["uniform"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/from_float64_to_float64", "varShapes": [[1]], "varTypes": ["float64"], "varInit": ["uniform"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/from_int32_to_float64", "varShapes": [[2]], "varTypes": ["int32"], "varInit": ["uniform_int2"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/from_int64_to_float64", "varShapes": [[2]], "varTypes": ["int64"], "varInit": ["uniform_int2"], "output": tf.float64},
#{"opName": "bitcast", "outName": "bitcast/emptyArrayTest/from_uint32_to_float64", "varShapes": [[0]], "varTypes": ["uint32"], "varInit": ["empty"], "output": tf.float64},
# {"opName": "bitwise_and", "outName": "bitwise_and/rank2_int32", "varShapes":[[1,2], [1,2]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_and", "outName": "bitwise_and/rank2_int64", "varShapes": [[1, 2], [1, 2]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_and", "outName": "bitwise_and/rank3_int32", "varShapes":[[1,1, 2], [1,1, 2]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_and", "outName": "bitwise_and/rank3_int64", "varShapes": [[1, 1, 2], [1, 1, 2]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_or", "outName": "bitwise_or/rank2_int32", "varShapes":[[1,2], [1,2]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_or", "outName": "bitwise_or/rank2_int64", "varShapes": [[1, 2], [1, 2]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_or", "outName": "bitwise_or/rank3_int32", "varShapes":[[1,1, 2], [1,1, 2]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_or", "outName": "bitwise_or/rank3_int64", "varShapes": [[1, 1, 2], [1, 1, 2]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/rank2_int32", "varShapes":[[0,0], [0,0]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "empty"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/rank2_int64", "varShapes": [[0,0], [0,0]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/partial_rank2_int64", "varShapes": [[1,0], [0,0]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/rank3_int32", "varShapes": [[0, 0, 0], [0, 0, 0]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/partial_rank3_int32", "varShapes": [[0, 1, 0], [1, 0, 0]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_or", "outName": "bitwise_or/emptyArrayTest/rank3_int64", "varShapes": [[0, 0, 0], [0, 0, 0]], "varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/rank2_int32", "varShapes":[[1,2], [1,2]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "uniform_int2"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/rank2_int64", "varShapes": [[1,2], [1,2]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_xor", "outName": "bitwise_xor/partial_rank2_int64", "varShapes": [[1,0], [0,0]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/rank3_int32", "varShapes": [[1, 1, 2], [1, 1, 2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int2"]},
# {"opName": "bitwise_xor", "outName": "bitwise_xor/partial_rank3_int32", "varShapes": [[1, 0, 2], [0, 0, 2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int2"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/rank3_int64", "varShapes": [[1, 1, 2], [1, 1, 2]], "varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "uniform_int2"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/emptyArrayTest/rank2_int32", "varShapes":[[0,0], [0,0]], "varTypes":["int32", "int32"], "varInit":["uniform_int10", "empty"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/emptyArrayTest/rank2_int64", "varShapes": [[0,0], [0,0]],"varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/emptyArrayTest/rank3_int32", "varShapes": [[0, 0, 0], [0, 0, 0]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "bitwise_xor", "outName": "bitwise_xor/emptyArrayTest/rank3_int64", "varShapes": [[0, 0, 0], [0, 0, 0]], "varTypes": ["int64", "int64"], "varInit": ["uniform_int10", "empty"]},
#{"opName": "is_non_decreasing", "outName": "is_non_decreasing/rank2_float32", "varShapes":[[1,2]], "varTypes":["float32"], "varInit":["uniform"]},
#{"opName": "is_non_decreasing", "outName": "is_non_decreasing/rank3_float32", "varShapes": [[1, 1, 2]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "is_non_decreasing", "outName": "is_non_decreasing/rank2_float32", "varShapes":[[1,2]], "varTypes":["float32"], "varInit":["stdnormal"]},
#{"opName": "is_non_decreasing", "outName": "is_non_decreasing/rank3_float32", "varShapes": [[1, 1, 2]], "varTypes": ["float32"], "varInit": ["stdnormal"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/rank2_float32", "varShapes":[[1,1]], "varTypes":["float32"], "varInit":["uniform"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/rank3_float32", "varShapes": [[1, 1, 2]],"varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/rank2_float32", "varShapes":[[1,1]], "varTypes":["float32"], "varInit":["stdnormal"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/rank3_float32", "varShapes": [[1, 1, 2]],"varTypes": ["float32"], "varInit": ["stdnormal"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/emptyArrayTest/rank1_float32", "varShapes": [[0]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/emptyArrayTest/rank2_float32", "varShapes": [[0, 0]], "varTypes": ["float32"], "varInit": ["uniform"]},
# {"opName": "is_strictly_increasing", "outName": "is_strictly_increasing/emptyArrayTest/rank3_float32", "varShapes": [[0, 0, 0]],"varTypes": ["float32"], "varInit": ["stdnormal"]},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_float32", "varShapes":[[1,2]], "varTypes":["float32"], "varInit":["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_float64", "varShapes": [[1, 2]], "varTypes": ["float64"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_half", "varShapes": [[1, 2]], "varTypes": ["half"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_float32_with_axis", "varShapes": [[1, 2],[]],"varTypes": ["float32","int32"], "varInit": ["uniform","zero"], "axis":1},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_float64_with_axis", "varShapes": [[1, 2],[]],"varTypes": ["float64","int32"], "varInit": ["uniform","zero"], "axis":1},
#{"opName": "log_softmax", "outName": "log_softmax/rank2_half_with_axis", "varShapes": [[1, 2],[]], "varTypes": ["half","int32"], "varInit": ["uniform","zero"],"axis":1},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_float32_with_axis", "varShapes": [[1, 2,3], []],"varTypes": ["float32", "int32"], "varInit": ["uniform", "zero"], "axis": 0},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_float64_with_axis", "varShapes": [[1, 2,3], []],"varTypes": ["float64", "int32"], "varInit": ["uniform", "zero"], "axis": 1},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_half_with_axis", "varShapes": [[1, 2, 3], []],"varTypes": ["half", "int32"], "varInit": ["uniform", "zero"], "axis": 2},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_float32", "varShapes": [[1, 2, 3],], "varTypes": ["float32"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_float64", "varShapes": [[1, 2, 3]], "varTypes": ["float64"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank3_half", "varShapes": [[1, 2, 3]], "varTypes": ["half"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank4_float32", "varShapes": [[1, 2, 3, 2]], "varTypes": ["float32"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank4_float64", "varShapes": [[1, 2, 3, 2]], "varTypes": ["float64"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/rank4_half", "varShapes": [[1, 2, 3, 2]], "varTypes": ["half"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/emptyArrayTest/rank4_half", "varShapes": [[0, 0, 0,0]], "varTypes": ["half"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/emptyArrayTest/rank4_float32", "varShapes": [[0, 0, 0, 0]], "varTypes": ["float32"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/emptyArrayTest/rank4_float64", "varShapes": [[0, 0, 0, 0]], "varTypes": ["float64"], "varInit": ["uniform"], "axis":0},
#{"opName": "log_softmax", "outName": "log_softmax/emptyArrayTest/partial_rank4_float64", "varShapes": [[0, 1, 0, 0]], "varTypes": ["float32"], "varInit": ["uniform"], "axis":0},
# {"opName": "log_softmax", "outName": "log_softmax/partial_rank3_float64", "varShapes": [[1, 0, 0]], "varTypes": ["float64"], "varInit": ["uniform"],"axis":0},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/float32", "varShapes": [[3, 4],[3],[]], "varTypes": ["float32","float32","int32"], "varInit": ["uniform","uniform","zero"]},
#{"opName": "non_max_suppression_v2", "outName": "non_max_suppression_v2/float32", "varShapes": [[3, 4], [3], []], "varTypes": ["float32","float32","int32"], "varInit": ["uniform","uniform","uniform_int10"]},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/float16", "varShapes": [[3, 4], [3], []],"varTypes": ["float16", "float16", "int32"], "varInit": ["uniform", "uniform", "uniform_int10"]},
#{"opName": "non_max_suppression_v2", "outName": "non_max_suppression_v2/float16", "varShapes": [[3, 4], [3], []],"varTypes": ["float16", "float16", "int32"], "varInit": ["uniform", "uniform", "uniform_int10"]},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/float32_with_thresholds", "varShapes": [[3, 4], [3], []], "varTypes": ["float32", "float32", "int32"], "varInit": ["uniform", "uniform", "zero"], "iou_threshold": 0.0, "score_threshold": 0.0},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/emptyArrayTest/float32_with_thresholds", "varShapes": [[0, 4], [0], []], "varTypes": ["float32", "float32", "int32"], "varInit": ["uniform", "uniform", "zero"], "iou_threshold": 0.2, "score_threshold": 0.5},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/emptyArrayTest/float16_with_thresholds", "varShapes": [[0, 4], [0], []], "varTypes": ["float16", "float16", "int32"], "varInit": ["uniform", "uniform", "zero"], "iou_threshold": 0.5, "score_threshold": 0.5},
#{"opName": "non_max_suppression", "outName": "non_max_suppression/emptyArrayTest/float32_with_thresholds", "varShapes": [[0, 4], [0], []], "varTypes": ["float16", "float16", "int32"], "varInit": ["uniform", "uniform", "one"], "iou_threshold": 0.4, "score_threshold": 0.4},
# {"opName": "betainc", "outName": "betainc/rank1_float32", "varShapes":[[4],[4],[4]], "varTypes":["float32","float32","float32"], "varInit":["uniform_0_1","uniform_0_1","uniform_0_1"]},
# {"opName": "betainc", "outName": "betainc/rank2_float32", "varShapes": [[3,4],[3,4],[3,4]], "varTypes": ["float32","float32","float32"], "varInit": ["uniform_0_1","uniform_0_1","uniform_0_1"]},
# {"opName": "betainc", "outName": "betainc/rank1_float64", "varShapes": [[4], [4], [4]], "varTypes": ["float64", "float64", "float64"], "varInit": ["uniform_0_1", "uniform_0_1", "uniform_0_1"]},
# {"opName": "betainc", "outName": "betainc/rank2_float64", "varShapes": [[3, 4], [3, 4], [3, 4]], "varTypes": ["float64", "float64", "float64"], "varInit": ["uniform_0_1", "uniform_0_1", "uniform_0_1"]},
# {"opName": "betainc", "outName": "betainc/emptyArrayTest/float64", "varShapes": [[0], [0], [0]], "varTypes": ["float64", "float64", "float64"], "varInit": ["empty", "empty", "empty"]},
# {"opName": "betainc", "outName": "betainc/emptyArrayTest/float32", "varShapes": [[0], [0], [0]], "varTypes": ["float64", "float64", "float64"], "varInit": ["empty", "empty", "empty"]},
#{"opName": "matrix_band_part", "outName": "matrix_band_part/float32", "varShapes": [[3,4]], "varTypes": ["float32"], "varInit": ["uniform"],"num_lower":1, "num_upper":-1},
#{"opName": "matrix_band_part", "outName": "matrix_band_part/float64", "varShapes": [[3, 4]], "varTypes": ["float64"], "varInit": ["uniform"], "num_lower": 1, "num_upper": -1},
#{"opName": "matrix_band_part", "outName": "matrix_band_part/float32_reverse_limits", "varShapes": [[3, 4]], "varTypes": ["float32"], "varInit": ["uniform"], "num_lower": 0, "num_upper": 1},
#{"opName": "multinomial", "outName": "multinomial/float32", "varShapes": [[3,3]], "varTypes": ["float32"], "varInit": ["uniform"], "num_samples":3},
#{"opName": "multinomial", "outName": "multinomial/float64", "varShapes": [[3, 4], [3, 4]], "varTypes": ["float64", "float64"], "varInit": ["uniform10", "uniform"]},
#{"opName": "multinomial", "outName": "multinomial/emptyArrayTest/float32", "varShapes": [[0, 0], [3, 4]], "varTypes": ["float32", "float32"], "varInit": ["empty", "uniform"]},
#{"opName": "polygamma", "outName": "polygamma/float32", "varShapes": [[3,4],[3,4]], "varTypes": ["float32","float32"], "varInit": ["uniform10","uniform"]},
#{"opName": "polygamma", "outName": "polygamma/float64", "varShapes": [[3, 4], [3, 4]], "varTypes": ["float64", "float64"], "varInit": ["uniform10", "uniform"]},
#{"opName": "polygamma", "outName": "polygamma/emptyArrayTest/float32", "varShapes": [[0, 0], [3, 4]], "varTypes": ["float32", "float32"], "varInit": ["empty", "uniform"]},
#{"opName": "lgamma", "outName": "lgamma/float32", "varShapes": [[3,4]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "lgamma", "outName": "lgamma/float64", "varShapes": [[3, 4]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "lgamma", "outName": "lgamma/emptyArrayTest/float32", "varShapes": [[0, 0]], "varTypes": ["float32"], "varInit": ["empty"]},
#{"opName": "igamma", "outName": "igamma/float32", "varShapes": [[3,4],[3,4]], "varTypes": ["float32","float32"], "varInit": ["uniform10","uniform"]},
#{"opName": "igamma", "outName": "igamma/float64", "varShapes": [[3, 4], [3, 4]], "varTypes": ["float64", "float64"], "varInit": ["uniform10", "uniform"]},
#{"opName": "igamma", "outName": "igamma/emptyArrayTest/float32", "varShapes": [[0, 0], [0, 0]], "varTypes": ["float32", "float32"], "varInit": ["empty", "uniform"]},
#{"opName": "igammac", "outName": "igammac/float32", "varShapes": [[3, 4], [3, 4]], "varTypes": ["float32", "float32"], "varInit": ["uniform10", "uniform"]},
#{"opName": "igammac", "outName": "igammac/float64", "varShapes": [[3, 4], [3, 4]], "varTypes": ["float64", "float64"], "varInit": ["uniform10", "uniform"]},
#{"opName": "igammac", "outName": "igammac/emptyArrayTest/float32", "varShapes": [[0, 0], [0, 0]], "varTypes": ["float32", "float32"], "varInit": ["empty", "uniform"]},
#{"opName": "digamma", "outName": "digamma/float32", "varShapes": [[3, 4]], "varTypes": ["float32"], "varInit": ["uniform10"]},
#{"opName": "digamma", "outName": "digamma/float64", "varShapes": [[3, 4]], "varTypes": ["float64"], "varInit": ["uniform10"]},
#{"opName": "digamma", "outName": "digamma/emptyArrayTest/float32", "varShapes": [[0, 0]], "varTypes": ["float32"], "varInit": ["empty"]},
#{"opName": "lgamma", "outName": "lgamma/float32", "varShapes": [[3, 4]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "lgamma", "outName": "lgamma/float64", "varShapes": [[3, 4]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "lgamma", "outName": "lgamma/emptyArrayTest/float32", "varShapes": [[0, 0]], "varTypes": ["float32"], "varInit": ["empty"]},
#{"opName": "random_crop", "outName": "random_crop/rank3_float32", "varShapes": [[8, 8, 3], [3]], "varTypes": ["float32", "int32"], "varInit": ["stdnormal", "uniform_int2"]},
#{"opName": "random_crop", "outName": "random_crop/rank3_float64", "varShapes": [[8, 8, 3], [3]], "varTypes": ["float64", "int32"], "varInit": ["stdnormal", "uniform_int2"]},
#{"opName": "random_crop", "outName": "random_crop/rank3_float64", "varShapes": [[8, 8, 3], [3]], "varTypes": ["float64", "int32"], "varInit": ["stdnormal", "uniform_int2"]},
#{"opName": "roll", "outName": "roll/rank1_float32", "varShapes": [[4]],"varTypes": ["float32"], "varInit": ["uniform"], "shift":2, "axis":0},
#{"opName": "roll", "outName": "roll/rank1_float64", "varShapes": [[4]], "varTypes": ["float64"], "varInit": ["uniform"], "shift": 2, "axis": 0},
#{"opName": "roll", "outName": "roll/rank2_float32", "varShapes": [[2,4]], "varTypes": ["float32"], "varInit": ["uniform"], "shift": 2, "axis": 0},
#{"opName": "roll", "outName": "roll/rank2_float32_zeroshift", "varShapes": [[2, 4]], "varTypes": ["float32"], "varInit": ["uniform"], "shift": 0, "axis": 1},
#{"opName": "roll", "outName": "roll/rank2_float32_axis", "varShapes": [[4,5]],"varTypes": ["float32"], "varInit": ["uniform"], "shift":2, "axis":1},
#{"opName": "roll", "outName": "roll/rank2_float64_axis", "varShapes": [[4,5]], "varTypes": ["float64"], "varInit": ["uniform"], "shift": 2, "axis": 1},
#{"opName": "roll", "outName": "roll/rank3_float32_axis", "varShapes": [[4, 5, 3]], "varTypes": ["float32"], "varInit": ["uniform"], "shift": 2, "axis": 2},
#{"opName": "roll", "outName": "roll/rank3_float64_axis", "varShapes": [[4, 5, 4]], "varTypes": ["float64"], "varInit": ["uniform"], "shift": 2, "axis": 1},
#{"opName": "roll", "outName": "roll/rank3_int32_axis", "varShapes": [[4, 5, 4]], "varTypes": ["int32"], "varInit": ["uniform_int10"], "shift": 2, "axis": 1},
#{"opName": "roll", "outName": "roll/rank4_float32_axis", "varShapes": [[4, 5, 3, 4]], "varTypes": ["float32"], "varInit": ["uniform"], "shift": 2, "axis": 3},
#{"opName": "roll", "outName": "roll/rank4_float32_axis", "varShapes": [[4, 5, 3, 4]], "varTypes": ["half"], "varInit": ["uniform"], "shift": 2, "axis": 2},
#{"opName": "roll", "outName": "roll/int32_long_axis", "varShapes": [[4, 5, 3, 4]], "varTypes": ["int64"], "varInit": ["uniform_int10"], "shift": 2, "axis": 3},
#{"opName": "roll", "outName": "roll_test/rank1_float32", "varShapes": [[1024]],"varTypes": ["float32"], "varInit": ["uniform"], "shift":2, "axis":0},
#{"opName": "roll", "outName": "roll_test/rank2_float32", "varShapes": [[102,204]], "varTypes": ["float32","float32"], "varInit": ["uniform","uniform"], "shift": 0, "axis": 1},
#{"opName": "roll", "outName": "roll_test/rank3_float32", "varShapes": [[102, 204, 102]], "varTypes": ["float32", "float32", "float32"], "varInit": ["uniform", "uniform","uniform"], "shift": 0, "axis": 1},
#{"opName": "roll", "outName": "roll_test/rank4_float32", "varShapes": [[20, 20, 30, 20]], "varTypes": ["float32", "float32", "float32","float32"], "varInit": ["uniform", "uniform", "uniform","uniform"],"shift": 0, "axis": 1},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank1_int8", "varShapes": [[4]], "varTypes": ["int8"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank1_int16", "varShapes": [[4]], "varTypes": ["int16"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank1_int32", "varShapes": [[4]],"varTypes": ["int32"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank2_int8", "varShapes": [[4,2]], "varTypes": ["int8"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank2_int16", "varShapes": [[4,2]], "varTypes": ["int16"],"varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank2_int32", "varShapes": [[4,2]], "varTypes": ["int32"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank3_int8", "varShapes": [[4, 2, 3]], "varTypes": ["int8"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank3_int16", "varShapes": [[4, 2, 3]], "varTypes": ["int16"], "varInit": ["uniform_int10"]},
#{"opName": "toggle_bits", "outName": "toggle_bits/rank2_int32", "varShapes": [[4, 2, 3]], "varTypes": ["int32"], "varInit": ["uniform_int10"]},
# {"opName": "right_shift", "outName": "right_shift/rank1_int32", "varShapes": [[4],[4]],"varTypes": ["int32","int32"], "varInit": ["uniform_int10","uniform_int10"]},
# {"opName": "right_shift", "outName": "right_shift/rank2_int32", "varShapes": [[4,2],[4,2]], "varTypes": ["int32","int32"], "varInit": ["uniform_int10","uniform_int10"]},
# {"opName": "right_shift", "outName": "right_shift/rank2_int32", "varShapes": [[4, 2, 3],[4,2,3]], "varTypes": ["int32","int32"], "varInit": ["uniform_int10","uniform_int10"]},
#{"opName": "right_shift", "outName": "right_shift/rank3_int8", "varShapes": [[4, 2, 3], [4, 2, 3]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
#{"opName": "right_shift", "outName": "right_shift/rank4_int16", "varShapes": [[4, 1, 1, 2], [4, 1, 1, 2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
#{"opName": "left_shift", "outName": "left_shift/rank1_int32", "varShapes": [[4], [4]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10","uniform_int10"]},
#{"opName": "left_shift", "outName": "left_shift/rank2_int32", "varShapes": [[4, 2], [4, 2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10","uniform_int10"]},
#{"opName": "left_shift", "outName": "left_shift/rank2_int32", "varShapes": [[4, 2, 3], [4, 2, 3]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10","uniform_int10"]},
#{"opName": "left_shift", "outName": "left_shift/rank3_int8", "varShapes": [[4, 2, 3], [4, 2, 3]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
#{"opName": "left_shift", "outName": "left_shift/rank4_int16", "varShapes": [[4, 1, 1, 2], [4, 1, 1, 2]], "varTypes": ["int32", "int32"], "varInit": ["uniform_int10", "uniform_int10"]},
#{"opName": "random_gamma", "outName": "random_gamma/rank1_float32", "varShapes": [[4], [4]], "varTypes": ["int32", "float32"], "varInit": ["uniform_int10","uniform"], "seeds":1},
#{"opName": "random_poisson", "outName": "random_poisson/rank1_float32", "varShapes": [[4], [4]], "varTypes": ["int32", "float32"], "varInit": ["uniform_int10", "uniform"]},
#{"opName": "random_shuffle", "outName": "random_shuffle/rank1_float32", "varShapes": [[4]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "fused_batch_norm", "outName": "fused_batch_norm/float16_nhcw", "varShapes": [[1, 2, 3, 4], [4], [4]], "varTypes": ["float16", "float32", "float32"], "varInit": ["uniform", "uniform_0_1", "uniform_0_1"], "epsilon": 0.5, "data_format": "NHWC"},
#{"opName": "fused_batch_norm", "outName": "fused_batch_norm/float32_nhwc_restr", "varShapes": [[1, 2, 3, 4], [4], [4]], "varTypes": ["float32", "float32", "float32"], "varInit": ["uniform_0_1", "uniform_0_1", "uniform_0_1"], "epsilon": 0.5 , "data_format": "NHWC"},
# The CPU implementation of FusedBatchNorm only supports NHWC tensor format for now.
#{"opName": "fused_batch_norm", "outName": "fused_batch_norm/float32_nchw", "varShapes": [[1, 2, 3, 4], [2], [2]], "varTypes": ["float32", "float32", "float32"], "varInit": ["uniform", "uniform", "uniform"], "epsilon": 0.5, "data_format": "NCHW"},
#{"opName": "fused_batch_norm", "outName": "fused_batch_norm/float32_nhwc", "varShapes": [[1, 2, 3, 4], [4], [4]], "varTypes": ["float32", "float32", "float32"], "varInit": ["uniform", "uniform", "uniform"], "epsilon": 0.0, "data_format": "NHWC"},
# hsv_to_rgb - no registered kernels for half, float16 types.
# {"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float16", "varShapes": [[1, 2, 3]], "varTypes": ["half"], "varInit": ["uniform"]},
# {"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float16", "varShapes": [[1, 2, 3]], "varTypes": ["float16"], "varInit": ["uniform"]},
#{"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float32_1", "varShapes": [[1, 1, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "hsv_to_rgb", "outName": "hsv_to_rgb/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "rgb_to_hsv", "outName": "rgb_to_hsv/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "rgb_to_hsv", "outName": "rgb_to_hsv/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "rgb_to_hsv", "outName": "rgb_to_hsv/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "rgb_to_hsv", "outName": "rgb_to_hsv/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "yiq_to_rgb", "outName": "yiq_to_rgb/float32_1", "varShapes": [[1, 1, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "yiq_to_rgb", "outName": "yiq_to_rgb/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "yiq_to_rgb", "outName": "yiq_to_rgb/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "yiq_to_rgb", "outName": "yiq_to_rgb/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "yiq_to_rgb", "outName": "yiq_to_rgb/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "rgb_to_yiq", "outName": "rgb_to_yiq/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "rgb_to_yiq", "outName": "rgb_to_yiq/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "rgb_to_yiq", "outName": "rgb_to_yiq/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "rgb_to_yiq", "outName": "rgb_to_yiq/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "rgb_to_grayscale", "outName": "rgb_to_grayscale/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "rgb_to_grayscale", "outName": "rgb_to_grayscale/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "rgb_to_grayscale", "outName": "rgb_to_grayscale/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "rgb_to_grayscale", "outName": "rgb_to_grayscale/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
# {"opName": "yuv_to_rgb", "outName": "yuv_to_rgb/float32_1", "varShapes": [[1, 1, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
# {"opName": "yuv_to_rgb", "outName": "yuv_to_rgb/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
# {"opName": "yuv_to_rgb", "outName": "yuv_to_rgb/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
# {"opName": "yuv_to_rgb", "outName": "yuv_to_rgb/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
# {"opName": "yuv_to_rgb", "outName": "yuv_to_rgb/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "rgb_to_yuv", "outName": "rgb_to_yuv/float32", "varShapes": [[1, 2, 3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "rgb_to_yuv", "outName": "rgb_to_yuv/float64", "varShapes": [[2, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform"]},
#{"opName": "rgb_to_yuv", "outName": "rgb_to_yuv/emptyArrayTest/float64", "varShapes": [[0, 4, 3]], "varTypes": ["float64"], "varInit": ["empty"]},
#{"opName": "rgb_to_yuv", "outName": "rgb_to_yuv/float64_from0_to1", "varShapes": [[5, 4, 3]], "varTypes": ["float64"], "varInit": ["uniform_0_1"]},
#{"opName": "lu", "outName": "lu/float32_rank2", "varShapes": [[3,3]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "lu", "outName": "lu/float32_rank3", "varShapes": [[2,2,2]], "varTypes": ["float32"], "varInit": ["uniform"]},
#{"opName": "lu", "outName": "lu/emptyArrayTest/float32", "varShapes": [[0, 2, 2]], "varTypes": ["float32"],"varInit": ["empty"]},
#{"opName": "lu", "outName": "lu/float32_rank3_returns_int64", "varShapes": [[2,2,2]], "varTypes": ["float32"], "varInit": ["uniform"], "output_idx_type": "int64"}
# {"opName": "triangular_solve", "outName": "triangular_solve/float32_rank2", "varShapes": [[3,3],[3,3]], "varTypes": ["float32","float32"], "varInit": ["uniform","uniform"], "lower":False,"adjoint":True},
# {"opName": "triangular_solve", "outName": "triangular_solve/float32_rank3", "varShapes": [[2,2,2],[2,2,2]], "varTypes": ["float32","float32"], "varInit": ["uniform","uniform"], "lower":False,"adjoint":False},
#{"opName": "triangular_solve", "outName": "triangular_solve/float64_rank2", "varShapes": [[3, 3], [3, 3]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"], "lower": False, "adjoint": True},
#{"opName": "triangular_solve", "outName": "triangular_solve/float64_rank3", "varShapes": [[2, 2, 2], [2, 2, 2]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"], "lower": False, "adjoint": False},
# {"opName": "triangular_solve", "outName": "triangular_solve/emptyArrayTest/float32", "varShapes": [[0, 2, 2],[0,2,2]], "varTypes": ["float32","float32"],"varInit": ["empty","empty"], "lower":True,"adjoint":True},
# {"opName": "triangular_solve", "outName": "triangular_solve/float32_rank3_returns_int64", "varShapes": [[2,2,2],[2,2,2]], "varTypes": ["float32","float32"], "varInit": ["uniform","uniform"], "lower":True,"adjoint":True}
# {"opName": "lstsq", "outName": "lstsq/float32_rank2", "varShapes": [[3,3],[3,3]], "varTypes": ["float32","float32"], "varInit": ["uniform","uniform"], "l2_regularizer":0.1,"fast":True},
#{"opName": "linear_solve", "outName": "linear_solve/float32_rank2", "varShapes": [[3, 3], [3, 3]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"], "adjoint": True},
#{"opName": "linear_solve", "outName": "linear_solve/float32_rank3", "varShapes": [[2, 2, 2], [2, 2, 2]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"], "adjoint": False},
#{"opName": "linear_solve", "outName": "linear_solve/float64_rank2", "varShapes": [[3, 3], [3, 3]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"], "adjoint": True},
#{"opName": "linear_solve", "outName": "linear_solve/float64_rank3", "varShapes": [[2, 2, 2], [2, 2, 2]], "varTypes": ["float64", "float64"], "varInit": ["uniform", "uniform"], "adjoint": False},
#{"opName": "linear_solve", "outName": "linear_solve/emptyArrayTest/float32", "varShapes": [[0, 2, 2], [0, 2, 2]], "varTypes": ["float32", "float32"], "varInit": ["empty", "empty"], "adjoint": True},
#{"opName": "linear_solve", "outName": "linear_solve/float32_rank3_returns_int64", "varShapes": [[2, 2, 2], [2, 2, 2]], "varTypes": ["float32", "float32"], "varInit": ["uniform", "uniform"], "adjoint": True}
]
'''
Ops requiring tests: (note that some of these might not support 0 shapes)
*assign
assign_add, assign_sub
batch_to_space
boolean_mask
broadcast_dynamic_shape
broadcast_static_shape
broadcast_to
clip_by_average_norm
clip_by_norm
clip_by_value
dynamic_partition
dynamic_stitch
edit_distance
eye
floormod
gather_nd
meshgrid
norm
no_op
one_hot
pad
parallel_stack
reverse_sequence
roll
*scatter_* (nd, nd_add, nd_sub, nd_update)
sequence_mask
*split (doesn't support num_or_size_splits=0?)
tile
unique
tf.linalg:
det
diag
diag_part
inv
logdet
tensor_diag
tensor_diag_part
trace
tf.math:
angle
atan2
bincount
erf
*invert_permutation
in_top_k
*scalar_mul
*segment_* (max, mean, min, prod, sum)
*unsorted_segment_* (max, mean, min, prod, sqrt_n, sum)
xdivy
xlogy
zeta
'''
for op in ops:
tf.compat.v1.reset_default_graph()
print("Running " + str(op))
test = OpTest(seed=19, op=op)
opName = op["opName"]
varShapes = op.get("varShapes")
varTypes = op.get("varTypes")
varInit = op.get("varInit")
phShapes = op.get("phShapes")
phTypes = op.get("phTypes")
phInit = op.get("phInit")
opCreator = OpCreator(op)
vars = test.createVars(varShapes, varTypes, varInit)
#ph = test.createPlaceholders(phShapes, phTypes, phInit)
opCreator.setVars(vars)
out = opCreator.execute(opName)
print(out)
# Run and persist
testName = op["outName"]
tfp = TensorFlowPersistor(save_dir=testName)
tfp.set_placeholders([]) \
.set_output_tensors(out) \
.set_test_data(test.get_test_data()) \
.set_verbose(True) \
.build_save_frozen_graph()
print("TF version: " + tf.version.VERSION)
tf.compat.v1.disable_eager_execution()
if __name__ == '__main__':
test_mathtransform()
| 149.103814
| 357
| 0.622641
| 41,505
| 351,885
| 5.080424
| 0.024744
| 0.076116
| 0.049098
| 0.039196
| 0.950997
| 0.926758
| 0.897004
| 0.853701
| 0.79684
| 0.753157
| 0
| 0.060676
| 0.107814
| 351,885
| 2,359
| 358
| 149.16702
| 0.610976
| 0.931884
| 0
| 0.315789
| 0
| 0
| 0.009591
| 0
| 0
| 0
| 0
| 0.000424
| 0
| 1
| 0.092105
| false
| 0
| 0.078947
| 0.013158
| 0.25
| 0.065789
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
8006dc561e30c89e84c6012cffe2c7a4a817c827
| 616
|
py
|
Python
|
installation_files/uninstall_root.py
|
drforse/search_images_online_context_menu
|
26f2df2864d8c3c4ad24d421fb4e58c4985495a2
|
[
"MIT"
] | null | null | null |
installation_files/uninstall_root.py
|
drforse/search_images_online_context_menu
|
26f2df2864d8c3c4ad24d421fb4e58c4985495a2
|
[
"MIT"
] | null | null | null |
installation_files/uninstall_root.py
|
drforse/search_images_online_context_menu
|
26f2df2864d8c3c4ad24d421fb4e58c4985495a2
|
[
"MIT"
] | null | null | null |
import winreg
def main():
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online\shell\Google\command")
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online\shell\Yandex\command")
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online\shell\Google")
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online\shell\Yandex")
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online\shell")
winreg.DeleteKey(winreg.HKEY_CLASSES_ROOT, r"*\shell\Search Image Online")
if __name__ == "__main__":
main()
| 41.066667
| 99
| 0.762987
| 85
| 616
| 5.294118
| 0.211765
| 0.2
| 0.28
| 0.333333
| 0.926667
| 0.926667
| 0.926667
| 0.926667
| 0.926667
| 0.926667
| 0
| 0
| 0.105519
| 616
| 14
| 100
| 44
| 0.816697
| 0
| 0
| 0
| 0
| 0
| 0.396104
| 0.087662
| 0
| 0
| 0
| 0
| 0
| 1
| 0.1
| true
| 0
| 0.1
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 0
| 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
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
339f1afb2069001f4eccb0380a6879ecb4bd32af
| 3,373
|
py
|
Python
|
saleor/payment/gateways/adyen/tests/webhooks/test_validate_notification.py
|
fairhopeweb/saleor
|
9ac6c22652d46ba65a5b894da5f1ba5bec48c019
|
[
"CC-BY-4.0"
] | 15,337
|
2015-01-12T02:11:52.000Z
|
2021-10-05T19:19:29.000Z
|
saleor/payment/gateways/adyen/tests/webhooks/test_validate_notification.py
|
fairhopeweb/saleor
|
9ac6c22652d46ba65a5b894da5f1ba5bec48c019
|
[
"CC-BY-4.0"
] | 7,486
|
2015-02-11T10:52:13.000Z
|
2021-10-06T09:37:15.000Z
|
saleor/payment/gateways/adyen/tests/webhooks/test_validate_notification.py
|
aminziadna/saleor
|
2e78fb5bcf8b83a6278af02551a104cfa555a1fb
|
[
"CC-BY-4.0"
] | 5,864
|
2015-01-16T14:52:54.000Z
|
2021-10-05T23:01:15.000Z
|
from django.contrib.auth.hashers import make_password
from ...webhooks import (
validate_auth_user,
validate_hmac_signature,
validate_merchant_account,
)
def test_validate_hmac_signature(adyen_plugin, notification_with_hmac_signature):
hmac_key = "8E60EDDCA27F96095AD5882EF0AA3B05844864710EC089B7967F796AC44AE76E"
plugin = adyen_plugin()
config = plugin.config
config.connection_params["webhook_hmac"] = hmac_key
assert validate_hmac_signature(notification_with_hmac_signature, config) is True
def test_validate_hmac_signature_missing_key_in_saleor(
adyen_plugin, notification_with_hmac_signature
):
plugin = adyen_plugin()
config = plugin.config
assert validate_hmac_signature(notification_with_hmac_signature, config) is False
def test_validate_hmac_signature_missing_key_in_notification(
adyen_plugin, notification
):
hmac_key = "8E60EDDCA27F96095AD5882EF0AA3B05844864710EC089B7967F796AC44AE76E"
plugin = adyen_plugin()
config = plugin.config
config.connection_params["webhook_hmac"] = hmac_key
assert validate_hmac_signature(notification(), config) is False
def test_validate_hmac_signature_without_keys(adyen_plugin, notification):
plugin = adyen_plugin()
config = plugin.config
assert validate_hmac_signature(notification(), config) is True
def test_validate_auth_user(adyen_plugin):
plugin = adyen_plugin()
config = plugin.config
config.connection_params["webhook_user"] = "admin@example.com"
password = make_password("admin")
config.connection_params["webhook_user_password"] = password
is_valid = validate_auth_user(
headers={"Authorization": "Basic YWRtaW5AZXhhbXBsZS5jb206YWRtaW4="},
gateway_config=config,
)
assert is_valid is True
def validate_auth_user_when_header_is_missing(adyen_plugin):
plugin = adyen_plugin()
config = plugin.config
config.connection_params["webhook_user"] = "admin@example.com"
password = make_password("admin")
config.connection_params["webhook_user_password"] = password
is_valid = validate_auth_user(headers={}, gateway_config=config)
assert is_valid is False
def test_validate_auth_user_when_user_is_missing(adyen_plugin):
plugin = adyen_plugin()
config = plugin.config
is_valid = validate_auth_user(
headers={"Authorization": "Basic YWRtaW5AZXhhbXBsZS5jb206YWRtaW4="},
gateway_config=config,
)
assert is_valid is False
def test_validate_auth_user_when_auth_is_disabled(adyen_plugin):
plugin = adyen_plugin()
config = plugin.config
is_valid = validate_auth_user(headers={}, gateway_config=config)
assert is_valid is True
def test_validate_merchant_account(adyen_plugin, notification_with_hmac_signature):
plugin = adyen_plugin()
config = plugin.config
notification_with_hmac_signature[
"merchantAccountCode"
] = config.connection_params.get("merchant_account")
assert validate_merchant_account(notification_with_hmac_signature, config) is True
def test_validate_merchant_account_invalid_merchant_account(
adyen_plugin, notification_with_hmac_signature
):
plugin = adyen_plugin()
config = plugin.config
notification_with_hmac_signature["merchantAccountCode"] = "test"
assert validate_merchant_account(notification_with_hmac_signature, config) is False
| 34.418367
| 87
| 0.781797
| 389
| 3,373
| 6.354756
| 0.131105
| 0.088997
| 0.080906
| 0.117314
| 0.881068
| 0.866505
| 0.842638
| 0.826456
| 0.781553
| 0.77589
| 0
| 0.032639
| 0.146161
| 3,373
| 97
| 88
| 34.773196
| 0.825694
| 0
| 0
| 0.618421
| 0
| 0
| 0.125111
| 0.069374
| 0
| 0
| 0
| 0
| 0.131579
| 1
| 0.131579
| false
| 0.065789
| 0.026316
| 0
| 0.157895
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
33c7e4410bdc217ab49235aa3443e2259c36a451
| 100
|
py
|
Python
|
simple_grpc_chat/backend/src/__init__.py
|
vdshk/simple-grpc-chat
|
09329a95108ea1cc72c901226112fdc65b3c3e76
|
[
"MIT"
] | null | null | null |
simple_grpc_chat/backend/src/__init__.py
|
vdshk/simple-grpc-chat
|
09329a95108ea1cc72c901226112fdc65b3c3e76
|
[
"MIT"
] | null | null | null |
simple_grpc_chat/backend/src/__init__.py
|
vdshk/simple-grpc-chat
|
09329a95108ea1cc72c901226112fdc65b3c3e76
|
[
"MIT"
] | null | null | null |
from simple_grpc_chat.backend.src.client import *
from simple_grpc_chat.backend.src.server import *
| 33.333333
| 49
| 0.84
| 16
| 100
| 5
| 0.5625
| 0.25
| 0.35
| 0.45
| 0.7
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08
| 100
| 2
| 50
| 50
| 0.869565
| 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
|
d5164621c6ba4b2d09e330994877fd67251e232f
| 87
|
py
|
Python
|
yandex-algorithm-training-2/homework_3_b/1.py
|
kirilllapushinskiy/code
|
8deea136de8d4559f7f2dad26005611b0e51790b
|
[
"MIT"
] | null | null | null |
yandex-algorithm-training-2/homework_3_b/1.py
|
kirilllapushinskiy/code
|
8deea136de8d4559f7f2dad26005611b0e51790b
|
[
"MIT"
] | null | null | null |
yandex-algorithm-training-2/homework_3_b/1.py
|
kirilllapushinskiy/code
|
8deea136de8d4559f7f2dad26005611b0e51790b
|
[
"MIT"
] | null | null | null |
print(len(set(map(int, input().split())).intersection(set(map(int, input().split())))))
| 87
| 87
| 0.666667
| 13
| 87
| 4.461538
| 0.615385
| 0.206897
| 0.310345
| 0.482759
| 0.655172
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022989
| 87
| 1
| 87
| 87
| 0.682353
| 0
| 0
| 0
| 0
| 0
| 0
| 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
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
1d19f82debb50ecb89538f474195f6f4c2f9e7fa
| 137
|
py
|
Python
|
resseg/utils.py
|
fepegar/resseg
|
ecf256c9ba8593c9fb2f33b184245125eb6cd91b
|
[
"MIT"
] | 3
|
2021-02-10T14:10:07.000Z
|
2021-06-09T21:23:50.000Z
|
resseg/utils.py
|
fepegar/resseg
|
ecf256c9ba8593c9fb2f33b184245125eb6cd91b
|
[
"MIT"
] | 2
|
2020-12-11T15:38:47.000Z
|
2022-01-08T17:59:55.000Z
|
resseg/utils.py
|
fepegar/resseg
|
ecf256c9ba8593c9fb2f33b184245125eb6cd91b
|
[
"MIT"
] | 1
|
2021-02-23T03:31:15.000Z
|
2021-02-23T03:31:15.000Z
|
import torch
def get_device():
# pylint: disable=no-member
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
| 19.571429
| 71
| 0.70073
| 20
| 137
| 4.7
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167883
| 137
| 6
| 72
| 22.833333
| 0.824561
| 0.182482
| 0
| 0
| 0
| 0
| 0.063636
| 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
|
1d29e6691fa376110a58474604f3bf4940fd61b0
| 38
|
py
|
Python
|
pyawscli/__init__.py
|
darshan-raul/Python-Package-
|
0a1efdbb0fd5bbcf1194373c3fb1f91dbc941166
|
[
"MIT"
] | 1
|
2019-05-04T00:40:00.000Z
|
2019-05-04T00:40:00.000Z
|
pyawscli/__init__.py
|
darshan-raul/Python-Package-for-CLI
|
0a1efdbb0fd5bbcf1194373c3fb1f91dbc941166
|
[
"MIT"
] | null | null | null |
pyawscli/__init__.py
|
darshan-raul/Python-Package-for-CLI
|
0a1efdbb0fd5bbcf1194373c3fb1f91dbc941166
|
[
"MIT"
] | null | null | null |
def joke():
return (u'works')
| 5.428571
| 21
| 0.5
| 5
| 38
| 3.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.315789
| 38
| 6
| 22
| 6.333333
| 0.730769
| 0
| 0
| 0
| 0
| 0
| 0.147059
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
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